general factors of personality in six datasets and a criterion-related validity study at the...

General Factors of Personality in Six Datasetsand a Criterion-Related Validity Study at theNetherlands Armed Forces

Dimitri van der Linden*, Jan te Nijenhuis**, Myckel Cremers**and Cyril van de Ven**

*Institute of Psychology, Erasmus University Rotterdam, Rotterdam, The Netherlands. [email protected]**Defence Behavioral Sciences Services Center, Dutch Ministry of Defense, The Hague, The Netherlands

Several papers showed that a general factor occupies the top of the hierarchical structure of

personality, the so-called General Factor of Personality (GFP). The first question is whether

the GFP behaves similar to the general factor of mental ability (g), in that GFP scores from

different personality questionnaires correlate highly. The second question is whether the

GFP is related to real-life outcomes. In six large datasets (total N¼ 21,754) collected in the

Netherlands armed forces, the GFPs extracted from six personality questionnaires generally

showed high degrees of correlation suggesting they measure the same construct. Moreover,

GFP was related to drop-out from military training. This evidence strengthens the view that

the GFP is a substantive construct with practical relevance.

1. Introduction

I n personality research, an important question is how

many basic dimensions of personality exist. Previous

studies that have addressed this question have led to

different personality models. One well-known example is

the Big Five (Goldberg, 1981), consisting of the traits

Openness to experience, Conscientiousness, Extraver-

sion, Agreeableness, and Neuroticism (OCEAN) that can

meaningfully explain and predict individual differences in a

wide range of settings, such as mental health, job

satisfaction, work performance, and salary (e.g., Barrick

& Mount, 1991; Dilchert & Ones, 2008; Dilchert, Ones,

Van Rooy, & Viswesvaran, 2006; Judge, Heller, & Mount,

2002). Yet, there are also several other models assuming

a different number of basic personality factors such as

Eysenck’s (1967) three factor model, Cattells’ (1950) 16-

factor model, and the HEXACO model in which a

Honesty–Humility factor is added to the Big Five (Ashton

& Lee, 2007).

Scientific discussions about personality, however, do

not only focus on the number of factors but also on their

hierarchical nature. For example, Digman (1997) and

DeYoung, Peterson, and Higgins (2002) showed that the

Big Five are not independent, but instead display inter-

correlations that justify the search for higher-order

factors. Subsequently, they identified the two meta-con-

structs a and b, which are often referred to as Stability

and Plasticity. Stability consists of Conscientiousness,

Emotional stability, and Agreeableness and refers to the

extent to which one is stable regarding motivation,

mood, and social interactions. Plasticity consists of

Extraversion and Openness to experience and refers to

the extent to which one actively searches for new and

rewarding experiences, either intellectually or socially.

More recently, it has been proposed that a general

factor of personality (GFP) occupies the top of the

hierarchical structure of personality (Hofstee, 2001;

Musek, 2007; Rushton, Bons, & Hur, 2008; van der

Linden, te Nijenhuis, & Bakker, 2010). The general factor

of personality has sometimes been compared with

the general factor g in cognitive ability that reflects

intelligence.

The current evidence on the GFP leaves little doubt

that a general factor can indeed be identified in many

different personality measures. For example, van der

Linden, te Nijenhuis et al. (2010) confirmed the GFP in

a meta-analytically derived correlation matrix based on

212 Big Five correlation matrices (N¼ 144,117). Rushton

and Irwing (2009a, 2009b, 2009c, 2009d) also identified

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International Journal of Selection and Assessment Volume 19 Number 2 June 2011

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GFPs in a range of other personality surveys not explicitly

based on the Big Five model. For example, they showed a

GFP in the Minnesota Multiphasic Personality Inventory,

the Multicultural Personality Questionnaire, the Guil-

ford–Zimmerman Survey, the Personality Assessment

Inventory, and in the Comrey Personality Survey.

Yet, the construct of GFP is not without its critics. The

main focus of the discussion is not so much on whether

there is a general factor but more on the interpretation

of this factor. Several researchers have argued that the

GFP arises from methodological or statistical artifacts.

For example, McCrae et al. (2008) argued that higher-

order factors based on the Big Five contain strong artifact

components reflecting the tendency to provide socially

desirable answers. Researchers have also related the GFP

to social desirability biases in self-report or halo biases in

other ratings (Anusic, Schimmack, Pinkus, & Lockwood,

2009; Backstrom, Bjorklund, & Larsson, 2009). On the

other hand, various studies have indicated that social

desirability does not strongly influence the factor struc-

ture of personality (Hogan, Barrett, & Hogan, 2007;

Kurtz, Tarquini, & Iobst, 2008; Ones & Viswesvaran,

1998; Ones, Viswesvaran, & Reiss, 1996). Moreover,

Rushton and Erdle (2010) found no evidence that social

desirability response set explains the GFP or its corre-

lates.

In contrast to the artifact account of the GFP, there are

also several researchers stating that the GFP may reflect a

substantive factor (Figueredo et al., 2005, 2006; Musek,

2007; Rushton et al., 2008). In this view, individuals with

high GFP scores are people who, in Big Five terms, can be

described as open to new experiences, hard working,

sociable, friendly, and emotionally stable. Recent studies

provide evidence for this substantive account of the GFP

and suggest that this construct is associated with beha-

vior in several major life domains, such as financial status,

self-directedness/planning, subjective well-being, and

health (Figueredo, Vasquez, Brumbach, & Schneider,

2007; Figueredo et al., 2005, 2006). The GFP also has

criterion-related validity as it predicted supervisor-rated

job performance (van der Linden, te Nijenhuis et al.,

2010) and classmates’ ratings of likeability and popularity

(van der Linden, Scholte, Cillessen, te Nijenhuis, &

Segers, 2010). The multimethod approach in these stud-

ies (i.e., self-reports and other ratings) makes it less likely

that the GFP is a mere artifact. In both studies, it was

shown that the GFP often accounted for a large share of

explained variance in supervisor-rated job performance

(van der Linden, te Nijenhuis et al., 2010; Study 2) and

classmate ratings of likeability (van der Linden, Scholte et

al., 2010). The authors stated that the GFP often shows

good predictive or criterion-related validity, but that in

some cases the unique contribution of the Big Five traits

can enhance the validity (van der Linden, Scholte et al.,

2010). This seems to indicate that similar to the g factor

in the cognitive domain, the GFP underlies part of the

validity of lower-order personality traits. Yet, compared

with g, the effect of the GFP leaves more space for

additional influence of the unique traits of the Big Five or

other lower-order personality traits.

Besides evidence derived from criterion-validity stu-

dies, there is also genetic research that supports the idea

of a substantive GFP. Specifically, the GFP has been shown

to have a substantial heritability of approximately 50%

(Rushton et al., 2008; Veselka, Aitken Schermer, Petrides,

& Vernon, 2009). Figueredo and Rushton (2009) argued

that the proportion of nonadditive genetic variance of the

GFP indicates that this construct has been subjected to

recent natural selection, which, again, makes it unlikely

that the GFP is solely a bias arising from the fact that

personality is usually measured using self-report ques-

tionnaires.

Despite this evidence suggesting a substantive GFP,

there are still several major research questions regarding

the nature of this construct. One fundamental question is

whether the GFP is consistent over different types of

personality measures. This is a critical issue in the GFP

debate because if such consistency is lacking, there would

simply be a different type of general factor in every

dataset. On the other hand, if GFPs from different

personality measures overlap substantially, this indicates

that the different general factors share an underlying

mechanism. To our knowledge, there are currently no

published articles that have examined the consistency of

the GFPs over different measures.

In testing GFP consistency, it is useful to look at the

literature on the consistency of g, the general factor of

mental ability, over different types of cognitive measures.

Regarding this, Spearman (1904) showed the principle of

the indifference of the indicator: all cognitive tests

measure the g factor. Jensen (1980, pp. 314–315) gives

a table of intercorrelations between total scores of

various IQ batteries, and concludes that the intercorrela-

tion is most typical in the range from about .67 to .77. It

seems plausible that the g scores of the various IQ

batteries would correlate even higher, possibly with a

mean correlation of about .75. To the extent that there is

a GFP, a proportion of variance in any measure of a single

dimension in personality will be due to that general

factor. This would imply that GFPs from two personality

questionnaires correlate highly. It currently remains an

open question whether the value of the intercorrelations

of various GFPs is comparable with the value of the

correlations of g scores from different IQ batteries.

The second question in this study relates to the

practical relevance of the GFP. More specifically, if the

GFP is a substantive construct it may predict behavior in a

wide range of domains. Currently, there are only very few

studies that showed criterion-related validity of the GFP

(van der Linden, te Nijenhuis et al., 2010; van der Linden,

Scholte et al., 2010). Hence, the current discussion about

the theoretical and practical value of the GFP would

158 Dimitri van der Linden, Jan te Nijenhuis, Myckel Cremers and Cyril van de Ven

International Journal of Selection and Assessment

Volume 19 Number 2 June 2011 & 2011 Blackwell Publishing Ltd.

https://www.researchgate.net/publication/24813820_Bias_in_Mental_Testing?el=1_x_8&enrichId=rgreq-e70e840a-4314-480b-be39-518d28bb8f0e&enrichSource=Y292ZXJQYWdlOzIyODI1Mzg1MTtBUzoxMDQ0NjY3ODczNDAzMDNAMTQwMTkxODIzMjAxNA==

https://www.researchgate.net/publication/23136166_Substance_and_Artifact_in_the_Higher-Order_Factors_of_the_Big_Five?el=1_x_8&enrichId=rgreq-e70e840a-4314-480b-be39-518d28bb8f0e&enrichSource=Y292ZXJQYWdlOzIyODI1Mzg1MTtBUzoxMDQ0NjY3ODczNDAzMDNAMTQwMTkxODIzMjAxNA==

https://www.researchgate.net/publication/43180410_No_Evidence_that_Social_Desirability_Response_Set_Explains_the_General_Factor_of_Personality_and_Its_Affective_Correlates?el=1_x_8&enrichId=rgreq-e70e840a-4314-480b-be39-518d28bb8f0e&enrichSource=Y292ZXJQYWdlOzIyODI1Mzg1MTtBUzoxMDQ0NjY3ODczNDAzMDNAMTQwMTkxODIzMjAxNA==

https://www.researchgate.net/publication/222298607_The_genetics_and_evolution_of_a_general_factor_of_personality?el=1_x_8&enrichId=rgreq-e70e840a-4314-480b-be39-518d28bb8f0e&enrichSource=Y292ZXJQYWdlOzIyODI1Mzg1MTtBUzoxMDQ0NjY3ODczNDAzMDNAMTQwMTkxODIzMjAxNA==

https://www.researchgate.net/publication/40036871_Evidence_for_Shared_Genetic_Dominance_Between_the_General_Factor_of_Personality_Mental_and_Physical_Health_and_Life_History_Traits?el=1_x_8&enrichId=rgreq-e70e840a-4314-480b-be39-518d28bb8f0e&enrichSource=Y292ZXJQYWdlOzIyODI1Mzg1MTtBUzoxMDQ0NjY3ODczNDAzMDNAMTQwMTkxODIzMjAxNA==

benefit from additional studies testing the validity of the

GFP. In line with this, we test whether the GFP is related

to drop-out from military training in the Netherlands

armed forces. Personality scores have been shown to

predict drop-out from initial military training in a pre-

vious study (te Nijenhuis, van de Ven, Vermeij, & Vos,

2008), but the GFP was never tested in this context.

1.1. Research questions

Thus, to test the consistency of the GFP and its criterion-

related validity, we adopt the following research ques-

tions. First, how strongly is the GFP present in the

different personality questionnaires used at the Nether-

lands armed forces? Second, how consistent is the GFP

over the different types of personality measures. Or

stated differently, how strongly do the different GFPs,

computed using different personality questionnaires, cor-

relate among each other? Third, do the different GFP

scores predict drop-out from initial military training?

2. Method

All studies in the present paper were carried out in the

Netherlands armed forces. In this study, we used six large

datasets. All datasets were originally collected for re-

search on personnel selection or performance prediction

in the military. In some of the datasets, there was a small

proportion of overlap of participants. However, for

comparing the different GFPs in the combined dataset,

all redundancy was removed. Thus, each participant was

present only once in the combined dataset.

In the Netherlands, selection of military personnel

typically consists of several steps. The first step consists

of the evaluation of scores on ability and personality tests

and checks on the minimum required level of education.

The second step consists of a semistructured interview.

Lastly, all personnel receive physical and medical exam-

ination and are security checked. Formal guidelines

regarding the ideal personality profile of military and

service personnel (see Table 1) indicates that employees

should display a positive mix of traits that in Big Five

terms can be defined as Open, Conscientious, Extravert,

Altruistic and Emotionally stable (as opposed to Neuro-

tic), which would result in a high GFP score.

2.1. Datasets

Six datasets from large selection studies in the Nether-

lands armed forces were available for the analyses in this

research project. All datasets consisted predominantly of

young males. The personality surveys are described

below under the heading instruments.

2.2. Dataset 1: Anonymous (1999–2000)

A large dataset (N¼ 18,649) with applicants from the

period 1999–2000, who took the Nederlandse Persoon-

lijkheid Vragenlijst (NPV) (The Dutch Personality Ques-

tionnaire), the Guilford LTP Temperament Survey

(GLTS), and the Prestatie Motivatie Test (PMT) (Perform-

ance Motivation Test). Ages varied from 15 to 47

(M¼ 19.37; SD¼ 3.34). The dataset consists of 15,465

males and 3,183 females (17.1%).

2.3. Dataset 2: van Amelsfoort and van Vliet(2003)

Van Amelsfoort and van Vliet (2003) predicted drop-out

from military training using different personality meas-

ures. Only data from selected applicants in the period

2000–2002 were used (N¼ 722). Participants filled out

the NPV, the GLTS, and the PMT. There were 637 males

and 93 females (12.7%) with ages varying from 16 to 32

(M¼ 18.37; SD¼ 2.33).

2.4. Dataset 3: Duel (2006)

Duel (2006) conducted a study on the usability of four

different questionnaires in the psychological evaluation of

future military personnel: the NEO-PI-R, the NPV, the

GLTS, and the PMT. In the period from November 1999

to January 2000, the personality questionnaires were

completed by candidates (N¼ 937) undergoing basic

psychological evaluation. They varied in age from 16 to

29 years (M¼ 20.06; SD¼ 3.14) and a minority of the

research group was female (16%).

Table 1. Profile of serviceman on a fixed-term contract, basedon Weterings (1998): requirements and relevant Big Fivepersonality dimensions

Requirements B5 dimen-sion

Stable/balanced NþResistant to stress and prolonged tension NþDeployable anywhere in the world OAbility to be away from home for a long time NþTeam-oriented A/EFlexible/adapts easily OPossesses self-discipline/sense of responsibility CCommunicative and socially skilled A/EMotivated to be or become a servicemanPossesses sufficient cognitive ability for the trainingSpeaks, reads, and understands the Dutch languageto a sufficient levelPossesses some knowledge of the organization andtraining of choiceWilling to devote himself to a cause C

Note. Nþ stands for emotionally stable.

General Factors of Personality 159

& 2011 Blackwell Publishing Ltd.


Volume 19 Number 2 June 2011

2.5. Dataset 4: van Kuijk and Vos (2006)

Van Kuijk and Vos (2006) focused on the question

whether there is a correlation between the personality

profile of candidates and drop-out during general military

training. Three different personality questionnaires were

used for the survey: the NEO-FFI, the NPV, and the PMT.

The research was carried out among the candidates of

four school battalions (N¼ 721) in August 2005, so the

dataset consists only of persons who passed the psycho-

logical evaluation. The results of the PMT were not

included in the present study because the scaling proce-

dure differed from the procedure in the other studies

using the PMT. Respondents varied in age from 15 to 29

years old (M¼ 17.76; SD¼ 2.39) and 43 of them were

females (8%).

2.6. Dataset 5: Cremers (2007)

Cremers (2007) focused on the use of integrity tests in

the military and used the NEO-FFI, the NPV, and the

Professional Integrity Test (PIT). Unfortunately, the re-

sults of the NEO-FFI could not be used for the present

study. Ages of the 315 male and 53 (14%) female

participants (total N¼ 368) varied from 15 to 51 years

old (M¼ 18.60; SD¼ 3.17).

2.7. Dataset 6: Anonymous (2001)

A dataset with applicants (N¼ 349) taking the NEO-PI-R,

the NPV, the GLTS, and the PMT. Ages varied from 16 to

36 (M¼ 20.46; SD¼ 3.78). Two hundred and ninety-one

male and 58 female (17%) applicants participated.

2.8. Combined dataset (dataset 7)

Datasets 1–6 were highly comparable in terms of re-

search participants and some samples used the same

questionnaires. We therefore also combined all the

samples (total N¼ 21,754). Ages varied from 15 to 47

years old (M¼ 19.34; SD¼ 3.30). 17,970 participants

were males and 3580 were females (17%). Dataset 2

consisted of participants who successfully participated in

the psychological evaluation, and all other datasets con-

sisted of both successful and unsuccessful applicants.

Because of the large sample size of dataset 1, the

combined dataset strongly resembles dataset 1.

2.9. Instruments

2.9.1. NEO-PI-R and NEO-FFI

The NEO-PI-R (Hoekstra, Ormel, & De Fruyt, 2003) is an

extensive questionnaire consisting of 240 items, with the

five dimensions of the Big Five model divided into 30

subdimension scores. The NEO-FFI (Hoekstra et al.,

2003) is a shortened version of the NEO-PI-R with 12

items for every Big Five dimension. The items of both

questionnaires are measured using a 5-point scale (1¼ I

completely disagree, 2¼ I disagree, 3¼ neutral, 4¼ I agree,

and 5¼ I completely agree).

The Neuroticism dimension covers the subdimensions

fear, irritation, depression, shame, impulsiveness, and

vulnerability. The Extraversion dimension includes the

subdimensions of cordiality, sociability, dominance, ad-

venturousness, and cheerfulness. The Openness dimen-

sion covers the subdimensions imagination, estheticism,

feelings, changes, ideas, and values. The Altruism dimen-

sion encompasses the subdimensions of trust, sincerity,

considerateness, agreeability, modesty, and compassion.

Finally, the Conscientiousness dimension includes the

subdimensions of efficiency, orderliness, reliability, ambi-

tion, self-discipline, and circumspection.

2.10. Dutch personality questionnaire (NPV)

The Nederlandse PersoonlijkheidsVragenlijst (Dutch Person-

ality Questionnaire [NPV]) (Luteijn, Starren, & van Dijk,

1985) comprises seven scales. Answers to 133 items are

given on a 3-point scale: 3¼ correct, 2¼ ?, and

1¼ incorrect. Scale scores are calculated by adding up

the points for every item. The Inadequacy scale consists

of 21 items that refer to the perception of undefined

physical symptoms, subdued moods, vague fears, and

feelings of inadequacy. The 15 items of the Social

inadequacy scale refer to avoiding or feeling unhappy in

social interaction. The 25 items of the Rigor scale relate

to the desire to make things run according to a fixed plan

and in accordance with fixed habits and principles. The 19

items of the Aggrievement scale focus on criticism and

mistrust of other people. The 16 items of the Compla-

cency scale highlight a possible feeling of complacency

and a lack of empathy with others and their problems.

The 17 items of the Dominance scale concern self-

confidence and the willingness to take initiatives and to

provide leadership to others. The 19 items of the Self-

confidence scale refer to having a positive attitude

toward work and being well adjusted and flexible.

2.11. Guilford LTP temperament survey (GLTS)

J. P. Guilford (1897–1987) may be regarded as the first to

systematically apply factor analytic techniques to person-

ality structure and arrive at substantive conclusions.

Beginning in the 1930s, his work culminated in the

publication of the Guilford–Zimmerman Temperament

Survey (GZTS; Guilford & Zimmerman, 1949). The

Dutch adaptation of the GZTS is called the Guilford

LTP Temperament Survey (GLTS; Laboratory for Applied

Psychology, 1984, Amsterdam). The GLTS consists of

seven personality and temperament factors: Restraint

(Seriousness vs. Impulsiveness; 17 items); Ascendance

(Social Boldness vs. Submissiveness; 26 items); Sociability




(Social Interest vs. Shyness; 23 items); Emotional Stability

(Evenness of Mood vs. Fluctuation of Moods; 19 items);

Objectivity (Thick-skinned vs. Hypersensitive; 14 items);

Masculinity (Hardboiled vs. Sympathetic; 20 items), and

Confidence, which measures how many times the answer ?

was chosen (Reflective vs. Disconnected; 19 items). The

items are measured on a 3-point scale (3¼ yes, 2¼ ?, and

1¼ no) and the scores on the items are summed.

Rushton and Irwing (2009b) reported a GFP for the

GZTS.

2.12. Achievement motivation test (PMT)

The Prestatie Motivatie Test (PMT, Achievement Motiva-

tion Test) (Hermans, 2004) measures three separate

constructs: achievement motive (P), negative fear of

failure (F�), and positive fear of failure (Fþ ). P measures

achievement motivation toward school and education,

and achievement motivation in general (41 items). People

with high scores on F� (26 items) are sensitive to

situations that are stressful, unstructured, and very

important. When confronted with a highly stressful

situation, these people may experience feelings of help-

lessness or personal inadequacy, fear of loss of status, or

low self-esteem. In general, these feelings will have a

negative effect on achievements. People with high scores

on Fþ (18 items) have feelings that have a positive effect

on their achievements when the situation they are

confronted with is unstructured, stressful, or very im-

portant.

2.13. Professional integrity test (PIT)

The Professional Integrity Test (PIT; CEBIR, 2007) is an

overt integrity questionnaire containing 56 items consist-

ing of statements on situations on which the respondents

must give their opinion. The respondents indicate the

degree to which they agree with those items on a 5-point

scale: 5¼ strongly disagree, 4¼ disagree, 3¼ neither agree

nor disagree, 2¼ agree, and 1¼ strongly agree. The 56

items are clustered into four scales and a composite

scale. Integrity in work behavior measures the extent to

which breaches of ethical conduct by oneself and others

are tolerated as well as the degree of blurring of moral

standards in relation to minor abuses (19 items). Orga-

nization orientation measures the willingness to engage in

activities that benefit the organization, performance of

the role of ambassador for the organization, and the

extent to which respondents defend the organization

against criticism (11 items). Altruistic work behavior

measures the support given to colleagues even if it

conflicts with one’s own interests (15 items). Construc-

tive criticism measures the willingness to confront

others, including superiors, with breaches of ethical

conduct, to point out shortcomings, and to make pro-

posals for improvement (11 items). The composite scale

of Integrity measures attitudes toward property, stan-

dards, rules, and other people as well as the attitude

toward work and the organization. The Integrity scale

combines the scales of Integrity in work behavior, Orga-

nization orientation, Altruistic work behavior, and Con-

structive criticism.

2.14. Drop-out from military training

The drop-out rates during training are substantial in the

Netherlands armed forces, with some trainees deciding

to leave as early as the first week of their training.

Reports since 1995 have shown fluctuating drop-out

rates, the average rate being around 20% (van de Ven,

2002). Differences exist between the various services and

training centers; the highest drop-out rates occur in the

Special Forces training program (Vos & Vermeij, 2007).

Following van Kuijk and Vos’ example, the combined

data from van Kuijk and Vos (2006) and van Amelsfoort

and van Vliet (2003) were first subdivided into (1) a group

who completed military training without delay (gradu-

ated; N¼ 690), and (2) a group who – of their own

accord (N¼ 440), involuntarily (N¼ 49), or due to

unknown causes (N¼ 40) – left the training program

prematurely (drop-out; N¼ 533). It was decided not to

include the candidates who incurred a delay during their

training. The reason for this is that it is unclear whether

this group will complete or has completed military

training. Another reason is that such delays often have

medical causes, which means they are less relevant for

the personality assessment. A second subdivision was

made – again following van Kuijk and Vos’ example – into

young candidates, aged 15–17, and older candidates, aged

18–28. Van Kuijk and Vos (2006) argue that groups of

adolescents and young adults are not always identical

with regard to personality and personality-related out-

comes. A score of 2 on the turnover variable meant

successfully having finished initial military training and a

score of 1 meant dropping out of the training.

2.15. Statistical analyses

Means and SDs were computed for every scale of every

personality questionnaire for the combined dataset.

Means were also computed for datasets 1–6. In the

present study, we report the outcomes of Principal

Factoring (PF) method, which extracts the shared var-

iance among lower-level personality traits without includ-

ing the unique variance of these traits. This method has

also been used in previous studies to extract a GFP (e.g.,

van der Linden, te Nijenhuis et al., 2010). It has been

established that using other factor-analytic techniques,

such as Maximum Likelihood (ML) and Principal Compo-

nent Analysis (PCA) does not have a strong effect on the

nature of the GFP and generally results in the same





conclusions (Musek, 2007; van der Linden, te Nijenhuis

et al., 2010).

Firstly, we conducted exploratory factor analyses using

Varimax and Oblimin rotations. In these analyses, we

used the standard criterion of eigenvalue 41 for the

extraction of the number of factors. We also inspected

the scree plot to test for the number of factors to

extract. In addition to the exploratory factor analyses, we

directly tested the feasibility of the one-factor solution.

2.16. GFP computation and correlations

The GFP score of the participants was computed by

summing the products of participant’s z scores and the

subtest’s loading on the general personality factor for all

the subtests. A GFP score was computed for every

personality questionnaire. Correlations between the

various GFP scores in the combined dataset were

computed.

2.17. Criterion-related validity

Using a combination of datasets 2 and 4, we calculated to

what degree a general personality factor predicted drop-

out from training. In addition, we also tested the relation-

ship between drop-out and the scores on all scales.

Criterion-related validities were computed by regressing

the various GFP scores on drop-out scores. As the GFP,

just as the g factor in the cognitive domain, is assumed to

partly underlie scores on personality scales, it is informa-

tive to examine the extent to which lower-order person-

ality traits show incremental validity beyond the GFP.

Testing this will show how the unique variance of the

lower-order traits relate to the outcome variables. In the

cognitive domain, it has been shown that, beyond the g

factor, the unique variance of specific cognitive tests

contributes only little to validity. In the personality

domain, it can be expected that the contribution to the

unique variance of individual traits is larger because the

GFP is less dominantly present in personality than g is in

cognitive abilities. We test the incremental value with

hierarchical regression analyses in which we enter the

GFP in Step 1 and the corresponding lower-order traits

in Step 2.

2.18. Correction for statistical artifacts

The research literature on meta-analysis has shown that

in individual studies, the relationship between variables is

attenuated by several statistical artifacts (Hunter &

Schmidt, 2004; Ones, Viswevaran, & Schmidt, 1993).

Correcting for statistical artifacts leads to better esti-

mates of the true strength of relationships. Therefore,

besides reporting uncorrected results, we also report the

results corrected for several statistical artifacts.

Salgado (2002) carried out a meta-analysis of how Big

Five scores predict turnover and reports a reliability of

.84 for measures of turnover. This leads to a correction

factor of 1.09. Means and SDs on the various personality

measures of applicants and persons admitted to initial

military training were not available, so we could not

directly compute the amount of restriction of range.

However, Salgado (2002) reports a meta-analysis of how

scores on Big Five factors predict turnover, including the

mean range restriction for the Big Five dimensions based

on large numbers of studies. The mean value of the range

restriction is .79 that corresponds with a correction

factor of 1.26. We will use this value here.

Drop-out from initial military training is a dichotomous

variable, which results in an attenuation of the correla-

tion between personality scores and drop-out. Hunter

and Schmidt (1990a, 1990b, p. 335, formula 2) give a value

to correct for this attenuation. Several publications (van

de Ven, 2002; van Kuijk & Vos, 2006; Vermeij, 2006)

suggest a drop-out percentage of 20% in the Netherlands

armed forces. This leads to an attenuation of the

correlation by 30%, so that the observed correlation

should be multiplied with a factor 1.20 to counterbalance

the effect of dichotomization.

We corrected the observed validities for attenuation

due to criterion unreliability, range restriction, and

dichotomization of the criterion. A correction factor

for reliability of drop-out of 1.09, for restriction of range

of 1.26, and for dichotomy of 1.20 leads to a total

correction factor of 1.65. We did not apply a correction

for predictor unreliability to the validity, because our

interest was in estimating the operational validities of

tests for selection purposes.

3. Results

3.1. Descriptive analyses and factor analyses

Table 2 shows the means for datasets 1–6, which are

highly similar for the same questionnaires. We computed

all correlation matrices of the scales of each personality

questionnaire; they are not incorporated in the present

manuscript, but can be obtained from the authors upon

request. Inspection of all the intercorrelations shows that

in general there were substantial correlations, which

justifies the search for higher-order factors. Table 3

shows that in the large majority of cases, the first factor

is strong: ranging from 27% to 63% of the variance in the

personality scales. Table 4 shows the loadings on the first

unrotated factors and it is clear that the large majority of

scales load moderately high to high on the general factor.

Thus, in line with previous studies (Musek, 2007; Rushton

& Irwing, 2009a, 2009b, 2009c; van der Linden, te

Nijenhuis et al., 2010), we found that a general factor

could be identified in each of the personality question-

naires. We repeated the above-described factor analyses




with other extraction methods (ML, PCA) and the

conclusions remained the same. The finding that a

general factor is present in the different personality

questionnaires can be compared with studies of the

general intelligence factor where it is clear that the g

factor is present in any collection of subsets of an IQ

battery and that this factor explains a substantial amount

of variance (Jensen, 1998).

Table 2. Means, N, and SDs for the combined dataset (set 7) and means of all other datasets

Dataset 7 Set 1 Set 2 Set 3 Set 4 Set 5 Set 6

Variable N M SD M M M M M M

NEO-Pi-RNeuroticism 1286 108.97 16.542 109.27 108.17Extraversion 1286 178.05 15.273 177.28 180.13Openness 1286 155.11 15.439 153.56 159.28Altruism 1286 172.49 13.517 172.23 173.17Conscientiousness 1286 178.77 16.351 177.98 180.90

NEO-FFINeuroticism 613 26.69 5.811 26.69Extraversion 613 45.69 4.411 45.69Openness 613 34.46 4.594 34.46Altruism 613 42.32 4.195 42.32Conscientiousness 613 46.34 5.069 46.34

NPVInadequacy 21,570 4.46 4.841 4.55 3.82 4.16 3.29 3.55 4.21Social inadequacy 21,570 3.44 4.703 3.54 2.79 2.78 2.78 2.66 2.90Rigor 21,570 24.98 6.350 25.01 24.77 24.92 25.07 24.34 24.41Aggrievement 21,570 11.42 6.276 11.51 11.15 11.37 10.34 10.20 10.19Complacency 20,484 9.44 4.139 9.50 9.63 9.17 9.18 8.76 8.30Dominance 20,484 17.98 6.285 17.92 16.77 19.03 17.36 18.50 20.97Self-confidence 20,477 33.17 4.925 33.09 33.46 33.64 33.60 33.74 33.91

GLTSSociability 19,986 35.43 7.578 35.39 35.51 36.04 36.09Emotional stability 19,986 9.92 7.195 9.97 8.60 9.49 8.84Restraint 19,986 17.41 5.233 17.43 17.53 17.13 16.80Ascendance 19,986 24.61 8.423 24.63 22.78 24.50 24.65Objectivity 19,986 15.32 6.056 15.35 16.43 15.04 14.25Masculinity 18,914 30.19 6.394 30.17 30.89 30.31 30.78Confidence 20,004 4.35 7.253 4.35 6.22 4.19 3.99

PMTAchievement motive 14,878 24.64 7.051 24.55 26.67 26.06Negative fear of failure (F�) 14,877 5.59 4.376 5.63 4.71 5.14Positive fear of failure (Fþ ) 13,978 14.32 3.669 14.27 15.12 15.41

PITIntegrity in work behavior 368 69.18 7.842 69.18Organization orientation 368 37.68 5.005 37.68Altruistic work behavior 368 53.45 6.393 53.45Constructive criticism 368 36.45 4.719 36.45

Note. GLTS¼Guilford LTP Temperament Survey; NEO-FFI¼Big Five Factor Inventory; NEO-Pi-R¼ Big Five Personality Inventory Revised;NPV¼Dutch Personality Questionnaire; PIT¼ Professional Integrity Test; PMT¼Achievement Motivation Test.

Table 3. Percentage variance explained by the first unrotated factor in all datasets

Variable Dataset

1 2 3 4 5 6 7

NEOPIR 50.93 45.88 49.49NEOFFI 41.21 41.21NPV 33.99 28.98 36.44 31.78 34.96 35.85 33.85GLTS 29.34 27.10 29.52 29.27 29.31PMT 56.67 51.21 52.45 56.56PIT 62.88 62.88






3.2. GFP intercorrelations

Table 5 shows the intercorrelations between the various

GFP scores in the combined dataset. In general, the GFPs

from the NEO-PR-I, the NEO-FFI, the NPV, the GLTS,

the PMT, and the PIT correlate substantially, with a mean

correlation of j:53j and a range from j:40j to j:67j. The

two highest correlations in the matrix involved the NPV

(with the NEO-PR-R and the GLTS, respectively). Yet, the

correlation with the lowest value was the one between

the NPV and the NEO-FFI. This latter finding is remark-

able as the NEO-FFI is assumed to measure the same

constructs as the NEO-PR-R. Overall, correlations be-

tween the GFPs in Table 5 range from positive r¼ .67 to

negative r¼�.64. The reason for this is that some

questionnaires contain relatively many scales that are

formulated in a negative way (e.g., Neuroticism, Inade-

quacy). Therefore, the corresponding general factor also

has a negative sign.

Table 4. Loadings on the first unrotated factor for all datasets and the combined dataset

Measure Dataset

1 2 3 4 5 6 7

NEO-Pi-RNeuroticism �.83 �.83 �.82Extraversion .79 .71 .77Openness .60 .25 .53Altruism .51 .61 .54Conscientiousness .79 .82 .80

NEO-FFINeuroticism �.71 �.71Extraversion .68 .68Openness .21 .21Altruism .65 .65Conscientiousness .79 .79

NPVInadequacy .79 .74 .80 .74 .73 .80 .79Social inadequacy .75 .73 .75 .76 .74 .78 .75Rigour .31 .17 .30 .12 .16 �.02 .30Aggrievement .69 .59 .68 .60 .71 .69 .68Complacency .48 .43 .58 .32 .49 .41 .48Dominance �.40 �.33 �.43 �.43 �.45 �.41 �.40Self-confidence �.49 �.52 �.51 �.66 �.62 �.68 �.50

GLTSSociability �.51 .58 .56 .49 �.51Emotional Stability .84 �.80 �.84 �.85 .84Restraint .10 .25 .04 �.15 .10Ascendance .57 �.37 �.48 �.53 .57Objectivity �.63 .57 .62 .65 �.63Masculinity �.54 .58 .59 .58 �.54Confidence .24 �.21 �.28 �.17 .24

PMTAchievement motive .66 .59 .59 .66Fn – negative fear of failure �.81 �.77 �.80 �.81Fp – positive fear of failure .78 .78 .76 .78

PITIntegrity in work behavior .67 .67Organization orientation .80 .80Altruistic work behavior .87 .87Constructive criticism .81 .81


Table 5. Intercorrelations between the various GFP scores inthe combined dataset

Variable N 1 2 3 4 5 76

1. NEOPIR 1286 –2. NEOFFI 613 a –3. NPV 20,477 �.64 �.40 –4. GLTS 18,914 �.48 a .67 –5. PMT 13,836 .59 a �.64 �.55 –6. PIT 368 a a �.49 a a –

Note. GLTS¼Guilford LTP Temperament Survey; NEO-FFI¼Big FiveFactor Inventory; NEO-Pi-R¼ Big Five Personality Inventory Revised;NPV¼Dutch Personality Questionnaire; PIT¼ Professional IntegrityTest; PMT¼Achievement Motivation Test. a¼Cannot be computed.




3.3. Criterion-related validity

Table 6 shows the correlations between personality

scores and drop-out from initial military training. A first

observation is that in general, the validities of all the

personality measures are quite low. For example, for the

Big Five scales, the mean uncorrected and absolute

validity was .072 in the adolescent sample and .096 in

the adult sample. The highest uncorrected validity was

for Neuroticism in the adult sample with a value of �.16.

Nevertheless, against this background of overall low

validities, the GFP scores of the NEO-FFI and NPV

were predictors that are often among the highest of

the personality variables. For example, the r¼ .11 for the

GFP in the adolescent sample was, together with Extra-

version and Conscientiousness, among the highest of the

validities.

It is useful to examine whether the criterion-related

validity that is found for the personality scales in this

sample, are partly due to their proportion of shared

variance, the GFP. We tested this with the results of the

NEO-based GFP in the adolescent group because we

found the relatively highest levels of validity in this group.

We conducted hierarchical regressions in which drop-out

was the dependent variable, the GFP was entered in Step

1, and the individual Big Five scales were entered in Step

2. Note that in such regression analyses, the focus is on

the level of explained variance in each step. The individual

b-weights in such an analysis are not readily interpretable

because the variable in Step 1 consists of a linear

combination of the variables in Step 2. With such linear

combination consisting of n variables, the number of

freely estimated weights in Step 2 is n�1.

We found that the GFP in Step 1 explained 1.2% of the

variance. This was marginally significant (p¼ .08). Beyond

that, the unique variance of the different Big Five factors

added another 1.4% (p¼ .47) of explained variance. Thus,

in this case, the GFP accounted for approximately 46% of

the validity of the Big Five, but beyond the GFP, the

unique variance of the five factors added a relevant (but

nonsignificant) amount of explained variance beyond that.

3.4. Corrections for statistical artifacts

We estimated the operational validities of tests for

selection purposes by correcting the observed validities

for attenuation due to criterion unreliability, range re-

striction, and dichotomization of the criterion. We

applied the 1.65 correction factor obtained as described

in Section 2. This means that for the group between 15

and 17, the observed predictive validity for the GFP

based on the NEO-FFI changes from .11 to a r of .18 (see

also Table 6). For the 18–28-year-old group, the correla-

tion of .07 changes to a r of .12. The validity of the NPV

in the two groups became �.13 after correction. These

values of criterion-related validity coefficients mean that

selection based on GFP scores can make a relevant yet

modest contribution to lowering drop-out in initial

military training.

4. Discussion

The evidence from this study contributes to the scientific

debate about the theoretical and practical value of the

GFP. We studied the GFP in six large datasets from the

Netherlands armed forces, with all datasets containing

the scores from at least two and maximally five person-

ality questionnaires. In all cases, a personality question-

naire yielded a first unrotated factor explaining a

relatively large amount of variance, so that it is evident

that there is a GFP in each of these questionnaires. The

percentage of variance explained by the first unrotated

factor is comparable with what is found in previous

studies on the GFP (Musek, 2007; Rushton & Irwing,

2009a, 2009b, 2009c; van der Linden, te Nijenhuis et al.,

2010; van der Linden, Scholte et al., 2010). General

factors were found in Big Five and non-Big Five ques-

tionnaires, in one achievement motivation test, and an

overt integrity test, which also measure personality

aspects.

The different GFPs computed using different broad

personality questionnaires, in the large majority of cases

correlated strongly with each other, supplying additional

support for the interpretation of the GFP as a substantive

construct. Specifically, an important aspect of the debate

about the GFP as a potentially substantive construct, is

whether the GFPs from different personality measures

show considerable overlap. The present study shows that

such an overlap is present.

It has to be noted that in the present study, the mean

correlation of r¼ .53 between the GFPs is lower than the

Table 6. Combined datasets 2 and 4: correlations (and cor-rected correlation between brackets) between the GFPs andthe scale scores of the NEO-FFI and the NPV with drop-outfrom initial military training for 15–17-year-olds (adolescent)and 18-year-olds and older (adults)

Measure Adolescent (r) Adult (r)

GFPNEO .11 (0.18) .07 (0.12)NEO-FFI: Neuroticism �.09 (�0.15) �.16 (�0.26)NEO-FFI: Extraversion .11 (0.18) .12 (0.20)NEO-FFI: Openness �.01 (�0.02) �.11 (�0.18)NEO-FFI: Altruism .04 (0.07) �.03 (�0.05)NEO-FFI: Conscientiousness .11 (0.18) .06 (0.10)

GFPNPV �.08 (�0.13) �.08 (�0.13)NPV: Inadequacy �.05 (�0.08) �.06 (�0.10)NPV: Social Inadequacy �.03 (�0.05) �.02 (�0.03)NPV: Rigour �.01 (�0.02) �.04 (�0.07)NPV: Aggrievement �.01 (�0.02) �.04 (�0.07)NPV: Complacency �.12 (�0.20) �.04 (�0.07)NPV: Dominance .08 (0.13) .08 (0.13)NPV: Self-confidence .02 (0.03) .06 (0.10)





mean correlation of r¼ .75 that is usually found among

different measures of the cognitive factor g (e.g., Jensen,

1980). One possible reason for the discrepancy is related

to the content of the subscales. From previous studies on

the GFP it became apparent that the unique variance in

individual personality scales, that is, the variance that is

not part of the GFP, is larger than the unique variance

beyond g in cognitive scales (e.g., Musek, 2007; van der

Linden, te Nijenhuis et al., 2010). Thus, the general

factors that are extracted from different personality

measures can be assumed to involve more influence

from the individual characteristics of the scales. Again it

is useful to make a comparison with the cognitive domain.

For example, one can extract a general factor from a

range of visual–spatial ability tasks, which will result in a g

factor with a visual–spatial ‘flavor’. This specific flavor will

attenuate the correlations with other gs, which may be

extracted from other types of cognitive tasks. Yet,

because g is so strongly present in most cognitive ability

tasks, the correlations will still be very high. As the

influence of the GFP in personality measures is often

found to be somewhat lower than the influence of g in

cognitive tests, the specific flavors of the GFP, caused by

different underlying scales, may have a somewhat stron-

ger attenuating effect.

Another way to look at this is in terms of the number

of subtest that are used to extract a general factor. For

example, Jensen (1998, pp. 103/104) introduced the

formula

1þX

r2sg= 1� r2

sg

� �h i�1� �� 0:5

where r2sg is the each subtest’s squared g loading, which

shows that the g loadedness of a sum score is an

asymptotic function of the size and the number of g

loadings of subtests. So, assuming comparable g loadings,

the more subtests in a battery, the more g-loaded its g

score. A battery with dozens of subtests will have a

g score which is 100% g-loaded, meaning it measures

g perfectly. This means that two cognitive test batteries

with a large number of subtests will have two g scores

with very high correlations. Smaller batteries will show

smaller correlations with other batteries. A battery using

only complex reasoning tests will have higher average g

loadings than a battery with the same number of subtests

using low-g psychomotor and memory tests.

Most likely, the lower value for the intercorrelations is

not caused by differences in reliabilities: it appears that

the reliabilities of the personality scales are not substan-

tially smaller than the reliabilities of the subtest scores of

IQ batteries. The most plausible explanation is simply the

fact that the number of scales in a personality question-

naire is smaller than the number of subtests in an IQ

battery. The GFP-loadedness of the sum score in person-

ality questionnaires is therefore lower than the g-loaded-

ness of the sum score of cognitive batteries, leading to

lower intercorrelations. For instance, the highest corre-

lation is found between the GFPs of the NPV and the

GLTS, which are also the questionnaires with the largest

number of scales, namely seven.

Regarding the criterion-related validity of the GFP, we

found that overall the validities of the personality vari-

ables were relatively low. Yet, several meta-analyses have

already shown that the effects sizes of personality on

work-related outcomes are modest in general. For ex-

ample, Barrick and Mount (1991) reported meta-analytic

validities of the Big Five and turnover in the range from

�.08 to .09. Salgado (2002) reported meta-analytic

relationships between the Big Five and turnover of .11,

.23, .14, .16, and .25 for O, C, E, A, and ES, respectively.

Thus, the present results on turnover fall in the range of

the effects sizes that are found in this area.

In the present sample, we found that the NEO-FFI-

based GFP predicted drop-out from military training with

a value of .11 for the adolescent group and .07 for the

adult group. These values became .18 and .12, respec-

tively, after correction for unreliability, range restriction,

and dichotomization. Hierarchical regression analyses

revealed that the GFP could account for approximately

half of the validities, but beyond the GFP, the unique

variance of the Big Five added a relevant amount of

explained variance.

The findings on drop-out contribute to the up-to-now

limited set of studies that have looked at the behavioral

correlates of the GFP. The picture that seems to emerge

from these studies is that the GFP may have criterion-

related validities for a range of different behavioral out-

comes. So, individual personality dimensions (e.g., the Big

Five) may sometimes show validities that are as strong or

sometimes stronger than the GFP, but the general con-

struct may have the most general predictive power over

different contexts. To illustrate this, in Study 2 of van der

Linden, te Nijenhuis et al. (2010), Openness, Conscien-

tiousness, and Emotional Stability displayed the highest

validities of the Big Five dimensions (.25, .19, and .14,

respectively) regarding supervisor-rated performance.

Yet, the validity of the GFP was approximately just as

high as .27. In van der Linden, Scholte et al. (2010), the Big

Five dimensions that showed the strongest relationships

to classmates’ ratings of likeability and popularity was

Extraversion (r¼ .37). But again, the GFP showed a

relationship with classmate ratings that was approxi-

mately as high as the highest Big Five dimensions

(r¼ .33). Finally in the present study, Extraversion and

Conscientiousness showed the highest validities regard-

ing drop-out, but the GFP was just as high. Thus, even

though the individual Big Five dimensions will differ in

their validities depending on the type of outcome mea-

sure one uses, there is consistency in the GFP being a

relatively good predictor over different situations and

different outcome measures. In this sense, it may have

some properties similar to the properties of the g factor




in the cognitive domain. Currently, there are too few

studies on this topic to be conclusive about this observa-

tion on the general predictive power of the GFP, but

future research will reveal whether it will hold in the face

of additional empirical studies.

4.1. Limitations of the study

In this study, we relied strongly on a number of factor-

analytical techniques. Different techniques may have

yielded different outcomes, leading to different conclu-

sions. However, classical studies into the use of different

techniques to measure the general mental ability factor

showed highly comparable outcomes for the different

techniques (Jensen & Weng, 1994): usually the correla-

tions between the different g scores are þ 1.00 or very

close to þ 1.00. As the intercorrelations between the

scores on subtest scores of an IQ battery and the

intercorrelations between the scale scores of personality

questionnaires are comparable in size, it does not seem

likely that the use of different factor-analytical techniques

would have led to different conclusions in the present

study.

4.2. GFP interpretations

If the GFP can indeed influence a wide range of behaviors,

then a subsequent question would be how to interpret

such a construct. In the cognitive domain, the interpreta-

tion of g is straightforward: an individual’s ability to solve

complex and novel problems. The interpretation of the

GFP, however, seems less obvious. Nevertheless, the

literature provides several indications. One is that the

GFP overlaps with emotional intelligence (Veselka et al.,

2009) defined as the ability to adjust one’s behavior in

order to reach social goals (e.g., make friends, make a

good impression on the boss, get an attractive partner). It

can be expected that if such a general ability exists, it will

have a broad influence on behavior. That is, it can be

expected to make people behave more openly, friendly,

sociable, emotionally controlled, and allow them to

uphold motivation in the face of tedious or strenuous

tasks. In fact, a meta-analysis of van Rooy and Viswes-

varan (2004) on self-report as well as ability measures of

emotional intelligence showed that emotional intelligence

is related to the positive pole of each of the Big Five

dimensions. The meta-analytic correlations of emotional

intelligence with the Big Five were .23, .31, .33, .34, and

.23 for O, C, E, A, and ES, respectively (Van Rooy &

Viswesvaran, 2004: Table 8, p. 85). Other studies have

also shown that emotional intelligence falls into the same

factor space as the GFP (Petrides et al., 2010). Thus, if

emotional intelligence exists and has a broad influence on

behavior, then it may influence the score on most of the

available personality scales; hence it may, at least partially,

explain the emergence of a general factor in personality.

4.3. Practical implications

Drop-out from training in the Netherlands armed forces

is a costly affair: each drop-out during training means that

an investment of thousands of Euros in recruitment,

selection, and training is lost. Having at our disposal, a

good predictor of drop-out will provide enormous

financial benefits. Repeated, large-scale research into

the causes of drop-out shows that the reply most

often given by recruits is ‘do not like military life’.

Adjustment to military life requires a profile of

OþCþ EþAþNþ , equivalent to a high GFP score.

Therefore, it does not seem unlikely that GFP can be

used as a predictor for drop-out from military training.

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