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THE DEVELOPMENT OF A MEASURE OF EMOTIONAL INTELLIGENCE IN
PRE-ADOLESCENT CHILDREN
Jennifer Wynne Lloyd
A dissertation submitted in partial fulfillment of the requirements for the degree of
Doctorate in Clinical Psychology
Discipline of Psychology
Swinburne University
Submitted April 2012
ii
Abstract The current dissertation developed a measure of pre-adolescent Ability Emotional
Intelligence (EI) based on Mayer and Salovey‟s (1997) model, with items derived from the
specific abilities outlined within the four branches. Specifically designed measures of EI in
pre-adolescent children, such as the Swinburne University Emotional Intelligence Test –
Early Years (SUEIT-EY) developed in this dissertation, are required for empirical
investigation of the developmental progression of EI and to facilitate the development of
theoretical models. Additionally, a measure of Ability EI in children may provide a means
of assessing the variety of EI development programs that have proliferated since the
popularisation of EI (Zeidner, Matthews, Roberts, & MacCann, 2003). Subsequent to initial
development of the measure in a convenience sample of 222 male primary school students
(aged 9-13 years, M = 10.87, SD = 0.99) the measure was administered to students of two
schools across two years. The data was divided for use as a calibration sample (N = 476;
250 males, 226 females; aged 9-13 years, M = 10.83, SD = 0.97) for exploratory factor
analysis and a validation sample (N = 566; 303 males, 263 females; aged 9-13 years, M =
10.93, SD = 1.00) for confirmation of model adjustment using confirmatory factor analysis.
As hypothesised, the pre-adolescent respondents provided valid and reliable estimates of
their own typical EI as measured by self-report and maximal EI as assessed by objective
items. While results for the branch level scores were mixed, support was found for the
hypothesised increase of measured EI with grade level and higher scores in females than
males. In line with the purpose of the current dissertation in developing a measure of EI in
pre-adolescent children, the prediction that the SUEIT-EY would reveal a structure in
accordance with the four branch model of Mayer and Salovey (1997) was also supported.
However, a two factor structure representing Experiential and Strategic EI was not
supported by the data. It was concluded that while still in the early stages of development,
the SUEIT-EY provides a valid and reliable measure of Ability EI that may be used for
educational purposes and identification of children who may benefit from intervention to
enhance their EI. Further development of the measure is needed for use in program
evaluation.
iii
Acknowledgements
Acknowledgement and thanks to:
My supervisors Professor Con Stough and Dr Karen Hansen. Particular thanks to Con for
giving encouragement when needed most and Karen for providing an empathic ear and the
measure title: “SUEIT-EY”.
The principals and teachers for facilitating completion of the measure and their students
who generously gave their time and energy in completing the measure. Additional thanks to
the teachers who gave comments on earlier drafts of the items.
Christopher Dowling for patiently modeling the emotional expressions for the photographs.
Dr Patrick Johnston for graciously giving his assistance in photograph selection.
Warm thanks to Andrew Cooke, Belinda Lloyd, Jill Lloyd, Yvette Montgomery and Victor
Sant for feedback on drafts.
Heartfelt thanks to my family and friends, who have been supportive in my long academic
journey and continuing to trepedatiously ask “How‟s the thesis going?”. Thanks to my
Facebook friends who have been there with me the whole time :-)
Special thanks to my darling Vic who has helped in so many ways but specifically with
“computer stuff” and reminding me to “back it up in several places”. Thanks also to my
beloved son Christo who, throughout his childhood had to share his mother with her “uni”;
and my darling Jessi who I can always count on for a motivating “cheer”.
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Dedication
This dissertation is dedicated to Marion Wyn O‟Driscoll,
who would have been so proud of me.
It is also dedicated to my mother Jill, who instilled in me a love of learning and my father
Peter, who patiently taught me about understanding through “first principles”.
v
Declaration
I certify that this dissertation contains no material which has been accepted for the award to
the candidate of any other degree or diploma except where due reference is made in the text
of the examinable outcome. To the best of my knowledge, this dissertation contains no
material previously published or written by another person except where due reference is
made in the text of the examinable outcome and where the work is based on joint research
or publications, the relative contributions of the respective workers or authors have been
disclosed.
Jennifer Wynne Lloyd …………………………
vi
Contents
Abstract ............................................................................................................................................ ii
Acknowledgements ......................................................................................................................... iii
Dedication ....................................................................................................................................... iv
Declaration ....................................................................................................................................... v
List of Tables ................................................................................................................................... xi
List of Figures ................................................................................................................................. xiii
Chapter 1: Introduction and Overview. ........................................................................................... 1
Chapter 2: Emotional Intelligence. .................................................................................................. 4
2.1 Models of Emotional Intelligence. ............................................................................ 4
2.2 The Ability model of Emotional Intelligence. .......................................................... 6
2.3 Emotional development and Emotional Intelligence. ............................................... 9
2.4 Summary and dissertation aims. ............................................................................. 17
Chapter 3: Emotional Intelligence Measures. ................................................................................ 19
3.1 Test validity and reliability. .................................................................................... 19
3.2 Typical and maximum performance. ...................................................................... 21
3.3 Objective measures of Ability Emotional Intelligence. .......................................... 24
3.3.1 Tests not specifically designed to measure Emotional Intelligence................. 24
3.3.2 The Mayer-Salovey-Caruso measures. ............................................................ 26
3.3.3 Situational Judgment Tests. ............................................................................. 31
3.3.4 Ability Emotional Intelligence Measure. ......................................................... 33
3.4 How objective measures fit within the nomological network. ................................ 34
3.5 Criticisms of objective measures. ........................................................................... 34
3.6 Subjective measures of ability Emotional Intelligence. .......................................... 36
3.6.1 Self-report Emotional Intelligence Scale. ........................................................ 36
3.6.2 Criticisms of the SSREIS. ................................................................................ 40
3.6.3 Wong Law Emotional Intelligence Scale. ........................................................ 42
3.7 General criticisms of subjective measures. ............................................................. 43
3.8 General summary and conclusions.......................................................................... 44
Chapter 4: Child Psychometrics and Test Development. ............................................................... 46
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4.1 Psychological measurement in children. ................................................................. 46
4.2 Test development. ................................................................................................... 50
4.2.1Response format – Typical Emotional Intelligence (Branches I, II and IV). ... 50
4.2.2 Instructions for self-report items. ..................................................................... 51
4.2.3 Item generation – Typical Emotional Intelligence (Branches I, II and IV). .... 52
4.2.4 Consistency items. ........................................................................................... 57
4.3 Instructions for objectively measured items. .......................................................... 57
4.3.1 Item generation and response format – Maximal Emotional Intelligence (Branch III). ...................................................................................................................... 58
4.4 Item generation – Maximal Emotional Intelligence (Branch I, ability 2 “identify emotions in others”). ........................................................................................... 68
4.3 Language and concepts. .......................................................................................... 75
4.4 Content validity. ...................................................................................................... 75
4.5 Summary and hypotheses. ....................................................................................... 75
Chapter 5: Factor Analytic Methods. ............................................................................................. 77
5.1 Factor analysis. ........................................................................................................ 77
5.1.2 Exploratory techniques for factor analysis. ...................................................... 77
5.1.3 Principal component analysis. .......................................................................... 77
5.1.4 Exploratory factor analysis. ............................................................................. 78
5.1.5 Decisions in principal components analysis and exploratory factor analysis. . 78
5.1.5 Principal components analysis and exploratory factor analysis comparison. .. 80
5.1.6 Confirmatory factor analysis. ........................................................................... 81
5.1.7 Maximum Likelihood estimation. .................................................................... 82
5.1.8 Assumptions underlying confirmatory factor analysis. ................................... 82
5.1.9 Univariate normality. ....................................................................................... 83
5.1.10 Multivariate normality. .................................................................................. 83
5.1.11 Sample size..................................................................................................... 84
5.2 Use of factor analytic methods in the assessment and development of EI measures85
5.2.1 MSCEIT. .......................................................................................................... 85
5.2.2 SSREIS. ............................................................................................................ 86
5.2.3 WLEIS. ............................................................................................................ 87
5.3 Summary and hypotheses. ....................................................................................... 88
Chapter 6: Method. ........................................................................................................................ 89
6.1 Initial Development and pilot testing. ..................................................................... 89
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6.2 Main analyses. ......................................................................................................... 89
6.2.1 Participants. ...................................................................................................... 89
6.2.2 Materials. .......................................................................................................... 90
6.2.3 Procedure.......................................................................................................... 91
6.2.4 Statistical design. ............................................................................................. 92
6.2.5 Analysis. ........................................................................................................... 92
6.2.6 Missing data. .................................................................................................... 92
6.2.7 Normality. ........................................................................................................ 92
6.2.8 Fit indices. ........................................................................................................ 93
6.2.9 Reliability. ........................................................................................................ 93
Chapter 7: Results. ......................................................................................................................... 94
7.1 Preliminary analyses. .............................................................................................. 94
7.1.1 Missing data. .................................................................................................... 94
7.1.2 Response consistency ........................................................................................... 94
7.1.3 Descriptive statistics. ....................................................................................... 94
7.1.4 Normality testing. ............................................................................................. 95
7.3 Principal components analysis. ............................................................................... 98
7.3.1 Assessing the factorability of the correlation matrix. ...................................... 98
7.3.2 Extracting the initial factors. ............................................................................ 98
7.3.3 Factor rotation. ............................................................................................... 105
7.3.4 Summary of principal components analyses. ................................................. 130
7.4 Confirmatory factor analysis: Branch I-Typical Emotional Intelligence. ............. 130
7.4.1 Branch I-Typical Emotional Intelligence: One factor model. ........................ 130
7.4.2 Branch I-Typical Emotional Intelligence: Four factor model. ....................... 131
7.4.3 Branch I- Typical Emotional Intelligence: Three factor model. .................... 134
7.4.4 Branch I- Typical Emotional Intelligence: Two factor model. ...................... 137
7.4.5 Branch I- Typical Emotional Intelligence: Model comparison...................... 139
7.4.6 Branch I- Typical Emotional Intelligence: measurement model and reliability............................................................................................................................ 140
7.4.7 Branch I- Typical Emotional Intelligence: Factor scores. ............................. 145
7.4.8 Branch I- Typical Emotional Intelligence grade level comparisons. ............. 147
7.4.9 Branch I- Typical Emotional Intelligence gender comparisons. .................... 148
7.4.10 Branch I- Typical Emotional Intelligence higher order factor comparisons.149
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7.5 Confirmatory factor analysis: Branch I-Maximal Emotional Intelligence ability two............................................................................................................................ 150
7.5.1 Branch I-Maximal Emotional Intelligence ability two: One factor model. ... 150
7.5.5 Branch I-Maximal Emotional Intelligence ability two: Model comparison. . 152
7.5.6 Branch I-Maximal Emotional Intelligence ability two: Measurement model and reliability. .......................................................................................................... 152
7.5.7 Branch I-Maximal Emotional Intelligence ability two: Factor scores. .......... 154
7.5.8 Branch I-Maximal Emotional Intelligence ability two: Grade level comparisons............................................................................................................................ 155
7.5.9 Branch I-Maximal Emotional Intelligence ability two: Gender comparisons.156
7.6 Confirmatory factor analysis: Branch II. .............................................................. 156
7.6.1 Branch II-Typical Emotional Intelligence: One factor model. ...................... 156
7.6.2 Branch II-Typical Emotional Intelligence: Four factor model. ..................... 158
7.6.3 Branch II-Typical Emotional Intelligence: Three factor model. .................... 159
7.6.4 Branch II-Typical Emotional Intelligence: Model comparison. .................... 160
7.6.5 Branch II-Typical Emotional Intelligence: Measurement model and reliability............................................................................................................................ 160
7.6.6 Branch II-Typical Emotional Intelligence: Factor scores. ............................. 162
7.6.7 Branch II-Typical Emotional Intelligence: Grade level comparisons. ........... 163
7.6.8 Branch II-Typical Emotional Intelligence: Gender comparisons. ................. 164
7.7 Confirmatory factor analysis: Branch III-Maximum Emotional Intelligence....... 164
7.7.1 Branch III-Maximum Emotional Intelligence: One factor model.................. 164
7.7.2 Branch III-Maximum Emotional Intelligence: Four factor model. ................ 166
7.7.5 Branch III- Maximum Emotional Intelligence: Measurement model and reliability. .......................................................................................................... 167
7.7.6 Branch III-Typical Emotional Intelligence: Factor scores. ............................ 169
7.7.7 Branch III- Maximum Emotional Intelligence: Grade level comparisons. .... 170
7.7.8 Branch III- Maximum Emotional Intelligence: Gender comparisons............ 170
7.8 Confirmatory factor analysis: Branch IV-Typical Emotional Intelligence. .......... 171
7.8.1 Branch IV- Typical Emotional Intelligence: One factor model. .................... 171
7.8.2 Branch IV-Typical Emotional Intelligence: Four factor model. .................... 172
7.8.3 Branch IV-Typical Emotional Intelligence: Three factor model. .................. 175
7.8.4 Branch IV-Typical Emotional Intelligence: Model comparison. ................... 177
7.8.5 Branch IV-Typical Emotional Intelligence: Measurement model and reliability............................................................................................................................ 179
7.8.6 Branch IV-Typical Emotional Intelligence: Factor scores............................. 183
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7.8.8 Branch IV-Typical Emotional Intelligence: Grade level comparisons. ......... 185
7.8.9 Branch IV-Typical Emotional Intelligence: Gender comparisons. ................ 186
7.8.10 Branch IV- Typical Emotional Intelligence higher order factor comparisons.187
7.9 Further analyses. ................................................................................................... 188
7.9.1 Full one factor Emotional Intelligence model. ............................................... 188
7.9.3 Experiential and Strategic Emotional Intelligence: Two factor model. ......... 189
7.9.5 Factor inter-correlations. ................................................................................ 190
Chapter 10 .................................................................................................................................... 199
10.1 Hypotheses and research questions ..................................................................... 199
10.1.1 Hypothesis 1: The factor structure of the SUEIT-EY will correspond to Mayer and Salovey‟s (1997) four branch model .......................................................... 199
10.1.2 Research question 1: Will the SUEIT-EY evidence a two factor structure corresponding to Experiential and Strategic EI? .............................................. 200
10.1.3 Research question 2: What factor structure will be displayed by the SUEIT-EY at the branch level? ........................................................................................... 200
10.1.2 Hypothesis 2: Pre-adolescents will provide valid and reliable responses to the SUEIT-EY......................................................................................................... 204
10.1.3 Hypothesis 3: SUEIT-EY scores will show increases according to grade level.207
10.1.4 Hypothesis 4: Females will have higher SUEIT-EY scores than males. ......... 208
10.1.5 Methodological considerations ........................................................................ 209
10.1.6 Implications ...................................................................................................... 209
10.1.7 Conclusions ...................................................................................................... 210
References ................................................................................................................................... 231
xi
List of Tables Table 1. Four Branch Hierarchical Model of Emotional Intelligence ............................... 8 Table 2. Descriptive Statistics for the Calibration and Validation Samples .................... 95 Table 3. Univariate Analysis ............................................................................................. 95 Table 4. Parallel Analysis ............................................................................................... 100 Table 5. Pattern/Structure for Coefficients(One Factor Solution) ................................. 100 Table 6. Pattern/Structure for Coefficients (Two Factor Solution) ................................ 105 Table 7. Pattern/Structure for Coefficients (Four Factor Solution, Varimax Rotation) 109
Table 8. Pattern/Structure for Coefficients (Five Factor Solution, Varimax Rotation) . 114 Table 9. Pattern/Structure for Coefficients (Six Factor Solution, Varimax Rotation).... 118 Table 10. Pattern/Structure for Coefficients (Sixteen Factor Solution, Varimax Rotation) ......................................................................................................................................... 123 Table 11. Sum of Squared Standardised Loadings, Error Variances and Variance
Extracted Estimates for Each Factor of the Branch I Four Factor Model ...... 134 Table 12. Sum of Squared Standardised Loadings, Error Variances and Variance Extracted
Estimates for Each Factor of the Branch I Three Factor Model ...................... 137 Table 13. Sum of Squared Standardised Loadings, Error Variances and Variance Extracted
Estimates for Each Factor of the Branch I Two Factor Model ........................ 139 Table 14. Branch I Model Comparison........................................................................... 140
Table 15. Branch I Measurement Models ....................................................................... 141 Table 16. Branch I Sum of Standardised Loadings, Error Variances and Construct
Reliability Estimates for the Four Factor Model .............................................. 143 Table 17. Branch I Higher Order Factors Sum of Standardised Loadings, Error Variances
and Construct Reliability Estimates.................................................................. 145 Table 18. Branch I Descriptive Statistics for Grades 4, 5 and 6 .................................... 146 Table 19. Branch I Descriptive statistics for Males and Females .................................. 147 Table 20. Branch I – Maximal Emotional Intelligence Model Comparison ................... 152 Table 21. Branch I Maximal Emotional Intelligence Measurement Models .................. 152 Table 22. Branch I Maximum Emotional Intelligence Sum of Standardised Loadings, Error
Variances and Construct Reliability Estimates .............................................. 154 Table 23. Branch I Maximum Emotional Intelligence Descriptive Statistics for Grades 4, 5
and 6 ............................................................................................................... 154 Table 24. Branch I Maximum Emotional Intelligence Descriptive statistics for Males and
Females ........................................................................................................... 155
Table 25. Branch II Model Comparison ......................................................................... 160 Table 26. Branch II Measurement Models ...................................................................... 161 Table 27. Branch II Sum of Standardised Loadings, Error Variances and Construct
Reliability Estimates for the One Factor Model ............................................. 162 Table 28. Branch II Descriptive Statistics for Grades 4, 5 and 6 ................................... 162
Table 29. Branch II Descriptive Statistics for Males and Females ................................ 163
Table 30. Branch III Measurement Model Comparison ................................................. 168
Table 31. Branch III Sum of Standardised Loadings, Error Variances and Construct Reliability Estimates for the One Factor Model ............................................. 169
Table 32. Branch III Descriptive Statistics for Grades 4, 5 and 6.................................. 169
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Table 33. Branch III Descriptive Statistics for Males and Females ............................... 169 Table 34. Branch IV Sum of Squared Standardised Loadings, Error Variances and
Variance Extracted Estimates for Each Factor of the Four Factor Model.... 175 Table 35. Branch IV Three Factor Model Sum of Squared Standardised Loadings, Error
Variances and Variance Extracted Estimates ................................................ 177 Table 36. Branch IV Model Comparison ........................................................................ 178 Table 37. Branch IV Measurement Model Comparison ................................................. 180 Table 38. Branch IV Four Factor Model Sum of Standardised Loadings, Error Variances
and Construct Reliability Estimates ............................................................... 181 Table 39. Branch IV Higher Order Factor Sum of Standardised Loadings, Error Variances
and Construct Reliability Estimates ............................................................... 183 Table 40. Branch IV Descriptive statistics for Grades 4, 5 and 6 .................................. 184 Table 41. Branch IV Descriptive statistics for Males and Females ................................ 185 Table 42. Sum of Standardised Loadings, Error Variances and Construct Reliability
Estimates for the One Factor Model of Emotional Intelligence ..................... 190 Table 43. Full Four Branch Model Factor Inter-correlations ....................................... 191 Table 44. Emotional Intelligence Descriptive statistics for Grades 4, 5 and 6 .............. 193 Table 45. Emotional Intelligence Descriptive statistics for Males and Females ............ 193 Table 46. Relationship of SUEIT-EY items with Social Desirability Scores .................. 195 Table 47. Relationship of SUEIT-EY factor scores with social desirability scores. ....... 197
xiii
List of Figures Figure 1. Response format. ............................................................................................... 50 Figure 2. Expression of anger. .......................................................................................... 69 Figure 3. Expression of happiness. ................................................................................... 70 Figure 4. Expression of surprise. ...................................................................................... 71 Figure 5. Expression of sadness. ...................................................................................... 72 Figure 6. Expression of fear.............................................................................................. 73 Figure 7. Expression of disgust. ....................................................................................... 74 Figure 8. Scree plot for items one to sixty-six. ................................................................. 99
Figure 9. Branch I four factor model. ............................................................................. 133 Figure 10. Branch I three factor model. .......................................................................... 136 Figure 11. Branch I two factor model. ............................................................................ 138 Figure 12. Branch I two factor higher order model. ....................................................... 144 Figure 13. Branch I – ability two: one factor model....................................................... 151 Figure 14. Branch II one factor model............................................................................ 158 Figure 15. Branch III one factor model. ......................................................................... 166 Figure 16. Branch IV four factor model. ........................................................................ 174 Figure 17. Branch IV three factor model. ....................................................................... 176 Figure 18. Branch IV four factor model with one higher order factor. .......................... 182 Figure 19. Four Branch Model of EI. ............................................................................. 188
Figure 20. One year temporal stability of EI. ................................................................. 192
Chapter 1: Introduction and Overview.
The purpose of this dissertation was to develop a measure of pre-adolescent children‟s
Ability Emotional Intelligence (EI) based on Mayer and Salovey‟s (1997) model. Such a
measure would allow empirical investigation of the developmental progression of EI in
children, as well as permit the testing of theoretical models of EI. Additionally, a measure
of Ability EI in children may provide a means of assessing the variety of EI development
programs that have proliferated since the popularisation of EI (Zeidner, Matthews, Roberts,
& MacCann, 2003).
Concepts related to models of Emotional Intelligence (EI) will be introduced in Chapter
two. The criticisms of the various EI models will be discussed and it is proposed that while
not without its detractors, the Ability Model holds the most promise for the investigation of
individual differences in emotional abilities. Mayer and Salovey‟s (1997) Hierarchical Four
Branch Model of EI is then explored in relation to extant research into the development of
emotional abilities in children. It is concluded that there is a need for a measure of Ability
EI in children so that the development of EI may be investigated. This endeavor may be
useful for testing claims about EI, as well as providing a means to identify children who are
lagging in terms of the development of EI and to assess programs purporting to enhance
children‟s EI.
Chapter three will provide a brief overview of the requirements for an acceptable measure
of a psychological construct and more specific requirements for establishing EI measures
within the nomological network. Objective and subjective measures of Ability EI will be
reviewed in this chapter, along with a discussion about the relative merits of Typical EI as
compared to Maximum EI in understanding development in children. The need for the
development of measures of both Maximum and Typical EI in children is proposed.
Typical EI measures may be important for for identification of children with difficulties in
dealing with and utilising emotions, while Maximum EI measures may provide further
information about whether the child is performing to their potential in terms of processing
2
and utilising emotion. It is suggested that the effects of training on Typical EI and
Maximum EI as well as causes and results of non-optimal EI performance should be
empirically determined.
In Chapter four the specific requirements for pre-adolescent respondents of self-report and
objective measures will be examined. Research on the development of emotional
capabilities will be referenced in the generation of items designed to measure Mayer and
Salovey‟s (1997) model of EI, with consideration of specific requirements for pre-
adolescent respondents. The process of reducing the initial item pool to a manageable
number of items will be described and hypotheses will be generated in reference to the
final test. It will be hypothesised that pre-adolescent respondents will provide valid and
reliable estimates of their own Typical EI as measured by self-report and Maximal EI as
assessed by objective measures. It is also hypothesised that pre-adolescent respondents will
show age related increases in both Typical EI and Maximal EI, with females scoring higher
than males.
Methods for examining the factorial validity of tests will be discussed in Chapter 5.
Specifically, exploratory factor analysis and confirmatory factor analytic methods will be
presented and the Maximum Likelihood estimation method along with associated fit criteria
will be explored. Research findings of the factorial structure of various measures of Mayer
and Salovey‟s (1997) four factor model will also be discussed along with the development
of specific hypotheses about the factorial structure of the Swinburne University Emotional
Intelligence Test –Early Years (SUEIT-EY). In line with the purpose of the current
dissertation in developing a measure of EI, it is predicted from the extant research that the
SUEIT-EY will reveal a four factor structure as hypothesised by the Mayer and Salovey
(1997) model. More specifically, it will be hypothesised that a one factor model specifying
Branches I, II, III, and IV will show good fit to the data. While Mayer and Salovey‟s (1997)
model implies a four factor structure at the branch level, there is no evidence to guide
hypotheses. Therefore, the factor structure of the branches will be posed as a research
question. There are mixed results for Experiential and Strategic area factors in performance
EI measures and no evidence for this two factor model in self-report measures. Therefore
3
the presence of a two factor model representing Experiential and Strategic EI will also be
posed as a research question.
In Chapter six, the methodology will be explained in detail in terms of pilot testing as well
as the main study. The study design will be outlined whereby subsequent to exploratory
factor analysis to explore the characteristics of the data set, a two step modeling approach
will be employed with validity of the separate factors at the item level established through
examining the measurement models, followed by an examination of the overarching
structural model. Furthermore, model modifications will be validated using a separate but
equivalent sample. The statistics used to assess model fit will be described as well as
decision rules which will be used to guide model modifications.
Chapter seven presents the results of the analyses described in Chapter 6. Subsequent to
exploration of the items and factor structure of the measure, model modifications will be
described in detail. The assessment of the final measure will be outlined, including the
determination of differences associated with gender and grade level, internal consistency,
temporal stability and association of the test items with a measure of socially desirable
responding.
Chapter eight provides a broad context discussion of the relevant results. Specifically,
support for the hypotheses is considered in the context of extant research and study
methodology. Conclusions and recommendations for further work are provided along with
a discussion outlining the positive and negative attributes of the work, its limitations and
further areas to be examined.
4
Chapter 2: Emotional Intelligence.
2.1 Models of Emotional Intelligence. The origins of the Emotional Intelligence (EI) construct have been traced to „social
intelligence‟, initially coined by E. L. Thorndike (1920) and later included in the multiple
abilities model of Gardner (1983). While the term “Emotional Intelligence” had been
previously referred to (Leuner, 1966; Payne, 1986; van Ghent, 1961), it is generally agreed
that Mayer and Salovey were first to introduce the construct to academia (Bar-On, 2005;
Boyatzis, Goleman, & Rhee, 2000; Matthews, Zeidner & Roberts, 2002).
Mayer and colleagues (2008) have subsequently named their model the Ability Model of EI
by way of differentiating it from other models, which they have termed Mixed Models.
Mixed Models (often called socio-emotional models by their proponents) are so termed
because they include a mix of non-cognitive abilities which include such constructs as
happiness, stress tolerance, and self-regard (Bar-On, 1997); adaptability, impulse control
and social competence (Boyatzis & Sala, 2004; Petrides & Furnham, 2001); and creative
thinking, flexibility, and intuition versus reason (Tett, Fox, & Wang, 2005). Mixed Models
of EI are typically measured by self-report or informant report (i.e. judgments of a person‟s
EI by another person) and are favored by applied (e.g. business, education, clinical
psychology), market oriented approaches (Conte, 2005). Conte (2005) and Landy (2005)
have argued that these commercial approaches tend to avoid the academic rigor imposed by
peer reviewed journals and often do not have data available for scrutiny (Conte, 2005;
Landy, 2005). The Ability Model is generally preferred by theoretically oriented
approaches and while the authors of the only available test of performance EI and others
(Brackett & Mayer, 2003; Day & Carroll, 2004; Matthews, Zeidner, & Roberts, 2004;
Mayer, Salovey, Caruso, & Sitarenios, 2001) argue that Ability EI is best measured using
performance measures, others have used self-report (Schutte, et al., 1998; Wong & Law,
2002). Mayer and Salovey (1990), classify EI models based on the operationalisation of the
model (i.e. whether the model is measured by self-report of performance tests) and include
the self-report measures based on their model as well as the Trait Emotional Intelligence
Questionnaire (TEIQ; Petrides & Furnham, 2001) as derived from a Mixed Model approach
5
(Mayer, Roberts, & Barsade, 2008). However, this classification is not shared by the
proponents of the measures. The self-report measures of the Ability Model have been
considered as indicating “typical” as opposed to “maximum” performance (Gignac, Palmer,
Manocha, & Stough, 2005; Gignac, 2010). Petrides differentiates the TEIQ from the Mixed
Model approaches differently, arguing that it is a measure of Trait EI (or Trait Emotional
Self Efficacy) and is located within the personality factor space (Petrides & Furnham,
2001). While some effort has been invested into the differentiation of the various models of
EI, others have suggested that the models are complementary and have argued for a focus
on identifying commonalities (Ciarrochi, Chan, & Caputi, 2000; Palmer, Gignac,
Ekermans, & Stough, 2008; Zeidner, Matthews, Roberts, & MacCann, 2003).
Ashkanasy and Daus (2005) have succinctly identified three streams of research in EI,
based on the test used to measure the construct. With tests usually (though not necessarily)
corresponding with the theoretical model of the authors, the three streams comprise
research that utilises the Mayer and Salovey performance test based on their four factor
model (e.g. MSCEIT; Mayer, Salovey, Caruso, & Sitarenios, 2003), self-report tests based
on Mayer and Salovey‟s model (e.g. Schutte, et al., 1998) and commercially available tests
that „go beyond‟ the Mayer and Salovey definition (e.g. EQI; Bar-On R. , 2004; ECI;
Boyatzis & Sala, 2004). As argued by Ashkanasy and Daus (2005) and admitted by one of
the authors of these Mixed Model measures (Bar-On, 2005), the term of “Emotional
Intelligence” is incorrectly applied to Mixed Model approaches. Indeed, much of the
criticism of EI is more applicable to the Mixed Model approaches than those based on the
Ability Model (Ashkanasy & Daus, 2005). Such criticisms include the paucity of data
available for scientific scrutiny (Conte, 2005) and unclear theoretical definitions (Becker,
2003; Matthews, Zeidner, & Roberts, 2004). Two meta-analyses have given support to the
validity of the Mixed Model compared to the Ability Model approaches (Schutte, Malouff,
Thorsteinsson, Bhullar, & Rooke, 2007; Martins, Ramalho, & Morin, 2010). However, the
stronger relationship found between EI and health for the Mixed Model approaches is likely
to be strongly influenced by the saturation of “wellbeing” within these measures. Mixed
Model approaches have been criticized for having widely encompassing definitions of EI
(Landy, 2005; Locke, 2005) incorporating any desirable characteristic not represented by
6
cognitive ability (Elfenbein, 2008; Matthews, Zeidner, & Roberts, 2002; Murphy, 2006;
Zeidner, Matthews, Roberts, & MacCann, 2003). It has also been argued that Mixed
Models have too much overlap with personality traits to justify a distinct construct (Conte,
2005; Daus & Ashkanasy, 2005; Van Rooy, Dilchert, Viswesvaran, & Ones, 2006). In an
extensive literature review, Roberts, MacCann, Matthews, and Zeidner (2010) argued that
Mixed Model approaches to EI have poor construct validity because they overlap
considerably with personality, have weak or negative correlations with intelligence and
weak correlations with emotion measures. However, these authors fail to consider the
possibility that the overlap between EI and personality may be due to common method
variance (Van ROoy, Viswesvaran, & Pluta). Additionally, it may be argued that a
relationship between EI and personality is to be expected because the constructs in question
represent phenomena which have common sub-elements (McCrae, 2000). While there are
clear disagreements and difficulties in regards to the Mixed models of EI, the Ability model
is not without its problems, including the assumption of a single correct answer inherent in
the performance-based scoring format (Van Rooy, Viswesvaran, & Pluta, 2005). In light of
the distinction between the Ability and Mixed Model approaches to EI and the
aforementioned problems with the Mixed EI models, the ensuing dissertation will focus on
the Ability Model of EI.
2.2 The Ability model of Emotional Intelligence. Critics of EI concede that the Ability Model compared to the Mixed Model approaches
holds some promise (e.g. Conte, 2005; Matthews, Zeidner, & Roberts, 2002; Matthews,
Zeidner, & Roberts, 2004). Roberts and colleagues (2010) consider that the Ability Model
more closely resembles other standard models of intelligence, attesting to its construct
validity. However, challenges to the Ability Model persist. These challenges are
predominantly in line with ensuring that as a new construct, EI fits within as well as adds
to the nomological network (cf. Cronbach & Meehl, 1955). While proponents of each of the
relevant fields of emotion (e.g. Izard , 2001) and intelligence (e.g. Davies, Stankov, &
Roberts, 1998; Locke, 2005) have added to the discourse, the strongest protestations have
come from intelligence researchers. While Salovey and Mayer (1990) placed EI under the
rubric of intelligence, they were careful to explain that they were referring to intelligence as
7
“a broad set of abilities” rather than more the restrictive notion proffered by various models
of intelligence such as Spearman‟s unifactorial “g” that holds that all mental abilities are
inter-correlated (Spearman, 1927). This, however, did not prevent them from being held to
task by critics questioning the assertion of EI as an intelligence (e.g. Roberts, Zeidner, &
Matthews, 2001; Locke, 2005). Mayer and colleagues countered such criticisms with a
detailed exposition (Mayer, Caruso, & Salovey, 1999; Mayer, Salovey, Caruso, &
Sitarenios, 2001), arguing that EI meets criteria as an intelligence owing to its
conceptualisation (comprising specific abilities), correlations (within EI factors as well as
with extant measures of intelligence) and that it develops with age and experience, and
provided empirical support for these claims. While some of the criticism has been
specifically directed at EI as a theoretical model (Locke, 2005), much of the discussion is
more relevant to the measurement tools and so will be further explored in the following
chapter.
Salovey and Mayer (1990) conceptualized EI as a guiding framework for the ostensibly
disparate research investigating appraisal, use and communication of emotions (for
example: Ekman, 1973; Dyer, 1983; Roseman, 1984; Sloman & Croucher, 1981; Smith &
Ellsworth, 1985). They initially defined EI as “the subset of social intelligence that involves
the ability to monitor one‟s own and other‟s feelings and emotions, to discriminate among
them and to use this information to guide one‟s thinking and actions” (p.189). They later
expanded the definition to include “use of emotions in thought” within a four branch
hierarchical model; “the ability to perceive accurately, appraise, and express emotion; the
ability to access and/or generate feelings when they facilitate thought; the ability to
understand emotion and emotional knowledge; and the ability to regulate emotions to
promote emotional and intellectual growth” (Mayer & Salovey, 1997, p.10). With their
1997 revision of the definition and conceptualization of EI, Mayer and Salovey enriched
the model with an outline of the developmental progression of four abilities within each of
the four branches from the basic to the more sophisticated (see Table 1 below).
8
Table 1. Four Branch Hierarchical Model of Emotional Intelligence
Branch IV Reflective Regulation of Emotion
Stay open to pleasant and unpleasant feelings
Reflectively engage or detach from an emotion
Reflectively monitor emotions in relation to self and others
Manage emotion in self and others without repressing or exaggerating information conveyed
Branch III Understanding and Analysing Emotion
Label emotions and recognise relations among emotions
Interpret the meanings emotions convey
Understand complex feelings, simultaneous feelings, blends
Recognise likely transitions among emotions
Branch II Emotional Facilitation of Thinking
Emotions prioritise thinking by directing attention to important information
Emotions are vivid and available to be generated as aids to judgment and memory
Mood swings change perspective to encourage multiple points of view
Emotion states differentially encourage specific problem approaches
Branch I Perception, Appraisal and Expression of Emotion
Identify emotion in one‟s physical states, feelings, thoughts
Identify emotions in others, designs, artwork, language, sound, appearance and behaviour
Express emotions accurately, and express needs related to feelings
Discriminate between accurate and inaccurate, honest and dishonest expressions of feelings
Note: Branches are ordered from the most basic (lowest row) to the more complex (highest row)
Abilities are ordered from the most basic (left) to the more complex (right)
Adapted from (Mayer & Salovey, 1997)
The four branches are differentiated in terms of complexity, with “Perception, Appraisal,
and Expression of Emotion” being considered to be the most simple, followed by
progressively more complex processes of “Emotional Facilitation of Thinking” and
“Understanding and Analysing Emotions; Employing Emotional Knowledge”; with
“Reflective Regulation of Emotions to Promote Emotional and Intellectual Growth”
considered to involve the most highly developed psychological processes (Mayer &
Salovey, 1997). Similarly, the abilities encompassed within each branch progress from
more basic, earlier developing abilities which are built upon to form more sophisticated
abilities as the individual develops (Mayer & Salovey). Adding to this model refinement,
Mayer and Salovey purported that people high in EI would progress more quickly through
the abilities and master more of them. This extension of the model seems pertinent to child
9
development, particularly in light of the plethora of programs purporting to enhance EI in
children (Zeidner, Matthews, Roberts, & MacCann, 2003). Despite the model being clearly
testable, there has been a surprising paucity of research investigating its veracity. While
aspects of EI may be garnered from extant research of children‟s emotional development,
the lack of a measure of Ability EI in young children has limited direct testing of Mayer
and Salovey‟s (1997) four branch model of EI. The ensuing paragraphs will expand upon
the abilities within each branch of the model, drawing on previous research of children‟s
emotional development to explore the developmental progression.
2.3 Emotional development and Emotional Intelligence. Branch I, “Perception, Appraisal, and Expression of Emotion”, is considered to comprise
the most basic emotion-related skill; the perception of emotion (Mayer & Salovey, 1997). It
involves the capacity to recognise emotion in others‟ facial and postural expressions and
non-verbal perception and expression of emotion in the face, voice, and related
communication channels (Mayer & Salovey). The abilities comprising this branch range
from the ability to identify emotions in oneself to the ability to discriminate between subtle
expressions of emotions 9Mayer & Salovey). These basic input processes are necessary
preconditions for the further processing of information in order to solve problems (Mayer,
Salovey, Caruso, & Siatarenios, 2003) and have been likened to the first phase of Gross and
Thompson‟s (2007) “modal” model of emotion (i.e. the sequence of attention to the
stimulus, appraisal and ultimately a response; Joseph & Newman, 2010).
Mayer and Salovey (1997) explain that early in development, infants and young children
learn to identify their own and other‟s emotional states and to differentiate among those
states. An infant can distinguish between facial expressions of emotion and respond to
parent‟s expressions (Barrera & Maurer, 1981; Caron, Caron, & MacLean, 1988; Klinnert,
Emde, Butterfield, & Campos, 1988; Maurer & Barrera, 1981; Moses, Baldwin, Rosicky, &
Tidball, 2001; Serrano, Iglesias, & Loeches, 1992; Walker-Andrews & Lennon, 1991), later
becoming more adept at labeling facial expressions of basic emotions (Denham &
Couchoud, 1990; Harrigan, 1984; Markham & Adams, 1992; Russell & Widen, 2002;
Widen & Russell, 2003) with the ability to discern “happy”, “sad” and “angry” faces
10
emerging earlier than for “scared”, “surprised” and “disgusted” faces (Widen & Russell,
2003). By the age of five years, most children are able to recognise different emotional
expressions (Pons, Harris, & de Rosnay, 2004). This developmental progression is seen to
occur across different modalities including verbal, prosodic as well as facial (Egan, Brown,
Goonan, Goonan, & Celano, 1998). As the child develops, they are better able to recognise
associated bodily sensations and other components of emotion schemata (culturally-based
cognitive associations and behavioural tendencies; Izard, 2009) and distinguish between
real and apparent emotions (Misailidi, 2006). The ability to express emotion also seems to
follow a fixed developmental sequence (Eibl-Eibesfeldt, 1973). Fridlund, Ekman and Oster
(1987) found that children‟s ability to recognise and produce facial expressions improves
until about age 10, at which time most children and adults appear equally capable of
encoding and decoding all major categories of emotion. Moreover, happiness and sadness
are generally found to be the easiest to recognise and produce, followed by anger and
disgust, with fear and surprise being the most difficult (Field & Walden, 1982; Kirouac &
Dore, 1983; Walden & Field, 1982). Gender differences have been found in adults, with
females outperforming males (Hall, 1984), however, such differences do not appear to
occur pre-pubertally (Battaglia, et al., 2004). Preliminary evidence of male infants being
more intensely expressive of emotion than girls (Brody, 1985) and that boys become
increasingly worse at expressing emotion with age (Buck, 1977; Shennum & Bugental,
1982) may be explained by the idea that boys learn to limit expression due to socialisation
pressures that encourage them to neutralize or mask emotions (Brody, 1985; Saarni, 1999).
Branch II, “Emotional Facilitation of Thinking” concerns the influence of emotions upon
intelligence and describes emotional events that assist intellectual processing (Mayer &
Salovey, 1997). This branch is perhaps the most applicable to Izard‟s (2001) question about
whether EI is merely attributable to the inherent adaptiveness of emotions rather than being
an intelligence per se. However, it is not suggested from this branch that a person who is
solely guided by the whims of emotional surges is emotionally intelligent but rather that the
emotionally intelligent individual selects and utilises emotions that are most suitable to
their goals. Mayer and Salovey (1997) propose that an aspect of this branch is the ability to
generate emotion, however Izard (1993) contends that while emotions can be activated and
11
influenced by perceptual, appraisal, conceptual and non-cognitive processes, they cannot be
created by them. Branch II entails both the capacity of emotions to assist thinking as well as
the generation and optimal utilisation of these emotions to enhance reasoning, problem
solving and planning (Mayer & Salovey, 1997). Branch II abilities range from use of
emotions as an alerting system for the prioritizing of thinking, to use of different emotional
states as a means of facilitating specific problem approaches. It is associated with the
“feeling” component of emotion (Davitz, 1969; Schwartz, 1990) and is akin to the part of
intelligence that involves drawing upon a knowledge base of experiences (Mayer &
Salovey, 1997). Knowledge of the link between emotions and thinking can be utilised to
direct one‟s planning (Izard, 2001) and research has identified that some types of problem
solving are specifically facilitated by some emotions but not others. For example, happy
moods facilitate a mental set that is useful for creative tasks requiring intuitive and
expansive thinking thus allowing novel associations, while sad moods engender a mental
set in which problems are solved more slowly with particular attention to detail, using more
focused and deliberate strategies (Isen, Daubman, & Nowicki, 1987). The influence of
positive emotion on creative thinking is thought to be underpinned by an expanded search
process which is facilitated when certain emotional states trigger a broadened associative
network (Russ & Kaugers, 2001). This has been linked to dopamine release in the anterior
cingulate improving cognitive flexibility and facilitating selection of a cognitive
perspective (Ashby, Isen, & Turken, 1999). Palfai and Salovey (1993) contend that the
different information processing styles (i.e. intuitive and expansive vs. focused and
deliberate) are specifically more effective for inductive and deductive logical tasks
respectively. The shifting perspective as a result of changes in mood may encourage
multiple points of view, and consequently encourage deeper and more creative thought
(Mayer, Salovey, & Caruso, 2000). The ability to harness the motivating qualities of
emotion is also considered to be a component of EI (Salovey, Bedell, Detweiler, & Mayer,
2000).
Mayer and Geher (1996) have proposed that the Affect Infusion Model (AIM) may be
relevant in explaining the processes involved in Branch II abilities. The AIM was
developed as a comprehensive, integrative theory to explain the influence of emotion on
12
cognitive processes as dependent upon the type of processing strategy used (Forgas, 2001).
“Affect infusion”, the process of affectively loaded information influencing cognitive
processes, is predicted to be most likely to occur in the course of constructive processing
that involves the substantial transformation rather than mere reproduction of existing
cognitive representations, requiring a relatively open information search strategy and a
significant degree of generative elaboration of the available stimulus details. Child
development research has found that reliance on external, situational cues in problem
solving decreases with age (ages four to seven) and increases with task difficulty (Ruble &
Nakamura, 1973). As children mature, they are more competent in determining
relationships among problem elements via their own resources (Ruble & Nakamura, 1973).
Therefore, according to the AIM, the influence of emotion in children‟s problem solving
and judgment is likely to increase with age and decrease with task difficulty. This may
explain the findings of state dependent learning found in five-year-old children for cued
(Bartlett & Santrock, 1979) but not uncued recall in children of the same age (Duncan,
Todd, Perlmutter, & Masters, 1985) and for uncued recall in older children (aged eight to
ten years; Forgas, Burnham, & Trimboli, 1988). Denham (1998) found that the utilisation
of emotions to facilitate cognition is limited in the preschool years by children‟s difficulties
in grasping the causes and time course of emotions. Affective decision making (in the form
of “hot cognitions” measured by the “Children‟s Gambling Task”) has been found to
develop with age (from three to six years; Hongwanishkul, Happaney, Lee, & Zelazo,
2005), with more pronounced effects of age-related improvements found for girls (Kerr &
Zelazo, 2004).
While Branch II focuses on the influence of emotion on cognition, Branch III relates to
cognitions about emotions and the building of the knowledge base of emotional
information and experiences which may be accessed for intelligent action. Branch III,
“Understanding and Analysing Emotions; Employing Emotional Knowledge” involves the
capacity to analyse emotions, appreciate their probable trends over time, and understand
their outcomes (Mayer & Salovey, 1997). The most fundamental competency at this level is
the ability to label emotions with words and to recognise the relationships among them. As
this ability develops, the individual is able to recognise groupings of emotions (Ortony,
13
Clore, & Collins, 1988), what emotions convey about relationships (Lazarus, Emotion and
Adaptation, 1991), the ways in which emotions can combine (Shaver, Schwatrz, Kirson, &
O'Connor, 1987), the co-occurrence of apparently opposite emotions (Reissland, 1985) and
the likely time course and transitioning of emotions (e.g. Tangney, Wagner, Fletcher, &
Gramzow, 1992). Research findings suggest that children initially rely on idosyncratic
situational cues or singular external body cues, then later elaborate on these examples by
using additional situations and body cues, and finally include mental states or inner
experiences in their understanding of emotional states (Carroll & Steward, 1984; Harris,
Olthof, & Meerum Terwogt, 1981; Rieffe, Meerum Terwogt, Koops, & Hagenaar, 2000;
Symons, McLaughlin, Moore, & Morine, 1997; Wellman, 1990).
Understanding of emotion is likely to be facilitated by development of perspective-taking
ability. Most three-year-olds perceive desires and beliefs as objective features of the world
and think that their ideas about desirability and their beliefs about the true state of affairs
apply to everyone (Wellman, 1990). Around the age of five, children appreciate the fact
that people have different desires and beliefs, and predict others‟ emotions accordingly,
even if they find those desires undesirable (Rieffe, Meerum Terwogt, Koops, Stegge, &
Oomen, 2001) or find others‟ beliefs to differ from their own situational knowledge
(Hadwin & Perner, 1991; Harris, Johnson, Hutton, Andrews, & Cooke, 1989).
Mayer, Salovey and Caruso (2004) proposed that development of Branch III coincides with
the growth of language and propositional thought. It may be that children‟s understanding
of emotion is related to the theory of emotion to which they subscribe (e.g. emotions as
separate categories or as dimensional entities; Ellsworth, 2007) which is likely to be
underpinned by their level of cognitive development (Izard, 1984)
Harris (1989) proposed that the early understanding of facial expressions leads to an
understanding of other aspects of emotions, which in turn leads to theory of mind. Denham
(1998) adds that the early understanding of emotion via facial expressions is the
“perceptual bedrock” (p.61) for all later understanding of emotion. Branch III abilities
appear with the beginnings of emotion language at age two, and become increasingly
sophisticated in three- and four- year olds (Denham, 1998). While pre-schoolers have
14
difficulties with understanding mixed emotions and complex, morally tinged emotions such
as shame and gratitude (Harris, Olthof, Meerum Terwogt, & Hardman, 1987), from the age
of seven years, most children are able to understand the role of desires, beliefs and the
possibility of hiding emotions (Pons, Harris, & de Rosnay, 2004). Between the ages of nine
and eleven years, children develop the ability to understand the mixed nature of emotions,
the possibility of regulating emotion via cognition and the influence of morality on
emotions (Pons, Harris, & de Rosnay, 2004). A comparable developmental progression is
found for concepts of simultaneity of emotions, with five year-olds unable to conceive of
the co-occurrence of emotions of opposite valence, seven year-olds being able to connect
the emotions sequentially and by ten years of age, children are able to conceive of
situations in which two emotions of opposite valence can occur simultaneously (Reissland,
1985). Harter and Buddin (1987) described children‟s progression through five stages of
understanding simultaneous emotions. They found developmental increases in the
understanding of simultaneous emotions were influenced by emotional valence and number
of targets of the emotions. That is, understanding of simultaneous emotions of the same
valence and target develops earlier than understanding of simultaneous emotions with
opposite valence and different targets. Preliminary evidence has found girls (N=27) to
outperform boys (N=23) in terms of understanding conflicting emotions (Brown & Dunn,
1996).
Branch IV, “Reflective Regulation of Emotions to Promote Emotional and Intellectual
Growth” encompasses the management of emotion and Mayer and colleagues (2004) argue
that it is integrally involved within the individual‟s personality such that emotions are
managed in the context of the individual‟s goals, self-knowledge, and social awareness.
Despite the claim that Mayer and Salovey intended for this branch to refer to only the
conscious regulation of emotion (Joseph & Newman, 2010), Mayer and Salovey (1995)
have specified three levels of consciousness operating within this branch; non-conscious,
low-level consciousness and higher consciousness. Non-conscious construction and
regulation of emotion occurs outside of conscious awareness because it operates at a
neurological level inaccessible to consciousness, is automatized and no longer attended to,
or because it has been repressed. This includes construction of basic emotions through
15
biologically programmed combinations of physiological experience and cognitive reactions
with automatic appraisals of the environment that arise from early reinforcement. Non-
conscious regulation of emotion involves the use of defenses against emotion which can
impede judgement because they reduce the information that the emotion provides, leading
to deficiencies in sensitivity to others, social understanding and health. Mayer and Salovey
(1995) contend that the use of the more sophisticated, cognitively complex defenses (such
as sublimation) exemplifies an emotionally intelligent non-conscious regulation strategy.
Although at this level, there is little intentional processing and so contributions to the
emotional system are judged to be more adaptive than intelligent, developed skills provide
a basis for the development of EI abilities (Mayer & Salovey, 1995). Such skills are
exemplified by the individual who is emotionally oriented, with an adaptive framework of
emotional reactions and employs minimal defensiveness.
Construction and regulation of emotion at the lower conscious level encompasses fleeting
awareness of emotions and strategies that involve directing mental action towards or away
from experiencing emotion (Mayer & Salovey, 1997). This level is involved in the
construction of the more complex, self conscious emotions (such as guilt, shame, envy and
jealousy) and occurs via transient self-instructions, societal rules and the reframing of
situations (Mayer & Salovey). Regulation of emotion at the lower conscious level involves
for example, redirection of attention through distraction and reinforcement through the
natural consequences of an emotion (e.g. happiness feels good, sadness feels bad). Those
with a high degree of competence at this level are seen as emotionally involved, with an
openness and willingness to intervene in the construction of emotions by reframing
appraisals to reassure oneself or achieve equanimity (Mayer & Salovey).
At higher levels of consciousness, construction of emotion involves more intentional,
extended attempts to understand, define and optimise emotion. Regulation of emotion at
this level consists of a reflective, or “meta-experience” of emotion (Mayer & Salovey,
1995, p. 203) whereby the individual reflectively monitors emotions by attending to them,
evaluating the qualities of the emotions and regulating the emotion or mood (if deemed
appropriate) through repair, maintenance or dampening. Individuals considered to have
16
gained accomplishment at this level are seen to have expert knowledge about emotions and
their regulation, which they are able to apply in accordance with their goals.
Individuals may utilise all three levels of consciousness at one time, but there is argued to
be a developmental progression from non-conscious to higher conscious strategies
underpinned by increasing cognitive involvement (Mayer & Salovey, 1995). For a neonate,
regulation of emotion is initially wholly conducted, and later facilitated, by the prime care
giver (Shore, 1994). Early, pre-verbal strategies for emotion regulation (including thumb-
sucking and gaze aversion) appear in the first weeks of life with a developmental
progression from simple learned strategies (e.g. seeking caregiver support) to more
sophisticated strategies (e.g. self soothing; Kopp, 1989). Pre-schoolers employ techniques
such as self distraction and apply simple rules for feeling and emotion display (Denham,
1998). Older children increasingly utilise cognitive rather than behavioural strategies which
become more highly developed in insightful coping strategies in adulthood (Saarni, 2000).
By the age of eight or nine, young children have learned to regulate their emotions by
means of cognitions and thoughts about themselves, their feelings or others‟ feelings
(Harris, 1989; Meerum Terwogt & Stegge, 1995; Saarni, 1999). In a longitudinal
investigation of the self-regulation of emotion of children over an eight-year period,
Raffaelli, Crockett, and Shen (2005) found evidence of age-related increases in self-
regulation, especially from early childhood (four to five years-of-age) to middle childhood
(eight to nine years-of-age), with girls showing a greater capacity for regulation of
emotions than boys at all ages (four to thirteen years-of-age). In terms of strategies used to
regulate emotion in other children, there is a shift with age (from four to twelve years-of-
age) from material intervention strategies to strategies involving verbal intervention
(McCoy & Masters, 1985).
The ability to use language enables toddlers to self-regulate such that they can talk
themselves through emotionally challenging situations or express their concerns to a person
who can help regulate their mood (Bretherton, Fritz, & Zahn-Waxler, 1986). Along with
understanding of display rules (culturally determined rules involving the masking of
emotional expression such that one‟s emotional expression does not necessarily reflect
17
one‟s emotional experience; Gnepp & Hess, 1986), children are better able to manage their
emotion in the application of these rules, for example by masking or altering emotion
expression. Ten-year-old children report using these rules more than six-year-old children
(Zeman & Shipman, 1996). While very young children have limited explicit knowledge
about ways they manage emotional responses, by six years-of-age, they report controlling
feelings by taking direct action (e.g. wipe away tears, hold in angry feelings, cuddle a soft
toy), and by age ten, through altering their own appraisals (e.g. think that the situation is
not that bad; Shaver, Schwatrz, Kirson, & O'Connor, 1987). By middle childhood, most
children have learned basic adaptive methods for regulation of emotion and are able to
effectively regulate emotion expression (Denham, 1998; Saarni, 1999). Understanding of
the rules and strategies associated with regulation of emotion is generally found to precede
application of this understanding and there may be a considerable time lag (Meerum
Terwogt & Stegge, 2001). There appears to be a general progression noted in development
of regulation of emotion, towards use of increasingly refined strategies. While simpler
strategies such as “counting to ten” before getting mad or smiling politely when receiving
an unwanted gift may be utilised by young children, by early adulthood, more sophisticated
strategies may be used including the ability to avoid feelings or to reframe appraisals to
reassure oneself or achieve equanimity (e.g. Erber, 1996; Larsen, 2000; Tice &
Bratslavsky, 2000; Wenzlaff, Rude, & West, 2002). McRae and colleagues (McRae,
Ochsner, Mauss, Gabrieli, & Gross, 2008) found no gender differences in emotion
regulation ability in adults, however their results suggested differences in the way that men
and women use cognitive regulation of emotion such that men expend less effort perhaps
due to greater use of automatic regulation and women use positive emotions in the service
of reappraisal of negative emotions.
2.4 Summary and dissertation aims. From the preceding literature review, it may be seen that in line with Mayer and Salovey‟s
(1997) hierarchical four branch model, there is both empirical and theoretical evidence for a
progression of EI abilities, consistent with extant approaches to human development (Izard,
1984). Cognition appears to develop in stages with revolutionary changes to the quality of
thinking (e.g. Bower & Peterson, 1972; Fischer, 1980; Piaget, 1985), while emotion is
18
thought to steadily develop with increasing complexity and involvement of cognitions
(Sroufe, 1995) and both develop in concert with one another (Bell & Wolfe, 2004; Carroll
& Steward, 1984). As the intersection between cognition and emotion (Salovey & Mayer,
1990), development of EI may be underpinned by dramatic shifts afforded by cognitive
development with an overlay of increasing sophistication of emotional abilities within each
cognitive stage. As mentioned previously, while gender differences for some emotional
abilities are found in adults, there is a lack of clear evidence of gender differences in
emotional abilities prior to puberty (Brody, 1985). This may be partially explained by
neurological changes during puberty (Felson & Haynie, 2002; Susman & Rogol, 2004;
Quevedo, Benning, Gunnar, & Dahl, 2009), the influence of socialisation (Adams, Kuebli,
Boyle, & Fivush, 1995), or a combination of both. Borrowing from Cattell‟s (1987)
financial analogy for the acquisition of knowledge and skills as the result of investment of
Fluid Intelligence in learning situations, Zeidner and colleagues (2003) have proposed a
“Multi-Level Investment Model of EI” that describes the changing influence of three
factors over time: biology/temperament, rule-based learning and insightful learning. While
the theoretical argument is persuasive, empirical investigation of the developmental
progression of EI is limited by the lack of a specific measure of Ability EI in children.
The current dissertation aimed to develop a measure of pre-adolescent children‟s Ability EI
based on Mayer and Salovey‟s (1997) model, with items generated based on the specific
abilities outlined within the four branches. As mentioned above, such a measure would
allow empirical investigation of the developmental progression of EI in children, as well as
permit the testing of other theoretical models (e.g. Zeidner, Matthews, Roberts, &
MacCann, 2003). Additionally, a measure of Ability EI in children may provide a means of
assessing the variety of EI development programs that have proliferated since the
popularisation of EI (Greenberg, et al., 2003; Payton, Warldlaw, Graczyk, Bloodworth,
Tompsett, & Weissberg, 2000). Extant measures of Ability EI will be reviewed in the next
chapter, along with a discussion about the relative merits of “typical” as compared to
“maximal” EI in understanding development in children.
19
Chapter 3: Emotional Intelligence Measures.
3.1 Test validity and reliability. With few constructs that may be measured directly, psychological research is challenged to
devise indirect but accurate methods of measurement. As new constructs are developed,
new measures are required to test the theoretical basis of such constructs. The scientific
process demands that exacting standards are applied to new constructs as well as the tests
developed to measure them. Many of the qualities required of a new psychological test are
closely linked to, and therefore limited by, the understanding of the construct. These centre
on establishing a place within the nomological network and include that the test must
adequately sample the construct (content validity), it must measure what it is purported to
measure (construct validity), be associated with similar measures (concurrent validity) and
not associated with dissimilar measures (divergent validity) and it must be related to
outcomes predicted by theory (criterion validity; Murphy & Davidshofer, 1994). With the
development of sophisticated programs for conducting factor analysis of items, factorial
validity is increasingly used to determine whether a measure holds the same structure as
that theorised for the construct (Tabachnick & Fidell, 2001). Minimizing non-random
sources of error in the form of response bias is also considered to be an aspect of validity
(King & Bruner, 2000) closely related to divergent validity (Holden & Fekken, 1989).
Reliability requirements more specifically pertain to the measure than the construct and
relate to the consistency of test scores. These include that test items should be related to one
another (internal consistency, split-half reliability), test scores should be similar to different
forms of the test (alternate forms reliability) or when measured at different times (test-retest
reliability; Murphy & Davidshofer, 1994). Test-retest reliability assumes that the construct
under assessment is stable over time, with variation attributed to measurement error.
Therefore a construct purported to develop with age and experience, should exhibit
moderate rather than high consistency over time. In this instance, test-retest reliability is
better understood as an indication of temporal stability (Murphy & Davidshofer, 1994).
Test reliability is underpinned by classical test theory, whereby test scores inevitably
incorporate not only the true score but also an amount of random error (Novick, 1966).
20
Cronbach and colleagues (1972) have argued that classical test theory is merely a special
case of the generalisability approach that recognises that error is not always random and
that it is often useful to identify specific, systematic sources of inconsistency in
measurement. That is, Generalisability theory identifies both systematic and random
sources of inconsistency that may contribute to errors of measurement. Under classical test
theory the goal of test development is to develop an accurate measure of the proposed
construct that minimizes error as much as possible. Generalisability theory insists that test
developers should also determine that the test is reliable for all purported applications of the
test (Murphy & Davidshofer, 1994). Generalisability coefficients may be determined from
generalisability studies, whereby test results are compared across a range of different
situations (e.g. age of respondent, test locations, time of day, presence of others, scoring
system). While classical test theory does not account for reliability of measures of
constructs argued to change or develop over time, generalisability theory may be more
useful (cf. van Agt, Essink-Bot, Krabbe, & Bonsel, 1994).
In terms of the nomological network for Mayer and Salovey‟s (1997) hierarchical four
branch Ability Model of EI, it is generally agreed that the construct should be distinct from
intelligence and personality (Day & Carroll, 2004; Fiori & Antonakis, 2011; Mayer,
Caruso, & Salovey, 1999; O'Boyle, Humphrey, Pollack, Hawver, & Story, 2010; Schulte,
Ree, & Carretta, 2004). However, some relationship with both intelligence (Farrelly &
Austin, 2007; Mayer, Caruso, & Salovey, 1999) and personality (O'Boyle, Humphrey,
Pollack, Hawver, & Story, 2010) is expected and is indicative of construct validity.
Theoretically, Ability EI should predict number and quality of interpersonal relationships
(Farrelly & Austin, 2007), job performance (Ashkanasy & Daus, 2005; Brackett & Mayer,
2003; Dulewicz & Higgs, 2000; Law, Wong, Huang, & Li, 2004; O'Boyle, Humphrey,
Pollack, Hawver, & Story, 2010), academic performance (Schutte, et al., 1998), and mental
health (Farrelly & Austin, 2007). O‟Boyle, Humphrey, Pollack, Hawver, & Story (2010)
recommend that for EI measures to be hold construct validity, they should predict these
factors over and above the influence of intelligence and personality. However, such a
requirement may be too strict for the development of the EI construct and its measures
21
which is in its nascent beginnings compared to the fields of intelligence and personality
(Van Rooy, Viswesvaran, & Pluta, 2005).
With a broad range of purported EI measures yielding disparate results, researchers have
attempted to explain discrepancies. Some have directed criticism towards the construct of
EI, arguing that disparate results attest to poor construct validity (Davies, Stankov, &
Roberts, 1998; Locke, 2005). However, as discussed in the previous chapter, many
researchers distinguish between measures based on Mixed and Ability models of EI.
Brackett & Mayer (2003) have argued that mode of measurement further distinguishes
Mixed and Ability EI. Despite being based on the Ability Model and showing factorial
validity (Ciarrochi, Chan, & Caputi, 2000; Saklofske, Austin, & Minski, 2003), Brackett &
Mayer have classified the SSREIS as operationalising a Mixed Model approach. This
argument is partially based on the measure being self-report and partially due to low
correlations with their performance measure (MSCEIT; Mayer, Salovey, Caruso, &
Siatarenios, 2003) with the arguable assumption that the MSCEIT is the gold standard
against which other measures should be definitively compared. Others have distinguished
the self-report measures variously as Trait EI (Petrides & Furnham, 2001) or Typical EI
(Schutte, Malouff, & Bhullar, 2009). While these terms have been used synonymously
(Schutte, Malouff, & Bhullar, 2009), Petrides and Furnham‟s (2003) conceptualization of
Trait EI, places EI firmly within the field of personality and as a different but related
construct to ability. As a typical ability, Typical EI would be considered to be dependent
upon motivation and personality characteristics such as optimism (DuBois, Sackett,
Zedeck, & Fogli, 1993) but also seen as being on a continuum with maximum performance
(Sackett, Zedeck, & Fogli, 1988).
3.2 Typical and maximum performance. Researchers have begun to conceive of EI in terms of typical and maximal modes of
performance (Gignac, 2010; Schutte, Malouff, & Bhullar, 2009). Cronbach (1960)
designated maximum performance tests as those that are intended to assess individual
differences within the ability domain, and typical performance as those that are intended to
assess individual differences outside of the ability domain. However, Sackett and
22
colleagues shifted the classification of the maximum and typical modes of performance,
viewing typical and maximum performance as existing on a continuum (Sackett, Zedeck, &
Fogli, 1988). Maximum performance measures have three defining characteristics;
awareness that performance is being monitored, instructions to focus full attention on
optimal performance of the task and a sufficiently brief task duration to allow sustained
effort throughout the measurement period. In contrast, typical performance is generally
unmonitored, is more dependent upon internal motivation and has fewer constraints on
attention to the task and length of measurement period (Sackett, Zedeck, & Fogli, 1988;
Sackett, 2007).
In line with Cronbach‟s classification, performance tests of Ability EI have been classified
as Maximum EI and self-report measures deemed as Typical EI (Schutte, Malouff, &
Bhullar, The Assessing Emotions Scale, 2009). However, the reverse is also possible. Self-
report measures of Maximum performance and performance measures of typical
performance have been used (Willerman, Turner, & Peterson, 1976). Generaliseability
studies investigating the effects of mode (typical versus maximum performance) for
performance and self-report measures may provide further understanding of differences
found between extant self-report and performance based measures of Ability EI.
After a meta-analysis of 13 studies, Van Rooy, Viswesvaran, and Pluta (2005) concluded
that performance and self-report measures of EI reflect distinctly different constructs (r =
.14). However they did not allow for the differing modes and models of EI upon which the
measures were based. That is, a test of maximum performance based on the Ability Model
(e.g. MSCEIT) was compared with measures of typical performance based on both the
Ability Model (e.g. SSREIS) and Mixed Models (e.g. EQ-I, ECI). As raised by Gignac
(2010), typical and maximum job performance measures have been found to have similar
inter-correlations (Sackett, Zedeck, & Fogli, 1988), which were interpreted as indicating
that the different measures involve relatively distinct but related approaches to the
assessment of job performance. Therefore, a fairer assessment would involve comparisons
of measures with only one degree of difference (e.g. compare performance and self-report
measures of typical performance of Ability EI).
23
While performance measures of Typical EI and self-report measures of Maximum EI have
not yet been developed, comparable research has been reported. Arguing that a failure to
account for distinctions between maximum and typical tests contributes to low predictive
validity of personality in predicting behavior, Willerman, Turner and Peterson (1976)
compared self-report of maximum and typical expressivity of emotion (anger and elation)
with performance based measures. They found that for angry expression, self reports of
maximum expressivity were superior to self reports of typical expressivity in predicting
both maximum and typical performance in the laboratory. Although a similar trend was
found for elation, the advantage of maximum self-report measures over typical self-report
measures was negligible. It is possible that „typical expressivity of anger‟ elicited a greater
level of arousal than „typical elation‟ (cf. Levenson, Ekman, & Friesen, 1990; Reisenzein,
1994), making it more analogous to a maximum performance situation (cf. Ebbeck &
Weiss, 1988). This may be associated with the negativity bias whereby negative
information is attended to more than positive stimuli, with greater informational value
requiring greater attention and cognitive processing (Peeters & Czapinski, 1990).
Freudenthaler and Neubauer (2005, 2007) also argue for the importance to distinguish
between measures of maximum and typical performance. They assert that maximum
measures of emotional management merely provide an indication of an individual‟s
emotional knowledge about the effectiveness of various behaviours, thereby representing
what they are capable of when highly motivated with only a moderate suggestion of the
extent to which this capacity is typically utilised. In their 2007 study, they measured typical
and maximum emotional management ability (EMA) utilising equivalent stimuli but
different instructions. They presented vignettes of emotional situations with respondents
indicating the most adequate behaviour (maximum performance condition) or the response
that typifies their own behaviour (typical performance condition). As predicted, maximum
EMA was significantly correlated with cognitive ability but not personality traits. The
reverse was the case for typical EMA, which was associated with personality traits but not
cognitive ability. Specifically, agreeableness and conscientiousness were more strongly
related to typical than maximum EMA. Freudenthaler & Neubauer (2007) propose that this
suggests that highly agreeable/conscientious individuals are more inclined to act closely to
24
their maximum EMA when behaving typically in emotional situations that less
agreeable/conscientious individuals.
Despite the typical/maximum performance distinction being cited in numerous theoretical
papers, there has been a lack of empirical research investigating the distinction, which has
only recently begun be corrected (Klehe, Anderson, & Viswesvaran, 2007). This
burgeoning field may be useful in guiding the direction of research in the application of EI
to social, school and work-related performance. Boudreau (1991) and Guion (1998) have
underlined the importance of matching predictor with criterion measures in terms of typical
and maximum performance. For example, while maximum performance measures are more
indicative of an individual‟s potential than typical performance measures, typical
performance measures are better indicators of an individual‟s usual performance (Deadrick
& Gardner, 2008).
3.3 Objective measures of Ability Emotional Intelligence.
3.3.1 Tests not specifically designed to measure Emotional Intelligence. There are various performance tests that are argued to measure individual branches of
Mayer and Salovey‟s hierarchical four branch model of Ability EI, but were not specifically
designed as an operationalisation of this model. For example, the Diagnostic Analysis of
Non-verbal Accuracy Scales (DANVA and DANVA-2; Nowicki & Duke, 1994), the
Japanese and Caucasian Brief Affect Recognition Test (JACBART; Matsumoto, LeRoux,
Wilson-Cohn, Raroque, & Kooken, 2000) as well as the Facial Expressions of Emotion:
Stimuli and Tests (FEEST; Young, Perrett, Clader, Sprengelmeyer, & Ekman, 2002) use
the Facial Affect Coding System (FACS; Ekman & Friesen, 1975), which is analogous to
the “identify emotions in others” aspect outlined in ability 2 of Branch I. The Emotion
Knowledge Test (EKT; Izard, Fine, Schultz, Mostow, Ackerman, & Youngstrom, 2001;
Mowstow, Izard, Fine, & Trentacosta, 2002; Trentacosta & Izard, 2007) is designed for
pre-school-aged children and measures aspects of both Branches I and III. It contains three
subscales; Facial Expressions (respondents classify the emotion expressed in a series of
faces), Social Situations (respondents estimate the emotion of the main character in a
vignette) and Social Behaviour (respondents estimate the emotion of the main character in a
25
vignette). Izard (2001) found that 5-year-olds‟ EKT scores positively predicted their
teacher-rated social skills and academic competencies and negatively predicted problem
behaviours at age eight. The Levels of Emotional Awareness Scale (LEAS; Lane, Quinlan,
Schwartz, Walker, & Zeitlan, 1990) and Levels of Emotional Awareness Scale for Children
(LEAS-C) appear to measure of Branch III, understanding emotions. In these tests,
respondents are asked to indicate how they and other vignette characters would feel in
certain situations. Responses are scored qualitatively and according to a continuum of low
emotional awareness (no emotional response) to high emotional awareness (appropriate
emotional responses given).
In the absence of a specifically designed performance measure of Ability EI for children,
Hall, Geher and Brackett (2004) composed a battery of tests to explore levels of EI in
children (aged 6 to 17 years) with reactive attachment disorder (RAD). They used caregiver
ratings of the Toronto Alexithymic Scale (TAS-20; Bagby, Taylor, & Parker, 1994) as a
measure of caregiver perceptions of difficulty identifying and describing feelings and
externally oriented thinking, The Questionnaire Measure of Emotional Empathy (QMEE;
Mehrabian & Epstein, 1972) as a measure of caregiver perceptions of empathy; the
Affective Communication Test (ACT; (Friedman, Prince, Riggio, & DiMatteo, 1980) as a
measure of caregiver perception of children‟s ability to effectively communicate emotional
stimuli; and the Emotion Control Questionnaire 2 (ECQ2; Roger & Najarian, 1989) to
measure caregiver perception of children‟s ability to control emotions, emotional inhibition
and rehearsal of emotion-relevant cognitions. Wording was modified to correspond with
caregiver‟s perceptions of their children, rather than behaviours of the caregivers
themselves. However, no other adjustments were made for use as informant reports of EI in
children. Their results indicated that children with RAD differ significantly from children
without RAD as assessed by their caregivers, in that caregivers of children with RAD (as
opposed to caregivers of children without RAD) report that their children have less control
of emotional aggression, less benign control of emotions, are less empathic and less
competent in non-verbal expression of emotions, as well as greater alexithymia, and greater
tendencies towards emotional rehearsal. They reported that their results suggest that
children who are allowed to develop bonds with a primary caregiver have advantages in the
26
development of EI skills. Limitations of this study include lack of peer review (i.e.
published in an edited book); the use of caregiver reports in which the proportion of non-
parent caregiver was greater for the RAD group compared to the non-RAD group; and the
use of measures designed for use in adults.
3.3.2 The Mayer-Salovey-Caruso measures. The most comprehensive tests of Ability EI are the Multifactor Emotional Intelligence
Scale (MEIS; Mayer, Caruso, & Salovey, 1999) and its successors, the Mayer-Salovey-
Caruso Emotional Intelligence Test (MSCEIT) for adults and the Mayer-Salovey-Caruso
Emotional Intelligence Test, Youth Version for adolescents (ages 12-17 years; MSCEIT-
YV; Mayer, Salovey, & Caruso, in press; cited in Peters, Kranzler, & Rossen, 2009).
The Multifactor Emotional Intelligence Scale (MEIS; Mayer, Caruso, & Salovey, 1999) is a
lengthy scale of 402 items which are divided into 12 tasks: faces, music, designs, stories,
synthesis, feeling biases, blends, progressions, transitions, relativity, managing others, and
managing oneself. These tasks represent the three branches of Salovey and Mayer‟s
original (1990) model: emotional perception, emotional understanding, and emotional
management. Scoring is conducted in three ways: general consensus (where an individual‟s
scores are compared to the proportion of responses from the sample of respondents), expert
scoring (where scores are compared against responses from a group of experts in the field)
and target scoring (veridical scoring based on report from the individual generating the
emotion). Mayer, Caruso and Salovey (1999) reported that reliabilities for the 12 tasks
range from poor (blends: r = .49) to excellent (music and transitions: r = .94).
The Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT; Mayer, Salovey,
Caruso, & Siatarenios, 2003) is a revision of the MEIS. It has recently been shown to
correlate highly with the MEIS (r = .80) and confirmatory factor analysis indicates that the
two tests measure the same construct (Maul, 2011). The MSCEIT is the primary test used
to measure the four branch hierarchical model of EI (Mayer & Salovey, 1997) and
comprises 141 items representing “perceiving emotion”, “using emotions to facilitate
27
thought”, “understanding emotion” and “managing emotion”. The response format varies
according to the Branch measured.
An example of Branch I shows a picture of a face, and asks the respondent to use a five
point Likert scale to rank whether the person displays „no happiness‟ to „extreme
happiness‟. An example of Branch II asks “What mood(s) might be helpful to feel when
meeting in-laws for the very first time?” The respondent then chooses an answer on a five-
point Likert scale which ranges from “not useful‟ to „useful‟ for moods such as tension,
surprise and joy. An example of Branch III states “Tom felt anxious, and became a bit
stressed when he thought about all the work he needed to do. When his supervisor brought
him an additional project, he (select best choice)” where the respondent chooses the best
answer from five multiple choice items. An example of Branch IV is “Debbie just came
back from vacation. She was feeling peaceful and content. How well would each action
preserve her mood? Action 1: She started to make a list of things at home that she needed to
do.” The respondent then uses a five-point Likert scale to rate each action between „very
ineffective‟ to „very effective‟.
The MSCEIT provides a range of scores: one total score, two area scores (factor analysis
has revealed that Branches I and II fall under an „experiential‟ area, while Branches III and
IV fall under a „strategic‟ area), four branch scores reflecting each of the hierarchical
branches and eight task scores. Scoring is done via the general consensus method as well as
expert scoring. Mayer, Salovey, Caruso and Sitarenios (2003) reported that the MSCEIT
full test split-half reliability ranged from .93 for general consensus scoring and .91 for
expert scoring. Brackett and Mayer (2003) found the MSCEIT‟s subtests had acceptable
split-half reliabilities, and the split-half reliability for the whole test was reported as .91.
Validity evidence for the MSCEIT has been provided through evidence confirming the
theorized hierarchical factor structure of the ability model. The MSCEIT measures are
generally found to form coherent, recognizable factors. Moreover, a single, global EI factor
has been found for both the MEIS and MSCEIT (Ciarrochi, Chan, & Caputi, 2000; Mayer,
Salovey, Caruso, & Siatarenios, 2003; Mayer, Panter, Salovey, & Sitareneos, 2005; Palmer,
28
Gignac, Manocha, & Stough, 2005; Roberts, Zeidner, & Matthews, 2001; although see
Fiori & Antonakis, 2011; Rossen, Kranzler, & Algina, 2008 for a different result). The
Experiential and Strategic area factors are often obtained (Ciarrochi, Chan, & Caputi, 2000;
Mayer, Salovey, Caruso, & Siatarenios, 2003; Roberts, Zeidner, & Matthews, 2001), as
well as three- or four-factor models with Branches I, III and IV emerging more consistently
than Branch II (Mayer, Caruso, & Salovey, 1999; Mayer, Salovey, Caruso, & Siatarenios,
2003; Palmer, Gignac, Manocha, & Stough, 2005; Roberts, Zeidner, & Matthews, 2001). It
has been suggested the poor construct validity evidence for Branch II may be partly due to
theoretical ambiguity over how it differs from Branch IV (Joseph & Newman, 2010).
Further exploration of the results of factor analyses will be presented later in Chapter Five.
Gil-Olarte Marquez, Palomera Martin, and Brackett (2006) used a Spanish translation of
the MSCEIT to investigate the relationship of EI to social competence and academic
achievement in adolescents aged 14 to 17 years (N = 77). They found EI to be positively
related to self-confidence, leadership, the general positive factor of prosocial behaviour and
final grades, and negatively related to shyness. This relationship held after controlling for
personality and intelligence. However, there was no relationship found with social
sensibility, social apathy, aggressiveness, conformity or the general negative factor of
maladaptive behaviour either before or after controlling for personality and intelligence.
While personality and intelligence were an appropriate choice of control variables, the
desicision to use these in separate rather than combined analyses may have overestimated
the effect of EI over and above these two factors. Unfortunately, internal consistency of the
MSCEIT in this sample was not reported. Such results would be particularly important due
to the use of a measure designed for adults being used in an adolescent sample.
Mayer, Salovey and Caruso have recently developed the MSCEIT-YV as a performance
measure of Ability EI in children and adolescents aged 10 to 18 years (Peters, Kranzler, &
Rossen, 2009). The structure of the test appears to be similar to the adult version. There are
101 items (of which 97 are scored) used to measure the four branches of the hierarchical
model. Internal consistency scores provided in the manual range from an alpha of .67 for
29
Branch I to an alpha of .86 for Branch III and an alpha of .91 for the overall measure
(Barlow, Qualter, & Stylianou, 2010).
In the first study to objectively measure academic achievement and its relationship to the
Ability EI, Peters, Kranzler, and Rossen (2009) provided preliminary support for the
construct validity of the MSCEIT-YV. They found that MSCEIT-YV measured Ability EI
was moderately associated with a youth specific self-report measure of Mixed Model EI
(EQ-i:YV), indicating that the two instruments largely measure different constructs. While
the MSCEIT-YV also correlated moderately with general cognitive ability and reading
achievement, there was no association with mathematics. Peters, Kranzler, and Rossen
(2009, p. 80) proposed that the higher correlations found between MSCEIT-YV and a
“high-stakes achievement test”, suggests that EI is more strongly related to academic
performance when there is a need for managing emotions under stressful conditions. This
also highlights that as a measure of Maximum EI, the MSCEIT-YV is a better predictor of
a maximum performance criterion than the EQ-i:YV, as a measure of typical performance.
Cha and Nock (2009) used the MSCEIT-YV to provide evidence of EI as a protective
factor for suicidal behavior in a sample of adolescents (aged 12 to 19 years; N = 54)
recruited from psychiatric clinics and the community. They found that childhood sexual
abuse was strongly predictive of suicidal behaviours (ideation and attempts) among those
with low EI, weakly predictive among those with medium EI and completely unrelated
among those with high EI. Further analyses revealed that the protective effects of EI were
primarily driven by differences in strategic EI (i.e. ability to understand and manage
emotions) but not experiential EI (i.e. ability to perceive emotions and integrate emotions
into thoughts). Unfortunately, the reliability of the MSCEIT-YV was not reported, likely
because the authors sent the data to Multi-Health Systems Inc for scoring (Cha & Nock,
2009) and therefore did not have access to individual items scores. This lack of reliability
data seriously diminishes the capacity for effective peer review.
Barlow, Qualter and Stylianou (2010) used the MSCEIT-YV to measure Ability EI in
children aged 8 to 11 years of age to investigate the relationships between EI,
30
Machiavellianism (the tendency to see others as able to be manipulated in social situations)
and Theory of Mind (TOM) in this age group. They found TOM and EI to be negatively
associated with Machiavellianism. Barlow and colleagues report that their results suggest
that low EI and poor TOM skills are important for girls but not boys. Unfortunately,
reliability results specific to the sample were not reported. However, correlations of the
MSCEIT-YV with a self-report measure of Trait EI specifically designed for children
(TEIQue-CF) were significant but moderate (males: r = .24; females: r = .33), suggesting
content validity. Girls in their sample had higher MSCEIT-YV than boys (girls EI =
110.42 (13.75), boys EI = 92.16 (14.34)) with a moderate effect size (Cohen‟s d = .59).
Qualter, Barlow and Stylianou (2011) investigated the relationship between performance
measured Ability EI, self-report measured Trait EI (TEIQue-CF; Mavroveli, Petrides,
Rieffe, & Bakker, 2007) and TOM in children aged 5-7 years and 8-10 years. The
MSCEIT-YV was used to measure EI in the 8-10 year-olds and the Emotion Recognition
and Perception test (ERP; measuring perception and recognition of emotion) was used in
the 5-7 year-olds. They found that for both age groups, only Ability EI was related to false
belief understanding. Regression analyses found that the understanding and managing
branches of Ability EI predicted unique variance in false belief understanding, controlling
for age, language and the other Ability EI branches. They reported only the reliability
scores from the manual reported above. Due to high branch score inter-correlations (.61
perceiving emotions - .84 managing emotions), initial analyses were restricted to global EI
scores. No gender differences were found for any of the measures of Ability or Trait EI.
Important research is being conducted in establishing the validity of the MSCEIT-YV in
adolescents as well as pre-adolescent children. However, non-reporting of study specific
reliability estimates in this age group is a limitation. While the test developers report
acceptable reliability for branch and total scores, the reliability of scores in one study does
not necessarily generalize to another testing group, time and situation (Gignac, 2009).
Many researchers have used global rather than branch scores in their analyses due to high
correlations between the branch scores. Such high inter-branch correlations may be
indicative of EI in younger age groups being a unitary factor rather than having the four
31
factor structure found in adults. Such a result is in line with the age differentiation
hypothesis that states that from childhood to early maturity, the structure of intelligence
changes from a unified, general ability to a broad set of more specific abilities (Garrett,
1946). Research exploring the factorial structure of the test across age groups would be
important in exploring the development of EI and in ensuring valid application of this test
in adolescents and pre-adolescent children.
Being the only objective measure of Ability EI for some time, the lack of an alternative to
the MSCEIT has limited EI research (Farrelly & Austin, 2007; Fiori & Antonakis, 2011).
The Situational Judgment Tests (SJTs; MacCann and Roberts, 2008) have partially
addressed this issue. The SJTs were specifically designed to measure Branches III and IV
of Mayer and Salovey‟s hierarchical four branch model of Ability EI and will be explored
in more detail below.
3.3.3 Situational Judgment Tests. MacCann and Roberts (2008) developed the Situational Test of Emotional Understanding
(STEU) and the Situational Test of Emotional Management (STEM) to measure the two
„strategic‟ area branches of Ability EI, “understanding emotion” and “managing emotion”.
This test involves having the respondent read a series of vignettes before choosing the most
appropriate response from a list of possible choices. Roberts and colleagues (2010) argue
that using an SJT approach is ecologically valid because it is more likely to represent
reality than self-report techniques. MacCann (2010) found that strategic EI (as measured by
short form of the STEU and STEM) was a latent factor distinct from fluid intelligence (Gf)
and crystallized intelligence (Gc), though strongly related to Gc.
The STEU (MacCann & Roberts, 2008) measures Branch III of the hierarchical EI model,
emotional understanding. There are 42 items comprising 14 within a workplace context, 14
in a personal-life context and 14 context reduced. The content of the STEU and its scoring
were based on Roseman‟s (1984) appraisal-based emotion model. This model states that
felt emotion is derived from features of appraisal of the situation. For example, “relief is
associated with the perception that an unpleasant situation has stopped or been averted”
32
(Austin, 2010, p. 566). The STEU items cover a variety of emotions including; sadness,
frustration, anger, fear, and dislike (MacCann, 2006). The short form of the STEU
(comprising 23 items) has been reported as having a low internal consistency (α = .50) with
a reasonable 10 weeks test-retest reliability (r = .55; MacCann, 2010).
The STEM (MacCann & Roberts, 2008) measures Branch IV of the hierarchical EI model,
emotional management. The development of the STEM used the situational judgment test
method (McDaniel, Morgeson, Finnegan, Campion, & Braverman, 2001). The test items
were created using semi-structured interviews with 50 individuals who designed scenarios
where emotion management was required (MacCann & Roberts, 2008). Potential responses
were created by an additional 99 individuals, and four responses were chosen for each item.
Experts then assessed the response to obtain score weights. The short form of the STEM
(comprising 21 items) has been reported as having a low internal consistency (α =.50) with
a reasonable 10 weeks test-retest reliability (r = .66; MacCann, 2010).
Strength of the SJTs is that, unlike the MSCEIT family of tests, they have transparent
scoring criteria that are available to researchers (Austin, 2010). Therefore, the research
community is able to work on developing and improving the scales. Indeed, this may be
required as the few data are available about their psychometric properties and tests of
reliability and validity are not very encouraging. While the SJTs are touted as a new
performance measure of Ability EI, Fiori and Antonakis (2011) remark that with test items
very similar to those of the MSCEIT, it is more a replication than an alternative.
With the MSCEIT as the only performance measure fully representative of the four-branch
hierarchical model, it has been difficult to distinguish between test and construct effects
(MacCann & Roberts, 2008), therefore the development of other comprehensive
performance measures has been important the progression of the field. Such a new measure
has recently been devised.
33
3.3.4 Ability Emotional Intelligence Measure.
Warwick, Nettelbeck and Ward (2010) developed the Ability Emotional Intelligence
Measure (AEIM) as a new performance based measure of Ability EI that corrected some of
the limitations of the MSCEIT. They aimed to improve the validity of the emotion
perception items by using Ekman, Friesen and Hager‟s (1978) well validated database of
facial expressions of emotion. Emotion management questions were improved by
increasing the question specificity (e.g. rather than asking which strategy is most likely to
be effective in helping participants to feel “better”, participants are asked which strategy is
most likely to be effective in helping participants to feel “more calm” or “more confident”).
Additionally, emotion management questions were made more specific in terms of
clarifying the objective; the management of emotion (e.g. improve, dampen or maintain
emotions). Warwick and colleagues argue that while consensus scoring assesses a person‟s
knowledge and not the ability to perform a task (Brody, 2004), confidence scores may be
used in conjunction with consensus scoring as means of assessing the capacity to select as
well as perform the best response emotional problems.
When calculated based on confidence scores, Warwick and colleagues reported that the
AEIM produced a single general EI component that was reliable, converged with fluid
ability, was distinct from personality and incrementally predicted stress. Meanwhile
consensus scores produced a general as well as two-component solution and scores
converged with fluid and crystallized ability and empathy and were distinct from
personality and incrementally predicted loneliness. However, Antonakis and Dietz (2010;
2011) have sternly criticized the violation of the assumption of homoscedasticity in the use
of hierarchical regression, use of extreme scores analysis and the lack of correction for
imperfectly measured regressors. In reanalyzing Warwick and colleague‟s data, Antonakis
and Dietz (2011) found that the reliability-corrected multiple correlation of the AEIM with
measures of personality and intelligence was up to .69, and that the AEIM did not predict
incremental variance in GPA, stress, loneliness, or well being. The AEIM is currently
withdrawn from circulation until these problems have been addressed (Warwick,
Nettelbeck, & Ward, 2010).
34
3.4 How objective measures fit within the nomological network. Performance measures of Ability EI are claimed to correlate with existing intelligences but
be independent of personality. These claims are supported by the meta-analysis conducted
by Van Rooy and Viswesvaran (2004) which demonstrates that performance measurement
of EI is strongly associated with general cognitive ability and with non-significant
correlations with personality, although results did show statistically significant correlations
with EI and some of the branch scores of personality measures. Mayer, Salovey, Caruso,
and Sitarenios (2001) claim that the MSCEIT meets the criteria for a test of intelligence
because it has a factor structure congruent with the four branches of the theoretical model,
the four abilities have expected convergent and discriminant validity (Brackett & Mayer,
2003; Gil-Olarte Marquez, Palomera Martin, & Brackett, 2006; Mayer, Salovey, & Caruso,
2004; Lopes, Salovey, & Straus, 2003; Van Rooy et al, 2005; Warwick & Nettelbeck,
2004), develops with age and experience and the abilities are measured objectively, with
answers considered to be correct or incorrect, based on consensus or expert scoring
(MacCann, Roberts, Matthews & Zeidner, 2004; Roberts, Zeidner, & Matthews, 2001).
However, Schulte, Ree and Carretta (2004) investigated Mayer and colleagues‟ contention
that EI is distinct from g and personality. Based on a moderate observed relationship
between EI and g (measured using the Wonderlich Personnel Test) and the prediction of EI
from g, personality and gender, concluded that EI is redundant with important individual
difference variables.
3.5 Criticisms of objective measures. Brackett, Rivers, and Salovey (2011) acknowledge that the MSCEIT family of measures do
not provide for assessment of certain skills such as the ability to regulate emotions in real
time or the appropriate expression of emotion. The MSCEIT has also been criticized for not
assessing a range of non-verbal aspects of emotional perception, such as tone of voice,
posture and gesture (O'Sullivan & Ekman, 2004) and for focusing exclusively on
conscious, declarative aspects of EI (Austin, 2009). Although others have argued that non-
conscious aspects of emotional ability should not be considered to represent EI (Joseph &
Newman, 2010).
35
Fiori (2009) emphasizes that most items of the MSCEIT represent performance in
hypothetical situations, rather than actual performance. While some individuals may be
good at contemplating and describing how they might behave in hypothetical situations,
they might not be good at actually performing the behavior. Conversely, a person may be
good at performing a particular behaviour but not be especially skillful at describing it. In
line with this argument, Spector and Johnson (2006) consider that the MSCEIT is a
measure of emotional knowledge which does not necessarily reflect the performance of EI
abilities. Ashton-James (2003; cited in O'Boyle, Humphrey, Pollack, Hawver, & Story,
2010) proposes that a true measure of EI should place respondents in a context where they
can actually experience the emotions they are asked about. Perhaps the most ecologically
valid are the measures of emotion perception. However, the lack of convergence of the
MSCEIT emotion perception items with established measures of emotion perception is
concerning, suggesting that these items require further development (Mayer, Roberts, &
Barsade, 2008).
Mayer, Salovey, Caruso, and Sitarenios (2001) have claimed that the objective scoring
methods of the MSCEIT contribute its status as a test of intelligence. However, Perez,
Petrides and Furnham (2005) counter that unlike standard cognitive ability tests, tests of
Ability EI cannot be objectively scored because there are no clear-cut criteria for what
constitutes a correct response. For example, identifying the correct response for higher-
order processes such as emotion management is problematic because the right decision will
vary due to differences in personality and cultural norms (Roberts, Zeidner, & Matthews,
2001). Developers of objective Ability EI measures have attempted to bypass this problem
by relying on alternative scoring procedures, including consensus, expert and target
scoring. However, these methods also have problems. The requirements for what entails an
“expert” should be clarified and target scoring is dependent upon the introspective ability of
the target (Conte, 2005). Day (2004) has suggested that agreement with the consensus may
actually reflect an average level of EI rather than high EI. Fiori and Antonakis (2011, p.
333) argue that the high correlation between expert and consensus scores “seriously
challenges the logic behind the scoring strategy” . They argue that similar responses from
experts and the majority of people puts the existence of „experts‟ under question. They
36
suggest that “rather than doubting the existence of experts in emotions”, it is more likely
that the test does not discriminate between high and low EI. The use of confidence scores
in the AEIM may provide an alternative (Warwick, Nettelbeck, & Ward, 2010); however
validity and reliability of this measure is yet to be established.
3.6 Subjective measures of ability Emotional Intelligence. According the Petrides, Frederickson and Furnham (2004), much of the intrapersonal
component of Ability EI is not amenable to objective scoring, because the information
required for such scoring is only available through respondent introspection. Self-report
measures of EI address this issue. While Davies and colleagues (1998) dismissed self-
report measures of EI as unreliable, more recent developments have found evidence of
reliable and valid self-report measures.
In a recent meta-analysis, self-report measures (Self-report Emotional Intelligence Scale;
SSREIS and Wong Law Emotional Intelligence Scale; WLEIS) but not the performance-
based measure of Ability EI (MSCEIT) predicted job performance beyond cognitive ability
and personality (O'Boyle, Humphrey, Pollack, Hawver, & Story, 2010). Additionally,
dominance analyses revealed that in predicting job performance, both modes of
measurement exhibited substantial relative importance in the presence of personality and
intelligence. They found self-report measures of Ability EI were more closely related to
intelligence than self-report measures of Mixed Model EI, which they argued supports the
notion of the self-report Ability EI measures as a type of intelligence test.
3.6.1 Self-report Emotional Intelligence Scale.
The Schutte (1998) Self-report Emotional Intelligence Scale (SSREIS; referred to by its
authors as the Assessing Emotions Scale, and alternately as Emotional Intelligence Scale,
or the Self-Report Emotional Intelligence Scale; Schutte, Malouff, & Bhullar, 2009) was
developed as a self-report measure of Salovey and Mayer‟s (1990) model. Despite having
items derived from this Ability model, the SSREIS has variously been described as being a
measure of Trait EI (e.g. Saklofske, Austin, & Minski, 2003; Perez, Petrides, & Furnham,
2005) or Mixed Model EI (e.g. Mayer, Roberts, & Barsade, Human abilities: Emotional
37
Intelligence, 2008). While it appears a shallow analysis to identify a construct merely based
on the response format utilised, it may be argued that SSREIS is more accurately depicted
as a measure of Typical EI (Gignac, Palmer, Manocha, & Stough, 2005; Schutte, Malouff,
& Bhullar, 2009).
The 33 items comprising the SSREIS were selected based on items loading above .40 on
the first factor of a Principal Components Analysis (PCA) of a larger pool of 62 items.
According to Schutte and colleagues (1998), these items represent a general measure of EI
and represent all portions of Salovey and Mayer‟s (1990) conceptual model and
recommended using total scores on the 33-item scale. The measure was considered to be
reliable (α = .90) with a two-week test-retest reliability of .78. Validity studies revealed that
those with high scores on the instrument possessed greater attention to feelings, greater
clarity of feelings, increased mood repair, greater optimism, less pessimism, less depression
and less impulsivity. Furthermore, college students‟ scores at the beginning of the school
year predicted grade point average at the end of the year [r(63) = .032, p < .01].
Discriminant validity analyses revealed that scores on the SSREIS were not significantly
correlated with most NEO PI-R dimensions (Costa & McCrae, 1992) with the exception of
openness to experience (r = .54, p < .009) which is argued to be associated with intelligence
(Gignac, Stough, & Loukomitis, 2004). With a Flesch-Kincaid reading level of 5.68,
indicating a fifth grade reading level, this test has also been used to measure EI in
adolescents and children.
Ciarrochi, Chan and Bajgar (2001) measured 13 to 15 year-old adolescents‟ EI using the
SSREIS. They reported the SSREIS to be a reliable measure of overall EI in that age group
(α =.84) with females scoring higher than males. They admitted that reliabilities were more
mixed at the Branch level (Perception: α = .76; Managing Others Emotions: α = .66;
Managing Self-Relevant Emotions: α =.63; Utilizing Emotions: α = .55). Total EI was
positively associated with skill at identifying emotional expressions, amount of social
support, extent of satisfaction with social support, and mood management behavior. This
attests to the validity of this measure in the adolescent sample. Furthermore, incremental
validity was supported, as these relationships held after controlling for self-esteem and trait
38
anxiety. Unexpectedly, they found that self reported emotion perception was unrelated to a
performance measure of recognition of facial expressions of emotion. While they attributed
the lack of relationship to poor self reporting ability of respondents, the performance
measure may not have held ecological validity. Additionally, the typical/maximum
distinction may account for this result, whereby the self-report of typical, usually low
effort, performance may not be reflective of the usually high effort maximum performance
given under testing conditions (Sackett, Zedeck, & Fogli, 1988; Sackett, 2007; Willerman,
Turner, & Peterson, 1976). Furthermore, performance may be impeded in the maximum
condition through the impact of anxiety associated with the testing conditions (Klehe,
Anderson, & Hoefnagels, 2007).
Charbonneau and Nicol (2002) reported poor validity of the SSREIS in 191 adolescents
(aged 12 to 18 years) enrolled in a three week summer camp providing military skills and
training. While they found an impressive reliability for their sample‟s overall SSREIS score
(α = .90), they argued that the moderate correlation (r = .40) of the SSREIS with a measure
of the tendency towards social desirable responding is problematic and they argue that the
SSREIS may not be suitable for use in adolescents. Additionally, they did not find the
gender differences reported in adult studies. They surmised that this may be indicative of
less developed EI in adolescents compared to adults, adding that there were lower scores
for the adolescents in their study compared to the adult sample reported by Shutte and
colleagues (1998).
Siu (2009) determined that the four factor but not the one factor structure holds for
adolescent secondary school students (N = 325; ages not provided). This suggests that the
branch but not the overall scores are meaningful in this population. While the overall score
provided a respectable reliability score (α = .86), the branch score reliabilities (Self
Management of Emotions: α = .68; Awareness of Others‟ Emotions: α = .73; Social Skills:
α = .77; Positive Use of Emotions: α = .57; also cf. Ciarrochi, Chan, & Bajgar, 2001), may
limit the utility of the SSREIS in adolescents. Siu (2009) found gender differences, with
females scoring higher than males on both the Self Management and the Social Skills
scales. This appears to contradict Charbonneau and Nicol (2002) findings of no gender
39
differences. However an overall measure of the SSREIS was used in the earlier study and
Branch scores were used by Siu (2009). Another explanation is that Siu‟s (2009) model
may have been “overfitted” to her own sample, limiting the applicability of this research to
other samples. After Siu (2009) modified the scale, by removal of poorly loading items, the
changed scale was not validated in a new sample (as recommended by Gerbing &
Hamilton, 1996). This also suggests that the finding of a four factor but not a one factor
model should be confirmed in a different sample.
Liau, Liau, Teoh and Liau (2003) used the SSREIS to examine the influence of EI on
problem behaviours in Malaysian adolescents (mean age = 15.9 years). They found that the
SSREIS scores were reliable in their sample (α = .76). Their results revealed low to
moderate negative correlations between EI and problem behaviours (r = - .21 to -.34).
When entered into a hierarchical regression analysis, they found that EI was a significant
moderator in the association between level of parental monitoring and aggression, as well
as between level of parental monitoring and delinquency. They also found level of parental
monitoring was inversely related to aggression and delinquency at high but not low levels
of adolescent EI.
The SSREIS has recently been studied in pre-adolescent children aged 10-11 years.
Williams, Daley, Burnside, and Rowley (2009) investigated the association between the
SSREIS and objective measures of emotional ability. These tasks included a task from the
Emotion Focusing Task (EFT; used to measure unprompted emotional perception in
response to photographic images of everyday behavioural scenes), a modified version of
Story Stems (SS; used as a measure of prompted emotional perception, the ability of
emotions to facilitate thought and the ability to manage emotions) and stimuli from the
Ekman-60 faces test (FEEST; used to measure recognition of the facial expression of
emotions). To make the SSREIS more appropriate for the age group, Williams and
colleagues simplified the wording for five items. For example, the item “Other people find
it easy to confide in me” was changed to “Other people find it easy to tell me things”. They
also removed the item “Some of the major events in my life have led me to re-evaluate
what is important and not important” because it was deemed to be inappropriate for
40
children in this age group. Another modification was that items were read aloud to
participants. The overall scale was found to have good reliability (α = .86), however branch
scores were not reported. Gender differences were not found. While a small positive
association (r = .19) was found with prompted emotional perception, the ability of emotions
to facilitate thought and the ability to manage emotions (as measured by SS), there was no
significant relationship found with unprompted emotional perception (as measured by the
EFT) or measure recognition of the facial expression of emotions (as measured by FEEST).
The findings of Siu (2009), suggest that the use of a unitary EI score rather than branch
scores may partially explain these low correlations. Additionally, different modes of
measurement and the typical/maximum ability distinction (cf. Freudenthaler & Neubauer
2005, 2007) may also account for the lack of association. Williams and colleagues‟ results
indicated construct validity through low to moderate associations between the SSREIS and
measures of psychopathology (Anxiety: r = -.22; Depression: r = -.31; Disruptive behavior:
r = -.31), a moderate association with self-concept (r = .57). Additionally, in accordance
with Mayer and colleagues (2001), there was a small, positive association with cognitive
ability (r = .14). William and colleagues also submitted their measures to a Principal
Components Analysis and found that the SSREIS loaded onto a different factor than the
objective measures. They asserted that this lends support to the argument for a distinction
between measures of typical and maximum performance of emotional skills in
preadolescence. However, the discrepancy may also be attributed to different modes of
measurement (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003).
3.6.2 Criticisms of the SSREIS. The decision by Schutte and colleagues (1998) to use an orthogonal rotation in the
development of the SSREIS has been called into question. With the measure being based
on a theoretical model which suggests that the underlying components are related, some
have argued that the use of an oblique rotation method would have been more suitable
(Petrides & Furnham, 2000; Gignac, Palmer, Manocha, & Stough, 2005). Additionally, as
the rotation method used distributes variance away from a general factor, the retention of
the first factor with the claim that it is a general factor is erroneous (Petrides & Furnham,
2000; Gignac, Palmer, Manocha, & Stough, 2005). Furthermore, the single factor retained
41
by Schutte and colleagues (1998), left 82.6% of the variance unexplained (Petrides &
Furnham, 2000). Gignac and colleagues (2005) have also argued that the demarcation
criterion of .40 for identification of significant factor loadings may have been too strict.
Petrides and Furnham (2000) reanalyzed Shutte‟s (1998) data using a confirmatory factor
analysis procedure. Their results were inconsistent with a one factor solution. Exploratory
Factor Analysis (EFA) yielded a four factor solution (corresponding to Optimism,
Appraisal of Emotions, Utilisation of Emotions, and Social Skills) accounting for 40.4% of
the total variance, a substantial improvement over Shutte‟s 17.4%. The four factors
identified by Ciarrochi and colleagues (2001) were described as: Perception of Emotions,
Managing Emotions in the Self, Social Skills or Managing Other‟s Emotions, and Utilising
Emotions. Saklofske and colleagues (2003) replicated the four factor model using
Confirmatory Factor Analysis (CFA). However this was likely to have been after a
considerable number of modifications to the model (Gignac, Palmer, Manocha, & Stough,
2005). Gignac and colleagues have argued that these solutions did not take into
consideration the six factor model (Salovey & Mayer, 1990) upon which the measure was
based. However, they could only identify four of the six factors. While “appraisal of
emotions in the self‟, „appraisal of emotions in others‟, „emotional regulation of the self‟,
and „utilising emotions in problem solving‟ were identified, „emotional regulation of
others‟ and „emotional expression‟ were not. A more complete discussion of the results of
factor analytic studies of the SSREIS will be presented later in Chapter Five.
A further criticism of the SSREIS has been the lack of negatively keyed items, making the
scale susceptible to bias from respondent acquiescence (Gignac, Palmer, Manocha, &
Stough, 2005). Austin, Saklofske, Huang, and McKenny (2004) constructed a revised
version of Schutte‟s (1998) SSREIS, with reversed wordings devised for nine of the
original 30 forward-keyed items and eight new items. However, they found that the
changes did not improve the scale‟s reliability, with the exception of a slight improvement
in the Utilisation of Emotions Branch. They could not explain a change in factor structure
with the minor changes to the scale and suggested that further development work on the
SSREIS is required.
42
3.6.3 Wong Law Emotional Intelligence Scale.
Wong and Law (2002) have also developed a self-report measure of Ability EI, specifically
designed for Chinese speakers in Hong Kong. Although Wong and Law (2002) specify that
their measure (Wong Law Emotional Intelligence Scale; WLEIS) is based on Mayer and
Salovey‟s (1997) model, their measure appears to be more closely associated with that of
Davies, Stankov, and Roberts (1998); which partially reflects Salovey and Mayer‟s (1990)
model (Ng, Wang, Zalaquett, & Bodenhorn, 2007). With Branch III distributed across two
dimensions, including it with „appraisal of emotions in self and others‟, there is no clear
representation of „understanding emotions‟. Wong and Law (2002; p 246) utilised 120
managers and undergraduate students to generate items after introduction to the four
dimensions defined as: Self Emotional Appraisal (SEA; “relates to the individual‟s ability
to understand their deep emotions and be able to express these emotions naturally”),
Other‟s Emotional Appraisal (OEA; “relates to peoples‟ ability to perceive and understand
the emotions of those people around them”), Regulation of Emotion (ROE; “relates to the
ability of people to regulate their emotions, which will enable a more rapid recovery from
emotional distress”) and Use of Emotion (UOE; “relates to the ability of individuals to
make use of their emotions by directing them towards constructive activities and personal
performance”).
From their initial item pool, a seven point Likert scale (where 1 = totally disagree to 7 =
totally agree) was utilised to garner responses from 189 undergraduate students. After
selecting 16 items (four per dimension) based on items with the highest factor loadings
Wong and Law (2002) found the final four factor solution to explain 71.5% of the variance.
A sample item from the SEA is “I have a good sense of why I have certain feelings most of
the time”. A sample item from the OEA is “I always know my friends‟ emotions from their
behavior”. And sample items from the UOE and ROE are “I always set goals for myself”
and “I have good control of my own emotions” respectively. Assessments of the test‟s
validity and reliability were conducted in another sample of 110 undergraduate students and
116 non-teaching employees of the university. The interscale correlations were mild to
moderate in one sample (r = .13 to .42) and higher in the other sample (r = .60 to .76;
Wong & Law, 2002). The ranges of coefficient alpha for the scales for the various studies
43
conducted as part of their scale development were .86 to .92 for SEA, .82 to .93 for OEA,
.84 to .88 for UOE, and .71 to .91 for ROE (Wong & Law, 2002). No test-retest reliability
was reported. Wong and Law (2002) determined convergent, incremental and discriminant
validities in additional independent samples. As measured by the WLEIS, they found EI to
be significantly positively correlated with job performance (r = .21), job satisfaction (r =
.40) and life satisfaction (r = .16 to .46), supporting convergent validity of the scale. In
support of discriminant validity, they found a significant negative correlation with
powerlessness (r = -.13 to -.39). Furthermore, the scale was moderately correlated with a
Mixed Model measure of EI (EQ-I; r = .63), and exhibited minimal correlations with IQ
estimates (r = -.19 to .06). Incremental validity of the WLEIS was demonstrated by using it
to predict life satisfaction in a hierarchical regression, controlling for personality (Big Five
dimensions of Neuroticism, Extraversion, Openness, Agreeableness and
Conscientiousness). The WLEIS significantly explained the additional portion of the
variance in life satisfaction. Further support for the construct and criterion validity of the
WLEIS in a management context was provided by Law, Wong and Song (2004).
Interestingly, the WLEIS has attracted very little research attention, with Ng, Wang,
Zalaquett, and Bodenhorn (2007) conducting the first study aside from those conducted by
the scale‟s developers. While Wong and colleagues‟ research has been focused on a
Chinese speaking population in Hong Kong, Ng and colleagues (2007) examined the
factorial validity of the WLEIS in a culturally broad sample of international students
attending universities across the United States of America. Results of their CFA supported
the factorial validity in this sample and confirmed the reliability of the four dimensions of
the WLEIS (SEA α = .84; OEA α = .84; UOE α = .85; ROE α = .87) and the entire scale (α
= .91). Further discussion of the results of factor analytic studies of the WLEIS will be
presented in Chapter Five.
3.7 General criticisms of subjective measures. While it is conceded that compared to performance assessment, self-report measures have
positive features including that they are relatively low cost, easy to administer and take
considerably less time to administer (Brackett, Rivers, Shiffman, Lerner, & Salovey, 2006),
researchers have raised concerns about self-report measures of EI (e.g. Mayer & Salovey,
44
1997; Zeidner, Matthews, & Roberts, 2004). Criticisms are generally centred on concerns
about impression management (the propensity to give responses considered to be socially
desirable), the ability of respondents to judge their own performance (having to give a
highly subjective weighted average of relevant behaviours at both positive and negative
extremes and decide with whom they should compare their abilites) and overlap with
personality measures (Conte, 2005; Davies, Stankov, & Roberts, 1998; Landy, 2005).
3.8 General summary and conclusions. In reviewing extant measures of Ability EI, it appears that the number of measures for
adults is growing and a performance measure has recently been developed for adolescents.
While adult measures of self-report EI have been used for children and adolescents,
possible structural variance across ages has not been considered. This research has garnered
mixed results in terms of factorial stability and test reliability. Notable in absence is a
specifically developed measure of Ability EI in pre-adolescent children, performance or
self report. As argued in the previous chapter, such measures are required for assessment of
the hierarchical four branch model of Ability EI (Mayer & Salovey, 1997) as well as
evaluation of programs purported to develop a child‟s EI. For these purposes, a measure
should be sufficiently sensitive to change, able to be administered to groups of children and
have items and a response format that are easily understood by children.
With its focus on day-to-day behavior, measures of Typical EI may be useful for screening
purposes, in identifying children with difficulties related to low EI. Children with low
Typical EI scores would include those who have a low level of Maximum EI and also those
who despite having a high Maximum EI, are not typically performing to their potential.
Further measurement of Maximum EI in these children could identify those who may
benefit from training in EI (low Maximum EI) or further investigation to determine reasons
for non-optimal performance (high Maximum EI). However, prior to this, it would be
important for the effects of training on Typical EI and Maximum EI as well as causes and
results of non-optimal EI performance to be empirically determined.
45
Typical EI and Maximum EI are both likely to be important for investigating the
development of EI in children. Indeed, the relationship between Typical EI and Maximum
EI may provide important insights as to why a child is not performing to their potential (cf.
Mangos, Steele-Johnson, LaHuis, & White, 2007). Inclusion of a comparison of Typical EI
and Maximum EI in the evaluation of EI development programs may be useful in
determining possible differential effects.
The next chapter will explore issues relevant to the development of a measure of Typical
and Maximal EI in pre-adolescent children. In particular, issues related to response bias,
wording of items and choice of response format will be discussed. The generation of self-
report and performance based test items will be detailed, with reference to empirical
evidence upon which the items are based. Specific hypotheses related to the measure will
be developed.
46
Chapter 4: Child Psychometrics and Test Development.
4.1 Psychological measurement in children. Cognitive functions related to language, literacy and memory continue to develop
throughout childhood and adolescence (Piaget, 1929). Cognitive immaturity may hinder a
young respondent‟s accuracy in answering both self-report and objective test questions.
While objective measurement in children is commonplace (e.g. Weschler scales of
intelligence, memory and achievement), use of self-report measures in assessing children‟s
experience is more contentious. With a large component of EI entailing subjective
experience, self-report measures of EI provide detail that cannot be tapped by other modes
of measurement. While parent and teacher reports may serve as proxies, there is evidence
that children are better able to provide reliable information about themselves than adults
who know them well (e.g. Ennett, Devellis, Earp, Kredich, Warren, & Wilhelm, 1998;
Tizard, 1986; Vogels, et al., 1998; however cf. Cremeens, Eiser, & Blades, 2006). Puig-
Antich and Chambers (1978) indicate that discepencies between child and parent reports
usually pertain to the child‟s subjective phenomena and private experience. Furthermore,
Christensen and James (2000) argue that when questions are relevant to the child‟s own
experience even quite young children can make insightful respondents.
There is empirical support for the use of self-report with children from the age of seven
years, albeit with carefully adapted questions (Borgers, De Leeuw, & Hox, 2000; Scott,
1997). For children to reliably self-report on EI abilities, they should have sufficient
capacity to perceive, reflect upon and express affect, otherwise termed “affect
consciousness” (cf. Mohaupt, Holgersen, Binder, & Hostmark, 2006). While affect
consciousness develops throughout childhood, by the age of five a child can “represent and
attribute different states of mind, to understand their relatedness, and to see that mind states
have the potential of becoming actions” (Mohaupt, Holgersen, Binder, & Hostmark, 2006,
p. 241). In line with this development, children of this age are also able to identify others‟
perspectives (Flavell, 2004) and predict other‟s emotions accordingly (Rieffe, Meerum
Terwogt, Koops, Stegge, & Oomen, 2001). Therefore, along with the ability to report on
their own experience, pre-adolescent children are likely to have the capacity to describe
47
how others view them. This would be necessary for self-report assessment of emotional
expressivity (e.g. “People can easily tell how I am feeling by the sound of my voice”). In a
study of children in families, primary school aged children (aged 8-9 years) were able to
give articulate and informative answers to questions about family circumstances as long as
the questions were about the present, or very recent past (Amato & Ochiltree, 1987).
Evidence suggests that the clarity of questions influences the quality of the data, especially
for younger children and that complex questions are problematic regardless of the child‟s
age (De Leeuw & Otter, 1995). Hypothetical questions and questions with a complex
structure such as double-barreled or negative questions cause particular problems for
children (Amato & Ochiltree, 1987; De Leeuw, Borgers, & Smits, 2004). Clear instructions
may assist the child in the understanding of content (Borgers & Hox, 2000; Holoday &
Turner-Henson, 1989) and with the often literal understanding of young children, care
should be taken to ensure language is direct and unambiguous (Holoday & Turner-Henson,
1989; Scott, Brynin, & Smith, 1995).
Asking questions that are meaningful to the child‟s own experience is not sufficient to
guarantee that the child will give meaningful answers. Another factor that is fundamental to
improving data quality concerns children‟s willingness and ability to answer questions and
articulate their subjective experience. This largely depends on the appropriateness, number
and order of the response alternatives. Response options (e.g., multiple choice responses)
and scaled responses (e.g., Likert scale) should adhere to the same requirements of
language clarity and simplicity as the questions themselves. Harris, Guz, Lipian and Man-
Shu (1985) discovered that four-year-olds‟ apparent inability to grasp the time course of
emotions was due to their misunderstanding of the response format. Pre-training in use of
the response format and reduction in the number of response options revealed that four-
year-olds were in line with the six-year-old understanding of the gradual waning of emotion
over time. While it is important to minimise the number of response options in order to
avoid overloading the child respondent‟s working memory capacity (Borgers, De Leeuw, &
Hox, 2000), requirements for statistical analyses also need to be considered. Pearson‟s r
progressively underestimates the true correlation as the number of categories reduces and
48
with fewer than five ordered categories, the underestimate becomes untenable (Alwin,
1992; Barrett, 2003; Bollen & Barb, 1981).
Scaled responses should be enhanced for young respondents making the options clear and
easy to interpret. Completely-labelled scales (where every point has a label) produce better
quality responses from children than partially labelled ones (where only the two extremes
are labelled; Borgers, Hox, & Sikkel, 2003). Verbal labels are more easily understood than
numeric ones (Borgers & Hox, 2000), while visual images also produce good results (Scott,
Brynin, & Smith, 1995). With a tendency for respondents (regardless of age) to attempt to
limit cognitive requirements for completing questionnaires (Krosnick, 1991; Vaillancourt,
1973), Bell (2007) recommends against the use of explicit “I don‟t know” options.
The order of responses should take into consideration systematic error introduced via the
“primacy effect”. The “primacy effect” occurs when earlier presented responses are chosen;
either through lack of motivation to read through the list or because earlier options persist
more strongly in memory (Krosnick & Alwin, An evaluation of cognitive theory of
response-order effects in survey measurement, 1987). Hershey and Hill (1976) found
evidence of a primacy effect with lists of five or more options, with children more likely to
choose the option that appeared first. To counteract this tendency, Bell (2007) recommends
positioning responses expected to be more salient nearer to the end of the list.
Other sources of systematic error include response biases such as social desirability (the
tendency give overly positive self descriptions; Furnham, 1986; Logan, Lewis Claar, &
Scharff, 2006; Ones & Viswesvaran, 1998; van de Mortel, 2008) and acquiescence bias (the
tendency to give affirmative responses to questions regardless of the content; Anastasi,
1976; Scott, 1968; Schriesheim & Kerr, 1974; Schriesheim & Kerr, 1981; Nunnally, 1978).
While well established in adults, it is important not to assume that findings applicable in
adults will generalize to children. For example, children may be less susceptible than adults
to social desirability bias (Bell, 2007). While many researchers argue for the use of social
desirability measures to control for positive impression management in self-report scales
(e.g. Crowne & Marlowe, 1964; Edwards, 1957; Jackson & Messick, 1958), researchers
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have questioned the interpretation of the social desirability construct. Kozma and Stones
(1987) argue that moderate correlations found between some measures of social desirability
and wellbeing are due to content overlap rather than response bias. Likewise, McCrae and
Costa (1983) have suggested that the positive relationship between social desirability scales
and the personality traits of conscientiousness and emotional stability is underpinned by the
social desirability scales measuring personality trait variance rather than merely indicating
a response bias. Mesmer-Magnus, Viswesvaran, Deshpande, and Joseph, (2006) found that
EI (measured using the Wong Law Emotional Intelligence Scale) explained significant
variance in socially desirable responding, over and above that explained by over-claiming
and self-esteem. They suggest that an implication of their finding that the EI subdimensions
of “emotion regulation”, “use of emotions” and “other-emotion appraisal” explain the most
variance in social desirability is that respondents actually behave in socially desirable ways.
However “self-emotions appraisal” was not found to significantly predict socially desirable
responding. They conceded that this may reflect Paulus and John‟s (1998) argument of a
self-deceptive tendency occurring at a non-conscious level, whereby respondents high in
other EI branches but low in “self-emotions appraisal” engage in self-deception and
actually believe their responses. Taking an intermediate approach (Helmes, 2000) suggests
that rather than removing any items that correlate with social desirability measures, item
selection should be based on stronger correlations with construct of interest than social
desirability measures.
While it is claimed that young children are particularly suggestible (Reed, 1996), there is
experimental evidence of minimal acquiescence bias among school-aged children (Hershey
& Hill, 1976). However, Christensen and James (2000) suggest that until further evidence
is provided, internal consistency checks should be used when testing children. While the
inclusion of an equal balance of positively and negatively phrased questions is argued to
counteract acquiescence bias for adult measures (Cloud & Vaughan, 1970), Schriesheim
and Hill (1981) report a deleterious effect on questionnaire validity. Negative questions,
which force the respondent to make a negative statement in order to deliver a positive
response, tend to be confusing (Benson & Hocvar, 1985) and are not advised in
questionnaires for children (Borgers, De Leeuw, & Hox, 2000; Marsh, 1986).
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4.2 Test development. For the purposes of determining the capacity of pre-adolescent children to give valid
responses to self-report and objectively measured items, Branches I, II and IV of Mayer and
Salovey‟s (1997) four branch model were considered to be most amenable to self-report as
a typical ability, while Branch III was considered to be most suited to objective assessment
as a maximal ability.
4.2.1Response format – Typical Emotional Intelligence (Branches I, II and IV). Self-report items were measured on a five-point Likert scale. As mentioned earlier in this
chapter, although fewer response categories are recommended for child self report, a
minimum of five categories is required for factor analysis of the correlation matrix. To
enhance clarity, each category was labeled and supplemented with a visual aid (circles of
increasing size and depth of colour, in line with categories). Categories indicated similarity
of the child to a statement of ability described in the item, and included “Not like me at all”,
“Not much like me”, “A little bit like me”, “A lot like me” and “Exactly like me”.
Respondents were instructed to “colour in the bubble inside the circle” that shows how
similar they are to the self statements in each item. The response format as shown in the
measure is presented below as Figure 1.
Figure 1. Response format.
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To promote understanding of the response format, „calibration items” were developed. This
entailed guiding the respondent through examples of using each extreme of the response
format, as well as an example of an intermediate response. “I am awake” and “I am asleep”
were chosen as categorical states to exemplify extreme scores. Forgetfulness was selected
as an example of characteristic that would be unlikely to exist in the extreme. Supervising
teachers read these instructions aloud and ensured child respondents understood what was
required of them. The instructions and calibration items are presented below:
For the first part of the SUEIT-EY, you will be asked to read sentences and say how well the sentence describes you. There are no right or wrong answers, we just want you to say what you are like. Read each sentence and think about how well each sentence describes what you are like. If the sentence describes you exactly, then you would answer “Exactly like me”. So you would colour in the bubble inside the biggest circle. Practise this using the example below. 1. I am awake. If the sentence does not describe you at all, then you would answer “Not like me at all”. So you would colour in the bubble inside the smallest circle. Practise using the example below. 2. I am asleep. If the sentence is a bit like you, then you should think about how much or how little the sentence describes you. You might choose “Not much like me”, “A little bit like me” or “A lot like me”. Practise using the example below. 3. I never forget things.
If you do not understand what to do, please ask your teacher.
4.2.2 Instructions for self-report items.
Instructions for item completion as well as the scoring key were repeated on each page of
the self-report measure. Instructions are presented below:
Below are sentences talking about what some people are like. Please read each sentence carefully and colour in the bubble inside the circle that shows how similar you are to what the sentence is saying. There are no right or wrong answers, we just want you to say the way you do things.
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If what the sentence is saying is not like you at all, colour in the bubble inside the smallest circle. If what the sentence is saying is exactly like you, then colour in the bubble inside the largest circle. If you make a mistake, cross it out and colour in the bubble that corresponds to your answer.
4.2.3 Item generation – Typical Emotional Intelligence (Branches I, II and IV).
Due to the inherent difficulties in asking a person to rate the level of their own ability (i.e.
to which benchmark does the respondent compare themselves?) items were developed to
focus on ease of enacting a particular skill or simply that the ability is present. Responses
were designed to ask about degree of similarity between the respondent and the particular
item description, rather than requiring the child to recollect different instances in order to
determine frequency. A small number of negative items were developed so that the
influence of negative wording on item reliability may be determined. Two items were
included to reflect the positive and negative wording of the same content. This was done as
an indicator of consistency of responding. Mayer and Salovey‟s (1997) hierarchical four
branch model was used to generate items. A brief reiteration of the model will be presented
below, along with the items designed to operationalise each ability. Please note that items
marked with an asterisk were retained after pilot testing (a convenience sample of N = 222
boys in grades 4 to 6; aged 9 – 13, M = 10.87, SD = 0.99), with other items removed due to
low internal consistency (Cronbach‟s alpha < .70; as recommended for early stage
research; Gignac, 2009).
4.2.3.1 Branch I. Branch I of the four branch model refers to the perception and appraisal of emotion. As
with all four branches, this branch comprises four abilities which are ordered from the
most basic, earlier developing abilities to those that are more sophisticated and develop
later. The first is the ability to identify emotion in one‟s physical states, feelings and
thoughts. The following items were developed to measure this ability:
I can easily tell if I am thinking happy or sad thoughts*
When I feel happy, my body feels different than when I am angry*
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When I am upset, I feel it in my body (such as lump in my throat, headache or sore
tummy)*
It’s hard for me to know if I am in a good or bad mood
When I am excited, I feel it in my body (such as butterflies in my tummy, or tingling
skin)
When I feel happy, my body feels different than when I’m angry
The second ability of Branch I entails capability in identifying emotion in others, designs,
artwork, language, sound, appearance and behavior. The following items were developed
to measure this ability:
I can easily tell how others are feeling by the look on their face*
I can easily tell how others are feeling*
I can easily tell if a song is happy or sad*
When someone has been in a bad mood, I can easily tell when they feel better
again*
I can easily tell how others are feeling by the sound of their voice
I can easily tell how others are feeling by the things they say
I can easily tell how others are feeling by how they move
I can easily tell if a painting is a calm, happy, sad or angry painting
The third ability of Branch I refers to the capacity to express emotions accurately, and
express needs related to feelings. The following items were developed to measure this
ability:
People can easily tell how I am feeling by the sound of my voice*
When I am upset, I can tell others how they can make me feel better*
People can easily tell how I am feeling by the way I do things (such as slamming
the door when I'm angry or singing when I'm happy)*
Others can easily tell how I am feeling
People don’t seem to know how I am feeling unless I tell them
People can easily tell how I am feeling by the look on my face
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When I am upset, I can tell others how they can make me feel better
It’s hard for me to talk about how I am feeling
The fourth ability of Branch I involves the more sophisticated processes of discriminating
between expressions of feelings in terms of accuracy and honesty. The following items
were developed to measure this ability:
I know when someone is trying to hide their true feelings*
I can tell if someone is only pretending to be angry*
I can tell if someone is not happy, even if they are smiling*
I can tell if someone is pretending to be more upset than they really are
I can tell if someone doesn’t like a present they have been given, even if they say
they like it
4.2.3.2 Branch II. Branch II pertains to the emotional facilitation of thought. The first ability entails the
prioritizing of thinking through the use of emotions to direct attention to important
information. The following items were developed to measure this ability:
When worrying thoughts distract me from enjoying something, I stop what I am
doing so I can try to fix what is worrying me*
I don't let my feelings get in the way of solving problems (Reverse scored)*
When I feel upset, I think more about what I'm upset about than my feelings
(Reverse scored)*
When I feel upset, I think more about my feelings than the actual problem
When I feel upset, I think more about what I’m upset about than my feelings
I think it is a bad idea to listen to my feelings when I have an important decision to
make
I don’t let my feelings get in the way of solving problems
The second ability entails emotions being vivid and available to be generated as aids to
judgment and memory. The following items were developed to measure this ability:
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It is easy for me to imagine how I might feel about something that hasn't happened
yet*
When I have made an important decision, my feelings tell me if I have made the
right decision*
When I try to remember something that happened a long time ago, it helps if I think
about how I was feeling at the time*
I can’t tell how I might feel about something until it happens
Ability three pertains to changes in mood encouraging multiple points of view. The
following items were developed to measure this ability:
When I’m in a bad mood, I tend to expect the worst*
When I am in a good mood, I think more positively about others*
When I can't solve a problem, if I wait until my mood has changed, I can usually
think of more solutions*
My good moods last longer than a day
My bad moods last longer than a day
My bad moods don’t last very long
My good moods last for only a short time
The fourth ability involves emotion states differentially encouraging specific problem
approaches. The following items were developed to measure this ability:
How I feel makes a difference to how fast I decide what to do*
Some moods make it easier to think through all possible solutions before deciding
what to do*
Some moods make me think more carefully about a problem*
Some moods make me faster in making decisions
Some feelings make me slower in making decisions
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4.2.3.3 Branch IV.
Branch IV refers to the reflective regulation of emotion. Ability one simply entails the
capacity to stay open to both pleasant and unpleasant emotions. The following items were
developed to measure this ability:
When someone is upset, I stay away from them (Reverse scored)*
I do not feel comfortable thinking about good feelings (Reverse scored)*
I am comfortable thinking about things that have upset me*
I enjoy stories that make me feel scared
I try not to think about things that upset me
When someone is upset, I stay away from them
When I’m really upset, I can’t think clearly
I don’t like sad stories
The second ability involves the capacity to reflectively engage or detach from an emotion.
The following items were developed to measure this ability:
I can make myself feel excited if I want to*
When I am really angry with someone, I can still think nice things about them*
When I am really angry with someone, I can’t think nice things about them
(Reverse scored)*
I can stop myself from feeling really upset*
I can stop myself from getting too excited if I need to
If I really want something, it is hard for me to wait for it
I can make myself feel happy by thinking about happy things
When I think about sad things, I can’t stop feeling sad
I can make myself feel sad if I need to
I get really angry if I don’t get my own way
When I am really excited about something, it is hard for me to calm down
When I am really excited, I can’t think clearly
When I am upset about something, I can still concentrate on something else
When I am upset about something, I can still do my school work
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Ability three involves reflectively monitoring emotions in relation to self and others. The
following items were developed to measure this ability:
I can easily tell if someone feels the same way as I do about something*
When I feel really excited about something, I can tell if others feel the same way as
me*
When someone is angry with me, I can think about why they feel that way
When I feel scared, I can tell if there is a real danger or not
When I am upset with someone, I can still tell how they are feeling
The fourth ability involves managing emotion in self and others without repressing or
exaggerating the information conveyed. The following items were developed to measure
this ability:
I can make others feel excited about something*
I can easily make people feel happy*
I can make people feel better when they are upset with me*
When I feel scared, I can tell if there is real danger or not*
I find it hard to make people get along with each other (Reverse scored)
4.2.4 Consistency items.
Two items in the first ability of Branch II were used as an indication of consistency of
responding. Items were designed to have opposite meanings but to both be worded
positively. These items are presented below.
When I feel upset, I think more about my feelings than the actual problem*
When I feel upset, I think more about what I’m upset about than my feelings*
4.3 Instructions for objectively measured items. The following instructions were read aloud by the supervising teachers after completion of
the self-report items. Instructions were designed to ensure maximal performance conditions
were adhered to.
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For Part 1, you were asked to read sentences and say how similar you are to what the sentence is saying. There were no right or wrong answers to these questions, they were just asking about what you are like. In Part 2, you will be asked questions about how you think a person is feeling. These questions will have right and wrong answers. Please consider each question carefully and put the answer that you think is most correct. Please answer each question in order, do not turn the page until you have answered each question and do not change your answers once you have turned the page.
4.3.1 Item generation and response format – Maximal Emotional Intelligence (Branch III).
Branch III of the four branch model entails the understanding and analysis of emotion. The
first and second abilities are to label and recognise the relations among emotions and to
interpret the meanings that emotions convey. These abilities were measured via brief
vignettes that described a situation likely to produce a particular emotion in the
protagonist. A multiple choice response format was used to garner responses. Responses
that were considered most likely were scored two and those that were possible but not
considered most likely were scored one. Correct responses are indicated by two asterisks
for two-point responses and one asterisk for one-point responses. Ability two was
measured by the respondent indicating which emotion was most likely to have been
experienced by the protagonist in each of three vignettes. Ability one was measured by the
respondent indicating which two of three protagonists in the preceding three vignettes were
feeling similar emotions. Vignettes were based on the prototypical antecedents to basic
emotions described by Shaver, Schwartz, Kirson and O‟Connor (1987; which apply to
children as well as adults, Harter & Whitesell, 1991) and scenarios developed by Ribordy,
Camras, Stefani and Spaccarelli (1988). Situations likely to elicit self conscious emotions
were determined using criteria suggested by Ellis and Weinstein (1986) and Lewis (2004).
Please note that items retained for further testing are marked with an asterisk. Other items
were removed based on non optimal item difficulty (p < .4 or > .6; Murphy & Davidshofer,
1994) determined through pilot testing.
Group 1
Jon is playing his favourite game with his best friend. What is Jon probably feeling? (choose one answer only)
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[Happy**, Disgusted, Angry, Sad, Surprised, Scared] Bob is watching a funny cartoon on television. What is Bob probably feeling? (choose one answer only) [Happy**, Disgusted, Angry, Sad, Surprised*, Scared] Eric’s favourite ice-cream melted before he could eat it. What is Eric probably feeling? (choose one answer only) [Happy, Disgusted, Angry*, Sad**, Surprised, Scared] Which two children are feeling MOST SIMILAR to each other? (choose one answer only) [Jon and Bob**, Jon and Eric, Bob and Eric] Group 2 *Jane’s friend is crying because she lost the race. Jane put her arm around her friend and gave her some chocolate. What is Jane probably feeling? (choose one answer only) [Happy, Disgusted, Angry, Sad**, Surprised, Scared] *When Lisa caught her little brother stealing her money yesterday, she asked him to give the money back. Lisa saw caught her little brother steal her money again today. What is Lisa probably feeling? (choose one answer only) [Happy, Disgusted*, Angry**, Sad*, Surprised*, Scared] *Sue’s favourite teacher is leaving the school today. What is Lisa probably feeling? (choose one answer only) [Happy, Disgusted, Angry*, Sad**, Surprised, Scared*] *Which two children are feeling the MOST SIMILAR to each other? (choose one answer only) [Jane and Lisa, Jane and Sue**, Lisa and Sue] Group 3 Jack lost his new toy he bought with his own money. What is Jack probably feeling? (choose one answer only) [Happy, Disgusted, Angry*, Sad**, Surprised, Scared] Bill saw a dead fish with maggots crawling through it. What is Bill probably feeling? (choose one answer only) [Happy, Disgusted**, Angry, Sad, Surprised, Scared] Tony stepped in dog poo. What is Tony probably feeling? (choose one answer only) [Happy, Disgusted**, Angry*, Sad, Surprised, Scared]
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Which two children are feeling the MOST SIMILAR to each other? (choose one answer only) [Jack and Bill, Jack and Tony, Bill and Tony**] Group 4 Julie always played with her friend Sarah at lunchtime at school. Today, Sarah said she wanted to play with Mary instead. Julie sat by herself and watched Sarah and Mary having fun. What was Julie probably feeling when she watched Sarah and Mary having fun? (choose one answer only) [Jealous**, Guilty, Grateful, Embarrassed*, Proud] Carla liked to be the teacher’s helper. The teacher always asked Carla to be the helper and said Carla was very responsible. But ever since Karen started at school, the teacher asks Karen to help her and doesn’t ask Carla anymore. What does Carla probably feel when the teacher asks Karen for help? (choose one answer only) [Jealous**, Guilty, Grateful, Embarrassed*, Proud] Becky threw a paper plane at her teacher while he was writing on the board. It hit the teacher in the head. When the teacher asked who threw the plane, nobody owned up so the teacher made everyone do schoolwork instead of having playtime. What was Becky probably feeling when the teacher made everyone do schoolwork instead of having playtime. [Jealous, Guilty**, Grateful, Embarrassed*, Proud] Which two children are feeling MOST SIMILAR to each other? (Choose one answer only) [Julie and Carla**, Julie and Becky, Carla and Becky] Group 5 *Bill wanted to play a trick on his friend. Just as his friend was about to sit down on his chair, Bill pulled the chair away. Bill’s friend fell on the floor and started crying. Bill helped his friend up off the floor. What was Bill probably feeling when his friend started crying? (choose one answer only) [Jealous, Guilty**, Grateful, Embarrassed*, Proud] *Jack always wanted a toy robot, just like the one his sister was given for her birthday. Jack watched his sister play with her robot. When she accidentally broke the robot, Jack smiled. What was Jack probably feeling when he watched his sister play with the robot? (choose one answer only) [Jealous**, Guilty, Grateful, Embarrassed, Proud]
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*Tom threw a ball against the wall, it bounced off and knocked his mother’s favourite lamp off the table. The lamp smashed into pieces. Tom tried to glue the lamp back together. What was Tom probably feeling after the lamp smashed into pieces? (choose one answer only) [Jealous, Guilty**, Grateful, Embarrassed*, Proud] Which two children are feeling the MOST SIMILAR to each other? (choose one answer only) [Bill and Jack**, Bill and Tom, Jack and Tom] Group 6 *Trish walked to the shop with her father. She wanted to buy her favourite cake but when they got to the shop, there was no cake left. Trish bought a sandwich instead. What was Trish probably feeling when she saw there was no cake left? (choose one answer only) [Annoyed*, Disappointed**, Frustrated*, Confused, Relieved] *Jill’s grandmother promised to send her a present in the mail. When Jill came home from school, her mother told her that the present had not arrived yet. What was Jill probably feeling when she was told the present had not arrived yet? (choose one answer only) [Annoyed*, Disappointed**, Frustrated*, Confused, Relieved] Mary was trying to reach through the fence to get her ball. She stretched her arm as far as she could but the ball was out of reach. She poked a stick through the fence, but it pushed the ball further away. What was Mary probably feeling when the ball moved further away? (choose one answer only) [Annoyed*, Disappointed*, Frustrated**, Confused, Relieved] Which two children are feeling the MOST SIMILAR to each other? (Choose one answer only) [Trish and Jill**, Trish and Mary, Jill and Mary] Group 7 Bill worked hard on a drawing and thought it was the best drawing he had ever done. His teacher put the picture on the wall so everyone could see what a good picture he had drawn. What was Bill probably feeling when the teacher put his picture on the wall? (choose one answer only) [Jealous, Guilty, Grateful*, Embarrassed, Proud**] Jon came first in the school swimming competition. As he got out of the pool, his friends ran over to him and patted him on the back.
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What was Jon probably feeling when his friends patted him on the back? (choose one answer only) [Jealous, Guilty, Grateful*, Embarrassed, Proud**] Tom’s teacher put all the student’s names in a hat. Tom really wanted to win because the prize was a toy that he really liked. Tom and his friends were excited and said the prize was really good. Tom’s name was pulled out of the hat and he won the prize. (choose one answer only) [Jealous, Guilty, Grateful**, Embarrassed, Proud*] *Which two children are feeling MOST SIMILAR to each other? [Bill and Jon**, Bill and Tom, Jon and Tom] Group 8 Carla was having lunch at her friend’s house. When the family was sitting down to eat, Carla accidentally knocked over her drink and everybody laughed. Carla put her hands over her face and hid under the table. What was Carla probably feeling when she hid under the table? (Choose one answer only) [Jealous, Guilty*, Grateful, Embarrassed**, Proud] Sue could not reach a book on the top shelf. An older girl saw that she was having trouble and showed her how to use the ladder to get the book. What was Sue probably feeling when the older girl showed her how to use the ladder? (Choose one answer only) [Jealous, Guilty, Grateful**, Embarrassed*, Proud] Trish was dressed up in a clown costume for her friend’s birthday party. When she got to the party, nobody else was dressed in a costume, they were just wearing their normal clothes. What was Trish probably feeling when she saw she was the only one wearing a costume? (Choose one answer only) [Jealous, Guilty, Grateful, Embarrassed**, Proud] Which two children are feeling the MOST SIMILAR to each other? (choose one answer only? [Carla and Sue, Carla and Trish**, Sue and Trish] Group 9 Jon’s favourite thing to eat is his chocolate chip cookies. When he got home from school, he smelled something nice and saw his mother was making chocolate chip cookies. Jon’s mother said he could have as many cookies as he wanted. Which word best describes what Jon was probably feeling when his mother said he could have as many cookies as he wanted? (choose one answer only) [Proud, Frustrated, Happy*, Sad, Disappointed, Relieved, Excited**]
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Bill accidentally dropped his favourite toy on the floor. As it dropped he waited for the sound of it smashing to pieces. But then he looked down and it wasn’t broken at all. Which word best describes how Bill was probably feeling when he saw that his toy didn’t break? (choose one answer only) [Proud, Frustrated, Happy*, Sad, Disappointed, Relieved**, Excited] Jack waited for the last book in the series of books he was reading. He really liked all the other books he read. When the last book was finally in the shops, he bought it. When he read the book, he said it wasn’t as good as the other books. Which word best describes what Jack was probably feeling after he read the last book? (choose one answer only) [Proud, Frustrated, Happy, Sad*, Disappointed**, Relieved, Excited] *Which two children had the MOST SIMILAR feelings? (choose one answer only) [Jon and Bill**, Bill and Jack, Jon and Jack] Group 10 *Jane was learning how to ride her bike. She practiced every day for a week. At the end of the week, she could ride really well. She showed her parents how good she was at riding. Which word best describes what Jane was probably feeling when she showed her parents how good she was at riding? (choose one answer only) [Proud**, Embarrassed, Happy*, Sad, Disappointed, Guilty, Excited] *Jill doesn’t like playing softball because she thinks she isn’t any good at catching the ball. But Jill wanted to play because everyone else was playing. Jill dropped a catch and all the other children groaned. Which word best describes what Jill was probably feeling when the other children groaned? (choose one answer only) [Proud, Embarrassed**, Happy, Sad, Disappointed*, Guilty, Excited] *Carla was looking forward to her friend’s party. Her friend said there was going to be a jumping castle and Carla loves jumping castles. When Carla got to the party, her friend said they couldn’t play on the jumping castle because it was broken. Which word best describes what Carla was probably feeling when her friend told her she couldn’t play on the jumping castle? (choose one answer only) [Proud, Embarrassed, Happy, Sad*, Disappointed**, Guilty, Excited] *Which two children had the MOST SIMILAR feelings? (choose one answer only) [Jane and Jill, Jane and Carla, Jill and Carla**] Group 11 Jim went to sleep in his own bed. But when he woke up, he was on the couch in the lounge room.
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Which word best describes what Jim was probably feeling when he woke up on the couch? (choose one answer only) [Frustrated, Sad, Disappointed, Excited, Surprised**, Bored, Scared*] Bill had some friends stay over at his place. It was night-time and they turned all the lights off and told each other stories about ghosts and monsters. When his friends said they wanted to go outside in the dark, Bill said he didn’t want to go. Which word best describes what Bill was probably feeling that made him not want to go outside? (choose one answer only) [Frustrated, Sad, Disappointed, Excited, Surprised, Bored, Scared**] Jack couldn’t find anything to do, so he asked his mother if she could think of anything for him to do. His mother said he could help her wash the dishes. Jack didn’t want to wash the dishes, so he sat on the steps and looked out the window. Which word best describes what Jack was probably feeling when he asked his mother if she could think of anything for him to do? (choose one answer only) [Frustrated, Sad, Disappointed, Excited, Surprised, Bored**, Scared] Which two children had the MOST SIMILAR feelings? (choose one answer only) [Jim and Bill**, Bill and Jack, Jim and Jack] Group 12 *Jill asked her friend Bob if she could read his new book. Bob said she could take the book for one night and asked her to be careful with it because it was very special to him. The next day, Jill looked for her book and couldn’t find it. Jill told Bob that she lost his book. Which word best describes what Jill was probably feeling when she told Bob that she lost his book? (choose one answer only) [Bored, Embarrassed*, Sad, Guilty**, Excited, Angry, Frustrated] Karen accidentally called her teacher “Mummy”. All the class laughed at Karen and she ran out of the classroom. Which word best describes what Karen was probably feeling that made her run out of the classroom? (choose one answer only) [Bored, Embarrassed**, Sad*, Guilty, Excited, Angry, Frustrated] *Carla was sitting by herself in her bedroom. She wanted to draw a picture for her grandmother. But every time she tried to draw with a pencil, it broke. Which word best describes what Carla was probably feeling after her pencils kept breaking? (choose one answer only) [Bored, Embarrassed, Sad, Guilty, Excited, Angry*, Frustrated**] Which two children had the MOST SIMILAR feelings? (choose one answer only) [Jill and Karen**, Karen and Carla, Jill and Carla]
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A different set of items were designed to capture the aspect of ability two that refers to the
capacity to interpret the meanings that emotions convey. These items were based on the
functionalist theory of emotion whereby the function of negatively and positively valenced
emotion is to encourage cessation or continuation of the current course of action
respectively (Scherer, 1984).
*Jane is reading her favourite book. As she is reading she starts to think about how her mother asked her to tidy her room before dinner. It is nearly dinner time and Jane has not tidied her room yet. What would Jane be feeling that would make her stop reading her book and tidy her room? (choose one answer only) [Sad, Guilty**, Grateful, Embarrassed*, Proud, Happy, Angry] What would Jane be feeling that would make her stop reading her book and forget about tidying her room? (choose one answer only) [Sad, Guilty, Grateful, Embarrassed, Proud*, Happy**, Angry]
The third ability of Branch III entails understanding complex feelings such as simultaneous
feelings and blends of emotion. The items tapping understanding of simultaneous feelings
were based on the five-stage developmental acquisition sequence developed by Harter and
Buddin (1987) to describe children‟s understanding of the simultaneity of emotion. Harter
and Buddin‟s found that children ages four through to twelve passed through stages in the
understanding of the simultaneous emotions in terms of target and valence of the emotions
(Level 0 – no acknowledgement of simultaneous emotion, occurs prior to 5 years of age;
Level 1 – understanding of the simultaneity of emotions of the same valence directed
toward one target, occurs around 7 years of age; Level 2 - understanding of the
simultaneity of emotions of the same valence directed toward different targets, develops by
around 8 years of age; Level 3 - understanding of the simultaneity of emotions of the
different valence each directed toward different targets, attained by around age 9 years;
and Level 4 – emotions of different valence directed toward the same target, develops by
around 11 years of age). The items generated to measure these different levels of the
understanding of simultaneous emotions are presented below. The response format
included a simple yes or no response, with yes being the correct answer to each item.
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Jane trained every day because she really wanted to win the school running competition. Jane ran faster than she ever had before and won the race. Could Jane feel HAPPY about winning and PROUD about winning at the same time? [Yes*, No]
*Karen invited her friend Jill to her birthday party. Jill said she would come. But when Karen’s party was starting, Jill didn’t arrive. Karen called Jill on the telephone and Jill said she didn’t want to come anymore as she was going to the beach. Could Karen feel MAD at Jill for not coming to the party and at the same time feel DISAPPOINTED with Jill because she didn’t come to the party? [Yes*, No]
Jack really wanted a bicycle or a computer game for his birthday. His parents gave him both a bicycle and a computer game. Could Jack feel HAPPY about getting both presents and at the same time feel SURPRISED that he got both presents? [Yes*, No]
Bob was sent to sit in the corner by himself because he accidentally broke his sister’s doll. Could Bob feel GUILTY because he broke his sister’s doll and at the same time feel ANGRY because his mother punished him? [Yes*, No] Jon had a difficult decision to make. He could either see a movie with his mother or go to the football with his father. He really wanted to do both but he could only choose one because they were on at the same time. Jon chose to go to the football with his father. Could Jon feel HAPPY to be going to the football and at the same time feel SAD that he was not going to see the movie? [Yes*, No] Mary’s friend Kate was trying to help Mary fix her bike. Mary wanted to ask her mother for help but Kate said she knew how to fix the wheel. Kate tried to fix the wheel with a hammer but then she broke the wheel because she banged on it too hard. Could Mary feel MAD at Kate for breaking the wheel and at the same time feel GRATEFUL to Kate for trying to help fix the bike? [Yes*, No]
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Blends of emotions were assessed using items based on Plutchik‟s (1980) emotion
combinations. These items are presented below.
*Mark is not sure what he is feeling. He said he feels a bit SAD and a bit SURPRISED. What best describes what Mark is probably feeling? [Disappointed**, Relieved, Embarrassed, Guilty] Jane is not sure what she is feeling. She said she feels a bit HAPPY and a bit SCARED. What best describes what Jane is probably feeling? [Excited, Disappointed, Furious, Guilty**] Bill is not sure what he is feeling. He said he feels a bit ANGRY and a bit SCARED. What best describes what Bill is probably feeling? [Annoyed, Jealous **, Excited, Bored] Kate is not sure what she is feeling. She said he feels a bit SCARED and a bit DISGUSTED. What best describes what Bill is probably feeling? [Jealous, Satisfied, Embarrassed**, Guilty]
The fourth ability of Branch III pertains to the capacity to recognise the likely transitions
among emotions. These items were loosely based on Frijda‟s (1987) action readiness
profiles and Lazarus‟s (1968) report of distinct cognitive appraisals associated with
emotions and are presented below.
*Bob is HAPPY because his friend gave him a present Jack is EXCITED because he is going on a holiday next week Tom is WORRIED because his dog is sick Bob, Jack and Tom are eating their lunch together when Jon shakes a bottle of soft drink and sprays it over them. Everyone is angry with Jon. Who is likely to feel the most angry? (choose one answer only) [Bob, Jack, Tom**] Jane is ANGRY because someone broke her new toy Mary is SAD because she forgot to bring her new toy to school Sue is HAPPY because her mother just gave her a new toy As the teacher walks by, she accidentally drops a pile of books.
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Who is LEAST likely to help the teacher pick up the books? (choose one answer only) [Jane**, Mary, Sue] *Chris didn’t get any sleep last night because he was excited about going to the school excursion to the zoo Mark was happy to be going to the zoo but would rather go to the beach Josh forgot all about the excursion When the teacher told the class that the excursion was cancelled, everyone was disappointed. Who would be MOST disappointed? (choose one answer only) [Chris**, Mark, Josh]
Jim, Chris and Bob are at the bus stop, waiting for the bus. A car drives over a puddle and splashes mud all over them. Jim thinks: “The driver did that deliberately.” Chris thinks: “Mum is going to be upset when she sees this mud on my clothes.” Bob thinks: “The driver should have driven around the puddle. I’ll have to wash my clothes.” Who would probably feel the MOST angry? [Jim**, Chris, Bob]
4.4 Item generation – Maximal Emotional Intelligence (Branch I, ability 2 “identify emotions in others”). Objective measurement of maximal ability to identify emotions in facial expressions was
developed using six posed photos of six basic emotions (one photo each of anger, disgust,
fear, happiness, sadness and surprise). The model was a fifteen year-old male. The model
was assisted to generate emotion (e.g. through music, guided imagery, exposure). Ekman‟s
(2003) criteria were used to identify expressions that typify expression of each basic
emotion (the final selection was done in consultation with Dr. Patrick Johnston, well-
published in the field of facial expression of emotion e.g. Johnston & Carr, 2002; Johnston,
McCabe, & Schall, 2003). Low intensity and partial expressions of emotion were selected
in an attempt to increase task difficulty (Ekman, et al., 1987; Nowicki & Mitchell, 1998).
Each photograph was shown separately in order to avoid relative judgment of expressions
(cf. Steblay, Dysart, Fulero, & Lindsay, 2001 for a review about the negative impact of
relative judgments on accuracy of eyewitness testimony). The final selection of
photographs are presented below. Please note that the photographs measured 620 mm x
1010 mm for the pilot study and were enlarged to 1070 mm x 1700mm for the final study.
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Figure 2. Expression of anger.
The photograph in Figure 2 was selected as a prototypical expression of anger. The
eyebrows are lowered and drawn together, the lower eyelids are slightly tensed and
straightened while the upper eyelid is raised causing a glaring look. The nostrils are flared
with pressed lips and a slight pushing up of the chin boss. In more intense expressions of
anger, the teeth may be bared in a grimace and the eyebrows drawn together so closely as
to crease the skin between the eyebrows. However, such expressions were considered to be
too easy for children to identify, thereby reducing the sensitivity of the item in
distinguishing between children with differing levels of ability.
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Figure 3. Expression of happiness.
The photograph in Figure 3 was selected as a prototypical expression of happiness. The
eyebrows relaxed, as are the nostrils. The eyes are slightly narrowed. The lips are relaxed
and drawn up at the corners through the action of raising the cheeks. The narrowing of the
eyelids and crow‟s-feet wrinkling unite with the smile to indicate an actual happy emotion,
referred to as a “Duchenne smile” indicating a genuine expression of happiness (Frank,
Ekman, & Friesen, 1993). This photograph was selected because only two of the three
components of the “Duchenne smile” were present (i.e. smile and the narrowing of the
eyes), thereby increasing item difficulty while maintaining the apparent authenticity of the
smile.
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Figure 4. Expression of surprise.
The photograph in Figure 4 was selected as a prototypical expression of surprise. The
eyebrows are raised straight up; with horizontal forehead creases, the lower eyelids are
relaxed while the upper eyelid is raised slightly revealing the sclera above the iris. The
nostrils are flared and the jaw is dropped slightly with relaxed lips.
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Figure 5. Expression of sadness.
The photograph in Figure 5 was selected as a prototypical expression of sadness. The inner
eyebrows are raised, the upper eyelid is slightly lowered and the lower eyelids are relaxed.
The nostrils are relaxed and the chin boss is raised with the mouth pulled laterally and
downwards. More extreme expressions would have creasing of the skin between the
eyebrows, increased narrowing of the eyes and a more obviously down-turned mouth.
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Figure 6. Expression of fear.
The photograph in Figure 6 was selected as a prototypical expression of fear. The
eyebrows are raised straight up, but without the horizontal forehead creases found in the
expression of surprise. The lower eyelids are tensed slightly while the upper eyelid is
raised revealing the sclera above the iris. The nostrils are flared and the jaw is dropped
slightly with the mouth stretched laterally.
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Figure 7. Expression of disgust.
The photograph in Figure 7 was selected as a prototypical expression of disgust. The
eyebrows are drawn down and together, with creasing of the skin over the nose bridge. The
lower eyelids are raised while the upper eyelid is lowered, partially obscuring the eyes.
The nostrils are drawn up, ostensibly narrowing the nostrils. The upper lip is raised slightly
and the lips are tightly closed with the chin boss raised.
Two forms of response categories were provided for the objective measurement of
identifying emotion in facial expressions. The first provided labels of the six basic
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emotions, and the second asked the respondent to identify likely antecedents to the
emotion. Antecedents were based on vignettes developed by Ribordy, Camras, Stefani and
Spaccarelli (1988). The response categories, which were identical for each item, are
presented below. Please note that the pilot study determined that the label and antecedents
did not yield different results. Therefore only the label responses were retained for further
testing.
Which word best describes how this person is probably feeling? (choose one answer only) Happy, Disgusted, Angry, Sad, Scared, Surprised What is likely to have made this person feel this emotion? (choose one answer only) The person just woke up after having a nightmare The person is just about to eat his favorite food The person just saw the television turn on by itself The person just saw his sister breaking his computer game after he told her not to touch it The person was just thinking about his dog who died last week The person just cleaned dog poo off his shoe
4.3 Language and concepts. Two primary school teachers independently checked the wording and concepts of the
instructions, items and response categories to ensure suitability for primary school aged
children. Two minor corrections were made according to their input (one typographical
error and one grammatical error). The Flesch-Kincaid reading grade level indicated that the
scale requires a fifth grade reading level (Flesch-Kincaid = grade level 5.65).
4.4 Content validity. Content validity was established with agreement amongst ten academics specializing in EI.
Items with less than 80% agreement were removed.
4.5 Summary and hypotheses. Research on the development of emotional capabilities was used to generate items based
on Mayer and Salovey‟s (1997) model of EI, with consideration of specific requirements
for pre-adolescent respondents. After pilot testing was conducted in a convenience sample
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of male primary school students, the initial pool of 159 items (77 Typical EI items
measured by self-report and 76 Maximal EI items measured through objective assessment)
was reduced to 66 items (38 Typical EI items measured by self-report and 28 Maximal EI
items measured through objective assessment). It was hypothesised that pre-adolescent
respondents would provide valid and reliable estimates of their own Typical EI as
measured by self-report and Maximal EI as assessed by objective items. It was also
hypothesised that the Typical and Maximal measures of EI would increase with grade level
and evidence higher scores in females than males.
The ensuing chapters will describe the assessment of the measure of Typical and Maximal
EI. This will entail reliability and validity analyses. The next chapter will provide a
summary of factorial analyses of Mayer and Salovey‟s (1997) hierarchical model of EI as
well as an outline of the analyses to be used in the current dissertation.
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Chapter 5: Factor Analytic Methods.
The following chapter will provide an outline and examination of various methods and
issues associated with factor analysis along with discussion of the methods that will be used
in the current dissertation. Following that, a summary of factorial analyses of Mayer and
Salovey‟s (1997) hierarchical model of EI will be presented.
5.1 Factor analysis. In line with the availability of sophisticated statistical software, researchers are increasingly
using factor analysis for the development of new measures of latent constructs (MacCallum
& Austin, 2000). For some time, Exploratory Factor Analysis (EFA; using principal
components or common factor analysis) has been used for item selection (Comrey, 1988).
More recently, Confirmatory Factor Analysis (CFA; using structural equation modeling)
has become an important tool in validating the dimensional structure of measures (Floyd &
Widaman, 1995; Tremblay & Gardner, 1996). Structural equation modeling may also be
used to explore a measure‟s structure and as a guide to adjustments (Gerbing & Hamilton,
1996; Noar, 2003).
5.1.2 Exploratory techniques for factor analysis. Factor analysis is used to summarize interrelationships among indicator variables as an aid
to conceptualization (Gorsuch, 1983). Principal components analysis (PCA) and common
exploratory factor analysis (EFA) are widely used methods of factor analysis.
5.1.3 Principal component analysis. Principal Components Analysis (PCA) derives a number of linear composites of measured
variables to account for maximum item variance (Tabachnick & Fidell, 2001). The first
linear composite extracts the maximum amount of possible variance while the second
orthogonal composite is formed by extracting the majority of the remaining variance and
this process continues until as many components are extracted as there are items
(Tabachnick & Fidell, 2001). While the components in PCA account for a mixture of
common and unique sources of variance, the distinction between common and unique
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sources of variance is not recognised and no attempt is made to separate unique variance
from the factors being extracted. The components in PCA are conceptually and
mathematically different from factors in EFA.
5.1.4 Exploratory factor analysis.
Exploratory Factor Analysis (EFA) is used to determine the number and nature of the latent
variables that explain the variation in a group of measured variables (Preacher &
MacCallum, 2003). The most common extraction methods used are Maximum Likelihood
(ML), Principal Axis Factoring (PAF), and Alpha Factoring (AF) (Tabachnick & Fidell,
2001). The extraction methods vary as to the statistical criteria used to determine factors
(Byrne, 2006). In EFA, the proportion of an item‟s variance that is shared with other items
is called the communality. EFA estimates the communalities for each variable and the
proportion of the unique variance in each item is the respective item‟s total variance minus
the communality. Thus, while PCA uses the total variance (i.e., common and unique) of the
items, EFA only uses the common variance.
5.1.5 Decisions in principal components analysis and exploratory factor analysis.
Both PCA and EFA require important decisions to be made; such as the number of factors
to retain and the rotation method to use. There are numerous decision rules that may be
used to guide the decision about the number of factors to retain in factor analysis (Zwick &
Velicer, 1986). These include Kaiser‟s Eigen-value-greater-than-one rule (K1; Guttman,
1954; Kaiser, 1960), Cattell‟s scree test (1966), and Horn‟s parallel analysis (1965).
However, it is important to remember that these decision rules do not necessarily yield the
same number of factors (Cattell & Vogelmann, 1977). In a Monte Carlo study, Zwick and
Velicer (1986) found that the K1 rule overestimated the number of factors to retain in the
majority of cases. While Cattell‟s scree test is touted to be more accurate (Costello &
Osborne, 2005), it also tends to over extract factors (Henson & Roberts, 2000).
As the title suggests, the K1 rule simply involves retaining the same number of factors as
there are eigen-values that exceed one. While this is the default option in most statistical
software packages, there is generally consensus that this is one of the least accurate
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methods for selecting the number of factors to retain (Fabrigar, Wegener, MacCallum, &
Strahan, 1999; Velicer & Jackson, 1990). The widely used Catell‟s scree test involves
examining the graph of eigen-values and looking for the natural breakpoint in the data,
where the curve flattens out. The number of data points above the break determines the
number of factors to retain. While more accurate than the K1, Catell‟s scree test has been
criticized for its subjectivity, requiring the researcher to interpret a graph that may be
“ambiguous and difficult to interpret” (Ledesma & Valero-Mora, 2007, p. 3) Horn‟s
parallel analysis is less commonly used, most likely because it is not available in most
statistical software packages. Parallel analysis entails the random generation of an
uncorrelated raw data matrix of the same sample size and with the same number of
variables as the actual raw data matrix (Horn, 1965). Eigen-values derived using the
random data are computed and compared to those based on the data under analysis. The
number of factors to extract is determined by the number of eigen-values in the real data
that exceed the value of that generated from the random data. While Horn‟s parallel
analysis has been found to be the most accurate method (Zwick & Velicer, 1986),
Thompson and Daniel (1996) suggest using multiple methods to determine the number of
factors to retain in performing exploratory factor analyses.
Once the factors are extracted, rotations are used to simplify and clarify the data structure.
Rotation orientates the correlations between the factors and the indicators so that the pattern
of values is more distinct (Tabachnick & Fidell, 2001). Orthogonal rotation generates
factors that are statistically uncorrelated, while oblique rotation allows factors to be
correlated. While orthogonal rotation is more simple, provides conceptual clarity, and is
more amenable to subsequent analysis (Nunnally, 1978), oblique rotation adds in the
information in factor inter-correlations and more accurately represents the complexity of
the examined variables because constructs in the real world are rarely uncorrelated (Ford,
MacCallum, & Tait, 1986). However, in a Monte Carlo study, Gerbing and Hamilton
(1996) found that the rotation methods were equally good in the recovery of the structure of
a model. In contrast to those who questioned the selection of orthogonal rotation for the
SSREIS (as discussed earlier on page 40; Petrides & Furnham, 2000; Gignac, Palmer,
Manocha, & Stough, 2005), Gerbing and Hamilton (1996) argue that the choice of
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extraction method is not so critical in EFAs because the methods seem to converge on very
similar solutions once the sample size is approximately 300 or greater. Furthermore, except
for in the case of factor inter-correlations of 0.80 and above, the orthogonal varimax
rotation did as well as the oblique rotations and generally yielded more accurate estimates.
Pallant (2005) recommends that the simpler orthogonal rotation is used for factor
correlations below .30.
5.1.5 Principal components analysis and exploratory factor analysis comparison. While PCA and EFA may seem superficially similar, they are essentially different methods
of extraction (Preacher & MacCallum, 2003). With PCA, the focus is on data reduction for
the purpose of explaining as much item variance as possible through a small number of
linear components. Meanwhile, the overall aim of EFA is to identify interpretable factors
that best explain the correlations among measured variables. In contrast to PCA, EFA does
not assume that the sample and population matrix are the same. The difference between the
matrices from which components and factors are extracted is that PCA places a value of
unity (the upper bound communality estimate) on the main diagonal which involves the
unlikely assumption that each variable is perfectly reliable. In contrast, factor analysis
places an estimate of common or reliable variance generated through an iterative process on
the main diagonal (Gorsuch, 1983). In their Monte Carlo study, Snook and Gorsuch (1989)
found that the factor loadings obtained through PCA were significantly inflated when
compared to the population factor loadings under various conditions (e.g., number of
variables and factors, magnitude of factor loadings). They attributed the inflated factor
loadings to PCA including both the common and unique item variance in the analysis while
EFA, which only uses the common variance between the items, produces solutions that are
more similar to the population pattern.
PCA and EFA has been criticized for being statistically rather than theoretically driven with
results that look impressive but are essentially meaningless (Costello & Osborne, 2005;
Kieffer, 1999). Therefore, it is important for adjustments to be based on theory (Armstrong,
1967) and for changes to be cross-validated in a new sample (Gerbing & Hamilton, 1996).
This avoids „over-capitalizing on chance‟ by fitting the model to the data which may not
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generalize to other populations. Indeed, while EFA is recommended as precursor to CFA in
model and measure development (Gerbing & Hamilton, 1996), confirmatory factor analysis
(CFA) is often preferred over EFA because it emphasizes a priori model testing (Bartko,
Carpenter, & McGlashan, 1988).
5.1.6 Confirmatory factor analysis. CFA techniques have become important for theory testing in applied multivariate analysis
(MacCallum & Austin, 2000). While the EFA may be used to generate hypotheses about
the factor structure of set of variables, CFA is generally based on a strong theoretical or
empirical foundation that allows researchers to specify the factor structure or model in
advance (Gerbing & Hamilton, 1996). When the researcher has a sufficiently strong
rationale for specifying the factors that should be in the data and what variables or items
should define each factor, CFA has considerable potential for construct validation as well
as theory development and testing (Anderson & Gerbing, 1988; Henson & Roberts, 2000;
MacCallum & Austin, 2000).
In CFA, the hypothesised model is used to estimate a population covariance matrix that is
then compared with the observed covariance matrix (Schreiber, Stage, King, Nora, &
Barlow, 2006). For the hypothesised model to be accepted, the difference between the
estimated and observed matrices should be minimal. CFA generates two types of
information; estimates of the parameters of the model (factor loadings, variances and
covariances of the factor, and residual error variances of the observed variables) and
various indices of the fit to assess whether the model provides a good representation of the
data.
While a strength of CFA is that it is theoretically driven, additional advantages include that
measurement errors are identified for variables under investigation, and measurement errors
are allowed to be correlated in a longitudinal model as indicators come from the same
source (Byrne , 2010). Floyd and Widaman (1995) argue that these advantages make CFA
a robust tool for assessing the factor structure of measurement instruments. However, for a
correct inference to be drawn from the results of a CFA analysis, additional assumptions
concerning the nature of the data and the estimation methods must be met (Byrne, 2010).
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The estimation method used in the current dissertation will be described below, followed by
a discussion of the associated assumptions.
5.1.7 Maximum Likelihood estimation. While there are several estimation methods available, Maximum Likelihood estimation
(ML) is the most widely used (Byrne, 2010; Hoogland & Boomsma, 1998). If the data is
relatively normally distributed, then ML is generally preferred because it allows for
estimation of means and provides a wide range of fit indices and confidence intervals that
allow hypothesis testing about models (Fabrigar, Wegener, MacCallum, & Strahan, 1999).
ML provides a comparison of the matrix of implied variances and covariances generated
from the hypothesised model with the matrix of variances and covariances from the sample.
The chi-square test is used to statistically test this hypothesis, with a non-significant result
meaning that the hypothesised model fits with the sample data, providing support for the
hypothesised model. Furthermore, the chi-square difference test may then be used to
compare nested models (alternate models where one model is a subset of the other; Byrne,
2010). A non-significant chi-square difference test indicates that the overall fit of the two
models is comparable and therefore the more parsimonious model (with the higher number
of degrees of freedom) is chosen. In conditions where the observed data stem from a
multivariate normal distribution, the model is specified correctly and the sample size is
sufficiently large, ML provides parameter estimates and standard errors that are
asymptotically unbiased, consistent, and efficient (Bollen, 1989).
5.1.8 Assumptions underlying confirmatory factor analysis.
A basic assumption underlying the use of CFA in structural equation modeling (SEM) is
that the observations are drawn from a continuous and multivariate normal population
(Tabachnick & Fidell, 2001). While ML can withstand moderate departures from normality
(e.g. Boomsma & Hoogland, 2001; West, Finch, & Curran, 1995; Curran, West, & Finch,
1996), if the distributions of continuous variables are severely non-normal, either corrected
statistics or an estimation method that does not assume normality should be used (Curran,
West, & Finch, 1996). However, Muthén and Kaplan‟s (1985) Monte Carlo study indicated
that larger sample sizes of around 500 cases can improve the resistance of ML to non-
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normality. Furthermore, if the distributions are non-normal because the indicators are
discrete with a small number of categories, then an appropriate estimation method based on
polychoric correlation as the input matrix for this type of data should be employed (Kline,
2004). As discussed earlier (Chapter 4), at least five ordered categories are required for the
estimation of the true correlation to be tenable (Alwin, 1992; Barrett, 2003; Bollen & Barb,
1981).
5.1.9 Univariate normality. Skewness and kurtosis statistics are used to provide information about the shape of a
distribution. Skewness reflects the symmetry of a distribution. A distribution in which most
of the scores are piled up to the left with a long tail extending towards more positive values
is regarded as positively skewed and a distribution in which most of the scores are gathered
to the right with a long tail extending towards more negative values is said to be negatively
skewed. Values of skewness within the range of -2 to +2 are interpreted to mean that the
data may be assumed to be normally distributed and absolute values of the univariate
skewness greater than 3 are described as “extremely” skewed (West, Finch, & Curran,
1995).
The peakedness of a distribution, referred to as kurtosis, is derived from the extent to which
scores cluster together (leptokurtic distribution) or are widely dispersed (platykurtic
distribution). While some researchers suggest that for a normal distribution, the value for
kurtosis should be within the +2 to -2 range, others use the +3 to -3 range (Curran, West, &
Finch, 1996). Absolute values of the univariate kurtosis index from 8.0 to over 20.0 have
been described as indicating “extreme” kurtosis (Kline, 1998). In general, West and
colleagues (1995) recommend that absolute values of skewness and kurtosis respectively
exceeding 2 and 7 are indicative of moderately non-normal distributions.
5.1.10 Multivariate normality.
With any substantial deviation from univariate normality of the observed variables,
multivariate distribution will also be non-normal. However, even if all the univariate
distributions are normal, the overall distribution may depart substantially from multivariate
normality. Therefore, it is also important to examine multivariate measures of skewness and
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kurtosis. Multivariate normality may be conceptualized similarly to bivariate normality,
with several variables interacting to form a multidimensional distributional model that will
vary in degree of normality (Henson, 1999). Multivariate outliers are usually detected
through examining the squared Mahalanobis distance for each case (DeCarlo, 1997). The
Mahalanobis distance, calculated using the inverse of the variance-covariance matrix,
indicates the multivariate distance between the scores of an individual case and the sample
means (De Maesschalck, Jouan-Rimbaud, & Massart, 2000). Mahalanobis distances may be
interpreted as chi-square statistics with degrees of freedom equal to the number of
variables. Tabachnick and Fidell (1996) suggest comparing the squared Mahalanobis
distance of a particular case against the appropriate critical value of chi-square in order to
determine whether the case differs significantly from all other cases.
5.1.11 Sample size. Researchers have made various recommendations as to the sample size required to obtain
unbiased test statistics and meaningful parameter estimates. Some recommend a sample
size of 200 is needed for small to medium models (Tabachnick & Fidell, 2001).
Meanwhile, Anderson and Gerbing (1984) argued that a sample size of 150 is usually
sufficient when there are three or more indicators per factor.
An insufficient sample size is more likely to yield nonconvergent and improper solutions
(Byrne, 2010). Nonconvergent solutions occur when the computational algorithm of an
estimation method is unable to arrive at values that meet prescribed termination criteria
within a set number of iterations. Solutions are improper (Heywood case) when the values
for one or more parameter estimates are not feasible, such as when negative variance
estimates or correlations greater than one are obtained (Gerbing & Anderson, 1987). While
this may indicate a misspecification of the model, these problems are frequently
encountered when very small samples are analyzed.
Problems of nonconvergence and improper solutions are compounded when more
parameters are estimated in a model with a smaller sample size (Boomsma & Hoogland,
2001). That is, when the estimation requirements are greater, more information is needed.
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Therefore with the sample size held constant, the likelihood of nonconvergence and
improper solutions increases as model complexity increases.
The number of indicators per factor has also been shown to affect solutions obtained from
CFAs. Gerbing and Anderson (1985) established that fewer indicators per factor produced
bias in the correlation among factors and instability in parameter estimates. Similarly,
Boomsma and Hoogland (2001) demonstrated that when the number of indicators per factor
increases, the number of nonconvergent solutions decline. Others have argued that a
smaller number of indicators per factor result in poorer fit between the expected and
obtained factor matrix (Velicer & Fava, 1998) and increasing the number of indicator
variables improves the value of fit indices (Breivik & Olsson, 2001; Kenny & McCoach,
2003). Kline (2005) recommends that factors comprised of at least three indicators per
factor are sufficient.
5.2 Use of factor analytic methods in the assessment and development of EI measures
5.2.1 MSCEIT. As with many fields of psychology research, those investigating measures of EI have used
factor analytic methods to explore construct validity. The various models implied from the
Maximal EI framework have yielded mixed results. Mayer, Salovey, Caruso, & Sitarenios
(2003) found evidence for a one-factor solution, with all eight tasks loading on a general EI
factor. Meanwhile, other tests of the one-factor model have obtained poor fits (Burns,
Bastian, & Nettelbeck, 2007; Gignac, 2005; Palmer, Gignac, Manocha, & Stough, 2005;
Rode, et al., 2008; Rossen, Kranzler, & Algina, 2008).
Mayer and colleagues (Mayer, Salovey, & Caruso, 2002; Mayer, Salovey, Caruso, &
Sitarenios, 2003) determined acceptible fit indices for an oblique two-factor model,
comprising two correlated factors (Experiential EI and Strategic EI). However others
(Burns, Bastian, & Nettelbeck, 2007; Day & Carroll, 2004; Rode, et al., 2008; Rossen,
Kranzler, & Algina, 2008) determined marginally acceptible fit for this model.
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Some studies have established an oblique four-factor model as the best fitting solution
(Burns, Bastian, & Nettelbeck, 2007; Day & Carroll, 2004; Mayer, Salovey, & Caruso,
2002; Mayer, Salovey, Caruso, & Sitarenios, 2003). Although excellent fits for the oblique
four-factor model have been replicated (e.g. Palmer, Gignac, Manocha, & Stough, 2005),
some have argued that this model is not preferable due to high correlations between
branches one and two (Gignac, 2005; Rode, et al., 2008; Rossen, Kranzler, & Algina, 2008)
or branches two and four (Palmer, Gignac, Manocha, & Stough, 2005).
Results of EFA fall in line with CFA studies. Roberts, Schulze, O'Brien, MacCann, Reid,
and Maul (2006) revealed that branches one and two were clustered into one factor in a
factor analysis combining the MSCEIT and two cognitive ability measures. Wang and Liu
(2008) extracted a new four-factor solution in a Chinese sample using EFA. The model,
with two second-order factors was found to fit extremely well with the data. However, a
factor loading greater than 1.0 indicated this was an improper model. Similar results were
observed for the integrated model with a general second-order factor (Rossen, Kranzler, &
Algina, 2008). In response to these results, some researchers have successfully trialed an
alternative three factor model, with Branches I and II combined to form one factor (Rode,
et al., 2008; Rossen, Kranzler, & Algina, 2008).
In recent research, Fan, Jackson, Yang, Tang and Zhang (2010) analysed the results of 18
studies in a meta-analytic structural equation modeling approach. They found that although
the four factor model showed excellent fit on four indices, it was not preferred due to a high
correlation (r = .90) between Branches I and II. Subsequently, a three-factor solution, with
Branches I and II combined to form one factor with Branches III and IV forming the other
two factors was concluded to be the best fitting model.
5.2.2 SSREIS. While the total scale score for the SSREIS has demonstrated good reliability (Schutte,
Thornsteinsson, Hine, Foster, Cauchi, & Binns, 2010), evidence for the scale‟s construct
validity is less clear. Schutte and colleagues (1998) recommended using a total scale score
to reflect a single factor or composite EI score, which has been supported by Brackett &
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Mayer (2003). However, further studies have focused on the primary factors argued to
comprise this measure. A four-factor solution has been reported (Ciarrochi, Chan, &
Bajgar, 2001; Ciarrochi, Deane, & Anderson, 2002; Petrides & Furnham, 2000; Saklofske,
Austin, & Minski, 2003). For instance, Ciarrochi and colleagues (2001) described the four
factors of the SSREIS as comprising perception of emotions, managing self-relevant
emotions, managing others‟ emotions and utilizing emotions. However, Saklofske, Austin
and Minski (2003) have replicated Petrides and Furnham‟s (2000) four factor structure
consisting of optimism, mood regulation, appraisal of emotions and utilsation of
emotions/social skills. Other studies have reported a three factor structure (Austin,
Saklofske, Huang, & McKenny, 2004), consisting of optimism/mood regulation, utilisation
of emotions, and appraisal of emotions. Meanwhile, Gignac and colleagues (2005) asserted
that investigation of the SREISS factor structure had neglected to consider the model upon
which the measure was based. That is, Salovey and Mayer‟s (1990) six factor model
incorporating appraisal of emotions in self, appraisal of emotions in others, expression of
emotion, utilisation of emotions, regulation of emotion in self and regulation of emotion in
others. However, they were only able to identify four of the factors, with “regulation of
emotion in others” and “emotional expression” not fitting the model. While the number of
specific factors found in the SSREIS is still under investigation, Gardner and Qualter
(2010) compared three commonly used trait EI measures and concluded that the total
SSREIS score is a valid measure of global trait EI. Chan‟s (2004) EFA and CFA results
supported the four factor structure of the SSREIS but only with a reduced 12-item set.
Fukuda, Saklofske, Tamaoka, Fung, Miyaoka and Kiyama (2011) have recently replicated
Schutte‟s (1998) single second order factor structure and Ciarrochi and colleagues‟ (2001)
four first order factors in a sample of Japanese university students.
5.2.3 WLEIS.
With no representation of expression of emotion, the four factors of the WLEIS comprise
appraisal of emotion in self, appraisal of emotion in others, use of emotion and regulation
of emotion. The WLEIS has primarily been examined in cross cultural studies which
support this four factor structure of the instrument in Chinese (Law, Wong, & Song, 2004;
Shi & Wang, 2007; Wong & Law, 2002), Japanese (Fukuda, Saklofske, Tamaoka, Fung,
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Miyaoka, & Kiyama, 2011) and Korean populations (Fukuda, Saklofske, Tamaoka, & Lim,
2011). The same four factor structure has also been replicated in two samples of
international university students studying in the United States (Ng, Wang, Zalaquett, &
Bodenhorn, 2007; Wang, Kim, & Ng, 2011).
5.3 Summary and hypotheses. While the one, two and four factor structure hypothesised from Mayer and Salovey‟s
(1997) hierarchical model of EI has moderate support, there is some indication of poor
discriminant validity between Branches I and II. Therefore a three factor model has been
proposed. The self-report measures are based on Salovey and Mayer‟s (1990) earlier model,
with Branch I divided into two factors distinguishing between the perception and appraisal
of emotion in self and perception and appraisal of emotion in others; and no representation
of Branch III. These measures have yielded the hypothesised one and four factor models.
However the two factor model, which distinguishes between Experiential and Strategic EI,
has not been tested in self-report measures. A three factor model was proposed for the
SSREIS which appears to combine Branches II and IV as one factor, with Branch I as the
second factor and a third factor representing “optimism/positivity”.
In line with the purpose of the current dissertation in developing a measure of EI
(Swinburne University Emotional Intelligence Test – Early Years; SUEIT-EY) in pre-
adolescent children, it was predicted that the SUEIT-EY would reveal a structure
corresponding to the four branch model of Mayer and Salovey (1997). More specifically, it
was hypothesised that a one factor model specifying Branches I, II, III, and IV would show
good fit to the data. With mixed results in terms of the two factors representing Experiential
and Strategic EI, a specific hypothesis about the presence of this structure in the SUEIT-EY
was not proposed. Instead, this was left as a question to be explored, rather than develop
hypotheses. Similarly, ideas about the branch level structures of the SUEIT-EY were posed
as research questions rather than as hypotheses. While Mayer and Salovey‟s (1997) four
branch model implies that each branch holds a four factor structure representing the
abilities comprising the branch, this has not been explicitly stated or tested.
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Chapter 6: Method.
6.1 Initial Development and pilot testing. An initial item pool of 160 items was generated, designed to capture the sixteen abilities of
Mayer and Salovey‟s (1997) four branch model of EI. Ninety-seven items garnered
agreement greater than or equal to 80% from ten academics with a specialty in EI and were
retained. The wording of instructions and items was checked by two primary school
teachers and adjustments were made upon their recommendations to ensure the measure
was within the reading and comprehension ability of the intended respondents.
Unfortunately, seven reverse scored items had to be adjusted towards a simpler, positive
meaning as they were deemed too convoluted for the younger students to understand. The
Flesch-Kincaid reading grade level indicated that the scale requires a fifth grade reading
level (Flesch-Kincaid = grade level 5.64). Reponses to the remaining items were assessed
in a convenience sample of 222 students in grades four to six of an independent school for
boys (Ethical clearance 0708/028; Appendix 1). Twenty-two self-report items were
removed based on low item reliability and nine performance items were removed based on
non-optimal item difficulty (p value less than .3 or greater than .8; Murphy & Davidshofer,
1994).
6.2 Main analyses.
6.2.1 Participants. Data was collected from two metropolitan Victorian primary schools in two separate
cohorts, during August across two years. One school comprised 644 students with 84% of
students coming from a language background other than English. The other school had 717
students enrolled with 24% of students coming from a language background other than
English. Those students in grades four to six who assented to participate, completed the
measure as part of a school-wide project for which parents had given. Results of
participants who completed the measure in both cohorts were retained for test-retest
analyses (N = 236). Data from both schools were combined for each cohort, resulting in
two separate samples used as calibration and validation samples.
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6.2.2 Materials.
6.2.2.1 SUEIT-EY. Subsequent to pilot testing of the Swinburne University Emotional Intelligence Test –
Early Years (SUEIT-EY), the final measure comprised 66 items (Appendix 2). The 38 self-
report items (items 1 to 38) were designed to measure three of the four Mayer and Salovey
(1997) branches of Emotional Intelligence; “Perception and Appraisal of Emotion”
(Branch I), “Emotional Facilitation of Thinking” (Branch II) and “Reflective Regulation of
Emotion” (Branch IV). Six items measuring an ability subsumed under Branch I; “Identify
emotions in others” (Branch I Maximum EI) was measured using performance based
assessment. The remaining 22 items were designed to measure Mayer and Salovey‟s
(1997) “Understanding and Analysing Emotion” (Branch III) using performance-based
assessment.
6.2.2.1.1 SUEIT-EY scoring. Self-report items were scored on a five point Likert scale where 1= “not like me at all” and
5 = “exactly like me”. Four items (items 7, 31, 36 and 37) were reverse scored. The Branch
I Maximum EI items (items 39 to 44) entailed presentation of a 1070 mm x 1700 mm
photograph of a male adolescent (15 years of age) modeling emotional expressions (happy,
disgusted, angry, sad, scared and surprised). These were scored using a six point response
format (happy, disgusted, angry, sad, scared and surprised). Each image was presented on a
single page (for paper based testing) or screen (for computer based testing) in the order of
“angry”, “happy”, “surprised”, “sad”, “scared” and “disgusted”. Each item yielded a score
of “zero” for an incorrect response and “one” for a correct response. The remaining
performance items were measured using a multiple choice format. Item 59 was measured
using a two point response (“yes” or “no”), items 45, 47, 54, 55, 56, 60, and 66 had three
point responses; items 57 and 58 were measured using four point responses, items 48, 49
50, 51 and 52 were measured using five point responses, item 46 was measured using a six
point response and items 53, 61, 62, 63, 64 and 65 were measured using seven point
responses.
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6.2.2.1.2 SUEIT-EY validity items.
Practice examples were developed to ensure children understood the response format prior
to responding to items. Three items were used to provide examples of the extreme
responses “Not like me at all” and “Exactly like me” as well as the intermediate responses
“Not much like me”, “A little bit like me” or “A lot like me”. These items were checked by
the classroom teacher to ensure understanding prior to the child proceeding with
completing the measure.
Additionally, two items with opposite meanings were included as an indication of
inconsistent responding. These were item 13 (“When I am really angry with someone, I can
still think nice things about them”) and item 37 (“When I am really angry with someone, I
can‟t think nice things about them”, which was reverse scored). The difference between
responses on these items was calculated for use as an indication of inconsistency of
responding (with higher scores indicating greater discrepancy between the scores).
6.2.2.2 Children’s Social Desirability Scale. Socially desirable responding was measured using the Children‟s Social Desirability Scale
(CSD; Crandall, Crandall, & Katkovsky, 1965). This test is modeled after the Marlowe-
Crowne scale to assesses socially desirable responding in children as motivated by a need
for approval. The scale comprises 48 statements in True-False format with 26 items keyed
true. Possible scores range from 0 to 48 with high scores indicating fear of disapproval.
Example items include “I never shout when I feel angry” and “I always help people who
need help”. Crandall and colleagues (1965) reported corrected split-half reliabilities ranging
from .82 to .95 and one-month test-retest reliability of .85. Split-half reliabilities in the
current dissertation were .88 (calibration sample) and .86 (validation sample).
6.2.3 Procedure. All data were collected in class time in the participants‟ usual class group. Teachers
supervised completion of measures, reading instructions aloud to participants (see
instructions for final measure in Appendix 2) and answering questions as required.
Measures took approximately 30-40 minutes to complete. Data was collected via a printed
test in the first wave of testing and via computer for the second wave.
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6.2.4 Statistical design.
A two step modeling approach was used as recommended by Anderson and Gerbing (1988)
whereby validity of the separate factors at the item level is established through examining
the measurement models, followed by an examination of the overarching structural model.
Initially, PCA was used to explore the characteristics of the data set, and then CFA was
used for examination and adjustment of the measurement model for each latent variable
representing the Branches of Mayer and Salovey‟s (1997) model. Furthermore, following
Jöreskog‟s (1993)‟s suggestion, model modifications were validated using a separate but
equivalent sample.
6.2.5 Analysis. Principal Components Analysis (PCA) was conducted using SPSS 18 (IBM Corporation,
2009). Maximum Likelihood Confirmatory Factor Analysis (CFA) using AMOS 18
(Arbuckle, 2009) was conducted to evaluate the significance of SUEIT-EY factor loadings
and the goodness of fit of measurement models.
6.2.6 Missing data. Structural equation modeling requires the data to have no missing cases. Therefore cases
may either be statistically imputed or removed (Byrne, 2010). If the number of cases with
missing data is small (less than 10%; Cohen & Cohen, 1983) and there is no clear pattern of
missing data (i.e. Little‟s Missing Completely At Random; MCAR chi-square statistic is
not significant at an alpha level of .001), then such cases will be removed prior to analysis.
Otherwise if more than 10% of data is missing, then the missing data will be imputed using
the Bayesian multiple imputation algorithm contained within AMOS. This method of
imputation has been found to be more consistent and accurate than other available methods
(Shafer & Graham, 2002).
6.2.7 Normality. Multivariate normality was evaluated by examining the Mahalanobis distance. In addition,
univariate indices of skewness and kurtosis were examined to determine extreme values.
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6.2.8 Fit indices.
Following the recommendations of Bollen and Long (1993), a variety of global fit indices
were used. These include the Root Mean Square Error of Approximation (RMSEA; which
should be less than .08 to indicate satisfactory fit; Steiger, 1990), the Comparative Fit Index
(CFI; which should be greater than .90; Hu & Bentler, 1995) ; The Tucker Lewis Index
(TLI; which should be greater than .90; Hu & Bentler, 1995); the standardized root mean
square residual (SRMR; which should be less than .05; Bentler, 1990) and the traditional
overall chi-square test of model fit (which should be statistically non-significant). While the
chi-square is known to be overly stringent as a test of model fit as sample size increases, it
is useful for comparison of nested models (Byrne , 2010). For non nested models, a
practical significance difference test will be used based on TLI values whereby a model
with a TLI value of .01 larger than another model will be considered practically better
fitting (Gignac, Palmer, Manocha, & Stough, 2005; Vandenberg & Lance, 2000). In
addition to the global fit indices, more focused tests of fit were used. These include
examination of factor loadings (should be significant at the .05 level and have loadings
>.32; Comrey & Lee, 1992) the standardized residual covariances (which should have a
magnitude of less than .20) and modification indices (which should be less than 10.00). The
parameter estimates were also examined for Heywood cases.
6.2.9 Reliability. Nunnally and Bernstein‟s (1994) and Gignac‟s (2009) recommendations of a reliability of
at least .70 for early stage research will be used to guide decisions.
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Chapter 7: Results.
7.1 Preliminary analyses.
7.1.1 Missing data. As there was no coherent pattern to the missing data, cases with missing data (comprising
less than 1% of the data) were removed prior to analysis. Upon this basis, there were eleven
cases removed from the validation file (six girls, five boys; two grade 4, five grade 5, four
grade 6). Missing data was primarily due to missed pages and ranged from 2 to 18 items.
There was no missing data in the calibration file, most likely due to the computerized
format for this group.
7.1.2 Response consistency Based upon the two items indicating consistency of responding, the majority of the
calibration sample responses were consistent. Thirty-seven percent received a difference
score of zero, 41.2% a difference score of one, 13.7% a difference score of two, 5.3% a
difference score of three and only 2.9% received the maximum difference score of four. A
paired samples t-test indicated that there was no significant difference between responses to
item 13 (M = 2.99, SD = 1.20) and the reverse score of item 37 (M = 2.99, SD = 1.30; t
(475) = 0.33, p = .974). Responses in the validation sample were slightly less consistent.
Twenty-three percent received a difference score of zero, 31.6% a difference score of one,
25.6% a difference score of two, 11.5% a difference score of three and 8.3% received the
maximum difference score of four. The paired samples t-test also indicated that there was
no significant difference between responses to item 13 (M = 2.89, SD = 1.30) and the
reverse score of item 37 (M = 2.90, SD = 1.40; t (566) = -0.22, p = .825), indicating
consistency of responses to these items.
7.1.3 Descriptive statistics.
Descriptive statistics are presented below in Table 2. In the calibration sample, the mean
age was 9.92 years (SD = 0.57 years) for fourth graders, 10.78 years (SD = 0.48 years) for
fifth graders and 11.87 years (SD = 0.56 years) for sixth graders. Similarly, in the validation
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sample the mean age for fourth graders was 9.89 years (SD = 0.59 years), for fifth graders
was 10.96 years (SD = 0.55 years) and for sixth graders was 11.90 years (SD = 0.52 years).
Descriptive statistics are presented below in Table 2.
Table 2. Descriptive Statistics for the Calibration and Validation Samples
Gender Grade Age (years)
Male Female 4 5 6 Range M (SD)
Calibration Sample 250 226 170 149 157 9 – 13 10.83 (0.97)
(N = 476)
Validation Sample 303 263 188 180 198 9 – 13 10.93 (1.00)
(N = 566)
7.1.4 Normality testing. Table 3 below presents means, standard deviations, skewness and kurtosis for all the items
used in the models.
Table 3. Univariate Analysis
Calibration Sample (N = 476) Validation Sample (N = 566) Item Mean (SD) Skew Kurtosis Mean (SD) Skew Kurtosis
1 3.79 (0.86) -0.43 -0.07 3.75 (0.84) -0.42 0.13
2 3.43 (1.01) -0.25 -0.29 3.39 (1.03) -0.29 -0.32
3 3.61 (1.10) -0.47 -0.45 3.59 (1.15) -0.49 -0.60
4 3.58 (0.98) -0.45 -0.15 3.58 (1.02) -0.52 -0.18
5 4.04 (1.08) -1.01 0.34 4.03 (1.10) -1.03 0.35
6R 2.97 (1.16) -0.16 -0.73 3.01 (1.22) -0.02 -0.89
7R 3.66 (1.08) -0.52 -0.33 3.66 (1.14) -0.54 -0.46
8 4.23 (0.94) -1.20 1.04 4.35 (0.92) -1.36 1.23
9 4.17 (0.93) -1.19 1.31 4.14 (0.94) -1.23 1.55
10 3.25 (1.03) -0.12 -0.51 3.29 (1.12) -0.30 -0.59
11 3.56 (1.23) -0.53 -0.67 3.62 (1.19) -0.55 -0.53
12 3.65 (0.97) -0.42 -0.07 3.62 (0.95) -0.37 -0.16
13 2.99 (1.20) -0.06 -0.85 2.89 (1.30) 0.05 -1.05
14 3.41 (1.14) -0.27 -0.70 3.42 (1.20) -0.31 -0.82
15R 4.26 (1.04) -1.46 1.45 4.44 (0.98) -1.92 3.11
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Calibration Sample (N = 476) Validation Sample (N = 566) Item Mean (SD) Skew Kurtosis Mean (SD) Skew Kurtosis
16 3.26 (1.17) -0.32 -0.61 3.34 (1.18) -0.39 -0.59
17 3.39 (1.06) -0.19 -0.49 3.43 (1.04) -0.26 -0.50
18 3.51 (1.05) -0.39 -0.41 3.70 (1.08) -0.55 -0.33
19 3.04 (1.03) -0.08 -0.46 3.05 (1.14) -0.06 -0.68
20 3.22 (1.18) -0.11 -0.82 3.26 (1.21) -0.26 -0.75
21 3.22 (1.18) -0.20 -0.82 3.27 (1.13) -0.21 -0.69
22 3.45 (1.19) -0.42 -0.60 3.45 (1.21) -0.37 -0.70
23 3.56 (1.15) -0.38 -0.79 3.66 (1.13) -0.56 -0.36
24 3.44 (1.16) -0.33 -0.65 3.51 (1.13) -0.42 -0.48
25 3.61 (1.12) -0.54 -0.45 3.57 (1.17) -0.55 -0.44
26 3.94 (1.14) -0.85 -0.19 3.86 (1.20) -0.91 -0.06
27 3.49 (1.00) -0.38 -0.07 3.54 (1.06) -0.41 -0.20
28 2.60 (1.32) 0.38 -0.97 2.80 (1.36) 0.10 -1.17
29 2.76 (1.22) 0.25 -0.82 2.77 (1.30) 0.216 -1.03
30 3.39 (1.39) -0.37 -1.14 3.54 (1.36) -0.56 -0.92
31R 2.72 (1.09) 0.16 -0.46 2.71 (1.09) 0.13 -0.52
32 3.59 (1.05) -0.39 -0.53 3.62 (1.13) -0.50 -0.43
33 3.54 (1.14) -0.39 -0.68 3.64 (1.10) -0.61 -0.27
34 3.17 (1.28) -0.16 -1.02 3.20 (1.32) -0.20 -1.05
35 3.82 (1.14) -0.77 -0.17 3.84 (1.16) -0.88 0.03
36R 2.71 (1.10) 0.22 -0.46 2.58 (1.15) 0.27 -0.64
37R 2.99 (1.30) -0.07 -1.06 2.90 (1.40) 0.11 -1.23
38 3.63 (1.05) -0.52 -0.16 3.63 (1.08) -0.52 -0.27
39 0.49 (0.50) 0.03 -2.00 0.52 (0.50) -0.08 -2.00
40 0.99 (0.09) -10.81 115.22 0.99 (0.11) -8.85 76.56
41 0.59 (0.49) -0.370 -1.87 0.79 (0.41) -1.40 -0.04
42 0.98 (0.14) -6.70 43.09 0.98 (0.14) -6.98 46.90
43 0.50 (0.50) 0.00 -2.01 0.67 (0.47) -0.72 -1.50
44 0.77 (0.42) -1.28 -0.36 0.77 (0.42) -1.28 -0.37
45 0.37 (0.48) 0.52 -1.73 0.39 (0.49) 0.45 -1.80
46 0.91 (0.22) -2.40 5.19 0.90 (0.23) -2.16 4.02
47 0.63 (0.48) -0.53 -1.72 0.58 (0.49) -0.34 -1.89
48 0.87 (0.29) -2.14 3.27 0.92 (0.22) -2.80 7.33
49 0.69 (0.46) -0.84 -1.30 0.83 (0.37) -1.78 1.18
50 0.89 (0.24) -2.17 3.96 0.90 (0.22) -2.13 3.83
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Calibration Sample (N = 476) Validation Sample (N = 566) Item Mean (SD) Skew Kurtosis Mean (SD) Skew Kurtosis
51 0.89 (0.22) -1.98 3.14 0.91 (0.22) -2.17 4.05
52 0.72 (0.33) -0.81 -0.46 0.70 (0.36) -0.76 -0.69
53 0.42 (0.49) 0.34 -1.89 0.53 (0.50) -0.12 -1.99
54 0.87 (0.34) -2.15 2.63 0.86 (0.34) -2.13 2.54
55 0.63 (0.48) -0.54 -1.71 0.74 (0.44) -1.08 -0.84
56 0.85 (0.36) -1.98 1.91 0.94 (0.24) -3.71 11.83
57 0.60 (0.42) -0.41 -1.45 0.62 (0.41) -0.46 -1.39
58 0.28 (0.30) 0.96 -0.93 0.31 (0.44) 0.83 -1.17
59 0.90 (0.30) -2.66 5.10 0.92 (0.27) -3.12 7.74
60 0.51 (0.50) -0.03 -2.01 0.65 (0.48) -0.65 -1.59
61 0.63 (0.43) -0.52 -1.42 0.75 (0.36) -1.08 -0.27
62 0.80 (0.36) -1.51 0.60 0.86 (0.30) -2.06 2.88
63 0.90 (0.27) -2.57 5.35 0.96 (0.140) -4.09 17.49
64 0.82 (0.27) -1.25 0.58 0.86 (0.24) -1.33 0.57
65 0.81 (0.34) -1.51 0.83 0.88 (0.26) -2.13 3.71
66 0.80 (0.40) -1.51 0.28 .89 (0.32) -2.42 3.88 Note: SD = Standard Deviation; Values of skew > 2 are in bold; Values of kurtosis > 7 are in bold
There were no absolute values of skewness greater than two or kurtosis greater than seven
in the self-report items, indicating that these univariate distributions were moderately
normal (West, Finch, & Curran, 1995). However several of the performance items in the
calibration sample (items 40, 42, 46, 50, 51, 59 and 63) and validation sample (items 40,
42, 48, 56, 59, 62, 63, 65 and 66) were skewed or kurtotic. Traditional ML methods of
SEM assume that the continuous variables in the model are multivariately normally
distributed (Tabachnick & Fidell, 2001). This was tested using the Mahalanobis distance,
which did not yield a statistically significant result (critical ratio = 107.258, p < .001);
indicating that there were no substantial multivariate outliers.
98
7.3 Principal components analysis.
7.3.1 Assessing the factorability of the correlation matrix.
The 38 items from the self-report scale as well as the scores from the 28 performance items
were subjected to principal components analysis (PCA; using SPSS version 16) to explore
trends in the data set. Prior to performing PCA, the suitability of the data for factor analysis
was assessed. Inspection of the correlation matrix revealed the presence of many
coefficients of .32 and above. The Kaiser-Meyer-Olkin (KMO) value was .80, exceeding
the recommended acceptable lower limit of .50 (Hutcheson & Sofroniou, 1999). This
indicates that the correlation matrix is not an identity matrix (i.e. that there is a relationship
among the items). The Bartlett‟s Test of Sphericity (Pallant, 2005) reached statistical
significance (chi-square (2145) = 6929.70, p < .001), indicating that there is sufficient
sample size relative to the number of items. Measures of Sampling Adequacy (MSA; anti-
image correlations) of below .50 for items 39 (.45), 41 (.48), 43 (.46) and 46 (.49) indicated
that these items should be removed due to low correlations with the other items. However,
it was decided to keep these items as other indicators suggested a relationship among the
items and the MSAs were not much higher than the cutoff.
7.3.2 Extracting the initial factors.
Principal components analysis revealed the presence of 21 factors with eigen-values
exceeding one, explaining 11.047, 6.714, 3.839, 3.182, 2.657, 2.402, 2.278, 2.226, 2.133,
2.096, 2.033, 1.991, 1.949, 1.839, 1.751, 1.721, 1.687, 1.662, 1.614 and 1.548 percent of
the variance respectively. An inspection of the scree plot (see Figure 8 below) revealed a
clear break after the fifth factor.
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Figure 8. Scree plot for items one to sixty-six.
Cattell‟s scree test (Pallant, 2005) suggests retaining five factors for further investigation.
However, the results of parallel analysis (1000 replications using Monte Carlo PCA for
parallel analysis; Watkins, 2000; see Table 4 below) showed six components with eigen-
values exceeding the corresponding criterion values for a randomly generated data matrix
of the same size (66 variables x 476 respondents); which suggests retaining six factors. It
was decided to explore both the five and six factor structures according to statistical criteria
as well as the one, two, four and 16 factor structures according to the model upon which the
items were based. While the one, four and sixteen factor models may correspond to total EI,
as well as the branch and the ability level scores respectively, the two factor structure may
represent either the distinction between Experiential and Strategic EI or distinguish
between Typical and Maximum EI or mode of measurement (i.e. self-report or
performance). It is possible that the five and six factor models will encompass the division
100
of branches according to the focus of the ability towards the self or others. For example; the
distinction between perception and appraisal of emotion in the self and others as well as the
regulation of emotion in the self and others outlined in Salovey and Mayer‟s (1990) model.
Table 4. Parallel Analysis
Component number Actual eigenvalue from
PCA
Criterion value from
parallel analysis
Decision
1 7.291 1.821 accept 2 4.431 1.753 accept 3 2.534 1.702 accept 4 2.100 1.657 accept 5 1.754 1.618 accept 6 1.585 1.582 accept 7 1.504 1.548 reject
The initial one factor solution explained 11.05% of the total variance. Item loadings for the
one factor solution included below in Table 5. Loadings equal to or above the cutoff of .32
are in bold.
Table 5. Pattern/Structure for Coefficients(One Factor Solution)
Factor
Item loading
Self-report Items
Branch I. Perception and appraisal of emotion
Identify emotion in one’s physical states, feelings and thoughts
i8. I can easily tell if I am thinking happy or sad thoughts
.373
i26. When I feel happy, my body feels different than when I am angry
.394
i30. When I am upset, I feel it in my body (such as lump in my throat, headache or sore tummy)
.340
Identify emotion in others, designs, artwork, language, sound, appearance and behavior
i1. I can easily tell how others are feeling by the look on their face
.574
i4. I can easily tell how others are feeling
.586
i5. I can easily tell if a song is happy or sad
101
Factor
Item loading
.495
i33. When someone has been in a bad mood, I can easily tell when they feel better again
.556
Express emotions accurately, and express needs related to feelings
i24. People can easily tell how I am feeling by the sound of my voice
.204
i29. When I am upset, I can tell others how they can make me feel better
.323
i35. People can easily tell how I am feeling by the way I do things (such as slamming the door when I'm angry
or singing when I'm happy)
.297
Discriminate between accurate and inaccurate, honest and dishonest expressions of feelings
i14. I know when someone is trying to hide their true feelings
.554
i23. I can tell if someone is only pretending to be angry
.474
i25. I can tell if someone is not happy, even if they are smiling
.568
Branch II. Emotional Facilitation of Thought
Emotions prioritise thinking by directing attention to important information
i20. When worrying thoughts distract me from enjoying something, I stop what I am doing so I can try to fix
what is worrying me
.373
i31R. I don't let my feelings get in the way of solving problems (R)
.412
i36R. When I feel upset, I think more about what I'm upset about than my feelings (R)
-.311
Emotions are vivid and available to be generated as aids to judgment and memory
i3. It is easy for me to imagine how I might feel about something that hasn't happened yet
.495
i18. When I have made an important decision, my feelings tell me if I have made the right decision
.616
i34. When I try to remember something that happened a long time ago, it helps if I think about how I was feeling
at the time
.391
Mood swings change perspective to encourage multiple points of view
i6. When I‟m in a bad mood, I tend to expect the worst
.079
i9. When I am in a good mood, I think more positively about others
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Factor
Item loading
.395
i16. When I can't solve a problem, if I wait until my mood has changed, I can usually think of more solutions
.415
Emotion states differentially encourage specific problem approaches
i2. How I feel makes a difference to how fast I decide what to do
.350
i27. Some moods make it easier to think through all possible solutions before deciding what to do
.580
i38. Some moods make me think more carefully about a problem
.553
Branch IV. Reflective Regulation of Emotion
Stay open to pleasant and unpleasant emotions
i7R. When someone is upset, I stay away from them
.068
i15R. I do not feel comfortable thinking about good feelings
.144
i28. I am comfortable thinking about things that have upset me.
.248
Reflectively engage or detach from an emotion
i11. I can make myself feel excited if I want to
.417
i13. When I am really angry with someone, I can still think nice things about them
.316
i21. I can stop myself from feeling really upset
.400
i37R. When I‟m really angry with someone, I can‟t think nice things about them
.009
Reflectively monitor emotions in relation to self and others
i10. I can easily tell if someone feels the same way as I do about something
.525
i32. When I feel really excited about something, I can tell if others feel the same way as me
.560
Manage emotion in self and others without repressing or exaggerating information conveyed
i12. I can easily make people feel happy
.484
i17. I can make others feel excited about something
.499
i19. I can make people feel better when they are upset with me
.426
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Factor
Item loading
i22. When I feel scared, I can tell if there is real danger or not.
.500
Performance items
Branch I. Perception and appraisal of emotion
Identify emotion in facial expressions
i39. (angry expression)
.011
i40. (happy expression)
-.065
i41. (surprised expression)
.013
i42. (sad expression)
.009
i43. (scared expression)
-.014
i44. (disgusted expression)
.118
Branch III. Understanding and Analysing Emotion
Label emotions and recognise relations among emotions
i45. .056
i47. .169
i60. .154
i66. .112
Interpret the meanings that emotions convey
i46. .079
i48. .146
i49. .017
i50. .037
i51. .135
i52. .026
i53. .145
i61. .127
i62. .098
i63. .111
i64. .090
i65. .167
Understand complex feelings; simultaneous feelings, blends of emotion
i57. .040
i58. .068
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Factor
Item loading
i59. .074
Recognise likely transitions among emotions
i54. .028
i55. .054
i56. .114
Of the self-report items, most factors were fully represented in the one factor solution.
However, only one of three items of the Branch I ability “Express emotions accurately and
express needs related to feelings” obtained a factor loading above the cutoff. Interestingly,
the items of this ability that did not sufficiently load were related to non-verbal expression
of emotion, whereas the item that did load was related to the verbal expression of emotional
needs. None of the three items representing the Branch IV ability “Stay open to pleasant
and unpleasant feelings” obtained factor loadings above the cut-off. Although item 36 was
reverse scored, it loaded negatively onto the one factor solution. This item may be
measuring Branch IV rather than Branch II as intended. The performance items
representing Branch III did not yield sufficiently high factor loadings in the one factor
model. This may be an indication of the distinction between self-report and performance EI
representing different constructs (Barchard & Hakstian, 2004). However, the low
correlations with the self-report items may also be due to differences in response format
and restricted range of scores for performance items (Tabachnick & Fidell, 2001). The one
factor solution appears to represent self reported Emotional Intelligence as per Salovey and
Mayer‟s (1990) model. While the one factor solution may be an artifact of the PCA
procedure (Harding, 2008), it is consistent with research suggesting the presence of an
overarching EI construct representing the ability to utilise and manage emotions, with the
exception of items tapping understanding of emotion. However, with a meagre 11.05
percent of the variance accounted for in this model, there is a lot of variance left to be
explained. The two, four, five, six and sixteen factor solutions were investigated after
submitting them to factor rotations for assessment of simple structure.
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7.3.3 Factor rotation.
While orthogonal rotations are simpler to interpret (Tabachnick & Fidell, 2001), EI factors
have been found to be inter-correlated and thus oblique rotations are more appropriate
(Petrides & Furnham, 2000). Therefore as suggested by Pallant (2005) both Varimax and
Oblimin rotations will be conducted. Factor correlations lower than .30 indicate that the
solutions will be similar (Pallant, 2005). In such cases, due to its relative simplicity only the
Varimax solution will be reported and analysed.
To aid in interpretation of the two factor solution, factor rotations were performed. The
initial two factor solution explained 17.61% of the total variance. With factor correlations
of less than .30 (r = .07), item loadings for the Varimax rotation of the two factor solution
are included below in Table 6. Loadings equal to or above the cutoff of .32 are in bold.
Table 6. Pattern/Structure for Coefficients (Two Factor Solution)
Factor loading
Item 1 2
Self-report Items
Branch I. Perception and appraisal of emotion
Identify emotion in one’s physical states, feelings and thoughts
i8. I can easily tell if I am thinking happy or sad thoughts
.355 .125
i26. When I feel happy, my body feels different than when I am angry
.361 .211
i30. When I am upset, I feel it in my body (such as lump in my throat, headache or sore tummy)
.327 .098
Identify emotion in others, designs, artwork, language, sound, appearance and behaviour
i1. I can easily tell how others are feeling by the look on their face
.559 .132
i4. I can easily tell how others are feeling
.573 .122
i5. I can easily tell if a song is happy or sad
.491 .066
i33. When someone has been in a bad mood, I can easily tell when they feel better again
.561 .030
Express emotions accurately, and express needs related to feelings
106
Factor loading
Item 1 2
i24. People can easily tell how I am feeling by the sound of my voice
.227 -.101
i29. When I am upset, I can tell others how they can make me feel better
.378 -.255
i35. People can easily tell how I am feeling by the way I do things (such as slamming the door when I'm angry
or singing when I'm happy)
.285 .092
Discriminate between accurate and inaccurate, honest and dishonest expressions of feelings
i14. I know when someone is trying to hide their true feelings
.554 .054
i23. I can tell if someone is only pretending to be angry
.473 .049
i25. I can tell if someone is not happy, even if they are smiling
.571 .039
Branch II. Emotional Facilitation of Thought
Emotions prioritise thinking by directing attention to important information
i20. When worrying thoughts distract me from enjoying something, I stop what I am doing so I can try to fix
what is worrying me
.382 -.010
i31R. I don't let my feelings get in the way of solving problems (R)
-.417 -.013
i36R. When I feel upset, I think more about what I'm upset about than my feelings (R)
-.303 -.072
Emotions are vivid and available to be generated as aids to judgment and memory
i3. It is easy for me to imagine how I might feel about something that hasn't happened yet
.482 .115
i18. When I have made an important decision, my feelings tell me if I have made the right decision
.626 .008
i34. When I try to remember something that happened a long time ago, it helps if I think about how I was feeling
at the time
.424 -.132
Mood swings change perspective to encourage multiple points of view
i6. When I‟m in a bad mood, I tend to expect the worst
.072 .040
i9. When I am in a good mood, I think more positively about others
.359 .223
i16. When I can't solve a problem, if I wait until my mood has changed, I can usually think of more solutions
.431 -.046
Emotion states differentially encourage specific problem approaches
107
Factor loading
Item 1 2
i2. How I feel makes a difference to how fast I decide what to do
.331 .129
i27. Some moods make it easier to think through all possible solutions before deciding what to do
.566 .129
i38. Some moods make me think more carefully about a problem
.547 .083
Branch IV. Reflective Regulation of Emotion
Stay open to pleasant and unpleasant emotions
i7R. When someone is upset, I stay away from them
.051 .091
i15R. I do not feel comfortable thinking about good feelings
.090 .295
i28. I am comfortable thinking about things that have upset me.
.271 -.095
Reflectively engage or detach from an emotion
i11. I can make myself feel excited if I want to
.451 -.142
i13. When I am really angry with someone, I can still think nice things about them
.358 -.187
i21. I can stop myself from feeling really upset
.437 -.153
i37R. When I‟m really angry with someone, I can‟t think nice things about them
.004 .024
Reflectively monitor emotions in relation to self and others
i10. I can easily tell if someone feels the same way as I do about something
.524 .055
i32. When I feel really excited about something, I can tell if others feel the same way as me
.553 .093
Manage emotion in self and others without repressing or exaggerating information conveyed
i12. I can easily make people feel happy
.506 -.069
i17. I can make others feel excited about something
.516 -.038
i19. I can make people feel better when they are upset with me
.461 -.143
i22. When I feel scared, I can tell if there is real danger or not.
.484 .132
Performance items
Branch I. Perception and appraisal of emotion
108
Factor loading
Item 1 2
Identify emotion in facial expressions
i39. (angry expression)
.015 -.018
i40. (happy expression)
-.052 -.073
i41. (surprised expression)
.017 -.021
i42. (sad expression)
-.013 .111
i43. (scared expression)
-.023 .046
i44. (disgusted expression)
.089 .162
Branch III. Understanding and Analysing Emotion
Label emotions and recognise relations among emotions
i45. .019 .196
i47. .146 .136
i60. .086 .365
i66. -.013 .656
Interpret the meanings that emotions convey
i46. .052 .146
i48. .052 .504
i49. -.018 .186
i50. -.043 .414
i51. .077 .315
i52. -.027 .281
i53. .091 .293
i61. .062 .346
i62. -.021 .624
i63. -.027 .726
i64. -.003 .491
i65. .505 .620
Understand complex feelings; simultaneous feelings, blends of emotion
i57. -.021 .324
i58. .026 .223
i59. .012 .329
Recognise likely transitions among emotions
i54. -.028 .295
i55. -.022 .398
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Factor loading
Item 1 2
i56. -.003 .619
From Table 6 it may be seen that the Typical EI items primarily load onto Factor one while
the Maximum EI items load onto Factor two. This may be due to the typical/maximum
performance distinction or mode of measurement. To aid in interpretation of the four factor
solution, factor rotations were performed. The orthogonal (Varimax) rotation is presented
below in Table 7. The oblique (Oblimin) solution failed to converge.
Table 7. Pattern/Structure for Coefficients (Four Factor Solution, Varimax Rotation)
Factor loading
Item 1 2 3 4
Self-report Items
Branch I. Perception and appraisal of emotion
Identify emotion in one’s physical states, feelings and thoughts
i8. I can easily tell if I am thinking happy or sad thoughts
.366 .086 .211 .094
i26. When I feel happy, my body feels different than when I am angry
.168 .176 .488 -.026
i30. When I am upset, I feel it in my body (such as lump in my throat, headache or sore tummy)
.104 .068 .494 -.001
Identify emotion in others, designs, artwork, language, sound, appearance and behaviour
i1. I can easily tell how others are feeling by the look on their face
.653 .098 .060 .060
i4. I can easily tell how others are feeling
.659 .087 .071 .069
i5. I can easily tell if a song is happy or sad
.492 .025 .227 -.021
i33. When someone has been in a bad mood, I can easily tell when they feel better again
.564 .003 .132 .147
Express emotions accurately, and express needs related to feelings
i24. People can easily tell how I am feeling by the sound of my voice
.090 -.136 .382 -.114
i29. When I am upset, I can tell others how they can make me feel better
.153 -.256 .291 .281
110
Factor loading
Item 1 2 3 4
i35. People can easily tell how I am feeling by the way I do things (such as slamming the door when I'm angry
or singing when I'm happy)
.083 .060 .099 -.043
Discriminate between accurate and inaccurate, honest and dishonest expressions of feelings
i14. I know when someone is trying to hide their true feelings
.624 .013 .123 .012
i23. I can tell if someone is only pretending to be angry
.655 .000 .013 -.127
i25. I can tell if someone is not happy, even if they are smiling
.637 .004 .091 .072
Branch II. Emotional Facilitation of Thought
Emotions prioritise thinking by directing attention to important information
i20. When worrying thoughts distract me from enjoying something, I stop what I am doing so I can try to fix
what is worrying me
.136 -.014 .361 .255
i31R. I don't let my feelings get in the way of solving problems (R)
-.270 -.017 -.172 -.330
i36R. When I feel upset, I think more about what I'm upset about than my feelings (R)
-.043 -.037 -.567 .041
Emotions are vivid and available to be generated as aids to judgment and memory
i3. It is easy for me to imagine how I might feel about something that hasn't happened yet
.479 .078 .211 .011
i18. When I have made an important decision, my feelings tell me if I have made the right decision
.470 -.009 .301 .292
i34. When I try to remember something that happened a long time ago, it helps if I think about how I was feeling
at the time
.183 -.145 .390 .215
Mood swings change perspective to encourage multiple points of view
i6. When I‟m in a bad mood, I tend to expect the worst
-.031 -.013 .457 -.369
i9. When I am in a good mood, I think more positively about others
.185 .215 .301 .195
i16. When I can't solve a problem, if I wait until my mood has changed, I can usually think of more solutions
.118 -.048 .441 .314
Emotion states differentially encourage specific problem approaches
i2. How I feel makes a difference to how fast I decide what to do
.192 .090 .429 -.087
i27. Some moods make it easier to think through all possible solutions before deciding what to do
.290 .109 .502 .249
111
Factor loading
Item 1 2 3 4
i38. Some moods make me think more carefully about a problem
.269 .060 .524 .207
Branch IV. Reflective Regulation of Emotion
Stay open to pleasant and unpleasant emotions
i7R. When someone is upset, I stay away from them
-.069 .127 -.028 .342
i15R. I do not feel comfortable thinking about good feelings
.031 .321 -.043 .275
i28. I am comfortable thinking about things that have upset me.
.197 -.108 .160 .087
Reflectively engage or detach from an emotion
i11. I can make myself feel excited if I want to
.262 -.133 .192 .400
i13. When I am really angry with someone, I can still think nice things about them
.118 -.137 .020 .691
i21. I can stop myself from feeling really upset
.286 -.140 .108 .422
i37R. When I‟m really angry with someone, I can‟t think nice things about them
.015 .086 -.371 .503
Reflectively monitor emotions in relation to self and others
i10. I can easily tell if someone feels the same way as I do about something
.579 .025 .082 .084
i32. When I feel really excited about something, I can tell if others feel the same way as me
.512 .061 .235 .100
Manage emotion in self and others without repressing or exaggerating information conveyed
i12. I can easily make people feel happy
.381 -.057 .086 .446
i17. I can make others feel excited about something
.315 -.119 .038 .526
i19. I can make people feel better when they are upset with me
.315 -.119 .038 .526
i22. When I feel scared, I can tell if there is real danger or not.
.453 .104 .203 .087
Performance items
Branch I. Perception and appraisal of emotion
Identify emotion in facial expressions
i39. (angry expression)
.178 -.030 -.164 -.119
i40. (happy expression)
112
Factor loading
Item 1 2 3 4
-.015 -.087 .023 -.154
i41. (surprised expression)
.062 -.040 .044 -.152
i42. (sad expression)
.045 .099 -.004 -.115
i43. (scared expression)
.072 .033 -.057 -.137
i44. (disgusted expression)
.257 .131 -.051 -.224
Branch III. Understanding and Analysing Emotion
Label emotions and recognise relations among emotions
i45. .104 .192 -.084 -.036
i47. .095 .123 .155 -.003
i60. .126 .359 .009 -.007
i66. -.009 .665 .007 .046
Interpret the meanings that emotions convey
i46. -.006 .157 .032 .129
i48. -.011 .509 .108 .073
i49. -.029 .183 .054 -.042
i50. -.074 .416 .072 -.011
i51. .178 -.030 -.164 -.119
i52. .018 .277 -.015 -.064
i53. .143 .280 .025 -.063
i61. .177 .332 -.051 -.104
i62. -.007 .628 .017 .004
i63. -.055 .740 .027 .087
i64. -.047 .500 .059 .066
i65. .084 .623 -.008 .037
Understand complex feelings; simultaneous feelings, blends of emotion
i57. .010 .322 -.002 -.041
i58. .121 .217 -.080 -.054
i59. -.018 .350 -.047 .176
Recognise likely transitions among emotions
i54. -.058 .294 .079 -.032
i55. .002 .382 .095 -.155
i56. .050 .622 -.034 -.001
113
Simple structure was achieved in the four factor model (Pallant, 2005), with a number of
strong loadings and most items loading substantially onto single factors. Cross-loadings
were apparent for item 6 and two of four items (12 and 17) representing the Branch IV
ability “Manage emotion in self and others without repressing or exaggerating information
conveyed”. While the oblique factor rotation failed to converge, moderate factor
correlations (Factors 1 and 4 =.62; Factors 3 and 4 = -.67) indicated that an oblique
rotation may be suitable (Pallant, 2005). The four factor solution explained only 24.78
percent of the variance. With Varimax rotation, Factor one contributed 8.00 percent,
Factor two contributed 6.78 percent, Factor three contributed 5.38 percent and Factor four
contributed 4.63 percent.
Items from Branches I, II and IV relating to others (or other external stimuli) tended to
load onto Factor One. Of the Branch III items that loaded on the four factor model, all
items loaded onto Factor Two. Factor Three loaded items from Branches I and II relating
to self. However, Branch IV items relating to both self and others loaded onto Factor Four.
Despite many items loading onto their intended factors, there were some notable
transgressions with cross-loadings between Factors One and Three as well as Factors One
and Four. While most of the items of the Branch I loaded onto Factor One, those relating to
intrapersonal processes (from the abilities: “Identify emotion in one‟s physical states,
feelings and thoughts” and “Express emotions accurately and express needs related to
feelings”) loaded onto Factor Three with Branch II items. In line with Mayer and
Salovey‟s (1997) contention that each ability builds upon previously developed abilities,
this might imply that in this sample, Branches I and II are undifferentiated from one
another. However, this Factor may represent Experiential EI. Alternatively, an underlying
construct such as “potency of emotion” may underpin all three constructs (“Emotional
facilitation of thought”, “Identify emotion in one‟s physical states, feelings and thoughts”
and “Express emotions accurately and express needs related to feelings”).
While many of the Branch IV items loaded onto Factor Four, those that related to
interpersonal regulation cross-loaded onto Factor One. As surmised earlier, the negatively
loading reverse scored item 31 (“I don't let my feelings get in the way of solving
114
problems”) loaded with Branch IV items rather than Branch II as intended. Another
reverse scored item (36R “When I feel upset, I think more about what I‟m upset about than
my feelings”) while intended to measure the extent to which feelings are incorporated into
thinking and problem-solving, negatively loaded onto this factor. This may indicate a
distinction between the ability for emotions to facilitate rather than upstage thinking.
Factor One appears to represent Branch I, Factor Two represents Branch III, Factor Three
may represent Experiential EI and Factor Four represents Branch IV. While all of the
intended branches were represented in the four factor solution, with just under a quarter of
the variance explained, the four factor solution did not explain substantially more variance
than the one factor solution.
Factor rotations were performed to aid in the interpretation of the five factor solution. With
low factor correlations (.02 to -.31), the oblique (Oblimin) solution was considered
unsuitable. The five factor solution explained 27.43% of the variance. The orthogonal
(Varimax) rotation is presented below in Table 8.
Table 8. Pattern/Structure for Coefficients (Five Factor Solution, Varimax Rotation)
Factor loading
Item 1 2 3 4 5
Self-report Items
Branch I. Perception and appraisal of emotion
Identify emotion in one’s physical states, feelings and thoughts
i8. I can easily tell if I am thinking happy or sad thoughts
.376 .070 .202 -.068 .065
i26. When I feel happy, my body feels different than when I am angry
.194 .172 .480 -.024 -.015
i30. When I am upset, I feel it in my body (such as lump in my throat, headache or sore tummy)
.080 .061 .509 .048 .138
Identify emotion in others, designs, artwork, language, sound, appearance and behaviour
i1. I can easily tell how others are feeling by the look on their face
.672 .086 .030 .078 -.003
i4. I can easily tell how others are feeling
.668 .074 .044 .098 .031
115
Factor loading
Item 1 2 3 4 5
i5. I can easily tell if a song is happy or sad
.481 .008 .220 .031 .118
i33. When someone has been in a bad mood, I can easily tell when they feel better again
.556 -.004 .113 .186 .043
Express emotions accurately, and express needs related to feelings
i24. People can easily tell how I am feeling by the sound of my voice
.049 -.151 .406 -.041 .205
i29. When I am upset, I can tell others how they can make me feel better
.081 -.250 .309 .360 .153
i35. People can easily tell how I am feeling by the way I do things (such as slamming the door when I'm angry
or singing when I'm happy)
.121 .059 .461 -.055 -.080
Discriminate between accurate and inaccurate, honest and dishonest expressions of feelings
i14. I know when someone is trying to hide their true feelings
.654 .000 .090 .026 -.036
i23. I can tell if someone is only pretending to be angry
.631 -.030 .007 -.051 .210
i25. I can tell if someone is not happy, even if they are smiling
.640 -.008 .067 .109 .039
Branch II. Emotional Facilitation of Thought
Emotions prioritise thinking by directing attention to important information
i20. When worrying thoughts distract me from enjoying something, I stop what I am doing so I can try to fix
what is worrying me
.143 .000 .349 .254 -.088
i31R. I don't let my feelings get in the way of solving problems (R)
-.215 -.024 -.179 -.386 -.121
i36R. When I feel upset, I think more about what I'm upset about than my feelings (R)
-.053 -.034 -.570 .023 -.021
Emotions are vivid and available to be generated as aids to judgment and memory
i3. It is easy for me to imagine how I might feel about something that hasn't happened yet
.494 .067 .192 .033 .018
i18. When I have made an important decision, my feelings tell me if I have made the right decision
.471 -.003 .278 .313 -.045
i34. When I try to remember something that happened a long time ago, it helps if I think about how I was feeling
at the time
.224 -.129 .361 .190 -.211
Mood swings change perspective to encourage multiple points of view
i6. When I‟m in a bad mood, I tend to expect the worst
.040 -.027 .445 -.398 -.102
116
Factor loading
Item 1 2 3 4 5
i9. When I am in a good mood, I think more positively about others
.228 .227 .275 .158 -.160
i16. When I can't solve a problem, if I wait until my mood has changed, I can usually think of more solutions
.074 -.036 .451 .363 .076
Emotion states differentially encourage specific problem approaches
i2. How I feel makes a difference to how fast I decide what to do
.241 .085 .410 -.102 -.088
i27. Some moods make it easier to think through all possible solutions before deciding what to do
.270 .114 .501 .288 .057
i38. Some moods make me think more carefully about a problem
.266 .065 .516 .232 -.002
Branch IV. Reflective Regulation of Emotion
Stay open to pleasant and unpleasant emotions
i7R. When someone is upset, I stay away from them
-.104 .146 -.022 .345 .010
i15R. I do not feel comfortable thinking about good feelings
.017 .334 -.045 .262 -.005
i28. I am comfortable thinking about things that have upset me.
.103 -.120 .194 .196 .328
Reflectively engage or detach from an emotion
i11. I can make myself feel excited if I want to
.253 -.114 .174 .409 -.100
i13. When I am really angry with someone, I can still think nice things about them
.087 -.097 .003 .686 -.155
i21. I can stop myself from feeling really upset
.264 -.122 .093 .441 -.061
i37R. When I‟m really angry with someone, I can‟t think nice things about them
-.039 .111 -.370 .506 .000
Reflectively monitor emotions in relation to self and others
i10. I can easily tell if someone feels the same way as I do about something
.610 .019 .047 .088 -.070
i32. When I feel really excited about something, I can tell if others feel the same way as me
.511 .053 .218 .134 .043
Manage emotion in self and others without repressing or exaggerating information conveyed
i12. I can easily make people feel happy
.359 -.042 .072 .476 -.011
i17. I can make others feel excited about something
.313 -.019 .209 .415 -.053
i19. I can make people feel better when they are upset with me
117
Factor loading
Item 1 2 3 4 5
.272 -.098 .026 .557 -.021
i22. When I feel scared, I can tell if there is real danger or not.
.449 .096 .190 .120 .058
Performance items
Branch I. Perception and appraisal of emotion
Identify emotion in facial expressions
i39. (angry expression)
.042 -.062 -.105 .030 .553
i40. (happy expression)
-.021 -.098 .032 -.135 .070
i41. (surprised expression)
.016 -.058 .069 -.089 .230
i42. (sad expression)
.036 .088 .004 -.098 .096
i43. (scared expression)
-.023 .008 -.011 -.031 .412
i44. (disgusted expression)
.139 .092 .004 -.080 .563
Branch III. Understanding and Analysing Emotion
Label emotions and recognise relations among emotions
i45. .025 .174 -.049 .044 .339
i47. .075 .117 .164 .026 .111
i60. .067 .344 .036 .052 .286
i66. .052 .672 -.015 -.033 -.144
Interpret the meanings that emotions convey
i46. -.028 .162 .040 .140 .059
i48. -.014 .510 .113 .062 .060
i49. -.057 .176 .071 -.016 .144
i50. -.126 .407 .103 .029 .250
i51. .047 .329 .015 .109 .006
i52. -.029 .264 .011 -.019 .240
i53. .112 .265 .040 -.025 .195
i61. .147 .313 -.036 -.066 .215
i62. .020 .628 .011 -.038 -.008
i63. -.005 .750 .010 .011 -.113
i64. .015 .511 .036 -.013 -.180
i65. .102 .621 -.014 .007 .022
Understand complex feelings; simultaneous feelings, blends of emotion
i57. .025 .319 -.005 -.061 .007
118
Factor loading
Item 1 2 3 4 5
i58. .115 .208 -.078 -.045 .084
i59. -.055 .355 -.033 .189 .117
Recognise likely transitions among emotions
i54. -.053 .292 .085 -.041 .038
i55. .024 .373 .096 -.171 .037
i56. .104 .624 -.055 -.067 -.102
From Table 8, it may be seen that Factors One and Three appear to represent EI directed
towards others and self respectively. Items from Branches I and IV referring to others
tended to load onto Factor One, while items from Branches I and II referring to self tended
to load onto Factor Three. Alternatively, Factor Three may also represent Experiential EI.
Factor Two, which although primarily loading items from Branch III, loads one item from
Branch IV. Therefore this may imply the existence of a Strategic EI factor. A few
Maximum EI items for Branch I loaded onto Factor Five along with a couple of Maximum
EI items for Branch III. The meaning for this factor is unclear and could represent either a
method factor or Maximum EI.
Orthogonal (Varimax) rotation of the six factor solution is presented below in Table 9.
Oblique (Oblimin) rotation resulted in generally low factor correlations with one factor
correlation marginally above the cut-off of .30 (Factors One and Four with a correlation of -
.33). Therefore, as they are likely to yield similar structures (Pallant, 2005), only the
orthogonal solution is presented.
Table 9. Pattern/Structure for Coefficients (Six Factor Solution, Varimax Rotation)
Factor loading
Item 1 2 3 4 5 6
Self-report Items
Branch I. Perception and appraisal of emotion
Identify emotion in one’s physical states, feelings and thoughts
i8. I can easily tell if I am thinking happy or sad thoughts
.373 .056 -.196 .121 -.030 -.026
i26. When I feel happy, my body feels different than when I am angry
119
Factor loading
Item 1 2 3 4 5 6
.394 .139 -.274 -.219 -.019 .101
i30. When I am upset, I feel it in my body (such as lump in my throat, headache or sore tummy)
.340 .034 -.266 .268 .176 -.116
Identify emotion in others, designs, artwork, language, sound, appearance and behaviour
i1. I can easily tell how others are feeling by the look on their face
.574 .204 .007 .335 -.152 .070
i4. I can easily tell how others are feeling
.586 .011 .008 .328 -.113 .075
i5. I can easily tell if a song is happy or sad
.495 -.028 -.154 .160 .020 -.148
i33. When someone has been in a bad mood, I can easily tell when they feel better again
.556 -.077 .034 .200 -.041 -.141
Express emotions accurately, and express needs related to feelings
i24. People can easily tell how I am feeling by the sound of my voice
.204 -.142 -.319 -.148 .220 .226
i29. When I am upset, I can tell others how they can make me feel better
.323 -.322 .045 -.206 .256 -.146
i35. People can easily tell how I am feeling by the way I do things (such as slamming the door when I'm angry
or singing when I'm happy)
.297 .036 -.289 -.249 -.067 .150
Discriminate between accurate and inaccurate, honest and dishonest expressions of feelings
i14. I know when someone is trying to hide their true feelings
.554 -.052 -.074 .298 -.181 .025
i23. I can tell if someone is only pretending to be angry
.474 -.041 -.132 .449 .025 -.191
i25. I can tell if someone is not happy, even if they are smiling
.568 -.070 -.008 .303 -.090 -.060
Branch II. Emotional Facilitation of Thought
Emotions prioritise thinking by directing attention to important information
i20. When worrying thoughts distract me from enjoying something, I stop what I am doing so I can try to fix
what is worrying me
.373 -.082 .010 -.262 -.013 .031
i31R. I don't let my feelings get in the way of solving problems (R)
-.412 .066 -.174 .084 -.174 -.104
i36R. When I feel upset, I think more about what I'm upset about than my feelings (R)
-.311 -.013 .340 .337 -.065 -.115
Emotions are vivid and available to be generated as aids to judgment and memory
i3. It is easy for me to imagine how I might feel about something that hasn't happened yet
.495 .021 -.112 .148 -.080 .128
120
Factor loading
Item 1 2 3 4 5 6
i18. When I have made an important decision, my feelings tell me if I have made the right decision
.616 -.111 .064 -.030 -.050 -.055
i34. When I try to remember something that happened a long time ago, it helps if I think about how I was feeling
at the time
.391 -.210 -.054 -.231 -.159 .050
Mood swings change perspective to encourage multiple points of view
i6. When I‟m in a bad mood, I tend to expect the worst
.079 .026 -.556 -.172 -.152 -.112
i9. When I am in a good mood, I think more positively about others
.395 .151 .014 -.175 -.143 .133
i16. When I can't solve a problem, if I wait until my mood has changed, I can usually think of more solutions
.415 -.126 .013 -.349 .196 .011
Emotion states differentially encourage specific problem approaches
i2. How I feel makes a difference to how fast I decide what to do
.350 .064 -.302 -.136 -.123 .230
i27. Some moods make it easier to think through all possible solutions before deciding what to do
.580 .019 -.064 -.263 .111 -.029
i38. Some moods make me think more carefully about a problem
.553 -.022 -.115 -.272 .046 .100
Branch IV. Reflective Regulation of Emotion
Stay open to pleasant and unpleasant emotions
i7R. When someone is upset, I stay away from them
.068 .080 .315 -.169 .115 -.001
i15R. I do not feel comfortable thinking about good feelings
.144 .273 .284 -.077 .039 .215
i28. I am comfortable thinking about things that have upset me.
.248 -.145 -.031 -.016 .353 -.011
Reflectively engage or detach from an emotion
i11. I can make myself feel excited if I want to
.417 -.225 .207 -.126 -.030 -.025
i13. When I am really angry with someone, I can still think nice things about them
.316 -.252 .547 -.220 .010 .165
i21. I can stop myself from feeling really upset
.400 -.233 .270 -.063 .002 .076
i37R. When I‟m really angry with someone, I can‟t think nice things about them
.009 .023 .629 .047 .090 .266
Reflectively monitor emotions in relation to self and others
i10. I can easily tell if someone feels the same way as I do about something
.525 -.045 .011 .268 -.192 -.076
121
Factor loading
Item 1 2 3 4 5 6
i32. When I feel really excited about something, I can tell if others feel the same way as me
.560 -.013 -.054 .116 -.033 -.091
Manage emotion in self and others without repressing or exaggerating information conveyed
i12. I can easily make people feel happy
.484 -.163 .307 -.003 .031 .013
i17. I can make others feel excited about something
.499 -.135 .195 -.110 .001 -.178
i19. I can make people feel better when they are upset with me
.426 -.228 .396 -.042 .058 -.054
i22. When I feel scared, I can tell if there is real danger or not.
.500 .038 -.041 .105 -.012 -.021
Performance items
Branch I. Perception and appraisal of emotion
Identify emotion in facial expressions
i39. (angry expression)
.011 -.021 -.018 .270 .499 .156
i40. (happy expression)
-.065 -.062 -.149 .042 .044 -.039
i41. (surprised expression)
.013 -.023 -.156 .073 .197 .171
i42. (sad expression)
.009 .111 -.085 .074 .053 .144
i43. (scared expression)
-.014 .049 -.082 .138 .378 .296
i44. (disgusted expression)
.118 .142 -.153 .280 .464 .282
Branch III. Understanding and Analysing Emotion
Label emotions and recognise relations among emotions
i45. .056 .189 .032 .128 .307 .031
i47. .169 .106 -.077 -.048 .105 -.361
i60. .154 .342 .022 .061 .253 .022
i66.
Interpret the meanings that emotions convey
i46. .079 .134 .106 -.084 .095 -.197
i48. .146 .485 .054 -.128 .070 -.219
i49. .017 .186 -.045 -.044 .146 -.257
i50. .037 .414 -.004 -.105 .269 -.369
i51. .135 .295 .123 -.045 .011 -.104
i52. .026 .282 -.018 -.037 .217 -.306
122
Factor loading
Item 1 2 3 4 5 6
i53. .145 .270 -.042 -.087 .141 -.013
i61. .127 .328 -.031 .174 .131 -.082
i62. .098 .617 .058 -.041 -.043 .045
i63. .111 .718 .135 -.115 -.126 -.066
i64. .090 .483 .074 -.114 -.188 .028
i65. .167 .599 .095 .019 -.027 .244
Understand complex feelings; simultaneous feelings, blends of emotion
i57. .040 .322 .000 .012 -.027 -.037
i58. .068 .214 .017 .142 .021 .077
i59. .074 .321 .207 -.065 .153 -.276
Recognise likely transitions among emotions
i54. .028 .295 -.038 .087 .037 .137
i55. .054 .395 -.142 -.013 -.017 .261
i56. .114 .608 .080 .031 -.166 .048
Simple structure was only partially achieved in the six factor model (Pallant, 2005). While
there were a number of strong loadings, many cross-loadings were apparent (items 1, 4,
16, 19, 23, 29 and 50). The six factor solution allowed many more of the performance
items to load compared to the one and four factor models, with three of the Maximum EI
“Identify Emotions in Facial Expressions” items (angry, scared, disgusted) loading onto
Factor Five and most of the Maximum EI Branch III items loading onto Factor Two. The
six factor solution explained a total of 29.84 percent of the variance. With Varimax
rotation, Factor One contributed 7.94 percent, Factor Two contributed 6.08 percent, Factor
Three contributed 5.24 percent, Factor Four contributed 4.67 percent, Factor Five
contributed 3.16 percent and Factor Six contributed 2.75.
With many items from the three Typical EI measures for Branch I, II and IV loading onto
Factor One; it may represent Typical EI. Factor Two appears to represent Branch III.
However, with the two different measures of Maximum EI (representing Branch I – ability
2 and Branch III) loading onto different factors, Factor Two is unlikely to represent
Maximum EI. Similarly, the loading of these items onto different factors attests to the
factors representing true factors rather than merely reflecting method variance. Factor
Three appears to represent positivity or optimism. Factor four primarily held loadings for
123
items of the second Branch I ability referring to perception of emotion in others. These
items had cross-loadings between Factors One and Four with stronger loadings on Factor
One than Four. These loadings may have been influenced by the Maximum EI items for
the same ability, which had items loading onto the next factor, Factor Five. Factor Five
appears to tap the ability to perceive emotions in faces. With only three loading items,
Factor Six is poorly defined and the meaning of the negative loadings of the three
performance items is unclear. This factor may represent difficulty with understanding
emotions; however other performance items did not load onto this factor. With the lack of
clear factor meanings, it is likely that this six factor model is not a suitable fit for the data.
Orthogonal (Varimax) rotation of the sixteen factor solution is presented below in Table 10.
The Oblique (Oblimin) rotation failed to converge.
Table 10. Pattern/Structure for Coefficients (Sixteen Factor Solution, Varimax Rotation)
Factor loading
Item 1 2 3 4 5 6 7 8
9 10 11 12 13 14 15 16
Self-report Items
Branch I. Perception and appraisal of emotion
Identify emotion in one’s physical states, feelings and thoughts
i8. I can easily tell if I am thinking happy or sad thoughts
.325 .001 .039 -.012 -.011 -.011 .299 .054
.108 .024 .161 .053 .514 .107 -.056 -.118
i26. When I feel happy, my body feels different than when I am angry
.185 .119 .471 -.226 .166 -.013 .051 -.009
.138 -.019 .159 .017 -.094 -.242 -.191 .058
i30. When I am upset, I feel it in my body (such as lump in my throat, headache or sore tummy)
.140 .011 .409 -.072 -.030 .214 .266 -.157
.108 .102 .039 -.174 -.147 -.037 -.035 -.044
Identify emotion in others, designs, artwork, language, sound, appearance and behaviour
i1. I can easily tell how others are feeling by the look on their face
.650 .070 .108 .174 -.155 -.013 .099 .008
-.129 -.022 .208 -.01 .049 .096 .060 -.207
i4. I can easily tell how others are feeling
124
Factor loading
Item 1 2 3 4 5 6 7 8
9 10 11 12 13 14 15 16
.645 .063 .054 .210 -.118 -.034 .175 -.053
-.015 .067 .267 .031 .058 .052 .103 -.199
i5. I can easily tell if a song is happy or sad
.519 -.042 .099 -.038 -.005 .236 .207 .034
.064 -.004 -.081 .140 .147 .029 -.094 .027
i33. When someone has been in a bad mood, I can easily tell when they feel better again
.588 -.036 .080 .071 .175 .141 .047 -.004
-.026 .048 -.056 -.063 -.037 -.111 -.098 .104
Express emotions accurately, and express needs related to feelings
i24. People can easily tell how I am feeling by the sound of my voice
.039 -.114 .127 -.017 -.036 -.006 .692 .195
-.010 .094 .028 -.088 .025 .021 .002 .116
i29. When I am upset, I can tell others how they can make me feel better
.107 -.194 .167 .183 .205 -.025 .269 .005
-.048 .416 -.176 .093 .070 .063 -.010 .263
i35. People can easily tell how I am feeling by the way I do things (such as slamming the door when
I'm angry or singing when I'm happy)
.122 .130 .157 -.089 .076 -.073 .644 -.082
.018 -.017 .052 .027 -.009 -.077 .013 .053
Discriminate between accurate and inaccurate, honest and dishonest expressions of feelings
i14. I know when someone is trying to hide their true feelings
.658 .063 .099 -.034 .120 .005 -.011 .014
-.015 -.056 -.122 -.110 -.067 .124 -.069 -.160
i23. I can tell if someone is only pretending to be angry
.667 -.027 .018 -.117 -.022 .005 -.110 .127
.093 .082 -.103 .002 .044 .016 .055 .236
i25. I can tell if someone is not happy, even if they are smiling
.688 .051 .097 .036 .023 -.069 .021 .007
.003 .007 -.170 .028 -.119 -.007 -.018 .042
Branch II. Emotional Facilitation of Thought
Emotions prioritise thinking by directing attention to important information
i20. When worrying thoughts distract me from enjoying something, I stop what I am doing so I can
try to fix what is worrying me
.134 068 .436 .107 .076 .088 .068 .023
125
Factor loading
Item 1 2 3 4 5 6 7 8
9 10 11 12 13 14 15 16
-.224 .010 -.289 -.096 .107 .340 -.043 -.060
i31R. I don't let my feelings get in the way of solving problems (R)
-.182 .028 -.327 -.359 -.004 -.054 -.037 .026
.013 -.235 -.197 .099 .028 -.125 .017 .014
i36R. When I feel upset, I think more about what I'm upset about than my feelings (R)
-.061 -.100 -.467 .151 .039 .062 -.266 .095
-.109 -.194 .087 -.137 -.061 -.075 .036 .191
Emotions are vivid and available to be generated as aids to judgment and memory
i3. It is easy for me to imagine how I might feel about something that hasn't happened yet
.416 .096 .203 -.005 .137 -.055 -.039 .116
.028 .195 .185 -.144 .290 -.028 -.125 -.027
i18. When I have made an important decision, my feelings tell me if I have made the right decision
.451 .010 .336 .164 .203 -.048 .001 -.033
-.148 .188 .047 .010 .063 .015 .045 -.027
i34. When I try to remember something that happened a long time ago, it helps if I think about how I
was feeling at the time
.170 -.048 .237 -.021 .475 -.082 .055 -.142
.125 .124 .020 .092 .193 .039 -.323 -.009
Mood swings change perspective to encourage multiple points of view
i6. When I‟m in a bad mood, I tend to expect the worst
.079 .026 -.556 -.172 -.152 -.112 -.003 .017
.128 .093 .021 .002 .000 .015 -.033 .058
i9. When I am in a good mood, I think more positively about others
.178 .193 .269 .069 .246 .124 .184 .159
-.120 -.306 .064 -.040 .183 -.107 -.061 .044
i16. When I can't solve a problem, if I wait until my mood has changed, I can usually think of more
solutions
.081 -.106 .671 .172 .006 .023 .012 .062
-.094 -.037 -.044 .048 -.022 .093 .102 .101
Emotion states differentially encourage specific problem approaches
i2. How I feel makes a difference to how fast I decide what to do
.145 .095 .418 -.231 .237 -.116 .030 .178
-.032 -.140 .227 .031 .095 .141 -.037 -.214
i27. Some moods make it easier to think through all possible solutions before deciding what to do
126
Factor loading
Item 1 2 3 4 5 6 7 8
9 10 11 12 13 14 15 16
.273 -.014 .558 .079 .161 .193 .103 -.022
-.014 .033 .128 .007 -.048 -.104 -.026 .013
i38. Some moods make me think more carefully about a problem
.268 .069 .587 .058 .087 -.087 .118 -.023
.092 .033 -.011 .086 .057 -.131 .037 .014
Branch IV. Reflective Regulation of Emotion
Stay open to pleasant and unpleasant emotions
i7R. When someone is upset, I stay away from them
-.045 .054 .104 .312 .038 .258 -.084 -.070
.132 -.124 -.176 .142 -.097 -.249 .018 -.289
i15R. I do not feel comfortable thinking about good feelings
-.001 .352 .060 .252 .132 -.065 .006 .304
.025 -.214 .049 .347 .042 -.196 .097 -.173
i28. I am comfortable thinking about things that have upset me.
.115 -.061 .092 .116 .044 -.019 .147 .046
.054 .649 -.068 -.087 -.067 .092 -.026 .038
Reflectively engage or detach from an emotion
i11. I can make myself feel excited if I want to
.219 -.085 .139 .205 .601 -.024 .008 .010
-.033 .035 -.046 -.153 -.025 -.091 .077 .098
i13. When I am really angry with someone, I can still think nice things about them
.072 -.047 .159 .682 .174 -.024 .092 -.134
-.066 .057 -.083 -.071 .047 -.027 -.111 -.023
i21. I can stop myself from feeling really upset
.238 -.069 .201 .353 .230 -.144 -.013 -.098
.012 .134 .044 .007 -.063 .113 -.073 .061
i37R. When I‟m really angry with someone, I can‟t think nice things about them
.009 .023 .629 .047 .090 .266 .044 .051
-.152 .030 -.020 -.181 .042 .027 -.006 .121
Reflectively monitor emotions in relation to self and others
i10. I can easily tell if someone feels the same way as I do about something
.580 .005 .055 .017 .219 -.040 -.016 .010
-.048 -.108 .177 .026 -.004 .083 .003 .128
i32. When I feel really excited about something, I can tell if others feel the same way as me
127
Factor loading
Item 1 2 3 4 5 6 7 8
9 10 11 12 13 14 15 16
.543 .046 .142 .011 .173 -.024 .095 -.044
.062 .122 .022 .143 -.074 -.222 .067 -.067
Manage emotion in self and others without repressing or exaggerating information conveyed
i12. I can easily make people feel happy
.329 -.066 .119 .401 .331 .050 .105 -.030
.018 -.092 .071 .101 -.133 .208 .163 -.077
i17. I can make others feel excited about something
.299 -.028 .147 .159 .579 .113 -.014 -.032
-.014 .063 -.052 .152 .047 .150 .055 .016
i19. I can make people feel better when they are upset with me
.276 -.181 .166 .511 .143 .089 .080 -.048
-.093 -.110 .090 .101 -.038 .037 .048 .099
i22. When I feel scared, I can tell if there is real danger or not.
.451 .113 .265 .012 .060 -.027 -.082 .103
.063 .085 -.083 -.031 .193 -.172 .038 .177
Performance items
Branch I. Perception and appraisal of emotion
Identify emotion in facial expressions
i39. (angry expression)
.005 -.012 -.070 .038 .011 .014 .067 .772
-.037 .047 .059 .097 -.041 .106 .038 .119
i40. (happy expression)
-.062 -.071 -.020 -.111 .089 -.132 .029 .180
.006 -.079 -.008 -.180 .242 -.013 .507 .203
i41. (surprised expression)
.023 -.021 -.077 -.025 .055 .031 .141 -.092
.773 -.019 -.064 .021 .052 .062 .067 .007
i42. (sad expression)
-.033 .173 .021 -.100 .078 -.024 -.072 .079
.156 .041 .027 -.060 -.072 .685 -.022 .012
i43. (scared expression)
-.043 -.035 .092 .022 -.063 .025 -.130 .189
.686 .028 .083 -.009 -.043 .043 -.093 -.011
i44. (disgusted expression)
128
Factor loading
Item 1 2 3 4 5 6 7 8
9 10 11 12 13 14 15 16
.134 .061 .071 .052 -.127 .067 .022 .658
.137 .099 -.078 -.090 -.111 -.055 -.001 -.136
Branch III. Understanding and Analysing Emotion
Label emotions and recognise relations among emotions
i45. .079 .055 -.019 -.009 -.004 .144 .049 .161
.041 .028 .133 .022 -.694 .019 -.019 -.034
i47. .118 .016 .174 -.120 -.030 .056 -.126 -.086
.063 .133 .076 .403 .059 -.017 .199 .222
i60. .123 .243 .249 .042 -.366 .127 -.059 .137
.033 .024 .036 .047 -.287 .007 -.081 .234
i66. .026 .734 .022 -.014 .008 .012 -.039 -.018
.035 -.060 .029 .020 -.014 -.006 .044 .049
Interpret the meanings that emotions convey
i46. .012 .077 .050 .035 -.007 .055 -.026 .035
.017 -.023 .059 .697 -.038 .015 -.025 -.024
i48. -.002 .333 .020 -.074 .252 .508 -.013 .010
-.085 .022 .113 -.050 -.152 -.100 .082 -.028
i49. -.030 .100 .029 -.057 .015 .082 -.015 -.030
-.028 .095 .043 .127 -.089 .032 .632 -.060
i50. -.063 .175 .033 -.096 -.029 .643 -.036 .040
-.015 .127 -.009 .149 -.028 -.040 -.038 .069
i51. .047 .290 -.013 .061 .080 .067 .099 -.013
-.002 -.018 .139 .014 -.058 -.017 .039 .530
i52. -.013 .158 -.019 -.134 .029 .193 -.214 .153
-.168 .440 .080 .182 .023 -.133 .140 -.057
i53. .074 .219 -.033 -.013 .044 .093 -.038 .144
.018 .384 .212 .053 .157 -.151 .143 -.215
i61. .155 .150 -.036 -.020 -.054 .435 -.022 .051
.105 -.059 .129 -.194 -.096 .060 .152 .070
i62. .003 .593 -.070 -.010 .031 .340 .112 .030
.009 -.051 .051 -.004 -.026 .136 -.001 -.083
i63. .004 .742 .037 -.035 .027 .188 -.077 -.023
-.095 -.019 -.009 .066 -.030 -.013 .108 .099
i64. .002 .564 .056 -.017 -.075 .084 -.017 -.068
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Factor loading
Item 1 2 3 4 5 6 7 8
9 10 11 12 13 14 15 16
-.190 .086 -.029 -.007 .059 .042 -.130 -.001
i65. .073 .705 -.004 .068 -.031 -.015 .080 .065
.149 .058 .045 -.054 -.069 .037 .012 .101
Understand complex feelings; simultaneous feelings, blends of emotion
i57. -.032 .152 .000 -.021 -.017 .084 .024 -.050
-.030 -.022 .627 .108 -.083 .000 .011 .184
i58. .058 .108 .120 .070 -.122 .105 -.259 -.065
.187 -.013 .301 -.376 .079 .044 .238 .038
i59. -.016 .165 .078 .159 -.133 .492 .122 -.025
.075 -.071 -.012 .069 .201 -.033 -.033 .374
Recognise likely transitions among emotions
i54. -.054 .227 .118 -.081 .038 .087 .026 .091
-.001 .013 .115 -.240 -.172 -.387 -.131 .028
i55. -.050 .322 .059 -.094 -.005 .050 .092 .110
.062 .013 .378 -.132 .001 -.089 -.011 -.165
i56. .085 .617 .005 .002 -.146 .045 -.054 -.067
-.001 -.014 .185 .099 .027 -.039 .042 .036
Simple structure was achieved in the sixteen factor model, with a number of strong
loadings and many items loading substantially onto single factors. However, most items
loaded onto the first few factors, leaving the other factors poorly defined with a few, weak
loadings. Cross-loadings were apparent for Typical EI items 12, 15R and 31R and the
Maximum EI items, 48, 55, 58, 59 and 62. The sixteen factor solution explained a total of
50.02 percent of the variance. With Varimax rotation, Factors One to Sixteen contributed
7.61 percent, 5.65 percent, 4.67 percent, 3.87 percent, 2.93 percent, 2.83 percent, 2.61
percent, 2.48 percent, 2.34 percent, 2.29 percent, 2.24 percent, 2.23 percent, 2.17 percent,
2.07 percent, 2.03 percent and 2.02 percent respectively. It is likely that this sixteen factor
model is not a suitable fit for the data.
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7.3.4 Summary of principal components analyses.
The one factor model included many of the self-report items but none of the performance
items and only accounted for a small portion of variance. While this may be due to different
modes of measurement, the distinction between Typical and Maximal EI may also account
for these items not loading on the same factor. The four factor model appeared to provide
the best fit as most of the items loaded onto single factors and had substantial loadings.
However, this model only accounted for a quarter of the variance, possibly due to item
redundancy. The five and six factor solutions did not substantially increase the amount of
explained variance. The pattern of loadings of the five and six factor solutions suggested
that the orientation of abilities to self or other may divide the branches. While the sixteen
factor model accounted for sufficient variance, the factor loadings were unclear,
particularly for later factors.
While the PCA gave a preliminary indication that there may be four factors in data with one
or two higher order factors, there was considerably low level of explained variance. This
may be due to problems with individual items within the measurement model. The next
sections will describe further assessment of the items through investigation of each of the
factors using a Structural Equation Modeling (SEM) approach. Once well fitting
measurement models were established, all factors were tested in the one model to establish
whether the one, two and four factor models were replicated in the data.
7.4 Confirmatory factor analysis: Branch I-Typical Emotional Intelligence.
7.4.1 Branch I-Typical Emotional Intelligence: One factor model. A one factor congeneric model with thirteen indicator items was specified to capture
Branch I. The variance of the latent variable was set to one so that all item loadings could
be explored. The data did not fit the model well with none of the selected criteria indicating
model fit ( (65) = 270.108, p < .001; TLI = .764; CFI = .804; RMSEA = .082 (.072-.092);
SRMR = .0648). With sample correlations ranging from a low of .03 (items 26 and 29) to a
high of .46 (items 14 and 25), none of the pairs of item indicators were so highly correlated
so as to indicate possible item redundancy. The eigen-values (3.547, 1.469, 1.051, 1.030,
.876, .852, .755, .709, .665, .611, .568, .520, .349) suggest that a four factor model may
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provide a better fit. The factor coefficients were all significant, however there were a few
items (i26, i30, i24, i29, i35) that did not substantially load (i.e. <.32) onto the factor and
should be considered for removal. Large standardized residuals (>2.58: Byrne, 2010) were
found for item 24 with items 35 (5.62), 29 (3.24), 30 (3.15) and between item 30 and items
26 (2.82) and 35 (2.99) and also between items 26 and 35 (3.45). This indicates that the
single Branch I model fails to account for much of the variance between these items.
Additionally, large modification indices (>10; Byrne, 2010) for the error terms between
items 4 and 5 (MI = 86.550) and for the error terms between items 24 and 35 (MI = 35.091)
indicate that the model would fit better if the covariance between these error terms were
freely estimated. Based on the eigen-values and the model that the items were based on, the
decision was made to re-specify the model as a four factor independent cluster
measurement model in which the factor inter-correlations were freely estimated. Three,
four, three and three indicator items were specified to capture the respective factors they
were designed to measure.
7.4.2 Branch I-Typical Emotional Intelligence: Four factor model.
The data for the newly specified four factor model showed improved model fit ( (59) =
156.883, p < .001; TLI = .876; CFI = .906; RMSEA = .049 (.034 - .064); SRMR = .0477).
The chi-square difference test for nested models (Thompson, 2004) shows that the
improvement is significant at the .005 level (critical value at six degrees of freedom =
18.55), and increase in the TLI by more than .01 (.11) also supports improved model fit
(Vandenberg & Lance, 2000). The factor coefficients were all significant, and item
loadings were greatly improved with only one item (29) not substantially loading (.293)
onto its intended factor. Large standardized residuals were found between item 33 and
items 23 (3.05) and 29 (2.71). This indicates that while more variance is accounted for by
the four factor model, variance in scores of these pairs of items that is not accounted for by
the model. Large modification indices for the error terms between items 1 and 4 (MI =
29.32) and for the error terms between item 33 and Factor 4 (MI = 22.21) indicate that the
model would fit better if the covariance between these error terms were freely estimated.
Modification indices suggested a regression weight be placed between item 5 and Factor 3
(“Express emotions accurately, and express needs related to feelings”) as well as between
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item 33 and Factor 4 („Discriminate between accurate and inaccurate, honest and dishonest
expressions of feelings”). Due to a low factor loading, item 29 was considered for removal.
However, this would leave only two indicators for the Factor 3 and the loading was only
marginally below the cut-off. With consideration of the meaning of item 29, it appears that
low correlations may have been due to item 29 referring to verbal expression of emotion
compared to the two other indicator items that refer to non-verbal expression of emotion.
While these may be better described as separate factors, it was considered important to
include both verbal and non-verbal expression of emotion within the one factor in order to
capture possible developmental changes (i.e. from focus on non-verbal expression to
verbal expression; e.g. Zeidner‟s investment model; Zeidner, Matthews, Roberts, &
MacCann, 2003). Therefore, item 33 was considered for removal. Although it had a
reasonable loading onto its intended factor (“Identify emotions in others, designs, artwork,
language, sound, appearance and behaviour”), Item 33 appeared problematic with a
number of large standardized residuals with other items and modification indices
suggesting shared error and a suggested regression weight with another factor (Factor 4:
“Discriminate between accurate and inaccurate, honest and dishonest expressions of
feelings”). Item 33 appears to reflect more sophisticated processes than the other indicator
items for Factor 2. Indeed it is more akin to the sophistication of ability suggested in
Factor 4. While consideration was directed towards utilizing item 33 as an indicator item
for Factor 4, the item does not fully capture the ability of discernment implied in the factor.
Therefore it was decided to remove item 33.
Removal of item 33 considerably improved fit statistics with most fit indices indicating
acceptable model fit ( (48) = 99.384, p < .001; TLI = .922; CFI = .943; RMSEA = .047
(.034 - .061); SRMR = .0496). With removal of an item, this is no longer a nested model,
therefore the chi-square difference test could not be used (Thompson, 2004). However an
increase in the TLI by more than .01 (.05) supports improved model fit (Vandenberg &
Lance, 2000). The final four factor model is presented below in Figure 9.
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Figure 9. Branch I four factor model.
The four factor model was further explored using Fornell and Larker‟s (1981) test of
discriminant validity (average of variance extracted estimates > squared correlation
between the constructs). The variance extracted estimates were calculated according to the
formula provided by Fornell and Larker (1981):
Where is the squared standardized loading for each observed variable and is the error
variance associated with each observed variable. The sum of the squared standardized
loadings and sum of associated error variance for each factor are presented below in Table
11.
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Table 11. Sum of Squared Standardised Loadings, Error Variances and Variance Extracted Estimates for Each Factor of the Branch I Four Factor Model
Sum of Squared
Standardised
Loadings
Sum of Error
Variances
Variance Extracted
Estimate
Squared Factor
Correlations
2 3 4
Factor 1 0.381 2.618 0.127 .49 .72 .39
Factor 2 1.406 1.624 0.464 .09 .47
Factor 3 0.674 2.326 0.227 .06
Factor 4 1.275 1.724 0.425
Average of Variance Extracted Estimates = 0.311 Note Factor 1 = Identify emotion in one‟s physical states, feelings and thoughts
Factor 2 = Identify emotions in others, designs, artwork, language, sound, appearance and behaviour
Factor 3 = Express emotions accurately, and express needs related to feelings
Factor 4 = Discriminate between accurate and inaccurate, honest and dishonest expressions of feelings
As can be seen from Table 11, while Factor 3 shows discriminant validity with Factors 2
and 4, there appears to be considerable overlap between Factor 1 and the other factors as
well as between Factors 2 and 4. Factors 1 and 3 may form one factor representing
“Perception, Appraisal and Expression of Emotions in the Self”. While poor discriminant
validity may be indicative of the presence of a higher order construct (Cunningham, 2008),
further exploration of the model is warranted. Based on item cross-loadings between these
factors as well as due to the factors being conceptually linked (they both pertain to the self),
the decision was made to re-specify the model as a three factor independent cluster
measurement model in which the factor inter-correlations were freely estimated. Six, three
and three indicator items were specified to capture the respective factors.
7.4.3 Branch I- Typical Emotional Intelligence: Three factor model.
The data did not fit the model well for the re-specified three factor model ( (51) =
119.637, p < .001; TLI = .902; CFI = .924; RMSEA = .053 (.041 - .066); SRMR = .0582).
The factor coefficients were all significant, however item 29 did not substantially load
(.28) onto its designated factor. Large standardized residuals were found for item 5 with
items 8, (4.521), 14 (3.269), 23 (2.880), 25 (2.205) and 29 (2.217) and between item 8 and
items 1 (3.094) and 4 (3.139). This indicates that the three-factor Branch I model fails to
account for much of the variance between these items. Additionally, there were large
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modification indices for the error term of item 5 with Factor 1 (“Perception, appraisal and
expression of emotion in the self; MI = 10.986), Factor 2 (“Identify emotions in others,
designs, artwork, language, sound, appearance and behaviour”; MI = 16.916) and Factor 4
(Discriminate between accurate and inaccurate, honest and dishonest expressions of
feelings”; MI = 22.258) and for the error term of item 8 with Factor 2 (MI = 17.995).
There were also large modification indices between item 5 with Factor 1 (MI = 10.648),
item 8 (MI = 12.942), item 14 (MI = 12.971) and item 23 (MI = 11.009) as well as
between item 8 and Factor 2 (MI = 15.655), item 1 (MI = 12.778), item 4 (MI = 12.398)
and item 5 (MI = 16.957). Items 5 and 8 both appear problematic and were considered for
removal. Interestingly, both items differ from other indicator items as they focus on
discerning emotion in an abstract construct (i.e. thought and song), indeed this may be the
shared variance that is unaccounted for in the model. Item 5 was selected for removal as it
is more dissimilar to other items of the same factor (perception of emotion in a song as
compared to other people) than item 8 (perception of emotions in one‟s thoughts as
compared to one‟s physicality). It is important to note that removal of item 5 changes the
meaning of the factor from “Identify emotion in others, designs, artwork, language, sound,
appearance and behavior” to a more specific “Identify emotion in others, appearance and
behavior”. Removal of item 5 considerably improved fit statistics with all fit indices
indicating acceptable model fit ( (41) = 64.479, p = .011; TLI = .961; CFI = .971;
RMSEA = .035 (.017 - .050); SRMR = .0466). An increase in the TLI by more than .01
(.059) supports improved model fit (Vandenberg & Lance, 2000). The final three factor
model is presented below in Figure 10.
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Figure 10. Branch I three factor model.
The hypothesis of a three factor model was further assessed using Fornell and Larker‟s
(1981) test of discriminant validity. The sum of the standardized loadings and sum of
associated error variance for each factor is presented below in Table 12.
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Table 12. Sum of Squared Standardised Loadings, Error Variances and Variance Extracted Estimates for Each Factor of the Branch I Three Factor Model Sum of Squared
Standardised
Loadings
Sum of Error
Variances
Variance Extracted
Estimate
Squared Factor
Correlations
1 2
Factor 1 0.966 6.851 0.119 .18 .17
Factor 2 1.281 0.606 0.730 .41
Factor 3 0.276 2.212 0.424
Average of Variance Extracted Estimates = 0.424 Note Factor 1 = Identify emotion in one‟s physical states, feelings and thoughts and Express emotions accurately, and
express needs related to feelings
Factor 2 = Identify emotions in others, appearance and behavior
Factor 3 = Discriminate between accurate and inaccurate, honest and dishonest expressions of feelings
From the results presented in Table 12, it may be seen that despite a moderate correlation
between Factors 2 and 3, the three factor model maintains discriminant validity. However,
it was decided to explore a more parsimonious two factor model where Factors 2 and 4
form one factor representing “perception and appraisal of emotions in others”. Based on
the moderate correlation between the factors as well as due to the factors being
conceptually linked (they both pertain to other people), the decision was made to re-
specify the model as a two factor independent cluster measurement model in which the
factor inter-correlations were freely estimated. Six and five indicator items were specified
to capture the respective factors.
7.4.4 Branch I- Typical Emotional Intelligence: Two factor model.
The data did not fit the model well for the re-specified two factor model ( (43) =
154.565, p < .001; TLI = .821; CFI = .860; RMSEA = .074 (.062 - .087); SRMR = .0593).
The factor coefficients were all significant, however item 29 did not substantially load
(.28) onto its designated factor. Large standardized residuals were found for item 25 with
items 14, (2.88), 25 (3.14) and 26 (2.18) as well as between items 8 and 1 (3.23) and 4
(3.37). This indicates that the two factor PAEE model fails to account for much of the
variance between these items. Additionally, large modification indices (≥10) for the error
term of item 1 with the error terms of item 4 (MI = 30.517), item 14 (MI = 11.406) and
item 25 (MI = 10.556) and for the error term of item 14 with the error terms of item 23 (MI
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= 15.020) and item 25 (MI = 23.679) and between the error terms of items 1 and 23 (MI =
10.596) as well as between the error term for item 8 and Factor 2 (MI = 19.485). There
were also large modification indices suggesting a regression weight from item 1 to items 4
(MI = 11.460), and 23 (MI = 17.300), from item 4 to item 1 (MI = 11.317), from item 8 to
items 1 (MI = 13.507), 4 (MI = 13.442) and Factor 2 (MI = 13.019), from item 14 to items
23 (MI = 10.836) and item 25 (MI = 14.686) as well as from item 23 to item 25 (MI =
14.810), and from item 25 to item 14 (MI = 14.882). With a substantial modification index
for their error terms, items 1 and 4 were considered for removal. Due to the meaning of
item 1 being subsumed by item 4, item 1 was chosen for removal. Removal of item 1
considerably improved fit statistics with all fit indices indicating acceptable model fit
( (34) = 63.455, p = .002; TLI = .925; CFI = .944; RMSEA = .043 (.026-.059); SRMR =
.0487). An increase in the TLI by more than .01 (.104) supports improved model fit
(Vandenberg & Lance, 2000). The final two factor model is presented below in Figure 11.
Figure 11. Branch I two factor model.
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The hypothesis of a two factor model was further assessed using Fornell and Larker‟s
(1981) test of discriminant validity. The sum of the standardized loadings and sum of
associated error variance for each factor is presented below in Table 13.
Table 13. Sum of Squared Standardised Loadings, Error Variances and Variance Extracted Estimates for Each Factor of the Branch I Two Factor Model Sum of Squared
Standardised
Loadings
Sum of Error
Variances
Variance Extracted
Estimate
Squared Factor
Correlations
2
Factor 1 2.369 5.029 0.162 .20
Factor 2 2.466 2.461 0.384
Average of Variance Extracted Estimates = 0.273 Note Factor 1 = Identify emotion in one‟s physical states, feelings and thoughts, express emotions accurately, and
express needs related to feelings
Factor 2 = Identify emotions in others, appearance and behavior, discriminate between accurate and inaccurate,
honest and dishonest expressions of feelings
From the results presented in Table 13, it may be seen that the factors of the two factor
model of PAEE hold discriminant validity.
7.4.5 Branch I- Typical Emotional Intelligence: Model comparison.
The final one, two, three and four factor models were re-assessed using the validation file.
A comparison of the fit indices for the models for both calibration (N = 476) and validation
files (N = 566) are presented below in Table 14.
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Table 14. Branch I Model Comparison
Fit Index
Model (df) p TLI CFI RMSEA (range) SRMR
One Factor (unmodified)
Calibration
sample
270.108 (65) < .001 .764 .804 .082 (.072 - .092) .0648
Two Factor (modified)
Calibration
sample
63.455 (34) .002 .925 .944 .043 (.026 - .059) .0487
Validation
sample
60.642 (34) .003 .950 .962 .037 (.021 - .052) .0364
Three Factor (modified)
Calibration
sample
64.479 (41) .011 .961 .971 .035 (.017 - .050) .0466
Validation
sample
69.462 (41) .004 .960 .970 .035 (.020 - .049) .0343
Four Factor (modified)
Calibration
sample
99.384 (48) < .001 .922 .943 .047 (.034 - .061) .0496
Validation
sample
115.287 (48) < .001 .913 .937 .050 (.038 - .062) .0445
It can be seen that model fit established through model modifications in the calibration
sample, was replicated in the validation sample. While the three factor model provided a
practically better fit for both samples, the four factor model was also established to have
adequate fit. As the four factor model is grounded in theory, it was further explored to
determine the type of measurement model (i.e. parallel, tau equivalent or congeneric).
7.4.6 Branch I- Typical Emotional Intelligence: measurement model and reliability.
The parallel (with all factor loadings and error variances set to equality for each factor), tau
equivalent (with all factor loadings set to equality for each factor and error variances left to
be freely estimated) and congeneric models (with all factor loadings and error variances
left to be freely estimated) were compared to determine whether scores could be combined
as equal scores or if factor loadings and error variances should be incorporated into factor
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scores. Table 15 below displays the fit criteria and chi-square difference tests for the
parallel, tau-equivalent and congeneric model for each data set.
Table 15. Branch I Measurement Models
Fit Index
Model (df) p TLI CFI RMSEA (range) SRMR
Congeneric model
Calibration
sample
99.384 (48) < .001 .922 .943 .047 (.034 - .061) .0496
Validation
sample
115.287 (48) < .001 .913 .937 .050 (.038- .062) .0445
Parallel model
Calibration
sample
294.656 (64) < .001 .738 .746 .087 (.077- .097) .0538
Validation
sample
363.412 (64) < .001 .709 .781 .091 (.082 - .100) .0634
Tau equivalent model
Calibration
sample
139.377 (56) < .001 .892 .908 .056 (.044 - .068) .0490
Validation
sample
208.285 (56) < .001 .831 .856 .069 (.059 - .080) .0653
tests (compared to congeneric model)
Parallel model
Calibration
sample
195.272 (16) < .001
Validation
sample
248.125 (16) < .001
Tau equivalent model
Calibration
sample
39.993 (8) < .001
Validation
sample
92.999 (8) < .001
From Table 15, it may be seen that while the Tau-equivalent model approaches fit in the
calibration sample, this was not replicated in the validation sample. Since the model
appears to be congeneric, factor loadings and error variances cannot be assumed to be equal
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and therefore scoring must take individual factor loadings and error variances into
consideration. Additionally, because Cronbach‟s alpha is based on the tau-equivalent
model, this measure may underestimate the reliability of the PAEE as a congeneric model
(Graham, 2008). Therefore the variance extracted estimates will be used to indicate
construct reliability as per Fornell and Larker‟s (1981) formula:
Fornell and Larker (1981) suggest that constructs should have estimates of at least .50; with
lower estimates indicating that variance due to measurement error is larger than the
variance captured by the factor. As the variances extracted estimate test is conservative,
reliabilities may be acceptable even if variances extracted estimates are less than .50
(Hatcher, 1994). Therefore, while the limit of reliability values of .70 or greater will be
followed, values above .50 may provide acceptable reliability. The sum of the standardized
loadings and sum of associated error variance for each factor is presented below in Table
16.
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Table 16. Branch I Sum of Standardised Loadings, Error Variances and Construct Reliability Estimates for the Four Factor Model Sum of
Standardised Loadings
Sum of Error Variances
Construct Reliability Estimates
(Cronbach‟s alpha)
Factor 1 Calibration Sample 1.067 2.618 0.30 (.31) Validation Sample 1.235 1.818 0.39 (.37) Factor 2 Calibration Sample 1.981 1.594 0.71 (.65) Validation Sample 1.839 1.818 0.65 (.61) Factor 3 Calibration Sample 1.375 2.326 0.45 (.43) Validation Sample 1.422 2.314 0.47 (.44) Factor 4 Calibration Sample 1.951 1.724 0.69 (.69) Validation Sample
1.881 1.791 0.66 (.66)
Note Factor 1 = Identify emotion in one‟s physical states, feelings and thoughts
Factor 2 = Identify emotions in others, designs, artwork, language, sound, appearance and behavior
Factor 3 = Express emotions accurately, and express needs related to feelings
Factor 4 = Discriminate between accurate and inaccurate, honest and dishonest expressions of feelings
Low reliability for the factor scores indicates that the factor-level scores should be
interpreted with caution. To explore whether a more reliable score could be produced by
incorporating the four factors, a higher order model was specified, representing general
PAEE. However, the model provided an inadequate fit to the data for both the calibration
( (50) = 126.082, p < .001; TLI = .889; CFI = .960; RMSEA = .057 (.044 - .069); SRMR
= .0569) and validation samples ( (50) = 158.909, p < .001; TLI = .864; CFI = .897;
RMSEA = .020 (.051 - .030); SRMR = .0544). When a model was specified with two
higher order factors representing “Perception and Appraisal of Emotion in Others” (Branch
I-A) and “Perception, Appraisal and Expression of Emotion in Self” (Branch I-B), the
solution was inadmissible with negative variance (-.134) for the error associated with
Factor 1 for the calibration sample. As per Gignac, Palmer, Manocha and Stough (2005); to
test the hypothesis that the negative error variance is due to sampling fluctuations, the
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residual error variance associated with Factor 1 was constrained to .0001, (in accordance
with Chen, Bollen, Paxton, Curran, & Kirby, 2001). The modified model with the
constrained error variance yielded a chi-square of 107.178 (df = 50, p < .001), which was
not statistically significantly worse fitting than the previous non-constrained model ( (49)
= 99.416, p < .001), indicating that the negative error variance was likely due to sampling
fluctuations, rather than a fundamentally inappropriate model specification. With the error
variance set to 0.0001, the model fit well ( (50) = 107.163, p < .001; TLI = .917; CFI =
.937; RMSEA = .049 (.036 - .062); SRMR = .0523). The final two factor higher order
model is presented below in Figure 12.
Figure 12. Branch I two factor higher order model.
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Reliability estimates for the two higher order factors are presented below in Table 17.
Table 17. Branch I Higher Order Factors Sum of Standardised Loadings, Error Variances
and Construct Reliability Estimates Sum of
Standardised Loadings
Sum of Error Variances
Construct Reliability Estimates
(Cronbach‟s alpha)
Branch I-A Calibration Sample 1.669 1.552 0.64 (.52) Validation Sample 1.872 1.245 0.74 (.57) Branch I-B Calibration Sample 1.654 1.628 0.63 (.72) Validation Sample 1.790 1.393 0.70 (.75) Note Branch I-A = Identify emotion in one‟s physical states, feelings and thoughts, Express emotions
accurately, and express needs related to feelings
Branch I-B = Identify emotions in others, designs, artwork, language, sound, appearance and behavior,
Discriminate between accurate and inaccurate, honest and dishonest expressions of feelings
From Table 17, it may be seen that with an acceptable reliability for Branch I-A in the
validation sample, the other reliabilities for the higher order factors approach an acceptable
level. Therefore, while these factors are more reliable than the lower order factors, scores
arising from these higher order factors should also be interpreted cautiously.
7.4.7 Branch I- Typical Emotional Intelligence: Factor scores. Factor scores were calculated by summing the product of each item and the associated
factor score weight generated by Amos (Arbuckle, 2009; calculations included in Appendix
3). Mean scores for grade and gender are presented below in Tables 18 and 19.
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Table 18. Branch I Descriptive Statistics for Grades 4, 5 and 6 Factor Grade Calibration Sample Validation Sample Mean (Standard Deviation) Mean (Standard Deviation) Factor 1 4 1.87 (0.30) 0.75 (0.15)
5 1.86 (0.28) 0.75 (0.13)
6 1.90 (0.26) 0.77 (0.12)
Factor 2 4 3.07 (0.65) 2.58 (0.53)
5 3.11 (0.60) 2.52 (0.50)
6 3.22 (0.53) 2.66 (0.42)
Factor 3 4 2.56 (0.45) 2.61 (0.53)
5 2.53 (0.46) 2.59 (0.46)
6 2.57 (0.43) 2.67 (0.42)
Factor 4 4 3.19 (0.69) 3.75 (0.80)
5 3.20 (0.68) 3.67 (.079)
6 2.57 (0.43) 3.88 (0.67)
Branch I A 4 4.94* (0.79*) 4.86 (0.95)
5 4.92* (0.75*) 4.83 (0.83)
6 5.03* (0.69*) 5.00 (0.74)
Branch I B 4 4.75* (0.90*) 4.51 (0.92)
5 4.79* (0.86*) 4.43 (0.90)
6 4.92* (0.84*) 4.67 (0.75) Note Calibration Sample: Grade 4 n = 170; Grade 5 n = 149; Grade 6 n = 157
Validation Sample: Grade 4 n = 188; Grade 5 n = 180; Grade 6 n = 198
* These calculations are influenced by constraint of an error variance to .0001
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Table 19. Branch I Descriptive statistics for Males and Females Factor Grade Calibration Sample Validation Sample Mean (Standard Deviation) Mean (Standard Deviation) Factor 1 Male 3.11 (0.82) 0.75 (0.13)
Female 3.28 (0.80) 0.77 (0.13)
Factor 2 Male 6.40 (1.58) 2.55 (0.49)
Female 6.61 (1.41) 2.64 (0.48)
Factor 3 Male 3.65 (1.13) 2.59 (0.47)
Female 3.81 (1.13) 2.66 (0.47)
Factor 4 Male 5.55 (1.69) 3.71 (0.76)
Female 6.00 (1.75) 3.84 (0.75)
Branch I A Male 4.87* (0.75*) 4.82 (0.85)
Female 5.06* (0.72*) 4.99 (0.83)
Branch I B Male 4.72* (0.86*) 4.46 (0.86)
Female 4.92* (0.81*) 4.62 (0.85) Note Calibration Sample: Grade 4 n = 170; Grade 5 n = 149; Grade 6 n = 157
Validation Sample: Grade 4 n = 188; Grade 5 n = 180; Grade 6 n = 198
* These calculations are influenced by constraint of an error variance to .0001
The Branch I factor scores for the two samples were normally distributed, with skews under
two (calibration sample: -.201 to -.442; validation sample: -.272 to -.385) and kurtosis
under seven (calibration sample: .071 to -.303); validation sample: .009 to -.255). There is
a clear pattern for females scoring more highly than males. However, while an increase
with higher grade level is apparent from grade 4 to grade 6 and from grade 5 to grade 6,
there is mixed evidence of changes from grade 4 to grade 5. Analyses were conducted to
determine whether these differences were significant.
7.4.8 Branch I- Typical Emotional Intelligence grade level comparisons.
A one-way between-groups analysis of variance was conducted on the calibration sample to
explore the impact of grade on the Branch I factors. Levene‟s test indicated significant
differences in homogeneity of variances between groups for Factor 2; therefore a robust test
of equality of means (Welch) was used for this factor. Contrary to expectations, there were
no significant differences for any of the Branch I factors across the three grade levels
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(Factor 1: F (2, 473) = .907, p = .404, Factor 2: Welch (2, 311.81) = 2.93, p = .055; Factor 3
F (2, 473) = .437, p = .646; Factor 4 F (2, 473) = .752, p = .472).
The one-way between-groups analysis of variance was repeated using the validation sample
to further explore the impact of grade and gender on level on the Branch I factors. Levene‟s
test indicated significant differences in homogeneity of variances between groups for the all
factors; therefore a robust test of equality of means (Welch) was used. While calibration
sample results failed to reveal the predicted grade level increase, there was a significant
difference for Factor 2 and Factor 4 across the three grade levels in the validation sample
(Factor 2: Welch (2, 366.174) = 4.276, p = .015, Factor 4 Welch (2, 367.715) = 3.857, p =
.022). Post-hoc comparisons using the Tukey HSD test indicated that the mean Factor 2 and
Factor 4 scores for grade 6 was significantly greater than the mean Factor 2 and Factor 4
scores for grade 5. While these differences were significant, the effect sizes were small
(Factor 2: eta squared = .01; Factor 4: eta squared = .01).
7.4.9 Branch I- Typical Emotional Intelligence gender comparisons. A one-way between-groups analysis of variance was conducted on the calibration sample to
explore the impact of gender on the Branch I factors. Levene‟s test indicated no significant
differences in homogeneity of variances between groups. As predicted, there was a
statistically significant difference in all Branch I factors (Factor 1: F (1, 474) = 7.645, p =
.006; Factor 2: F (1, 474) = 4.310, p = .038; Factor 3: F (1, 474) = 5.300, p = .022; Factor
4: F (1, 474) = 9.438, p = .002), with females scoring higher than males or all factors.
While these differences were significant, the effect sizes were small (Factor 1: eta squared
= 02; Factor 2: eta squared = .01; Factor 3: eta squared = .01; Factor 4: eta squared = .02).
For the gender comparisons in the validation sample, Levene‟s test indicated no significant
differences in homogeneity of variances between groups. As expected and in line with the
calibration sample results, there was a statistically significant difference in all Branch I
factors (Factor 1: F (1, 564) = 5.450, p = .020; Factor 2: F (1, 564) = 4.957, p = .026;
Factor 3: F (1, 564) = 3.068, p = .080; Factor 4: F (1, 564) = 4.515, p = .034), with
females scoring higher than males or all factors. While these differences were significant,
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the effect sizes were small (Factor 1: eta squared = 01; Factor 2: eta squared = .01; Factor
3: eta squared = .01; Factor 4: eta squared = .01).
7.4.10 Branch I- Typical Emotional Intelligence higher order factor comparisons. Analyses were conducted in the calibration sample to explore the impact of grade level on
the higher order Branch I factors. Levene‟s test indicated no significant differences in
homogeneity of variances across grade levels for both higher order factors. Contrary to
expectations, one-way between groups analysis of variance revealed no significant
differences in estimated factor scores across the grade levels (Branch I-A: F (2, 473) =
0.745, p = .634; Branch I-B: F (2, 473) = 1.803, p = .166).
The one-way between-groups analysis of variance was repeated in the validation sample to
explore the impact of grade level on the higher order Branch I factors. Levene‟s test
indicated significant differences in homogeneity of variances across grade levels for both
higher order factors; therefore a robust test of equality of means (Welch) was used. In line
with predictions but contrary to results in the calibration sample, one-way between groups
analysis of variance revealed a significant difference in estimated higher order factor scores
across the grade levels for Branch I -B (Welch (2, 366.894) = 4.125, p = .017).
A one-way between-groups analysis of variance was conducted to explore the impact of
gender on the higher order Branch I factors. Levene‟s test indicated no significant
differences in homogeneity of variances across gender. As predicted, there were significant
differences for the Branch I higher order factors across gender (Branch I -A: F (1, 474) =
7.639, p = .006; Branch I -B F (1, 474) = 6.992, p = .008), with females scoring
significantly more highly than males. While these differences were significant, the effect
sizes were small (Branch I -A: eta squared = .02; Branch I -B eta squared = .02).
The one-way between-groups analysis of variance was repeated in the validation sample to
explore the impact of gender on the higher order Branch I factors. Levene‟s test indicated
no significant differences in homogeneity of variances across gender. In line with the
calibration sample results, there were significant differences in the expected direction for
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the higher order Branch I factors across gender (Branch I -A: F (1, 564) = 5.311, p = .022;
Branch I –B: F (1, 564) = 4.910, p = .027), with females scoring significantly more highly
than males. The effect sizes were small (Branch I -A: eta squared = .01; Branch I -B eta
squared = .01). Post-hoc comparisons using the Tukey HSD test indicated that the mean
Branch I -B score for grade 6 was significantly greater than the mean score for grade 5.
While this difference was significant, the effect size was small (eta squared = .01).
7.5 Confirmatory factor analysis: Branch I-Maximal Emotional Intelligence ability two.
7.5.1 Branch I-Maximal Emotional Intelligence ability two: One factor model. A one factor congeneric model with six indicator items was specified to capture a specific
aspect of ability two of Branch I “Identify emotion in other‟s facial expressions” (Branch I
ability 2). The variance of the latent variable was set to one so that all item loadings could
be explored. The data did not fit the model well with none of the selected criteria indicating
model fit ( (9) = 46.322, p < .001; TLI = .341; CFI = .604; RMSEA = .093 (.068-.121);
SRMR = .0643). With sample correlations ranging from a low of .00 (items 40 and 43) to a
high of .31 (items 39 and 44), none of the pairs of item indicators were so highly correlated
so as to indicate possible item redundancy. The eigen-values (1.445, 1.220, 1.004, .970,
.698, .662) suggest that a three factor model may provide a better fit. However, with six
indicator items, a three factor solution would be untenable leaving only two indicator items
for each factor. The factor coefficients for items 39, 43 and 44 were significant, while items
40, 41 and 42 did not have significant loadings. Items 40, 41, 42 and 43 did not
substantially load (i.e. <.32) onto the factor and should be considered for removal. A large
standardized residual (>2.58: Byrne, 2010) between items 41 and 43 (5.530) indicates that
the model fails to account for much of the variance between these items. Additionally, large
modification indices (>10; Byrne, 2010) for items 41 and 43 (MI = 15.391) and for the
error terms between these items (MI = 33.696) indicate that the model would fit better if the
covariance between these error terms were freely estimated. Item difficulty (p value;
measured by dividing the number of individuals answering the item correctly by the total
number of individuals completing the item; Murphy & Davidshofer, 1994) was calculated
for each item. While difficulty level of items 39 (anger) , 41 (surprise) and 44 (disgust)
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were optimal (p values of .50, .59 and .77 respectively), items 40 (happy), 42 (sad) and 43
(scared) appeared to be too easy (p values of .98, .98 and .99). These latter items were not
removed due to the importance of these being included as basic emotions (Ekman, et al.,
1987). However, future versions of the test should attempt to increase difficulty of these
items. Based on the statistical considerations and Oatley and Johnson-Laird‟s (1987)
contention that surprise is not a distinct emotion but a reaction to an unexpected event that
may be the precursor to an emotion, the decision was made to remove item 41.
The data for the newly specified four factor model showed improved model fit ( (5) =
5.495, p = .359; TLI = .983; CFI = .991; RMSEA = .014 (.000-.067), SRMR = .0238). The
chi-square difference test for nested models (Thompson, Exploratory and confirmatory
factor analysis: Understanding concepts and applications, 2004) shows that the
improvement is significant at the .005 level (critical value at two degrees of freedom =
10.597), and increase in the TLI by more than .01 (.642) also supports improved model fit
(Vandenberg & Lance, 2000). While only the factor coefficients for items 39, 43 and 44
were significant, and despite the item loadings for item 40, 42 and 43 being below the
cutoff, the model was retained due to excellent fit. The final one factor model is presented
below in Figure 13.
Figure 13. Branch I – ability two: one factor model.
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7.5.5 Branch I-Maximal Emotional Intelligence ability two: Model comparison.
The hypothesis of a one factor model was re-assessed using the validation sample. A
comparison of the fit indices for the models for both calibration (N = 476) and validation
sample (N = 566) are presented below in Table 20.
Table 20. Branch I – Maximal Emotional Intelligence Model Comparison
Fit Index
Model (df) p TLI CFI RMSEA (range) SRMR
One Factor (unmodified)
Calibration
sample
46.322 (9) < .001 .341 .604 .093 (.068 - .121) .0643
One Factor (modified)
Calibration
sample
5.495 (5) .359 .983 .991 .014 (.000 - .067) .0238
Validation
sample
12.135 (5) .033 .908 .954 .051 (.013 - .087) .0351
It may be seen that model fit established through model modifications in the calibration
sample, were replicated in the validation sample. The modified one factor model was
further explored to determine the type of measurement model (i.e. parallel, tau equivalent
or congeneric).
7.5.6 Branch I-Maximal Emotional Intelligence ability two: Measurement model and reliability.
The parallel (with all factor loadings and error variances set to equality for each factor), tau
equivalent (with all factor loadings set to equality for each factor and error variances left to
be freely estimated) and congeneric models (with all factor loadings and error variances left
to be freely estimated) were compared to determine whether scores could be combined as
equal scores or if factor loadings and error variances should be incorporated into factor
scores. Table 21 below displays the fit criteria and chi-square difference tests for the
parallel, tau-equivalent and congeneric model for each data set.
Table 21. Branch I Maximal Emotional Intelligence Measurement Models
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Fit Index
Model (df) p TLI CFI RMSEA (range) SRMR
Congeneric model
Calibration
sample
5.495 (5) .359 .983 .991 .014 (.000 - .067) .0238
Validation
sample
12.135 (5) .033 .908 .954 .050 (.013- .087) .0351
Parallel model
Calibration
sample
1640.893 (13) < .001 -20.710 .000 .513 (.493- .535) .0761
Validation
sample
1785.873 (13) < .001 -7.783 .000 .491 (.472 - .511) .1078
Tau equivalent model
Calibration
sample
66.099 (9) < .001 -.100 .010 .11 (.090 - .142) .0964
Validation
sample
150.234 (9) < .001 -.011 .090 .167 (.144 - .191) .1332
tests (compared to congeneric model)
Parallel model
Calibration
sample
1635.398 (8) < .001
Validation
sample
1770.738 (8) < .001
Tau equivalent model
Calibration
sample
60.604 (4) < .001
Validation
sample
138.099 (4) < .001
From Table 21, it may be seen that neither the parallel nor the tau-equivalent model
approach fit in either sample. Since the model appears to be congeneric, factor loadings and
error variances cannot be assumed to be equal and therefore scoring must take individual
factor loadings and error variances into consideration. Additionally, construct reliability
will be calculated using Fornell and Larker‟s (1981) formula. The sum of the standardized
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loadings and sum of associated error variance for each factor is presented below in Table
22.
Table 22. Branch I Maximum Emotional Intelligence Sum of Standardised Loadings, Error Variances and Construct Reliability Estimates
Sum of Standardised Loadings
Sum of Error Variances
Construct Reliability Estimates
(Cronbach‟s alpha)
Factor 1 Calibration Sample 0.640 4.310 0.337 (.342) Validation Sample 1.739 4.031 0.429 (.436)
Low reliability for the factor scores indicates that the factor-level scores should be
interpreted with caution. Using Pallant‟s suggestion of calculation of mean inter-item
correlations for scales with fewer than 10 items indicated sufficient reliability. The mean
inter-item correlation for both the calibration and validation samples was .2 (the
recommended optimal range is .2 to .4; Briggs & Cheek, 1986).
7.5.7 Branch I-Maximal Emotional Intelligence ability two: Factor scores.
Factor scores were calculated as described earlier. Mean scores for grade and gender are
presented below in Tables 23 and 24.
Table 23. Branch I Maximum Emotional Intelligence Descriptive Statistics for Grades 4, 5 and 6 Factor Grade Calibration Sample Validation Sample
Mean (Standard Deviation) Mean (Standard Deviation)
Branch I ability 2 4 1.83 (0.74) 1.86 (0.81)
5 1.97 (0.74) 1.72 (0.06)
6 2.07 (0.62) 2.05 (0.68) Note Calibration Sample: Grade 4 n = 170; Grade 5 n = 149; Grade 6 n = 157
Validation Sample: Grade 4 n = 188; Grade 5 n = 180; Grade 6 n = 198
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Table 24. Branch I Maximum Emotional Intelligence Descriptive statistics for Males and Females
Factor Grade Calibration Sample Validation Sample Mean (Standard Deviation) Mean (Standard Deviation) Branch I ability 2 Male 1.94 (0.71) 1.87 (0.80)
Female 1.98 (0.71) 1.90 (0.78) Note Calibration Sample: Grade 4 n = 170; Grade 5 n = 149; Grade 6 n = 157
Validation Sample: Grade 4 n = 188; Grade 5 n = 180; Grade 6 n = 198
The Branch I maximum EI factor scores for the two samples showed a normal distribution,
with skews under two (calibration sample: -.684; validation sample: -.715) and kurtosis
under seven (calibration sample: -.915); validation sample: -.717). There is a pattern for
females to evidence slightly higher scores than males. However, while an increase with
higher grade level is apparent from grade 4 to grade 6 and from grade 5 to grade 6, there is
mixed evidence of changes from grade 4 to grade 5. Analyses were conducted to determine
whether these differences were significant.
7.5.8 Branch I-Maximal Emotional Intelligence ability two: Grade level comparisons.
A one-way between-groups analysis of variance was conducted on the calibration sample to
explore the impact of grade on level of Branch I ability 2. Levene‟s test indicated a
significant difference in homogeneity of variances between groups; therefore a robust test
of equality of means (Welch) was used. As hypothesised, there was a significant difference
for Branch I ability 2 across the three grade levels (Welch (2, 311.326) = 5.223, p = .006).
Post-hoc comparisons using the Tukey HSD test indicated that the mean Branch I ability 2
score for grade 6 was significantly greater than the mean score for grade 4. While this
difference was significant, the effect size was small (eta squared = .02).
The one-way between-groups analyses of variance were repeated in the validation sample
to further explore the impact of grade on level of Branch I ability 2. Levene‟s test indicated
a significant difference in homogeneity of variances between grade levels; therefore a
robust test of equality of means (Welch) was used for comparisons. As predicted and in
support of the results in the calibration sample, there was a significant difference for
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Branch I ability 2 across the three grade levels (Welch (2, 366.741) = 9.028, p < .001). Post-
hoc comparisons using the Tukey HSD test indicated that the mean Branch I ability 2 score
for grade 6 was significantly greater than the mean score for grades 4 and 5 and the mean
score for grade 5 was significantly greater than the mean scores for grade 4. The effect
sizes were small to moderate (eta squared = .03).
7.5.9 Branch I-Maximal Emotional Intelligence ability two: Gender comparisons.
A one-way between-groups analysis of variance was conducted to explore the impact of
gender on level of Branch I ability 2. Levene‟s test indicated no significant differences in
homogeneity of variances between groups. Contrary to expectations, there was no
statistically significant difference in Branch I ability 2 across gender (F (1, 474) = .385, p =
.535).
For gender comparisons in the validation sample, Levene‟s test indicated no significant
differences in homogeneity of variances between groups. As in the calibration sample,
contrary to hypotheses there was no statistically significant difference in Branch I ability 2
across gender (F (1, 564) = .180, p = .672).
7.6 Confirmatory factor analysis: Branch II.
7.6.1 Branch II-Typical Emotional Intelligence: One factor model.
A one factor congeneric model with twelve indicator items was specified to capture Branch
II. The variance of the latent variable was set to one so that all item loadings could be
explored. The data did not fit the model well with most of the selected criteria indicating
model misfit ( (54) = 123.899, p < .001; TLI = .878; CFI = .900; RMSEA = .052 (.040-
.064); SRMR = .0484). With sample correlations ranging from a low of .056 (items 6 and
36) to a high of .393 (items 27 and 38), none of the pairs of item indicators were so highly
correlated so as to indicate possible item redundancy. The eigen-values (3.170, 1.240,
1.075, .894, .840, .801, .722, .678, .578, .536) suggest that a three factor model may
provide a better fit. The factor coefficients were all significant, however item 6 did not
substantially load (.142) onto the factor and should be considered for removal. Large
standardized residuals (>2.58: Byrne, 2010) were found for item 6 with items 31 (3.264)
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and 36 (2.735). This indicates that the single Branch II model fails to account for much of
the variance between these items. Additionally, large modification indices (>10; Byrne,
2010) for the error terms between items 6 and 31 (MI = 13.619) and for the error terms
between items 3 and 16 (MI = 10.106) indicate that the model would fit better if the
covariance between these error terms were freely estimated. Item 6 was selected for
removal based upon the statistical criteria outlined above as well as consideration that the
item may reflect pessimism. While Schutte and colleagues‟ (1998) inclusion of optimism in
the title of their regulation factor suggests it is integral to effective regulation of emotion
(as a component of EI), incremental validity of EI over optimism in predicting mental and
somatic health (Mikolajczak, Luminet, & Menil, 2006) suggests that they are separate
constructs. While it is plausible that optimism is related to Branch II whereby effective
utilisation of positive thinking is beneficial to the individual (Carver & Scheier, 1981),
optimism as an attitude is unlikely to be integral to Branch II which refers to the effective
use of current mood or emotion in thinking and problem solving. Removal of item 6
improved model fit ( (44) = 92.325, p < .001; TLI = .910; CFI = .928; RMSEA = .048
(.034 - .062); SRMR = .0430). The final one factor model is presented below in Figure 14.
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Figure 14. Branch II one factor model.
Based on the model from which the items were derived, as well as the eigen-values, the
decision was made to re-specify the model as a four as well as a three factor independent
cluster measurement model in which the factor inter-correlations were freely estimated.
7.6.2 Branch II-Typical Emotional Intelligence: Four factor model.
For the four factor model, two, four, three and three indicator items were specified to
capture the respective factors they were designed to measure. With two inter-factor
correlations greater than one (Factor 1 with Factor 3, and Factor 1 with Factor 4) the
solution for the four factor model was not admissible. The high factor correlations may be
due to indicator items of the factors having cross-loadings between the factors or may mean
that the items of factor one should be redistributed onto factors three and four. The factor
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coefficients were all significant, however item 6 did not substantially load (.164) onto its
designated factor and should be considered for removal. A large standardized residual was
found for item 6 with item 31 (3.188). Additionally, a large modification index for the error
terms between items 6 and 31 (MI = 10.703). Item 6 was removed based on statistical and
theoretical considerations discussed above. Removal of item 6 did not make the solution
admissible and there were no other indications for item removal or re-specification. The
four factor model was also inadmissible for the validation sample, suggesting that sample
particularities (such as identical patterns of item responses across cases) are not responsible
for model misfit. Therefore it was decided that the four factor model is not appropriate for
the current sample and the three factor model was investigated.
7.6.3 Branch II-Typical Emotional Intelligence: Three factor model. The three factor model was initially specified by loading the items of factor one onto the
second factor. Factors 1 and 2 were chosen because the Factor 1 items are more similar in
meaning (all items refer to the vividness of emotions) than the other factors. The solution
was also inadmissible for this model. Removal of items 20 and 36 did not change the
inadmissibility of the solution. The three factor model was re-specified with two, four and
five indicator items respectively. This re-specification was organized according to item
similarities. The new factors were considered to represent “Generation of Emotion”,
“Utilisation of Emotion” and “Potency of Emotion”. The re-specified three factor model
was admissible but did not have adequate model fit ( (51) = 114.187, p < .001; TLI =
.883; CFI = .909; RMSEA = .051 (.039-.064); SRMR = .0464). The factor coefficients
were all significant, and item loadings were greatly improved with only one item (item 6)
not substantially loading (.156) onto its intended factor. Providing further evidence that
item 6 should be excluded from the model, a large standardized residual was found between
item 6 and item 31 (-3.301). Large modification indices for the error terms between items 6
and 31 (MI = 11.362) and for between factor one and the error terms of item 16
(MI=11.362) and item 18 (MI = 11.815) indicate that the model would fit better if the
covariance between these terms were freely estimated. Removal of item 6 improved model
fit ( (41) = 83.886, p < .001; TLI = .914; CFI = .936; RMSEA = .047 (.032-.061); SRMR
= .0408). However this model appears to be unstable because the solution was inadmissible
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in the validation sample due to high correlations between factors. High inter-factor
correlations found for both the three and four factor models in both samples suggests that a
one factor solution may be more appropriate.
7.6.4 Branch II-Typical Emotional Intelligence: Model comparison.
The final one factor model was re-assessed using the validation file. A comparison of the fit
indices for the models for both calibration (N = 476) and validation files (N = 566) are
presented below in Table 25.
Table 25. Branch II Model Comparison
Fit Index
Model (df) p TLI CFI RMSEA (range) SRMR
One Factor (unmodified)
Calibration
sample
123.899 (54) < .001 .878 .900 .052 (.040 - .064) .0484
One Factor (modified)
Calibration
sample
92.325 (44) < .001 .910 .928 .048 (.034 - .062) .0430
Validation
sample
96.611 (44) < .001 .913 .931 .046 (.034 - .058) .0398
It can be seen that model fit established through the model modification in the calibration
sample, was replicated in the validation sample. The modified one factor model provided a
better fit after modification according an increase in TLI of greater than .01 and the chi-
square difference test ( = 31.547 (10), p < .005). The one factor model was further
explored to determine the type of measurement model (i.e. parallel, tau equivalent or
congeneric).
7.6.5 Branch II-Typical Emotional Intelligence: Measurement model and reliability.
The parallel (with all factor loadings and error variances set to equality for each factor), tau
equivalent (with all factor loadings set to equality for each factor) and congeneric models
(with all factor loadings and error variances left to be freely estimated) were compared to
determine whether scores could be combined as equal scores or if factor loadings and errors
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should be incorporated into factor scores. Table 26 below displays the fit criteria and chi-
square difference tests for the parallel, tau-equivalent and congeneric model for each
sample.
Table 26. Branch II Measurement Models
Fit Index
Model (df) p TLI CFI RMSEA (range) SRMR
Congeneric model
Calibration
sample
92.325 (44) < .001 .910 .928 .048 (.034 - .062) .0430
Validation
sample
96.611 (44) < .001 .913 .931 .046 (.034- .058) .0398
Parallel model
Calibration
sample
232.168 (64) < .001 .784 .749 .074 (.064- .085) .0654
Validation
sample
295.225 (64) < .001 .739 .696 .080 (.071 - .089) .0734
Tau equivalent model
Calibration
sample
135.775 (54) < .001 .876 .878 .056 (.045 - .068) .0607
Validation
sample
166.596 (54) < .001 .849 .852 .061 (.050 - .071) .0659
tests (compared to congeneric model)
Parallel model
Calibration
sample
139.843 (20) < .005
Validation
sample
198.614 (20) < .005
Tau equivalent model
Calibration
sample
43.450 (10) < .005
Validation
sample
69.985 (10) < .005
From Table 26, it may be seen that model fit was not established for the Parallel or the Tau-
equivalent models. Since the model appears to be congeneric, factor loadings and error
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variances cannot be assumed to be equal and therefore scoring must take individual factor
loadings and error variances into consideration. Additionally, as argued earlier, construct
reliability should be calculated using Fornell and Larker‟s (1981) formula.
The sum of the standardized loadings and sum of associated error variance for each factor
is presented below in Table 27.
Table 27. Branch II Sum of Standardised Loadings, Error Variances and Construct Reliability Estimates for the One Factor Model
Sum of Standardised Loadings
Sum of Error Variances
Construct Reliability Estimates
(Cronbach‟s alpha)
Factor 1 Calibration Sample 5.034 8.604 0.75 (.74) Validation Sample 4.901 8.677 0.74 (.73)
The total factor reliability of test scores (.75) for the calibration sample is acceptable and is
replicated in the validation sample (.74).
7.6.6 Branch II-Typical Emotional Intelligence: Factor scores. Factor scores were calculated as per Branch I factor scores, described above.
Mean scores for grade and gender are presented below in Tables 28 and 29. Table 28. Branch II Descriptive Statistics for Grades 4, 5 and 6
Factor Grade Calibration Sample Validation Sample Mean (Standard Deviation) Mean (Standard Deviation) Branch II 4 4.99 (0.96) 4.98 (0.96)
5 5.07 (0.93) 4.96 (0.85)
6 5.23 (0.72) 5.08 (0.81) Note Calibration Sample: Grade 4 n = 170; Grade 5 n = 149; Grade 6 n = 157
Validation Sample: Grade 4 n = 188; Grade 5 n = 180; Grade 6 n = 198
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Table 29. Branch II Descriptive Statistics for Males and Females
Factor Grade Calibration Sample Validation Sample Mean (Standard Deviation) Mean (Standard Deviation) Branch II Male 5.03 (0.88) 5.01 (0.89) Female 5.17 (0.88) 5.01 (0.86) Note Calibration Sample: Males n = 250; Females n = 226
Validation Sample: Males n = 303; Females n = 263
The Branch II factor scores for the two samples showed a normal distribution, with skews
under two (calibration sample: -.410 to -.44; validation sample: -.272 to -.385) and kurtosis
under seven (calibration sample: .451 to 429; validation sample: .009 to -.255). While
females scored more highly than males in the calibration sample, the genders scored
equally in the validation sample. However, while an increase in EI with higher grade level
is apparent from grade 4 to grade 6 and from grade 5 to grade 6, there is mixed evidence of
changes from grade 4 to grade 5. Analyses were conducted to determine whether the
differences found were significant.
7.6.7 Branch II-Typical Emotional Intelligence: Grade level comparisons.
A one-way between-groups analysis of variance was conducted on the calibration sample to
explore the impact of grade level on the Branch II factor. Levene‟s test indicated a
significant difference in homogeneity of variances between grade levels; therefore a robust
test of equality of means was used. As predicted, there was a significant difference in
Branch II across the three grade levels (Welch (2, 308.689) = 3.617, p = .028). Post-hoc
comparisons using the Tukey HSD test indicated that the mean Branch II score for grade 6
was significantly greater than the mean score for grade 4. While the differences were
significant, the effect size was small (eta squared = .01).
The one-way between-groups analysis of variance was repeated in the validation sample to
explore the impact of grade level on the Branch II factor. Levene‟s test indicated no
significant differences in homogeneity of variances between groups. Contrary to
expectations and the calibration sample results, there were no significant differences for
Branch II across the three grade levels (F (2, 563) = 1.75, p = .342).
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7.6.8 Branch II-Typical Emotional Intelligence: Gender comparisons.
A one-way between-groups analysis of variance was conducted on the calibration sample to
explore the impact of gender on the Branch II factor. Levene‟s test indicated no significant
differences in homogeneity of variances. Contrary to expectations, there was no significant
difference for Branch II across gender (F (1, 474) = 3.00, p = .084).
The one-way between-groups analysis of variance was repeated in the validation sample to
explore the impact of gender on the Branch II factor. Levene‟s test indicated no significant
differences in homogeneity of variances. Contrary to expectations but in line with
calibration sample results, there were no significant differences for Branch II across gender
(F (1, 564) = .005, p = .945).
7.7 Confirmatory factor analysis: Branch III-Maximum Emotional Intelligence.
7.7.1 Branch III-Maximum Emotional Intelligence: One factor model. A one factor congeneric model with twenty-two indicator items was specified to capture
Branch III. The variance of the latent variable was set to one so that all item loadings could
be explored. The data fit the model well with all the selected criteria indicating model fit
( (209) = 310.353, p < .001; TLI = .903; CFI = .912; RMSEA = .032 (.024 - .039);
SRMR = .0437). With sample correlations ranging from a low of -.003 (items 58 and 45) to
a high of .509 (items 63 and 66), none of the pairs of item indicators were so highly
correlated so as to indicate possible item redundancy. The eigen-values (4.090, 1.369,
1.251, 1.148, 1.127, 1.074, 1.033, .979, .935, .923, .857, .839, .806, .770, .739, .720, .699,
.599, .522, .447, .426) suggest that while the one factor model is suitable, a seven factor
model may also be suitable. The factor coefficients were all significant, however all items
except 48, 50, 56, 62, 63, 64, 65 and 66 substantially loaded onto the factor. Large
standardized residuals were found for item 46 with item 58 (-3.378), item 48 with item 61
(2.649), item 50 with items 52 (3.305) and 59 (3.220) and between items 52 and 53 (2.838).
This indicates that the single Branch III model fails to account for much of the variance
between these items. Additionally, large modification indices for the error terms of item 46
and item 58 (12.083), between the error terms of items 48 and 61 (MI = 10.202) and
between the error terms of item 50 with the error terms of items 52 (MI = 13.663) and 66
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(MI = 13.144) indicates that that the model would fit better if the covariance between these
error terms were freely estimated. Correlations for the items of UAE would be expected to
be underestimated because there is a limited range of values for these performance items
(Tabachnick & Fidell, 2001). Therefore, given that the model shows a good fit to the data,
items will not be removed solely on the basis of low factor correlations. Item difficulties
were mostly within the optimal range of p = .3 to .7, with item 45 being the most difficult
(p = .37) and item 59 being the easiest (p = .90). Item 50 was considered for removal based
on statistical considerations. However as no theoretical basis could be identified for
removal of this or other items and the model showed adequate fit, the model was retained.
Model fit was only partially replicated in the validation sample ( (209) = 269.730, p <
.001; TLI = .878; CFI = .890; RMSEA = .023 (.014-.030); SRMR = .0399). The one factor
model for Branch III is presented below in Figure 15.
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Figure 15. Branch III one factor model.
7.7.2 Branch III-Maximum Emotional Intelligence: Four factor model. Based on the model from which the items were derived the model was also specified as a
four factor independent cluster measurement model in which the factor inter-correlations
were freely estimated. Four, twelve, three and three indicator items were specified to
capture the respective factors they were designed to measure. The four factor model was
inadmissible due to a factor correlation exceeding one (factors one and two). The high
factor correlations for the other factors (ranging from .742 to .952) provide further support
for the one factor model. The one factor model was further explored to determine the type
of measurement model (i.e. parallel, tau equivalent or congeneric).
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7.7.5 Branch III- Maximum Emotional Intelligence: Measurement model and reliability.
The parallel (with all factor loadings and error variances set to equality for each factor), tau
equivalent (with all factor loadings set to equality for each factor) and congeneric models
(with all factor loadings and error variances left to be freely estimated) were compared to
determine whether scores could be combined as equal scores or if factor loadings and errors
should be incorporated into factor scores. Table 30 below displays the fit criteria and chi-
square difference tests for the parallel, tau-equivalent and congeneric model for each
sample.
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Table 30. Branch III Measurement Model Comparison
Fit Index
Model (df) p TLI CFI RMSEA (range) SRMR
Congeneric model
Calibration
sample
310.353 (209) < .001 .903 .912 .032 (.024 - .039) .0437
Validation
sample
269.730 (209) < .001 .878 .890 .023 (.014- .030) .0399
Parallel model
Calibration
sample
105.701 (251) < .001 .482 .000 .125 (.120- .130) .0921
Validation
sample
3072.054 (251) < .001 -3.713 .000 .141 (.137 - .146) .0625
Tau equivalent model
Calibration
sample
635.795 (230) < .001 .646 .648 .061 (.055- .067) .0852
Validation
sample
421.474 (230) < .001 .651 .652 .038 (.033 - .044) .0621
tests (compared to congeneric model)
Parallel model
Calibration
sample
1795.348 (42) < .001
Validation
sample
2802.324 (42) < .001
Tau equivalent model
Calibration
sample
325.442 (21) < .001
Validation
sample
151.744 (21) < .001
From Table 30, it may be seen that model fit was not established for the parallel or the tau-
equivalent models. Since the model appears to be congeneric, factor loadings and error
variances cannot be assumed to be equal and therefore scoring must take individual factor
loadings and error variances into consideration. Additionally, as argued earlier, construct
reliability should be calculated using Fornell and Larker‟s (1981) formula.
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The sum of the standardized loadings and sum of associated error variance for each factor
is presented below in Table 31.
Table 31. Branch III Sum of Standardised Loadings, Error Variances and Construct Reliability Estimates for the One Factor Model
Sum of Standardised Loadings
Sum of Error Variances
Construct Reliability Estimates
(Cronbach‟s alpha)
Branch III Calibration Sample 7.628 18.642 0.76 (.73) Validation Sample 5.881 20.066 0.63 (.63)
Although the total factor reliability of test scores for the calibration sample is acceptable,
reliability was not replicated in the validation sample. Therefore, until replicated in other
samples, this factor should be interpreted with caution.
7.7.6 Branch III-Typical Emotional Intelligence: Factor scores. Factor scores were calculated as per Branch I factor scores, described above.
Mean scores for grade and gender are presented below in Tables 32 and 33.
Table 32. Branch III Descriptive Statistics for Grades 4, 5 and 6 Factor Grade Calibration Sample Validation Sample Mean (Standard Deviation) Mean (Standard Deviation) Branch III 4 3.49 (0.80) 5.52 (0.81)
5 3.70 (0.78) 5.59 (0.77)
6 3.78 (0.82) 5.90 (0.71) Note Calibration Sample: Grade 4 n = 170; Grade 5 n = 149; Grade 6 n = 157
Validation Sample: Grade 4 n = 188; Grade 5 n = 180; Grade 6 n = 198
Table 33. Branch III Descriptive Statistics for Males and Females
Factor Grade Calibration Sample Validation Sample Mean (Standard Deviation) Mean (Standard Deviation) Branch III Male 3.54 (0.89) 5.59 (0.88)
Female 3.79 (0.69) 5.77 (0.65) Note Calibration Sample: Grade 4 n = 170; Grade 5 n = 149; Grade 6 n = 157
Validation Sample: Grade 4 n = 188; Grade 5 n = 180; Grade 6 n = 198
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The Branch III factor scores for the two samples were normally distributed, with skews
under two (calibration sample: -1.681; validation sample: -1.821) and kurtosis under seven
(calibration sample: 4.257; validation sample: 3.072). There is a clear pattern for females
scoring more highly than males and an increase in Branch III score with grade level.
Analyses were conducted to determine whether these differences were significant.
7.7.7 Branch III- Maximum Emotional Intelligence: Grade level comparisons.
A one-way between-groups analysis of variance was conducted on the calibration sample to
explore the impact of grade level on the Branch III factor. Levene‟s test indicated no
significant differences in homogeneity of variances between grade levels. As predicted,
there was a significant difference for Branch III across the three grade levels (F (2, 473) =
5.891, p = .003). Post-hoc comparisons using the Tukey HSD test indicated that the mean
Branch III score for grade 6 was significantly greater than grade 4. While these differences
were significant, the effect size (eta squared = .02) was small.
The one-way between-groups analysis of variance was repeated in the validation sample to
explore the impact of grade level on the Branch III factor. Levene‟s test indicated no
significant differences in homogeneity of variances between groups. In line with
expectations and findings in the calibration sample, there was a significant difference for
Branch III across the three grade levels (F (2, 565) = 13.001, p < .001). Post-hoc
comparisons using the Tukey HSD test indicated that the mean Branch III score for grade 6
was significantly different from both grades 4 and 5. The effect size (eta squared = .04) was
small to moderate.
7.7.8 Branch III- Maximum Emotional Intelligence: Gender comparisons.
A one-way between-groups analysis of variance was conducted on the calibration sample to
explore the impact of gender on the Branch III factor. Levene‟s test indicated significant
differences in homogeneity of variances between all groups; therefore the robust equality of
means test was used. As hypothesised, there were significant differences for Branch III
across gender (Welch (1, 462.571) = 10.890, p = .001) with females scoring higher than
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males. Despite the difference being significant, the effect size (eta squared = .02) was small
to moderate.
The one-way between-groups analysis of variance was repeated in the validation sample to
explore the impact of gender on the Branch III factor. Levene‟s test indicated significant
differences in homogeneity of variances between all groups; therefore the robust equality of
means test was used. In line with expectations and as per findings in the calibration sample,
there were significant differences for Branch III across gender (Welch (1, 551.062) = 7.677,
p = .006) with females scoring higher than males. Despite the difference being significant,
the effect size (eta squared = .01) is small.
7.8 Confirmatory factor analysis: Branch IV-Typical Emotional Intelligence.
7.8.1 Branch IV- Typical Emotional Intelligence: One factor model. A one factor congeneric model with twelve indicator items was specified to capture Branch
IV. The variance of the latent variable was set to one so that all item loadings could be
explored. The data did not fit the model well with all the selected criteria indicating model
misfit ( (54) = 154.573, p < .001; TLI = .819; CFI = .852; RMSEA = .063 (.051-.074);
SRMR = .0545). With sample correlations ranging from a low of .124 (item 15R) to a high
of .622 (item 12), none of the pairs of item indicators were so highly correlated so as to
indicate possible item redundancy. The eigen-values (3.024, 1.287, 1.166, .980, .870, .816,
.782, .732, .680, .590, .569, .503) suggest that three factor model may provide a better fit.
The factor coefficients were all significant; however items 7R, 15R, 22 and 28 did not
substantially load onto the factor and should be considered for removal. Large standardized
residuals were found for item 10 with items 7R (-2.778) and 32 (3.058) and for item 7R
with item 15R (3.085). This indicates that the single RRE model fails to account for much
of the variance between these items. Additionally, large modification indices for the error
term of item 10 and the error terms of item 7R (MI = 10.199) and the error term of item 32
(16.018) and between the error terms of item 13 and 19 (MI = 10.534) indicate that the
model would fit better if the covariance between these error terms were freely estimated.
Items 7R, 15R, 22 and 28 were selected for removal based upon insufficient factor
loadings. While removal of these items improved model fit, indices continue to suggest
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model misfit ( (20) = 73.084, p < .001; TLI = .871 CFI = .908; RMSEA = .075 (.057 -
.094); SRMR = .0511). High standardized residuals between item 10 and 32 (3.317) and
large modification indices suggested improvement in fit with the covariances between the
error terms of items 10 and 32 (18.166) and the error terms of items 13 and 19 (MI =
10.336) allowed to be freely estimated. In line with statistical considerations and due to the
item pertaining more to the perception of emotion than emotional regulation, item 10 was
selected for removal. While model fit improved upon removal of item 10, most indices
continued to suggest model misfit ( (14) = 49.720, p < .001; TLI = .891 CFI = .928;
RMSEA = .073 (.052 - .096); SRMR = .0451). With no other statistical indications for
model modification in the calibration sample, the model was assessed in the validation
sample. Indices also suggested close but inadequate model fit in the validation sample
( (14) = 79.245, p < .001; TLI = .835 CFI = .890; RMSEA = .091 (.072 - .111); SRMR =
.0569). Based on the model from which the items were derived, as well as the eigen-values,
the decision was made to re-specify the model as a four as well as a three factor
independent cluster measurement model in which the factor inter-correlations were freely
estimated.
7.8.2 Branch IV-Typical Emotional Intelligence: Four factor model. For the four factor model, three, three, two and four indicator items were specified to
capture the respective factors they were designed to measure. The four factor model did not
appear to be a good fit to the data ( (42) = 137.025, p < .001; TLI = .820 CFI = .869;
RMSEA = .062 (.050-.075); SRMR = .0599). The factor loadings were all significant
except item 28. Item 22 and 28 did not substantially load (.299, -.050) onto their designated
factors and should be considered for removal. Large standardized residuals were found for
item 28 with items 19 (2.760), 21 (3.961) and 32 (2.657) and between items 22 and 32
(2.771). Additionally, there was a large modification index for the error terms of items 13
and 19 (MI = 10.796) and between the error term of item 13 and the Factor 3. Items 28 and
22 were removed based on non-significant and insufficient loadings. While removal of
these items improved model fit, indices suggest insufficient fit to the data (29) =
70.447, p < .001; TLI = .893; CFI = .931; RMSEA = .055 (.039-.071); SRMR = .0424).
While there were no large standardized residuals in the modified model, there was a large
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modification index for the error terms of items 13 and 19. Item 13 was selected for removal
based on statistical considerations and due to the item reflecting more sophisticated
processes than the other items loading onto this factor. Removal of item 13 resulted in
much improved model fit ( (21) = 37.759, p <.05; TLI = .942; CFI = .966; RMSEA =
.041 (.018-.062); SRMR = .0331). When model fit was assessed in the validation sample,
the solution was inadmissible due to negative error variance (-.050) for item 7R. To test the
hypothesis that the negative error variance may be due to sampling fluctuations, item 7R‟s
residual error variance was constrained to .0001. The modified model with the constrained
error variance yielded a chi-square (306) = 570.72, p < .001, which was not statistically
significantly worse fitting than the previous non-constrained model, indicating that the
negative error variance was likely due to sampling fluctuations, rather than a fundamentally
inappropriate model specification. With the error variance set to 0.0001, the model fit well
( (22) = 32.078, p = .076; TLI = .976 CFI = .985; RMSEA = .028 (.000 - .049); SRMR =
.0244). The final four factor model is presented below in Figure 16.
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Figure 16. Branch IV four factor model.
While the four factor model appeared to provide a fit for both the calibration and validation
samples, low and non-significant loadings between factor one and the other factors (.22,
.09, .33), suggest that a three factor model may provide a better fit to the data. The four
factor model was further explored using Fornell and Larker‟s (1981) test of discriminant
validity. The sum of the squared standardized loadings and sum of associated error variance
for each factor are presented below in Table 34.
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Table 34. Branch IV Sum of Squared Standardised Loadings, Error Variances and
Variance Extracted Estimates for Each Factor of the Four Factor Model Sum of Squared
Standardised
Loadings
Sum of Error
Variances
Variance Extracted
Estimate
Squared Factor
Correlations
2 3 4
Factor 1 0.330 1.670 0.165 .05 .01 .11
Factor 2 0.498 1.502 0.249 .61 .81
Factor 3 0.649 1.351 0.325 .42
Factor 4 1.138 1.863 0.379
Average of Variance Extracted Estimates = 0.280 Note Factor 1 = Stay open to pleasant and unpleasant feelings
Factor 2 = Reflectively engage or detach from an emotion
Factor 3 = Reflectively monitor emotions in relation to self and others
Factor 4 = Manage emotion in self and others
As may be seen from Table 34, while Factor 1 shows discriminant validity with Factors 2,
3 and 4, there appears to be considerable overlap among Factors 2, 3 and 4. Indeed Factor 1
appears to be unrelated to the other factors. While poor discriminant validity may be
indicative of the presence of a higher order construct (Cunningham, 2008), further
exploration of the model is warranted. Based on the low correlations of factor one with the
other factors, the decision was made to re-specify the model as a three factor independent
cluster measurement model in which the factor inter-correlations were freely estimated.
Two, two and three indicator items were specified to capture the respective factors.
7.8.3 Branch IV-Typical Emotional Intelligence: Three factor model. The three factor model was specified as per factors two, three and four of the original four
factor model. The three factor model did not display adequate fit ( (24) = 80.505, p <
.001; TLI = .865 CFI = .910; RMSEA = .070 (.054 - .088); SRMR = .0498). All factor
loadings except for item 22 (.300) were sufficient and significant. No items were correlated
so highly as to indicate item redundancy and there were no high standardized residuals.
Large modification indices for the error terms between items 13 and 19 (MI = 11.774) and
for between factor three and the error term of item 22 (MI = 16.267) indicate that the model
would fit better if the covariance between these terms were freely estimated. Item 22 was
selected for removal based on insufficient loading onto its intended factor. Removal of item
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22 improved model fit with most of the selected indices indicating model fit ( (17) =
50.052, p < .001; TLI = .905; CFI = .943; RMSEA = .064 (.044 - .085); SRMR = .0418).
Model fit was partially replicated in the validation sample ( (17) = 52.136, p < .001; TLI
= .924; CFI = .954; RMSEA = .060 (.042-.080); SRMR = .0394). The final three factor
model is presented below in Figure 17.
Figure 17. Branch IV three factor model.
The hypothesis of a three factor model was further assessed using Fornell and Larker‟s
(1981) test of discriminant validity. The sum of the standardized loadings and sum of
associated error variance for each factor is presented below in Table 35.
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Table 35. Branch IV Three Factor Model Sum of Squared Standardised Loadings, Error Variances and Variance Extracted Estimates Sum of Squared
Standardised
Loadings
Sum of Error
Variances
Variance Extracted
Estimate
Squared Factor
Correlations
1 2
Factor 1 0.499 1.501 0.250 .61 .83
Factor 2 0.649 1.352 0.324 .42
Factor 3 1.135 1.865 0.378
Average of Variance Extracted Estimates = 0.317 Note Factor 1 = Reflectively engage or detach from an emotion
Factor 2 = Reflectively monitor emotions in relation to self and others
Factor 3 = Manage emotion in self and others
From the results presented in Table 35, it may be seen with moderate correlations between
the factors, the three factor model does not maintain discriminant validity. This suggests the
presence of a higher order factor.
7.8.4 Branch IV-Typical Emotional Intelligence: Model comparison.
A comparison of the fit indices for the one, three and four factor models for both calibration
(N = 476) and validation files (N = 566) are presented below in Table 36.
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Table 36. Branch IV Model Comparison Fit Index
Model (df) p TLI CFI RMSEA (range) SRMR
One Factor (unmodified)
Calibration
sample
154.573 (54) < .001 .819 .852 .063 (.051 - .074) .0545
One Factor (modified)
Calibration
sample
49.720 (14) < .001 .891 .928 .073 (.052 - .096) .0451
Validation
sample
79.245 (14) < .001 .835 .890 .091 (.072 - .111) .0569
Three Factor (modified)
Calibration
sample
50.052 (17) < .001 .905 .943 .064 (.044 - .085) .0418
Validation
sample
52.136 (17) < .001 .924 .954 .060 (.042 - .080) .0394
Four Factor (modified)
Calibration
sample
37.759 (21) < .001 .942 .966 .041 (.018 - .062) .0331
Validation
sample
32.078 (22*) .076 .976 .985 .028 (.000 - .049) .0244
Note *The modified four factor model in the validation sample has an extra degree of freedom due to restraint of an error variance
It may be seen that model fit for the four factor model established through the model
modification in the calibration sample, was replicated in the validation sample. The
modified four factor model provided a better fit than the other models as indicated by an
increase in TLI of greater than .01 and significant chi-square difference tests between the
four factor model and both the one factor ( (8) = 47.167, p < .005) and three factor
( (5) = 20.058, p < .005) models. The four factor model was further explored to
determine the type of measurement model (i.e. parallel, tau equivalent or congeneric).
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7.8.5 Branch IV-Typical Emotional Intelligence: Measurement model and reliability.
The parallel (with all factor loadings and error variances set to equality for each factor), tau
equivalent (with all factor loadings set to equality for each factor) and congeneric models
(with all factor loadings and error variances left to be freely estimated) were compared to
determine whether scores could be combined as equal scores or if factor loadings and errors
should be incorporated into factor scores. Table 37 below displays the fit criteria and chi-
square difference tests for the parallel, tau-equivalent and congeneric model for each data
set.
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Table 37. Branch IV Measurement Model Comparison
Fit Index
Model (df) p TLI CFI RMSEA (range) SRMR
Congeneric model
Calibration
sample
37.759 (21) < .05 .942 .966 .041 (.018 - .062) .0331
Validation
sample
32.077 (21) < .05 .972 .984 .041 (.000- .051) .0244
Parallel model
Calibration
sample
127.150 (37) < .001 .824 .819 .072 (.058- .085) .0507
Validation
sample
166.462 (37) < .001 .814 .809 .079 (.067 - .091) .0622
Tau equivalent model
Calibration
sample
54.849 (29) < .01 .935 .948 .043 (.025 - .061) .0436
Validation
sample
90.873 (29) < .05 .887 .909 .061 (.047 - .076) .0572
tests (compared to congeneric model)
Parallel model
Calibration
sample
89.391 (16) < .001
Validation
sample
134.384 (16) < .001
Tau equivalent model
Calibration
sample
17.090 (8) < .001
Validation
sample
58.796 (8) < .001
From Table 37, it can be seen that while model fit was not established for the parallel
model, the tau-equivalent model yielded adequate fit indices. However, the chi-square
difference test indicated a significant difference between the models, suggesting that the
congeneric and tau equivalent models are not equivalent and therefore the congeneric
model should be used. Since the model appears to be congeneric, factor loadings and error
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variances cannot be assumed to be equal and therefore scoring must take individual factor
loadings and error variances into consideration. Additionally, as argued earlier, construct
reliability should be calculated using Fornell and Larker‟s (1981) formula.
The sum of the standardized loadings and sum of associated error variance for each factor
is presented below in Table 38.
Table 38. Branch IV Four Factor Model Sum of Standardised Loadings, Error Variances and Construct Reliability Estimates
Sum of Standardised Loadings
Sum of Error Variances
Construct Reliability Estimates
(Cronbach‟s alpha)
Factor 1 Calibration Sample 1.067 2.618 0.30 (.31) Validation Sample 1.102* 0.990* 0.55* (.18) Factor 2 Calibration Sample 0.991 1.502 0.34 (.39)
Validation Sample 1.040 1.450 0.43 (.42) Factor 3 Calibration Sample 1.139 1.351 0.49 (.49)
Validation Sample 1.339 1.101 0.62 (.62)
Factor 4 Calibration Sample 1.839 1.863 0.64 (.64)
Validation Sample
1.924 1.762 0.68 (.67)
Note Factor 1 = Stay open to pleasant and unpleasant thoughts
Factor 2 = Reflectively engage or detach from and emotion
Factor 3 = Reflectively monitor emotions in relation to self and others
Factor 4 = Manage emotions in self and others
* These calculations are influenced by constraint of the error variance to .0001
Ranging from .20 (Factor 1) to .65 (Factor 4), reliability of individual factor scores for the
calibration sample is not acceptable. The higher order model was assessed to determine
whether factor scores could be combined, with an increased number of indicator items
likely to yield improved reliability. The initial solution was inadmissible, with a negative
error variance for the error associated with the second factor. To test the hypothesis that the
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negative error variance may be due to sampling fluctuations, the second factor‟s residual
error variance was constrained to .0001. The modified model with the constrained error
variance yielded a chi-square of 40.410 (df = 24; p = .019) which was not statistically
significantly worse fitting than the previous non-constrained model ( (23) = 40.333, p =
.014), indicating that the negative error variance was likely due to sampling fluctuations,
rather than a fundamentally inappropriate model specification. The model showed good fit
according to the selected indices ( (24) = 40.410, p = .019, TLI = .951, CFI = .967,
RMSEA = .038 (.015-.058), SRMR = .0342). Good fit for the higher order factor model
was replicated in the validation sample ( (23) = 35.409, p = .047, TLI = .971, CFI = .982,
RMSEA = .031 (.003-.050), SRMR = .0261). The higher order four factor model is
presented below in Figure 18. Note that the Factor 2 loading is 1.00 due to the restraint of
the associated error variance.
Figure 18. Branch IV four factor model with one higher order factor.
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Factor reliability was explored in the calibration and validation samples. The sum of the
standardized loadings and sum of associated error variance for each factor is presented
below in Table 39.
Table 39. Branch IV Higher Order Factor Sum of Standardised Loadings, Error Variances and Construct Reliability Estimates
Sum of Standardised Loadings
Sum of Error Variances
Construct Reliability Estimates
(Cronbach‟s alpha)
Higher Order Factor
Calibration Sample 2.932* 1.561* 0.85* (.66) Validation Sample 2.698 1.967 0.79 (.68) Note * These calculations are influenced by constraint of the error variance to .0001
It may be seen from Table 39, that the reliabilities of .85 and .79 for the higher order factor
representing Branch IV are acceptable. While the reliability estimate for the calibration
sample may be inflated due to the constraint of an error variance, the respectable reliability
estimate in the validation sample suggests that the higher order factors may be interpreted
with confidence. However, lower order factor scores should be interpreted with caution due
to low reliability estimates.
7.8.6 Branch IV-Typical Emotional Intelligence: Factor scores. Factor scores were calculated for the calibration and validation samples as per Branch I
factor scores, described above. Mean scores for grade and gender are presented below in
Table 40 and 41.
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Table 40. Branch IV Descriptive statistics for Grades 4, 5 and 6
Factor Grade Calibration Sample Validation Sample Mean (Standard Deviation) Mean (Standard Deviation) Factor 1 4 2.68 (0.64) 1.49* (0.26*)
5 2.56 (0.57) 1.49* (0.23*) 6 2.54 (0.55) 1.48* (0.22*) Factor 2 4 2.63 (0.60) 3.06* (0.66*)
5 2.56 (0.52) 3.06* (0.56*)
6 2.65 (0.46) 3.14* (0.52*)
Factor 3 4 2.66 (0.58) 2.32* (0.56*) 5 2.66 (0.51) 2.35* (0.48*) 6 2.78 (0.48) 2.47* (0.45*)
Factor 4 4 2.97 (0.63) 2.93* (0.66*) 5 2.91 (0.58) 2.93* (0.56*) 6 3.02 (0.52) 3.00* (0.52*) Branch IV 4 4.25 (0.86) 2.93* (0.66*)
5 4.17 (0.78) 2.93* (0.56*)
6 4.33 (0.69) 2.95* (0.58*) Note Calibration Sample: Grade 4 n = 170; Grade 5 n = 149; Grade 6 n = 157
Validation Sample: Grade 4 n = 188; Grade 5 n = 180; Grade 6 n = 198
* These calculations are influenced by constraint of an error variance to .0001
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Table 41. Branch IV Descriptive statistics for Males and Females
Factor Grade Calibration Sample Validation Sample Mean (Standard Deviation) Mean (Standard Deviation) Factor 1 Male 2.51 (0.59) 1.44* (0.24*) Female 2.69 (0.58) 1.53* (0.22*)
Factor 2 Male 2.60 (0.51) 3.03* (0.59*)
Female 2.63 (0.56) 3.16* (0.56*)
Factor 3 Male 2.63 (0.53) 2.35* (0.51*)
Female 2.77 (0.52) 3.04* (0.57*)
Factor 4 Male 2.90 (0.57) 2.88* (0.59*)
Female 3.04 (0.58) 3.04* (0.57*)
Branch IV Male 4.17 (0.77) 4.40* (0.85*)
Female 4.34 (0.79) 4.59* (0.83*) Note Calibration Sample: Males n = 250; Females n = 226
Validation Sample: Males n = 303; Females n = 263 * These calculations are influenced by constraint of an error variance to .0001
The Branch IV factor scores for the two samples were normally distributed, with skews
under two (calibration sample: -.174 to -.635; validation sample: -.177 to -.500) and
kurtosis under seven (calibration sample: -.195 to .590); validation sample: -.028 to -.263).
There is a clear pattern for females scoring more highly than males. However, there do not
appear to be any clear increases with grade level. Analyses were conducted to determine
whether any differences were significant.
7.8.8 Branch IV-Typical Emotional Intelligence: Grade level comparisons.
A one-way between-groups analysis of variance was conducted on the calibration sample to
explore the impact of grade level on the Branch IV factors. Levene‟s test indicated
significant differences in homogeneity of variances between grade levels for Factor 2,
Factor 3 and Factor 4; therefore a robust equality of means test was used for these
comparisons. Contrary to predictions, there were no significant differences across the three
grade levels for the Branch IV factors (Factor 1: F (2, 473) = 2.528, p = .081; Factor 2:
Welch (2, 312.481) = 1.303, p = .273; Factor 3: Welch (2, 313.907) = 2.762, p = .065;
Factor 4: Welch (2, 313.032) = 1.452, p = .236).
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The one-way between-groups analysis of variance was repeated in the validation sample to
explore the impact of grade level on the Branch IV factors. Levene‟s test indicated
significant differences in homogeneity of variances between grade levels for Factor 2,
Factor 3 and Factor 4; therefore a robust equality of means test was used for these
comparisons. As hypothesised but contrary to calibration sample findings, there were
significant differences across the three grade levels for Factor 3 (Welch (2, 369.553) =
5.007, p = .007. Post-hoc comparisons using the Tukey HSD test indicated that the mean
Factor 3 score for grade 6 was significantly greater than grades 4 and 5 at the .05 level.
While this difference was significant, the effect size was small (eta squared = .02).
7.8.9 Branch IV-Typical Emotional Intelligence: Gender comparisons. A one-way between-groups analysis of variance was conducted on the calibration sample to
explore the impact of gender on the Branch IV factors. Levene‟s test indicated a significant
difference in homogeneity of variances between genders for Factor 2; therefore a robust
equality of means test was used for these comparisons. As expected, there were significant
differences across gender for Factor 1 (F (1, 474) = 10.284, p =.001), Factor 3 (F (1, 474) =
8.712, p = .003) and Factor 4 (F (1, 474) = 6.271, p = .013) with females scoring
significantly higher than males for all factors. While these differences were significant, the
effect sizes were small for all comparisons (eta squared = .02, .02 and .01 respectively).
The one-way between-groups analysis of variance was repeated in the validation sample to
explore the impact of gender on the Branch IV factors. Levene‟s test indicated no
significant differences in the homogeneity of variances between genders. As expected but
contrary to calibration sample findings, there were significant differences across gender for
Factor 1 (F (1, 564) = 20.334, p <.001), Factor 2 (F (1, 564) = 7.360, p = .007) and Factor 4
(F (1, 564) = 11.666, p = .001) with females scoring significantly higher than males for all
factors. While these differences were significant, the effect sizes were small to moderate
(Factor 1: eta squared = .04; Factor 2: eta squared = .01; Factor 4: eta squared = .02).
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7.8.10 Branch IV- Typical Emotional Intelligence higher order factor comparisons.
Analyses were conducted in the calibration sample to explore the impact of grade level on
the higher order Branch IV factor. Levene‟s test indicated a significant difference in
homogeneity of variances between grade levels; therefore a robust test of equality of means
(Welch) was used. Contrary to expectations, One-way between groups analysis of variance
revealed no significant difference in estimated Branch IV factor score across the grade
levels (Welch (2, 312.951) = 1.664, p = .191).
The analyses were repeated in the validation sample to explore the impact of grade level on
the higher order RRE factor. Levene‟s test indicated a significant difference in
homogeneity of variances between grade levels; therefore a robust test of equality of means
(Welch) was used. As for the validation sample results, contrary to expectations, one-way
between groups analysis of variance revealed no significant differences in estimated Branch
IV factor scores across the grade levels (Welch (2, 369.371) = 1.352, p = .232).
The one-way between-groups analysis of variance was repeated in the calibration sample to
explore the impact of gender on the higher order Branch IV factor. Levene‟s test indicated
no significant difference in homogeneity of variances across gender. As hypothesised, there
was a significant difference for Branch IV across gender (F (1,474) = 6.137, p = .014), with
females scoring significantly higher than males. While this difference was significant, the
effect size was small (eta squared = .01).
The one-way between-groups analysis of variance was repeated to explore the impact of
gender on the higher order RRE factor. Levene‟s test indicated no significant differences in
homogeneity of variances across gender. In line with for the calibration sample findings,
the results indicated the expected significant difference of Branch IV scores (F (1, 564) =
7.368, p = .007), with females scoring significantly higher than males. While this difference
was significant, the effect size was small (eta squared = .01).
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7.9 Further analyses. Once well fitting measurement models were established for each factor, all factors were
tested in the one model to establish whether the original four factor model was replicated in
the data.
7.9.1 Full one factor Emotional Intelligence model. In line with Mayer and Salovey‟s (1997) four branch model of EI and PCA findings
discussed earlier, a one factor congeneric model with four indicator items was specified to
capture the full EI model. Branch I was represented by three indicator items; the Typical EI
measures of Branch I A and Branch I B as well as the Maximum EI measure of Branch I
ability 2. Branches II, III and IV were represented by unitary factors. These branch scores
were generated based on well fitting measurement models developed in the preceding
paragraphs. The variance of the latent variables (Total EI and Branch I) were set to one so
that all item loadings could be explored.
Figure 19. Four Branch Model of EI.
The model showed moderate fit to the data, with most fit indices indicating good fit ( (8)
= 39.446, p < .001; TLI = .940; CFI = .968; RMSEA = .091 (.064 - .120); SRMR = .0297).
Model fit was replicated in the validation file ( (8) = 44.708, p < .001; TLI = .942; CFI =
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.969; RMSEA = .090 (.065 - .117); SRMR = .0348). The loadings of the Maximum EI
measures (Branch I Maximum EI and Branch III Maximum EI) were low, possibly due to
restricted variance. While the loadings of the Maximum EI measures were low, the model
fit established for the four branch model of EI supports the hypothesis that the SUEIT-EY
would reveal a structure in line with Mayer and Salovey‟s (1997) four factor model.
To further test the hypothesis of the four branch model, an alternative nested one factor
congeneric model with six indicator items was specified. This model was the same as that
specified above but with the Branch I factor removed, leaving the Branch I sub-factors to
load directly onto the EI factor. Model fit evidenced mixed support from the fit indices in
the calibration sample ( (9) = 84.854, p < .001; TLI = .871; CFI = .922; RMSEA = .133
(.108 - .160); SRMR = .0488) with adequate fit evidenced in the validation sample ( (9) =
65.901, p < .001; TLI = .920; CFI = .952; RMSEA = .106 (.083 - .130); SRMR = .0410).
The chi-square difference test indicated that one factor, four branch model was significantly
better fitting than the alternative model for both the calibration sample (Δ = 45.408, p <
.001) and validation sample (Δ = 21.193, p < .001). While it is acknowledged that there
may be other better fitting models for this data, this provides further evidence in support of
the hypothesis that the SUEIT-EY factor structure is in line with Mayer and Salovey‟s
(1997) four branch model.
7.9.3 Experiential and Strategic Emotional Intelligence: Two factor model. To explore the research question as to whether that the measure would evidence two higher
order factors representing Experiential and Strategic EI, the decision was made to re-
specify the model as a two factor independent cluster measurement model in which the
factor inter-correlations were freely estimated. A two factor model was specified based on
Mayer, Salovey, Caruso, & Siatarenios (2003) Strategic and Experiential distinction. The
branches were represented as per the one factor model initially specified. Two higher order
factors were specified representing Experiential EI (with Branch I and II as indicator items)
and Strategic EI (with Branches III and IV as indicator items). The variance of each latent
variable (Experiential EI, Strategic EI and Branch I) was set to one so that all item loadings
could be explored. However, the solution was not admissible, with negative variances
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associated with the two latent variables: Experiential EI and Strategic EI. When each error
variance was constrained to one, the chi-square difference test indicated a significant
change in model fit. Therefore the model was considered to be inappropriate. Model
misspecification was also evident when the model was tested in the validation file. These
results indicate that that the SUEIT-EY does not evidence a two factor structure
representing Experiential and Strategic EI.
7.9.4 Full EI model: Construct reliability.
Construct reliability was calculated using Fornell and Larker‟s (1981) formula. The sum of
the standardized loadings and sum of associated error variance for each factor is presented
below in Table 42.
Table 42. Sum of Standardised Loadings, Error Variances and Construct Reliability Estimates for the One Factor Model of Emotional Intelligence
Sum of Standardised
Loadings
Sum of Error Variances
Construct Reliability Estimates
(Cronbach‟s alpha)
EI Calibration Sample 2.368 2.266 0.71 (.70) Validation Sample 2.471 2.137 0.74 (.72) Note EI = total EI score
From Table 42, it can be seen that with an acceptable reliability for the EI factor in both the
calibration and validation samples, this score may be interpreted with some confidence.
7.9.5 Factor inter-correlations. The relationship between the factors was explored through bivariate correlations. The
results are presented below in Table 43.
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Table 43. Full Four Branch Model Factor Inter-correlations
Branch II Branch III Branch IV
Branch I .59** .11 .52**
Branch II .09* .55**
Branch III -.001
Branch IV Note * significant at the p < .05 level
** significant at the p < .01 level
While Branches I, II and IV showed convergent validity, Branch III did not appear to be
related to the other branches with the exception of a significant but low correlation with
Branch I. This significant correlation with Branch I was initially considered to represent
common method variance associated with the inclusion of the Maximum EI measure of
Branch I ability two within the Branch I score. However further analysis revealed that
Branch III was uncorrelated with the Maximum EI measure of Branch I ability two (r = .04,
p =.387).
To examine the relationship between the Maximum EI measure of Branch I ability two with
self reported ability to perceive the emotions of others, bivariate correlations were
conducted between the Maximum EI measure of Branch I and the Branch I higher order
factors as well as with item 1 that directly assesses self-report of this ability (“I can easily
tell how others are feeling by the look on their face”). While there was a weak significant
relationship between the Maximum EI measure of Branch I and the Branch I higher order
factor “Perception and appraisal and expression one‟s own emotions”, this relationship was
not replicated in the validation sample. There was no significant relationship found with the
Branch I higher order factor that relates to perception of emotion in others or with item 1.
7.9.6 Temporal stability.
Temporal stability of the four factor model was determined through structural equation
modeling with measurement errors allowed to be correlated as the indicators come from the
same source (Byrne , 2010).
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Figure 20. One year temporal stability of EI.
As indicated in Figure 20, the full EI score showed moderate temporal stability over one
year (r = .38, p < .001). Moderate rather than high temporal stability was anticipated, due
to the expected development of EI over time, particularly for this pre-adolescent sample.
By way of comparison, the reported one month test-retest reliability for the CSD,
considered to be a relatively stable trait is .85 (Crandall, Crandall, & Katkovsky, 1968).
Meanwhile, the temporal stability of CSD scores over one year in the current sample was
.50 (p < .001).
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7.9.7 Full EI model: Factor scores.
Factor scores were calculated for the calibration and validation samples. Mean scores for
grade and gender are presented below in Tables 44 and 45.
Table 44. Emotional Intelligence Descriptive statistics for Grades 4, 5 and 6 Factor Grade Calibration Sample Validation Sample Mean (SD) Mean (SD) EI 4 5.84 (0.96) 5.59 (1.03)
5 5.87 (0.94) 5.63 (0.89) 6 6.06 (0.77) 5.80 (0.78) Note Calibration Sample: Grade 4 n = 170; Grade 5 n = 149; Grade 6 n = 157
Validation Sample: Grade 4 n = 188; Grade 5 n = 180; Grade 6 n = 198
Table 45. Emotional Intelligence Descriptive statistics for Males and Females
Factor Grade Calibration Sample Validation Sample Mean (Standard Deviation) Mean (Standard Deviation) EI Male 5.82 (0.90) 5.60 (0.91) Female 6.04 (0.89) 5.76 (0.89) Note Calibration Sample: Males n = 250; Females n = 226
Validation Sample: Males n = 303; Females n = 263
The EI factor scores for the calibration and validation samples showed a normal
distribution, with skews under two (-.241 and -.348 respectively) and kurtosis under seven
(-.915 and .606 respectively). The results of and Table 45 show an increased mean EI score
with increasing grade level as well as greater mean EI score for females compared to males.
This pattern of differences was the replicated in the validation sample and appears to be in
support of the hypothesised differences. Analyses were conducted to determine whether
these differences were significant and are described in the next section.
7.9.8 Full EI model: Grade level comparisons.
A one-way between-groups analysis of variance was conducted on the calibration sample to
explore the impact of grade level on EI. Levene‟s test indicated a significant difference in
the homogeneity of variances between grade levels; therefore a robust equality of means
test was used for these comparisons. As hypothesised, there was a significant difference
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across the three grade levels for EI (Welch (2, 311.155) = 3.177, p = .043). Post hoc
comparisons revealed that grade 6 EI score was significantly greater than the grade 4 EI
score. While this difference was significant, the effect size was small (eta squared = .01).
The one-way between-groups analysis of variance was repeated in the validation sample to
explore the impact of grade level on EI. Levene‟s test indicated significant differences in
homogeneity of variances between grade levels; therefore a robust equality of means test
was used. In line with calibration sample results and as hypothesised, there was a
significant difference across the three grade levels for EI (Welch (2, 366.367) = 3.475 p
=.032. Post hoc comparisons revealed that grade 6 EI score was significantly greater than
the grade 4 EI score. While this difference was significant, the effect size was small (eta
squared = .01).
7.9.9 Full EI model: Gender comparisons.
A one-way between-groups analysis of variance was conducted on the calibration sample to
explore the impact of gender on EI. Levene‟s test indicated no significant differences in
homogeneity of variances between genders. As predicted, there was a significant difference
across gender for EI (F (1, 474) = 7.414, p =.007) with females scoring significantly higher
than males. While this difference was significant, the effect size was small (eta squared =
.01).
The one-way between-groups analysis of variance was repeated in the validation sample to
explore the impact of gender on the Typical EI. Levene‟s test indicated no significant
difference in the homogeneity of variances between genders. As predicted and in line with
the calibration sample results, there was a significant difference across gender for EI (F (1,
564) = 4.370, p =.037) with females scoring significantly higher than males. While this
difference was significant, the effect size was small (eta squared = .01).
7.9.10 Relationship with social desirability
Using the approach suggested by (Helmes, 2000) the correlations of items with factors were
compared with correlations of items with social desirability as measured by the Children‟s
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Social Desirability Scale (Crandall, Crandall, & Katkovsky, 1968). Bivariate correlations
are presented below in Table 46.
Table 46. Relationship of SUEIT-EY items with Social Desirability Scores
Calibration Sample Validation Sample Item/Factor Factor Social Desirability Factor Social Desirability Branch I Factor 1
8 .544** .054 .271** .070 26 .614** -.042 .751** .002 30 .766** -.034 .825** .022 Branch I Factor 2
1 .850** .119** .791** .169** 4 .890** .144** .856** .111** 5 .553** .023 .606** .057 Branch I Factor 3
24 .769** .024 .766** .001 29 .483** .097* .600** .192** 35 .764** -.093* .700** -.093* Branch I Factor 4
14 .794** .104* .833** .089* 23 .749** .005 .658** .085* 25 .809** .083 .809** .028 Branch I A
Factor 1 .830** -.016 .847** .026 Factor 2 .801** -.009 .828** .036
Branch I B
Factor 1 .869** .134** .831** .151** Factor 2 .878** .085 .920** .085*
Branch I ability 2
39 .777* .035 .842** -.061 40 .068 -.005 .028 -.036 42 .086 .008 .197** -.016 43 .385** -.051 .404** -.071 44 .793** -.031 .808** -.029 Branch II
2 .464** -.095* .480** -.010 3 .477** .014 .528** .060 9 .412** .067 .467** .179** 16 .569** .044 .606** .005 18 .610** .153** .606** .137** 20 .490** .058 .488** .187** 27 .669** .057 .674** .055 31 .474** -.155** .358** .214** 34 .501** .093* .465** .108** 36 .462** .041 .332** -.032 38 .679** .026 .677** .090*
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Calibration Sample Validation Sample Item/Factor Factor Social Desirability Factor Social Desirability Branch III 45 .085 -.058 .095* -.020 46 .154** .004 .022 .112 47 .130** .055 .266** -.046 48 .145** .013 .267** -.039 49 .495** -.002 .275** .004 50 .198** .010 .371** -.033 51 .382** -.021 .292** -.047 52 .318** .048 .206** .052 53 .278** -.011 .528** -.090* 54 .319** -.134 .194** -.028 55 .281** -.104 .528** -.120** 56 .403** .001 .387** -.075 57 .634** -.060 .304** -.120** 58 .346** .058 .170** .042 59 .236** .028 .235** -.072 60 .341** -.056 .364** .012 61 .385** -.029 .543** -.037 62 .351** -.108* .517** -.077 63 .631** -.068 .466** -.070 64 .731** -.104* .352** -.121** 65 .497** -.103* .341** .017 66 650** -.085 .406** -.030 Branch IV Factor 1 7R .846** .161** .996** .155** 15R .661** .064 .186** .076 Branch IV Factor 2 11 .849** .098* .860** .085* 21 .718** .190** .721** .223** Branch IV Factor 3 10 .824** .095* .831** .050 32 .803** .115* .869** .091* Branch IV Factor 4 12 .814** .200** .767** .166** 17 .766** .070 .803** .148** 19 .703** .208** .766** .183** Branch IV Factor 1 .135** .157** .297** .160** Factor 2 .627** .174** .624** .179** Factor 3 .753** .129** .756** .084* Factor 4 .840** .204** .862** .212** Note: Instances where the correlation of the item with social desirability is greater than the
correlation with the factor are in bold * Significant at the .05 level; ** Significant at the .01 level
From Table 46 it may be seen that only the Branch IV Factor 1 was more highly correlated
with social desirability than the factor it was designed to measure. However, this was not
replicated in the validation sample. With similar levels of correlation with social
desirability as the other factors loading onto the RRE factor, it is possible that this result is
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due to the low correlation of RRE-1 with RRE rather than an excessive relationship with
social desirability. Bivariate correlations were calculated between the factor scores and
social desirability and are presented below in Table 47.
Table 47. Relationship of SUEIT-EY factor scores with social desirability scores.
Calibration sample Validation sample
EI .139 ** .170 **
Branch I A .047 .074
Branch I B .125** .123*
Branch I Max -.037 -.059
Branch II .093* .153**
Branch III -.073 -.131*
Branch IV .228** .225** Note: * Significant at the .05 level; ** Significant at the .01 level It may be seen from Table 47 that all self reported EI factor scores except Branch I A
(“perception, appraisal and expression of one‟s own emotions”) held a significant but small
positive relationship with social desirability scores. As would be expected, most
performance measures were unrelated. However, the negative relationship between Branch
III and social desirability scores was unexpected.
7.10 Summary
Measurement models were established for each branch of Mayer and Salovey‟s (1997) four
branch model. A four factor model was established to be the best fit for Branch I. While the
lower order factors were insufficiently reliable, a model with two higher order factors
showed improved, although moderate reliability. The Maximum EI measure of ability two
of Branch I was best represented by a one factor model, with the item measuring surprise
removed to allow adequate model fit. This measure yielded insufficient reliability for use as
a solitary measure. Branch II was best described as a one factor model and showed
adequate reliability. The Maximum EI measure of Branch III was best described by a one
factor model and the reliability estimate was also adequate. A four factor model was found
to best describe Branch IV. While the ability level scores for this branch were unreliable, a
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higher order factor representing the total branch yielded good reliability. As expected, for
overall EI, a four factor model including the four branches showed a good fit with the data.
However, the two factor higher-order model was not supported.
While the branch scores generally increased slightly with grade level, most of these
differences were not significant. More consistent significant differences were found for
gender with females scoring higher than males. Of the more reliable higher order factor
scores (Branches I and IV and Typical EI) and lower order factor scores (Branches II and
III), only Branch III showed significant differences with grade level. Gender differences
with small to moderate effect sizes were apparent for all factor scores except Branch II.
With seven items deleted for poor factor loadings or misfit to the model, the final 59 item
SUEIT-EY was found to hold adequate construct reliability and validity for a measure early
in its development. While branch level scores had poor to adequate reliability, the internal
reliability of the full score was sufficient to allow meaningful comparisons. The validity of
the measure was evidenced by model fit for the hypothesised four factor structure and small
but significant gender and grade level differences. The measure also showed convergent
and discriminant validity, with moderate inter-factor correlations and items evidencing
stronger correlations with their intended factors than with socially desirable responding as
measured by the CSD.
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Chapter 8: Discussion.
8.1 Hypotheses and research questions The current dissertation aimed to develop a measure of pre-adolescent children‟s Ability EI
based on Mayer and Salovey‟s (1997) model, with items generated based on the specific
abilities outlined within the four branches. The results supported the prediction that the
SUEIT-EY would reveal a structure corresponding to the four branch model from which it
was derived. With a lack of evidence for a two factor model representing Experiential and
Strategic EI in self-report measures, the presence of such a structure in the current data was
posed as a research question. Results indicated that this two factor model did not provide a
good fit with the data. Similarly, the implied four factor structure of each branch of Mayer
and Salovey‟s (1997) model has not been explicitly stated or tested. Therefore, while the
factor structure of the branches was explored, no specific hypotheses were developed. A
one factor model was found to best fit the data for Branches II, III and the Maximum EI
measure of ability two of Branch I. Branch I revealed a four factor structure with two
higher order factors representing “perception, appraisal and expression of one‟s own
emotions” and “perception and appraisal of other‟s emotions”. Similarly, Branch IV was
found to hold a four factor structure in line with the division of abilities but with a single
higher order factor. As expected, pre-adolescent respondents were able to provide valid and
reliable estimates of their own Typical EI as measured by self-report and Maximal EI as
assessed by objective items. Additionally while branch level results were mixed, the results
supported the hypothesised increase of EI with grade level and higher scores in females
than males for total EI scores.
8.1.1 Hypothesis 1: The factor structure of the SUEIT-EY will correspond to Mayer and Salovey’s (1997) four branch model
The four factor structure of the SUEIT-EY, along with the MSCEIT (Burns, Bastian, &
Nettelbeck, 2007; Day & Carroll, 2004; Mayer, Salovey, & Caruso, 2002; Mayer, Salovey,
Caruso, & Sitarenios, 2003), is in support of Mayer and Salovey‟s (1997) four branch
model of EI. Available self-report measures of Ability EI, based on Salovey and Mayer‟s
(1990) model, have also evidenced a four factor structure (Ciarrochi, Chan, & Bajgar,
2001; Ciarrochi, Deane, & Anderson, 2002; Petrides & Furnham, 2000; Saklofske, Austin,
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& Minski, 2003). However, with no representation of Branch III, this structure is accounted
for by the division of Branch I (Gignac, Palmer, Manocha, & Stough, 2005) or Branch IV
(Ciarrochi, Chan, & Bajgar, 2001) into abilities referring to self and others. While the same
pattern was found in Branch I of the SUEIT-EY, when the branches were included in one
model, the model fit better when the Branch I abilities were subsumed within a single
higher order factor. While other research has found a self/other division for Branch IV
(Ciarrochi, Chan, & Bajgar, 2001), the SUEIT-EY Branch IV abilities were best explained
within a four factor model with one higher order factor. This is more in line with Mayer and
Salovey‟s (1997) model than their earlier model (Salovey & Mayer, 1990).
8.1.2 Research question 1: Will the SUEIT-EY evidence a two factor structure corresponding to Experiential and Strategic EI?
The SUEIT-EY did not evidence a two factor structure akin to Experiential and Strategic
EI. This structure has not previously been explored in self-report measures, most likely
because there is no representation of one half of Strategic EI (i.e. Branch III) in these
measures. However, it may be that this structure is peculiar to Maximum EI. Nevertheless,
the two factor structure of the MSCEIT has mixed support. The developers of the measure
have argued in support of the model (Mayer, Salovey, & Caruso, 2002; Mayer, Salovey,
Caruso, & Sitarenios, 2003) while others have found only marginal support (Burns,
Bastian, & Nettelbeck, 2007; Day & Carroll, 2004; Rode, et al., 2008; Rossen, Kranzler, &
Algina, 2008).
8.1.3 Research question 2: What factor structure will be displayed by the SUEIT-EY at the branch level?
8.1.3.1 Branch I Branch I yielded good fit for a two, three or four factor model. While the three factor model
was practically better fitting than the other two, the four factor model was chosen due to its
similarity to Mayer and Salovey‟s (1997) model. The three factor model combined the
ability to perceive and appraise one‟s own emotions with the ability to accurately express
emotions into one factor, with perception and appraisal of others‟ emotions and the ability
to discriminate between expressions of emotions as the other two factors. With further
investigation of the four factor model, a model with two higher order factors was found to
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be a suitable representation. While the lower order factors were unreliable, the two higher
order factors, representing “perception, appraisal and expression of one‟s own emotions”
and “perception and appraisal of others‟ emotions” were reliable in the validation (r = .74, r
= .70) but not the calibration sample (r = .64, r = .63). While these reliabilities were
calculated according to Fornell and Larker (1981); alpha coefficients across the two
samples ranged from .52 (“perception, appraisal and expression of one‟s own emotions”) to
.70 (“perception and appraisal of others emotions”). The Branch I higher order factor alpha
coefficients for the SUEIT-EY are lower than those found for other Typical EI measures of
Branch I; including the original SSREIS (α = .71; Saklofske, Austin, & Minski, 2003), the
modified SSREIS (α = .76; Austin, Saklofske, Huang, & McKenny, 2004), the SSREIS in a
sample of adolescents (Awareness of other‟s emotions: α = .73; Siu, 2009) and the WLEIS
(Appraisal of emotions in the self: α = .79 - .92; Appraisal of others‟ emotions: α = .82 -
.93; (Wong & Law, 2002; Fukuda, Saklofske, Tamaoka, & Lim, 2011).
On reflection, the two factor higher order model of Branch I may be a result of bias in the
design of the items. While the first ability is clearly intended to refer to the self and the
second ability to others, the target of the fourth ability is less distinct. In the SUEIT-EY,
items representing the fourth ability “discriminate between accurate and inaccurate, honest
and dishonest expressions of feelings” all relate to the expressions of others. However, it is
possible that an awareness of the plausibility of one‟s own expressions of emotion may be
an important aspect of this ability. Such an awareness may be associated with the use of
display rules. While Mayer and Salovey (1997) did not specifically refer to this ability in
relation to the self, further investigation may be useful in expanding the construct validity
of the model.
The Maximum EI measure of Branch I, measuring the ability to identify emotion in faces,
showed an excellent fit as a one factor model. However, the item measuring the ability to
recognise an expression of surprise was removed to allow model fit. This decision was
based on statistical considerations as well as the argument that surprise is not an emotion
but merely a reaction to an unexpected event (Oatley & Johnson-Laird, 1987). Closer
examination of this issue may be important for improving the reliability of measures of
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emotional recognition. While acceptable reliability was concluded based on sufficient inter-
item correlations, the alpha correlation calculated for the Maximum EI measure of Branch I
did not favourably compare with the corresponding MSCEIT measure. Coefficient alpha
for the SUEIT-EY was .34 (calibration sample) to .43 (validation sample), while the
MSCEIT coefficient alphas are considerably larger (α = .80 and 84). Increasing the number
of items may improve reliability. However, poorly loading items are likely to have
contributed to the low reliability. The item loadings for recognition of the expression of
happiness, sadness and fear were insufficient. This may be a result of range restriction
associated with a ceiling effect for these items or due to an underestimation of the pearson
correlation associated with a low number of possible scores for each item. The range of
scores could be increased through the use of a Likert scale to indicate the level of various
emotions perceived (as per the MSCEIT). Underestimation of the Pearson correlation may
be addressed through use of polychoric correlations (Olsson, 1979). Additionally,
increasing item difficulty may be beneficial. This could be done by using more subtly and
mixed expressions of emotions.
Interestingly, there was no relationship found between the Maximum EI measure of Branch
I and self-report of ability to perceive others‟ emotions. Problems with such measures are
not uncommon. For example others have also found self-report and performance measures
of perception of emotion in faces to be unrelated (Ciarrochi, Chan, & Bajgar, 2001;
Ciarrochi, Deane, & Anderson, 2002). While the authors suggested that this may indicate
respondent inaccuracy in self-describing their ability to perceive emotions, conversely, this
could reflect inadequate validity of the performance measure. That is, while self-report
involves the description of the behavior in a range of situations and over a length of time
(i.e. Typical EI), presentation of a single photograph of a posed expression may not hold
ecological validity. Similarly, as discussed earlier, this difference may be accounted for by
the distinction between typical and maximum performance. Brief presentation of micro-
expressions or film clips of expressions of emotions may better approximate the application
of this ability in natural settings. Alternatively, measurement of a “just noticeable
difference” (cf. Watson, 1973) of morphed facial expressions (cf. Young, Rowland, Calder,
Etcoff, Seth, & Perrett, 1997) may prove useful. Additionally, it may be that persons high
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in EI may be able to discern genuine from acted expression of emotion. Therefore, use of
actual rather than acted emotion may improve validity.
8.3.2 Branch II Branch II of the SUEIT-EY was found to hold a one factor structure with a reliable factor
score. The Branch II factor has also been extracted as a unitary factor from the SSREIS
(e.g. Austin, Saklofske, Huang, & McKenny, 2004) but the MSCEIT results are more
mixed. With high correlations between the MSCEIT Branches I and II, some studies have
combined these branches to form one factor (Rode, et al., 2008; Rossen, Kranzler, &
Algina). The SUEIT-EY Branch II factor score reliability estimation calculated according
to Fornell and Larker (1981) was close to the coefficient alpha reliability. The coefficient
alpha of .74 for the SUEIT-EY Branch II factor score compares favorably to the other
Typical EI measures of the construct including the original SSREIS Branch II (α = .57;
Saklofske, Austin, & Minski, 2003) as well as the modification of the test by Austin and
colleagues (2004; α = .68), and is on par with the WLEIS Branch II reliability (α = .73;
Fukuda, Saklofske, Tamaoka, & Lim, 2011).
8.3.3 Branch III Branch III of the SUEIT-EY was also found to have a one factor structure with a reliable
factor score. The alpha reliability for this branch (α = .63-.73) is comparable to the
MSCEIT Branch III reliabilities (α = .69 - .73; Palmer, Gignac, Manocha, & Stough, 2005).
As discussed above in relation to the Maximum EI Branch I measure, reliability of scores
for Branch III may be improved through increasing the range of possible scores for each
item and possible underestimation of the pearson correlation may be addressed through use
of polychoric correlations.
8.1.3.4 Branch IV In line with the structure implied by Mayer and Salovey (1997), a four factor measurement
model with one higher order factor was found to be the best fit for Branch IV. However,
while the second, third and fourth factors held strong inter-correlations, the first factor was
only minimally correlated with the other factors. This may have been due to the negatively
worded indicator items representing this factor. Indeed, other negatively worded items
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included in the SUEIT-EY were problematic, with seven items considered too convoluted
to be understood by pre-adolescent respondents, four items removed during pilot testing
due to low reliability and a further two items removed in subsequent factor analysis due to
poor factor loadings and model misfit. Additionally, the utility of negatively worded items
is questionable. While the SSREIS has been criticized for the lack of negatively worded
items (Gignac, Palmer, Manocha, & Stough, 2005), Austin and colleagues‟ (2004)
inclusion of negatively worded items did not improve the test‟s reliability and
unaccountably altered the measure‟s factor structure. The SUEIT-EY Branch IV would
benefit from the development of positively worded indicator items for the first factor.
Acceptable reliability was determined for the Branch IV higher order factor score using
Fornell and Larker‟s formula (1981). Despite coefficient alpha tending to underestimate
congeneric models, the alpha co-efficient is typically reported for other EI measures
without the determination of tau equivalence. Nevertheless, the coefficient alphas for the
SUEIT-EY Branch IV higher order factor were lower (α = .66-.68) than that reported for
the original SSREIS (α = .73; Saklofske, Austin, & Minski, 2003), the modified SSREIS (α
= .78; Austin, Saklofske, Huang, & McKenny, 2004) and the WLEIS (α = .88; Fukuda,
Saklofske, Tamaoka, Fung, Miyaoka, & Kiyama, 2011).
8.1.2 Hypothesis 2: Pre-adolescents will provide valid and reliable responses to the SUEIT-EY. As outlined in the preceding sections, the SUEIT-EY was found to hold the hypothesised
factor structure and all SUEIT-EY branch level score reliabilities were acceptable,
supporting the hypothesis that pre-adolescent respondents are able to provide valid and
reliable responses. The SUEIT-EY EI factor score reliability estimations calculated
according to Fornell and Larker (1981) were also acceptable and the values were close to
the coefficient alpha reliability. The SUEIT-EY EI factor score coefficient alpha of .70 to
.72, while acceptable, is lower than the reliability reported for established measures of
Mayer and Salovey‟s (1997) four branch model. The typical EI measures of SSREIS and
WLEIS yield reported reliabilities ranging from .84 to .90 (Austin, Saklofske, Huang, &
McKenny, 2004; Saklofske, Austin, & Minski, 2003; Schutte, et al., 1998) and .85 to .89
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(Fukuda, Saklofske, Tamaoka, & Lim, 2011; Whitman, Van Rooy, Chockalingam, &
Kraus, 2009) respectively. Split-half reliability for the Maximum EI measure is reported to
range from .91 to .93 (Mayer, Salovey, Caruso, & Sitarenios, 2003). A similar range of
reliability estimates are also found for the use of the SREISS in adolescents (α = .76 to .90;
Charbonneau & Nicol, 2002; Ciarrochi, Chan, & Bajgar, 2001; Liau, Liau, Teoh, & Liau,
2003; Siu, 2009) as well as in pre-adolescents (α = .86; Williams, Daley, Burnside, &
Hammond-Rowley, 2009). While still in the early stages of development, the manual
reported split-half reliability of the MSCEIT-YV in children and adolescents aged 10-18
years appears impressive (α = .91; Barlow, Qualter, & Stylianou, 2010). However, until the
reliability analyses are published in peer-reviewed journals, this result should be considered
with caution.
Temporal stability has not commonly been reported for the EI measures reviewed in this
dissertation. Shutte and colleagues (1998) reported the two week test-retest reliability of the
SSREIS as .78 in their adult sample. While Ability EI is considered to develop from
childhood to adulthood (Mayer, Caruso, & Salovey, 1999), it appears to be a relatively
stable trait in adults (Chapman & Hatslip, 2006; Parker, Saklofske, Wood, Eastabrook, &
Taylor, 2005) the correlation of test scores over time is mostly an indication of the
reliability of the measure. However, expected developmental shifts of EI will impact
differenctly on the tests scores over time. Therefore in a sample of pre-adolescent children,
the correlation of test scores over time is also an indication of the temporal stability of the
measured trait. As expected, the one-year temporal stability in the current sample was
moderate. This indicates that the SUEIT-EY is sufficiently sensitive to developmental
change. While a shorter time period for retesting the current sample was not possible,
further research including a two week test-retest of the SUEIT-EY may be useful in
determining the short-term stability of test scores.
While the reliability of the SUEIT-EY scores are at an acceptable level for early stage
research (Gignac, 2009), they do not favourably compare with the reliabilities of extant
measures of Mayer and Salovey‟s (1997) model of EI. As outlined in the preceding
sections, further development is required to improve the reliability of the SUEIT-EY.
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Specifically, such development may include improvement of the reliability through
generation of more items. Inclusion of more items for each of the abilities within each
branch may improve ability level reliability to allow testing of Mayer and Salovey‟s (1997)
hypothesised developmental progression of such abilities. However, care should be taken
to not unecessarily lengthen the measure making it too onerous for young respondents to
complete. Reliability may also be improved through ensuring that the instructions are well
understood and that the response categories suitably reflect respondents‟ answers. This may
be done through use of cognitive interviews in a small sample of respondents of various
ages (see Irwin, Varni, Yeatts & DeWalt, 2009 and Woolley, Bowen & Bowen, 2004 for
examples of cognitive interviewing of children to gain feedback on level of understanding
of instructions, items and response format). A strength of the SUIET-EY is that is was
specifically developed for use with pre-adolescent children, with a factor structure
determined and replicated in this population.
Some critics have expressed concern about the influence of socially desirable responding in
self-report of EI (e.g. Conte, 2005), and still others have shown scepticism in the ability of
children and adolescents to self-report (Maccoby & Maccoby, 1954). Indeed, Charbonneau
and Nicol (2002) argued that the SSREIS may not be suitable for use in adolescents due to
a moderate correlation (r = .40) with a measure of social desirability. While a low degree of
association was found between SUEIT-EY items and a measure of social desirability, the
items held stronger associations with their intended factors. Additonally, SUEIT-EY factor
scores were found to have significant but small associations with socially desirable
responding. This gives further support to the expectation that pre-adolescents are able to
provide valid reponses to questions about themselves.
A further indication of validity is provided by the consistency of responses to two items
with the same content, but with one item worded negatively. The majority of responses to
these items were consistent, suggesting that respondents were thinking carefully about their
answers. Furthermore, this finding was replicated in the validation sample. While none of
the reviewed measures of Ability EI include validity items, this may be an important tool
for determining test validity. Further development of the SUEIT-EY should include a
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greater number of items to assess response consistency. This may improve the reliability of
such indicators, which may be used as an indication of the overall validity of responses.
Training in use of response categories may have facilitated the pre-adolescents to provide
valid responses. However, this was not tested in the current dissertation. While others
(Harris, Guz, Lipian, & Man-Shu, 1985) have argued that such pre-training is required in
younger samples, research is required to determine any substantial influence on the quality
of responses to the SUEIT-EY. Cognitive pretesting, whereby the child respondent is asked
a series of questions to determine the child‟s ability in reading, understanding and
responding to items (cf. Woolley, Bowen, & Bowen, 2004), may be useful in further
assessment of the validity of pre-adolescent responses.
The results appear to support the expectation that pre-adolescent respondents are capable of
providing reliable and valid responses to the SUEIT-EY. Further validity was determined
through the expected increase in EI with grade level and gender differences. These are
discussed in the following sections.
8.1.3 Hypothesis 3: SUEIT-EY scores will show increases according to grade level. With the exception of the Branch II and Branch IV higher order factor score, all other
factor scores revealed the expected increase with grade level. Attesting to the reliability of
this outcome, results were replicated in the validation sample. The finding of age-related
increases the Branch I and III abilities as well as for total EI is in line with past research
indicating the development of EI related abilities. While affective decision making has been
found to develop with age (Hongwanishkul, Happaney, Lee, & Zelazo, 2005), the
utilization of emotions to facilitate cognition (Branch II) as measured by the SUEIT-EY did
not reveal age-related improvements. This may attest to poor validity of the SUEIT-EY
Branch II factor score. However Branch II has not been reliably determined in the factor
structure of other measures (Austin, Saklofske, Huang, & McKenny, 2004; Ciarrochi,
Chan, & Bajar, 2001; Fan, Jackson, Yang, Tang, & Zhang, 2010; Gignac, 2005; Palmer,
Gignac, Manocha, & Stough, 2005; Rode et al., 2008; Rossen, Kranzler, & Algina, 2008).
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Further research is needed to determine the validity of this branch in the SUEIT-EY and
other measures.
Despite the developmental progression of regulation of emotion being well established (e.g.
Cicchetti, Ackerman, & Izard, 1995; Raffaelli, Crockett, & Shen, 2005), the SUEIT-EY did
not evidence the expected increase in reported regulation of emotion (Branch IV) with
grade level. While the reliability of this measure was acceptable, the presence of negatively
worded items to represent the first ability may have altered the validity of the branch
scores. The negatively-worded indicator items were the only items to hold a stronger
relationship with the social desirability measure than their intended factor. Additionally,
while still low, Branch IV held the strongest correlation with the social desirability measure
than the other branches. It is possible that the pre-adolescent respondents‟ ability to
candidly answer questions about their ability to regulate their own and others emotions was
limited by a desire to give a positive impression. This would be understandable within a
school context where children are likely to be strongly encouraged to regulate their
emotions in the pursuit of a well behaved classroom. Conversely, qualitative and/or non-
conscious emotion regulation capacity, not measured by the SUEIT-EY may be responsible
for developmental shifts in ability to regulate emotion.
8.1.4 Hypothesis 4: Females will have higher SUEIT-EY scores than males. As hypothesized, females evidenced higher EI than males as measured by the SUEIT-EY.
This difference was consistent across the branch scores as well as for total EI and was
replicated in the validation sample. It is important to keep in mind that while significant,
these differences are small. Indeed, these gender differences are less than those found for
intelligence subtests (Halpern & LaMay, 2000). However, such gender differences are
consistently found in emotion research and are yet to be conclusively explained. While
differences in biological influences (e.g. Buck, Miller, & Caul, 1974) and socialisation (e.g.
Brody, 1985; Saarni, 1999) have been considered, further research exploring possible
causes and impact of gender differences in EI may provide insight into such issues as the
so-called “gender gap” in school performance (Burgess, McConnell, Propper, & Wilson,
2004).
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8.1.5 Methodological considerations While the SUEIT-EY factor scores revealed expected differences across gender and grade
level, there is a need to establish structural invariance in order to establish whether
meaningful comparisons can be made. Further research should establish whether the four
factor structure of the SUEIT-EY holds across gender and grade level. With the exception
of Wang, Kim and Ng (2011) and Whitman, Van Rooy, Chockalingam and Kraus (2009)
who determined structural invariance for the WLEIS across gender and cultural groups,
structural invariance has not yet been established for the EI measures reviewed in this
dissertation.
A strength of the current dissertation is the determination of type of measurement model in
terms of congeneric, parallel or tau equivalent. The measurement model (in terms of
congeneric, parallel or tau equivalent) of EI measures reviewed in this dissertation have not
yet been established. Determination of specific measure model for measures such as the
SSREIS, would provide guidance on the preferred method of calculating scores. For
example, if the factor loadings are not equivalent, then summing item scores does not give
an adequate representation of the relative influence of each item (ref). There is a need for
CFA studies to determine the measurement model of EI measures, to ensure calculation of
scores is done according to the model.
8.1.6 Implications The current dissertation has shown that pre-adolescent children are able to provide reliable
and valid responses to self-report as well as performance measures of EI. The reliability of
the SUEIT-EY in its current form is acceptable for a measure in the early stages of
development (Gignac, 2009) and may be used for screening decisions or as an educational
tool. While the SUEIT-EY appears to have sufficient sensitivity to determine change in EI
related to development programs, improvement of the measure‟s reliability is required prior
to applications such as program evaluation. Reliability may be improved through increasing
the number of items; however caution should be taken to not inordinately extend the
measure so as to not tax the respondents. An excessively long measure is likely to reduce
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the quality of responses. An increase in the number of scoring points for the Maximum
Branch III items may improve reliability for this branch.
8.1.7 Conclusions While it appears to measure a unitary construct, the SUEIT-EY in its current state is a
conglomeration of self-report measures of Typical EI and performance measures of
Maximum EI. Further development is needed to expand the test to measure Branch III as a
typical ability and Branches I, II and IV as maximum abilities. This may better develop the
construct validity of the measure and allow for targeted predictive validity. The
development of performance measures of Typical EI self-report measures of Maximum EI
may assist in distinguishing between the possible influence of mode of measurement and
the Typical/Maximal EI distinction.
Initial results in the present dissertation were garnered from a measure based on models
developed through CFA techniques with changes based on statistical and theoretical
considerations. Such results should be considered tentative as they are likely to be
particular to the sample. However, while replication of the results in a second sample lends
support to the reliability of these results, further replication is required. Additional testing
in more diverse samples and different settings will establish whether the present results
may be generalised to the broader population of pre-adolescent children. Determination of
factor loadings and errors for use in calculation of test scores should be conducted in a
larger, broader sample, taking care to include children from a range of cultures and
socioeconomic backgrounds. Additionally, the development of age-related norms may
provide a useful reference for determining developmental lag or giftedness.
It is hoped that the development of the SUEIT-EY as a measure of EI in pre-adolescent
children will facilitate research into how Ability EI develops with age, environmental
enrichment, education and coaching.
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Appendix 1 Ethical Clearance
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Appendix 2 Swinburne University Emotional Intelligence Test – Early Years
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Appendix 2 Swinburne University Emotional Intelligence Test – Early Years
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Appendix 2 Swinburne University Emotional Intelligence Test – Early Years
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Appendix 2 Swinburne University Emotional Intelligence Test – Early Years
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Appendix 2 Swinburne University Emotional Intelligence Test – Early Years
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Appendix 2 Swinburne University Emotional Intelligence Test – Early Years
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Appendix 2 Swinburne University Emotional Intelligence Test – Early Years
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Appendix 2 Swinburne University Emotional Intelligence Test – Early Years
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Appendix 2 Swinburne University Emotional Intelligence Test – Early Years
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Appendix 2 Swinburne University Emotional Intelligence Test – Early Years
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Appendix 2 Swinburne University Emotional Intelligence Test – Early Years
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Appendix 2 Swinburne University Emotional Intelligence Test – Early Years
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Appendix 2 Swinburne University Emotional Intelligence Test – Early Years
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Appendix 2 Swinburne University Emotional Intelligence Test – Early Years
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Appendix 2
Swinburne University Emotional Intelligence Test – Early Years
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Appendix 2
Swinburne University Emotional Intelligence Test – Early Years
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Appendix 2 Swinburne University Emotional Intelligence Test – Early Years
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Appendix 3 Calculation of Factor Scores
PAEE1 = .38 * (i8 - .856) + .32 * (i26 - .896) + .37 * (i30 - .866) PAEE2 = .78 * (i1 - .398) + .80 * (i4 - .36) + .40 * (i5 - .836) PAEE3 = .53 * (i24 - .719) + .56 * (i35 - .691) + .29 * (i29 - .916) PAEE4 = .67 * (i14 - .551) + .59 * (i23 - .654) + .69 * (i25 - .519) EFT = .40 * (i20 - .606) + .54 * (i18 - .61) + .39 * (i36 - .842) + .40 * (i3-.756) + .41 * (i31-.871) + .40 * (i34 - .836) + .36 * (i9 - .83) +.49 * (i16 - .842) + .40 * (i2 - .852) + .62 * (i27 - .714) + .63 * (i38 - .84) UAE = .015 * (i45 - .586) + .13 * (i46-.632) + .12 * (i47 - .78) + .45 * (i48 - .445) + .15 * (i49 - .664) + .35 * (i50 - .922) + .30 * (i51-.908) + .24 * (i52 - .909) + .24 * (i53 - .968) + .24 * (i54 - .928) + .32 * (i55 - .652) + .59 * (i56 - .9) + .27 * (i57 - .944) + .18 * (i58scored - .945) + .3 * (i59 - .944) + .3 * (i60 - .909) + .28 * (i61 - .88) + .58 * (i62 - .976) + .74 * (i63 - .801) + .47 * (i64 - .987) + .61 * (i65 - .984) + .64 * (i66 - .978) RRE1 = .46 * (i7R - .715) + .34 * (i15R - .603) RRE2 = .56 * (i11 - .545) + .44 * (i21 - .698) RRE3 = .59 * (i10 - .653) + .55 * (i32 - .81) RRE4 = .67 * (i12 - .692) + .63 * (i17 - .882) + .53 * (i19 - .788) PAEE2MaximalEI = .51 * (i39 - .543) + .07 * (i40 - .935) + .08 * (i42 - .965) + .22 * (i43 - 1) + .61 * (i44 - .543) RRE = .294 * (RRE1 - .196) + 1 * (RRE2 - .451) + .741 * (RRE3 - 0) * .897 * (RRE4 - .914) PAEE-A = 1*(PAEE1-.001) + .669*(PAEE3 - .552) PAEE-B = .872 * (PAEE2 - .239) + .782 * (PAEE4 - .388) TypicalEI = .554 * (PAEE-A- .693) + .673 * (PAEE-B- .547) + .785 * (EFT - .384) + .691 * (RRE - .522) Total EI = .025 * (UAE-FSW - .99) + .005 * (MaximumPAEE-FSW - .991) + .304 * (RRE-FSW - .555) + .552 * (EFT-FSW - .334) + .168 * (PAEE-B-FSW - .21) + .275 * (PAEE-A-FSW - .151) Branch I = .001 * (MaximumPAEE-FSW - .991) + .03 * (PAEE-A-FSW - .210) + .049 * (PAEE-B-FSW - .151)
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