Whereas meta-analytic evidence has accumulated about the important role personality characteristics such as the Big Five traits play for academic performance in secondary school, less is known about the specific behavioral and emotional repertoire that drives these associations. A closer look at the facet-level of Big Five traits can provide this knowledge helping to better understand why students differ in their success at school. We therefore studied associations between Big Five facets and academic performance (grades and standardized test scores in German and mathematics) in three independent secondary student samples (Study 1 N = 3866, Study 2 N = 2290, and Study 3 N = 366), using regression analytic, structural equation modeling, and meta-analytic procedures to test facet-level associations and incremental associations above traits. We found facet-differential associations with academic performance indicators, particularly for facets of conscientiousness, openness, and extraversion. Evidence for incremental effects of facets beyond traits was weak. Self-discipline (conscientiousness) was the only facet that consistently showed incremental effects across Samples 1 and 2. The results can stimulate research and practice to identify the underlying mechanisms that drive these associations and also highlight the specific emotional and behavioral repertoire that should be supported to help students to succeed in school.
Plain language summary
People differ in the way they act, think, and feel—their personality traits—and these trait differences have been shown to be predictive for how well students perform in school. Besides findings on the broader trait level, less is known about which specific behavioral and emotional repertoire might drive these associations. A closer look at the narrower facet-level can provide this knowledge helping to better understand why students differ in their success at school. We therefore studied associations between personality facets and academic performance (grades and standardized test scores in German and mathematics) in three independent secondary student samples (Study 1 N = 3866, Study 2 N = 2290, and Study 3 N = 366) and integrated these findings later on. Particularly, we wanted to know whether facets can explain differences in academic performance above what is explained by broader traits. We found evidence for facet-specific associations with grades and test scores; however, evidence for explanations of facets above traits was weak. Self-discipline was the only facet that explained differences above what is explained by the broader trait conscientiousness.
BeckE. D.JacksonJ. J. (2022). A mega-analysis of personality prediction: Robustness and boundary conditions. Journal of Personality and Social Psychology, 122(3), 523–553. https://doi.org/10.1037/pspp0000386
4.
BergoldS.SteinmayrR. (2018). Personality and intelligence interact in the prediction of academic achievement. Journal of Intelligence, 6(2), 27. https://doi.org/10.3390/jintelligence6020027
5.
BrandtN. D.BeckerM.TetznerJ.BrunnerM.KuhlP. (2021). What teachers and parents can add to personality ratings of children: Unique associations with academic performance in elementary school. European Journal of Personality, 35(6), Article 089020702098843. https://doi.org/10.1177/0890207020988436
6.
BrandtN. D.BeckerM.TetznerJ.BrunnerM.KuhlP.MaazK. (2018). Personality across the lifespan. Exploring measurement invariance of a short Big Five inventory from ages 11 to 84. European Journal of Psychological Assessment, Advance Online, 1–12. https://doi.org/10.1027/1015-5759/a000490
7.
BrandtN. D.LechnerC. M.TetznerJ.RammstedtB. (2020). Personality, cognitive ability, and academic performance: Differential associations across school subjects and school tracks. Journal of Personality, 88(2), 249–265. https://doi.org/10.1111/jopy.12482
8.
ChenF. F.HayesA.CarverC. S.LaurenceauJ.-P.ZhangZ. (2012). Modeling general and specific variance in multifaceted constructs: A comparison of the bifactor model to other approaches: Bifactor modeling of multifaceted constructs. Journal of Personality, 80(1), 219–251. https://doi.org/10.1111/j.1467-6494.2011.00739.x
9.
CondonD. M.RevelleW. (2014). The international cognitive ability resource: Development and initial validation of a public-domain measure. Intelligence, 43, 52–64. https://doi.org/10.1016/j.intell.2014.01.004
10.
CorkerK. S.OswaldF. L.DonnellanM. B. (2012). Conscientiousness in the classroom: A process explanation. Journal of Personality, 80(4), 995–1028. https://doi.org/10.1111/j.1467-6494.2011.00750.x
11.
CostaP. T.McCraeR. (2017). NEO inventories as instruments of psychological theory. In WidigerT. A. (Ed.), The Oxford handbook of the five-factor (pp. 11–38). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199352487.013.10
12.
CostaP. T.McCraeR. R. (1995). Domains and facets: Hierarchical personality assessment using the revised NEO personality inventory. Journal of Personality Assessment, 64(1), 21–50. https://doi.org/10.1207/s15327752jpa6401_2
13.
DannerD.LechnerC. M.SotoC. J.JohnO. P. (2021). Modelling the incremental value of personality facets: The domains‐incremental facets‐acquiescence bifactor model. European Journal of Personality, 35(1), 67–84. https://doi.org/10.1002/per.2268
14.
DeYoungC. G.QuiltyL. C.PetersonJ. B. (2007). Between facets and domains: 10 aspects of the Big Five. Journal of Personality and Social Psychology, 93(5), 880–896. https://doi.org/10.1037/0022-3514.93.5.880
15.
DongY.DumasD. (2020). Are personality measures valid for different populations? A systematic review of measurement invariance across cultures, gender, and age. Personality and Individual Differences, 160, Article 109956. https://doi.org/10.1016/j.paid.2020.109956
16.
EndersC. K. (2010). Applied missing data analysis. Guilford Press.
GanzeboomH. B. G.De GraafP. M.TreimanD. J. (1992). A standard international socio-economic index of occupational status. Social Science Research, 21(1), 1–56. https://doi.org/10.1016/0049-089X(92)90017-B
19.
GatzkaT.HellB. (2018). Openness and postsecondary academic performance: A meta-analysis of facet-, aspect-, and dimension-level correlations. Journal of Educational Psychology, 110(3), 355–377. https://doi.org/10.1037/edu0000194
20.
GöllnerR.DamianR. I.RoseN.SpenglerM.TrautweinU.NagengastB.RobertsB. W. (2017). Is doing your homework associated with becoming more conscientious?Journal of Research in Personality, 71, 1–12. https://doi.org/10.1016/j.jrp.2017.08.007
21.
GuoJ.TangX.MarshH. W.ParkerP.BasarkodG.SahdraB.RantaM.Salmela-AroK. (2023). The roles of social–emotional skills in students’ academic and life success: A multi-informant and multicohort perspective. Journal of Personality and Social Psychology, 124(5), 1079–1110. https://doi.org/10.1037/pspp0000426
22.
HallquistM. N.WileyJ. F. (2018). MplusAutomation: An R package for facilitating large-scale latent variable analyses in Mplus. Structural Equation Modeling, 25(4), 621–638. https://doi.org/10.1080/10705511.2017.1402334
23.
HeavenP. C. L.CiarrochiJ. (2008). Parental styles, conscientiousness, and academic performance in high school: A three-wave longitudinal study. Personality and Social Psychology Bulletin, 34(4), 451–461. https://doi.org/10.1177/0146167207311909
HuL. T.BentlerP. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424–453. https://doi.org/10.1037/1082-989X.3.4.424
26.
IsraelA.BrandtN. D.GrundS.KöllerO.LüdtkeO.WagnerJ. (2022). Personality and psychosocial functioning in early adolescence: Age-differential associations from the self- and parent perspective. European Journal of Personality, 36(2), 218–244. https://doi.org/10.1177/08902070211005636
27.
IsraelA.BrandtN. D.SpenglerM.GöllnerR.LüdtkeO.TrautweinU.WagnerJ. (2023). The longitudinal interplay of personality and school experiences in adolescence. European Journal of Personality, 37(2), 131–153. https://doi.org/10.1177/08902070211062326
28.
IsraelA.LüdtkeO.WagnerJ. (2019). The longitudinal association between personality and achievement in adolescence: Differential effects across all Big Five traits and four achievement indicators. Learning and Individual Differences, 72, 80–91. https://doi.org/10.1016/j.lindif.2019.03.001
29.
JohnO. P.DonahueE. M.KentleR. L. (1991). The Big five inventory—versions 4a and 54. University of California, Berkeley, Institute of Personality and Social Research.
30.
JohnO. P.NaumannL. P.SotoC. J. (2008). Paradigm shift to the integrative Big Five taxonomy. History, measurement, and conceptual issues. In JohnO. P.RobinsR. W.PervinL. A. (Eds.), Handbook of personality. Theory and research (pp. 114–158). Guilford.
31.
JonkmannK.RoseN.TrautweinU. (Eds.), (2013). Tradition und Innovation: Entwicklungsverläufe an Haupt- und Realschulen in Baden-Württemberg und Mittelschulen in Sachsen - Abschlussbericht für die Länder Baden-Württemberg und Sachsen [Tradition and innovation: Student development in two German states]. Projektbericht an die Kultusministerien der Länder.
32.
KretzschmarA.SpenglerM.SchubertA.-L.SteinmayrR.ZieglerM. (2018). The relation of personality and intelligence—what can the Brunswik symmetry principle tell us?Journal of Intelligence, 6(3), 30. https://doi.org/10.3390/jintelligence6030030
33.
LangF. R.LüdtkeO.AsendorpfJ. B. (2001). Testgüte und psychometrische Äquivalenz der deutschen Version des Big Five Inventory (BFI) bei jungen, mittelalten und alten Erwachsenen. Diagnostica, 47(3), 111–121. https://doi.org/10.1026//0012-1924.47.3.111
34.
LechnerC. M.DannerD.RammstedtB. (2017). How is personality related to intelligence and achievement? A replication and extension of Borghans et al. And Salkever. Personality and Individual Differences, 111, 86–91. https://doi.org/10.1016/j.paid.2017.01.040
35.
LechnerC. M.KnopfT.NapolitanoC. M.RammstedtB.RobertsB. W.SotoC. J.SpenglerM. (2022). The behavioral, emotional, and social skills inventory (BESSI): Psychometric properties of a German-language adaptation, temporal stabilities of the skills, and associations with personality and intelligence. Journal of Intelligence, 10(3), 63. https://doi.org/10.3390/jintelligence10030063
36.
MacCannC.LipnevichA. A.PoropatA. E.WiemersM. J.RobertsR. D. (2015). Self- and parent-rated facets of conscientiousness predict academic outcomes: Parent-reports are more predictive, particularly for approach-oriented facets. Learning and Individual Differences, 42, 19–26. https://doi.org/10.1016/j.lindif.2015.07.012
37.
MammadovS. (2022). Big Five personality traits and academic performance: A meta‐analysis. Journal of Personality, 90(2), 222–255. https://doi.org/10.1111/jopy.12663
38.
MarshH. W.MorinA. J. S.ParkerP. D.KaurG. (2014). Exploratory structural equation modeling: An integration of the best features of exploratory and confirmatory factor analysis. Annual Review of Clinical Psychology, 10, 85–110. https://doi.org/10.1146/annurev-clinpsy-032813-153700
39.
MarshH. W.NagengastB.MorinA. J. S. (2013). Measurement invariance of Big-five factors over the life span: ESEM tests of gender, age, plasticity, maturity, and la dolce vita effects. Developmental Psychology, 49(6), 1194–1218. https://doi.org/10.1037/a0026913
McAbeeS. T.OswaldF. L.ConnellyB. S. (2014). Bifactor models of personality and college student performance: A broad versus narrow view: Personality and academic performance. European Journal of Personality, 28(6), 604–619. https://doi.org/10.1002/per.1975
42.
McCraeR. R. (2015). A more nuanced view of reliability: Specificity in the trait hierarchy. Personality and Social Psychology Review, 19(2), 97–112. https://doi.org/10.1177/1088868314541857
43.
McCraeR. R.CostaP. T. (2008). The five-factor theory of personality. In JohnO. P.RobinsR. W.PervinL. A. (Eds.), Handbook of personality: Theory and research (pp. 159–181). Guilford.
44.
MeyerJ.FleckensteinJ.RetelsdorfJ.KöllerO. (2019). The relationship of personality traits and different measures of domain-specific achievement in upper secondary education. Learning and Individual Differences, 69, 45–59. https://doi.org/10.1016/j.lindif.2018.11.005
45.
MõttusR.KandlerC.BleidornW.RiemannR. (2017). Personality traits below facets: The consensual validity, longitudinal stability, heritability, and utility of personality nuances. Journal of Personality and Social Psychology, 112(3), 474–490. https://doi.org/10.1037/pspp0000100.supp
46.
MõttusR.SinickJ.TerraccianoA.HřebíčkováM.KandlerC.AndoJ.MortensenE. L.Colodro-CondeL.JangK. L. (2019). Personality characteristics below facets: A replication and meta-analysis of cross-rater agreement, rank-order stability, heritability, and utility of personality nuances. Journal of Personality and Social Psychology, 117(4), e35–e50. https://doi.org/10.1037/pspp0000202
NoftleE. E.RobinsR. W. (2007). Personality predictors of academic outcomes: Big Five correlates of GPA and SAT scores. Journal of Personality and Social Psychology, 93(1), 116–130. https://doi.org/10.1037/0022-3514.93.1.116
49.
NyeC. D.RobertsB. W.SaucierG.ZhouX. (2008). Testing the measurement equivalence of personality adjective items across cultures. Journal of Research in Personality, 42(6), 1524–1536. https://doi.org/10.1016/j.jrp.2008.07.004
50.
ParkC.MurakiE. (2003). Bias of Ability Estimates Using Warm’s Weighted Likelihood Estimator (WLE) in the Generalized Partial Credit Model (GPCM). In YanaiH.OkadaA.ShigemasuK.KanoY.MeulmanJ. J. (Eds.), New Developments in Psychometrics. Springer. https://doi.org/10.1007/978-4-431-66996-8_21
51.
PoropatA. E. (2009). A meta-analysis of the five-factor model of personality and academic performance. Psychological Bulletin, 135(2), 322–338. https://doi.org/10.1037/a0014996
52.
PoropatA. E. (2014). A meta-analysis of adult-rated child personality and academic performance in primary education. British Journal of Educational Psychology, 84(2), 239–252. https://doi.org/10.1111/bjep.12019
53.
RammstedtB.DannerD. (2009). Die Facettenstruktur des Big Five Inventory (BFI). Psychologische Rundschau, 60(3), 212–214. https://doi.org/10.1026/a000002
54.
RammstedtB.DannerD.SotoC. J.JohnO. P. (2020). Validation of the short and extra-short forms of the Big Five inventory-2 (BFI-2) and their German adaptations. European Journal of Psychological Assessment, 36(1), 149–161. https://doi.org/10.1027/1015-5759/a000481
55.
R Core Team. (2021). R: A language and environment for statistical computing [Computer software]. R Foundation for Statistical Computing.
56.
RobertsB. W. (2009). Back to the future: Personality and Assessment and personality development. Journal of Research in Personality, 43(2), 137–145. https://doi.org/10.1016/j.jrp.2008.12.015
57.
SaucierG. (1994). Mini-markers: A brief version of Goldberg’s unipolar Big-Five markers. Journal of Personality Assessment, 63(3), 506–516. https://doi.org/10.1207/s15327752jpa6303_8
58.
Schermelleh-EngelK.MoosbruggerH.MüllerH. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8(2), 23–74. https://doi.org/10.23668/psycharchives.12784
59.
SotoC. J. (2019). How replicable are links between personality traits and consequential life outcomes? The life outcomes of personality replication project. Psychological Science, 30(5), 711–727. https://doi.org/10.1177/0956797619831612
60.
SotoC. J.JohnO. P. (2009). Ten facet scales for the Big Five inventory: Convergence with NEO PI-R facets, self-peer agreement, and discriminant validity. Journal of Research in Personality, 43(1), 84–90. https://doi.org/10.1016/j.jrp.2008.10.002
61.
SotoC. J.JohnO. P. (2017a). Short and extra-short forms of the big five inventory–2: The BFI-2-S and BFI-2-XS. Journal of Research in Personality, 68, 69–81. https://doi.org/10.1016/j.jrp.2017.02.004
62.
SotoC. J.JohnO. P. (2017b). The next Big Five Inventory (BFI-2): Developing and assessing a hierarchical model with 15 facets to enhance bandwidth, fidelity, and predictive power. Journal of Personality and Social Psychology, 113(1), 117–143. https://doi.org/10.1037/pspp0000096
63.
SpenglerM.BrunnerM.MartinR.LüdtkeO. (2016). The role of personality in predicting (change in) students’ academic success across four years of secondary school. European Journal of Psychological Assessment, 32(1), 95–103. https://doi.org/10.1027/1015-5759/a000330
64.
SpenglerM.LüdtkeO.MartinR.BrunnerM. (2013). Personality is related to educational outcomes in late adolescence: Evidence from two large-scale achievement studies. Journal of Research in Personality, 47(5), 613–625. https://doi.org/10.1016/j.jrp.2013.05.008
65.
TettR. P.BurnettD. D. (2003). A personality trait-based interactionist model of job performance. Journal of Applied Psychology, 88(3), 500–517. https://doi.org/10.1037/0021-9010.88.3.500
66.
TetznerJ.BeckerM.BrandtN. D. (2020). Personality‐achievement associations in adolescence—examining associations across grade levels and learning environments. Journal of Personality, 88(2), 356–372. https://doi.org/10.1111/jopy.12495
67.
Van LissaC. J. (2020). Small sample meta-analyses: Exploring heterogeneity using MetaForest. In Van De SchootR.MiočevićM. (Eds.), Small Sample Size Solutions (Open Access): A Guide for Applied Researchers and Practitioners. CRC Press. https://doi.org/10.4324/9780429273872-16
VazireS. (2010). Who knows what about a person?: The self–other knowledge asymmetry (SOKA) model. Journal of Personality and Social Psychology, 98(2), 281–300. https://doi.org/10.1037/a0017908
70.
WarmT. (1989). Weighted likelihood estimation of ability in item response theory. Psychometrika, 54(3), 427–450. https://doi.org/10.1007/BF02294627
71.
WillinghamW. W.PollackJ. M.LewisC. (2002). Grades and test scores: Accounting for observed differences. Journal of Educational Measurement, 39(1), 1–37. https://doi.org/10.1111/j.1745-3984.2002.tb01133.x
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
