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British Journal of Educational Psychology (2014)
© 2014 The British Psychological Society
www.wileyonlinelibrary.com
Implicit theories about intelligence and growth(personal best) goals: Exploring reciprocalrelationships
Andrew J. Martin*School of Education, University of New South Wales, Sydney, New South Wales,Australia
Background. There has been increasing interest in growth approaches to students’
academic development, including value-added models, modelling of academic trajecto-
ries, growth motivation orientations, growth mindsets, and growth goals.
Aims. This study sought to investigate the relationships between implicit theories about
intelligence (incremental and entity theories) and growth (personal best, PB) goals –withparticular interest in the ordering of factors across time.
Sample. The study focused on longitudinal data of 969 Australian high school students.
Method. The classic cross-lagged panel design (using structural equationmodelling) was
employed to shed light on the ordering of Time 1 growth goals, incremental theories, and
entity theories relative to Time 2 (1 year later) growth goals, incremental theories, and
entity theories.
Results. Findings showed that Time 1 growth goals predicted Time 2 incremental
theories (positively) and entity theories (negatively); Time 1 entity and incremental
theories negatively predicted Time 2 incremental and entity theories respectively; but,
Time 1 incremental theories and entity theories did not predict growth goals at Time 2.
Conclusion. This suggests that entity and incremental theories are negatively
reciprocally related across time, but growth goals seem to be directionally salient over
incremental and entity theories. Implications for promoting growth goals and growth
mindsets are discussed.
In educational psychology, there has been increasing interest in growth approaches tostudent development, including value-added models, modelling of academic trajectories,
growth motivation orientations, and growth goals (Anderman, Anderman, Yough, &
Gimbert, 2010; Anderman, Gimbert, O’Connell, & Riegel, 2014; Betebenner, 2008, 2009;
Briggs & Betebenner, 2009; Elliot, Murayama, & Kobeisy, 2014; Elliot, Murayama, &
Pekrun, 2011; Harris, 2011; Martin, 2006, 2012; Martin & Liem, 2010; Masters, 2013;
Parker, Marsh, Morin, Seaton, & Van Zanden, 2014). At the same time, educational
psychology and other psychological disciplines have emphasized the concept of ‘growth
mindset’, with particular focus on implicit theories about ability and intelligence(Dweck, 2000, 2006, 2010). This study investigates the relationship between two salient
growth-related phenomena: students’ implicit theories about intelligence and academic
*Correspondence should be addressed to Andrew J. Martin, School of Education, University of New South Wales, Sydney,NSW 2052, Australia (email: [email protected]).
DOI:10.1111/bjep.12038
1
growth (personal best, PB) goals. The former are said to reflect basic schema regarding
ability and individuals’ capacity to develop and build on their intelligence. The latter are
seen as personal and specific growth targets towards which students strive. In a recent
analysis of new directions in goal setting, Locke and Latham (2006) recommended furtherresearch be conducted to better understand the relationship between cognition and
goals; the present study seeks to do so in relation to implicit theories about intelligence
and growth goals. Specifically, it seeks to understand the directional salience of implicit
theories and growth goals over time. The extent to which one is salient over the other, or
to the extent that they are reciprocally related, holds implications for psycho-educational
intervention aimed at promoting growth mindsets and growth goals.
Implicit theories about intelligence
As described byDweck (2006, 2010) and Burnette, O’Boyle, VanEpps, Pollack, and Finkel
(2013), individuals develop theories, implicit beliefs, and deeply held schema about
human attributes to explain and understand their world. According to Burnette et al.,
‘these lay theories are frequently implicit; that is, they are not explicitly articulated in the
mind of the person holding them. Implicit theories are schematic knowledge structures
that incorporate beliefs about the stability of an attribute and organize the way people
ascribe meaning to events’ (2013, p. 657).Intelligence is one major and influential attribute about which individuals develop
such theories and schema. Implicit theories about intelligencebroadly refer to individuals’
belief that intelligence is something that is fixed (an ‘entity’ view) or something that is
malleable (an ‘incremental’ view; Dweck, 2000, 2006). Important for shaping and
understanding the present study and argument is the notion that these implicit theories
tend to be deeply held, reflect fundamental orientations towards the phenomenon of
intelligence, and represent an individual’s characteristic disposition towards intelligence,
its nature and development. It is proposed that these deeper implicit theories stand insome contrast to goals (including growth goals) and goal setting that tend to be more
specific, flexible, and responsive to one’s proximal circumstance and situation (Locke &
Latham, 2002; Martin, 2011).
Growth (PB) goals
Growth goals (elsewhere referred to as ‘personal best’, PB goals) are defined as specific,
challenging, competitively self-referenced targets towards which students strive (Martin& Liem, 2010). In the academic domain, examples include doing better on a forthcoming
test than on a previous test or trying harder on a forthcoming assignment than on a
previous assignment (Martin, 2006, 2011; Martin & Liem, 2010). Goals vary on three
dimensions – Difficulty/challenge, specificity, and reference (Locke & Latham, 2002;
Martin, 2006, 2012) – and growth goals are no exception. The level of challenge or
difficulty prescribed by a growth goal must be at least as high as or higher than that of a
previous best. The specificity prescribed by a growth goal refers to how clearly defined
the goal is (Locke & Latham, 2002). Growth goals are specific to the extent that the targetis known (i.e., the student knows their previous result andhencewhat target is required to
exceed it). Goals also vary in terms of their reference. For growth goals, the reference is
personal and personalized (to exceed one’s own previous attainment) – rather than
external (to exceed other students). Growth goals are different frommastery goals under
major achievement goal frameworks (Elliot, 2005; Elliot & Church, 1997) in that growth
2 Andrew J. Martin
goals are focused on exceeding oneself (i.e., self-focused), whereas mastery goals tend to
be focused on the task and mastery and/or learning of it.
Most growth goal research has focused on PB goals. In cross-sectional work, PB
goals have been found to predict students’ educational aspirations, enjoyment ofschool, class participation, and persistence (Martin, 2006); in longitudinal research, PB
goals predict subsequent literacy, numeracy, test effort, enjoyment of school,
persistence, class participation, homework completion, educational aspirations, and
(negatively) disengagement (Martin & Liem, 2010); longitudinal work also finds PB
goals predict various learning strategies, class cooperation, and positive in-class
relationships across time (Liem, Ginns, Martin, Stone, & Herrett, 2012); in research
with at-risk (ADHD) students, PB goals predict achievement and behavioural
engagement (Martin, 2012, 2013b); and in cross-cultural research, PB goals predictacademic engagement among Chinese student samples (Yu & Martin, 2014). In all
these studies, the focus was on the effects of growth (PB) goals. No research has yet
investigated the predictors of growth goals nor the possible reciprocity between
implicit theories about intelligence and growth goals.
Elliot, Murayama, and Pekrun (2011) have explored self-based approach goals. These
goals ‘use one’s own intrapersonal trajectory as the evaluative referent’ (p. 322).
However, findings for this factor were not striking. To note, though, all items referred to
examinations and so may have elicited test anxiety concerns more than a self-improve-ment orientation.1 Elliot et al.’s (2014) work in this special issue explores potential-based
(growth-oriented) goals, finding separability between them and past-based goals.
However, these goals are also focused on examinations2 and Yu and Martin (2014) have
thus suggested a need to further examine self-approach and potential-based goals beyond
a focal reference to examinations (partly a purpose of this study).
Major concepts and processes relevant to implicit theories and growth goalsHowmight implicit theories of intelligence and growth goals be related? Major models of
human functioning and its attendant processes argue that dispositional or characteristic
orientations give rise to specific strategies that individuals use to navigate demands in their
environment (Buss & Cantor, 1989; Cury, Elliot, Da Fonseca, &Moller, 2006; Elliot, 2006;
Elliot & Church, 1997; Martin, Marsh, & Debus, 2001). This approach stipulates how
dispositions may be adaptively expressed via specific strategies to respond to different
stimuli, circumstances, and situations to effect positive outcomes (Cantor, 1990;
Kyl-Heku & Buss, 1996). In relation to the present study, this may suggest that students’implicit theories about intelligence reflect a characteristic orientation to their intelligence
and ability while goals and goal settingmay represent strategies or tactics used to respond
to demands in their environment. Thus, students’ implicit theories about intelligence
would be hypothesized to predict the goals that they pursue. Interestingly, meta-analysis
has shown that personality significantly predicts goal setting (Judge & Ilies, 2002;
Locke & Latham, 2006).
1 Although the self-based goals in the Elliot et al. (2012) study were focused on examinations, self-based goals are not confined toexaminations. Other self-based goals can include goals related to class participation, homework completion, and the like. It is quitelikely that such goals would not evoke performance concerns that exam-oriented goals evoke.2 As with self-based goals, past- and potential-based goals can also apply to activities that are not likely to evoke the performanceconcerns that examinations evoke. Thus, although Elliot et al. (2014) focused on examinations, application to other activities canbe readily conducted.
Implicit theories and growth (PB) goals 3
Another approach is that by Burnette et al. (2013) who integrate implicit theories
conceptswithCarver and Scheier’s (1998)model of self-control. They argue for amodel in
which implicit theories impact self-regulatory strategies that individuals use, including
their goal setting. Their meta-analysis testing this idea found that incremental theories, incontrast to entity theories, negatively predicted performance goals and positively
predicted learning goals. They concluded that ‘mindsets matter’ in the goal-setting
process. While this is suggestive of an argument for implicit theories predicting goals, in
their study limitations, they identified the need to test bidirectional relationships – amajor
focus of this study. Indeed, a recent experimental study by Dinger and Dickh€auser (2013)suggested that implicit theories do in fact predict achievement goals, in particularmastery
goals. However, as with most studies in this area, their research was not longitudinal and
so it could not be establishedwhether implicit theories predictmastery goals beyondpriorvariance in mastery goals. Again, this study redresses this methodological gap through its
longitudinal investigation.
In other work, the directional nature of individuals’ characteristic orientations and
their goal setting is not so clear-cut. For example, in their discussion of goal setting and
its processes, Locke and Latham (2002) explored the notion that specific goals mediate
the relationship between personality and outcomes. They suggested that assigned goals
are probably not overly influenced by personality or dispositional orientations – which
may suggest no substantial link between implicit theories and specific growth goals. Insuch cases, they see proximal and specific goals as ‘strong’ influences that are salient
over orientations and dispositions. Indeed, some of their research has suggested that
specific goals may even ‘neutralize’ (Locke & Latham, 2002) implicit orientations.
Notwithstanding this, however, they also suggest that when goals are self-set (such as
many personalized growth goals), personality and dispositions may yield a greater
impact (Latham, Ganegoda, & Locke, 2011) – which may suggest a directional
relationship between implicit theories predicting growth goals. Similarly, in models
representing individuals’ deeper goal orientations, incremental orientations (e.g.,learning goal orientations) have been posited as preceding individuals’ proximal desire
for challenging goals and tasks – with research suggesting as much (Dweck, 2000;
VandeWalle, Cron, & Slocum, 2001). Taken together, although a directional relationship
between implicit theories and growth goals receives some support, this idea is not
without qualification.
Three possible relationships between implicit theories and growth goalsIn linewithmindset theorizing (Dweck, 2006), itmight be proposed that studentswith an
incremental viewwill perceive academic growth and development as something that can
be attained and thus they will be likely to pursue goals that target growth. Similarly, it
might also beproposed that studentswhohold an entity viewwill see their competence as
relatively fixed and difficult to address, leading to less inclination to aim for and target
growth. Hence, it might be predicted that incremental theories positively predict growth
goals, whereas entity theories negatively predict growth goals. This represents something
of a top-down approach to the mindset framework: broader theories and schemaimpacting specific and proximal goals.
However, Dweck (2006, 2010) has suggested that implicit theories are not
immutable; it is possible for individuals to adjust their beliefs about intelligence and its
development. If implicit theories can change, then this brings into consideration
factors that might lead to such shifts. The present study explores the role of growth
4 Andrew J. Martin
goals in predicting implicit theories about intelligence. Such an effect would represent
something of a bottom-up effect: specific and proximal goals bringing about shifts in
theories and schema about intelligence. This is in line with other bottom-up
approaches to self-system factors and processes. Such approaches argue for low-level,specific factors giving rise to broader and more global constructs (Martin, 2013a;
Shavelson, Hubner, & Stanton, 1976).
A third possibility exists: a reciprocal relationship that reflects both top-down and
bottom-up effects. Indeed, the elemental concept underpinning this relationship is not
distinct from goals research such as this. For example, Marsh (2007; Marsh & Craven,
2006; Marsh & Martin, 2011) described a parallel reciprocal effects model in relation to
academic self-concept and academic achievement, seeking to answer the ‘chicken–egg’
f
Time 1 Growth
(PB) Goals
Time 1 Incremental
Theories
Time 2 Growth
(PB) Goals
Time 2 Incremental
Theories
Time 1 Entity
Theories
Time 2 Entity
Theories
Socio-demographic Covariates - SES - Parent education - Non-English background - Gender - Age - Ability
Figure 1. Hypothesized cross-lagged relationships between growth (personal best, PB) goals,
incremental theories, and entity theories. Note: Bolded single-headed arrows represent cross-lagged
path coefficients; dashed lines represent auto-lagged path coefficients or covariate parameters;
double-headed arrows represent unlagged correlation coefficients.
Implicit theories and growth (PB) goals 5
problem of whether self-concept impacts achievement or achievement impacts self-con-
cept. Marsh and Craven (2006) advised that numerous variations of this reciprocal effects
model are feasible, depending on the substantive issue at hand. This study therefore also
explores a possible reciprocal effects model: implicit theories and growth goals evincingmutual directional salience.
Taken together, three patterns of relationships between implicit theories and growth
goals are possible: top-down, bottom-up, and reciprocal. Marsh and Craven (2006) have
provided specific advice as to how to appropriately estimate models seeking to test three
such possible effects. They advised using structural equationmodelling (SEM) comprising
latent variables, correlating uniquenesses of parallel items across time, correlating
predictors, correlating outcomes, and employing a fully forwardmodel inwhich there are
test–retest (auto-lagged) parameters and cross-lagged paths in the one model. Theseessential features are incorporated into this study tomost appropriately gain a sense of the
ordering of Time 1 implicit theories and growth goals predicting each of Time 2 implicit
theories and growth goals. Figure 1 demonstrates.
To this process, further controls are included, with socio-demographic and ability
covariates as predictors of all Time 1 and 2 implicit theories and growth goal factors. Prior
research has shown these to be significantly associated with growth (PB) goals (Martin,
2012, 2013b) and implicit theories (Martin, Nejad, Colmar, & Liem, 2013), and thus, it is
important to control for their influence in order to disentangle unique varianceattributable to implicit theories and goals, from any variance attributable to covariates.
These covariates are also shown in Figure 1.
Method
Participants and procedureA total of 969 high school students participated from junior high, aged 11–14 years (54%),
and senior high, aged 15–19 years (46%). Students were from nine high schools in four
major urban areas on Australia’s east coast. The schools comprised students of mixed
ability (though, they were slightly higher in achievement and socio-economic status [SES]
than the nation’s average). Four schools were co-educational (comprising boys and girls),
three schools were single-sex girls’ schools, and two were single-sex boys’ schools. Just
over half (52%) of the respondents were male. The average age at Time 1 was 14.40
(SD = 1.55) years. Just under one in five (16%) of the sample spoke a language other thanEnglish at home. For the most part, targeted students in attendance on the day of the
survey participated in the survey. The dataset on which this study is based is shared with
that reported in Martin et al. (2013) investigating predictors and consequences of
adaptability.
Teachers administered the instrument to students during class time. Students were
first explained the rating scale along with a sample item. They were then asked to
complete the instrument on their own and to return it at the conclusion of class. Students
completed the survey twice, once in Term 1 2010 and then 1 year later in Term 1 2011.After accounting for students for whom it was not possible to have completed both
surveys (i.e., students beginning high school at Time 2 who were new to the school and
not part of Time 1; students in their final year at Time 1who had graduated by Time 2; new
students who joined the school in any year group; students who left the school in any year
group; students whowere absent for any reason at either Time 1 or Time 2), the retention
rate was estimated at 58%.
6 Andrew J. Martin
Measures
Growth (PB) goals
To measure growth (PB) goals, the four Personal Best Scale items from Martin (2006) and
Martin and Liem (2010)were used. These items are as follows: ‘When I domy schoolwork
I try to do the best that I’ve ever done’; ‘When I domy schoolwork I try to do it better than
I’ve done before’; ‘When I do my schoolwork I try to improve on how I’ve done before’;
and ‘When I do my schoolwork I try to get a better result than I’ve got before’. To eachgrowth goal item, students rated themselves on a scale of 1 (‘Strongly Disagree’) to 7
(‘Strongly Agree’). Table 1 presents descriptive statistics, reliability coefficients, and
factor loadings for the PB Scale.
Implicit theories about intelligence (entity and incremental theories)
Implicit theories are operationalized using Martin et al.’s (2001) adaptation of Stipek and
Gralinski’s (1996) ability–performance beliefs and effort-related beliefs factors – referredto here as entity and incremental theories, respectively. Martin et al.’s adaptation
involved minor word adjustments such as changing ‘kids’ to ‘people’ and selecting the
highest loading items. The entity theories factor holds ability as the primary determinant
of intelligence, irrespective of effort (e.g., ‘People can learn new things but how smart
they are doesn’t change’; ‘There isn’t much some people can do to make themselves
smarter’). The incremental theories factor holds that intelligence can be primarily
developed, such as through the application of effort (e.g., ‘Any person could get smarter if
they worked hard’; ‘A person who works really hard can be very smart’). Five itemscomprise the entity theories scale, and five items comprise the incremental theories scale.
Students rated items on a 1 (‘Strongly Disagree’) to 7 (‘Strongly Agree’) scale. Table 1
presents descriptive statistics, reliability coefficients, and factor loadings.
Socio-demographic and ability covariates
Data were also collected on socio-demographic and other characteristics including
gender, age, language background, parent education, SES, and ability. On languagebackground, participants were asked whether they spoke English (0) or another
language (1 – non-English-speaking background, NESB) at home. Gender was coded 0 for
females and 1 for males. Age was retained as a continuous variable. Students’ SES was
scoredon the basis of their homepostcode using theAustralianBureau of Statistics relative
Table 1. Descriptive, reliability, and confirmatory factor analysis (CFA) statistics
Mean (SD) Skew Kurtosis
Cronbach’s
alpha CFA Loading range
Time 1 Time 2 Time 1 Time 2 Time 1 Time 2 Time 1 Time 2 Time 1 Time 2
Growth
(PB)
goals
5.52 (1.09) 5.44 (1.11) �.78 �.56 .71 .01 .90 .89 0.78–0.85 0.79–0.83
Incremental
theories
5.81 (0.99) 5.72 (1.08) �.99 �.94 1.15 .98 .84 .87 0.57–0.80 0.70–0.83
Entity
theories
2.68 (1.25) 2.62 (1.31) .63 .58 �.11 �.41 .79 .84 0.58–0.75 0.66–0.78
Implicit theories and growth (PB) goals 7
advantage/disadvantage index, with higher scores reflecting higher SES. A parent
education score was calculated through the mean of mother’s (or female caregiver)
education and father’s (or male caregiver) education. Ability was based on students’
scores in annual nation-wide assessment of literacy and numeracy (National AssessmentProgram in Literacy and Numeracy) administered by the Australian Curriculum and
Assessment and Reporting Authority. It is a nationally standardized test for which school
students receive a score for numeracy and literacy. An ability factor was calculated via the
mean of literacy and numeracy scores.
Statistical analysis
Using Mplus version 7 (Muth�en & Muth�en, 2012), a series of analyses examined therelative salience of Time 1 growth goals, Time 1 incremental theories, and Time 1 entity
theories in predicting growth goals, incremental theories, and entity theories at Time 2.
Preliminary analyses involved confirmatory factor analysis (CFA) to ensure the factor
structure underlying the substantive model was sound. Central analyses then employed a
‘cross-lagged panel design’ to disentangle and differentiate the strength of competing
‘directional’ interpretations between the factors assessed on two different occasions
(Huck, Cormier, & Bounds, 1974).
Structural equation modelling represented growth goals, incremental theories, andentity theories as latent factors purged of unreliability. In all models, socio-demographics
and abilitymeasureswere included as covariates, predicting all Time 1 and Time 2 growth
goals, incremental theories, and entity theories factors. Correlated uniquenesses for
parallel items at Time 1 and Time 2 (e.g., between Time 1 growth goal item 1 and Time 2
growth goal item 1) were included in the SEM. This was because it is known that if the
same measurements are used on multiple occasions, then corresponding residual error
variables will tend to be correlated. Thus, to get accurate estimates of relations among the
central factors, correlations among residuals ought to be included in the statistical model(Marsh, Balla, & Hau, 1996).
The root mean square error of approximation (RMSEA) and the comparative fit index
(CFI) were used as indicators of model fit. RMSEAs at or less than .08 and .05 reflect close
and excellent fits, respectively; CFIs at or greater than .90 and .95 reflect acceptable and
excellent fits, respectively (McDonald & Marsh, 1990). Maximum likelihood with
robustness to non-normality and non-independence of observations (Muth�en & Muth�en,2012) was used to estimate models. Less than 5% of data were missing, and this was
estimated using the Expectation Maximization Algorithm.Although there were insufficient schools for multilevel modelling, and the study is
focused on psychological constructs not considered to vary at school level, there was an
adjustment for the clustering of students within schools using the Mplus ‘cluster’
command with the ‘complex’ method. This provides adjusted standard errors and thus
does not bias statistical significance testing due to clustering of students within schools
(Muth�en & Muth�en, 2012).
Results
Descriptive statistics and factor analysis
The skewness and kurtosis values in Table 1 indicate that the factors are approximately
normally distributed. Cronbach’s alphas indicate that the factors are reliable at both
8 Andrew J. Martin
Times 1 and 2. CFA yields a very good fit to the data, chi square = 1004.03, df = 431,
CFI = .95, RMSEA = .037. Factor-loading ranges derived from the CFA are also shown in
Table 1; these are generally high and indicate sound measurement bases upon which to
conduct cross-lagged panel analyses with SEM – the focus of the study.Correlations derived from the CFA are shown in Table 2. These demonstrate
preliminary support for cross-lagged relationships between Time 1 growth goals and
Time 2 incremental theories (r = .35, p < .001) and Time 2 entity theories (r = �.26,
p < .001), between Time 1 incremental theories and Time 2 growth goals (r = .30,
p < .001) and Time 2 entity theories (r = �.38, p < .001), and between Time 1 entity
theories and Time 2 growth goals (r = �.33, p < .001) and Time 2 incremental theories
(r = �.37, p < .001). However, these correlations do not control for shared variance
among all growth factors nor do they control for variance shared with socio-demographicand ability covariates. Cross-lagged panel analysis using SEM is the appropriate approach
to account for such controls.
Cross-lagged panel analyses
Results from the cross-lagged analyses (Figure 2 and Table 3) suggested that the data fit
the models well, as indicated by good fit indices, chi square = 1423.56, df = 434,
CFI = .92, RMSEA = .049. Significant substantive growth-related parameters areshown in Figure 2. All parameters (including covariates) are presented in Table 3. Time
1 and Time 2 auto-lagged parameters are significant; thus, any significant variance
attributable to cross-laggedparameters is notable. After controlling for socio-demographic
and ability covariates, the cross-lagged relationship between Time 1 growth goals and
Time 2 incremental theories is statistically significant, as is the cross-lagged relationship
between Time 1 growth goals and Time 2 entity theories (negatively). That is, beyond the
variance explained by Time 1 implicit theories, socio-demographics and ability, Time 1
growth goals is a significant positive predictor of Time 2 incremental theories (b = .18,
Table 2. Correlations from the confirmatory factor analysis
Growth (PB) goal Incremental theory Entity theory
Time 1 Time 2 Time 1 Time 2 Time 1 Time 2
Age �.19*** �.23*** �.15*** �.17*** .13** .18***
Language (NESB) .07* .07* .04 .01 �.04 �.04
Gender (M) �.11*** �.10 �.02 �.05 .11 .15**
SES �.06 �.01 �.09* �.04 �.06 �.05
Ability .17*** .14*** .01 .02 �.13* �.12*
T1 Growth (PB) goal –T2 Growth (PB) goal .63*** –T1 Incremental theory .48*** .30*** –T2 Incremental theory .35*** .49*** .48*** –T1 Entity theory �.26*** �.19** �.62*** �.37*** –T2 Entity theory �.26*** �.33*** �.38*** �.74*** .56*** –
Notes. Chi square = 1004.03; df = 431; comparative fit index = .95; root mean square error of
approximation = .037.
*p < .05; **p < .01; ***p < .001.
Implicit theories and growth (PB) goals 9
Time 1 Growth (PB)
Goals
Time 1 Incremental
Theories
Time 2 Growth (PB)
Goals
Time 2 Incremental
Theories
Time 1 Entity
Theories
Time 2 Entity
Theories
.18***
.59***
–.08*
.48***
.34***
–.15***
–.09**
Figure 2. Significant cross-lagged relationships between growth (personal best, PB) goals, incremental
theories, and entity theories (controlling for gender, age, parent education, socio-economic status,
language background, ability) – see Table 3 for all parameters. Note: Model fit: chi square = 1423.56;
df = 434; comparative fit index = .92; root mean square error of approximation = .049.
Table 3. Standardized beta coefficients from the cross-lagged panel analyses based on structural
equation modelling
Growth (PB) goal Incremental theory Entity theory
Time 1 b Time 2 b Time 1 b Time 2 b Time 1 b Time 2 b
Age �.20*** �.11*** �.16*** �.07 .13*** .08
Language (NESB) .05* .02 .03 �.03 �.03 �.01
Gender (M) �.12*** �.03 �.03 �.02 .09 .08*
SES �.13*** .02 �.11** �.02 �.01 �.01
Ability .20*** .05 .03 �.02 �.13** �.05
T1 Growth (PB) goal – .59*** – .18*** – �.09**
T2 Growth (PB) goal – – – – – –T1 Incremental theory – .03 – .34*** – �.08*
T2 Incremental theory – – – – – –T1 Entity theory – �.01 – �.15*** – .48***
T2 Entity theory – – – – – –
Notes. Model fit: chi square = 1423.56; df = 434; comparative fit index = .92; rootmean square error of
approximation = .049.
*p < .05; **p < .01; ***p < .001.
10 Andrew J. Martin
p < .001) and is a significant negative predictor of Time 2 entity theories (b = �.09,
p < .01).
Figure 2 and Table 3 also show that after controlling for socio-demographic and ability
covariates, the cross-lagged relationship between Time 1 incremental theories and Time 2entity theories is statistically significant, as is the cross-lagged relationshipbetweenTime1
entity theories and Time 2 incremental theories. That is, beyond the variance explained by
Time 1 incremental theories, growth goals, socio-demographics and ability, Time 1 entity
theories is a significant negative predictor of Time 2 incremental theories (b = �.15,
p < .001) and Time 1 incremental theories is a significant negative predictor of Time 2
entity theories (b = �.08, p < .05). Notably, there is no cross-lagged relationship
between incremental theories and growth goals. Nor is there a significant cross-lagged
relationship between entity theories and growth goals.
Discussion
The present study sought to investigate the relationships between implicit theories and
growth goals – with particular interest in the ordering of factors across time. The classic
cross-lagged panel design (Huck et al., 1974) was employed to shed light on the orderingof Time 1 growth goals, incremental theories, and entity theories relevant to each of Time
2 growth goals, incremental theories, and entity theories (Figure 1). Findings showed that
Time 1 growth goals predicted Time 2 incremental theories (positively) and entity
theories (negatively) but that Time 1 incremental theories and entity theories did not
predict growth goals at Time 2 (Figure 2 and Table 3). This suggests that entity and
incremental theories are reciprocally related across time but that growth goals seem to be
directionally salient over incremental and entity theories.
Findings of note
For theory, the findings offer further insight into the nature of factors and processes
operating under the broader growth mindset framework. Cross-lagged models seek to
address ‘chicken–egg’ questions, encouraging theorists tomove beyond unidirectional or
static self-systemmodels tomore appropriately recognize the dynamic interplay between
factors andprocesses in human functioning (Marsh, 2007;Marsh&Craven, 2006;Marsh&
Martin, 2011). The findings, then, may lend some specificity to these two importantconcepts that are salient under a growth mindset framework. Whereas the broad
relationship between implicit theories and goals is a matter of agreement (Burnette et al.,
2013; Dweck, 2006), there was a need to better understand their specific connections
across time using appropriate modelling inclusions that are possible under a cross-lagged
SEM design. Not only does this specificity further inform the growth mindset framework;
as described below, it also has significant implications for psycho-educational
intervention.
The directional salience of growth goals over implicit theories seems to lend supportfor arguments suggesting that individuals can develop specific cognitions (and behaviour
and affect) that can impact (or counter) deeper schema and orientations held by an
individual. For example, a review by Ginns, Liem, and Martin (2011) describes how
students can be taught to change cognition, behaviour, and affect in the face of personality
attributes or global schema thatmight otherwise leave them ‘stuck’. Similarly, work under
free trait theory suggests that implicit orientations and dispositions are not immutable
Implicit theories and growth (PB) goals 11
(Jorm, 1989; Little, 1996; Little & Joseph, 2007). The present study indicates growth goals
as one specific cognitive (and consequently, behavioural) approach to navigating one’s
implicit theories about intelligence.
Aligned with these arguments, earlier research has suggested that specific goals mayimpact or even ‘neutralize’ (Locke & Latham, 2002) implicit orientations. For example, it
has been shown that individuals with performance orientations who are assigned specific
and difficult learning goals perform at a similar level to students with learning orientations
(Seijts & Latham, 2001). In this instance, a specific goal disinhibited a potentially
problematic performance-oriented schema. The present findings are also in line with
Latham et al. (2011) who argue for goals as ‘strong’ factors that are not necessarily
influenced by personality or dispositional orientations.
These findings also confirm the importance of testing for bidirectional relationshipsbetween implicit theories and goals. Whereas Burnette et al. (2013) found that
incremental theories negatively predicted performance goals and positively predicted
learning goals, their advice to further test this relationship across time is well made: when
the present study did so, the relationship between implicit theories was not quite asmany
theorists and researchers might have posited. A cross-lagged panel design (controlling for
auto-regression, controlling for correlated residuals and with numerous covariates
operating through the entire model) shed light on unique effects of growth goals relevant
to implicit theories. While a bidirectional relationship was derived for incremental andentity theories, growth goals were directionally salient over these implicit theories.
Another finding worth noting is the negative reciprocal relationship between
incremental and entity beliefs across the course of a year. Incremental beliefs in one
year predicted reduced entity beliefs a year later, and entity beliefs in one year predicted
reduced incremental beliefs a year later (beyond the effects of auto-regression). Thus,
alongside PB goals as onemeans bywhich to potentially address implicit beliefs, targeting
implicit beliefs themselves will also have yields. The finding also holds implications for
operationalizing implicit beliefs in research. Clearly, modelling incremental and entitybeliefs as correlated but distinct factors revealed some interesting patterns in this study –including their own inter-relationships across time. Whereas some research operation-
alizes implicit beliefs via a unidimensional construct, the present research also supports a
bidimensional approach. However, this leaves open the issue of why and how the two do
correlate and how individuals coordinate the two. Further work to understand and
disentangle in this area would be useful.
Implications for intervention
The applied implications of findings are noteworthy. If implicit theories were salient over
growth goals, then practitioners would need to direct educational attention to the views
that students hold about intelligence, encouraging them to see intelligence and
competence as potentially malleable through increased effort. However, because growth
goals were salient over implicit theories, then well-established guidance for effective goal
setting becomes relevant (Ford, 1992; Lemos, 1996; Locke & Latham, 2002; Martin, 2011,
2013b).Because findings supportedmore of a bottom-upmodel, intervention seems to be best
directed at growth goals as ameans of promoting incremental theories and reducing entity
theories. Practitioners may adopt two approaches to promoting growth goals: ‘process
growth goals’ and ‘outcome growth goals’. Process growth goals would focus on targets
such as enhanced effort, engagement, skill development, participation, attendance,
12 Andrew J. Martin
persistence, and enjoyment. Examples include reading one more book for the present
assignment than on the previous assignment, preparing for a test at the weekend when
previously no study had been carried out at weekends, asking a teacher for help when
previously the teacher had been avoided, spending an extra hour doing homework,staying at school all day when previously non-attendance had been a problem, remaining
in one’s seat for longer periods each day, or calling out in class less each day in a given
week (Martin, 2011, 2013b). Outcome growth goals would focus on targets such as
improved achievement, attainment, performance, and productivity. Examples include
getting a higher mark in end-of-year examinations than in the half-yearly examinations,
making greater reading progress than prior progress, increasing one’s word bank, getting
more sums correct in one’s mathematics homework, and completing a task when
previously one’s work was rarely completed (Martin, 2011, 2013b).Significant covariates are also important to consider in intervention designed to
promote growth goals. For example, at p < .001, findings showed that older students,
males, students higher in SES, and lower achieving students were less likely to pursue
growth goals. This provides some direction as to the student profile that might be a target
for growth goal intervention. The present data were unable to shed light on why these
studentswere less inclined to endorse growthgoals. However, prior researchmayprovide
some insight: males are found to be less inclined to pursue mastery-related goals (Martin,
2007); due to the competitive and high stakes nature of senior school, older students aremore inclined to pursue competitive goals (Anderman et al., 2010); lower achieving
students are more inclined to pursue avoidance-oriented goals (Covington, 1992); and
high SES students may be under pressure from parents to attain highly and the
intergenerational transfer of fear of failure and avoidance goals is known (Elliot & Thrash,
2004). In the light of findings such as these, perhaps it is not surprising as to why growth
goals were not endorsed by these students.
Limitations and future directions
There are limitations important to consider when interpreting findings, which provide
some direction for further research. The data presented in this study are self-reported. It is
thus important to examine the same processes using data derived from additional sources
such as from teachers. Locke and Latham (2002) proposed functions by which goals
operate (e.g., provide clarity, energize, create pressure to self-compete, motivate
dissonance reduction). The present data were unable to uncover which of these
functionsmay have operated to predict implicit theories. Further research can assist here.It may also be useful to explore growth goal structures – that is, the extent to which there
exist class-level growth goals and how these may impact individual students’ implicit
theories. Similarly, Dweck (2010) has emphasized the importance of teachers holding
incremental theories to foster growth mindsets in students. Further work might explore
the effect of teachers’ implicit theories on students’ growth goals. Also to note is that this
study assessed domain-general academic constructs; future research should examine
these constructs and their relationships in specific academic subjects. It is also important
to recognize that growth goals are not a panacea. Growth goals still establish a standardagainst which one is judged. Growth goals may not ‘protect’ students from negative affect
resulting from failing to meet this standard. Future research might seek to test the effects
of failing to meet one’s own growth goal. Finally, although the study’s design was robust
in that it assessed growth goals and implicit theories across a full academic year,
additional time points are required to better test processes and loops that are prominent
Implicit theories and growth (PB) goals 13
in models of self-regulatory processes involving implicit theories and goals (Burnette
et al., 2013).
Conclusion
In the past decade, there has been increasing attention given to the role of growth
mindsets in students’ academic development. Two constructs that fall under the growth
mindset framework are implicit theories about intelligence and growth (PB) goals. One
important applied question concerns the relative salience of each of these factors in
predicting the other over time. Are factors reciprocally related or is one more salient over
another in its predictive role? The present investigation sought to answer this question
and demonstrated that whereas incremental and entity theories are reciprocally related,growth goals are directionally salient in predicting implicit theories. These results shed
additional light on major self-system models relevant to goals and goal setting and also
provide guidance as to where to direct and emphasize intervention when seeking to
promote and foster growth mindsets.
Acknowledgement
The author would like to thank the Australian Research Council for funding this research.
References
Anderman, E., Anderman, L., Yough, M., & Gimbert, B. (2010). Value-added models of assessment:
Implications for motivation and accountability. Educational Psychologist, 45, 123–137.doi:10.1080/00461521003703045
Anderman, E. M., Gimbert, B., O’Connell, A., & Riegel, L. (2014). Approaches to academic growth
assessment. British Journal of Educational Psychology. Advance online publication
Betebenner,D. (2008).Norm- and criterion-referenced student growth. Paper presented atCCSSO,
Washington, DC, 16 June 2008.
Betebenner, D. (2009). Growth, standards and accountability. Dover, NH: Center for
Assessment.
Briggs, D., & Betebenner, D. (2009). Is growth in student achievement scale dependent? Paper
presented at the invited symposiumMeasuring and Evaluating Changes in Student Achievement:
A Conversation about Technical and Conceptual Issues at the annual meeting of the National
Council for Measurement in Education, San Diego, CA, 14 April 2009.
Burnette, J. L., O’Boyle, E. H., VanEpps, E. M., Pollack, J. M., & Finkel, E. J. (2013). Mind-sets matter:
A meta-analytic review of implicit theories and self-regulation. Psychological Bulletin, 139,
655–701. doi:10.1037/a0029531Buss, D. M., & Cantor, N. (1989). Personality psychology: Recent trends and emerging directions.
New York, NY: Springer-Verlag.
Cantor, N. (1990). From thought to behavior: ‘Having’ and ‘doing’ in the study of personality and
cognition. American Psychologist, 45, 735–750. doi:10.1037/0003-066X.45.6.735Carver, C. S., & Scheier, M. F. (1998).On the self-regulation of behavior. NewYork, NY: Cambridge
University Press.
Covington, M. V. (1992). Making the grade: A self-worth perspective on motivation and school
reform. Cambridge, UK: Cambridge University Press.
Cury, F., Elliot, A. J., Da Fonseca, D., & Moller, A. C. (2006). The social-cognitive model of
achievement motivation and the 2 9 2 achievement goal framework. Journal of Personality
and Social Psychology, 90, 666–679. doi:10.1037/0022-3514.90.4.666
14 Andrew J. Martin
Dinger, F. C.,&Dickh€auser,O. (2013).Does implicit theory of intelligence cause achievement goals?
Evidence from an experimental study. International Journal of Educational Research, 61,
38–47. doi:10.1016/j.ijer.2013.03.008Dweck, C. S. (2000). Self-theories: Their role in motivation, personality, and development.
Philadelphia, PA: Psychology Press.
Dweck, C. S. (2006). Mindset: The new psychology of success. New York, NY: Random House.
Dweck, C. S. (2010). Mind-sets and equitable education. Principal Leadership, January, 26–29.Elliot, A. J. (2005). A conceptual history of the achievement goal construct. In A. J. Elliot &
C. S. Dweck (Eds.), Handbook of competence and motivation (pp. 52–72). New York, NY:
Guilford Press.
Elliot, A. J. (2006). The hierarchical model of approach-avoidance motivation. Motivation and
Emotion, 30, 111–116. doi:10.1007/s11031-006-9028-7Elliot, A. J., & Church, M. A. (1997). A hierarchical model of approach and avoidance achievement
motivation. Journal of Personality and Social Psychology, 72, 218–232. doi:10.1037/
0022-3514.72.1.218
Elliot, A. J., Murayama, K., & Kobeisy, A. (2014). Potential-based achievement goals.British Journal
of Educational Psychology. Advance online publication
Elliot, A. J., Murayama, K., & Pekrun, R. (2011). A 3 9 2 achievement goal model. Journal of
Educational Psychology, 103, 632–648. doi:10.1037/a0023952Elliot, A. J., & Thrash, T. M. (2004). The intergenerational transmission of fear of failure.
Personality and Social Psychology Bulletin, 30, 957–971. doi:10.1177/0146167203262024Ford, M. E. (1992). Motivating humans: Goals, emotions, and personal agency. Newbury Park,
CA: Sage.
Ginns, P., Liem, G. A. D., & Martin, A. J. (2011). The role of personality in learning processes and
learning outcomes in applied settings. In S. Boag & N. Tiliopoulos (Eds.), Personality and
individual differences: Theory, assessment, and application. New York, NY: Nova Science
Publishers.
Harris, D. N. (2011). Value-added measures in education. Cambridge, UK: Harvard Educational
Press.
Huck, S. W., Cormier, W. H., & Bounds, W. G. (1974). Reading statistics and research. New York,
NY: Harper and Row.
Jorm, A. F. (1989). Modifiability of trait anxiety and neuroticism: A meta-analysis of the
literature. Australian and New Zealand Journal of Psychiatry, 23, 21–29. doi:10.3109/00048678909062588
Judge, T. A., & Ilies, R. (2002). Relationship of personality to performance motivation: A
meta-analytic review. Journal of Applied Psychology, 87, 797–807. doi:10.1037/0021-9010.87.4.797
Kyl-Heku, L. M., & Buss, D. M. (1996). Tactics as unit of analyses in personality psychology:
An illustration using tactics of hierarchy negotiation. Personality and Individual Differences,
21, 497–519. doi:10.1016/0191-8869(96)00103-1Latham,G. P., Ganegoda,D. B., & Locke, E. A. (2011).Goal-setting: A state theory but related to traits.
In T. Chamorro-Premuzik, S. von Stumm&A. Furnham (Eds.),TheWiley-Blackwell handbookof
individual differences (pp. 577–587). Oxford, UK: Blackwell Publishing.
Lemos,M. (1996). Students’ and teachers’ goals in the classroom. Learning and Instruction,2, 151–171. doi:10.1016/0959-4752(95)00031-3
Liem, G. A., Ginns, P., Martin, A. J., Stone, B., & Herrett, M. (2012). Personal best goals and academic
and social functioning: A longitudinal perspective. Learning and Instruction, 22, 222–230.doi:10.1016/j.learninstruc.2011.11.003
Little, B. R. (1996). Free traits, personal projects and ideo-tapes: Three tiers for personality
psychology. Psychological Inquiry, 7, 340–344. doi:10.1207/s15327965pli0704_6Little, B. R., & Joseph, M. F. (2007). Personal projects and free traits: Mutable selves andwell beings.
In B. R. Little, K. Salmela-Aro & S. D. Phillips (Eds.), Personal project pursuit: Goals, action and
human flourishing (pp. 375–400). Mahwah, NJ: Lawrence Erlbaum Associates.
Implicit theories and growth (PB) goals 15
Locke, E., & Latham, G. (2002). Building a practically useful theory of goal setting and task
motivation. American Psychologist, 57, 705–717. doi:10.1037/0003-066X.57.9.705Locke, E., & Latham, G. (2006). New directions in goal-setting theory. Current Directions in
Psychological Science, 15, 265–268. doi:10.1111/j.1467-8721.2006.00449.xMarsh, H. W. (2007). Self-concept theory, measurement and research into practice: The role of
self-concept in educational psychology. Leicester, UK: British Psychological Society.
Marsh, H.W., Balla, J. R., &Hau, K. T. (1996). An evaluation of incremental fit indices: A clarification
of mathematical and empirical processes. In G. A. Marcoulides & R. E. Schumacker (Eds.),
Advanced structural equation modeling techniques (pp. 315–353). Hillsdale, NJ: Erlbaum.
Marsh, H. W., & Craven, R. G. (2006). Reciprocal effects of self-concept and performance from a
multidimensional perspective: Beyond seductive pleasure and unidimensional perspectives.
Perspectives on Psychological Science, 1, 133–163. doi:10.1111/j.1745-6916.2006.00010.xMarsh, H. W., & Martin, A. J. (2011). Academic self-concept and academic achievement: Relations
and causal ordering. British Journal of Educational Psychology, 81, 59–77. doi:10.1348/000709910X503501
Martin, A. J. (2006). Personal bests (PBs): A proposed multidimensional model and empirical
analysis. British Journal of Educational Psychology, 76, 803–825. doi:10.1348/
000709905X55389
Martin, A. J. (2007). Examining a multidimensional model of student motivation and engagement
using a construct validation approach.British Journal of Educational Psychology,77, 413–440.doi:10.1348/000709906X118036
Martin, A. J. (2011). Personal best (PB) approaches to academic development: Implications for
motivation and assessment. Educational Practice and Theory, 33, 93–99. doi:10.7459/ept/33.1.06
Martin, A. J. (2012). The role of Personal Best (PB) goals in the achievement and behavioral
engagement of students with ADHD and students without ADHD. Contemporary Educational
Psychology, 37, 91–105. doi:10.1016/j.cedpsych.2012.01.002Martin, A. J. (2013a). Improving the achievement, motivation, and engagement of students with
ADHD: The role of personal best goals and other growth-based approaches. Australian Journal
of Guidance and Counselling, 23, 143–155. doi:10.1017/jgc.2013.4Martin, A. J. (2013b). Academic buoyancy and academic resilience: Exploring ‘everyday’ and
‘classic’ resilience in the face of academic adversity. School Psychology International, 34, 488–500. doi:10.1177/0143034312472759
Martin, A. J., & Liem, G. A. (2010). Academic Personal Bests (PBs), engagement, and achievement:
A cross-lagged panel analysis. Learning and Individual Differences, 20, 265–270.doi:10.1016/j.lindif.2010.01.001
Martin, A. J., Marsh, H. W., & Debus, R. L. (2001). Self-handicapping and defensive pessimism:
Exploring a model of predictors and outcomes from a self-protection perspective. Journal of
Educational Psychology, 93, 87–102. doi:10.1037/0022-0663.93.1.87Martin, A. J., Nejad, H. G., Colmar, S., & Liem,G. A. D. (2013). Adaptability: How students’ responses
to uncertainty and novelty predict their academic and non-academic outcomes. Journal of
Educational Psychology, 105, 728–746. doi:10.1037/a0032794Masters, G. (2013). Towards a growth mindset in assessment. Melbourne, Victoria: ACER.
McDonald, R. P., & Marsh, H. W. (1990). Choosing a multivariate model: Noncentrality and
goodness-of-fit. Psychological Bulletin, 107, 247–255. doi:10.1037/0033-2909.107.2.247Muth�en, L. K., & Muth�en, B. O. (2012). Mplus user’s guide. Los Angeles, CA: Muth�en & Muth�en.Parker, P. D., Marsh, H. W., Morin, A. J. S., Seaton, M., & Van Zanden, B. (2014). If one goes up the
other must come down: Examining ipsative relationships between mathematics and English
self-concept trajectories across high school. British Journal of Educational Psychology.
Advance online publication
Seijts, G. H., & Latham, B. W. (2001). Can goal orientation be induced? Further exploration of the
state versus trait debate. In C. Sue-Chan (Chair), Justice, efficacy, goal orientation, culture, and
16 Andrew J. Martin
creativity: New findings in motivation. Symposium conducted at the annual meeting of the
Canadian Psychological Association, St. Foy, QC.
Shavelson, R. J., Hubner, J. J., & Stanton, G. C. (1976). Validation of construct interpretations.
Review of Educational Research, 46, 407–441. doi:10.3102/00346543046003407Stipek, D., & Gralinski, H. J. (1996). Children’s beliefs about intelligence and school performance.
Journal of Educational Psychology, 88, 397–407. doi:10.1037/0022-0663.88.3.397VandeWalle, D., Cron, W., & Slocum, J. (2001). The role of goal orientation following performance
feedback. Journal of Applied Psychology, 86, 629–640. doi:10.1037/0021-9010.86.4.629Yu, K., & Martin, A. J. (2014). Personal best (PB) and ‘classic’ achievement goals in the Chinese
context:Their role in predicting academicmotivation, engagement, and buoyancy.Educational
Psychology: An International Journal of Experimental Educational Psychology. Advance
online publication. doi:10.1080/01443410.2014.895297
Received 6 February 2014; revised version received 1 May 2014
Implicit theories and growth (PB) goals 17
