The Influence of Personality on Three Aspects of Cognitive Performance,

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).


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


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


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 Àngstfragebogen 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 Ànstrengungsvermeidungstest' (AVT; Rollett & Bartram, 1977) measures duty orientation

(`Pichteifer') and avoidance of eort (Ànstrengungsvermeidung').

The Àrbeitsverhaltensinventar' (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



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);



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)


Test anxiety 0.21 3.58 0.000

Independence 0.16 2.81 0.005

Working speed 0.16 2.79 0.006



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


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)


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


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)


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



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


APM Multiple correlation: 0.42 (shrunken 0.41)

Predictors: Mathematic competence (SBI-KJ), relationship to opposite sex (SBP), test anxiety (AFS)


Math competence 0.23 3.95 0.000

Opposite sex 0.22 3.92 0.000

Test anxiety 0.22 3.81 0.000


APM Multiple correlation: 0.41 (shrunken 0.40)

Predictors: Mathematic interest (SBI-KJ), test anxiety (AFS), relationship to opposite sex (SBP)


Mathematic interest 0.21 3.62 0.000

Test anxiety 0.25 4.28 0.000

Opposite sex 0.24 4.27 0.000



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


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)


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


Test anxiety 0.15 3.15 0.002

General competence 0.16 3.07 0.002



Predictors: Avoidance of failures motivation (AVI),


test anxiety (AFS), autonomous performance control (AVI),

relationship to opposite sex (SBP),

relationship to teachers (SBI-KJ),

external causal attribution (AVS)


Avoid. of failures 0.20 3.89 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


External attribution 0.13 2.88 0.006



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)



Predictors: KFT-general (KFT),

avoidance of failures motivation (AVI),


relationship to opposite sex (SBP), processing speed (KDT and ZVT),

autonomous performance control (AVI),

relationship to teachers (SBI-KJ),

diligence (AVS)


KFT-general 0.31 6.36 0.000

Avoid. of failures 0.23 4.77 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


Diligence 0.12 2.66 0.008



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.



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.



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.



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.


Documents

The Influence of Personality on Three Aspects of Cognitive Performance,