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Learning and Individual Di ﬀerences
journal homepage: www.elsevier.com/locate/lindif
The relationship of personality traits and di ﬀerent measures of domain-
speciﬁc achievement in upper secondary education
Jennifer Meyera,⁎, Johanna Fleckensteina,b, Jan Retelsdorfc, Olaf Köllera
aLeibniz Institute for Science and Mathematics Education (IPN), Kiel, Germany
bUniversity of Applied Sciences and Arts Northwestern Switzerland (FHNW), Basel, Switzerland
cUniversity of Hamburg (UHH), Hamburg, Germany
ARTICLE INFO
Keywords:
Personality traits
Five-factor modelDomain-speci ﬁc achievement
Secondary educationAchievement measuresABSTRACT
This study examined the relationship of personality traits with academic achievement, while controlling for
cognitive ability. We considered two domains (Mathematics and English as a foreign language) and threeachievement measures, capitalizing on a sample of N= 3637 students in upper secondary education (year 13,
ageM= 19.92 years) in Germany. First, we aimed to replicate previous results on grades and test scores. Second,
we aimed to extend the body of research by adding ﬁnal examinations —a school-based performance test —as a
third measure. Our ﬁndings indicate an incremental predictive validity of personality traits for domain-speci ﬁc
academic achievement beyond cognitive ability. Conscientiousness predicted grades and ﬁnal exams in both
domains. Results for test scores were domain-speci ﬁc: conscientiousness predicted mathematics test score,
whereas openness was associated with English test score. Relationships with personality traits varied dependingon the domain, the measure used, and the covariates included in the model.
1. Introduction
Personality traits explain a unique amount of variance in students'
achievement in secondary education (e.g., Poropat, 2009 ;De Raad &
Schouwenburg, 1996; Laidra, Pullmann, & Allik, 2007). So far, some
studies have considered academic domains separately (e.g., mathe-
matics and languages; Lüdtke, Trautwein, Nagy, & Köller, 2004;
Spengler, Brunner, Martin, & Lüdtke, 2016); and a few have also takendiﬀerent achievement measures into account (e.g., grades and stan-
dardized test scores; Spengler, Lüdtke, Martin, & Brunner, 2013 ). Those
detailed investigations obtained di ﬀerential domain- and measure-
speciﬁc results, indicating the importance of taking into account dif-
ferent (a) domains and (b) achievement measures.
We aim to contribute to this body of research and validate results
from previous studies by simultaneously investigating two domains
(mathematics and English as a foreign language) using both grades and
standardized test scores. Going beyond replication, we include ﬁnal
exams as a third achievement measure. We argue including a thirdmeasure with properties distinct from grades and test scores may help
to provide a more detailed understanding of the relationship of per-
sonality traits with domain-speci ﬁc achievement.2. Theoretical background
2.1. Personality and intelligence
Relations of personality and intelligence have been prominently
debated in the literature regarding the distinctiveness versus conceptual
overlap of the two constructs (see De Young, 2011 ). Among others,
Cattell (1957) ,Ackerman and Heggestadt (1997) , and Aitken Harris
(2004) assume a substantial overlap between intelligence and person-
ality dispositions, naturally resulting in signi ﬁcant associations between
personality and academic achievement. However, even if personality
and intelligence are seen as independent constructs (e.g., Eysenck,
1994 ), it can be argued that individuals varying in cognitive ability
diﬀer in their use of intellectual resources in relation to personality
traits. This would, in turn, result in achievement di ﬀerences associated
with personality ( Allik & Realo, 1997 ). These considerations imply that
personality, intelligence, and academic achievement are related to eachother and need to be considered simultaneously.
Further, it has to be kept in mind that these relationships should not
be seen as ﬁxed but rather as evolving over time, as certain personality
traits might enhance achievement but, at the same time, achievementexperiences can a ﬀect personality development (see Göllner et al.,
2017 ). This view is empirically supported by studies showing that the
https://doi.org/10.1016/j.lindif.2018.11.005
Received 15 November 2017; Received in revised form 8 November 2018; Accepted 15 November 2018⁎Corresponding author at: Leibniz Institute for Science and Mathematics Education, Olshausenstr. 62, 24118 Kiel, Germany.
E-mail address: jmeyer@ipn.uni-kiel.de (J. Meyer).Learning and Individual Differences 69 (2019) 45–59
1041-6080/ © 2018 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/BY/4.0/).
T

personality of older adolescents is more stable, which is associated with
the growth of mental capabilities ( Allik, Laidra, Realo, & Pullmann,
2004 ).
Another theoretical distinction has to be made between personality
and personality traits, with personality referring to a wide range ofvariables and theoretical constructs. In this study we focused on per-
sonality as speci ﬁc personality traits, namely, the ﬁve traits proposed in
theﬁve-factor model ( Costa & McCrae, 1992 ). The ﬁve-factor model has
been established as an important and productive taxonomy to classify
the structure of human personality (e.g., Bakker, Van der Zee, Lewig, &
Dollard, 2006; Barrick & Mount, 1991 ;Roberts, Kuncel, Shiner, Caspi,
& Goldberg, 2007 ;Soldz & Vaillant, 1999), containing the dimensions
of extraversion, agreeableness, conscientiousness, neuroticism (oremotional stability), and openness to experience ( McCrae & Costa,
1987 ).
2.2. Personality traits and academic achievement in secondary education
To give an overview on previous research we consider results on a)
general academic achievement, b) domain-speci ﬁc achievement, and c)
associations with di ﬀerent achievement measures.
2.2.1. Personality and general academic achievement
Previous studies investigating general academic achievement in-
dicate varying associations with agreeableness, neuroticism, and ex-traversion, depending on student age and academic level (see Poropat,
2009 ;von Stumm, Hell, & Chamorro-Premuzic, 2011). Eﬀ ect sizes were
small ( ρ= 0.05 with agreeableness when controlling for cognitive
ability; Poropat, 2009 ) and inconsistent across studies (see Farsides &
Woodﬁeld, 2003). For extraversion and neuroticism, similar e ﬀect sizes
were obtained when controlling for cognitive ability ( ρ=−0.03/
−0.01, respectively; Poropat, 2009). Conscientiousness and openness,
on the other hand, have consistently been shown to be associated with
achievement in secondary education beyond cognitive ability, with
smaller e ﬀects for openness ( ρ= 0.23/0.09, respectively; Poropat,
2009 ).
Trait-speci ﬁcﬁndings for conscientiousness . Conscientiousness is closely
related to traits and behaviors known to be crucial for school
performance, such as self-discipline, ambition, persistence, diligence,
dutifulness, and grit ( Credé, Tynan, & Harms, 2016; Dumfart &
Neubauer, 2016 ;Ivcevic
& Brackett, 2014 ;Schmidt, Fleckenstein,
Retelsdorf, Eskreis-Winkler, & Möller, 2017 ;Schmidt, Nagy,
Fleckenstein, Möller, & Retelsdorf, 2018 ). Further, conscientiousness
has been linked to learning behaviors that result in good grades (see
Credé & Kuncel, 2008 ;Kling, Noftle, & Robins, 2013), such as self-
regulated learning ( Bidjerano & Dai, 2007), goal orientations ( Sorić ,
Penezi ć, & Buri ć, 2017 ), systematic studying and methodical learning
styles (e.g., Geisler-Brenstein, Schmeck, & Hetherington, 1996 ;
Komarraju, Karau, Schmeck, & Avdic, 2011), as well as academic
eﬀort (see De Raad & Schouwenburg, 1996 ;Noftle & Robins, 2007;
Trautwein, Lüdtke, Roberts, Schnyder, & Niggli, 2009 ;Trautwein,
Lüdtke, Schnyder, & Niggli, 2006).
Trait-speci ﬁcﬁndings for openness . Openness is related to academic
achievement constructs such as intellectual curiosity, aesthetic
sensitivity, vivid imagination, preference for novelty and variety
(Costa & McCrae, 1992; McCrae & Costa Jr, 1997), and intellectual
investment ( von Stumm & Ackerman, 2013). Individuals with high
openness scores are characterized as being deep and complex with a
positive attitude toward challenging learning experiences ( Barrick &
Mount, 1991 ), as opposed to being more down-to-earth and narrow-
minded ( McCrae & Costa, 1987 ). Higher curiosity and intellectual
engagement results in positive correlations between openness and
approaches to learning ( Chamorro-Premuzic & Furnham, 2009;
Diseth, 2003; Geisler-Brenstein et al., 1996; Vermetten, Lodewijks, &Vermunt, 2001) and critical thinking ( Bidjerano & Dai, 2007), as well as
elaborate learning strategies, including deep-processing of information(Blickle, 1996; Komarraju et al., 2011).
However, some studies found a negative relationship between
openness and mathematics grade ( Lipnevich, Preckel, & Krumm, 2016).
This could be related to di ﬀerent scales measuring various aspects of
openness. According to Gatzka and Hell (2017) openness can be viewed
as a two-dimensional construct including two di ﬀerent aspects: senseo-
aesthetic openness and intellectual openness.
2.2.2. Personality traits and domain-speci ﬁc
achievement
Many previous studies used domain-general composite scores such
as GPA to measure academic achievement (see Poropat, 2009; von
Stumm et al., 2011 ). This approach, however, neglects the hetero-
geneity of academic domains. There are two major reasons for studying
achievement domain-speci ﬁcally, even if this comes at the cost of
measurement errors associated with the use of domain-speci ﬁc mea-
sures such as single grades.
First, previous research on noncognitive factors such as self-concept
(e.g., Marsh, 1987 ) and other motivational constructs (e.g., expectancy-
value theory; Eccles et al., 1983; Trautwein et al., 2012 ) shows a
moderating role of the domain when considering the nature of the as-
sociation between personality traits and achievement.
Second, in Germany, many universities demand a certain domain-
speciﬁc grade before admitting a student to their programs. For ex-
ample, English grades higher than 12 points (which corresponds toGrade B+ in the US system) are required for admission to language
programs ( University of Kiel, 2008 ). This shows the practical relevance
of understanding students' achievement in di ﬀerent domains, for ex-
ample, in English as a foreign language.
Domain-speci ﬁcﬁndings for conscientiousness . Conscientiousness is
related to agentic learning strategies (see Schmeck, Geisler-Brenstein,
& Cercy, 1991 ), including methodical study and fact retention ( Geisler-
Brenstein et al., 1996 ;Komarraju et al., 2011 ). This association with
academic e ﬀort and hard work makes conscientiousness a bene ﬁcial
trait for students in all areas of academic achievement ( Noftle & Robins,
2007 ;Trautwein et al., 2009). Taking a domain-speci ﬁc view, it could
be argued that conscientiousness is even more valuable in mathematics
as persistent learning behavior and analytical thinking are required in
order for students to understand complex equations and solve di ﬃcult
problems (see Duckworth & Seligman, 2005 ;MacCann, Duckworth, &
Roberts, 2009).
Domain-speci ﬁcﬁndings for openness . Students scoring high on openness
prefer activities that potentially enhance verbal competencies: they are
known to apply more elaborate and creative learning strategies ( Blickle,
1996 ;Geisler-Brenstein et al., 1996 ;Komarraju et al., 2011 ), are more
curious and interested in cognitively demanding free-time activities
(Schwaba, Luhmann, Denissen, Chung, & Bleidorn, 2017 ), such as
reading books or watching movies in foreign languages, and in culturalactivities (e.g., visits to the opera). Whereas a positive relationship of
openness with academic achievement is hypothesized for both verbaland
nonverbal domains, domain-speci ﬁcd iﬀerences can be expected:
Gatzka and Hell (2017) found a moderating e ﬀect of academic major in
postsecondary education, showing that openness might be less
beneﬁcial for majors that require abilities related to the revision of
default procedures and that emphasize rules and regulations. This isreﬂected in the previously mentioned negative relationship of openness
with mathematics grade ( Lipnevich et al., 2016 ). In contrast, majors
that include rewards for critical thinking and creative ideas arecorrelated more strongly with openness ( Gatzka & Hell, 2017 ). The
same line of reasoning can be followed for academic domains insecondary education, with mathematics being more closely related to
rules and regulations; therefore, a high amount of openness might in
fact be detrimental, whereas, in languages, more creative thinking andJ. Meyer et al. Learning and Individual Differences 69 (2019) 45–59
46

varying solutions might be appreciated. This hypothesis is in line with
previous research that found a positive e ﬀect of openness on
achievement in German and French ( Spengler et al., 2013 ) as well as
in English ( Lüdtke et al., 2004), measured by standardized tests.
2.2.3. Personality traits and multiple achievement measures
Beyond domain-speci ﬁc relationships, in previous research di ﬀer-
ential results were obtained depending on the measure used: con-
scientiousness predicted mathematics achievement when using grades
(Spengler et al., 2013; Spengler et al., 2016 ) but not when using test
scores ( Lüdtke et al., 2004 ;Noftle & Robins, 2007 ). The results suggest
that the association of personality traits with academic achievement
may also vary with the operationalization of achievement. So far, few
studies have considered multiple measures simultaneously (see Noftle &
Robins, 2007; Spengler et al., 2013 ).
Although there is a large overlap between measures, it can be ar-
gued that di ﬀerent measures are related to di ﬀerent aspects of
achievement ( Willingham, Pollack, & Lewis, 2002). Analyzing these
aspects could provide more detailed insights, which might be helpful tobetter understand the associations with personality traits. One aspect,
for example, could be the di ﬀerent types of task that are used in dif-
ferent testing situations and that require creative versus more analytical(algorithmic) solutions (see Chamorro-Premuzic, 2006). Thus, in the
following, we describe the characteristics of three practically relevanttypes of achievement measures in the German school system and their
relationship with personality traits: grades, standardized test scores,
andﬁnal exams as a school-based performance measure. Similar
achievement measures are used in most other European countries (e.g.,Austria, Switzerland, Italy, Finnland, Luxembourg; see Centre for
Educational Research, 1999 ).
Report card grades . Report card grades contain teachers' assessments of
students' learning performance, accumulated over an entire schoolterm, consisting of multiple written class examinations as well as
evaluations of oral class participation. Thus, teachers' observations
play an important role, making grades a subjective measure. Grades are
also dependent on class composition and vary between teachers,
schools, and federal states (see Baumert & Watermann, 2000 ; see also
Holmeier, 2013 for a detailed description and discussion of grades in
upper secondary schools in Germany). Still, report card grades in upper
secondary education are highly relevant achievement outcomes for
students as they contribute signi ﬁcantly to ﬁnal GPA, and thus have a
major impact on students' futures. A growing number of studies provideevidence on the incremental value of conscientiousness in predicting
grades (e.g., Spengler et al., 2013 ;Steinmayr & Spinath, 2009 ). Similar
eﬀects have been shown for self-control and academic procrastination –
constructs closely related to consistent study behaviors ( Hofer, Kuhnle,
Kilian, & Fries, 2012 ).
It can be argued that consistent study behaviors, and thus con-
scientiousness, in ﬂuence grading for three reasons. First, e ﬀort
reg-
ulation and persistent studying have positive e ﬀects on the amount of
material learned, contributing to academic success ( Bidjerano & Dai,
2007 ;Noftle & Robins, 2007; Steel, 2007). Second, teachers' expecta-
tions and judgments can in ﬂuence grading (e.g., self-ful ﬁlling pro-
phecies and perceptual biases; Jussim & Harber, 2005). Third, grades
are multicriterial; teachers are encouraged to evaluate classroom be-havior and participation during class and incorporate these into
grading. Thus, grades reward conscientious study habits that are ob-
servable by the teacher through students' academic engagement
whereas poor work habits are penalized (e.g., Marsh, 1987; Willingham
et al., 2002; Zimmermann, Schütte, Taskinen, & Köller, 2013 ).
Further, because of the multicriterial nature of grades, classroom
participation becomes more relevant. Especially in the language class-
room, participation and oral performance are vital and inﬂ uence
grading. It can thus be hypothesized that personality traits are posi-tively associated with social adjustment (i.e., agreeableness; see Ehrler& Evans, 1999 ) and oral performance (i.e., extraversion; for a discus-
sion see Dewaele & Furnham, 1999). More extraverted students might
be more active during lessons and might learn in a more e ﬀective way
during this time because of this behavior.
Standardized tests . Standardized tests are constructed by researchers
and administered by trained personnel. While such tests are often
conceptualized in line with curricular demands, the content and form
can di ﬀer from classroom learning and assessment ( Willingham et al.,
2002 ). Tests can be regarded as an objective measure given the
standardized administration and evaluation performed by researchers.
The ability to solve novel problems (i.e., ﬂuid reasoning) is more
relevant for performance on standardized tests than for grades,resulting in a stronger impact of cognitive ability ( Borghans,
Golsteyn, Heckman, & Humphries, 2011; Borghans, Golsteyn,
Heckman, & Humphries, 2016; Lechner, Danner, & Rammstedt, 2017;
Willingham et al., 2002 ). Further, because of the low-stakes nature of
standardized tests, students have no opportunity and little incentive to
study for them. Consequently, characteristics such as study habits,
eﬀort, and persistence are unlikely to a ﬀect test performance (see
Marsh, Trautwein, Ludtke, Koller, & Baumert, 2005 ).
However, openness has been shown to predict test scores, especially
in languages (English; β= 0.18, controlling for cognitive ability, SES,
gender and school track; Lüdtke et al., 2004 ; German; β= 0.20; con-
trolling for self-concept, interest, cognitive ability and anxiety; Spengler
et al., 2013). This may be because students scoring high on openness
tend to seek out intellectually stimulating situations ( Schwaba et al.,
2017 ).
Knowledge acquired during these intellectual free-time activitiescan enhance performance in standardized tests ( Willingham et al.,
2002 ).
Written ﬁnal exams . The impact of written ﬁnal exams at the end of
upper secondary education in Germany on students' life choices andsuccess is substantial as they make up one third of the ﬁnal GPA and,
consequently, are important for college admission criteria.
On the one hand, ﬁnal exams are similar to standardized tests: to
ensure greater objectivity, ﬁnal exam assignments in most German
federal states are not given by the instructing teacher but by the
Ministry of Education in each federal state ( Zentralabitur ; see Klein,
Kühn, van Ackeren, & Block, 2009). Thus, unfamiliar content can beencountered during the test.
On the other hand, the rate of standardization of exams in Germany
is low to moderate compared to international standards with respect to
all stages of the assessment process, especially as evaluation is per-
formed by teachers ( Klein et al., 2009 ). Moreover, students' preparation
before the exams is crucial and dependent on students' learning beha-vior, as a lot of material has to be covered beyond just one school term.
However, in contrast to grades the ﬁnal exam is a performance situation
for the individual student. It is strongly characterized by a certainamount of pressure, as all the knowledge and competencies have to be
retrieved on one particular occasion with little room for compensation
in the case of failure.
Furnham and Monsen (2009) analyzed ﬁnal examinations in a
British sample, showing a positive relationship with conscientiousness
in both mathematics and English. However, in contrast to previously
summarized results they did not ﬁnd any relation between openness
and languages. Those rather inconsistent ﬁndings indicate the need to
take a closer look at ﬁnal examinations in another context, as it could
be argued that e ﬀects can be explained by the di ﬀerent nature of
achievement measures.
3. The present study
The major aim of this study was to gain more detailed insights into
the predictive value of personality traits on academic achievement in
upper secondary education over and above cognitive ability, using aJ. Meyer et al. Learning and Individual Differences 69 (2019) 45–59
47

large sample of students shortly before graduating from upper sec-
ondary school (Grade 13). Achievement was measured in two domains:
mathematics and English as a foreign language. Previous research has
shown domain-speci ﬁc relationships of personality traits with
achievement for similar domains (German, French, and mathematics;Spengler et al., 2013). Our ﬁrst objective was to validate the existing
ﬁndings in a sample of older adolescents ( Research Question 1). Our
second objective was to extend the knowledge base regarding these
relations by simultaneously considering three di ﬀerent measures of
achievement, including ﬁnal exams ( Research Question 2). Final exams
represent school-based, but more standardized performance measures.
To complete this systematic approach, we used GPA as an aggregated
and general measure with high practical implications, thereby aiming
to support previous ﬁndings ( Research Question 3). Although the focus
of this study is on conscientiousness and openness as the two traits mostrelevant for achievement, we investigated e ﬀects of extraversion,
agreeableness and neuroticism systematically ( Research Question 4 ).
We tested our hypotheses while also controlling for cognitive ability as
we were interested in the incremental e ﬀect of personality traits. Fur-
thermore, we included several control variables as it has been shownthat socioeconomic status (SES; Sirin, 2005 ), gender ( Feingold, 1994;
Schmitt, Realo, Voracek, & Allik, 2008), and track and course level(Leucht, Retelsdorf, Pant, Möller, & Köller, 2015; Lüdtke et al., 2004 )
are related to achievement.
3.1. Research Question 1
First, we considered the trait-speci ﬁcd iﬀerential relationships of
conscientiousness and openness with all measures in both domains. We
structured our hypothesis by outcome measure.
a) Hypotheses on grades.
In line with Spengler et al. (2013) , we expected conscientiousness to
be an important predictor of grades in both domains due to the im-portance of study behavior and teacher expectations for grading.
However, in line with previous research the e ﬀect was expected to be
stronger for mathematics than for English.
For the e ﬀect of openness on grades domain-speci ﬁcd iﬀerences
were hypothesized as well. On the one hand, we expected openness topredict English grade. On the other hand, previous results on the as-
sociation of openness and mathematics grades are inconsistent.
Spengler et al. (2013) did not ﬁnd a signi ﬁcant e ﬀect, whereas
Lipnevich et al. (2016) found a negative e ﬀect. We aimed to test sys-
tematically whether this can be resolved by using di ﬀerent covariates
explaining the changing patterns of results. Theoretically, both out-
comes are feasible. On the one hand, it can be argued that students who
are
high in openness are less interested in more analytical, less creative
thinking as required in mathematics, which could result in a negativeeﬀect of openness. On the other hand, openness has been shown to be
positively related to intellectual investment in general ( von Stumm &
Ackerman, 2013 ). Thus, this relationship might be found in the
mathematics domain as well. However, we expected the e ﬀect sizes to
be smaller in mathematics compared to English in view of the di ﬀerent
characteristics of the domains.
b) Hypotheses on test scores.
Openness was hypothesized to predict English test scores because of
its relation to verbal knowledge and intellectual curiosity. As e ﬀects of
openness on mathematics test score have not been observed in previous
research we expected domain-speci ﬁc results here as well (see Lüdtke
et al., 2004 ;Spengler et al., 2013 ). On the basis of previous studies, we
expected no or very small signi ﬁcant eﬀects of conscientiousness on test
scores in both domains in line with Lüdtke et al. (2004) andSpengler
et al. (2013) .3.2. Research Question 2
Second, we aimed to extend previous research by adding ﬁnal
exams as a third achievement measure. Because of the hypothesized
similarity to grades, we expected conscientiousness to predict ﬁnal
exam scores in both mathematics and English, with stronger e ﬀect sizes
in mathematics. We expected the same domain-speci ﬁc pattern as hy-
pothesized for grades for e ﬀects of openness on ﬁnal exams, with po-
sitive e ﬀects in English and non-signi ﬁcant/negative e ﬀects in mathe-
matics.
3.3. Research Question 3
For GPA, we expected to ﬁnd strong relationships with con-
scientiousness as the most important noncognitive predictor of aca-
demic achievement (see Dumfart & Neubauer, 2016 ;Poropat, 2009 ).
3.4. Research Question 4
For extraversion, agreeableness, and neuroticism, our research was
rather exploratory in view of the inconsistent results from previousresearch. However, in general, we expected to ﬁnd positive associations
of extraversion and agreeableness with grades, especially in English,due to the higher relevance of oral participation and performance in the
interactive language classroom ( Ehrler & Evans, 1999; for a discussion
Dewaele & Furnham, 1999).
3.5. Research Question 5
In our ﬁfth and ﬁnal research question we addressed changes in
eﬀects of the ﬁve traits depending on the set of covariates used.
4.
Method
4.1. Sample
The present study is based on the LISA 6 study –“Educational
Outcomes of Students from Vocational and Academic Upper Secondary
Schools ”, which was conducted in the German Federal State of
Schleswig-Holstein ( N= 3775; see Leucht, Kampa, & Köller, 2016 ). In
Schleswig-Holstein, there are two school tracks in upper secondary
education (school years 11– 13): the vocational and the academic track.
The academic track refers to the traditional Gymnasium , which provides
general pretertiary education, whereas the vocational track focuses on
more applied subjects such as technical and economical courses, in
addition to compulsory education (e.g., languages and mathematics). In
both tracks, students can obtain the general higher education entrance
qualiﬁcation ( Abitur ) after successful completion of classes and ﬁnal
exams at the end of Grade 13.
Participation in the LISA 6 achievement tests was mandatory for all
students in Grade 13 at randomly drawn academic-track schools(N= 1433 students from 17 schools) and at all of the vocational-track
schools ( N= 2342 students from 27 schools) with the consent of the
Ministries of Education, Science and Cultural A ﬀairs in Schleswig-
Holstein, whereas participation in the questionnaires was voluntary.This study was carried out in accordance with the ethical guidelines for
research with human participants as proposed by the American
Psychological Association (APA). The study materials and procedures
were approved by the Ministries of Education, Science and Cultural
Aﬀairs of the Federal State of Schleswig-Holstein. About 50% of stu-
dents voluntarily took part in the questionnaire, thus data were avail-able for N= 2234 students. We excluded students from the original
sample who were no longer enrolled in English instruction ( N= 9) and
students di ﬀering in years of English instruction because they had fo-
cused on another ﬁrst foreign language ( N= 129); this resulted in a
ﬁnal sample of N= 3637.J. Meyer et al. Learning and Individual Differences 69 (2019) 45–59
48

4.2. Measures
4.2.1. Personality traits
We assessed the ﬁve-factor personality traits using the German short
version of the Big Five Inventory (BFI-K; Rammstedt & John, 2005 ).
Example items include “I am someone who does a thorough job ”,
measuring conscientiousness, and “I am someone who is complex, a
deep thinker ”, measuring openness. A ﬁve-point response format was
used, ranging from “strongly disagree ”to“strongly agree ”. Internal con-
sistencies were su ﬃcient: α= 0.78 for extraversion (four items),
α= 0.62 for agreeableness (four items), α= 0.67 for conscientiousness
(four items), α= 0.69 for neuroticism (four items), and α= 0.70 for
openness ( ﬁve items). To correct for measurement error in our analyses,
we used a latent modeling approach (see Section 4.4 ).
4.2.2. Covariates
Information on gender, socioeconomic status, and course level was
collected from school administrations. Socioeconomic status was mea-
sured by parents' occupational status in order to compute the Highest
International Socio-Economic Index of Occupational Status (HISEI;
Ganzeboom, de Graaf, & Treiman, 1992).
General cognitive ability was assessed using the verbal and ﬁgural
reasoning subscales of the cognitive ability test (KFT4-12R; Heller &
Perleth, 2000 ). The reliabilities of the subscales, as provided in the
manual according to the Kuder-Richardson Formula 20, were satisfac-
tory, ranging from α= 0.68 (verbal; V3) to 0.81 ( ﬁgural; N2). Data
were available for N= 3172 students. To obtain reliable scores for each
student, we computed ﬁve plausible values (see Section 4.3 ).
As detailed above, upper secondary education in Schleswig-Holstein
contains di ﬀerent school tracks. Student characteristics di ﬀer system-
atically between tracks, with academic-track students showing higher
academic achievement as well as higher SES and cognitive ability (see
Leucht & Köller, 2016). We took these di ﬀerences into account by using
school track as a dummy-coded covariate. A descriptive comparison ofthe school tracks in our sample can be found in Table 2.
A number of other school system features are related to learning
outcomes. In Schleswig-Holstein, students in both academic- and vo-
cational-track schools can decide which speci ﬁc domain they want to
prioritize, for example, science or languages. If they opt for a coursewith higher demands in science or languages, they spend more hours
per week in science or language instruction, respectively. It could be
argued that the course level is associated with teaching factors that arerelated
to academic achievement, for example, varying grading criteria
or preparation for exams. As a consequence, accounting for theseschooling di ﬀerences is important when examining the relationship
with personality traits. We used a dummy-coded variable for each track,indicating whether a student participated in more demanding science or
language courses (0 = no higher demand course, 1 = higher demand
course).
4.2.3. Academic achievement
Report card grades and GPA . Domain-speci ﬁc end-of-school-year report
card grades were collected via school administration lists for Grade 13.In Germany, grades at upper secondary school range from zero to 15
points, with higher values indicating better grades. Data on grades in
English were available for N= 3619 students, on grades in
mathematics for N= 3617 students.
GPA was collected via school administration lists after the end of the
school year. GPA is a general measure of academic achievement at theend of upper secondary school used as college entrance quali ﬁcation. It
is computed of all end-of-term grades in upper secondary school (fourterms; 2/3rd of ﬁnal GPA) as well as ﬁnal examination results (1/3rd of
ﬁnal GPA)
Standardized tests . Mathematics achievement was assessed using a 20-
item mathematics test from the National Educational Panel Study(NEPS), which is based on the literacy concept and designed in line
with the German educational standards ( Neumann et al., 2013 ; see also
Kampa, Köller, Schmidt, & Leucht, 2016). Data were available forN= 3171 students. In the NEPS framework, mathematical competence
is conceptualized as being able to successfully work with concepts andprocedures embedded in everyday life contexts that are typical for a
particular age group, thereby covering the literacy aspect of
mathematical competence relevant for future life. As mathematical
concepts and procedures are typically learned in school, they follow a
particular curriculum. Following the NEPS framework, mathematical
competencies can be described with two dimensions: a) content areas in
theﬁeld of mathematics (quantity [4 items], change and relationship
[6], space and shape [3], data and chance [7]) and b) the cognitivecomponent of mathematical competence, covering processes related to
solving mathematical problems. Five cognitive processes are measured
in the test: technical abilities and skills (9 items), modeling (1),
mathematical problem solving (4), using representational forms (5),
and mathematical communication (1). The quality and appropriateness
of the items was ensured by extensive pilot studies conducted by the
NEPS team (see Neumann et al., 2013 ).
English achievement was measured with listening and reading
comprehension exercises, using a subset of items from the German
National Assessment (e.g., Stanat, Böhme, Schipolowski, & Haag,
2016 ). The test items were designed to monitor the implementation of
educational standards in Germany (see Köller, Knigge, & Tesch, 2010;
see also Leucht, Fleckenstein, & Köller, 2016 ) and therefore represent
competencies based on the national curricula for the English language
classroom. Three to four tasks consisting of di ﬀerent items were pre-
sented in four 15-minute blocks. Blocks were balanced in di ﬃculty and
rotated in eight di ﬀerent booklets to control for position e ﬀects and
performance decline with test duration ( multimatrix design ). Data were
available for N= 3191 students. The reliability and validity of the test
have been shown in previous studies; results can be linked to similar
standardized tests such as PISA (see Fleckenstein, Leucht, Pant, &
Köller, 2016).
Written ﬁnal
exams . We collected information on grades received in
written ﬁnal exams ( Abitur ) in both domains via school administration
lists. Exam grades range from zero to 15 points, with higher valuesindicating better performance. Centralized Abitur tasks are provided by
the Ministries of Education, Science and Cultural A ﬀairs in Schleswig-
Holstein for both tracks; however, the tasks given to vocational- andacademic-track schools di ﬀer in content and conceptualization.
Academic-track students have 5 h to complete the exam. Forvocational-track students, the duration of the ﬁnal exams depends on
the course level: time allowed to complete the test for courses withhigher demands is 5 h, for those with lower demands, it is 4 h (see also
Landesverordnung über die Gestaltung der Oberstufe und der Abiturprüfung
in den Gymnasien und Gemeinschaftsschulen [OAPVO, 2007 ] for
regulations in the academic track, and Landesverordnung über das
beruﬂiche Gymnasium [BGVO, 2012 ] for the vocational track). To
account for these di ﬀerences, we used a dummy-coded variable to
control for school track (1 = academic track, 0 = vocational track [see
Covariates ]) and included course level in our model (see Statistical
analyses ). The ﬁnal exams of the two school tracks also have some
characteristics in common: In both school types, two teachers evaluate
the exams independently, using criteria issued by the Ministries of
Education, Science and Cultural A ﬀairs and based on subject-speci ﬁc
demands. In each domain, students can choose between at least twoassignments: In mathematics, competencies in di ﬀerent ﬁelds are
captured, for example, calculus and geometry, with coherent
superordinate assignments consisting of several subtasks ( Standing
Conference of the Ministers of Education and Cultural A ﬀairs [KMK],
2002a ). In English, assignments consist of text comprehension, for
example, ﬁctional and non ﬁctional texts combined with essay writing
tasks ( KMK, 2002b ).J. Meyer et al. Learning and Individual Differences 69 (2019) 45–59
49

To some extent, students can choose which subjects they take their
ﬁnal exams in. Thus, not all students take ﬁnal exams both in mathe-
matics and English. In English, ﬁnal exams results were available for
N= 2902 students, in mathematics for N= 2996 students. To test for
potential di ﬀerences between students who choose to take ﬁnal exams
in one domain and students who do not, we used logistic regression
analysis to predict whether a student chose mathematics or English as
theirﬁnal exam, respectively. The results are displayed in Table 1,
which shows signi ﬁcant di ﬀerences between the samples (e.g., in con-
scientiousness for English exam participation). We used the full in-
formation maximum likelihood approach (FIML) to estimate missing
values because the number of missing values on these variables was low
in comparison to the rest of the sample ( N= 735 in English, N= 642 in
mathematics) and signi ﬁcant di ﬀerences were accounted for by in-
cluding the relevant variables in the model. FIML also seemed feasible
as the measure of the ﬁve traits displayed appropriate distributions (see
Supplement E). However, we cannot ensure that the missing valueswere at random for the unobserved third variables. Thus, we also
conducted analyses on the subsamples of students who actually parti-
cipated in the respective exam (results are given in Supplement A).
4.3. Estimation of plausible values
To obtain more reliable pro ﬁciency scores for mathematics and
English test performance as well as for cognitive ability, ﬁve plausible
values were computed using item response theory (IRT) scaling tech-
niques in ConQuest ( Wu, Adams, & Wilson, 1998 ). To improve the
validity of results, we used a background model including informationon gender, age, English and mathematics course level, as well as grades.
When drawing plausible values, students who were not present on the
day of testing were included, in which case pro ﬁciency scores were
estimated based on the background variables (see Leucht, Kampa, &
Köller, 2016). For all subsequent analyses, we combined the ﬁve PVs for
ﬁnal estimations, following Rubin (1987) . PV reliabilities were sa-
tisfactory, ranging between 0.80 (cognitive ability), 0.81 (English), and
0.92 (mathematics).
4.4. Statistical analyses
We applied path analysis in a structural equation modeling frame-
work using M plus(Version 7.1; Muthén & Muthén, 1998– 2012). Con-
sidering the measurement of personality traits, a latent approach is
especially bene ﬁcial in light of the moderate alpha reliabilities of the
BFI-K instrument. Thus, we applied a single-indicator approach: wecomputed scales for the ﬁve personality traits, accounting for mea-
surement error by estimating the error variance as a function of relia-bility ( Hayduk, 1987 ).
1To gain a more detailed perspective on the
diﬀerential predictive value of conscientiousness and openness for do-
mains and measures, we investigated the changing patterns of the re-
lation between personality traits and academic achievement associated
with using di ﬀerent covariates. We considered a) only the ﬁve per-
sonality traits, b) only all covariates, c) the ﬁve personality traits,gender, SES, and IQ, using covariates on the individual student level, d)theﬁve personality traits, school track, and course level using covari-
ates on system level, and e) the full model. We tested for domain spe-ciﬁcity of standardized parameters in all models using Wald tests of
parameter constraints in Mplus.
All models were based on maximum likelihood estimation. Because
of the hierarchical data structure with students clustered in schools, itwas necessary to control for dependencies in the data. Thus, in all
models, we took account of the hierarchical data structure by com-
puting robust estimates of the model parameters by using type=com-
plex in Mplus7 (see Muthén & Satorra, 1995 ). FIML, implemented in
Mplus 7.1, was used (see Enders, 2010 ) to deal with missing values.
Several robustness checks
2were performed; the results are presented in
the Supplement.
5. Results
5.1. Descriptive statistics and bivariate correlations
The descriptive statistics of personality traits, covariates, and
achievement measures are provided in Table 2 , the bivariate correla-
tions in Table 3. The intercorrelations that we found between the ﬁve
traits re ﬂectﬁndings from previous research (e.g., Rammstedt & John,
2005 ). Correlations of the ﬁve traits with gender are similar to ﬁndings
from previous research ( Schmitt et al., 2008 ) with girls showing higher
manifestations in extraversion, agreeableness, neuroticism and con-
scientiousness. However, we found girls to score higher on openness inTable 1
Logistic regression results (standardized regression coe ﬃcients and standard
errors), predicting exam participation in English and mathematics, respectively.
English exam Mathematics exam
β SE β SE
E −0.02 0.04 −0.05 0.03
A 0.03 0.04 0.04 0.03
C −0.10⁎0.03 0.06 0.03
N −0.03 0.04 −0.07 0.04
O −0.01 0.04 −0.02 0.04
Gendera0.06 0.03 0.06⁎0.03
SES 0.06 0.03 0.02 0.03
Cognitive ability −0.00 0.03 0.02 0.03
School trackb−0.10⁎⁎0.03 −0.46⁎⁎0.04
English grade 0.29⁎⁎0.03 −0.04 0.03
Math grade −0.07⁎0.03 0.27⁎⁎0.03
English test 0.16⁎⁎0.04 −0.03 0.03
Math test −0.10⁎0.04 0.07 0.04
GPA 0.15⁎⁎0.03 0.11⁎⁎0.02
Note. E = Extraversion, A = Agreeableness, C = Conscientiousness,
N = Neuroticism, O = Openness, SES = Socioeconomic status. GPA =Grade
point average.
aGender: 0 = female, 1 = male.
bSchool track: 0 = vocational track, 1 = academic track.
⁎p≤.05.
⁎⁎p≤.01.
1We decided not to use con ﬁrmatory factor analyses (CFA) modeling in our
analyses because of inadequate model ﬁt (CFI = 0.724; TLI = 0.676;
SRMR = 0.090; RMSEA = 0.092). This is a common problem when modelingtheﬁve traits as latent factors (e.g., Borkenau & Ostendorf, 1990 ;Church &
Burke, 1994 ;McCrae, Zonderman, Costa, Bond, & Paunonen, 1996 ), because of
the moderate intercorrelations between the ﬁve factors. Exploratory Structural
Equation Modeling (ESEM), as suggested by Booth and Hughes (2014) , yielded
better but still not satisfactory results (CFI = 0.886; TLI = 0.793;
SRMR = 0.038; RMSEA = 0.073). To provide further information on the ro-
bustness of our ﬁndings, we included the ESEM model with outcome variables
(CFI = 0.94; TLI = 0.904; SRMR = 0.032; RMSEA = 0.033), yielding largely
parallel results to the single indicator solution. Results can be found in Sup-
plement C.2The following robustness checks are provided:
a) Subsamples participating in the ﬁnal examinations for each subject (see
Supplement A)
b) Multigroup analysis with unconstrained parameters (see Supplement B)
c) Analysis providing results for the full model using ESEM instead of single
indicator measurement (see Supplement C)
d) Analyses based on the sample who participated in the questionnaire
(N= 1646) (see Supplement E)
e) A comparison of responders and non-responders to the questionnaire
(Supplement D).J. Meyer et al. Learning and Individual Differences 69 (2019) 45–59
50

our sample, di ﬀering from previous results. The intercorrelations show
that di ﬀerent measures of achievement were correlated strongly but not
perfectly.
5.2. Covariates
Model a (see Table 4 ) shows the e ﬀects of all the covariates used
when predicting the di ﬀerent measures. The general pattern of results is
in line with previous research, with cognitive ability being the strongest
predictor across all measures, and gender, SES, school track, and course
levels all having smaller e ﬀects, depending on the measure used.
5.3. Predictive value of personality traits
The major objective of this study was to investigate the predictive
value of personality traits on di ﬀerent measures of academic achieve-
ment beyond cognitive ability in upper secondary education. In thissection, we consider the associations with each of the ﬁve traits and the
changing patterns of results depending on the covariates added. Ourresults are displayed in Tables 4– 8.
5.3.1. Results from multiple regression analysesResults on Research Question 1 .
a) Results on grades.
Conscientiousness was shown to predict grades in all models in both
domains (model b, c, d and e, see Tables 5 to 8, respectively). The sizesof the standardized regression coe ﬃcients were similar in all models.
We tested for domain speci ﬁcity of e ﬀects showing stronger e ﬀect sizes
of conscientiousness in mathematics compared to English (see Table 9).
Openness positively predicted English grades in model b and d
(Tables 5 and 7), but e ﬀects were non-signi ﬁcant when cognitive
ability, SES and gender were controlled for (models c and e; see Tables
6 and 8). This means that the e ﬀect might be explained by the strong
bivariate correlation of openness and gender. We tested for domainspeciﬁcity of results (see Table 9), showing that results for mathematics
diﬀered systematically. In all models, openness negatively predicted
mathematics grade. In view of the non-signi ﬁcant bivariate correlation
this might indicate a suppression e ﬀect caused by the multicollinearity
of the ﬁve traits.
b) Results on test scores.
Conscientiousness did not predict test scores in English independent
of the covariates used. In mathematics, there were signi ﬁcant positive
eﬀects of conscientiousness when gender, SES and cognitive ability
were controlled for. There were no domain-speci ﬁcd iﬀerences con-
cerning
eﬀects of conscientiousness in model b and d, as both coe ﬃ-
cients were non-signi ﬁcant (see Table 9).
Openness positively predicted English test score in all models. There
were no signi ﬁcant eﬀects on mathematics test score in all models (see
Table 9 ).
Results on Research Question 2 . Conscientiousness predicted ﬁnal exams
in mathematics in all models. E ﬀects for English were non-signi ﬁcant,
except when school-system-variables were controlled for (models c and
e). In view of the non-signi ﬁcant bivariate correlation of
conscientiousness and English ﬁnal exam this indicates a suppression
eﬀect. Eﬀ ects di ﬀered signi ﬁcantly between domains (see Table 9).
Openness predicted English ﬁnal exams (model b), but only when
none of the covariates were included in the model. In view of the non-signiﬁcant bivariate correlation this indicates a suppression e ﬀect
caused by the multicollinearity of the ﬁve traits. In mathematics, there
were negative e ﬀects of openness on ﬁnal exams, but only when cog-
nitive ability, SES and gender (models c and e) were controlled for,again indicating a suppression e ﬀect. Eﬀ ects were domain-speci ﬁc (see
Table 9 ).
Results on Research Question 3 . Conscientiousness strongly predicted
GPA in all models with independent of included covariates with similar
eﬀect sizes. There were no signi ﬁcant e ﬀects of openness independent
of included covariates in spite of the signi ﬁcant bivariate correlation.
Results on Research Question 4
Results on extraversion. Extraversion predicted grades in English, but
only when gender, SES and cognitive ability were controlled for. Inview of the signi ﬁcant bivariate correlation this might indicate
suppression e ﬀects associated with the multicollinearity of the ﬁve
traits. Eﬀ ects in mathematics were negative but only when gender, SES
and cognitive ability were not controlled for. Again, this might indicatea
suppression e ﬀect. Extraversion had negative e ﬀects on mathematics
ﬁnal exams and test scores in all models. There were no signi ﬁcant
eﬀects for English in all models.
Results on agreeableness . Agreeableness negatively predicted English
grades when gender, SES and cognitive ability were controlled for. In
view of the non-signi ﬁcant bivariate correlation this indicates a
suppression e ﬀect. Eﬀ ects on mathematics grade were signi ﬁcant only
in the full model.
Agreeableness had negative e ﬀects on English ﬁnal exams but only
if gender, SES and cognitive ability were not controlled for. Results ontest scores were similar but inconsistent as there was a signi ﬁcant ne-
gative e ﬀect in the full model. E ﬀects of agreeableness on mathematics
ﬁnal exams were non-signi ﬁcant in all models. E ﬀects on test scoresTable 2
Comparison of school types (academic vs. vocational track) considering mean
and standard deviation of the ﬁve traits, control variables, and di ﬀerent in-
dicators of academic performance (manifest).
Complete
sampleAcademictrackVocationaltrackCohen's d
M (SD) M (SD) M (SD)
E 3.61
(0.85)3.62
(0.88)3.60
(0.83)0.02
A 3.01
(0.76)2.98
(0.76)3.03
(0.76)−0.07
C 3.65
(0.68)3.59
(0.71)3.68
(0.67)−0.13
N 2.88
(0.80)2.86
(0.77)2.89
(0.81)−0.04
O 3.60
(0.77)3.62
(0.80)3.59
(0.75)0.04
Cognitive
ability
a−0.01
(0.69)0.32
(0.63)−0.20
(0.65)0.81
SES (HISEI) 57.44
(18.10)64.29(17.22)53.56
(17.36)0.62
Math grade
b8.34
(3.26)8.86
(3.23)8.04
(3.23)0.25
English gradeb8.77
(2.75)9.29
(2.83)8.47
(2.66)0.30
Math test scorec−0.02
(0.74)0.30
(0.68)−0.20
(0.72)0.71
English test
scorec0.00
(0.95)0.48
(0.87)−0.27
(0.88)0.86
Math examb6.32
(3.48)7.15
(3.51)5.84
(3.39)0.38
English examb7.91
(2.85)8.72
(2.97)7.45
(2.68)0.45
Note. E = Extraversion, A = Agreeableness, C = Conscientiousness,
N = Neuroticism, O = Openness. SES = Socioeconomic status. The ﬁve traits
were measured on a scale ranging from one to ﬁve.
aTest scores were computed using ﬁve PVs (see Measures ).
bGrades and ﬁnal exams range from 0 to 15 points, higher values indicating
better performance.
cPVs for mathematics, English, and cognitive ability were z-standardized.J. Meyer et al. Learning and Individual Differences 69 (2019) 45–59
51

Table 3
Bivariate correlations.
Personality traits Covariates Outcomes
E A C N O IQ SES Gender School track Course level
MathCourse level
EnglishMath grade English
gradeMath test English test Math exam English
examGPA
A 0.15⁎⁎
C 0.27⁎⁎0.15⁎⁎
N −0.31⁎⁎−0.11⁎−0.02
O 0.23⁎⁎0.12⁎⁎0.20⁎⁎0.19⁎⁎
IQ −0.10⁎⁎−0.11⁎⁎−0.06 −0.07⁎0.06
SES 0.13⁎⁎−0.06 −0.04 −0.08⁎0.07 0.12⁎⁎
Gendera−0.07⁎⁎−0.22⁎⁎−0.30⁎⁎−0.32⁎⁎−0.20⁎⁎0.15⁎⁎0.08⁎⁎
School trackb0.01 −0.03 −0.08 −0.03 0.02 0.36⁎⁎0.30⁎⁎0.06
Course level
Mathc−0.07⁎⁎−0.07⁎⁎−0.01 −0.08⁎⁎−0.08⁎−0.39 −0.14⁎⁎0.18⁎⁎−0.39⁎⁎
Course level
Englishc0.02 0.04 0.00 0.02 0.00 −0.42 −0.07⁎⁎−0.09⁎−0.42⁎⁎−0.29⁎⁎
Math grade −0.01 0.04 0.25⁎⁎−0.03 −0.04 0.30⁎⁎0.10⁎⁎−0.02 0.12⁎⁎0.01 −0.03
English grade 0.11⁎⁎−0.04 0.17⁎⁎0.07⁎0.16⁎⁎0.21⁎⁎0.19⁎⁎−0.06 0.14⁎⁎−0.14⁎⁎0.06 0.36⁎⁎
Math test −0.11⁎⁎−0.16⁎⁎−0.06⁎−0.20⁎⁎−0.06⁎0.64⁎⁎0.15⁎⁎0.46⁎⁎0.32⁎⁎0.06 −0.26⁎⁎0.44⁎⁎0.24⁎⁎
English test −0.02 −0.11⁎⁎−0.05 −0.01 0.13⁎⁎0.46⁎⁎0.20⁎⁎0.03 0.38⁎⁎−0.24⁎⁎0.06⁎0.22⁎⁎0.53⁎⁎0.45⁎⁎
Math exam −0.06 0.00 0.15⁎⁎−0.08⁎⁎−0.07⁎0.38⁎⁎0.14⁎⁎0.07⁎⁎0.19⁎⁎0.06⁎⁎−0.11⁎0.69⁎⁎0.36⁎⁎0.52⁎⁎0.32⁎⁎
English exam −0.01 −0.08⁎0.05 0.04 0.08⁎0.27⁎⁎0.19⁎⁎0.03 0.22⁎⁎−0.15⁎⁎0.02 0.30⁎⁎0.77⁎⁎0.28⁎⁎0.56⁎⁎0.37⁎⁎
GPA −0.05 0.02 −0.25⁎⁎−0.05 −0.08⁎−0.28⁎⁎−0.16⁎⁎0.04−0.14⁎⁎0.04 0.04 −0.62⁎⁎−0.62⁎⁎−0.36⁎⁎−0.38⁎⁎−0.63⁎⁎−0.56⁎⁎
Note. E = Extraversion, A = Agreeableness, C = Conscientiousness, N = Neuroticism, O = Openness, IQ = Cognitive ability, SES = Socioeconomic status, GPA = Grade point average.
aGender: 0 = female, 1 = male.
bSchool track: 0 = vocational track, 1 = academic track.
cCourse level: 0 = no attendance, 1 = attendance.
⁎p≤.05.
⁎⁎p≤.01.J. Meyer et al. Learning and Individual Differences 69 (2019) 45–59
52

were negative when cognitive ability, gender and SES were not con-
trolled for. In the other models they were non-signi ﬁcant.
Results on neuroticism . Neuroticism signi ﬁcantly predicted English
grades controlling for gender, SES and cognitive ability. There were no
signiﬁcant e ﬀects on mathematics grades. Regarding ﬁnal exams,
neuroticism predicted mathematics exams negatively when cognitiveability, SES and gender were not controlled for. The same was found for
mathematics test scores. In English, e ﬀects were non-signi ﬁcant for
both ﬁnal exams and test scores. The results on extraversion,
neuroticism, and agreeableness need to be interpreted with caution
because they were not consistent in any of the robustness checks (see
Supplement A).
Results on Research Question 5 . The pattern of change in R
2when adding
sets of variables is similar for the di ﬀerent measures. In general, the ﬁve
traits explained a small amount of variance. Adding system-level
covariates only had minor impact on the amount of explained
variance. Gender, SES and cognitive ability increased R2most for test
scores, especially in mathematics. In the full model, adding the system-related variables increased the amount of explained variance to only a
small degree. This is in line with model c generally showing a similarpattern as the full model e.6. Discussion
The major goal of our study was to gain a more detailed under-
standing of the di ﬀerential predictive value of personality traits for
academic achievement in upper secondary education. To do this, weconsidered ﬁnal exams as an academic outcome alongside GPA, grades,
and test scores, and compared three di ﬀerent achievement measures in
view of their relation to personality traits. Thus, we extend the body of
systematic research on this topic.
Our results indicate domain- and measure-speci ﬁcd iﬀerences in the
predictive validity of personality traits for academic outcomes beyond
cognitive ability. Even though our study was limited to a cross-sectional
design, it provides new insights and raises questions about the detailed
relationship of personality traits with academic achievement.
6.1. Personality traits and academic achievement
6.1.1. Measures of academic achievement
Regarding di ﬀerent measures of academic achievement our study
showed high, but not perfect inter-correlations between measures. This
is in line with previous studies, suggesting that di ﬀerent measures are
related to di ﬀerent aspects of achievement ( Willingham et al., 2002).
The highest correlations were found for ﬁnal exams with grades in bothTable 4
Model a: Standardized regression coe ﬃcients using all covariates as predictors with standard errors in parenthesis.
Grades Final exams Test scores GPA
English Math English Math English Math
Gendera−0.09⁎⁎
(0.02)−0.08⁎⁎
(0.02)−0.06⁎⁎
(0.02)−0.01
(0.02)−0.03
(0.02)0.34⁎⁎
(0.01)0.09⁎⁎
(0.02)
SES 0.15⁎⁎
(0.03)0.07⁎⁎
(0.03)0.13⁎⁎
(0.03)0.09⁎⁎
(0.03)0.07⁎
(0.03)0.04
(0.04)−0.13⁎⁎
(0.03)
IQ 0.22⁎⁎
(0.02)0.30⁎⁎
(0.02)0.25⁎⁎
(0.03)0.34⁎⁎
(0.02)0.41⁎⁎
(0.02)0.52⁎⁎
(0.02)−0.28⁎⁎
(0.02)
School trackb0.05
(0.04)0.07
(0.04)0.13⁎⁎
(0.04)0.12⁎
(0.05)0.33⁎⁎
(0.03)0.20⁎⁎
(0.03)−0.02
(0.04)
Course level Englishc0.12⁎⁎
(0.04)0.09⁎⁎
(0.04)0.13⁎⁎
(0.03)0.07
(0.04)0.30⁎⁎
(0.02)0.04(0.02)−0.04
(0.03)
Course level Math
c−0.06
(0.04)0.08⁎
(0.03)−0.04
(0.03)0.13⁎⁎
(0.04)−0.02
(0.02)0.19⁎⁎
(0.02)−0.01
(0.03)
Multiple R20.10 0.11 0.13 0.16 0.35 0.58 0.11
Note. SES = Socioeconomic status, IQ = Cognitive ability, GPA = Grade point average.
aGender: 0 = female, 1 = male.
bSchool track: 0 = vocational track, 1 = academic track.
cCourse level: 0 = no attendance, 1 = attendance.
⁎p≤.05.
⁎⁎p≤.01.
Table 5
Model b: Standardized regression coe ﬃcients using the ﬁve personality traits as predictors with standard errors in parenthesis.
Grades Final exams Test scores GPA
English Math English Math English Math
E 0.07
(0.04)−0.09⁎
(0.04)−0.03
(0.04)−0.14⁎⁎
(0.04)−0.06
(0.04)−0.18⁎⁎
(0.04)0.01
(0.04)
A −0.07⁎
(0.03)0.01
(0.03)−0.09⁎
(0.04)−0.03
(0.03)−0.11⁎⁎
(0.04)−0.18⁎⁎
(0.04)0.06⁎
(0.03)
C 0.14⁎⁎
(0.04)0.28⁎⁎
(0.03)0.06
(0.04)0.19⁎⁎
(0.03)−0.06
(0.04)−0.02
(0.04)−0.25⁎⁎
(0.03)
N 0.07
(0.04)−0.04
(0.04)0.00
(0.04)−0.11⁎⁎
(0.04)−0.07
(0.04)−0.29⁎⁎
(0.04)−0.04
(0.04)
O 0.12⁎⁎
(0.04)−0.07⁎
(0.04)0.09⁎
(0.04)−0.05
(0.04)0.19⁎⁎
(0.04)0.06
(0.04)−0.03
(0.04)
Multiple R20.06 0.08 0.02 0.05 0.04 0.11 0.07
Note. E = Extraversion, A = Agreeableness, C = Conscientiousness, N = Neuroticism, O = Openness, GPA = Grade point average.
⁎p≤.05.
⁎⁎p≤.01.J. Meyer et al. Learning and Individual Differences 69 (2019) 45–59
53

domains, supporting our expectations as described above. Un-
expectedly, the bivariate correlation between openness and cognitive
ability was not signi ﬁcant. This might indicate that the instrument used
captured the senseo-aesthetic aspect of openness more than the in-tellectual aspect.
6.1.2. Relationships of personality traits and academic achievement
Our analyses provided systematic ﬁndings on the relationships be-
tween personality traits and academic achievement. We discuss both
the replication of results from previous research and the integration of
newﬁndings into the literature, with a focus on ﬁnal examinations as
an additional measure.
First, we considered the trait-speci ﬁcd iﬀerential relationships of
conscientiousness and openness with all measures in both domains(Research Question 1).
a) Grades
In line with Spengler et al. (2013) , we found conscientiousness to be
an important predictor of grades in both domains due to the importanceof study behavior and teacher expectations for grading. In line with
previous research the e ﬀect was stronger for mathematics compared to
English. Eﬀ ects of openness on grades were domain-speci ﬁc as well:
openness predicted English grade, whereas in mathematics we found a
negative e ﬀect of openness on grades. It can be argued that students
high in openness are less interested in more analytical, less creativethinking as required in mathematics, which could result in a negative
eﬀect of openness (see Lipnevich et al., 2016 ). As mentioned above, thisTable 6
Model c: Standardized regression coe ﬃcients using the ﬁve traits and covariates on the individual level as predictors with standard errors in parenthesis.
Grades Final exams Test scores GPA
English Math English Math English Math
E 0.09⁎
(0.04)−0.05
(0.04)−0.01
(0.04)−0.08⁎
(0.04)−0.01
(0.04)−0.07⁎
(0.03)−0.03
(0.03)
A −0.04
(0.04)0.06
(0.03)−0.06
(0.04)0.04
(0.03)−0.07
(0.04)−0.03
(0.03)0.01
(0.03)
C 0.15⁎⁎
(0.04)0.30⁎⁎
(0.03)0.06
(0.04)0.23⁎⁎
(0.04)−0.05
(0.04)0.11⁎⁎
(0.03)−0.28⁎⁎
(0.03)
N 0.11⁎⁎
(0.04)0.03
(0.04)0.04
(0.04)−0.02
(0.04)−0.02
(0.04)−0.05
(0.04)−0.11⁎⁎
(0.04)
O 0.07
(0.04)−0.13⁎⁎
(0.03)0.04
(0.04)−0.12⁎⁎
(0.04)0.10⁎⁎
(0.04)−0.01
(0.03)0.02
(0.04)
Gendera−0.01
(0.03)0.01
(0.03)−0.05
(0.03)0.05
(0.03)−0.07⁎
(0.03)0.37⁎⁎
(0.02)−0.02
(0.03)
SES 0.16⁎⁎
(0.03)0.09⁎⁎
(0.03)0.17⁎⁎
(0.03)0.11⁎⁎
(0.03)0.14⁎⁎
(0.03)0.06
(0.04)−0.14⁎⁎
(0.03)
IQ 0.21⁎⁎
(0.02)0.32⁎⁎
(0.02)0.25⁎⁎
(0.03)0.37⁎⁎
(0.02)0.44⁎⁎
(0.02)0.57⁎⁎
(0.02)−0.29⁎⁎
(0.02)
Multiple R20.13 0.19 0.11 0.21 0.25 0.56 0.18
Note. E = Extraversion, A = Agreeableness, C = Conscientiousness, N = Neuroticism, O = Openness, SES = Socioeconomic status, IQ = Cognitive ability,
GPA = Grade point average.
aGender: 0 = female, 1 = male.
⁎p≤.05.
⁎⁎p≤.01.
Table 7Model d: Standardized regression coe ﬃcients using all of the ﬁve personality traits, school track, and course level as predictors with standard errors in parenthesis.
Grades Final exams Test scores GPA
English Math English Math English Math
E 0.07
(0.04)−0.08⁎
(0.04)−0.04
(0.04)−0.12⁎⁎
(0.04)−0.07
(0.04)−0.16⁎⁎
(0.04)0.01
(0.04)
A −0.07⁎
(0.03)0.03
(0.03)−0.09⁎
(0.04)0.00
(0.03)−0.11⁎⁎
(0.03)−0.15⁎⁎
(0.03)0.06
(0.03)
C 0.16⁎⁎
(0.04)0.30⁎⁎
(0.03)0.08⁎
(0.04)0.21⁎⁎
(0.03)−0.01
(0.04)0.01
(0.03)−0.26⁎⁎
(0.03)
N 0.07
(0.04)−0.02
(0.04)0.01
(0.04)−0.08⁎
(0.04)−0.06
(0.04)−0.24⁎⁎
(0.04)−0.05
(0.04)
O 0.11⁎⁎
(0.04)−0.08⁎
(0.04)0.08
(0.04)−0.06
(0.03)0.17⁎⁎
(0.04)0.05
(0.04)−0.03
(0.04)
School tracka0.21⁎⁎
(0.04)0.24⁎⁎
(0.04)0.27⁎⁎
(0.04)0.31⁎⁎
(0.05)0.51⁎⁎
(0.03)0.46⁎⁎
(0.04)−0.21⁎⁎
(0.03)
Course level Englishb0.14⁎⁎
(0.04)0.11⁎⁎
(0.04)0.14⁎⁎
(0.03)0.08
(0.04)0.29⁎⁎
(0.03)0.04
(0.03)−0.06
(0.03)
Course level mathb0.00
(0.04)0.13⁎⁎
(0.04)−0.01
(0.03)0.18⁎⁎
(0.04)0.04
(0.03)0.32⁎⁎
(0.04)−0.07⁎
(0.03)
Multiple R20.10 0.11 0.08 0.11 0.24 0.28 0.09
Note. E = Extraversion, A = Agreeableness, C = Conscientiousness, N = Neuroticism, O = Openness, GPA = Grade point average.
aSchool track: 0 = vocational track, 1 = academic track.
bCourse level: 0 = no attendance, 1 = attendance.
⁎p≤.05.
⁎⁎p≤.01.J. Meyer et al. Learning and Individual Differences 69 (2019) 45–59
54

pattern had already been found in a sample of adolescents in Germany
in a study by Lipnevich et al. (2016) . However, this relation could
possibly be due to students with lower scores in openness tending to bemore interested in mathematics: scoring low on openness means being
practical, conventional, and rational ( McCrae & Costa, 1987 ). Students
with these characteristics might enjoy analytical thinking and tasks thathave one correct answer, as opposed to creative and open-solution tasks
with high verbal demands. As conscientiousness was held constant in
the regression, it could be argued that, with the same level of con-
scientiousness, a relationship between openness and interest could be
hypothesized, which could be associated with mathematics achieve-
ment. This needs to be tested in future research.
Further, reasons for inconsistent results in our study and previous
studies need to be considered. As mentioned above, in Spengler et al.
(2013) there was no association of openness and mathematics grades,but as in Lipnevich et al. (2016) in our study a negative e ﬀect was
observed. One reason for this di ﬀerential pattern is the use of the dif-
ferent covariates. We used a similar set of covariates as used byLipnevich et al. (2016) , whereas Spengler et al. (2013) used self-con-
cept and domain-speci ﬁc interest as additional covariates. It can be
argued that the negative association with openness had been hidden
because of potentially mediating e ﬀects of these constructs. For ex-
ample, domain-speci ﬁc interest might be related with openness, thus
explaining the negative association with mathematics grade, followingthe argumentation above. This needs to be further investigated in future
research.
a) Test scores
As hypothesized, openness predicted English but not mathematics
test scores. Concerning e ﬀects of conscientiousness, we found positive
eﬀects on test scores in mathematics. This pattern of results di ﬀered
from previous research where no or very small signi ﬁcant e ﬀects on
mathematics test scores had been obtained ( Lüdtke et al., 2004;
Spengler et al., 2013 ). One possible explanation may be that students
with higher scores in conscientiousness invest more academic e ﬀort
throughout the school year, thereby improving their learning outcomes(Trautwein et al., 2006 ;Trautwein et al., 2009 ;Trautwein & Lüdtke,
2007 ). Thus, their accumulated knowledge could enhance their test
performance. Another explanation could be the di ﬀerences in test-
taking behavior; motivation during a low-stakes test is assumed to berather low. Thus, concentration might be negatively a ﬀected, resulting
in a higher number of careless mistakes that are detrimental, especiallyin mathematics (e.g., writing down a wrong number and using it for
further calculations). It may be hypothesized that, even if motivation is
low, these mistakes are less common for individuals scoring high on
conscientiousness, thereby explaining better test scores with aTable 8
Model e: Standardized path coe ﬃcients estimated simultaneously in one model with standard errors in parenthesis for the relations of personality traits, cognitive
ability, and covariates (gender, school track, ses, course level) to academic outcomes in both domains.
Grades Final exams Test scores GPA
English Math English Math English Math
E 0.08⁎
(0.04)−0.05
(0.03)−0.02
(0.04)−0.08⁎
(0.04)−0.02
(0.03)−0.07⁎
(0.03)−0.03
(0.04)
A −0.04
(0.04)0.07⁎
(0.03)−0.07
(0.04)0.05
(0.03)−0.08⁎
(0.03)−0.02
(0.03)0.01
(0.03)
C 0.17⁎⁎
(0.04)0.31⁎
(0.03)⁎0.08⁎
(0.04)0.23⁎⁎
(0.04)−0.02
(0.04)0.11⁎⁎
(0.03)−0.28⁎⁎
(0.03)
N 0.11⁎⁎
(0.04)0.03
(0.04)0.03
(0.03)−0.01
(0.04)−0.02
(0.04)−0.04
(0.04)−0.11⁎⁎
(0.04)
O 0.07
(0.04)−0.12⁎
(0.03)⁎0.04
(0.04)−0.11⁎⁎
(0.04)0.11⁎⁎
(0.04)−0.01
(0.03)0.02
(0.04)
Gendera0.01
(0.03)0.00
(0.03)−0.03
(0.03)0.04
(0.03)−0.04
(0.03)0.35⁎⁎
(0.02)−0.02
(0.02)
SES 0.14⁎⁎
(0.03)0.09⁎
(0.03)⁎0.13⁎⁎
(0.04)0.10⁎⁎
(0.03)0.06
(0.03)0.04
(0.04)−0.13⁎⁎
(0.03)
IQ 0.22⁎⁎
(0.03)0.31⁎
(0.03)⁎0.24⁎⁎
(0.04)0.34⁎⁎
(0.03)0.40⁎⁎
(0.02)0.51⁎⁎
(0.02)−0.29⁎⁎
(0.03)
School trackb0.08
(0.04)0.09⁎
(0.04)0.14⁎⁎
(0.04)0.13⁎⁎
(0.05)0.34⁎⁎
(0.03)0.20⁎⁎
(0.03)−0.05
(0.04)
Course level Englishc0.14⁎⁎
(0.04)0.11⁎
(0.04)⁎0.14⁎⁎
(0.03)0.08
(0.04)0.30⁎⁎
(0.02)0.04
(0.02)−0.06
(0.03)
Course level Mathc−0.04
(0.04)0.08⁎
(0.04)−0.04
(0.03)0.12⁎⁎
(0.04)−0.02
(0.02)0.18⁎⁎
(0.02)−0.02
(0.03)
Multiple R20.15 0.19 0.14 0.22 0.37 0.59 0.18
Note. E = Extraversion, A = Agreeableness, C = Conscientiousness, N = Neuroticism, O = Openness, GPA = Grade point average. SES = Socioeconomic status.
IQ = Cognitive ability.
aGender: 0 = female, 1 = male.
bSchool track: 0 = vocational track, 1 = academic track.
cCourse level: 0 = no attendance, 1 = attendance.
⁎p≤.05.
⁎⁎p≤.01.
Table 9
Tests of domain speci ﬁcity (Wald tests).
Openness Conscientiousness
Value p-Value Value p-Value
Model b Grades 23.003 < 0.0001 13.157 0.0003
Tests 7.020 0.0081 0.555 0.4563
Final exams 12.225 0.0005 11.403 0.0007
Model c Grades 23.824 < 0.0001 13.919 0.0002
Tests 7.065 0.0079 12.089 0.0005
Final exams 12.860 0.0003 14.807 0.0001
Model d Grades 22.583 < 0.0001 13.063 0.0003
Tests 7.054 0.0079 0.173 0.6774
Final exams 12.175 0.0005 10.636 0.0011
Model e Grades 24.050 < 0.0001 12.596 0.0004
Tests 7.547 0.0060 8.998 0.0027
Final exams 13.231 0.0003 12.652 0.0004J. Meyer et al. Learning and Individual Differences 69 (2019) 45–59
55

compensatory e ﬀect ( Trautwein et al., 2015). Results di ﬀer domain-
speciﬁcally; with no signi ﬁcant eﬀects on English test score. It could be
argued that this compensation e ﬀect would not hold for English, as
careless mistakes would not a ﬀect the total performance as much be-
cause of the di ﬀerent nature of the two domains.
The question of why the relation between conscientiousness and the
mathematics test score seems to vary across studies remains. Possible
explanatory factors could be student age and population di ﬀerences. A
study by Lüdtke et al. (2004) investigated a sample (Grade 13, upper
secondary school) similar to the one that we used in this study, whereas
Spengler et al. (2013) used a more heterogeneous and younger student
population (Grade 9, di ﬀerent school types). Also, the studies used
diﬀerent covariates in addition to controlling for cognitive ability. Si-
milar to our study, Lüdtke et al. controlled for background character-istics such as SES, gender, and school system features, whereas Spengler
et al. (2013) used domain-speci ﬁc self-concept and interest. It can be
hypothesized that self-concept and interest fully mediate the relation-
ship of openness and academic achievement, resulting in non-sig-
niﬁcant e ﬀects if these variables are controlled; explaining the di ﬀer-
ential results. Future research is needed to provide stronger evidence tosupport these explanations.
Investigating Research Question 2 we aimed to extend previous
research by adding ﬁnal exams as a third achievement measure. Con-
scientiousness predicted ﬁnal exams in mathematics in all models. Ef-
fects for English were non-signi ﬁcant, except when school-system
variables were controlled for, indicating a suppression e ﬀect. Similarly,
openness predicted ﬁnal exams in both domains with varying patterns
of results depending on the sets of covariates used. These results in-dicate that there might be e ﬀects of openness on ﬁnal exams in both
domains, but just as with the results on grades as described above, theyhave to be interpreted carefully because of potential suppression ef-
fects.
InResearch Question 3 we found conscientiousness to strongly
predict GPA but no e ﬀects of openness. This is in line with previous
research showing conscientiousness to be the most important non-
cognitive predictor of academic achievement (see Dumfart & Neubauer,
2016 ;Poropat, 2009).
Results on Research Question 4 showing e ﬀects
of extraversion,
agreeableness, and neuroticism need to be interpreted carefully becauseof the inconsistent results across our robustness checks. More sys-
tematic research is needed concerning potential factors causing these
inconsistencies. Positive e ﬀects of extraversion were found on the
English grade, potentially related to oral participation during lessons.Findings on agreeableness were rather inconsistent across domains and
measures. Neuroticism had positive relationships with both the English
grade and the GPA. This could be related to the more careful learning
behavior that is associated with neuroticism and that is bene ﬁcial for
cumulative assessment but not for direct testing situations.
In summary, these patterns of results suggest the inconsistencies in
the eﬀects (especially those of agreeableness, neuroticism, and extra-
version) found in previous research might be explained with di ﬀerent
sets of covariates and domains. At least for some of the traits, our studyprovides more consistent patterns and ideas for interpretation, but re-
plications are be needed for further interpretations and to account for
the stability of results. Here, we focus on conscientiousness and open-
ness, as most results on neuroticism, agreeableness, and extraversion
could not be shown in any of our robustness checks. This might indicate
that the association of these personality traits varies for di ﬀerent sub-
populations, which should be considered in future research, in additionto covariates and domains. Further, the possibility that the e ﬀects found
are related to measurement errors or that they are speci ﬁc to our
sample cannot be ruled out in this approach.
InResearch Question 5 we addressed changes in e ﬀects of the ﬁve
traits depending on the set of covariates used. In general, the ﬁve traits
explain a small amount of variance. Adding system-level covariatesonly had minor impact on the amount of explained variance. Gender,SES and cognitive ability increased R
2most in test scores, especially in
mathematics, as can be expected concerning the e ﬀect of cognitive
ability on these tests as known from previous research (see Lechner
et al., 2017; Noftle & Robins, 2007). In general, student-related vari-
ables explain a greater amount of variance in academic achievement in
comparison to system-related covariates, and they are more strongly
related to the ﬁve traits. This indicates the importance of controlling for
student-level characteristics when investigating the e ﬀects of person-
ality traits on academic achievement, as ﬁndings can change depending
on the covariates used. Further, cognitive ability is the only predictorthat has a large and consistent e ﬀect for all measures.
In addition, the multicollinearity of the ﬁve traits has impacted
several of our results. This emphasizes the importance of using the
other four traits as potential covariates when investigating e ﬀects of
speciﬁc personality traits.
It can be concluded that our results provide evidence on the gen-
eralizability of these ﬁndings to another relevant achievement measure,
indicating the importance of these associations. However, the ﬁndings
on neuroticism, agreeableness, and extraversion were rather incon-sistent across subgroups, indicating an association with population
characteristics that should be considered in future studies.
Our results were largely consistent with patterns from previous
studies concerning the correlations with achievement measures and
intercorrelations between the ﬁve traits. However, in our sample
openness was correlated with gender, and girls scored higher in open-ness than boys. This should not a ﬀect our results as we considered the
impact of using gender as covariate by entering di ﬀerent sets of cov-
ariates. Still, it should be acknowledged that in our sample the pattern
diﬀered from previous research indicating that girls tend to show
higher scores in all traits except for openness (see Schmitt et al., 2008).
6.2. Limitations and directions for future research
As argued above, our study contributes to the literature on the re-
lationship between personality traits and academic achievement inupper secondary education. However, the following critical issues
should be kept in mind when interpreting the results.
First, as is the case with all studies using cross-sectional data, further
studies with longitudinal data are needed to provide evidence on the
stability and direction of the relationships investigated. Moreover, our
study does not account for the possibility of alternative causal pathways
(e.g., pleiotropic genes) that could also explain the relationships we
found between personality traits and academic achievement. Potential
third variables should be addressed in future studies in order to develop
a more comprehensive understanding.
Second, students from academic- and vocational-track schools di ﬀer
signiﬁcantly and it has to be kept in mind that grading depends on the
frame of reference, which di ﬀers between school tracks, as do the ﬁnal
examination tasks that are administered. We addressed these issues by
using school track as a covariate, and our results were mostly robust
when additional analyses were conducted in a multigroup framework.
However, future studies should focus on the potential di ﬀerences be-
tween school tracks that could be associated with personality traits.
Third, our data suggest a selection e ﬀect considering the choice of
ﬁnal exams, with, for example, less conscientious students choosing
English. We took this into account by conducting additional separate
analyses for both samples (i.e., students choosing mathematics andstudents
choosing English, respectively), yielding similar results.
Overall, the robustness checks we carried out showed that the pathcoeﬃcients of personality traits on academic achievement did not
change across analyses. As a related issue, it has to be kept in mind that
our results only consider academic achievement in the German context
of upper secondary education. Whether a similar pattern of results
could be observed for other educational systems and age groups re-
mains a question for future research.
Fourth, there was a large amount of missing values on theJ. Meyer et al. Learning and Individual Differences 69 (2019) 45–59
56

questionnaire data. This is the result of the voluntary nature of students'
questionnaire as required by law and ethical standards. We handled
these missing data with FIML to include all accessible information. As
we controlled for a large set of covariates in our analyses and conducted
robustness analyses yielding largely the same results, the use of FIML
seemed defensible. However, the possibility of uncontrolled third
variables cannot be excluded. This should be kept in mind when in-
terpreting our results as these might di ﬀer if all students had partici-
pated in the questionnaire.
Another important issue is the measurement of personality with the
BFI-K, which is a very short instrument. Previous research has shownthat di ﬀerent scales conceptualize personality in di ﬀerent ways. In
particular, very short scales cannot represent all personality facets, andit could be argued that some facets of openness and conscientiousness
are more strongly related to academic achievement than others
(Roberts, Chernyshenko, Stark, & Goldberg, 2005 ;Schmidt et al.,
2018 ). Thus, in addition to measures of achievement, future research
needs to consider the impact of the personality measure chosen on the
results obtained.
Another limitation can be seen in our conceptualization of cognitive
ability, as only the ﬂuid aspects of mental ability were covered in this
study and it could be argued that they are only one facet of generalcognitive ability. This means that the associations between achieve-
ment and personality traits could be di ﬀerent when a broader con-
ceptualization of cognitive ability is used.
In addition, we were not able to include the two aspects of openness
into our models because of our use of a short instrument with only ﬁve
items. This should be addressed in future research, especially because
ourﬁndings concerning mathematics that are not in line with previous
research might be explained by the fact that our openness instrumentemphasized the senseo-aesthetic aspect. This would be in line with
previous research as this aspect is not related to this kind of academic
achievement, especially in domains such as mathematics that requiremore conventional thinking and have a stronger emphasis on rules and
regulations, as pointed out by Gatzka and Hell (2017) .
On a conceptual level, it could be argued that, if standardized tests
are seen as academic achievement measures, cognitive ability measures
might also be indicators of achievement. This opens up the question of
how personality is associated with cognitive ability, using it as a de-
pendent variable instead of a covariate. This question should be con-
sidered in future research, focusing on the distinctive aspects that se-
parate cognitive ability tests from achievement tests. This is related to
the debate on whether and how intelligence, personality, and academic
achievement relate and overlap; and it could have the potential to
provide answers some of those questions. In our study, we focused on
the incremental predictive value of personality on academic achieve-
ment beyond cognitive ability, and on the achievement-related aspects
of achievement tests, but we believe further investigations from dif-
ferent theoretical angles could foster deeper understanding.
Although we approached our research questions systematically and
attempted to further validate ﬁndings from previous studies by using a
rather strict categorization, we cannot rule out the possibility that thereis a lot of overlap between achievement measures. It could be argued
that some measures are prone to measurement errors. For example,
single grades might not be as reliable as accumulated measures such as
GPA. This was not accounted for in this study. Nonetheless, we argue
that this approach is a worthwhile attempt to gain a more detailed
picture of the di ﬀerential relationships between academic achievement
and personality traits in upper secondary education. Finally, in futureresearch, the possibility of a nonlinear relationship between personality
and academic achievement in secondary education should be con-
sidered (see Ziegler, Knogler, & Bühner, 2009, for a study in a college
sample).6.3. Conclusions
Considering the cross-sectional nature of our data, our results need
to be treated cautiously and require further consideration in futureresearch, especially in longitudinal studies. Still, our study provides
new insights into the relationship between personality traits and aca-
demic achievement, showing the domain-speci ﬁc relationships of con-
scientiousness and openness with grades, test scores, and ﬁnal ex-
aminations —a school- and performance-based test with great
implications
for students' academic careers. Our results provide evi-
dence on the generalizability of ﬁndings from previous research not
only to a di ﬀerent student population and age group, but also to an
additional achievement outcome. Whereas the e ﬀect of personality was
small in magnitude, its impact may cumulate across school careers,inﬂuencing students' educational paths in signi ﬁcant ways. The un-
derlying mechanisms and possible mediators for domain-speci ﬁc asso-
ciations need to be further investigated in order to determine ways in
which students' individual academic achievement can be enhanced and
successful educational careers can be fostered. Thus, our study extends
the body of research by providing important insights and introducing
new and interesting prospects for future research.
Appendices. Supplementary data
Supplementary data to this article can be found online at https://
doi.org/10.1016/j.lindif.2018.11.005 .
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