Learning and Individual Differences 21 (2011) 196–200 Contents lists available at ScienceDirect Learning and Individual Differences j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / l i n d i f Goal orientations predict academic performance beyond intelligence and personality Ricarda Steinmayr a,⁎, Tanja Bipp b, Birgit Spinath c a b c Philipps-University Marburg, Germany Eindhoven University of Technology, The Netherlands Heidelberg University, Germany a r t i c l e i n f o Article history: Received 18 May 2010 Received in revised form 5 November 2010 Accepted 30 November 2010 Keywords: Academic achievement Intelligence Big Five Goal orientations a b s t r a c t Goal orientations are thought to be an important predictor of scholastic achievement. The present paper investigated the joint influence of goal orientations, intelligence, and personality on school performance in a sample of N = 520 11th and 12th graders (303 female; mean age M = 16.94 years). Intelligence, the Big Five factors of personality (Neuroticism, Extraversion, Openness to Experience, Agreeableness, and Conscientiousness) as well as goal orientations (learning, performance-approach, -avoidance, and work-avoidance goals) were assessed. When school performance was regressed on all variables simultaneously, intelligence, Openness to Experience, Conscientious, and learning goals predicted school performance. Learning goals additionally partially mediated the association of Openness to Experience and Conscientious, respectively, with GPA. Results are discussed with regard to the importance of goal orientations in academic settings. © 2010 Elsevier Inc. All rights reserved. 1. Empirical background Different goal orientations have recently received a great deal of attention in achievement motivation research (e.g., Wigfield & Cambria, 2010). One focus of interest is the association between goal orientations and academic achievement. Numerous studies have found a substantial relationship between goal orientations and academic achievement in both school and university settings (e.g., Murayama & Elliot, 2009; Steinmayr & Spinath, 2009). Although these studies have impressively underlined the importance of goal orientations in academic contexts, the interplay of goal orientations with other well-established predictors of academic achievement has not yet been sufficiently investigated. The present study was designed to investigate the incremental validity of goal orientations over intelligence and personality in predicting academic achievement, as well as the potential mediation effects of goal orientations regarding the association between personality and academic achievement. 1.1. Prediction of academic achievement Intelligence is frequently considered to be the strongest predictor of school performance (Kuncel, Hezlett & Ones, 2004). Research has also identified associations between personality traits in the Big Five Model of personality (e.g., McCrae & Costa, 1987) and school performance. The five personality traits comprise Neuroticism (N), ⁎ Corresponding author. Department of Psychology, Philipps-Universität Marburg, Gutenbergstr. 18, 35032 Marburg, Germany. Tel.: + 49 6421 2823450. E-mail address: steinmay@staff.uni-marburg.de (R. Steinmayr). 1041-6080/$ – see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.lindif.2010.11.026 Extraversion (E), Openness to Experience (O), Agreeableness (A), and Conscientiousness (C). In his recent meta-analysis, Poropat (2009) found GPA to be weakly associated with O (r = .12) and C (r = .21) in secondary education after controlling for intelligence. Concerning achievement goals, we concentrated on the trichotomous goal framework (e.g., Elliot & Church, 1997). This framework comprises learning goals (LGs; focus on the increase of one's competence), performance-approach goals (P-ApGs; striving to demonstrate competence), and performance-avoidance goals (P-AvGs; striving not to demonstrate a lack of competence). Additionally, we considered work avoidance (WA; the goal to invest as little work and effort as possible; e.g., Nicholls, 1984). The pattern of correlations between achievement goals and academic performance supports a multiple goal perspective (Harackiewicz, Barron, Tauer & Elliot, 2002) in which both LGs and P-ApGs are positively related to academic performance and further educational outcomes. A recent meta-analysis demonstrated a weak positive correlation between the LG trait and academic achievement (Payne, Youngcourt & Beaubien, 2007). The results of this meta-analysis concerning performance goals should be interpreted cautiously because coding of the P-ApGs was not unequivocal (for a more detailed critique, cf. Bipp, Steinmayr & Spinath, 2008, p. 1456). Single studies have most often found P-ApGs to be weakly positively related to achievement, whereas P-AvGs have been found to be uncorrelated or weakly negatively correlated with performance (e.g., Zusho, Pintrich & Cortina, 2005). Whether LGs or P-ApGs are better predictors of school performance is an issue as yet unresolved and may depend on the context in which the goals are pursued (cf. Murayama & Elliot, 2009). WA has consistently been shown to be weakly negatively correlated with achievement (e.g., Steinmayr & Spinath, 2009). Thus, 197 R. Steinmayr et al. / Learning and Individual Differences 21 (2011) 196–200 Table 1 Means (M), standard deviations (SD), internal consistencies (α), and intercorrelations among all predictors. Descriptives Intercorrelations M SD 4.01 27.92 .60 4.08 .71 2.78 3.52 3.21 3.51 3.42 .64 .51 .53 .52 .58 .85 .78 .72 .78 .84 3.94 3.22 2.31 2.37 .54 .72 .81 .81 .79 .81 .89 .89 α a Gender GPAb Intelligence (Int) Personality Neuroticism (N) Extraversion (E) Openness to Experience (O) Agreeableness (A) Conscientiousness (C) Goal orientation Learning (LG) Performance-approach (P-ApG) Performance-avoidance (P-AvG) Work avoidance (WA) GPA Int N E O A C LG P-ApG P-AvG WA −.06 .11 .22 −.37 −.17 −.06 −.09 −.01 −.02 −.08 .18 .11 −.14 .05 −.07 −.07 .32 −.02 −.10 .24 −.01 .00 .11 −.02 −.02 −.05 .00 .23 −.11 .10 −.31 .04 −.05 −.08 .20 −.03 −.18 .10 .04 .20 −.01 .07 .25 .12 .34 .02 .05 −.01 −.22 .22 .31 −.10 −.09 −.21 .00 .05 −.08 −.12 −.25 −.34 .30 −.01 .56 −.30 .16 .44 Note. N = 509–520. The intelligence scale ranged from 0 to 50. All other scales ranged from 1 to 5 with 1 indicating lower personality trait and goal orientation values. Correlations≥ |.09|, p b .05; correlations≥ |.11|, p b .01. a Gender was coded as 1 = female and 2 = male. b The GPA mean was based on the reversed grades. goal orientations are significant though not very strong predictors of academic achievement. To evaluate the importance of goals for academic achievement, not only should the absolute magnitude of the association be considered but also the incremental contribution over other well-established predictors such as intelligence and personality. However, this research question has scarcely been addressed (cf. Payne et al., 2007). Steinmayr and Spinath (2009) demonstrated that LGs (positively) and WA (negatively) predicted GPA after controlling for intelligence. Freudenthaler, Spinath and Neubauer (2008) investigated the joint impact of intelligence, personality traits, goals orientations, and further motivational variables on school achievement separately for boys and girls in a sample of low-achieving middle school students. WA predicted school achievement beyond the other variables for girls, and P-AvGs did so for boys. However, the beta weights of these variables did not significantly differ between the sexes. Whether these beta weights were significant also for the total sample was not reported. Because personality traits and all goal orientations have frequently been demonstrated to be correlated (for a summary, cf. Bipp et al., 2008), investigating the joint influence of personality traits and achievement goals on academic achievement is of special importance. We expected goal orientations to possess incremental validity over personality when explaining school performance because the goal construct specifically focuses on achievement situations, whereas personality traits are much broader and less focused on predicting outcomes in specific situations. Furthermore, we hypothesized the effect of personality traits on school performance to be partially mediated by goal orientations. On the one hand, goal orientations are thought to be influenced by personality traits such as the Big Five (e.g., Table 2 Sex-specific means (M) and standard deviations (SD) of those variables in which boys (n = 217) and girls (y = 303) differed, as well as results testing for sex differences in variances (Levene's test) and means (t test and Welch test). Girls M Boys SD M Levene's test t or Welch test SD F p t p d Intelligence 27.54 4.08 28.45 4.03 .16 .69 − 2.50 .01 −.22 Neuroticism 2.98 .62 2.50 .54 8.61 b.001 9.40 b.001 .21 Extraversion 3.56 .51 3.46 .51 .02 .90 2.03 .04 −.47 Agreeableness 3.57 .53 3.43 .50 .23 .63 3.17 b.01 .83 Learning goals 3.99 .55 3.87 .51 .87 .35 2.39 .02 .18 Work avoidance 2.21 .76 2.58 .84 2.92 .09 − 5.28 b.001 .28 Note. The Welch test was used when Levene's test resulted in an F value with p ≤ .05. Elliot & Thrash, 2002). On the other hand, goal orientations are thought to be a more proximal predictor of specific behaviors such as school performance (Elliot & Church, 1997). Thus, we examined both the shared (mediation hypothesis) and unique (incremental validity hypothesis) variance explanations of goal orientations and personality traits in predicting school performance. 1.2. Hypotheses Hypothesis 1. Goal orientations predict school performance after controlling for intelligence and personality. Hypothesis 2. The relationship between personality traits and school performance is partially mediated by goal orientations. 2. Method 2.1. Sample and procedure The sample was recruited from three different German schools that prepare children for university (Gymnasium) in three midsized Table 3 Hierarchical multiple regression of general school achievement on measured general intelligence, personality, and learning goals. Model 1 Model 2 Model 3 GI GI E N O A C GI E N O A C LG P-ApG P-AvG WA Beta t p R R2 R2adj. ΔF(df) Δp .22 .20 −.07 −.12 .15 .01 .30 .20 −.08 −.11 .12 .02 .26 .11 .06 −.05 .02 5.12 4.96 − 1.71 − 2.70 3.71 −.27 7.28 5.01 − 1.93 − 2.45 2.86 .39 5.58 2.41 1.03 −.80 .37 b.001 b.001 .09 .01 b.001 .78 b.001 b.001 .05 .01 b.01 .70 b.001 .01 .30 .42 .71 .22 .44 .05 .19 .05 .18 26.24 (1, 507) 17.64 (5, 502) b.001 b.001 .46 .21 .20 2.38 (4, 498) .03 Note. N = 509. GI = General Intelligence; E = Extraversion; N = Neuroticism; O = Openness to Experience; A = Agreeableness; C = Conscientiousness; LG = Learning Goal; P-ApG = Performance-Approach Goal; P-AvG = Performance-Avoidance Goal; WA = Work Avoidance. 198 R. Steinmayr et al. / Learning and Individual Differences 21 (2011) 196–200 Table 4 Relative weights and percentage of explained criterion variance (%) for all predictors of school performance. Predictor RW % Intelligence Neuroticism Extraversion Openness to Experience Agreeableness Conscientiousness Learning goals Performance-approach goals Performance-avoidance goals Work avoidance R² total .05 .02 .00 .02 .00 .07 .03 .01 .00 .01 .21 22.4 9.3 1.4 10.5 .8 35.7 12.8 3.6 1.3 2.3 100 towns. The sample consisted of 520 students (303 female). The average age was 16.94 years (SD = .71), ranging from 16 to 19. Testing was voluntary, but apart from students who were ill on the day of testing, all students in grades 11 and 12 participated. We received signed consent forms from parents of underage students. All students took part in a wider assessment (duration: approximately 90 min). In the following sections, we will focus on the variables that were crucial for the present study. 2.2. Measures Goals. A German self-report measure was used to assess goal orientations (“Skalen zur Erfassung der Lern- und Leistungsmotivation”; SELLMO; Spinath, Stiensmeier-Pelster, Schöne & Dickhäuser, 2002). The instrument (“In school, it is important for me …”) contains four subscales measuring LGs (e.g., “…to learn as much as possible”; 8 items), P-ApGs (e. g., “…that others think I am smart”; 7 items), and P-AvGs (e.g., “….not to embarrass myself by giving wrong answers or asking dumb questions”; 8 items), as well as WA (e. g., “…to do as little work as possible”; 8 items). Items were answered on a 5-point scale ranging from 1 (totally disagree) to 5 (totally agree). Personality. The Big Five factors of personality were assessed with the German version of the NEO-FFI (Borkenau & Ostendorf, 1993), with 12 items assessing each trait. Intelligence. Intelligence was assessed by means of the German version of Cattell's Culture-Fair-Test 3 (CFT 3; Weiß, 1971). The CFT measures nonverbal, fluid intelligence. The test was administered in its short version, which is highly correlated with the full test version (r = .92; Weiß, 1971). School performance. Overall school performance was indicated by students' grade point average (GPA) excluding arts, music, and sports. Grades ranged from 1 to 6 and were recoded so that higher grades indicate better performance. All but 11 report cards the school delivered could be assigned to the students. 3. Results Table 1 presents descriptive statistics and intercorrelations of all variables from the present study. Beside means, standard deviations, and alpha coefficients, all analyses were based on schoolwise z-standardized variables to control for potential school effects. Boys and girls significantly differed in intelligence, N, E, A, LG, and WA1 (cf. Table 2 for detailed statistics). To test Hypothesis 1 concerning the incremental validity of achievement goals over intelligence and personality, we performed hierarchical multiple regression analyses with GPA as the dependent 1 All analyses were performed separately for girls and boys. Because no genderspecific results were found (cf. Freudenthaler et al., 2008 for the same variables), results are reported only for the total sample. Table 5a Results of the regression analyses that regressed those personality traits on those goal orientations that fulfilled prerequisites for being a mediator of the association between personality and GPA. Personality trait (predictor) Goal orientation (criterion) Conscientiousness Learning goal Conscientiousness Performance-approach goal Conscientiousness Work avoidance Openness to Learning goal Experience Openness to Work avoidance Experience B SE .37 .04 .17 .03 Beta .34 .22 t p 8.30 b.001 5.03 b.001 −.24 .03 −.34 − 8.11 b.001 .25 .04 .25 5.97 b.001 −.08 .03 −.12 − 2.73 .006 Note. N = 520. variable. In each regression analysis, intelligence was entered in the first model (Model 1). The second model additionally contained all investigated personality traits (Model 2), and the third model (Model 3) also contained all goal orientations. Because the variables were intercorrelated, we also determined relative weights of all predictors when predicting school performance by all variables simultaneously to determine the percentage of predicted criterion variance uniquely attributed to each variable (cf. LeBreton, Hargis, Griepentrog, Oswald & Ployhart, 2007). Results are depicted in Tables 2 and 3. Intelligence was a significant predictor (Model 1), but only accounted for 5% of the variance in school achievement (Table 4). The personality traits together explained 14% of the variance beyond intelligence (Model 2). N, O, and C were significant predictors of school performance. C contributed the largest amount of unique variance explained (UVE; 36%). The shares of UVE for O (11%) and N (9%) were smaller. Model 3 demonstrated that only LGs incrementally contributed to the prediction beyond personality and intelligence. P-ApGs, P-AvGs, and WA did not significantly add to the prediction of school performance.2 Additionally, LGs had the highest share of UVE (13%). The contributions of the other achievement goals were rather small. Thus, the results partly confirmed the assumptions made in Hypothesis 1. In Hypothesis 2, we expected the relationships among personality traits and school performance to be mediated by goal orientations. According to Baron and Kenny (1986), mediation analyses require that both predictors are correlated with each other and with the criterion. This applied to C and LGs, P-ApGs, and WA, as well as to O and LGs and WA. Regression analyses were performed to test the mediation effects. First, the personality trait was regressed on the learning goal orientation (Table 5a). Second, the specific learning goal was regressed on school performance controlling for the personality trait because the mediator must be correlated with the criterion while also controlling for the initial variable (Table 5b; Supplementary data). Third, in hierarchical multiple regressions, the personality trait was entered in Model 1 and the goal orientation in Model 2 (Table 5c). The significance of the mediation effects were tested by means of the Sobel test if the prerequisites were fulfilled, which applied to C and LGs as well as O and LGs and WA. LGs significantly mediated the impact of C (z = 2.96, p b .001) and O (z = 3.64, p b .001) on school achievement. WA did not significantly mediate the relationship between O and GPA (z = 1.65, p = .10). Additionally controlling for intelligence did not alter the results. 2 We also inspected Hypothesis 1 by separately testing the incremental validity of each goal orientation beyond both intelligence and personality. Results were comparable to those found in Model 3: LGs incrementally contributed to the prediction (β=.12, t=2.62, p=.009) above personality and intelligence. Performance-approach (β=.06, t=1.45, p=.147), performance-avoidance (β=−.004, t=−0.10, p=.924) goals, and WA (β=−.01, t=0.22, p=.824) did not significantly add to the prediction of school performance. 199 R. Steinmayr et al. / Learning and Individual Differences 21 (2011) 196–200 Table 5c Results of the hierarchical multiple regression analyses that first regressed those personality traits on grade point average (Model 1) and then those goal orientations in Model 2 that fulfilled prerequisites for a mediation analysis concerning the association between specific personality traits and GPA. Analysis 1 Model 1 Model 2 Analysis 2 Model 1 Model 2 Analysis 3 Model 1 Model 2 C C LG O O LG O O WA B SE Beta t p R R2 ΔF(df) Δp .37 .31 .17 .22 .16 .25 .22 .21 .07 .05 .05 .05 .05 .06 .05 .05 .05 .04 .33 .28 .14 .18 .13 .20 .18 .17 .09 7.73 6.21 3.15 4.16 2.88 4.54 4.16 3.89 2.08 b.001 b.001 .002 b.001 .004 b.001 b.001 b.001 .04 .33 .35 .11 .12 59.68 (1, 507) 9.90 (2, 506) b.001 .002 .18 .27 .03 .07 17.30 (1, 507) 20.58 (2, 506) b.001 b.001 .18 .20 .03 .04 17.30 (1, 507) 4.32 (2, 506) b.001 .04 Note. N = 509. C = Conscientiousness; O = Openness to Experience; LG = Learning Goal; WA = Work Avoidance. 4. Discussion The present paper investigated the joint influence of intelligence, personality traits, and achievement goals on academic achievement. Intelligence, N, O, C, LGs, P-ApGs, and WA goals were significant predictors of school achievement. However, when school achievement was regressed on all predictors simultaneously, only intelligence, O, C, and LGs significantly predicted school achievement. LGs significantly mediated the association between O and C, respectively, and school achievement. As in other studies (e.g., Bipp et al., 2008), intelligence and goal orientations were mostly uncorrelated. Thus, those goal orientations associated with school achievement (LGs, P-ApGs, and WA) possess incremental validity above and beyond intelligence. However, in our analyses we additionally controlled for personality traits (Hypothesis 1). Then, only LGs were incrementally valid. The results diverged from those found by Freudenthaler et al. (2008) because we found neither WA nor P-AvGs to be incrementally valid. Furthermore, we found no gender-specific results. Because those authors focused on a larger and different sample (low-achieving students), considered more variables, and used different or only short measures of all constructs, the reasons for the divergent results may lie in one or all of these differences from the present study. Our results showed that only LGs incrementally predicted a share of variance in school achievement beyond both personality traits and intelligence. The pursuit to increase one's competencies is an aspect of LGs that is not included in any other personality trait or intelligence considered in the present study. We suggest that this aspect may be the cause for the incremental validity of LGs. Performance goals and WA, on the other hand, seem not to possess such unique aspects. Whatever causes their relationship to academic achievement is already captured by the Big Five. Bipp et al. (2008) demonstrated a distinct correlational pattern between the facets of the Big Five and goal orientations. Further studies should investigate which facets are related to both goal orientations and school performance. Moreover, the presented results contribute to the discussion of multiple goal perspectives (Harackiewicz et al., 2002) in school settings. Unlike LGs, P-ApGs do not cover a share of variance in school achievement not accounted for by personality. It might be that P-ApGs are a domain-unspecific tendency to demonstrate one's competencies, and these goals may be closely related to one's personality (see also Bong, 2001). This thought is supported by a study that demonstrated a domain-specific operationalization of LGs but not of P-ApGs (Sparfeldt, Buch, Wirthwein & Rost, 2007). Whereas performance goals may be a part of a persons' personality, LGs are thought to be influenced by context factors such as classroom variables (e.g., Murayama & Elliot, 2009). These factors may also account for the incremental validity of LGs in comparison to performance goals. The importance of LGs is further supported by the mediation analyses. Partly supporting the assumptions made in Hypothesis 2, LGs but not P-ApGs and WA significantly mediated the relationships of C and O, respectively, with school performance. Both C and O seem to promote the school-related goal to improve one's competencies, which in turn improves one's academic achievement. Concerning a possible causal relationship from C to LGs, the facets Competence and Achievement Striving may foster LGs. The trait of feeling competent and the wish for high achievement in life may promote the goal to improve one's competencies. This thought is in line with positive correlations between those facets and LGs (Bipp et al., 2008). Furthermore, the general tendency to value new ideas in different areas may also foster LGs. If one wants to improve one's competencies, one has to be open to new ideas. This rationale is supported by positive correlations between LGs and all facets of O (Bipp et al., 2008). However, because the data are cross-sectional, they do not allow for any causal conclusions; thus, further studies should explore the relationships between goal orientations and personality traits longitudinally or experimentally. The results of the relative weight analysis show that C was the strongest predictor of all variables even in the presence of intelligence.3 LGs explained as much unique variance as O and N. Thus, the presented results suggest that some personality traits are as important as goal orientations in academic contexts, or even more important. This is in line with Caspi, Roberts and Shiner (2005), who consider personality traits to be crucial personal prerequisites for success. However, context variables having a potential impact on the relationship between personality traits and achievement have not yet been investigated. For example, in a collaborative learning classroom setting, as compared to the traditional lecture-based learning environment prevalent in the present study, more social aspects of the Big Five, such as A, may predict school achievement.4 Following the same rationale, a domain-specific operationalization of goal orientations as suggested by Murayama and Elliot (2009) may increase their importance for certain subjects. Domain-specifically operationalized constructs have higher criterion-validity than domain-unspecifically operationalized constructs when predicting domain-specific criteria (e.g., Steinmayr & Spinath, 2009). Further studies should consider these two context aspects. Supplementary materials related to this article can be found online at doi:10.1016/j.lindif.2010.11.026. References Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. 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