Upload
sipos-gabriella
View
216
Download
0
Embed Size (px)
Citation preview
7/30/2019 The Influence of Personality on Three Aspects of Cognitive Performance,
1/14
The inuence of personality on three aspects of cognitive
performance: processing speed, intelligence and school
performance
Heiner Rindermann a,*, Aljoscha C. Neubauer b
aInstitute of Psychology, Otto-von-Guericke-University, Postfach 4120, 39016 Magdeburg, GermanybInstitute of Psychology, Karl-Franzens-University, Graz, Germany
Received 27 October 1999; received in revised form 29 February 2000; accepted 7 April 2000
Abstract
According to the mental speed approach, measures of speed of information processing represent cogni-
tive ability in a comparatively `pure' form, i.e. less inuenced by cultural and learning factors than psy-
chometric intelligence tests. In contrast school performance is assumed to be strongly inuenced by
cultural and personality factors like motivation, diligence, relationship to teachers etc. Former research hasshown, that the speed-intelligence relationship cannot be explained by higher cognitive processes like
motivation. But no research has simultaneously investigated the impact of personality on processing speed
measures, psychometric intelligence test scores and school performance in comparison. The more `culture
fair' processing speed tests should be less inuenced by personality. To test this hypothesis, stepwise
regressions between personality scales and dierent processing speed measures (Zahlen-Verbindungs-Test,
Coding Test), psychometric intelligence tests (Kognitiver Fa higkeits-Test, Advanced Progressive Matrices)
and school performance (grades) were calculated. In a sample of 280 students from German gymnasiums
(class-levels 9 and 10) results show a weak multiple correlation of personality with processing speed
(R=0.32), a medium correlation with intelligence (R=0.51) and a high correlation with grades (R=0.69).
Processing speed tests allows one to measure cognitive abilities in a less biased form than intelligence tests,
whereas school performance could be inuenced in a positive or negative way by personality factors likeself-concept, anxiety or motivation. # 2001 Elsevier Science Ltd. All rights reserved.
Keywords: Processing speed; Intelligence; School performance; Personality
0191-8869/01/$ - see front matter # 2001 Elsevier Science Ltd. All rights reserved.P I I : S 0 1 9 1 - 8 8 6 9 ( 0 0 ) 0 0 0 7 6 - 3
Personality and Individual Differences 30 (2001) 829842
www.elsevier.com/locate/paid
* Corresponding author. Tel.: +49-391-6711919; fax: +49-391-6711965.
E-mail address: [email protected] magdeburg.de (H. Rindermann).
7/30/2019 The Influence of Personality on Three Aspects of Cognitive Performance,
2/14
1. Introduction
In the last two decades a lot of evidence for a relation between microlevel measures of speed of
information processing and macrolevel measures of intelligence has been collected (e.g. Beauducel& Brocke, 1993; Lindley, Smith & Thomas, 1988; Neubauer, 1995, 1997). The use of latency
measures (choice reaction time, reading rates, coding of numbers or letters) is an old and in the
last 15 years often used, approach to study mental processes in intelligence research, but also in
personality research. Results show a relationship between psychometric intelligence and speed
of information processing of about r=0.30 (Neubauer, 1997): The faster a person can solve easy
tasks, the higher is the psychometric intelligence. In personality research it was shown, that extreme
answers were found quicker than medium answers; here persons need longer time to nd answers,
because they have to compare dierent situations, persons and stimuli (Amelang, Eisenhut &
Rindermann, 1991).
The relationship between intelligence and processing speed is explained by neural eciency,oscillation rate or specic cortical activation (Haier, 1993; Jensen, 1982; Neubauer, Freudenthaler
& Pfurtscheller, 1995; Neubauer, Sange & Pfurtscheller, 1999). Higher IQ individuals use their
brain more eciently. Intelligence is seen as a characteristic of the central nervous system to
process information quickly and correctly. The mental speed theory regards speed and eciency
of information processing in elementary cognitive tasks as an important basis of individual dif-
ferences in cognitive abilities. Explanations referring to a biological substrate (eciency of bio-
logically-determined central nervous system) are sometimes labelled `bottom-up' approaches as
opposed to `top-down' explanations, which try to explain the IQ-speed-correlations by higher
cognitive processes: personality factors (motivation, test anxiety, speed-accuracy tradeo) or
other non-biological abilities or circumstances like strategies (response practice), training, order
eects or the use of timed tests (see Neubauer, 1997, for a review).Among the personality factors assumed to inuence performance in cognitive tests are moti-
vation, task-orientation and test-anxiety. Persons with higher motivation and task-orientation or
lower test-anxiety should show higher scores in intelligence tests as well as in mental-speed tests.
However, former research has shown (Lindley & Smith, 1992; Neubauer, Bauer & Ho ller, 1992;
Rindermann & Neubauer, 2000), that the relationship between intelligence and processing speed
could not be explained by motivation or other `top-down' explanations like strategies or con-
centration ability. In the Rindermann and Neubauer study (2000) the focus of research was on
the relationship between processing speed, intelligence and school performance. The results sup-
ported the singularity of mind view, predicting correlations between dierent measures of intelli-
gence presumably because of the eciency of central nervous system operations. But in theRindermann and Neubauer study only a few personality scales were used to test, whether the
relationship between processing speed and intelligence or performance criteria could be explained
by `top-down' explanations. By using a large set of personality scales we want here to extend this
approach and investigate which of three areas of cognitive performance (processing speed, psycho-
metric intelligence, school performance) is most and which is least inuenced by personality factors.
In his model on three dierent conceptions of intelligence, Eysenck (1986) postulated that
biological intelligence should be inuenced only by biological factors, psychometric intelligence
(or IQ) by biological and cultural factors and social intelligence by psychometric intelligence and
many environmental, personality and cultural factors. It is not possible to measure biological
830 H. Rindermann, A.C. Neubauer / Personality and Individual Dierences 30 (2001) 829842
7/30/2019 The Influence of Personality on Three Aspects of Cognitive Performance,
3/14
intelligence directly, but elementary cognitive tasks designed to measure processing speed are
presumed to be one of the best indicators for it (apart from physiological or biological indicators).
Results in these tests should be less inuenced than IQ or especially school performance by cultural
or personality factors. Social intelligence is a construct very dicult to conceptualize and measure;therefore, we prefer school performance as the most important external criterion for intelligence here.
Following the theory of mental speed approach, measures of speed of information processing
represent cognitive ability in a comparatively `pure' form, i.e. less inuenced by cultural and
learning factors than intelligence-measures. School performance is supposed to be inuenced by
cultural and motivational factors like status of family, diligence, relationship to teachers etc. So we
expect low correlations between personality factors and processing speed, a higher one between
personality factors and intelligence and the highest between personality and school performance.
To prove these assumptions we used dierent personality factors in a new sample to predict (a)
performance on paper-and-pencil processing speed tests, (b) performance on general and factorial
intelligence-tests and (c) success in school (school grades); i.e. we try to compare the predictabilityof processing speed, intelligence and school performance by personality factors.
On the basis of these relationships we can ask a second question that relates to the practical
usefulness (validity) of processing speed, cognitive ability and personality measures in predicting
school performance. In applied diagnostical psychology, intelligence tests as well as personality
questionnaires are often used to predict real world phenomena, like success in school. On the
basis of our data we can also turn our main question around and ask which combination of
measures from the cognitive as well as from the personality domain might provide the best pre-
diction of school success. For this question we treat mean school grades as the criterion and test
by which set of predictors (processing speed, cognitive ability, personality) as well as of combi-
nations of these sets the school performance can be predicted best. We hypothesize that a com-
bination of measures from the cognitive domain and from the personality domain should give thebest prediction. The open question, however, will be if personality combined with measures from
both cognitive domains are really necessary or if only one of them (which one?) is necessary for a
good prediction of success in schools.
2. Method
2.1. Instruments
2.1.1. Speed-of-processing testsSpeed of information processing was measured using two paper-and-pencil tests, which can be
administered in groups: the Zahlen-Verbindungs-Test (ZVT; Oswald & Roth, 1978) and the
Kodierungstest (KDT; Lindley et al., 1988; Sitzwohl, 1995). In both instruments participants
have to solve easy mental tasks within a short time-limit (30 s).
In the ZVT, which is a trail-making test consisting of four matrices, randomly arranged numbers
have to be connected in an ascending order (from 1 to 90) with a pencil. Assuming that a number
matrix involves 136 bits of information, an index for speed of information processing (i.e. bit/s)
can be calculated from the number of correctly connected signs within a given time limit of 30 s
per matrix.
H. Rindermann, A.C. Neubauer / Personality and Individual Dierences 30 (2001) 829842 831
7/30/2019 The Influence of Personality on Three Aspects of Cognitive Performance,
4/14
In the KDT (Coding-Test) letters, numbers or circle segments have to be copied (no coding,
copy condition), or written one sequence forward (code forward: next letter in alphabet, next
number/add one, enlarge the circle segment in a clockwise direction; simple coding) or one
sequence backward (code backward: preceding letter or number or circle segment; complex cod-ing). The Coding-Test (or `substitution-test') was developed by Lindley et al. (1988) with verbal
and numerical tasks, gural tasks were added by Sitzwohl (1995, cf. also Neubauer & Bucik,
1996). In each version (verbal, numerical, gural) participants were given one row of 10 items as
practice and then two pages with seven rows of 10 items (instructions stressed speed and accu-
racy). The time limit per page was 30 s. The dependent variable was the number of correct items
within the time limit for each condition (Copy, Code For, Code Back), aggregated over both
repeated presentations of each condition.
Both, the ZVT and KDT measurements were collected from 1995 to 1999.
2.1.2. Intelligence testsIntelligence was measured by the Kognitiver Fa higkeitstest (KFT; Heller, Gaedike & Weinla -
der, 1985) and Advanced Progressive Matrices (APM; Raven, 1958; Heller, Kratzmeier & Leng-
felder, 1998). The KFT (Cognitive Abilities Test) assesses verbal, numerical and nonverbal/gural
abilities. We were using six subscales: vocabulary and word analogies, comparison of quantities
and number series, classication of gures and gure-analogies. The KFT is used in two parallel
forms (A in year 1 and B in year 2); with each class-level (4, 5, 6, F F F) the tasks get more dicult
(class-level-adaptive testing). The APM measure logical reasoning using abstract gures. The
APM are considered a good indicator of general intelligence (Spearmans g).
2.1.3. Grades
Grades or marks were taken from the nal year school report (all tests and grades were indi-vidually assigned by using anonymous numbers). Subject grades were grouped into six areas by
content and by cluster analysis: Languages, mathematics and physics, natural sciences, huma-
nities, music and art, behaviour and co-operation/diligence. The correlations between grades of
the six areas are around r=0.50. Grades were collected from 1995 to 1998.
2.1.4. Personality scales (self-assessment questionnaires)
The `Angstfragebogen fu r Schu ler' (AFS; Wieczerkowski, Nickel, Janowski, Fittkau & Rauer,
1986), an anxiety questionnaire for school children, measures two facets of anxiety, test anxiety
and general anxiety and also allows the computation of a school frustration score.
The `Leistungsmotivationstest' (LMT; Hermans, Petermann & Zielinski, 1978) is measuringachievement motivation by means of three scales perseverance, stimulating anxiety and repressive
anxiety.
The `Anstrengungsvermeidungstest' (AVT; Rollett & Bartram, 1977) measures duty orientation
(`Pichteifer') and avoidance of eort (`Anstrengungsvermeidung').
The `Arbeitsverhaltensinventar' (AVI; Thiel, Keller & Binder, 1979) measures behaviour and
attitudes towards work and learning at school using the following scales: delay of gratication,
extrinsic vs intrinsic motivation, motivation to avoid failure (`Mierfolgsmotivation'), self-assessment
of working speed, relaxation in test situations, learning conditions, impulsive vs reexive style,
learning style (learning of facts vs learning by insight), self-regulated performance control, resistance
832 H. Rindermann, A.C. Neubauer / Personality and Individual Dierences 30 (2001) 829842
7/30/2019 The Influence of Personality on Three Aspects of Cognitive Performance,
5/14
against stress, independence of teachers and peers in learning, independence of moods and interests
in learning, learning strategies, attitude towards school, satisfaction with own performance.
The `Fragebogen zum Arbeitsverhalten von Schu lern' (AVS; Heller, 1992) measures behaviour,
cognitive styles and attitudes towards work and learning at school. Scales that are redundant withthe AVI are omitted. We used the following scales: instability of thinking, success in school,
external causal attribution, attention control, concentration, diligence.
The `Fragebogen zur Erfassung des Erkenntnisstrebens' (FES; Lehwald, 1981) measures in one
scale the motivation to achieve more knowledge and better understanding.
Two self-concept-questionnaires were used, both translations and adaptions of Marsh's Self-
Description-Questionnaires (Marsh, 1990). One version was used in class 9 (Selbstbeschrei-
bungsinventar fu r Kinder und Jugendliche, SBI-KJ; Tanzer & Marsh, 1996), the other in class 10
(Selbstbeschreibungsfragebogen, SBP; Ho rmann, 1986). Identical scales were averaged arithmetically.
These scales were administered: language, German competence, German interest, mathematic,
mathematic competence, mathematic interest, general competence, general school competence, generalschool interest, relationship to teachers, relationship to parents, relationship to peers, relationship to
opposite sex.
The two Selbstwirksamkeit scales (SW; Jerusalem & Schwarzer, 1981) measure the belief in
control and solution of school problems and of general life problems.
Only those scales are listed and used, which correlate about r!0.20 with at least one criterion
(processing speed, intelligence, grades). Scales of dierent instruments measuring the same con-
tent are averaged after checking the correlation between the scales (r!0.50).
2.2. Participants
Two-hundred and eighty students of classes 9 and 10 in German gymnasiums (high schools),between 14 and 16 years of age, participated in the study. One-hundred and seventy-ve students
(63%) are members of four high ability gymnasiums, 150 students (37%) of two regular gymna-
siums. German gymnasiums are for students with abilities above average. Only students from the
upper 60% of ability distribution of German students attend these schools (gymnasiums). The
combination of a sample of high ability students and a sample of average and above average
students ensure a sucient variance in intelligence and personality measures.
2.3. Procedure
Every student and every scale is used only once in the nal sample. Results of tests, which are
used in class 9 and 10, are averaged across class.In a rst step identical scales of dierent tests are summed up (relationship to parents in SBI-
KJ and SBP, physical self-concept or general self-esteem of both self-concept-questionnaires).
For similar but not identical scales, those scales are eliminated that displayed lower correlations
with the criteria. In a second step, all scales were excluded that correlate less than r=0.20 with all
criteria. Thirdly, stepwise regressions (probability level p=0.01 for inclusion, p=0.05 for exclu-
sion) with personality scales as predictors were calculated with the following criteria:
. Processing speed (KDT and ZVT summed or separate);
. Intelligence (KFT-general, APM, separate KFT-verbal, KFT-numerical, KFT-gural);
H. Rindermann, A.C. Neubauer / Personality and Individual Dierences 30 (2001) 829842 833
7/30/2019 The Influence of Personality on Three Aspects of Cognitive Performance,
6/14
. School performance (all grades without behaviour grades, music and art; or separate lan-
guages, mathematics and physics, natural sciences, humanities, music and art, behaviour).
In a next step these analyses were repeated excluding personality scales that describe school per-formance (self assessment of school abilities or school performance, e.g. German competence,
satisfaction with own performance).
Finally, personality scales (excluding self assessment of school abilities or school performance),
processing speed tests and intelligence measures are compared as predictors for the external cri-
terion of school performance.
Results are presented in tables only for general processing speed, KFT-general, APM and
grades-general. Signicant deviations of results in separate criterion measures are described in the
text. Results of regressions with and without self-assessment of school abilities/performance are
listed separately. Complete information about the intercorrelations of all variables can be
obtained from the author.
3. Results
3.1. Prediction of processing speed by personality
Processing speed measures cannot be predicted very well by personality scales (see Table 1).
The multiple correlation reaches a maximum value of R=0.34, with test anxiety, independence
Table 1Stepwise regression for processing speed
Regression including self assessment of school abilities or performance
Processing speed Multiple correlation: 0.30 (shrunken 0.28)
Predictors: general school competence (SBI-KJ), test anxiety (AFS)
Scale Stand. beta T Signicance
School competence 0.18 2.71 0.007
Test anxiety 0.17 2.60 0.010
Regression excluding self assessment of school abilities or performance
Processing speed Multiple correlation: 0.34 (shrunken 0.33)
Predictors: Test anxiety (AFS), independence of moods and interests in learning (AVI),
self-assessment of working speed (AVI)
Scale Stand. beta T Signicance
Test anxiety 0.21 3.58 0.000
Independence 0.16 2.81 0.005
Working speed 0.16 2.79 0.006
834 H. Rindermann, A.C. Neubauer / Personality and Individual Dierences 30 (2001) 829842
7/30/2019 The Influence of Personality on Three Aspects of Cognitive Performance,
7/14
and working speed in the regression. For the regression including self assessment of school abil-
ities or performance, only two predictors entered the regression: general school competence (self-
concept, SBI-KJ) and test anxiety (AFS). The beta weights indicate that the better academic self-
concept and the lower test anxiety is, the higher scores in speed of processing are reached.For the separate analysis of the KDT as criterion the prediction is even worse (R=0.21 or 0.25;
including/excluding self assessment of school abilities or performance); in the rst case only gen-
eral school competence is included, in the second test anxiety and independence. The multiple
correlation for ZVT is similar to the summed processing speed measure (R=0.32 inclusive or 0.32
exclusive school abilities). Therefore, the ZVT-results can be predicted slightly better than KDT-
results.
3.2. Prediction of psychometric intelligence by personality
Tables 2 and 3 show that personality scales display a higher multiple correlation with results inthe KFT (Cognitive Abilities Test, R=0.62) than with the APM scores (R=0.42). An explana-
tion might be that KFT-tasks are more similar to school-tasks (e.g. calculations, verbal tasks)
Table 2
Stepwise regression for KFT-general
Regression including self assessment of school abilities or performance
KFT-general Multiple correlation: 0.62 (shrunken 0.61)
Predictors: Test anxiety (AFS), mathematic competence (SBI-KJ), success in school (AVS),
relationship to peers (SBP), German competence (SBI-KJ), diligence (AVS)
Scale Stand. beta T Signicance
Test anxiety 0.31 5.52 0.000
Math competence 0.23 4.62 0.000
Success in school 0.21 3.49 0.001
Peer-relationship 0.15 3.12 0.002
German comp. 0.16 2.93 0.004
Diligence 0.14 2.81 0.005
Regression excluding self assessment of school abilities or performance
KFT-general Multiple correlation: 0.58 (shrunken 0.57)
Predictors: Test anxiety (AFS), relationship to teachers (SBI-KJ), general anxiety (AFS),
learning strategies (AVI), external causal attribution (AVS), relationship to peers (SBP)
Scale Stand. beta T Signicance
Test anxiety 0.51 7.63 0.000
Teacher-relationship 0.17 3.29 0.001
General anxiety 0.17 2.55 0.011
Learning strategies 0.15 2.91 0.004
External attribution 0.14 2.74 0.007
Peer-relationship 0.13 2.60 0.010
H. Rindermann, A.C. Neubauer / Personality and Individual Dierences 30 (2001) 829842 835
7/30/2019 The Influence of Personality on Three Aspects of Cognitive Performance,
8/14
than the gural APM-tasks. Academic self-concept-scales, which partly reect success and per-
formance in school, are therefore important predictors for intelligence as measured by the KFT.
But when self assessment of school abilities or performance is excluded, KFT results are still
better predictable than APM results (R=0.58 vs 0.41). The APM measure more basic logicalthinking and are, therefore, less amenable to social and cultural inuences like learning attitudes
or motivation.
For a separate analysis of the three KFT factors we found the following: For KFT-verbal the
multiple R is 0.53 (0.47 excluding self-assessment of school abilities or performance). Signicant
predictors are success in school, German competence, test anxiety and teacher-relationship.
The multiple correlation between KFT-numerical and the predictors is R=0.59 (R=0.53).
Most important predictors are mathematic competence, test anxiety and mathematic interest.
The multiple correlation between KFT-nonverbal and the predictors is R=0.50 (R=0.44).
Here, important scales are test anxiety, mathematic and mathematic interest.
KFT-subscales are somewhat less predictable than KFT-general, which is probably due to thehigher reliability of the aggregated measure. The numerical tasks are most similar to school-tasks
(numeric operations). Attitudes and motivations, relevant for learning and performance at
school, are important for acquisition of mathematical abilities as measured by KFT too.
3.3. Prediction of school performance by personality
Compared to speed-of-processing and psychometric intelligence, school performance reaches
the largest multiple R (R=0.72; see Table 4) when being predicted by personality variables. In
Table 3Stepwise regression for APM
Regression including self assessment of school abilities or performance
APM Multiple correlation: 0.42 (shrunken 0.41)
Predictors: Mathematic competence (SBI-KJ), relationship to opposite sex (SBP), test anxiety (AFS)
Scale Stand. beta T Signicance
Math competence 0.23 3.95 0.000
Opposite sex 0.22 3.92 0.000
Test anxiety 0.22 3.81 0.000
Regression excluding self assessment of school abilities or performance
APM Multiple correlation: 0.41 (shrunken 0.40)
Predictors: Mathematic interest (SBI-KJ), test anxiety (AFS), relationship to opposite sex (SBP)
Scale Stand. beta T Signicance
Mathematic interest 0.21 3.62 0.000
Test anxiety 0.25 4.28 0.000
Opposite sex 0.24 4.27 0.000
836 H. Rindermann, A.C. Neubauer / Personality and Individual Dierences 30 (2001) 829842
7/30/2019 The Influence of Personality on Three Aspects of Cognitive Performance,
9/14
contradiction to our hypothesis, the exclusion of self assessment of school abilities or perfor-
mance does not lead to a much worse prediction (R=0.66). It, therefore, seems that most of the
school grades variance is already included in the non school-related personality variables; the
school-related personality measures add only little to the prediction of an individual's success inschool.
For the separate analysis of areas of school performance we found the following. The multiple
correlation between languages and the personality predictors is R=0.70 (R=0.63 excluding self
assessment of school abilities or performance). The predictors do not change a lot except for the
Table 4
Stepwise regression for general school grades
Regression including self assessment of school abilities or performance
School grades Multiple correlation: 0.72 (shrunken 0.71)
Predictors: Satisfaction with own performance (AVI),
general school competence (SBI-KJ),
relationship to opposite sex (SBP),
independence of moods and interests in learning (AVI),
test anxiety (AFS),
general competence (SBP)
Scale Stand. beta T Signicance
Satisf. with perfor. 0.30 5.78 0.000
School competence 0.28 5.47 0.000
Opposite sex 0.20 4.68 0.000
Independence 0.14 3.24 0.001
Test anxiety 0.15 3.15 0.002
General competence 0.16 3.07 0.002
Regression excluding self assessment of school abilities or performance
School grades Multiple correlation: 0.66 (shrunken 0.65)
Predictors: Avoidance of failures motivation (AVI),
independence of moods and interests in learning (AVI),
test anxiety (AFS), autonomous performance control (AVI),
relationship to opposite sex (SBP),
relationship to teachers (SBI-KJ),
external causal attribution (AVS)
Scale Stand. beta T Signicance
Avoid. of failures 0.20 3.89 0.000
Independence 0.21 4.52 0.000
Test anxiety 0.26 5.07 0.000
Perform. control 0.19 3.95 0.000
Opposite sex 0.22 4.69 0.000
Teacher-relationship 0.17 3.59 0.000
External attribution 0.13 2.88 0.006
H. Rindermann, A.C. Neubauer / Personality and Individual Dierences 30 (2001) 829842 837
7/30/2019 The Influence of Personality on Three Aspects of Cognitive Performance,
10/14
new scales language competence, language and idleness. The multiple correlation between
mathematics/physics and the personality predictors is R=0.72 (R=0.62). Important additional
predictors are mathematic, mathematic competence and mathematic interest. The multiple corre-
lation between sciences and the personality predictors is R=0.64 (R=0.61). Important new pre-dictors are mathematic and mathematic interest.
For humanities the multiple correlation is R=0.61 (R=0.54). Now the variables idleness and
attention controlentered into the regression. The weakest relationship was found between grades
in music/art and the personality predictors (R=0.38; the same R resulted when excluding school-
related personality measures). Important additional predictors are school frustration and parent-
relationship.
Finally, the multiple correlation between behaviour and the personality predictors is R=0.53
(R=0.52). One important new predictor is attention control.
When comparing school performance in main subjects (languages, mathematics/physics) we
found this to be better predictable than performance in subsidiary subjects (R=0.73 vs 0.64,respectively).
3.4. Prediction of school performance by processing speed, intelligence and personality
In this section we will deal with the question of how school performance can be predicted by
combinations of processing speed, intelligence and personality measures. If we combine variables
from all three domains to predict the mean school grade we nd a quite good prediction of
R=0.72 (shrunken R=0.70; for the predictors' weights see Table 5), i.e. 49% of the variance
in school performance can be predicted. The most important predictor is the KFT-sum-score
Table 5
Stepwise regression for general school grades (processing speed, intelligence and personality as predictors)
Regression excluding self assessment of school abilities or performance
School grades Multiple correlation: 0.72 (shrunken 0.70)
Predictors: KFT-general (KFT),
avoidance of failures motivation (AVI),
independence of moods and interests in learning (AVI),
relationship to opposite sex (SBP), processing speed (KDT and ZVT),
autonomous performance control (AVI),
relationship to teachers (SBI-KJ),
diligence (AVS)
Scale Stand. beta T Signicance
KFT-general 0.31 6.36 0.000
Avoid. of failures 0.23 4.77 0.000
Independence 0.18 4.02 0.000
Opposite sex 0.17 3.93 0.000
Processing speed 0.17 3.66 0.000
Perform. control 0.16 3.48 0.001
Teacher-relationship 0.14 3.12 0.002
Diligence 0.12 2.66 0.008
838 H. Rindermann, A.C. Neubauer / Personality and Individual Dierences 30 (2001) 829842
7/30/2019 The Influence of Personality on Three Aspects of Cognitive Performance,
11/14
(KFT-general). The APM are not used by the regression model because of its high intercorrela-
tion with KFT-general. Therefore, the APM cannot explain additional variance in school per-
formance. In the cognitive domain, also processing speed is among the signicant predictors.
More important predictors are personality scales like avoidance of failures motivation (the higherthis motivation the worse are the grades; AVI), independence of moods and interests in learning
(AVI) and relationship to opposite sex (the better the relationships are, the worse the grades;
SBP).
A much weaker prediction of R=0.52 (shrunken R=0.51) is found when only measures from
the cognitive domain (processing speed and psychometric intelligence) serve as predictors. Here
only KFT-general (T=7.94) and processing speed (T=3.21) are allowed to enter the regression.
KFT-general and school performance correlate at r=0.49, APM and grades r=0.33 and proces-
sing speed and grades r=0.32. APM is not considered because of its high correlation with KFT-
general (r=0.54; KFT-general and processing speed r=0.34). Therefore, the APM contains
mainly redundant information. Using only psychometric intelligence as predictor for school per-formance only KFT-general is included (APM excluded), R is identical with the bivariate corre-
lation (R=0.49, shrunken R=0.49). When using only processing speed as predictor for school
performance only ZVT is included (KDT excluded), R is identical with the bivariate correlation
(R=0.31, shrunken R=0.31).
Using psychometric intelligence (KFT-general, APM) and personality scales in a stepwise
regression on school performance KFT-general is again the most important predictor (R=0.69,
shrunken R=0.68). The next predictors are independence of moods and interests in learning (AVI),
avoidance of failures motivation (the higher this motivation the worse grades; AVI), autonomous
performance control (AVI), relationship to opposite sex (the better the relationships are, the worse
the grades; SBP) and general school interest (SBI-KJ).
When using processing speed (KDT and ZVT together) and personality scales in a stepwiseregression on school performance then relationship to opposite sex (the better the relationships
are, the worse the grades; SBP) is the most important predictor (R=0.70, shrunken R=0.68).
Next is processing speed, avoidance of failures motivation (the higher this motivation the worse
grades; AVI), autonomous performance control (AVI), text anxiety (the higher the lower the
grades; AFS), relationship to teachers (SBP), independence of moods and interests in learning (AVI)
and external causal attribution (the higher the lower the grades; AVS). Considering personality
variables only for the prediction of school success leads to 44% of explained variance (excluding
self assessment of school abilities or performance, R=0.66, shrunken R=0.65).
4. Discussion
First we will deal with the main question of our study, i.e. the predictability of three kinds of
cognitive measures (processing speed, psychometric intelligence, school grades) by personality
variables. In conrmation of our hypotheses school performance is better predictable than psy-
chometric intelligence test results or speed-of-processing measures by personality variables like
self-concept, motivation, anxiety, learning-, working- and cognitive-styles. Most important pre-
dictors are academic and social self-concept scales, test anxiety and interests. Speed of processing
is barely predictable by personality scales.
H. Rindermann, A.C. Neubauer / Personality and Individual Dierences 30 (2001) 829842 839
7/30/2019 The Influence of Personality on Three Aspects of Cognitive Performance,
12/14
Intelligence as measured by the KFT displays a higher relationship with personality traits than
the more `culture-fair' assessment of cognitive ability by means of the Raven's APM. This is
probably due to the KFT-tasks being more similar to school-tasks; presumably the test items are
more strongly subjected to cultural and personality inuences. Especially, the verbal and numer-ical items can only be solved with the help of knowledge acquired at school. And for this acqui-
sition of knowledge and school competencies, personality attributes like motivation, learning
styles and control of anxiety are very important. The gural part of the KFT-tests displays the
weakest relationship with personality measures. Figural tasks seem to measure school knowledge
to a smaller degree. Like the APM they are rather measuring basic logic reasoning employing
gural material. The weakest inuence of personality on cognitive performance can be observed
for Ravens APM.
Speed-of-processing as measured by the two paper-and-pencil tests ZVT and KDT seems to be
rather independent of personality. Self-concept, motivation, anxiety, learning-, working- and
cognitive-styles have only weak inuence on processing speed. Therefore, the concept of processingspeed as a basic indicator of intelligence that should be less inuenced by personality and style
variables is supported by our empirical evidence. On the other hand, school performance seems to
depend strongly not only on the individual's cognitive ability but also on his/her personality.
It should be pointed out, however, that (multiple) correlations do not indicate causality.
Therefore, we cannot conclude whether school performance, intelligence and processing speed are
inuenced by personality or vice versa or both? Academic self-concept (general school, language
or mathematic competence self-concept) and self-assessment of school performance (success in
school, AVS, satisfaction with own performance, AVI) are inuenced by perceived successes or
failures at school (Helmke & van Aken, 1995). Therefore, in a second attempt we excluded the
scales self assessment of school abilities and performance from the regressions. By and large, this
had only marginal eects on the multiple correlations. But also in other scales like test anxietyand teacher-relationship a reverse inuence of school performance on personality is not abso-
lutely negligible: Good performance might support low test anxiety and good teacher-relation-
ships. For self-concept, longitudinal studies (Helmke & van Aken, 1995; Marsh & Yeung, 1997)
have shown reciprocal eects of personality and school performance.
For processing speed it was demonstrated (Neubauer, 1997), that higher cognitive processes
like motivation, training or strategies have a relatively small impact on test-results. Our results
support this assumption. Processing speed tests allow one to measure cognitive abilities better by
intelligence tests than in a culture, learning or personality inuenced form. On the other hand,
school performance and grades could be heavily inuenced by personality (Schnabel, 1996). This
leads to an optimistic pedagogical view, that school performance could be inuenced by motiva-tion, self-concept, learning strategies etc. But it also demonstrates the responsibility of teachers
and educators, to promote a supportive background for children and adolescents to develop a
favourable personality structure.
Summarizing, for the main question of our study we can conclude that in correlational terms
there is a considerable overlap between an individual's personality and his/her school perfor-
mance, but also psychometric intelligence seems to be related to a considerable degree to per-
sonality. On the other hand, processing speed seems relatively independent of personality factors.
These relationships are interesting with respect to the basic question of the relationship between
psychological traits from the cognitive ability vs the personality domain.
840 H. Rindermann, A.C. Neubauer / Personality and Individual Dierences 30 (2001) 829842
7/30/2019 The Influence of Personality on Three Aspects of Cognitive Performance,
13/14
Keeping in mind that processing speed measures seem to measure aspects of cognitive ability
better by psychometric intelligence tests than by a personality or style variables inuenced form,
we can ask for the practical, diagnostical consequences of these ndings. In practice psychometric
intelligence tests as well as personality questionnaires are often used to predict real world phe-nomena, e.g. like success in school. In view of the nding that intelligence and personality are
rather strongly related one can ask if measures of both kinds are really necessary for the predic-
tion of school success? Maybe one of these two domains might be sucient? Which one? Or
might a combination of the much less strongly correlated processing speed tests and personality
test provide a good prediction which at the same time would be rather economical in view of the
short duration of speed-of-processing-tests?
The combination of the two `classical' domains (psychometric intelligence and personality)
resulted in an R of 0.69. However, a quite similar quality of prediction can be achieved by the
combination of processing speed measures with personality (R=0.70) and when combining
measures from all three domains the prediction is barely better (R=0.72). With the reservation asto replications of this result, we could conclude that the prediction of success in schools can be
achieved economically by a combination of processing speed tests and personality measures; in
this combination the processing speed measures seem to explain the cognitive part of the variance
in school performance and the personality measures the non-cognitive part of the variance. On
the other hand, it has been shown that a prediction of school performance by psychometric
intelligence or by processing speed alone does not allow an equally good prediction.
References
Amelang, M., Eisenhut, K., & Rindermann, H. (1991). Responding to Adjective Check List Items: a reaction timeanalysis. Personality and Individual Dierences, 12, 523533.
Beauducel, A., & Brocke, B. (1993). Intelligence and speed of information processing: Further results and questions on
Hick's paradigm and beyond. Personality and Individual Dierences, 15, 627636.
Eysenck, H.-J. (1986). Intelligence: the new look. Psychologische Beitrage, 28, 332365.
Haier, R. J. (1993). Cerebral glucose metabolism and intelligence. In P. A. Vernon, Biological approaches to the study of
human intelligence (pp. 317332). Norwood, NJ: Ablex.
Heller, K. A. (1992). Hochbegabung im Kindes- und Jugendalter. Go ttingen: Hogrefe.
Heller, K. A., Gaedike, A.-K., & Weinla der, H. (1985). Kognitiver Fahigkeits-Test (KFT). Weinheim: Beltz.
Heller, K. A., Kratzmeier, H., & Lengfelder, A. (1998). Matrizen-Test-Manual, Bd. 2. Ein Handbuch zu den Advanced
Progressive Matrices von Raven. Go ttingen: Hogrefe.
Helmke, A., & van Aken, M. A. G. (1995). The causal ordering of academic achievement and self-concept of ability
during elementary school: A longitudinal study. Journal of Educational Psychology, 87, 624637.
Hermans, H., Petermann, F., & Zielinski, W. (1978). Leistungsmotivationstest (LMT). Amsterdam: Swets en Zeitlinger.
Ho rmann, H.-J. (1986). Selbstbeschreibungsfragebogen (SBP; SDQ-III-G). In R. Schwarzer, Skalen zur Bendlichkeit
und Personlichkeit (pp. 4771). Berlin: Institut fu r Psychologie, FU Berlin, Forschungsbericht Nr. 5.
Jensen, A. R. (1982). Reaction time and psychometric g. In H. J. Eysenck, A model for intelligence (pp. 93132). Hei-
delberg: Springer.
Jerusalem, M., & Schwarzer, R. (1981). Selbstwirksamkeit WIRKSUL. In R. Schwarzer, Skalen zur Bendlichkeit und
Personlichkeit (pp. 1528). Berlin: Forschungsbericht 5 der FU Berlin.
Lehwald, G. (1981). Verfahren zur Untersuchung des Erkenntnisstrebens. In J. Guthke, & G. Witzlack, Zur Psycho-
diagnostik von Personlichkeitsqualitaten bei Schulern (pp. 345406). Berlin: Volk und Wissen.
Lindley, R. H., & Smith, W. R. (1992). Coding tests as measures of IQ: Cognition or motivation?. Personality and
Individual Dierences, 13, 2529.
H. Rindermann, A.C. Neubauer / Personality and Individual Dierences 30 (2001) 829842 841
7/30/2019 The Influence of Personality on Three Aspects of Cognitive Performance,
14/14
Lindley, R. H., Smith, W. R., & Thomas, J.Th (1988). The relationship between speed of information processing as
measured by timed paper-and-pencil tests and psychometric intelligence. Intelligence, 12, 1725.
Marsh, H. W. (1990). The structure of academic self-concept: The Marsh/Shavelson Model. Journal of Educational
Psychology, 82, 623636.Marsh, H. W., & Yeung, A. S. (1997). Causal eects of academic self-concept and academic achievement: Structural
equation models of longitudinal data. Journal of Educational Psychology, 89, 4154.
Neubauer, A. C. (1995). Intelligenz und Geschwindigkeit der Informationsverarbeitung. Wien: Springer.
Neubauer, A. C. (1997). The mental speed approach to the assessment of intelligence. In J. Kingma, & W. Tomic,
Advances in cognition and educational practice: Reections on the concept of intelligenc (pp. 149174). Greenwich,
Connecticut: JAI Press.
Neubauer, A. C., Bauer, C., & Ho ller, G. (1992). Intelligence, attention, motivation and speed-accuracy tradeo in the
Hick paradigm. Personality and Individual Dierences, 13, 13251332.
Neubauer, A. C., & Bucik, V. (1996). The mental speed-IQ relationship: Unitary or modular. Intelligence, 22, 2348.
Neubauer, A., Freudenthaler, H. H., & Pfurtscheller, G. (1995). Intelligence and spatiotemporal patterns of event-
related desynchronization (ERD). Intelligence, 20, 249266.
Neubauer, A. C., Sange, G., & Pfurtscheller, G. (1999). Psychometric intelligence and event-related desynchronisation
during performance of a letter matching task. In G. Pfurtscheller, & F. H. Lopes da Silva, Event-Related Desyn-chronization (ERD) Lopes da Silva, Handbook of EEG and Clinical Neurophysiology, vol. 6 (pp. 219231).
Amsterdam: Elsevier.
Oswald, W. D., & Roth, E. (1978). Der Zahlenverbindungstest (ZVT). Go ttingen: Hogrefe.
Raven, J. C. (1958). Advanced progressive matrices. London: Lewis.
Rindermann, H., & Neubauer, A. C. (2000). Informationsverarbeitungsgeschwindigkeit und Schulerfolg: Weisen
basale Mae der Intelligenz pra diktive Validita t auf? Diagnostica, 46, 817.
Rollett, B., & Bartram, M. (1977). Anstrengungsvermeidungstest. Braunschweig: Westermann.
Schnabel, K. (1996). Leistungsangst und schulisches Lernen. In J. Mo ller, & O. Ko ller, Emotionen, Kognitionen und
Schulleistung (pp. 5367). Weinheim: Beltz.
Sitzwohl, E. M. (1995). Konstruktion und Uberprufung eines Papier-Bleistift-Tests zur Erfassung von Informationsver-
arbeitungsgeschwindigkeit: Der Coding-Test. Unpublished diploma's thesis, University of Graz, Austria.
Tanzer, N. K., & Marsh, H. W. (1996). Das Selbstbeschreibungsinventar fur Kinder und Jugendliche (SBI-KJ). Unpub-lished Test. Graz: Karl-Franzens-University.
Thiel, R.-D., Keller, D., & Binder, A. (1979). Arbeitsverhaltensinventar (AVI). Braunschweig: Westermann.
Wieczerkowski, W., Nickel, H., Janowski, A., Fittkau, B., & Rauer, W. (1986). AFS Angstfragebogen fur Schuler.
Braunschweig: Westermann.
842 H. Rindermann, A.C. Neubauer / Personality and Individual Dierences 30 (2001) 829842