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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
Copyright # 2006 John Wiley & Sons, Ltd. Eur. J. Pers. 20: 5–28 (2006)
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.
8 P. Y. Herzberg and M. Roth
Copyright # 2006 John Wiley & Sons, Ltd. Eur. J. Pers. 20: 5–28 (2006)
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.
Beyond resilients, undercontrollers, and overcontrollers 9
Copyright # 2006 John Wiley & Sons, Ltd. Eur. J. Pers. 20: 5–28 (2006)
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
10 P. Y. Herzberg and M. Roth
Copyright # 2006 John Wiley & Sons, Ltd. Eur. J. Pers. 20: 5–28 (2006)
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).
Beyond resilients, undercontrollers, and overcontrollers 11
Copyright # 2006 John Wiley & Sons, Ltd. Eur. J. Pers. 20: 5–28 (2006)
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.
12 P. Y. Herzberg and M. Roth
Copyright # 2006 John Wiley & Sons, Ltd. Eur. J. Pers. 20: 5–28 (2006)
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.
Beyond resilients, undercontrollers, and overcontrollers 13
Copyright # 2006 John Wiley & Sons, Ltd. Eur. J. Pers. 20: 5–28 (2006)
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.
14 P. Y. Herzberg and M. Roth
Copyright # 2006 John Wiley & Sons, Ltd. Eur. J. Pers. 20: 5–28 (2006)
(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.
Beyond resilients, undercontrollers, and overcontrollers 15
Copyright # 2006 John Wiley & Sons, Ltd. Eur. J. Pers. 20: 5–28 (2006)
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
16 P. Y. Herzberg and M. Roth
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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
Beyond resilients, undercontrollers, and overcontrollers 17
Copyright # 2006 John Wiley & Sons, Ltd. Eur. J. Pers. 20: 5–28 (2006)
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
18 P. Y. Herzberg and M. Roth
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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
Beyond resilients, undercontrollers, and overcontrollers 19
Copyright # 2006 John Wiley & Sons, Ltd. Eur. J. Pers. 20: 5–28 (2006)
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.
20 P. Y. Herzberg and M. Roth
Copyright # 2006 John Wiley & Sons, Ltd. Eur. J. Pers. 20: 5–28 (2006)
Figure2.
Fivepersonalityprototypes
characterizedbytheirBig-Fivez-score
patternsbytwodifferentapproaches:Leftpanel,sample-based
method;rightpanel,population-based
method.
Beyond resilients, undercontrollers, and overcontrollers 21
Copyright # 2006 John Wiley & Sons, Ltd. Eur. J. Pers. 20: 5–28 (2006)
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.
22 P. Y. Herzberg and M. Roth
Copyright # 2006 John Wiley & Sons, Ltd. Eur. J. Pers. 20: 5–28 (2006)
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).
Beyond resilients, undercontrollers, and overcontrollers 23
Copyright # 2006 John Wiley & Sons, Ltd. Eur. J. Pers. 20: 5–28 (2006)
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,
24 P. Y. Herzberg and M. Roth
Copyright # 2006 John Wiley & Sons, Ltd. Eur. J. Pers. 20: 5–28 (2006)
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.
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