Discriminant and Incremental Validity of Self-concept And

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Discriminant and incremental validityof self-concept and academic self-efficacy: a meta-analysisChiungjung Huang aa Graduate Institute of Education, National Changhua University ofEducation, Changhua, 500, TaiwanVersion of record first published: 08 Oct 2012.

To cite this article: Chiungjung Huang (2012): Discriminant and incremental validity of self-conceptand academic self-efficacy: a meta-analysis, Educational Psychology: An International Journal ofExperimental Educational Psychology, 32:6, 777-805

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Discriminant and incremental validity of self-concept andacademic self-efficacy: a meta-analysis

Chiungjung Huang*

Graduate Institute of Education, National Changhua University of Education, Changhua500, Taiwan

(Received 24 May 2012; final version received 18 September 2012)

Two studies examined the discriminant and incremental validity of self-conceptand academic self-efficacy. Study 1, which meta-analysed 64 studies comprising74 independent samples (N= 24,773), found a strong mean correlation of .43between self-concept and academic self-efficacy. The domains of self-conceptand self-efficacy, and the domain matching between them, moderate the strengthof the correlation between self-concept and academic self-efficacy. Global self-concept was associated with weaker correlations than were academic and sub-ject-specific self-concept. Academic self-efficacy had higher incremental validitythan self-concept. Study 2, which examined data-sets from Programme for Inter-national Student Assessment 2000, 2003 and 2006, found that the mean correla-tion ranged from .31 to 54. Self-concept sometimes had higher incrementalvalidity than academic self-efficacy. The higher incremental validity of self-con-cept may result from the wording and domain of self-concept measure as wellas specificity matching between self-concept and academic achievement.

Keywords: discriminant validity; incremental validity; meta-analysis; academicself-efficacy; self-concept

Self-concept and self-efficacy, two important components of self-beliefs, are deter-minants of academic achievement. Self-concept denotes an individual’s self-percep-tion (Rosenberg, 1965), while self-efficacy refers to the perceived capability of anindividual to perform certain tasks successfully (Bandura, 1977, 1982, 1986). Sev-eral studies have elucidated the differences and similarities between these two con-structs. For example, Bong and Clark (1999) and Bong and Skaalvik (2003)suggested that self-concept comprises affective and cognitive components of self,with normative standards used to determine an individual’s self-concept, while self-efficacy comprises only the cognitive component of self, with an absolute standardused to determine an individual’s self-efficacy. Since self-efficacy is more specificin scope than self-concept, Bong and Clark (1999) proposed that the power of self-efficacy in predicting academic achievement exceeded that of self-concept.

To determine the importance of self-concept and self-efficacy, one must considerboth conceptual and empirical differences between these two constructs. Althoughconceptual differences have been established (e.g. Bong & Clark, 1999; Bong &Skaalvik, 2003; Byrne, 1996), empirical differences between self-concept and

*Email: [email protected]

Educational PsychologyVol. 32, No. 6, October 2012, 777–805

ISSN 0144-3410 print/ISSN 1469-5820 online� 2012 Taylor & Francishttp://dx.doi.org/10.1080/01443410.2012.732386http://www.tandfonline.com

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self-efficacy have not been examined thoroughly. To examine empirical differences,one must consider the strength of the correlation between self-efficacy and self-con-cept. Discriminant validity is the degree to which two constructs are uncorrelated.When the correlation between self-concept and self-efficacy is weak, discriminantvalidity of self-concept and self-efficacy is high. However, a strong correlationbetween self-concept and self-efficacy does not necessarily imply that differentiationbetween the two constructs is meaningless or that the concepts are conceptuallysimilar. Self-concept and self-efficacy should each make a unique contribution topredicting such important outcomes as academic achievement. Hence, one way ofassessing the value of self-concept is to investigate the extent to which it predictsacademic achievement above and beyond self-efficacy. Similarly, the importance ofself-efficacy depends on the degree to which it predicts academic achievementabove and beyond self-concept. Self-concept is valuable to educational researchersand practitioners when it reveals incremental validity – the degree to which self-concept uniquely explains academic achievement. The importance of self-efficacyincreases when demonstrating incremental validity. Hence, when examining empiri-cal distinctiveness, one must consider the strength of the correlation between self-concept and self-efficacy, and their incremental validities.

Although several primary studies have determined the discriminant validity ofself-concept and academic self-efficacy (e.g. Ku, 2002; Lent, Brown, & Gore,1997), a meta-analysis has not evaluated the discriminant validity of self-conceptand academic self-efficacy. Some meta-analyses have documented the criterion-related validity of self-concept (Hansford & Hattie, 1982; Möller, Pohlmann, Köller,& Marsh, 2009; Valentine, DuBois, & Cooper, 2004), and one meta-analysis hasdone the same for self-efficacy (Multon, Brown, & Lent, 1991). Despite the impor-tance of meta-analysis focusing on univariate validity, which assesses the relationbetween self-concept and academic achievement, and that between self-efficacy andacademic achievement, predictions of academic achievement rarely rely on a singlepredictor variable. This creates a need for research that considers incremental valid-ity, namely, the increase in validity resulting from adding a new predictor to anexisting explanatory model. As such academic achievement predictors as self-concept are correlated with academic self-efficacy, the incremental validity of aca-demic self-efficacy over and above self-concept and that of self-concept over andabove academic self-efficacy warrant further investigation. Incremental validity ofacademic self-efficacy is determined when it explains the considerable variance inacademic achievement, after statistically controlling for self-concept. Similarly,incremental validity of self-concept is established when self-concept accounts forsubstantial variance in academic achievement, after controlling for academic self-efficacy.

In addition to empirical studies, examining the discriminant and incrementalvalidity of self-concept and academic self-efficacy that provide the data for meta-analysis, this study analyses data from the Programme for International StudentAssessment (PISA). The organization for economic cooperation and development(OECD) has conducted four international studies, three of which assessed both self-concept and self-efficacy. Students in the PISA 2000 study of 32 countries wereaged 15, and the total number of students exceeded 250,000. The OECD releasedPISA 2000 data from questionnaire and cognitive test results, including reading asa primary focus, and mathematical and scientific literacy. The PISA 2003 projectfocused on mathematics as the major subject, and reading, science and problem

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solving in 41 countries. The primary focus of PISA 2006 was science. These dataare ideal for examining the discriminant and incremental validity of self-conceptand academic self-efficacy for the following three reasons. First, different domainsof self-concept and academic self-efficacy are assessed in the three databases, facili-tating examination of domain effects. Second, sample sizes are large. Third, analyti-cal results provide support for the universal differences between self-concept andacademic self-efficacy.

Self-efficacy

Bandura (1997, p. 3) defines self-efficacy as ‘an individual’s belief in one’s capacityto organise and execute the courses of action required to produce given attain-ments’. According to the social cognitive theory of self-efficacy (Bandura, 1977,1982, 1986), mastery experience, vicarious learning, verbal persuasion and physio-logical state are four determinants on self-efficacy that affect individual choicesabout whether to engage in a specific task and persist in completing a specific task.Mastery experiences are the most significant determinant of individual sense of self-efficacy. Academic self-efficacy is a belief in one’s ability to accomplish a particulartask regarding an academic domain (e.g. I can do the most challenging tasks inclass if I try). Domain-specific self-efficacy refers to the perceived ability to suc-cessfully complete a specific task regarding a subject area (e.g. I can do the mostdifficult mathematics problems).

Self-concept

Self-concept denotes personal perceptions of the self with different levels ofspecificity or domains. Byrne (1996) suggested that possible theoretical models ofself-concept can be either unidimensional or multidimensional. Early literature onself-concept assumes such models as unidimensional. For example, Rosenberg(1965) defined self-concept as attitudes toward an object (Rosenberg, 1965), wherethese attitudes include favourable or unfavourable facts, opinions and valuesregarding the self. One of the most popular measures of self-concept developed byRosenberg (1965), the Self-Esteem Scale, was based on the unidimensional perspec-tive. Shavelson, Huber, and Stanton (1976) claimed that self-concept was multidi-mensional and hierarchically structured. That is, global self-concept, located at theapex of the hierarchy, can be categorised as either academic or non-academic. Eachof these subfactors of general self-concept can then be classified into more specificself-concepts. For instance, academic self-concept comprises domain-specific (e.g.math, science and English) self-concepts. Global self-concept measures global per-ceptions of the self. For example, ‘I have positive feelings about myself’. Mean-while, academic self-concept comprises overall individual perceptions regarding anacademic domain. For example, ‘I have generally performed well in school sub-jects’. The subject-specific self-concept refers to overall perceptions regarding a par-ticular subject area. For example, I find mathematics class easy. Marsh and Craven(2006) suggested that smaller correlations were obtained between self-concept andacademic achievement when using global measure of self-concept than when usingacademic/subject-specific measures.

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Although self-concept and self-efficacy shared a similar factor structure (hierar-chical and multidimensional), researchers observed a conceptual distinction betweenthem. For example, Pajares and Miller (1994) stated that self-efficacy is more task-,context- and situation-specific than self-concept. Self-concept thus can be domain-specific (e.g. I am good at mathematics), but not task-specific (e.g. I can solve thismathematics problem). Marsh, Walker, and Debus (1991) proposed that self-conceptis based on normative standards, and thus on an external frame of reference inwhich individuals make judgements by comparing their performance with theirpeers. Self-efficacy is evaluated based on intrapersonal standards of success in acertain achievement task, and thus previous mastery experience rather than normalcomparison was frequently used to determine individual self-efficacy (Bong &Clark, 1999). Self-efficacy thus is determined based on an internal frame of refer-ence. Furthermore, Bong and Clark (1999) and Bong and Skaalvik (2003) sug-gested that self-efficacy contains cognitive facet of self, while self-concept containsboth cognitive and affective (e.g. I certainly feel useless at times) components ofself. Bong and Clark (1999, p. 142) thus concluded the following.

Self-concept is judged to be more inclusive, at least in its theoretical (in contrast tooperational) content, because it embraces a broader range of descriptive and evaluativeinferences, with ensuing affective reactions. Self-efficacy emerges as a relatively unidi-mensional construct that largely embodies one’s cognitive perceptions of competencein a given domain.

Self-esteem

Self-esteem comprises overall evaluations of the self. For example, I feel goodabout myself. Byrne (1996) identified conceptual differences between self-conceptand self-esteem and made the following suggestion.

Whereas self-concept connotes a relatively broad definition of the construct thatincludes cognitive, affective, and behavior aspects, self-esteem is thought to be a morelimited evaluative component of the broader self-concept term. (p. 5)

Despite the identification of conceptual differences between self-concept and self-esteem, these differences have not been empirically differentiated. Byrne (1996)indicated that general self-concept and self-esteem are usually measured using aself-report inventory. Both general self-concept and self-esteem comprise self-description and self-evaluation, making empirical differentiation challenging.

Discriminant validity of self-concept and academic self-efficacy

As two important components of self-belief, self-efficacy and self-concept likelyoverlap. However, whether these two concepts are correlated is not vitally impor-tant, rather the strength of their correlation is important. A strong correlation mayimply difficulty when empirically differentiating these two constructs. Althoughmany studies have identified the discriminant validity of self-concept and academicself-efficacy, the strength of the correlation between self-concept and academic self-efficacy varies. For example, Lent et al. (1997) applied structural equation model-ling to examine the discriminant validity of three academic self-efficacy measuresthat differed in domain and academic self-concept for 205 university students.

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Although they considered academic self-efficacy and academic self-concept as twodistinct constructs, the two concepts were strongly correlated (.57) when assessed atthe general academic level. Chong (2007), who examined the relation between gen-eral self-concept and academic self-efficacy for 1304 Grade 7 students in Singapore,found that the correlation between academic self-efficacy and general self-conceptwas weak at �.13. Given the diversity of findings, meta-analysis is valuable sinceit clarifies the degree to which academic self-efficacy and self-concept differ.

Incremental validity

Self-concept and academic self-efficacy together explain more variance in academicachievement than either can separately. Nevertheless, the degree of incrementalvalidity of these two concepts is a salient issue. Meyer (2000) defined incrementalvalidity in three ways. The first definition is based on the comparative criterion.When academic achievement is more strongly correlated with academic self-efficacythan with self-concept, academic self-efficacy is a better predictor of academicachievement than self-concept and, thus, has superior incremental validity to self-concept. The second definition is based on contribution. When both academic self-efficacy and self-concept are correlated with academic achievement, and when bothvariables uniquely explain academic achievement, both are useful predictors. Thethird definition is based on the incremental criterion. For instance, incrementalvalidity of academic self-efficacy is the degree to which it uniquely explains vari-ance in academic achievement after controlling for self-concept. When academicself-efficacy is strongly correlated with self-concept, its ability to account uniquelyfor variance in academic achievement is limited.

Several empirical studies (Cantrell, 2001; Choi, 2005; Fleming, 1998; Migray,2002; Piccirillo, 1995) predicted academic performance using academic self-efficacyand self-concept. The incremental validity of academic self-efficacy and self-conceptis important when using these two concepts to predict academic achievement. Thestrong correlation between academic self-efficacy and self-concept limited the vari-ance in academic achievement for which each factor accounts. Although some stud-ies have identified the incremental validity of academic self-efficacy, their findingsare inconclusive. Piccirillo (1995) investigated the ability of self-concept and aca-demic self-efficacy to explain variance in general educational development (GED)test scores for a sample of 150 students who took test preparation classes. Whenthe regression model included demographic variables, self-concept and causal attri-bution, adding academic self-efficacy increased explained variance in GED testscores by only 1%. Choi (2005) examined the ability of self-efficacy and self-concept to predict academic achievement in a sample of 230 college students takingfour general education classes. Choi used three measures of self-efficacy: generalself-efficacy, which measured generalised self-efficacy instead of task-specificefficacy; academic self-efficacy, which assessed college student confidence in per-forming typical academic tasks; and course-specific efficacy, which measuredself-efficacy specific to a course. Choi found that including general, academic andcourse-specific self-efficacy in the regression model explained 10% of the variancein term grades. Moreover, including academic and course-specific self-concept aswell as the three self-efficacy components in the regression model explained 21%of the variance in term grades. Hence, adding two self-concept components to the


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three self-efficacy components in the regression model increased the varianceprediction of academic achievement by 11%.

Primary studies examining the incremental validity of academic self-efficacy andself-concept in predicting academic achievement have yielded mixed findings. Toextend and clarify previous findings, this study combines analytical results obtainedby studies investigating the relations among academic self-efficacy, self-concept andacademic achievement.

Previous meta-analysis studies of self-efficacy

Judge, Erez, and Bono (2002), who conducted a meta-analysis of the discriminantvalidity of generalised self-efficacy, self-esteem, neuroticism and locus of control,investigated whether a common core construct explained the relations among theseconstructs. The discriminant validity of general self-efficacy and self-esteem waslow; the mean correlation between these two constructs was strong at .85 for ninesamples. Notably, this study has at least three limitations. First, the discriminantvalidity of general self-efficacy and self-esteem was based on a small number ofstudies. Second, moderator analyses were not performed to examine variation indiscriminant validity for general self-efficacy and self-esteem. Finally, only thePsycINFO database was searched and no comprehensive literature search wasundertaken.

Variability in discriminant validity between academic self-efficacy andself-concept

Self-concept can be assessed in different domains. Notably, the self-conceptdomains may affect the relationship between academic self-efficacy and self-concept. Swalander and Taube (2007) examined the correlations among academicself-efficacy, academic self-concept and verbal self-concept for a sample of 4018Grade 8 students. The correlation between academic self-efficacy and verbal self-concept was .49, while that between academic self-efficacy and academic self-con-cept was .69. Furthermore, Tabassam and Grainger (2002) examined the strength ofthe correlation between academic self-efficacy and self-concept measures and howself-concept differed in various domains. Math self-efficacy had correlations coeffi-cients of .46, .63 and .66 with general, academic and math self-concept, respec-tively; the corresponding correlation coefficients for reading self-efficacy were .44,.63 and .65, respectively. The primary research identified an effect for the self-con-cept domain and, consequently, the self-concept domain was examined as a poten-tial moderator.

Similarly, academic self-efficacy can be evaluated in different domains, and theself-efficacy domain may affect the relation between academic self-efficacy andself-concept. Britner and Pajares (2004) examined correlations among science self-efficacy, academic self-efficacy and science self-concept for 319 students in Grades5–8. The correlation between science self-efficacy and science self-concept was .61,while that between academic self-efficacy and science self-concept was .52. Lentet al. (1997), who assessed the discriminant validity of academic self-concept, aca-demic self-efficacy and math self-efficacy, found that academic self-concept hadcorrelations at .20, .43 and .57 with math problem self-efficacy, math courseself-efficacy and academic self-efficacy, respectively. Since the strength of the

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correlation between academic self-efficacy and self-concept varies with the self-effi-cacy domain, examining this effect of self-efficacy domain is essential.

Empirical evidence suggests that the matching domain between academic self-efficacy and self-concept may be related to the discriminant validity between thesetwo concepts. Self-concept and academic self-efficacy are closely related when theyare measured within the same domain. For instance, Choi (2005) examined the rela-tions between measures of self-concept and academic self-efficacy in differingdomains. The correlation between academic self-concept and academic self-efficacywas r= .61 and that between course-specific self-concept and course-specific self-efficacy was r= .81. Conversely, the correlation between academic self-efficacy andcourse-specific self-concept was r= .38, while that between course-specific self-efficacy and academic self-concept was with r= .51. These findings support thatrelative strong correlations were obtained given matching domain of self-conceptand academic self-efficacy.

Bandura (1977, 1986) claimed that self-efficacy can change with experience andmaturity. Substantial research (Alvarez, Ruble, & Bolger, 2001; Case, 1991) hassuggested that young children have difficulty in predicting their ability to success-fully complete certain tasks, and that the capacity to make accurate judgementsregarding future experiences increases with age. Consequently, the correlationbetween academic self-efficacy and self-concept likely varies with participant age.Most primary research has provided age data, and so this meta-analysis exploredthe potential moderating effect of age on the relation between academic self-efficacyand self-concept.

Purpose

Although primary research has examined the relation between academic self-effi-cacy and self-concept, no study has applied meta-analyses to determine the discrim-inant validity of self-concept and academic self-efficacy. To estimate the overallrelation between self-concept and academic self-efficacy, this study first appliesmeta-analyses to determine the discriminant validity between self-concept and aca-demic self-efficacy. The magnitudes of correlation coefficients determine whetherself-concept can be differentiated from academic self-efficacy.

Previous primary studies identified inconsistency in the correlation between self-concept and academic self-efficacy. A major advantage of meta-analysis is that itcan be used to examine the impact of potential moderators on the relation betweenself-concept and academic self-efficacy. Moderators that may explain variance inthe relation between self-concept and academic self-efficacy are participant age,self-concept domain, academic self-efficacy domain, matching domain between aca-demic self-efficacy and self-concept and publication status. Hence, the secondobjective of this study is to examine the effects of moderators on the relationbetween academic self-efficacy and self-concept, and explain the variability of thisrelation.

The predictive power of self-concept (Hansford & Hattie, 1982; Möller et al.,2009; Valentine et al., 2004) and self-efficacy (Multon et al., 1991) to predict aca-demic achievement has been established in empirical research and meta-analyses.To expand extant research, meta-analysis should be applied to examine the incre-mental validity of self-concept over academic self-efficacy and that of academicself-efficacy over self-concept. The third purpose of this study is to examine the


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incremental validity of self-concept and academic self-efficacy in predicting aca-demic achievement.

As PISA projects provided data for examining the discriminant and incrementalvalidity of self-concept and academic self-efficacy, the fourth aim of this study is tocompare discriminant and incremental validity in this meta-analysis with those inPISA data-sets. Comparable discriminant and incremental validity increases the gen-eralisability of findings.

Study 1

Method

Literature search

To identify potential studies for inclusion in the analysis, this study performed com-puterised searches of articles listed on ERIC, PsycINFO and ProQuest dissertationsand theses. All possible keyword combinations for self-efficacy and self-conceptterms (i.e. self-concept, self-esteem, self-image and self-worth) were used to searchfor studies published through May 2011.

As mentioned previously, self-concept and self-esteem have not been empiricallydifferentiated. Byrne (1996) suggested that although researchers claim self-conceptand self-esteem as distinct concepts, scales measuring these concepts generally com-prised of both descriptive and evaluative items, making it impossible to distinguishthe two constructs. Consequently, general self-concept and self-esteem were com-bined into a single category. Academic self-efficacy was defined as an individual’sbelief in his/her ability to perform a certain academic task. Since self-efficacy forself-regulated learning indicates the efficacy of using self-regulated learning strate-gies (Zimmerman, 1990), self-efficacy for self-regulated learning does not indicatethe efficacy for executing an academic task. Therefore, this study does not examineself-efficacy for self-regulated learning. Furthermore, this investigation excludesstudies including items measuring other constructs than academic self-efficacy. Forexample, items not measuring academic self-efficacy (e.g. My family cares aboutmy academic performance) were included in the academic self-efficacy scale devel-oped by Yeager (2009); therefore, Yeager’s study was excluded.

The specific inclusion criteria are as follows: first, studies are included onlywhen they administered at least one measure of academic self-efficacy (academicand domain-specific) and self-concept (general, academic and domain-specific) andreported at least one correlation between these two constructs. Studies that exam-ined general self-efficacy, career-decision self-efficacy, health self-efficacy, familyself-efficacy or gender-role self-efficacy are excluded. Given the low power of non-academic self-concept (e.g. social and physical self-concept) in predicting academicachievement, non-academic self-concepts are excluded. Studies that had items mea-suring variables other than academic self-efficacy (social efficacy) are also excluded.For instance, Currence (2007) measured course efficacy, room-mate efficacy andsocial efficacy using the College Self-Efficacy Instrument (Solberg, O’Brien,Villareal, & Davis, 1993); therefore, this study is excluded. Second, studies measur-ing self-concept and self-efficacy using self-reported questionnaires are included,while those measuring self-esteem and self-efficacy using information from infor-mants other than the participants (e.g. Castellanos (2002) used teacher ratings) areexcluded. Third, naturalistic studies measuring trait self-concept and self-esteem

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are included, while experimental studies manipulating self-concept or self-esteemare excluded (e.g. Norwich, 1987). Finally, only studies written in English areconsidered.

Searches identified 2113 studies. The author of this study reviewed article titles,abstracts and keywords to determine whether they should be subject to analysisusing selection criteria. The full text of a study was obtained when abstracts con-tained insufficient information to determine inclusion or exclusion. In total, 518studies were retrieved for further review based on titles, keywords and abstracts.

Studies were then reviewed to determine whether correlation coefficients wereobtained from independent samples. For studies utilising the same sample, the studyselected was the study providing additional effect sizes (i.e. the correlations amongself-concept, academic self-efficacy and academic achievement). If the number ofeffect sizes were the same, the study with the larger sample size was selected.

Coding

In addition to information for estimating effect sizes and weights (correlation coeffi-cients among self-concept, academic self-efficacy, academic achievement and sam-ple size), several study characteristics were coded.

Mean age

Participant mean age was recorded. For studies reporting an age range, the medianage was used as an estimate. In studies where grade level was reported, age wasestimated by adding 5 years to the grade.

Self-concept domain

The self-concept domain was coded as global, academic, verbal, mathematics, sci-ence or other self-concept domains.

Academic self-efficacy domain

Academic self-efficacy was classified as general academic, verbal, mathematics, sci-ence or other self-efficacy domains.

Matching domain between self-concept and academic self-efficacy

A match in the assessment domain between academic self-efficacy and self-conceptwas coded as either matching or non-matching.

All studies were coded by the author and a doctoral student. For categoricalvariables (domains of academic self-efficacy and self-concept, the matching domainbetween academic self-efficacy and self-concept, and publication status), inter-rateragreement exceeded 81% for all coding categories. For continuous variables, samplemean age, sample size and the correlation between self-concept and academic self-efficacy, that between self-concept and academic achievement, and that betweenacademic self-efficacy and academic achievement, coder reliabilities estimated bythe correlation between coders were .89, .90, .87, .87 and .88, respectively. Codingdiscrepancies were resolved through discussion.


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Analysis

Effect size in this meta-analysis is the Pearson product-moment correlation coeffi-cient, r. When participants had multiple self-efficacy and self-concept measures,only correlations between self-efficacy and its corresponding self-concept werecoded. For example, when math and English self-efficacy and math and Englishself-concept were measured, correlations between math self-efficacy and math self-concept and between English self-efficacy and English self-concept were coded.When the domain between self-concept and academic self-efficacy did not match,all relevant correlations between self-concept and academic self-efficacy werecoded. For instance, when a participant had multiple academic self-efficacy mea-sures (e.g. math and English self-efficacy) and multiple self-concept measures (e.g.global and academic self-concept), four different correlation coefficients (i.e. corre-lations between math self-efficacy and global self-concept, between math self-effi-cacy and academic self-concept, between English self-efficacy and global self-concept and between English self-efficacy and academic self-concept) were coded.The independence issue arises when multiple measures of academic self-efficacyand self-concept are obtained from the same participant sample. Mean correlationswere computed to address this dependence issue. In analysing the effects of thematching domain between academic self-efficacy and self-concept and domain ofacademic self-efficacy and self-concept on discriminant validity between academicself-efficacy and self-concept, multiple correlations of various academic self-efficacyand self-concept measures for the same participant sample were consideredindependent.

Weighted mean correlation coefficients were computed. Specifically, each corre-lation coefficient was weighted by sample size (Hunter & Schmidt, 2004). The sumof these products was then divided by the total sample size to compute the samplesize weighted mean. The significance of the mean correlation coefficient was testedby computing the 95% confidence interval. If the 95% confidence interval includes0, the mean correlation does not significantly differ from zero. Otherwise, it doessignificantly differ from zero.

A major advantage of meta-analysis is that it can identify the potential effect ofmoderators on the relation between academic self-efficacy and self-concept. Thehomogeneity of effect size was tested by Q, which is distributed approximately asχ2 with k-1 degrees of freedom, where k is the number of effect sizes. A significantQ indicates heterogeneity among effect sizes and moderators are therefore intro-duced to explain variability.

In fixed-effect models, variation in observed effect size must result from within-study variability associated with sampling error. In random-effect models, studiescan have various true effect sizes. Restated, both the within-study (sampling error)and the between-study (variation of true effect size across studies) variances explaineffect size variation. Because of the implausible assumptions of the fixed-effectsmodel, the random-effects models were used to compute the mean correlation andexamine the variance explained by each moderator.

Results

In total, 64 studies satisfied inclusion criteria, yielding 74 independent samplesinvolving 24,773 participants. Of these studies, 37 were journal articles, 23 were

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doctoral dissertations, one was a master’s thesis, two were conference papers andone was a book chapter. These studies are listed in the Appendix. The correlationbetween self-concept and academic self-efficacy ranged from �.13 to .76.

Outlier analyses

Two outlier analyses were performed to determine the robustness of mean correla-tion between self-concept and academic self-efficacy after excluding extreme corre-lation coefficients and sample sizes. As sample size was utilised to compute themean correlation coefficients, studies involving large sample sizes were assignedrelatively large weights. The mean correlation coefficient was thus defined by stud-ies with large sample sizes. For the 74 independent effect sizes, the mean correla-tion between academic self-efficacy and self-concept was r= .43, with a 95%confidence interval of .38–.48. Overall, these analytical findings indicate that astrong correlation exists between academic self-efficacy and self-concept. Onepotentially extreme value (�.13) was analysed to determine its effect on theweighted mean correlation coefficient. After excluding this extreme value, the meancorrelation coefficient was high at .44. This study did not markedly influence themean correlation coefficient and was therefore retained. Excluding a study withsample size of N= 4018 only slightly changed the mean correlation coefficient (i.e..42); thus, this study was retained for subsequent analysis. Notably, all eligible stud-ies were subjected to further analysis.

Description of included studies

Table 1 lists the mean age, sample size, domains of academic self-efficacy and self-concept, as well as correlations among self-concept, academic self-efficacy and aca-demic achievement. Seventy samples reported mean participant age. Moreover, twosamples were assessed longitudinally over a one-year period. In the study by Gra-ham (2000), academic self-efficacy and self-concept of students aged 11–13 weremeasured six times over a three-year period. Lee (1997) measured academic self-efficacy and self-concept of students aged 18 and 19. Of the remaining 68 samples,mean subject age was 17.15 (range, 8–40). Mean population size was 334.77(range, 15–4018 participants).

Moderator analyses

Participant age

As mentioned previously, 70 samples reported mean participant age. Two studiesmeasured self-concept and academic self-efficacy longitudinally. Specifically, self-concept and academic self-efficacy were first measured when participants were aged18 and then were remeasured when participants were aged 19 (Lee, 1997). In thestudy by Graham (2000), participants were assessed two times at 11–13 years old.Weighted regression analysis using age as a continuous variable was applied toassess the effect of age on the relation between self-concept and academic self-effi-cacy based on 70 samples. Age did not significantly affect the relation between aca-demic self-efficacy and self-concept (b=�.01, p> .05).


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Table 1. Summary of studies included in the meta-analysis.

Study Age NDomainof SC

Domainof SE

r: SC& SE

r: SC &AA

r: SE &AA

Anderman (1994) 11.50 678 S S .34 NA NABangert (1995) 40.00 34 M M .55 .48 .36Bowler (1982) 16.00 525 G GA .38 NA NABritner (2001) #1 12.00 127 S S .64 .38 .56Britner (2001) #2 12.00 135 S S .62 .45 .63Britner (2006) 11.50 319 S S .61 .49 .62Cantrell (2001) 24.15 264 G O .24 .05 .03Cara (2000) 36.50 92 G O .13 NA NACashin (2000) NA 226 M O .33 .36 .18Castillo (2002) 16.00 96 GA GA .32 NA NAChoi (2005) 20.50 230 GA, O GA, O .58 .40 .27Chong (2007) 12.00 1304 G GA �.13 NA NACole (2004) 20.77 164 G GA .20 NA NAD’Amico (2003) 13.40 151 G V, M .22 .04 .48Farran (2004) 19.31 186 GA, G GA .33 NA NAFerla (2010) 18.00 512 GA GA .41 .14 .27Fleming (1998) 26.29 232 M M .53 .38 .41Gloria (2001) 23.42 83 G GA .40 NA NAGraham (2000) A 207 M M .64 .45 .48Gungor (2007) 19.90 890 S S .54 NA NAHampton (1996)-1 16.00 128 G GA .24 .28 .41Hampton (1996)-2 16.00 150 G GA .26 .16 .61Hunt (2002)-1 19.50 164 G V .11 -.01 .27Hunt (2002)-2 19.50 98 G V -.01 .02 .29Karl (1994) 21.73 90 G O .29 .09 .10Klassen (2002)-1 12.38 112 M M .54 .57 .53Klassen (2002)-2 12.38 158 M M .41 .57 .50Klassen (2008) 23.33 261 G GA .28 .07 .36Ku (2002) NA 625 GA GA .40 NA NALane (2004) 27.50 205 G GA .32 NA NALee (1997) B 192 G, GA GA .43 .25 .25Lent (1997) 19.75 205 GA GA, M .44 .23 .38Lim (2000) NA 235 GA O .24 NA NALing (2006) 20.00 163 G GA .39 .21 .46Lopez (1992) 16.42 50 GA M .51 .60 .50Malone (2004) 16.50 159 G GA .26 .23 .25Marsh (1991) 9.00 410 V, M V, M .13 .17 .20Migray (2002) 11.50 651 GA M .38 .41 .46Mills (2007) 19.50 303 V V .59 .25 .24Mone (1995) 19.50 215 G O .24 .09 .33Oberman (2002) 16.17 314 O O .6 .47 .61Pajares (2007) 12.50 1258 V V .58 NA .33Pajares & Graham(1999)

11.00 273 M M .66 .48 .57

Pajares & Johnson(1995)

14.00 181 V V .51 .47 .60

Pajares and Miller(1994)

19.50 350 M M .61 .54 .70

Pajares, Miller, andJohnson (1999) #1

8.00 105 V V .47 .36 .58


9.00 123 V V .61 .38 .57

(Continued)

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Publication status

Whether the strength of the correlation between self-concept and academic self-effi-cacy varied as a function of publication status was tested. Table 2 lists the analyticalresults for four categorical moderators. A master’s thesis and book chapter each hadone data point and mean correlations were therefore not computed for these two pub-lication outlets. In this meta-study, QB denotes the variability between group means.When QB is significant, the mean correlations differ by more than sampling error.The effect of publication status was not supported with QB = 3.24 (p= .20). Effectsizes for journal articles and doctoral dissertations differed significantly from zero.

Self-concept domain

The self-concept domain was coded as global, academic, verbal, mathematics,science or other self-concept domains. Coding multiple effect sizes for various

Table 1. (Continued).

Study Age NDomainof SC

Domainof SE

r: SC& SE

r: SC &AA

r: SE &AA


10.00 135 V V .63 .39 .54

Pajares and Valiante(1999) #1

13.00 376 V V .60 .39 .38

Pajares and Valiante(1999) #2

13.00 366 V V .57 .32 .21

Paraskeva (2008) NA 286 G O .10 NA NAPeterson (2007) 21.00 306 GA GA .43 NA NAPhan (2010) 19.00 290 G O .25 �.03 .04Piccirillo (1995) 29.00 511 G, M, V,

GAGA .34 NA NA

Pietsch (2003) 15.00 416 M M .41 .31 .43Rao (2000) 16.00 94 M M .44 .57 .15Seidman (1996) 14.01 330 G GA .19 .09 .08Sharrow (1993) 13.50 15 M, V M, V .07 .12 .11Skaalvik (1997) 13.00 253 G, GA GA .56 NA NASkaalvik, E. (2002) 11.00 295 M M .62 .30 .30Skaalvik (2004) 16.60 483 V, M V, M .62 .65 .66Skaalvik (2006) 13.90 246 M M .64 NA NASkaalvik (2004) #1 10.90 277 M M .53 NA NASkaalvik (2004) #2 13.90 236 M M .63 NA NASkaalvik (2004) #3 15.80 263 M M .57 NA NASkaalvik (2004) #4 27.70 124 M M .76 NA NAStrelnieks (2003) 13.00 240 GA GA .55 .44 .35Swalander (2007) 14.67 4018 GA, V GA .59 .30 .28Tabassam (2002) 10.13 172 GA, M,

V, GM, V .57 NA NA

Tao (2006) 29.47 100 G GA .56 NA NAThomas (2007) 18.00 161 G GA .35 �.06 .41Usher (2009) 13.00 784 M M .61 .56 .46White (2006) 21.10 193 GA GA .59 NA NAYau (1995) 16.32 170 G, GA GA .21 NA NA

Notes: SC = self-concept; SE = self-efficacy; V=Verbal; M=math; S = science; O = other subject-specificdomains; GA= general academic; G = global; AA= academic achievement; NA= not available.A Participants were measured twice at 11–13 years old.B Participants were measured at both 18 and 19 years old.


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academic self-efficacy and self-concept measures for the same participant sampleyielded 95 effect sizes. Fourteen (14.74%) data points measured verbal self-concept,22 (23.16%) measured mathematics self-concept, five (5.26%) measured scienceself-concept, three (3.16%) measured other domains, 21 (22.11%) measured generalacademic self-concept and 30 (31.58%) measured global self-concept. The self-con-cept domain was strongly related to the relation between self-concept and academicself-efficacy with QB = 42.23 (p< .001). As indicated by the 95% confidence inter-vals, the mean correlation based on global self-concept (r= .27) was weaker thancorrelations among verbal self-concept (r= .51), mathematics self-concept (r= .53),science self-concept (r= .54) and general academic self-concept (r= .47).

Academic self-efficacy domain

Academic self-efficacy was classified as general academic, verbal, mathematics, sci-ence or other self-efficacy domains. In total, 17 (17.89%) data points measured ver-bal self-efficacy, 26 (27.37%) measured mathematics self-efficacy, five (5.26%)measured science self-efficacy, 11 (11.58%) measured self-efficacy for otherdomains and 36 (37.89%) measured general academic self-efficacy. The effect ofthe self-efficacy domain was also significantly supported by QB = 10.08 (p< .05),indicating that the domain of academic self-efficacy explains a significant amountof variance in the correlation between self-concept and academic self-efficacy. How-ever, comparisons between pairs of mean correlations were non-significant.

Table 2. Moderator analyses.

95% CI

Moderator k Mean r Lower Upper QB

Publication status 3.24Journal 44 .46 .39 .52dissertation 26 .36 .27 .45Conference paper 2 .44 �1.44 2.32

SC domain 42.23⁄⁄Verbal 14 .51 .41 .61Mathematics 22 .53 .45 .60Sciences 5 .54 .33 .75Other domains 3 .60 .18 1.00General academics 21 .47 .39 .55Global self-concept 30 .27 .20 .33

SE domain 10.08⁄Verbal 17 .44 .33 .55Mathematics 26 .52 .43 .61Sciences 5 .54 .28 .81Other domains 11 .34 .20 .49General academics 36 .38 .31 .45

Matching between SC and SE 35.60⁄⁄⁄Match 52 .53 .48 .58No match 43 .31 .26 .37

Notes: SC = self-concept; SE = academic self-efficacy.⁄p < .05; ⁄⁄p< .001.

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Matching domain between self-concept and academic self-efficacy

Analyses determined whether the matching domain between self-concept andacademic self-efficacy moderated the relation between self-concept and academicself-efficacy. For instance, a correlation between math self-efficacy and math self-concept was considered a match, whereas a correlation between math self-conceptand academic self-efficacy was not considered a match. Fifty-two data points repre-sented the domain match between self-concept and academic self-efficacy and 43points represented no match. The effect of the matching domain between self-con-cept and academic self-efficacy was significant by QB = 35.60, p< .001. Matchesbetween academic self-efficacy and self-concept were associated with larger effectsfor matching studies (r= .53) than were those for non-matching studies (r= .31).


Via inclusion criteria, 49 samples reported the relation between self-concept andacademic achievement. A meta-analysis was thus conducted to estimate the meancorrelation between self-concept and academic achievement. As shown in Table 3,the mean correlation was r= .30 with a 95% confidence interval of .25–.35. In syn-thesising over 800 meta-analyses of influences on academic achievement, Hattie(2009) provided reference points for assessing influences on academic achievement.Specifically, r= .1 was considered weak, r= .2 was considered moderate and r= .3was considered strong. According to guidelines postulated by Hattie, the correlationbetween self-concept and academic achievement in this study was strong. Fifty sam-ples reported that a correlation existed between academic self-efficacy and academicachievement; the mean correlation was .39 with a 95% confidence interval of.34–.43.

Cohen (1992) provided guidelines for effect sizes in multiple regression analy-sis. Specifically, R2/(1�R2) = .02 was considered weak, R2/(1�R2) = .15 was consid-ered moderate and R2/(1�R2) = .35 was considered strong. Thus, R2 = .02 wasconsidered weak, R2 = .13 was considered moderate and R2 = .26 was consideredstrong. When academic self-efficacy was entered into the regression model first,self-concept accounted for an additional 2% of variance in academic achievement(i.e. ZAA = βZSE first, then ZAA = β1ZSE + β2ZSC in equation forms). Based on theseguidelines, self-concept contributed little to prediction variance in academicachievement when academic self-efficacy was entered first into the regressionmodel. When self-concept was entered into the regression model first, academicself-efficacy accounted for an additional 8% of variance in academic achievement(i.e. ZAA = βZSC first, then ZAA = β1ZSC + β2ZSE in equation forms). Thus, academic

Table 3. Mean correlations to be used in regression analyses.

Total samples Matching Non-matching

Exclusion of three studies

Matching Non-matching

r: SC & SE .43 .53 .31 .53 .31r: SC & AA .30 .39 .19 .38 .18r: SE & AA .39 .42 .31 .40 .30

Notes: SC = self-concept; SE= academic self-efficacy; AA= academic achievement.


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self-efficacy explained a small to moderate amount of variance in academic achieve-ment after controlling for self-concept.

As the moderating effect of the matching domain between academic self-efficacyand self-concept was statistically significant, this study examines the incrementalvalidity of matching versus not matching. When the assessment focus of academicself-efficacy and self-concept was the same, incremental validity for academic self-efficacy was 7%, while that for self-concept was 4%. When the assessment focus ofacademic self-efficacy and self-concept differed, self-concept accounted for an addi-tional 1% of variance in academic achievement. Thus, self-concept contributes littleto the prediction of variance in academic achievement beyond that accounted for byacademic self-efficacy. When self-concept was entered into the regression modelfirst, academic self-efficacy accounted for an additional 7% of variance in academicachievement beyond that accounted for by self-concept.

The predictive power of academic self-efficacy can be inflated when identicalitems are used to determine academic self-efficacy and evaluate academic achieve-ment (Bong & Clark, 1999). This study examines the effect of item matchingbetween academic self-efficacy and academic achievement. Three studies (Klassen,2002; Migray, 2002; Pajares & Miller, 1994) had matching items between academicself-efficacy and academic achievement. Table 3 shows the correlations amongself-concept, academic self-efficacy and academic achievement after excluding thestudies by Klassen (2002), Migray (2002), and Pajares and Miller (1994). Afterexcluding these three studies, incremental validity for academic self-efficacy was6%, while that for self-concept was 4% for studies with the matching domainbetween self-concept and self-efficacy (Table 4). For studies with non-matchingdomains, incremental validity for academic self-efficacy was 7%, while that forself-concept was 1%.

Study 2

Method

PISA 2000

Data from 32 countries, representing a sample of 78,206, 15-year-old students, wereused to determine the strengths of correlations among self-concept, self-efficacy andacademic achievement. When PISA 2000 data were entered into weighted meananalysis in Study 1, this would generate a disproportionate influence on analysis.Thus, PISA data-sets were examined separate from data in Study 1.

In the PISA 2000, academic self-concept was assessed using the following threeitems: ‘I learn things quickly in most school subjects’, ‘I am good at most schoolsubjects’ and ‘I do well in tests in most school subjects’. The internal consistencyestimate of reliability, α, of scores for this scale was .77.

Math self-concept was also assessed using three items: ‘I get good marks inmathematics’, ‘Mathematics is one of my best subjects’ and ‘I have always donewell in mathematics’. The internal consistency estimate was .87 for this scale.

Verbal self-concept was assessed using the following three items: ‘I am hopelessin test language classes’, ‘I learn things quickly in test language class’ and ‘I getgood marks in test language’. This scale has an internal consistency estimate of .70.

To assess academic self-efficacy, each student responded to the following threeitems: ‘I’m certain I can understand the most difficult material presented in texts’,

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‘I’m confident I can do an excellent job on assignments and tests’ and ‘I’m certainI can master the skills being taught’. The internal consistency estimate of reliability,α, of scores for this scale was .69. Responses were on a 4-point Likert scale, rang-ing from one for ‘disagree’ to four for ‘agree’. Both math and reading test scoresare available in the PISA 2000 database.

PISA 2003

Data from 41 countries for a sample of 244,547, 15-year-old students were used toexamine the correlations among math self-concept, math self-efficacy and mathachievement. Math self-concept was assessed using five items. Participants indicatedtheir degree of agreement with statements such as ‘I learn mathematics quickly’.The estimated internal consistency was .88 for this scale.

Math self-efficacy comprised eight items. Participants rated their confidence intheir ability to complete eight mathematics tasks (e.g. solving an equation like 3x+ 5 = 17) using a scale ranging from ‘very confident’ to ‘not at all confident’. This

Table 4. Regression results.

Variable β ΔR2 R2

Total sample1. Self-efficacy .30 .15 .152. Self-concept .16 .02 .17

1. Self-concept .16 .09 .092. Self-efficacy .30 .08 .17

Match1. Self-efficacy .30 .18 .182. Self-concept .23 .04 .22


Not matching1. Self-efficacy .28 .10 .102. Self-concept .10 .01 .11


Exclusion of 3 studiesMatch1. Self-efficacy .28 .16 .162. Self-concept .23 .04 .20


Not matching1. Self-efficacy .27 .09 .092. Self-concept .10 .01 .10



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scale has an estimated internal consistency of .83. Prior math grades and math testscores are provided by PISA 2003.

PISA 2006

Data from 57 countries with a total sample size of 351,994 participants providedthe correlations among science self-concept, science self-efficacy and science testscores. Science self-concept was assessed using six items (e.g. Learning advancescience topics would be easy for me.). Response was on a 4-point Likert scale,ranging from ‘strongly agree’ to ‘strongly disagree’. This scale has an estimatedinternal consistency of .91.

Participants rated their confidence in their ability to solve eight scientific taskson a science self-efficacy instrument (e.g. Describe the role of antibiotics in thetreatment of disease.) Responses were on a 4-point Likert scale, ranging from ‘Icould do this easily’ to ‘I could not do this’. The estimated internal consistency was.82 for this scale. Test scores were used to indicate academic achievement.

Results

Mean Correlations for PISA 2000

Table 5 presents the mean correlation among self-concept, academic self-efficacyand academic achievement for PISA 2000, 2003 and 2006. For PISA 2000, the

Table 5. Mean correlation for PISA 2000, 2003 and 2006.

k Mean r Minimum Maximum

95% CI

Lower Upper

PISA 2000ASC-ASE 34 .54 .44 .68 .52 .57MSC-ASE 34 .35 .13 .51 .32 .37VSC-ASE 34 .31 .19 .48 .28 .33ASC-MT 34 .24 .02 .44 .20 .28ASC-RT 34 .26 �.01 .47 .22 .30MSC-MT 34 .26 �.01 .48 .22 .30VSC-RT 34 .25 .09 .38 .22 .27ASE-MT 34 .21 .02 .39 .18 .24ASE-RT 34 .21 .04 .38 .18 .24

PISA 2003MSC-MSE 41 .53 .34 .74 .50 .55MSC-MT 41 .30 .04 .52 .27 .33MSE-MT 41 .46 .20 .60 .44 .49MSC-M 16 .55 .03 .72 .46 .63MSE-M 16 .37 .01 .61 .29 .44

PISA 2006SSC-SSE 57 .41 .25 .60 .38 .43SSC-ST 57 .18 .02 .34 .16 .20SSE-ST 57 .24 .02 .38 .22 .26

k= number of countries; ASC= academic self-concept; ASE= academic self-efficacy; MSC=math self-concept; VSC= verbal self-concept; MT=PISA math test scores; RT= PISA reading test scores;MSE=math self-efficacy; M= prior math grades; SSC= science self-concept; SSE= science self-effi-cacy; ST= PISA science test scores.

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mean correlations of academic self-efficacy with academic self-concept, math self-concept and verbal self-concept were .54, .35 and .31, respectively. These findingssupport the effect of domain matching between self-concept and self-efficacy. Thestrengths of mean correlations for matching versus non-matching were also com-parable with those obtained by Study 1 (.53 vs. .31). The mean correlationbetween self-concept and academic achievement was in the range of .24–.26,approaching that (i.e. .30) in Study 1. However, the correlation between academicself-efficacy and the reading and math PISA 2000 test scores was .21, markedlysmaller than that in Study 1 (.39).

Mean correlations for PISA 2003

The strength of the correlation between math self-concept and math self-efficacywas in the range of .34–.74 and the mean was .53, equal to that for domainmatching in Study 1 and close to the mean correlation between academic self-concept and academic self-efficacy of .54 in PISA 2000. The differencebetween correlation coefficients of mathematics self-concept with PISA mathe-matics test scores (.30) and of mathematics self-efficacy with PISA mathematicstest scores (.46) was noticeable. This can be explained by matching specificitybetween self-measures with test scores. Bandura (1986) asserted that the predic-tive power of a measure depends on the match between the specificities of apredictor and of a criterion. Specificity matched between math self-efficacy andmath test scores because both were task-specific. Conversely, mathematics self-concept was domain-specific, while mathematics testing was task-specific. Math-ematics test grades were available for 16 countries, for a total sample of79,587 students. The differences in correlations between mathematics self-con-cept with prior mathematics grades (.55) and of mathematics self-efficacy withprior mathematics grades (.37) were also noticeable. The likely reason for thisdifference was again the specificity match between self-measures and academicachievement. Both mathematics self-concept and math grades are domain-spe-cific, such that specificity matching existed. Mathematics self-efficacy was mea-sured at a task-specific level, while mathematics grades are at a domain-specificlevel. Thus, specificity matching between math self-efficacy and math gradeswas not achieved.

Mean correlations for PISA 2006

The mean correlation between science self-efficacy and science self-concept was.41, lower than that for domain matching in Study 1, PISA 2000 and PISA2003. The correlation between science self-concept and PISA science test scorewas .18, and weaker than correlations between self-concept and achievement inStudy 1, PISA 2000 and PISA 2003. The correlation between science self-effi-cacy and PISA science test score was also relatively weak at .24. In summary,the strengths of the correlations among self-concept, self-efficacy and academicachievement for PISA 2006 were weaker than those in study 1, PISA 2000 andPISA 2003.


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PISA 2000

The mean correlations listed in Table 5 were used as inputs for regression anal-yses. Table 6 shows that when academic self-efficacy was entered into theregression model first, academic self-concept explained an additional 3% of vari-ance in the PISA math test scores (i.e. ZAA = βZSE first, then ZAA = β1ZSE + β2ZSCin equation forms). Total explained variance of self-concept and academic self-efficacy was approximately 7%. Moreover, when academic self-concept wasentered into the regression model first, adding academic self-efficacy (i.e.ZAA = βZSC first, then ZAA = β1ZSC + β2ZSE in equation forms) increased theexplained variance by 1%. Thus, incremental validity was weak for academicself-concept and academic self-efficacy.

As the specificity of self-concept and academic achievement may moderate theincremental validity of self-concept and self-efficacy, the incremental validity ofmath and verbal self-concepts in predicting PISA reading and math test scores isexamined. The incremental validities for math and verbal self-concepts wereroughly 4% and were 1–2% for academic self-efficacy. Again, the incrementalvalidity for self-concept and self-efficacy was weak.

Table 6. Regression results for PISA 2000.

Variable β ΔR2 R2

Math1. ASE .11 .04 .042. ASC .18 .03 .07

1. ASC .18 .06 .062. ASE .11 .01 .07

Reading1. ASE .10 .04 .042. ASC .21 .03 .07

1. ASC .21 .07 .072. ASE .10 .00 .07

Math1. ASE .14 .04 .042. MSC .21 .04 .08

1. MSC .21 .07 .072. ASE .14 .01 .08

Reading1. ASE .15 .04 .042. VSC .20 .04 .08

1. VSC .20 .06 .062. ASE .15 .02 .08

Notes: ASC= academic self-concept; MSC=math self-concept; VSC= verbal self-concept; ASE= aca-demic self-efficacy.

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PISA 2003

Table 7 lists the incremental validity for PISA 2003. Both math test scores and pre-vious math marks are available in PISA 2003, and these two variables were used ascriterion variables. For the PISA 2003 math test score, math self-efficacy had amuch stronger incremental validity than math self-concept. Specifically, when mathself-efficacy was entered into the regression model first, adding math self-conceptincreased explained variance by 1%. When self-concept was entered into the regres-sion model first, math self-efficacy explained an additional 13% of variance in thePISA math score, suggesting a moderate incremental validity of math self-efficacyafter controlling for math self-concept. Regression of the PISA math test scores onmath self-efficacy and math self-concept yielded an R2 = .22. When prior mathmarks were used as a criteria variable, math self-concept exhibited moderate to highincremental validity. When math self-efficacy was entered into the regression modelfirst, math self-concept explained a further 17% of variance in PISA 2003 mathscores. When math self-concept was entered into the regression model first,explained variance increased by only 1%.

PISA 2006

When science self-efficacy was entered into the regression model first, science self-concept explained an additional 1% of the variance in PISA science test scores.Thus, science self-concept contributed little to predicting variance in science testscores when science self-efficacy was entered into the regression model first. Whenscience self-concept was entered into the regression model first, science self-efficacyaccounted for an additional 4% of variance.

Discussion

Study 1 meta-analysed 74 independent samples (N= 24,773), yielding a strongmean correlation between self-concept and academic self-efficacy of .43. Study 2

Table 7. Regression results for PISA 2003 and 2006.

Variable β ΔR2 R2

2003 Test score1. Self-efficacy .42 .21 .212. Self-concept .08 .01 .22


2003 Prior math mark1. Self-efficacy .11 .14 .142. Self-concept .49 .17 .31


20061. Self-efficacy .20 .06 .062. Self-concept .10 .01 .07



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focused on PISA data-sets and provided additional estimates of the strength of thecorrelation between self-concept and academic self-efficacy; estimates were in therange of .31–.54. Although the consistency of the approximately strong correlationbetween self-concept and academic self-efficacy academic self-efficacy scores stillcontain variance not shared by self-concept and vice versa. This finding supportsthe discriminant validity between self-concept and academic self-efficacy and sug-gests construct separation between self-concept and academic self-efficacy.

An investigation of five potential moderators reveals that the self-conceptdomain moderates the correlation between academic self-efficacy and self-concept.Global self-concept exerted a smaller influence on academic achievement than didacademic self-concept and subject-specific self-concept. Similarly, the self-efficacydomain also moderated the correlation between self-concept and academic self-effi-cacy. These findings underscore the importance of measurement specificity in gen-eral self-belief literature (Marsh & Craven, 2006; Pajares & Miller, 1994, 1995).

Analyses examined whether the matching domain between self-concept and aca-demic self-efficacy affected the strength of their correlation. As expected, correla-tions were strong when an identical assessment focus was shared for academicself-efficacy and self-concept. In Study 1, the strength of the correlation betweenself-concept and academic self-efficacy for domain matching between self-conceptand academic self-efficacy was strong at .53. A similar correlation was obtainedwith PISA 2000 and 2003 data. The strength of the correlation for the non-match-ing domain in Study 1 was moderate to strong at .31. The PISA 2000 data alsogenerated similar estimates.

Bong and Clark (1999) argued that academic self-efficacy had better predictivepower than self-concept because self-efficacy was more context-specific. The moregeneral scope of self-concept reduced its predictive utility. To determine their practi-cal utility, this study examines the incremental validity of self-concept and academicself-efficacy. Overall, the incremental validity of academic self-efficacy (6�8%)exceeded that for self-concept (1–4%) in Study 1. This finding supports the notionthat the predictive power of academic self-efficacy exceeds that of self-concept(Bandura, 1981; Bong & Clark, 1999; Bong & Skaalvik, 2003). According toCohen (1992), the incremental validity of self-concept over was weak, while that ofself-efficacy was weak to moderate. Although self-efficacy was developed to assessthe confidence of individuals in their ability to successfully perform certain tasks,and self-concept was developed to assess individual perceptions of the self, includ-ing both self-concept and self-efficacy in the regression may be redundant.

In Study 2, the incremental validity of self-concept sometimes exceeded that ofacademic self-efficacy. For example, the incremental validity of self-conceptexceeded that of academic self-efficacy in predicting PISA 2000 reading and mathtest scores. These analytical results contrast with the view that self-efficacy outper-forms self-concept in predicting academic achievement. For PISA 2003, the incre-mental validity for math self-concept exceeded that for academic self-efficacy inpredicting past math grades. Some possible explanations exist for the ambiguitiesregarding the relative incremental validity between self-concept and academic self-efficacy. First, math self-concept and math grades were measured at the subject-spe-cific level. Matching specificity between math self-concept and math grades mayincrease the incremental validity of math self-concept. Second, the wording of theitem used to measure prior math concept resembled that of one of the five itemsused to measure self-concept. Specifically, past math grades were measured using

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the item ‘In your last school report, what was your mark in mathematics?’ Mean-while, one of the items used to measure self-concept was ‘I get good marks inmathematics’. The similarity in wording between the items used to measure mathself-concept and past math grades may increase the incremental validity of mathself-concept. Conversely, math self-efficacy in PISA 2003 was measured at a task-specific level (e.g. participants were asked to rate their confidence in their ability tosolve the following equation 2(x+ 3) = (x+ 3)(x� 3)). The measures of math self-efficacy and prior math grades were worded quite differently, reducing the incre-mental validity of math self-efficacy.

Examining the discriminant and incremental validity of self-concept and aca-demic self-efficacy yielded the following observations. First, academic self-efficacydid not necessarily have higher predictive power than self-concept. Educational psy-chologists thus should not be overconfident regarding the relative predictive powerof self-efficacy. Meanwhile, educational researchers should realise that the wordingand domain specificity of self-measures, as well as domain matching of self-mea-sures and academic achievement, affect predictive power. Second, researchers orpractitioners need to carefully consider the similarities and differences between self-concept and self-efficacy; they should also consider which they should favour ifforced to choose. Self-concept was developed to measure individual self-evalua-tions, whereas self-efficacy measures the confidence of individuals in their ability tosuccessfully execute a task. Hence, if assessed using performance in similar tasks,self-efficacy can be a powerful predictor. Furthermore, self-concept measures affec-tive and cognitive components of the self, whereas self-efficacy only measurescognitive components. When the criterion variables are only cognitive outcomes,self-efficacy can be a better predictor. In contrast, when emotional outcomes areinvolved, self-concept should be used (Bong & Skaalvik, 2003).

Future directions and limitations

Self-concept and academic self-efficacy are two important explanatory factors usedwhen predicting academic achievement. Analytical findings suggest that the jointeffects of these two constructs depend on the wording and domain of self-measuresas well as the match specificity of self-concept and self-efficacy with academicachievement. As academic achievement was the only criterion variable used in thisstudy, future research should broaden the incremental validity of self-efficacy byusing motivational and affective constructs as criterion variables.

Although the domains of self-concept and self-efficacy significantly affected therelation between academic self-efficacy and self-concept, the effect of the self-con-cept and academic self-efficacy domains on incremental validity of academic self-efficacy was not examined due to the small number of data points. Future researchcan examine the moderating effects of the academic self-efficacy and self-conceptdomains on incremental validity.

The relation between academic self-efficacy and self-concept across scales couldnot be compared due to the large number of scales and small number of studiesusing the same scale. Future research should examine whether the relation betweenacademic self-efficacy and self-concept depends on instrument scales. The non-equivalence of measures has been a minor issue for self-concept, as most studiesused the Self-Esteem Scale (Rosenberg, 1979) and Self-Description Questionnaire(Marsh, 1992a, 1992b, 1992c).


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Despite its contributions to literature, this study has limitations. The first limita-tion is that all studies analysed used self-reported measures to assess academic self-efficacy and self-concept. Owing to the shared method variance, the correlationbetween academic self-efficacy and self-concept may be biased upwards. Second,despite conceptual differences between global self-concept and self-esteem, empiri-cal differentiation was difficult. Therefore, this study combined these two terms intoa single category.

Conclusions

As the variance in academic self-efficacy shared by self-concept was below 30%, asubstantial amount of academic self-efficacy was not shared by self-concept. Dis-criminant validity between academic self-efficacy and self-concept was thusachieved. For researchers interested in using one of the two constructs to predictacademic achievement, academic self-efficacy and self-concept can be equally validmeasures of academic achievement. Domain-specific self-measures will be moreuseful than global measures in predicting academic achievement. The claim that thepredictive power of academic self-efficacy was higher than that of self-concept wasnot supported fully. The predictive power of self-measures increased given matchingspecificity between self-measures and academic achievement.

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Appendix: included studies

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Britner, S.L., & Pajares, F. (2001). Self-efficacy beliefs, motivation, race, and gender in mid-dle school science. Journal of Women and Minorities in Science and Engineering, 7, 269–283.

Britner, S.L., & Pajares, F. (2006). Sources of science self-efficacy beliefs of middleschool students. Journal of Research in Science Teaching, 43, 485–499. doi:10.1002/tea.20131

Cantrell, S.W. (2001). Self-efficacy, causal attribution, self-esteem, and academic successin baccalaureate nursing students (Doctoral dissertation). Available from ProQuest Disserta-tions and Theses database. (UMI No. 3000897)

Cara, E.D. (2000). The effects of a prepracticum educational module on self-efficacy,self-esteem, anxiety, and performance (Doctoral dissertation). Available from ProQuestDissertations and Theses database. (UMI No. 3006452)

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Castillo, E.M. (2002). Psychosociocultural predictors of academic persistence decisionsfor Latino adolescents (Doctoral dissertation). Available from ProQuest Dissertations andTheses database. (UMI No. 3060555)

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Choi, N. (2005). Self-efficacy and self-concept as predictors of college students' aca-demic performance. Psychology in the Schools, 42, 197–205. doi:10.1002/pits.20048

Chong, W.H. (2007). The role of personal agency beliefs in academic self-regulation: anAsian perspective. School Psychology International, 28, 63–76. doi:10.1177/0143034307075681

Cole, J.S., & Denzine, G.M. (2004). ‘I’m not doing as well in this class as I’d like to’:Exploring achievement motivation and personality. Journal of College Reading and Learn-ing, 34, 29–44.

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Discriminant and Incremental Validity of Self-concept And