beyond resilients under controllers and over controllers

European Journal of Personality

Eur. J. Pers. 20: 5–28 (2006)

Published online 31 January 2006 in Wiley InterScience

(www.interscience.wiley.com). DOI: 10.1002/per.557

Beyond Resilients, Undercontrollers, and Overcontrollers?An Extension of Personality Prototype Research

PHILIPP YORCK HERZBERG* and MARCUS ROTH

Technical University Dresden, Germany

Abstract

Prototypes of personality were investigated in two studies. In study I, clusters of Big-

Five-based prototypes were examined using a general population sample of 1908

German adults. Convergent evidence suggested the appropriateness of a five-cluster

solution, which corresponds to previously identified temperament based prototypes. In

study II, the five-cluster solution was cross-validated in a sample of 256 prisoners.

Moreover, it was shown that a population-based approach (using discriminant functions

derived from study I) was superior over the traditional sample-based cluster

approach (using Ward followed by k-means). The authors argue that future typological

research can be sufficiently grounded on a five-prototype conception rather than on a

three-prototype conception, and suggest a new and flexible assignment procedure.

Copyright # 2006 John Wiley & Sons, Ltd.

INTRODUCTION

Dimensional or variable-centred approaches (see Pervin, 2003) fail to take into account

one important aspect of personality, i.e. the configuration of the characteristics within a

person. It is precisely this aspect, however, that is the focus of the typological or person-

oriented approaches. Person-centred research focuses on the configuration of different

variables within the person. It is concerned with how different dimensions are organized

within the individual, something that subsequently defines different types of person. In

contrast, the variable-centred approach focuses on the differences among individuals

within a single dimension. Caspi (1998) has argued that it is still not understood which of

the two approaches is better suited to provide a more accurate description of the

organization of personality. The majority of personality researchers seem to have resolved

the question of ‘how many personality dimensions’ there are in respect of the variable-

centred approach by accepting the Five-Factor Model (John & Srivastava, 1999). The

*Correspondence to: Philipp Yorck Herzberg, Institut fur Padagogische Psychologie und Entwicklungspsycho-logie, Technische Universitat Dresden, Weberplatz 5, 01062 Dresden, Germany.E-mail: [email protected]

Received 13 September 2004

Revised 1 March 2005

Copyright # 2006 John Wiley & Sons, Ltd. Accepted 22 March 2005

question of how many types are needed for sufficient and efficient description, prediction,

and explanation of personality has yet to be answered for the person-oriented approach.

Nevertheless, the Five-Factor model could serve as a ‘solid ground in the wetlands of

personality’ (Costa & McCrae, 1995) in prototype research. Indeed, typological concep-

tions of personality based on Big-Five measures have recently been enjoying a renaissance

in personality psychology (for an overview see Asendorpf, Caspi, & Hofstee, 2002).

Across numerous studies, three major personality prototypes have been proposed (see e.g.

Asendorpf & Aken, 1999; Robins, John, Caspi, Moffitt, & Stouthamer-Loeber, 1996):

Resilient, Overcontrolled, and Undercontrolled. Resilients showed a generally well

adjusted profile with below average Neuroticism and above average or intermediate scores

on the remaining four dimensions. Overcontrollers scored high in Neuroticism and low in

Extraversion, whereas Undercontrollers had low scores in Conscientiousness and Agree-

ableness. The evidence supporting the three types was taken from the fact that they were

consistent throughout different sample characteristics (age, nationality), different instru-

ments (questionnaire, adjective list, Q-sort), different methods of deriving types (Q-factor

analysis, cluster analysis), and different judgements (self-, other-ratings).

First we give an overview of the problems regarding the Big-Five-based prototype

research. We conclude our overview by addressing the question of the most appro-

priate partitioning of participants into Big-Five-based prototypes. In a second study we

propose an algorithm-based approach to assign individuals to prototypes that avoid many

of the outlined problems of prototype research listed in the next section and compare this

new approach with the current sample-based approach.

Problems regarding the Big-Five-based personality prototypes

Consistency of the prototypes across studies

The consistency of these three prototypes across different studies is, however, far from

being perfect. Table 1 provides an overview of the profiles within the three prototypes

derived from cluster analysis across different studies based on self-ratings. When one

considers Table 1 it becomes clear that there is a notable variability across the studies.

Only Neuroticism for Resilients and Overcontrollers shows consistency across different

studies, whereas the other dimensions showed substantial fluctuations, for instance

Extraversion or Openness for Undercontrollers varies between z-scores smaller than 0.05

and greater than 0.50. In general, the comparison of three cluster solutions from NEO-PI-R

studies of non-risk samples, reveals a low consistency between the three-cluster solutions

from different nationalities (Asendorpf, 2002). Measures of consistency (Cohen’s kappa;

Cohen, 1960) ranged from 0.22 to 0.72, with less than 30% of the kappas meeting the

minimum criterion of values greater 0.60 (Asendorpf, 2002).

This variability is not limited to the empirical cluster solutions described in Table 1.

Comparing the three prototypes’ median pattern based on cluster analysis with the three

Q-sort-based prototypes also revealed notable differences (Robins, John, & Caspi, 1998).

Whereas the prototypic Undercontroller was characterized by medium Neuroticism and

medium to high scores in Openness according to Table 1, Robins et al. (1998) described

them as high in Neuroticism, and low in Openness. This indicates that the postulated

similarity between the cluster-analytic and Q-sort-derived prototypes is far from being

perfect.

Labelling different prototypes with the same names disguises the problem of hetero-

geneity between different prototypes from different studies. Instead of fostering this

6 P. Y. Herzberg and M. Roth

Copyright # 2006 John Wiley & Sons, Ltd. Eur. J. Pers. 20: 5–28 (2006)

Table

1.

Overview

offindingsforthethreetypes

ofResilients,Overcontrollers,andUndercontrollers(cluster

solutionsandself-reportsonly,noQ-sorts)

Dim

ension

Study

Measure

Resilients

Overcontrollers

Undercontrollers

Neuroticism

Asendorpfet

al.,2001

NEO-FFI(N

¼730)

��þþ

0NEO-A

djec.

(N¼568)

��þþ

þNEO-PI-R(N

¼786)

��þþ

þBoehm

etal.

NEO-PI(N

¼758)

��þþ

0NEO-PI(N

¼460)

��þþ

þþBarbaranellia

NEO-PI(N

¼421)

��þþ

0DeFruytet

al.

NEO-PI-R(N

¼464)b

��þþ

0HiPIC

(N¼464)

�þþ

��Ram

mstedtet

al.

NEO-PI-R(N

¼515)

��þ

þþEkeham

mar

andAkrami

NEO-PI(N

¼156)

�þþ

�Van

Leeuwen

etal.

QBF(N

¼484)

��þþ

0Extraversion

Asendorpfet

al.,2001

þ��

þþ

��þþ

þ��

þþBoehm

etal.

þ��

0þ

��

Barbaranelli

0��

þDeFruytet

al.

þþ0

�0

��þþ

Ram

mstedtet

al.

þ��

þEkeham

mar

andAkrami

þ��

0Van

Leeuwen

etal.

þþ��

0Openess

Asendorpfet

al.,2001

00

0þ

��0

0��

þþBoehm

etal.

0�

þ0

�0

Barbaranelli

0�

þþDeFruytet

al.

þþ

��0

��þþ C

ontinues

Beyond resilients, undercontrollers, and overcontrollers 7


Table

1.

Continued

Dim

ension

Study

Measure

Resilients

Overcontrollers

Undercontrollers

Ram

mstedtet

al.

0��

þEkeham

mar

andAkrami

��

þVan

Leeuwen

etal.

þ0

0Agreeableness

Asendorpfet

al.,2001

00

0þ

��

00

0Boehm

etal.

þ��

0þ

��

Barbaranelli

0��

þþDeFruytet

al.

þþ0

��

0þ

Ram

mstedtet

al.

00

�Ekeham

mar

&Akrami

0��

þVan

Leeuwen

etal.

þþ0

�Conscientiousness

Asendorpfet

al.,2001

þþ0

��þþ

0��

þþ0

��Boehm

etal.

þþ0

��þ

þ��

Barbaranelli

þþ�

��DeFruytet

al.

þþ��

0��

0þþ

Ram

mstedtet

al.

þþ0

��Ekeham

mar

andAkrami

þþ��

��Van

Leeuwen

etal.

þþþ

��z-scores

>0.50

þþ>0.25

þ0.25<z>�0

.25

0

<�0

.25

�<�0

.50

��aBarbaranelli(2002)reported

concernsin

labellingtheovercontrolled

cluster,because

itiscontraryto

thedefinitionofovercontrol.

bDeFruytet

al.(2002)could

only

derivetheresilienttypeclearlyfrom

NEO-PI-Rscores.



nominalistic fallacy (Cliff, 1983) in order to enhance comparability of one’s own research

with other published studies, it would be more productive to explore the reasons behind the

heterogeneity of different outcomes. The substantial variability of the prototypes across

the studies can be taken either as an indication of a problem within the prototypical

approach itself, or as a problem based on the premature adoption of the three prototype

solution.

Doubts about the number of prototypes

Both the heterogeneity of the number of clusters itself and the heterogeneity within the

published three prototype solutions make it necessary to once again focus on the question

of ‘how many’ prototypes there should be. Indeed, other researchers have extracted more

than three prototypes, ranging from four (York & John, 1992) to seven (Pulkinnen, 1996).

Virtually all of the cited studies relied solely on the criterion replicability to determine the

number of prototypes. Krieger and Green (1999, p. 352) summarize intensive simulation

studies with the cautionary note ‘that the prevailing practice of split-half data set testing is

not analogous to cross validation in multiple regression and is fraught with difficulties. In

particular, the extension of this practice to determining the ‘‘correct’’ number of clusters is

problematic’. We argue, however, that replicability is only one empirical standard among

others, such as construct validity or generalizability (Overall & Magee, 1992). Construct

validity could be established by using different internal criteria that have been proposed

for determining the number of clusters (Milligan & Cooper, 1985). In the method section

we will report several internal criteria in order to determine the number of clusters.

Sample size and sample composition as crucial issues in prototype research

Cluster analysis is known for its sensitivity to sample size and sample composition

(Aldenderfer & Blashfield, 1996). Most of the studies reported above consisted of sample

sizes with less than 500 Ss, which are by no means sufficient to retain stable classifications

(Schweizer, 1993). The problem concerning sample sizes becomes more salient if the

replicability approach is considered. The within-study replicabilities were based on half

samples, which denote increasing sampling error. Sufficiently large samples are therefore

needed to determine the number of prototypes.

Furthermore, sample composition is just as important as sample size. For instance, in his

gender-separated Q-sort-analyses, Block (1971) differentiated six female and five male

prototypes. Likewise, Pulkinnen (1996) found different clusters for females and males.

This issue can become even more complicated in the case of samples for which an a priori

particular configuration is unlikely to exist in this population. For example, populations of

prisoners are endowed with higher prevalence of mental and personality disorders, alcohol

and substance abuse/dependence (see e.g. Rasmussen, Storsaeter, & Levander, 1999).

Furthermore, delinquent samples differ from general populations in the Big-Five variables

(see e.g. Dennison, Stough, & Birgden, 2001). More specifically, male prisoners score

higher on Neuroticism and lower on Extraversion and Openness than the general male

population sample (Kunst & Hoyer, 2003). Thus, the configuration of the personality

dimension that constitutes the resilient prototype is less likely to be found in samples of

prisoners or offenders. Recently cluster based prototype research has relied on

homogeneous college samples. The prototype issue should therefore be investigated in a

variety of samples that stem from heterogeneous populations, such as patients with

different disorders, prisoners, or different professional groups in order to explore the

characteristic features of prototypes in these populations.



Sample-based versus algorithm-based prototype assignment

The question of sample-based clustering versus assignment to prototypes by means of an

algorithm is linked to both the issue of assignment of individuals to a prototype

membership in applied settings and the distribution of these prototypes in different

populations. Sample-based clustering entails a clustering procedure being carried out for

every particular data set, regardless of sample size and composition of the data set in

question. An algorithm derived from a large representative, general-population-based

sample can serve as a useful tool for diagnostic or other applied settings in order to assign

individuals to a prototype membership even in the case of small samples or in single case

analysis. The rationale of this approach is similar to the handling of questionnaires and

tests in research and application, where the item–scale assignment is not recomputed for

every investigated sample. Common practice is to refer to the test manual and compute the

scores as recommended in the manual. This procedure enables comparability of question-

naires and tests as one standard for educational and psychological testing. Another parallel

could be drawn from variable-centred research, where starting with a representative set of

variables was a crucial prerequisite for developing a sophisticated taxonomy of variables

for personality research (Pervin, 2003). Analogously, persons in cluster analysis are equal

to variables in a principal component analysis (PCA). Just as the factors in a PCA depend

critically on the set of variables, so the clusters in a cluster analysis depend on the set of

persons. Starting with a representative sample of persons is the same as starting with a

representative set of variables. By definition, the results would be superior to analysis that

starts with a peculiar subset. Therefore, we believe that an algorithm-based approach could

foster prototype research in terms of better comparability between studies as well as for

diagnostic and applied settings.

In summary, we have identified a variety of problems inherent in present prototype

research practice. The substantial variability of the prototypes across different studies, the

insufficient consideration of multiple statistical criteria for determining how many clusters

to retain, and relying on small to moderate samples sizes for deriving prototypes are the

most obvious. Because the first aim of the present study 1 is to examine the number of

prototypes for the person-centred approach based on Big-Five measures, we based the

analysis of the number of prototypes on a large representative, general-population-based

sample. Such a sample avoids biased results due to selected populations. Furthermore,

instead of relying on a single index for determining the number of clusters to retain, we

used several indices that have proven to work successfully.

The second goal of the article is to compare sample-based clustering versus assignment

to prototypes by means of an algorithm. It is assumed that the algorithm-based approach

maximizes the between-type variance relative to the within-type variance. This should be

demonstrated in a selected sample that differs from a representative sample.

STUDY 1

In their comparison of the current status of the dimensional and typological approaches,

Robins et al. (1998) stated that the Five-Factor Model provides a widely accepted

taxonomy of personality traits, whereas there is no generally accepted taxonomy for

personality types. Study 1 is the first investigation within the prototype research paradigm

that contributes to the important issue of how many basic prototypes are reliably distin-

guishable by utilizing a general-population-based sample. This representative sample



enables us to create an algorithm for person-prototype assignment, the features of which

will be examined in study 2.

METHOD

Sample

The sample consisted of 1908 subjects, aged between 18 and 96 years (M¼ 47.7;

SD¼ 16.9) constituting a representative, general-population-based sample of Germany.

Trained interviewers interviewed the subjects at home. The selection of the households

was made according the random-route procedure (192 sample points). Korner, Geyer, and

Brahler (2002) describe the sample and procedure of this study in detail. Due to missing

values, the number of subjects decreased to 1692 subjects. As a means of obtaining full

information on the data set we applied the imputation technique described by Schafer

(1997).

Measures

All subjects were administered the German version of the NEO-FFI (Borkenau &

Ostendorf, 1993). The NEO-FFI measures the personality domains Neuroticism, Extra-

version, Openness to Experience, Agreeableness, and Conscientiousness with 12 items

each. Internal consistencies of the scales (Cronbach’s alpha) ranged between 0.71

(Agreeableness and Openness) and 0.85 (Neuroticism and Conscientiousness).

Derivation of the prototypes

The prototypes were derived by applying a two step clustering procedure, which combines

the hierarchical analysis method of Ward (1963) with the non-hierarchical k-means

clustering procedure (MacQueen, 1967) in order to optimize the cluster solutions (see

Blashfield & Aldenderfer, 1988).

Determining the number of clusters

Determining the right number of clusters has been one of the major issues in numerical

classification since its inception. Even today, cluster analysis suffers from establishing a

suitable null hypothesis to determine the number of clusters to retain. Virtually all

clustering algorithms implemented in commonly available statistical software1 fail to

provide sufficient information as to the number of clusters present in a data set. Therefore,

in order to alleviate the problem of obtaining either too few or too many clusters from a

given data set, a bulk of procedures for determining the appropriate number of clusters has

been proposed (see Milligan, 1981). Unfortunately, recent research of personality types

tends to adopt only one of the measures for determining the number of clusters, namely

Cohen’s � (Cohen, 1960). This is despite the fact that both theoretical (Hubert & Arabie,

1One notable exception is the SAS software package, with computes some criteria (e.g. pseudo-F-statistic,decrease in overall between-cluster variance), but a study with real and simulated data (Steinley, 2003) revealsthat the SAS k-means clustering algorithm is most likely to provide just a local optimum solution (and not the bestpossible outcome).



1985) and empirical studies (Milligan & Cooper, 1985) have demonstrated the superiority

of the Rand index over � when comparing the numbers of clusters to retain. Furthermore,

Breckenridge (2000) has shown that selecting the number of clusters by means of

replication indexes is strongly biased towards choosing fewer clusters. This procedure is

roughly three times more likely to result in an estimation of fewer than five clusters

(Breckenridge, 2000), therefore underestimating the correct number of clusters. Overall

and Magee (1992) stated that ‘in the presence of highly overlapping populations, the

replication criterion tended to underestimate the actual number of latent populations’

(p. 124).

In more general terms and from a methodical point of view, the ability to demonstrate

that the same clusters appear across different subsets when the same clustering method is

used—precisely what � indicates—does not constitute strong evidence supporting the

validity of a solution (Blashfield & Aldenderfer, 1988); see also Krieger and Green (1999).

In order to circumvent the problems associated generally with relying on a single index

and, in particular, with �, we decided to broaden the basis for determining the numbers of

clusters to be retained. We adopt a two step approach for determining the number of

clusters to retain. First, we single out solutions that are at least moderately replicable.

Second, if more solutions are potentially viable, we use the following criteria that have

been proposed for determining the correct number of clusters in a data set (Bacher, 1996;

Milligan, 1981): PREk, point biserial, C-index, Gamma, W/B, and G(þ ).2 This is similar

to the decision process in structural equation modelling, where the use of different fit

indices is indispensable (Hu & Bentler, 1995). Furthermore, as recommended in assessing

the fit of competitive models in structural equation modelling, we utilize the information-

theoreticmeasureAIC (Akaike, 1973). Interpretations of the criteria are provided in Table 2.

We present our results in two sections. First, we report � in order to provide

comparability with previous studies. In order to alleviate the problems associated with �,we report the more reliable Rand index and its adjusted form. In the second section, we

report the decision regarding the number of clusters based on the six criteria listed above

and the AIC.

RESULTS

Replicability as criteria for the number of prototypes

Following the suggestion by Asendorpf et al. (2001), we first split the total sample into

random halves and compared the cluster solutions by means of Cohen’s � in order to

evaluate the replicability of the final cluster solutions. The entire two step procedure (Ward

followed by k-means) was applied to both halves. The two cluster solutions were

compared for agreement by assigning the participants of each random half to new clusters.

This was achieved by using the Euclidean distances between their personality profiles and

the cluster centres of the other random half. The replicability of the cluster solutions was

then computed by comparing the new clusters to the original clusters using �. For eachrandom split, the �-coefficients were computed and were subsequently averaged. As in

other applications, an agreement of at least 0.60 was considered acceptable (cf. Asendorpf,

2002). This condition was only met for the three-, four-, and five-cluster solutions.

2Computing details are available on request from the first author.



Therefore, only these results were considered as at least moderately replicable (�¼ 0.73,

0.60, and 0.83 for three-, four-, and five-cluster solutions respectively; see Table 2). The

Rand index indicates only minor differences between the values for the three- and four-

factor solutions, and indicates the five-cluster solution as most appropriate. This held true

for the adjusted Rand index as well, indicating the five-cluster solution as matter of choice.

Table 2. Comparison of three- to five-cluster goodness of fit for k-means cluster analysis solutions

Criterion 3-cluster 4-cluster 5-cluster Interpretation Vote for Nsolution solution solution of clusters

PREk 0.13 0.09 0.08 Minimum 50.14 0.12 0.10 5

Point biserial 0.32 0.33 0.34 50.37 0.37 0.33 3/40.226 0.239 0.245 Maximum 5(0.018) (0.015) (0.013) 3

Bootstrapped 0.287 0.281 0.274(0.021) (0.014) (0.016)

C-index 0.05 0.04 0.02 50.08 0.07 0.08 40.133 0.131 0.131 Minimum 4/5(0.015) (0.013) (0.014)

Bootstrapped 0.115 0.110 0.107 5(0.011) (0.013) (0.013)

Gamma 0.49 0.55 0.62 50.53 0.61 0.62 50.291 0.343 0.384 5(0.022) (0.020) (0.019) Maximum

Bootstrapped 0.375 0.410 0.443 5(0.028) (0.019) (0.022)

W/B 0.51 0.46 0.40 50.44 0.38 0.36 50.722 0.676 0.638 Minimum 5(0.019) (0.017) (0.016)

Bootstrapped 0.624 0.595 0.567 5(0.020) (0.015) (0.016)

G(þ ) 0.11 0.09 0.07 50.11 0.08 0.06 50.165 0.130 0.106 Minimum 5(0.008) (0.007) (0.006)

Bootstrapped 0.146 0.120 0.098 5(0.007) (0.007) (0.006)

AIC 5888.06 5776.99 5774.25 Minimum 55857.52 5823.29 5833.71 4

Cohen’s kappaa 0.73 0.60 0.83 Maximum 50.79 0.46 0.76 3

Rand indexa 0.81 0.77 0.90 50.83 0.77 0.85 Maximum 5

Adjusted Rand indexa 0.58 0.43 0.72 Maximum 50.65 0.43 0.58 3

N¼ 1908. Values in the second line are from ipsatized variables.

Bootstrapped values from 100 samples. Due to the relevance of these results, we report three decimals instead of

two. SD in brackets.

AIC: Akaike’s information criterion.aValues are averaged across both halves of the sample.



Internal fit measures as criteria for the number of prototypes

In order to decide between the three-cluster solutions which are sufficiently replicable, we

computed the above-mentioned fit indices for the three-, four-, and five-cluster solutions.

Table 2 presents the fit indices for the solutions. The criteria support the five-cluster

solution in preference to the three- and four-cluster solutions in most of the cases.

We also adopt the powerful bootstrap procedure (see e.g. Shao & Tu, 1995) in order to

further evaluate the goodness-of-fit criteria for the three-cluster solutions. Due to hardware

working memory limitations, we are obliged to proceed with fewer than 1000 cases. We

therefore draw a random sample without replacement of N¼ 999 cases from the 1692

cases without missing values. Although 20 bootstrap runs for all practical purposes are

enough (personal communication, B. M. El-Khouri, 18 February 2004), we decided to run

100 bootstrap runs. The results and the corresponding standard deviations are shown in

Table 2. All of the five bootstrapped criteria indicate that the five-cluster solution is more

appropriate than the three- or four-cluster solutions. The bootstrap approach is based on

drawing repeated samples from the initial sample, meaning that different drawings could

be regarded as independent samples. Because the initial sample itself is a representative,

general-population-based sample, the bootstrap results provide strong evidence that the

five-cluster solutions is superior to the three- and four-cluster solution in terms of internal

criteria.

Finally, we compared Akaike’s information criterion for the cluster solutions. AIC-

values for five clusters are lower than for both remaining clusters. In concert with the

above-reported fit indices, the information-theoretic measure AIC indicates the five-cluster

solution is the most appropriate.

As a further step in enhancing the generalizability of the five-cluster solution, we split

the data into five subsets based on gender (male n¼ 835 versus female n¼ 1055) and age

(young, 18–30 years n¼ 650; middle, 31–59 years, n¼ 569; old, 60–96, n¼ 473) and

recomputed the criteria for the subsets. The evidence for the five-cluster solution is

heightened by the subsample comparison.3

To rule out the possibility that we extracted artificial factors due to participants’

idiosyncrasies in using the rating scales or due to acquiescence or other response styles we

ipsatized the Big-Five variables to remove this undesired source of variation (see Hendriks

et al., 2003) and re-evaluated the cluster solutions. Table 2 reveals that the conclusions

drawn from initial data analysis do not have to be altered. The majority of the multiple

criteria still indicate the five-cluster solution as the most appropriate partition of the data.

In summary, the results of the comparison of the cluster solutions provide at least some

evidence in support of the five-cluster solution.

Description of the prototypes

Figure 1 presents the pattern of mean z-scores on the five factors for the five-cluster

solution. The first cluster (N¼ 276) was characterized by its low scores on Neuroticism

and high scores on Extraversion, Agreeableness, and Conscientiousness and moderately

positive scores on Openness to Experience. The second cluster (N¼ 206) had pronounced

scores on Neuroticism, low scores on Extraversion and medium to low scores on

Openness, Agreeableness, and Conscientiousness, respectively. The third cluster

3Due to space limitations, subsample results are not presented here. They are available on request from the firstauthor.



(N¼ 406) was characterized by its high scores on Neuroticism, moderate scores on

Extraversion and Openness, and low scores on Agreeableness and Conscientiousness. The

fourth cluster (N¼ 374) had medium scores on Neuroticism, Agreeableness, and

Conscientiousness and moderately high scores on Extraversion and Openness. Finally,

the fifth cluster (N¼ 430) tended to have low scores on Neuroticism, Extraversion, and

Openness, and moderately positive scores on Agreeableness and Conscientiousness.

Males and females were differently distributed within each cluster (�2½4� ¼ 34.67,

p< 0.01). Post hoc analyses revealed that more females are assigned to cluster two

(70%) and four (58%), whereas in the remaining cluster males and females were equally

distributed.

Mean ages were statistically distinct between the clusters (F[4, 1687]¼ 17.68, p< 0.01)

but small in effect size (f¼ 0.20). Post hoc comparisons revealed significant age dif-

ferences ( p< 0.01) between the fifth prototype (M5¼ 52.6, SD¼ 16.6) and clusters one,

three, and four (M1¼ 46.0, SD¼ 15.0; M3¼ 44.8, SD¼ 16.4; M4¼ 44.5, SD¼ 16.5,

Figure 1. Five personality prototypes characterized by their Big-Five z-score patterns in the representative,general-population-based German sample. N, Neuroticism; E, Extraversion; O, Openness; A, Agreeableness; C,Conscientiousness.



respectively). The mean age for cluster two is 49.4 years (SD¼ 17.9). All clusters incorpo-

rated the full age range of participants from 18 years up to 90 years.

The next step following the description of the five prototypes is to find appropriate labels

for them. The first cluster clearly represents the Resilient prototype. The second cluster

depicts the Overcontrolled prototype and the third cluster the Undercontrolled prototype.

These prototypes showed strong similarity with the three core prototypes extracted in the

majority of person-centred studies on personality (see Table 1). The fourth cluster

resembles the assertive prototype identified by Schnabel et al. (2002) or recently by

Gramzow, Sedikides, Panter, Harris, and Insko (2004), who labelled this prototype as

resilient undercontrolled. The assertive prototype of Schnabel et al. (2002) is a subtype of

the resilient prototype. Indeed, tracing the bifurcation from the three- to the five-cluster

solution, 37% from the resilient and 58% from the undercontrolled cluster members

migrated into cluster four. At the first glance this resemblance suggests the label resilient

undercontrolled for cluster four, but cluster four has positive z-scores for both Consci-

entiousness and Agreeableness, which is not compatible with undercontrol. Instead,

cluster four is mainly characterized by high Openness and Extraversion. Therefore, and

because of the similarity to a temperament prototype described by Caspi and Silva (1995)

and Caspi et al. (2003), we suggest labelling this cluster as Confident. Finally, the fifth

resembles the resilient overcontrolled cluster identified by Gramzow et al. (2004), the well

adjusted prototype from Schnabel et al. (2002), and chiefly the reserved prototype from

Caspi et al. (2003), and is mainly characterized by low Openness; we labelled it as

Reserved.

So far, the presented five-cluster solution has sufficiently corresponded to previous

outcomes from typological research. However, all samples of the above mentioned studies

are restricted in age range, i.e. no study includes subjects older than 50 years. This raises

the possibility of different cluster patterns for different age groups. Although the

developmental aspects of personality prototypes are beyond the scope of the present

article, we would like to point out that the number of clusters does not vary across age

groups. Furthermore, Staudinger and Herzberg (2003) demonstrated that cluster patterns

continue quite similarly between age groups. For the present data, correlations between

age and the Big-Five variables (N¼�0.03, E¼�0.22, O¼�0.16, A¼ 0.12, C¼ 0.09) as

mean differences between the young, middle, and old groups were small, effect sizes ("2)ranging between 0.00 for Neuroticism and 0.04 for Extraversion, respectively. Never-

theless, for the three age groups overall mean differences between prototypes were found

(Wilks �¼ 0.96, F[40, 7295.23]¼ 1.87, p< 0.01). Post hoc analyses revealed age differences

for Neuroticism and for Openness. The young resilient group had higher values for

Neuroticism than the remaining groups and higher values for Openness than the older

group. Contrary to results from variable-centred research, the old reserved group had

higher values for Neuroticism than the middle group but not compared with the young

group. For the remaining prototypes no mean differences emerged.

Discussion

The main aim of study 1 was to examine the number of prototypes for the person-centred

approach based on Big-Five questionnaires. This is anchored in a number of factors such

as concern regarding the substantial variability of the prototypes across different studies as

outlined in Table 1, the insufficient consideration of statistical criteria for determining the

number of clusters to retain, and the fact that small to moderate samples sizes were often



relied upon to derive prototypes in previous research. Therefore, we based the analysis of

the number of prototypes on a large representative, general-population-based sample in

order to avoid biased results due to selected populations. Furthermore, instead of relying

on a single index for determining the number of clusters to retain, we used different and

independent indices that have been proven to work well (Bacher, 1996; Milligan, 1981).

As has been noted by several researchers (e.g. De Fruyt et al., 2002; York & John, 1992),

prototype research would benefit from considering other prototypes in addition to the

Resilient, Under-, and Overcontrolled prototypes. In this regard, results presented in study

1 support the presence of two additional prototypes based on NEO-FFI data. In accordance

with previous studies, we yielded a fourth and a fifth prototype (see Gramzow et al., 2004;

York & John, 1992). The remaining three clusters resemble the Under- and Overcontrolled

and the Resilient prototypes hypothesized in previous research (e.g. Caspi, 1998). It is

interesting to note that the five prototypes share some similarity with the five temperament

prototypes (labelled well adjusted, inhibited, undercontrolled, reserved, and confident)

identified by Caspi and Silva (1995) based on behavioural ratings by examiners when the

children were three years old. Whereas the congruence of the well adjusted with the

resilient, the inhibited with the overcontrolled, and undercontrolled with undercontrolled

from Caspi and Silva (1995) with the common derived prototypes, respectively, is

noted elsewhere (Robins et al., 1998), the remaining two temperament prototypes have

stayed isolated until now. Plotting their Big-Five z-scores at age 26 (Caspi et al., 2003) and

comparing them with our cluster solution shows similarity between the reserved prototype

of Caspi et al. (2003) and our reserved prototype as well as between their confident and our

confident prototype. At age 3, the confident prototype was defined by lack of control and

elevated scores on approach, whereas the reserved prototype was shy and fearful, but

unlike their inhibited counterparts their orientation to cognitive tasks was not reduced.

Demonstrating congruence between prototypes from the largest samples (more than 800

children) used so far for generating prototypes put further evidence beyond the internal

criteria reported above on the five-cluster solution presented here. Moreover, it possibly

stimulates the focus on developmental aspects of prototype research.

Furthermore, we controlled the classification of prototypes for age differences. In

general, only a few age differences were found in this representative, general-population-

based sample. Most noteworthy are the age differences in Neuroticism for the old reserved

group. Whereas age differences are in concert with variable-centred research for the young

resilient group, i.e. that Neuroticism and Openness decreases with age (Costa & McCrae,

1997), they are contradictory for the old reserved group. This group had higher values for

Neuroticism than the middle group but not compared with the young group. Although it

could not be answered with the current cross sectional data, it raises the question of

differential trajectories for different prototypes. The remaining prototypes are not affected

by age differences.

STUDY 2

The criticism has already been made that most published typologies depend on the unique

characteristics of the sample used to generate them, leading to very different results. Until

now, the most common way of comparing results was by labelling them identically.

Contrary to this approach, which only masks the differences between the prototypes, we

proposed a new approach for generating prototypes, independent of specific sample

compositions. This approach assigns individuals to prototypes using an algorithm derived



from the cluster results based on the general population-based sample as described in

Study 1. As mentioned above, this approach is especially appropriate when a sample

differs from the population-based samples in important aspects. Therefore, one goal in this

second study is to compare the sample-based procedure with the algorithm-based

approach in a sample of prisoners.

As a second goal of study 2 we investigated the external validity of the prisoner

prototypes. For comparison of personality prototypes, we employed Moffitt’s theory of

delinquent behaviour (Moffitt, 1993). The theory describes two developmental pathways

into delinquent behaviour: an ‘adolescence-limited’ occurrence of delinquent behaviour

and a pathway characterized by an early onset and a stable course of delinquent behaviour

(‘life-course-persistent’; LCP). The latter is of interest to us in this study. LCP males

reported more frequent and serious offences than others. Breaking down the offences by

type revealed that LCPs differed from other delinquents in two ways: they reported a

higher frequency of drug-related offences (e.g. trafficking) and violent offences (e.g.

robbery). In addition and of special interest in the present context is the fact that

differences were also found with respect to personality traits: LCPs had elevated scores for

Neuroticism and Agreeableness at age 26 (Moffitt, Caspi, Harrington, & Milne, 2002). In

summary, the taxonomy of delinquent behaviour by Moffitt proposed a relationship

between a special form of delinquency and personality. We consequently expect a

relationship between personality type and indices of the ‘LCP syndrome’ (e.g. childhood

delinquency and drug consumption).

Before investigating the main goals of study 2, we used the prisoner sample to cross-

validate the number of prototypes to retain.

Sample

Participants in this study were 265 detained offenders from nine prisons in Germany.

Responses from subjects who clearly falsified their answers (e.g. accidental responses

or those who had more than 10 missing data were excluded. This left a final sample of

241 males and 15 females, aged between 25 and 35 years (M¼ 29.5 years, SD¼ 3.1). Of

the sample, 29% were arrested because of property offences, 27% because of violent

crime, 28% because of motoring offences, and five per cent because of other offences (e.g.,

sex offences). Missing information concerning offence was 11%. Sentence length ranged

from 1 month to 15 years (M¼ 3.5 years, SD¼ 3.8).

Measures

In addition to demographic data (age, gender, secondary school qualifications, and number

of previous convictions) and data concerning the prison sentence (offence, length of the

prison sentence) the following variables were assessed.

Personality

The dimensions of the Five-Factor Model were assessed using the German NEO-FFI

(Borkenau & Ostendorf, 1993) as described above. The internal reliabilities ranged from

0.66 to 0.79.

Delinquency

Delinquency during childhood was measured using 10 items from the subscale ‘Conduct

Problems’ from the Self-Appraisal Questionnaire by Loza, Dhaliwal, Kroner, and Loza-

Fanous (2000). The 10 items describe delinquent behaviour (e.g. ‘stealing’). Responding



with either yes or no, the subjects indicated whether they behaved in that manner before

the age of 15. The German translation of the scale reached a reliability of �¼ 0.84.

Social support

To measure the social support that offenders obtained before imprisonment, the short form

of the Social-Support Questionnaire (SOZU-K-22; Sommer & Fydrich, 1989) was

administered. This self-report questionnaire consists of 22 items (e.g. ‘I often see myself

as an outsider’). In the present study, the SOZU-K-22 was modified insofar as the subjects

were requested to respond with respect to the time before imprisonment. The internal

reliability was �¼ 0.91.

Family environment

The family environment during childhood and adolescence was measured using a German

short form of the Family Environmental Scale by Moos and Moos (1981). The German

short form consists of 30 items constituting five scales: ‘Positive Emotional Climate’,

‘Active Recreational Orientation’, ‘Organization’, ‘Control’, and ‘Intellectual–Cultural

Orientation’. Internal reliabilities were estimated to be 0.63–0.82.

Lifetime prevalence of drug use

Subjects had to indicate the frequency of heroin, ecstasy, and LSD use in their lifetime to

the present.

Procedure

Subjects between the ages of 25 and 35 were asked to volunteer in the study via notice

boards. The inmates were promised a reward (3 s) for their participation and they were

assured of the confidentiality and anonymity of the data. The subjects completed the

questionnaires in groups of six to 10.

RESULTS

The number of clusters in the prisoner sample

With the same rationale as in study 1, we conducted the two-step clustering procedure for

three, four and five clusters, respectively. The comparison of cluster goodness of fit for k-

means cluster analysis solutions is given in Table 3. The five-cluster solution also seems

preferable in the prisoner sample. The point-biserial index favours a three-cluster solution,

whereas the AIC favour a four-cluster solution, but the majority of indices in concert with

their bootstrapped results clearly advocates a five-cluster solution.

Derivation of prototypes

The same procedure as described above was used for the sample-based derivation of

prototypes. The population-based derivation was conducted by applying the discriminant

functions (via SPSS discriminant) of study 1 to the NEO values of the prisoner sample, by

which the sample was divided into five groups (see study 1 for the rationale of this

approach and a more detailed description). According to study 1, the resulting clusters

were labelled Resilient, Overcontrolled, Undercontrolled, Confident, and Reserved.

Figure 2 presents the Big-Five mean z-scores for the five-cluster solution. The left panel

depicts the sample-based clustering results and the right panel the population-based



derivation. In terms of their z-scores, only the resilient prototype resembles sufficiently the

common resilient prototype pattern. The remaining prototypes show remarkable

differences from the corresponding prototypes derived from the general population-based

sample described in study 1. This prohibits a straightforward comparison between

prisoners’ prototypes and prototypes derived from normal samples, because an

unambiguous categorization of prisoners to existing prototypes appears questionable. In

contrast to the poor recovery of prototype categories from sample-immanent clustering,

the algorithm-based approach clearly reproduces the prototype categories (Figure 2, right

panel). A meaningful correspondence between prisoner prototypes and prototypes from

previous research is given. Noteworthy are some differences in degree. For instance, the

resilient prisoner, although having the same pattern as the Resilient from normal samples,

has lower values for Neuroticism, Extraversion, Agreeableness, and Conscientiousness.

The undercontrolled prisoner shows less Agreeableness; the confident type shows less

Extraversion, Openness, Agreeableness, and Conscientiousness; and the Reserved shows

less Conscientiousness than their normal sample counterparts.

External validation of prisoner prototypes

In order to examine the relationship between the personality prototypes and educational

degree, sentence length, previous convictions, and heroin, ecstasy, and LSD consumption

we used contingency tables. As shown in Table 4, the personality group’s classification

using population-based discriminant coefficients significantly differed with respect to

Table 3. Comparison of three- to five-cluster goodness of fit for k-means cluster analysis solutionsfor the prisoner sample

Criterion 3-cluster- 4-cluster- 5-cluster- Vote for Nsolution solution solution of clusters

PRE 0.16 0.11 0.09 5Point biserial 0.37 0.36 0.34 3Bootstrapped 0.293 0.303 0.309 5

(0.021) (0.025) (0.018)C-index 0.20 0.21 0.19 5Bootstrapped 0.189 0.187 0.185 5

(0.022) (0.021) (0.022)Gamma 0.49 0.54 0.56 5Bootstrapped 0.375 0.426 0.477 5

(0.027) (0.031) (0.022)W/B 0.55 0.51 0.49 5Bootstrapped 0.669 0.624 0.581 5

(0.021) (0.022) (0.016)G(þ ) 0.11 0.09 0.07 5Bootstrapped 0.145 0.115 0.090 5

(0.009) (0.008) (0.005)AIC 930.13 911.68 924.34 4Cohen’s kappaa 0.61 0.51 0.66 5Rand indexa 0.76 0.79 0.84 5Adjusted Rand indexa 0.47 0.48 0.52 5

N¼ 256.

Bootstrapped values from 100 samples. SD in brackets.

AIC: Akaike’s information criterion.aValues are averaged across both halves of the sample.



Figure2.

Fivepersonalityprototypes

characterizedbytheirBig-Fivez-score

patternsbytwodifferentapproaches:Leftpanel,sample-based

method;rightpanel,population-based

method.



the educational degree, sentence length, and prevalence of ecstasy use, as well as LSD

use. More precisely, Resilient prisoners are more likely to have a higher educational

degree than over- and undercontrolled prisoners. Reserveds are more likely to have a

sentence length of less than three years. Undercontrollers and Confidents reported ecstasy

consumption more frequently, whereas Resilients and Reserveds rarely report LSD

consumption.

Differences in the psychometric scales assessing childhood delinquency, family

environment, and social support between the five personality groups were tested using

multivariate analyses of variance with cluster groups as independent variables. Multiple

comparisons of means according to the Scheffe test were also implemented. As can be

seen in Table 5, with the exception of the FE-scale Control, all the scales included showed

significant differences between the five personality groups classified using population-

based discriminant coefficients. More specifically, Resilients reported higher endorsement

for positive-emotional climate than Overcontrollers and Confident’s and more active

recreational orientation in their families than Overcontrollers and Reserved. They also

reported higher values for organization than the remaining prototype members and viewed

their families with an intellectual-cultural orientation. Undercontrolled prisoners reported

more often childhood delinquency than the other prisoners. Finally, Resilients received

more social support before prison sentence.

DISCUSSION

The results of Study 2 support a five-cluster solution as the most appropriate partition of

the prisoner Big-Five data. Regardless of retaining a five-cluster solution, the sample

Table 4. Comparison of educational degree, prison sentence, and drug use for population-basedprototype assignment

Over- Under- Significance testResilient controlled controlled Confident Reserved

% % % % % �2 df p

Educational degreea 9.64 4 < 0.05low 36 60 59 40 54high 64 40 41 60 46

Sentence lengthb 11.79 4 < 0.05less than 3 years 45 57 48 57 783 years and more 55 43 52 43 22

Previous convictions 7.31 4 > 0.05no 71 81 90 85 73yes 29 19 10 15 27

Heroin 7.21 4 > 0.05no 88 70 68 72 78yes 12 30 32 28 22

Ecstasy 22.75 4 < 0.01no 86 63 57 55 85yes 14 37 43 45 15

LSD 16.28 4 < 0.01no 86 70 68 63 90yes 14 30 32 37 10

aLow (no educational degree and 8th class qualification), high (‘O-level’, 10th class qualification, technical

college, ‘A’-levels).bNo, no previous conviction before the actual prison sentence; yes, one previous conviction or more.



clustering approach could reproduce only the Resilient prototype with sufficient resemb-

lance to the target prototype. The remaining prototypes are not in agreement with their

counterparts derived from normal samples. Only the algorithm-based approach reproduces

prisoner personality groups that meet sufficient congruence with their normal sample

counterparts. The fact that only the population-based approach could recover meaningful

prisoner prototypes is taken as an argument supporting the validity of this approach. The

advantages of the population-based approach for further research and for applied

assessment will be discussed in the general discussion below. Nevertheless, some

difference between algorithm-based prisoner prototypes and prototypes already reported

in Table 1 and study 1 emerged, although the overall pattern is highly comparable. The

most salient differences are in general lower values for prisoner prototypes in Agree-

ableness and Conscientiousness. These differences appear meaningful and could therefore

be regarded as a first reference of validity, because both traits should be less pronounced in

populations that have been involved in violent crime, and other severe offences such as sex

offences. Moreover, Confidents show less Extraversion and Openness.

The validation of the prototypes indicates that the resilient prisoner prototype appears

better adjusted than the remaining prototypes. This parallels results from normal sample

research, where the resilient prototype is described in terms of high adjustment and

effective functioning in both interpersonal and task domains (Robins et al., 1998). On the

other hand, Undercontrollers show the typical LCP features such as adverse family climate

in childhood, low social support and, above all, a high level of childhood delinquency,

which is the defining characteristic. This parallels findings from Caspi (2000), who reports

more self-reported criminal offences as well as official records of criminal recidivism for

Undercontrollers than Resilients and Overcontrollers.

GENERAL DISCUSSION

There is concern regarding a variety of problems in the current prototype research practice,

such as substantial variability of the prototypes across different studies, insufficient

consideration of multiple statistical criteria, and the fact that small to moderate sample

sizes are relied upon for deriving prototypes. The principal aim of the present study,

Table 5. Comparison of family environment, childhood delinquency, and social support forpopulation-based prototype assignment

Scales Over- Under-

Resilient controlled controlled Confident Reserved ANOVA

M SD M SD M SD M SD M SD F p Scheffe

FE-PEC 3.56 0.77 2.96 0.91 3.00 0.66 3.19 0.84 2.98 0.76 5.56 < 0.01 1> 2, 5

FE-ARO 3.67 0.87 2.88 1.02 3.07 0.87 3.02 0.91 3.18 0.83 5.95 < 0.01 1> 2, 4

FE-ORG 3.94 0.67 3.40 0.91 3.37 0.75 3.38 0.96 3.45 0.70 5.11 < 0.01 1> 2, 3, 4, 5

FE-CON 3.48 0.81 3.45 1.10 3.29 0.73 3.18 0.83 3.49 0.84 1.15 > 0.05 —

FE-ICO 2.89 0.76 2.75 0.93 2.61 0.88 2.40 0.82 2.50 0.74 3.08 < 0.05 1> 4

CHDE 1.53 0.26 1.60 0.25 1.77 0.22 1.58 0.22 1.57 0.25 7.78 < 0.01 3> 1, 2, 4, 5

SOSU 4.34 0.62 3.57 0.86 3.67 0.73 3.80 0.78 4.00 0.66 8.65 < 0.01 1> 2, 3, 4

PEC¼Positive Emotional Climate, ARO¼Active Recreational Orientation, ORG¼Organization, CON¼Control, ICO¼ Intellectual–Cultural Orientation, CHDE¼Childhood Delinquency, SOSU¼Social Support

(before prison sentence).



therefore, was to examine the number of clusters of Big-Five-based prototypes. Contrary

to previous research, which relies predominantly on Cohen’s � as criterion for determining

the number of clusters, we employed a sequential validation framework (Morey,

Blashfield, & Skinner, 1983). This framework included derivation, replication, cross

validation, and external validation. The prototypes were derived using the current state-of-

the-art sample clustering approach (Ward’s method followed by k-means clustering) and

compared this approach with a population-based approach (via discriminant function) in

study 2. In the replication stage we extended the commonly �-based internal replication

approach by utilizing a variety of criteria that meet the current standards in cluster research

(Milligan & Cooper, 1985). The prototypes based on a representative, general-population-

based sample were cross-validated using a prisoner sample. After this, we conducted an

external validation of the prototypes within the prisoner sample.

The results of our sequential validation framework analyses were able to shed some

light on the dilemma of selecting the right number of personality prototypes based on Big-

Five measures. While it is true that some researchers have found evidence for prototypes

beyond the Resilient, Overcontrolled, and Undercontrolled prototype (Barbaranelli, 2002;

Caspi & Silva, 1995; York & John, 1992), most of the studies extracted three prototypes

when relying on one single criterion. As study 1 has demonstrated, it seems premature to

fix the empirically derived personality prototypes only on the Resilient, Overcontrolled,

and Undercontrolled types. From our point of view, it is likely that using Cohen’s � as the

single criterion for determining the number of prototypes has misled most prototype

researchers. The conviction that the presented five-cluster solution is not artificial stems

from the following the fact that we (a) based our analysis on a large representative,

general-population-based sample; (b) applied the current most reliable criteria for

determining the number of clusters; (c) found convergence in subsamples and (d) bootstrap

analyses; (e) replicated the five-cluster solution in a prisoner sample; and (f) were able to

relate our findings to studies that also extracted five prototypes, especially to those

identified by Caspi and Silva (1995) and Caspi et al. (2003).

In addition to demonstrating the evidence supporting five rather than three prototypes,

we also investigated a new approach for assigning individuals to prototypes. Instead of

conducting a cluster analysis for every specific sample under investigation, which can be

sensitive to sample size and composition, we proposed a discriminant function based

approach derived from a representative, general-population-based sample. This popula-

tion-based approach avoids the tendency of defining equal-size clusters, which is a feature

of Ward’s cluster method (Blashfield & Aldenderfer, 1988). This becomes especially

important for samples with different base rates, such as prisoner samples, where the

resilient prototype is a priori less likely to be represented. An advantage of the population-

based approach is the fact that it allows one to circumvent the heterogeneity of previous

personality types in different samples. The rationale involved in summing up scores from

psychometric measures, is that, because the factorial structure has been established on

representative samples, it does not need to be recomputed for every new data set under

investigation. By also adopting this rationale, the suggested population-based approach

could make results from different samples more comparable.

Furthermore, we were able to demonstrate that the algorithm-based prototypes were

related to demographic variables, variables of prison sentence and drug use, family

environment, and childhood delinquency variables in terms of Moffitt’s theory (Moffitt,

1993). In contrast, when using the sample-inherent approach to create prototypes, we did

not discover the expected pattern of relationships between personality, socio-demographic,



and family environment variables postulated by Moffitt. Using a nomological validation

framework, we therefore conclude that the algorithm-based approach reproduces perso-

nality prototypes more validly than the sample inherent derivation.

Finally, we would like to pinpoint three issues with regard to future research.

(1) While cluster analysis conveys every individual into one cluster, the algorithm-based

approach could easily be modified to avoid assigning those individuals with less clear

configurations to a particular prototype. This modification does not classify

individuals who possess a relatively unique personality structure, or those with a

personality structure that resembles more than one prototype; in contrast, it defines an

entirely separate group of residuals that should subsequently be excluded from further

analysis. This ensures that individuals are sufficiently matched to the personality

configuration defining the prototype, therefore minimizing within-cluster hetero-

geneity. On the other hand, this maximizes between-cluster homogeneity and should

make the prototypes more distinctive. Relationships to important variables should

therefore become more salient. Due to space limitations, we were not able to evaluate

this particular capability of the algorithm-based approach in this article.

(2) A further feature of the algorithm-based approach is that it allows the assignment of

single individuals to prototypes. The Big-Five-based prototype idea thus becomes

attractive for applied assessment issues in which thinking in terms of types is the

norm, such as in the case of clinicians and counsellors.

(3) One limitation of the algorithm-based approach that should not be ignored is the

possibility that the discriminant function is not culture invariant. While cross-cultural

research on personality traits has revealed that Big-Five inventories provide reliable

and valid measures of personality in a wide variety of cultures (e.g. Hendriks et al.,

2003), the appropriateness of the discriminant function for cultures other than German

still needs to be established empirically.

ACKNOWLEDGEMENTS

We thank Jens B. Asendorpf and two anonymous reviewers for their valuable comments

on an earlier draft.

The authors wish to thank Annett Korner, who permitted us to use the NEO-FFI data,

and Inge Seiffge-Krenke for access to parts of the prisoner data.

REFERENCES

Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. InB. N. Petrov, & F. Csaki (Eds.), Proceedings of the 2nd International Symposium of InformationTheory (pp. 267–281). Budapest: Akademiai Kiado.

Aldenderfer, M. S., & Blashfield, R. K. (1996). Cluster analysis. Newbury Park, CA: Sage.Asendorpf, J. B. (2002). Editorial: The puzzle of personality types. European Journal of Personality,

16, S1–S5.Asendorpf, J. B., & Aken, M. A. G. v. (1999). Resilient, overcontrolled, and undercontrolled

personality prototypes in childhood: Replicability, predictive power, and the trait-type issue.Journal of Personality and Social Psychology, 77, 815–832.

Asendorpf, J. B., Borkenau, P., Ostendorf, F., & Aken, M. A. G. v. (2001). Carving personalitydescription at its joints: Confirmation of three replicable personality prototypes for both childrenand adults. European Journal of Personality, 15, 169–198.



Asendorpf, J. B., Caspi, A., & Hofstee, W. K. B. (Eds.). (2002). The puzzle of personality types.European Journal of Personality, 16, S1–S96.

Bacher, J. (1996). Clusteranalyse [Cluster analysis]. Munchen: Oldenbourg.Barbaranelli, C. (2002). Evaluating cluster analysis solutions: An application to the Italian NEOpersonality inventory. European Journal of Personality, 16, S43–S55.

Blashfield, R. K., & Aldenderfer, M. S. (1988). The methods and problems of cluster analysis. In J.R. Nesselroade, & R. B. Cattell (Eds.), Handbook of multivariate experimental psychology (2nded., pp. 447–473). New York: Plenum.

Block, J. (1971). Lives through time. Berkeley, CA: Bancroft.Boehm, B., Asendorpf, J. B., & Avia, M. D. (2002). Replicable types and subtypes of personality:Spanish NEO-PI samples. European Journal of Personality, 16, S25–S41.

Borkenau, P., & Ostendorf, F. (1993). Neo-Funf-Faktoren Inventar (NEO-FFI) nach Costa undMcCrae [The Five Factor Inventory]. Gottingen: Hogrefe.

Breckenridge, J. N. (2000). Validating cluster analysis: Consistent replication and symmetry.Multivariate Behavioral Research, 35, 261–285.

Caspi, A. (1998). Personality development accross the life course. In W. Damon, & N. Eisenberg(Eds.),Handbook of child psychology: Social, emotional, and personality development (Vol. 3, pp.311–388). New York: Wiley.

Caspi, A. (2000). The child is the father of the man: Personality continuities from childhood toadulthood. Journal of Personality and Social Psychology, 78, 158–172.

Caspi, A., Harrington, H., Milne, B., Amell, J. W., Theodore, R. F., &Moffit, T. E. (2003). Children’sbehavioral styles at age 3 are linked to their adult personality traits at age 26. Journal ofPersonality, 71, 495–514.

Caspi, A., & Silva, P. A. (1995). Temperamental qualities at age three predict personality traitsin young adulthood: Longitudinal evidence from a birth cohort. Child Development, 66, 486–498.

Cliff, N. (1983). Some cautions concerning the application of causal modeling methods.MultivariateBehavioral Research, 18, 115–126.

Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and PsychologicalMeasurement, 20, 37–46.

Costa, P. T., & McCrae, R. R. (1995). Solid ground in the wetlands of personality: A reply to Block.Psychological Bulletin, 117, 216–220.

Costa, P. T., & McCrae, R. R. (1997). Longitudinal stability of adult personality. In R. Hogan, J.Johnson, & S. Briggs (Eds.), Handbook of personality psychology (pp. 269–290). San Diego:Academic.

De Fruyt, F., Mervilde, I., & van Leeuwen, K. (2002). The consistency of personality typeclassification across samples and five-factor measures. European Journal of Personality, 16, S57–S72.

Dennison, S. M., Stough, C., & Birgden, A. (2001). The big 5 dimensional personality approach tounderstanding sex offenders. Psychology, Crime and Law, 7, 243–261.

Ekehammar, B., & Akrami, N. (2003). The relation between personality and prejudice: A variable-and a person-centred approach. European Journal of Personality, 17, 449–464.

Gramzow, R. H., Sedikides, C., Panter, A. T., Harris, J., & Insko, C. A. (2004). Patterns of self-regulation and the Big Five. European Journal of Personality, 18, 367–385.

Hendriks, A. A. J., Perugini, M., Angleitner, A., Ostendorf, F., Johnson, J. A., De Fruyt, F.,Hrebickova, M., Kreitler, S., Murakami, T., Bratko, D., Conner, M., Nagy, J., Rodrıguez-Fornells,A., & Ruisel, I. (2003). The Five-Factor Personality Inventory: Cross cultural generalizabilityacross 13 countries. European Journal of Personality, 17, 347–373.

Hu, L., & Bentler, P. M. (1995). Evaluating model fit. In R. H. Hoyle (Ed.), Structural equationmodeling: Concepts, issues, and applications (pp. 76–99). Thousand Oaks, CA: Sage.

Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2, 193–218.John, O. P., & Srivastava, S. (1999). The big five trait taxonomy: History, measurement, andtheoretical perspectives. In L. A. Pervin, & O. P. John (Eds.), Handbook of personality theory andresearch (pp. 102–138). New York: Guilford Press.

Korner, A., Geyer, M., & Brahler, E. (2002). Das NEO-Funf-Faktoren Inventar (NEO-FFI).Validierung anhand einer deutschen Bevolkerungsstichprobe [German validation of the NEOFive-Factor Inventory]. Diagnostica, 48, 19–27.



Krieger, A. M., & Green, P. E. (1999). A cautionary note on using internal cross validation to selectthe number of clusters. Psychometrika, 64, 341–353.

Kunst, H., & Hoyer, J. (2003). Psychometrische Analysen von IIP und Neo-FFI bei Straftatern[Psychometric analyses of IIP and Neo-FFI in offenders]. Diagnostica, 49, 24–33.

Loza, W., Dhaliwal, G., Kroner, D. G., & Loza-Fanous, A. Q. (2000). Reliability, construct, andconcurrent validities of the Self-Appraisal Questionnaire. Criminal Justice and Behavior, 27,356–374.

MacQueen, J. (1967). Some methods to classification and analysis of multivariate observations. InL. M. Necam, & J. Neymann (Eds.), Proceedings of the fifth Berkeley symposium of mathematicalstatistics and probability 1965/66 (Vol. 1, pp. 281–297). Berkeley: University of CaliforniaPress.

Milligan, G. W. (1981). AMonte Carlo study of thirty internal criterion measures for cluster analysis.Psychometrika, 46, 187–198.

Milligan, G. W., & Cooper, M. C. (1985). An examination of procedures for determining the numberof clusters in a data set. Psychometrika, 50, 159–179.

Moffitt, T. E. (1993). Adolescence-limited and life-course-persistent antisocial behavior. Psycho-logical Review, 100, 674–701.

Moffitt, T. E., Caspi, A., Harrington, H., & Milne, B. J. (2002). Males on the life-course-persistentand adolescence-limited antisocial pathways: Follow up at 26 years. Development andPsychopathology, 14, 179–207.

Moos, R., & Moos, B. S. (1981). Family Environment Scale. Palo Alto: Consulting PsychologistsPress.

Morey, L. C., Blashfield, R. K., & Skinner, H. A. (1983). A comparison of cluster analysis techniqueswithin a sequential validation framework. Multivariate Behavioral Research, 18, 309–329.

Overall, J. E., & Magee, K. N. (1992). Replication as a rule for determining the number of clusters inhierarchial cluster analysis. Applied Psychological Measurement, 16, 119–128.

Pervin, L. A. (2003). The science of personality. Oxford: Oxford University Press.Pulkinnen, L. (1996). Female and male personality styles: A typological and developmental analysis.

Journal of Personality and Social Psychology, 70, 1288–1306.Rammstedt, B., Riemann, R., Angleitner, A., & Borkenau, P. (2004). Resilients, Overcontrollers, and

Undercontrollers: The replicability of the three personality prototypes across informants.European Journal of Personality, 18, 1–14.

Rasmussen, K., Storsaeter, O., & Levander, S. (1999). Personality disorders, psychopathy, andcrime in a Norwegian prison population. International Journal of Law and Psychiatry, 22,91–97.

Robins, R. W., John, O. P., & Caspi, A. (1998). The typological approach to studying personality. InR. B. Cairns, L. Bergman, & J. Kagan (Eds.), Methods and models for studying the individual(pp. 135–160). Thousand Oaks, CA: Sage.

Robins, R. W., John, O. P., Caspi, A., Moffitt, T. E., & Stouthamer-Loeber, M. (1996). Resilient,overcontrolled, and undercontrolled boys: Three replicable personality types. Journal ofPersonality and Social Psychology, 70, 157–171.

Schafer, J. L. (1997). Analysis of incomplete multivariate data. London: Chapman and Hall.Schnabel, K., Asendorpf, J. B., & Ostendorf, F. (2002). Replicable types and subtypes of personality:

German NEO-PI-R versus NEO-FFI. European Journal of Personality, 16, S7–S24.Schweizer, K. (1993). Comparison of various methods for classifying variables on the basis of

correlations in small numbers of variables. In R. Steyer, K. F. Wender, & K. F. Widaman (Eds.),Psychometric methodology. Proceedings of the seventh European meeting of the PsychometricSociety (pp. 475–478). Stuttgart: Fischer.

Shao, J., & Tu, D. (1995). The jacknife and the bootstrap. New York: Springer.Sommer, G., & Fydrich, T. (1989). Soziale Unterstutzung. Diagnostik, Konzepte, Fragebogen F-

SozU [Social support, assessement, concepts, questionnaire F-SozU]. Tubingen: dgvt.Staudinger, U. M., & Herzberg, P. Y. (2003). Halten Personlichkeitstypen der Ontogenese stand?

[Do personality prototypes change?] Paper presented at the seventh. Arbeitstagung derFachgruppe fur Differentielle Psychologie, Personlichkeitspsychologie und PsychologischeDiagnostik der Deutschen Gesellschaft fur Psychologie, Halle.

Steinley, D. (2003). Local optima in k-means clustering: What you don’t know may hurt you.Psychological Methods, 8, 294–304.



Van Leeuwen, K., De Fruyt, F., & Mervielde, I. (2004). A longitudinal study of the utility of theresilient, overcontrolled, and undercontrolled personality types as predictors of children’s andadolescents’ problem behaviour. International Journal of Behavioral Development, 28, 210–220.

Ward, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the AmericanStatistical Association, 58, 236–244.

York, K. L., & John, O. P. (1992). The four faces of Eve: A typological analysis of women’spersonality at midlife. Journal of Personality and Social Psychology, 63, 494–508.



beyond resilients under controllers and over controllers

Documents