Adults’ Perceptions of Child Well-Being. Developing and Validating a Helpful Measuring Instrument

Eva Expósito(1), Esther López(1),

Enrique Navarro(2) & Bianca Thoilliez(2)

(1) National Open University (Spain)

(2) Complutense University of Madrid (Spain)

Introduction

– Growing interest for research on child well- being.

– Increasing interest from different institutions and organizations to develop indicators capable of measuring the specificity of child well-being.

– In the Spanish context, we can highlight different activities carried out under the Program for the Child Friendly Cities and the recent publication of the report “Proposal of a System of Indicators of Child Well-Being in Spain”, driven by the Spanish Committee for UNICEF.

Objective

To build and validate an instrument that allows us to know what are the determinants of child well-being that adults recognize as

more important.

Research Design

• PHASE A: Construction of the instrument.

• PHASE B: Analyze the underlying structure of the data matrix.

• PHASE C: To evaluate the psychometric properties of the instrument. Two perspectives: Classical Test Theory and Item Response Theory.

Phase A: Construction of the instrument (I)• Content matrix, based on the report “Child poverty in perspective: an

overview of child wellbeing in rich countries” published by the Innocenti Research Center in 2007 (Unicef, 2007).

DIMENSIONS SUB-DIMENSIONS

MATERIAL WELL-BEING

Household IncomeUnemploymentFamily ResourcesCultural level

HEALTH AND SAFETY

Health ServicesPersonal situationHealth behaviorsRisk BehaviorsExperience of violence

EDUCATION WELL-BEING

Access educational servicesEducational performanceParticipation in the educational CommunityFirst job Access

RELATIONS WITH THE ENVIRONMENT

FamilyPeersSchoolCommunity

SUBJECTIVE WELL-BEING

Attitude to lifeSelf-esteemEvaluation of relations with the environmentReactions and somatic complaints

Phase A: Construction of the instrument (II)

• Future education professionals evaluate in a scale from 1 to 6, the degree of importance they confer to the various aspects related with child well-being proposed

Phase A: Construction of the instrument (III)

• SAMPLE: 805 students registered during the academic course 2010/2011 in different degrees related with the field of education (schoolteaching, pedagogy and social education) in publics and privates universities of the Region of Madrid (Spain).

28.45%

18.26%28.20%

24.97%

Degree

Pedagogy

Social Education

Early Child Education

School Teaching Career79.38%

20.12%

Ownership

PublicPrivate

Phase B: Structure of the data matrix

• Factoring process was developed using:– Components extraction method– Varimax rotation

• 19 factors => 69,67%.

• A greater weight or saturation of the item, notes that is more important in the factor explanation. Those with less weight may be candidates for disposal.

Phase C: Psychometric properties of the instrument (I)

• Classical Test Theory: Provides information on the precision of the test. That is, the instrument measures with little error

• Reability (Cronbach’s Alfa) = 0.95• Item # = 95

Cronbach's Alfa Number of itemsMATERIAL WELL-BEING 0.80 14HEALTH AND SAFETY 0.92 22EDUCATIONAL WELL-BEING 0.89 23RELATIONSHIP WITH THEIR ENVIRONMENT

0.88 19

SUBJECTIVE WELL-BEING 0.92 17

Phase C: Psychometric properties of the instrument (II)

• Classical Test Theory: Correlation item- total dimension

Correlation Item-Total dimension lower to 0.5MATERIAL WELL-BEING 1. Cultural activities in family

6. Unemployment situationof of one parent9. The mother is housewife or the father is househusband

HEALTH AND SAFETY 28. Attend for eye examinations29. Receive care by a pediatrician (vaccinations, annual reviews, etc.)30. Access to health services31. Attend for oral examinations32. Good personal hygiene (washing hands, brushing teeth,...)33. Balanced and varied diet34. Provide a content filtering systems to have access to the Internet and social networks35. Do some sport regularly36. Live under optimal hygiene conditions

EDUCATION WELL-BEING 54. Repeat one or more course in Primary Education55. Repeat one or more course in Secondary Education56. Leave school without having completed compulsory education58. Enrollment in higher education59. Be enrolled before the age of 6

RELATIONS WITH THE ENVIRONMENT NoneSUBJECTIVER WELL-BEING None

Fase C: Psychometric properties of the instrument (III)

• Item Response Theory: The key assumption of IRT models is that there is a functional relation between the values in the variable that measure the items and the subjects’ probability of getting right.

• Subjects who score high in their perceptions of child well-being tend to give the highest ratings in a given item (>5). By contrast, subjects with lower scores on the construct "child well-being" tend to give lower ratings on the item.

Fase C: Psychometric properties of the instrument (IV)

• Item Response Theory:

4 different models (extensions to Rasch’s simple logistic model –suitable for use when items are scored polytomously-)

– Partial Credit Model (PC): Masters (1982) Allows the analysis of a collection of cognitive or attitudinal items that can have more than two levels of response.

– ONE- DIMENSIONAL– MULTI-DIMENSIONAL

– Rating Scale Model (MEC): (Andrich, 1978) Allows the analysis of sets of rating items that have a common, multiple-category response format. The rating scale model is of particular value when examining the properties of the Likert-type items that are commonly used in attitude scales.

– ONE- DIMENSIONAL– MULTI-DIMENSIONAL

MODEL 1One-Dimensional;

Rating Scale

MODEL 2One-Dimensional;

Partial Credit

MODEL 3Multidimensional;

Rating Scale

MODEL 4Multidimensional;

Partial CreditDeviance 163486.76350 161579.41012 158598.53564 155566.58788

Parámetros 84 395 98 409Diff deviance 1 vs. 2 1907.353 4 vs. 3 3031.948

D. F. 311 311p. value. Ji2 0.00 0.00

- Model 4 model fits better than the other models do.

- Differences between the desviance of the models are significant.

Fase C: Psychometric properties of the instrument (V)

• Item Response Theory

Unwighted Fit Weighed fititem step ESTIMATE ERROR^ MNSQ CI T MNSQ CI T

19 0 0.71 ( 0.90, 1.10) -6.3 1.00 ( 0.76, 1.24) 0.119 1 -0.252 0.086 1.38 ( 0.90, 1.10) 6.8 1.02 ( 0.77, 1.23) 0.219 2 -0.989 0.079 1.03 ( 0.90, 1.10) 0.6 0.99 ( 0.89, 1.11) -0.219 3 -0.134 0.073 0.98 ( 0.90, 1.10) -0.3 0.99 ( 0.92, 1.08) -0.319 4 0.403 0.083 1.07 ( 0.90, 1.10) 1.3 1.01 ( 0.92, 1.08) 0.219 5 0.972* 0.95 ( 0.90, 1.10) -1.0 0.97 ( 0.89, 1.11) -0.464 0 6.56 ( 0.90, 1.10) 52.1 1.24 ( 0.62, 1.38) 1.264 1 -0.516 0.086 7.66 ( 0.90, 1.10) 58.0 1.03 ( 0.67, 1.33) 0.264 2 -1.207 0.082 2.25 ( 0.90, 1.10) 18.6 1.06 ( 0.85, 1.15) 0.964 3 -0.352 0.075 1.16 ( 0.90, 1.10) 3.0 1.04 ( 0.92, 1.08) 0.964 4 0.635 0.080 1.07 ( 0.90, 1.10) 1.4 1.03 ( 0.94, 1.06) 0.964 5 1.440* 0.95 ( 0.90, 1.10) -0.9 1.02 ( 0.91, 1.09) 0.4

Fase C: Psychometric properties of the instrument (VI)

• Item Response Theory: Example of items that fit well

Fase C: Psychometric properties of the instrument (VI)• Item Response Theory: Characteristic Curve of Item

Fase C: Psychometric properties of the instrument (VII)• Item Response Cumulative Probability Curves

Unwighted Fit Weighed fititem step ESTIMATE ERROR^ MNSQ CI T MNSQ CI T75 0 0.00 ( 0.90, 1.10) -53.7 0.09 ( 0.00, 3.24) -1.175 1 -1.705 0.096 0.05 ( 0.90, 1.10) -37.5 0.90 ( 0.00, 2.31) 0.175 2 -2.563 0.096 0.57 ( 0.90, 1.10) -10.0 1.05 ( 0.73, 1.27) 0.475 3 -0.134 0.089 1.61 ( 0.90, 1.10) 10.2 1.06 ( 0.88, 1.12) 0.975 4 1.199 0.083 1.00 ( 0.90, 1.10) 0.0 0.93 ( 0.94, 1.06) -2.575 5 3.202* 1.61 ( 0.90, 1.10) 10.3 0.86 ( 0.91, 1.09) -3.179 0 0.00 ( 0.90, 1.10) -53.1 0.10 ( 0.00, 2.78) -1.5

79 1 -3.484 0.100 698.77 ( 0.90, 1.10) 470.7 1.46 ( 0.58, 1.42) 2.079 2 -1.211 0.095 133.81 ( 0.90, 1.10) 246.0 1.12 ( 0.81, 1.19) 1.279 3 -0.020 0.086 24.79 ( 0.90, 1.10) 114.5 1.15 ( 0.90, 1.10) 2.979 4 1.304 0.081 1.86 ( 0.90, 1.10) 13.8 1.13 ( 0.95, 1.05) 4.479 5 3.412* 7.44 ( 0.90, 1.10) 56.9 1.31 ( 0.90, 1.10) 5.7

Fase C: Psychometric properties of the instrument (VIII)

• Item Response Theory: Items that fit bad

Fase C: Psychometric properties of the instrument (IX)• Item Response Theory: Curvas Características de los ítems

Fase C: Psychometric properties of the instrument (X)• Item Response Cumulative Probability Curves

• The reability of the instrument is good.• Partial credit model fits betther than Rating Scale

do.• Multidimensional model fits well. So, theoretical

matrix is supported.• In some items, the categories 0 and 1 haven’t any

frequency.• Number of items is high. So, It would be interesting

to eliminate malfunctioning items.• Apply the instrument to other groups of adults.

Conclusions

References

• Andersen, E. B. (1977): Sufficient statistics and latent trait models. Psychometrika, 46, 69-81.

• Andrich, D. (1978): A rating formulation for ordered response categories. Psychometrika, 43, 561-573.

• Masters, G. N. (1982): A Rasch model for partial credit scoring. Psychometrica, 47, 149-174.

• Wu, M. L, Adams, R. J., Wilson, M. R., Haldane, S A. (2007): ACERConQuest Version 2.0: generalised item response modelling software. HACER Press: Victoria

Thank you for your attention!!!