Person-centered approaches 1 Person-centered Approaches to Personality Jens B. Asendorpf

Person-centered approaches 1

Person-centered Approaches to Personality

Jens B. Asendorpf

Department of Psychology, Humboldt University Berlin, Germany

Chapter prepared for M. L. Cooper & R. Larsen (Eds.), Handbook of personality processes and

individual differences. Washington, DC: American Psychological Association.

Final draft, February 20, 2013

Author address

Jens B. Asendorpf, Department of Psychology, Humboldt University, Unter den Linden 6, 10099

Berlin, Germany. E-mail: [email protected]


Personality can be defined as "the dynamic organization within the individual of those

psychophysical systems that determine his [or her] unique adjustments to his [or her]

environment" (Allport, 1937, p. 48, italics added). Lay psychological concepts, textbook

definitions, and theoretical reviews of personality agree with this person-centered view of

personality. In contrast, empirical research has treated personality mainly from a variable-

centered perspective, focusing on single personality traits. Person-centered approaches studying

individual personality patterns or profiles comprising many different traits has been the

exception rather than the rule.

This chapter attempts to highlight the specific merits of a person-centered perspective as

compared to a variable-centered perspective and to provide an overview of the historical

development of the person-centered view, the main methods of implementing this view in

empirical research, and problems of these methods. Finally it highlights the advantages of

multilevel analysis for the simultaneous study of personality from both the person- and the

variable-centered perspective.

Within the person-centered literature, numerous terms are used that are often confusing

because they have similar but non-identical meaning and are not explicitly defined. Table 1

provides an overview of most of these terms and their definition in order to clarify these terms

and to assist readers throughout the chapter.

- Table 1 -

Dark Spots of the Variable-Centered Approach

The variable-centered approach to personality isolates psychologically meaningful behavioral

characteristics on which individuals reliably differ (traits), and studies their correlational

structure, stability over time, and predictive validity for important life outcomes. Note that this


type of structure, stability, and validity is a property of a sample of persons, not of an individual

person. For any individual person, variable-centered approaches provide no information about

the person-specific intra-individual organization of psychological processes and behavior, no

information about the person-specific intra-individual dynamics of this organization, and no

information about the person-specific predictive validity of this organization and dynamics for

important life outcomes.

Instead, variable-centered approaches provide information about the trait structure,

stability, and validity for an average person in the sample. This information can be translated for

a particular individual into a probability that two different traits are consistent, that a trait is

stable, and that the trait is valid for this one individual but this probability is based only on

average information about all individuals in the sample. This is a point often misunderstood and

needs some discussion.

In all three cases, trait structure, stability and validity are measured by correlations. A

correlation is a property of a sample of individuals, not of an individual. Asendorpf (1990) has

proven that the Pearson correlation between two Variables X, Y can be decomposed into

individual consistency scores i(X,Y) = 1 – (z(X) – z(Y))²/2 such that the mean of these scores is

identical with the correlation (see Asendorpf, 1992, for an application to trait stability).

Individuals with highly similar standardized scores on both variables contribute more to a

positive correlation than individuals with less similar scores. Thus, the Pearson correlation is a

measure of the average similarity of the standardized scores of the two correlated variables.

Therefore, for any individual in the sample, a positive correlation informs us about the

probability that there is an association between the correlated variables (such that the

standardized scores are similar) but this probability is identical for all individuals in the sample,


ignoring the fact that the individual consistency scores vary between individuals. In other words,

the variable-centered approach ignores inter-individual differences in individual consistency.

From a person-centered perspective, much more informative is the individual consistency i(X,Y)

itself; this consistency provides unique information based on the individual personality.

In addition to its neglect of inter-individual differences in individual consistency, the

variable-centered approach derives the relevant information from traits studied in isolation from

each other. Therefore it misses the key point, as pointed out by Allport (1937), that different

psychological processes, behaviors and traits do not function in isolation from each other within

a person but function as a coordinated system of processes, behaviors, and traits. Although the

variable-centered approach can be enriched by adding interactions to the main effects of traits,

such an approach can only consider a few traits simultaneously because the number of

interactions exponentially increases with the number of traits, quickly leading to overly complex

designs. If we wish to take the concept of personality seriously, a person-centered perspective is

required where the unit of analysis is the person, not a trait, and the organization of many traits is

studied, not only interactions among few traits.

From Variables to Persons: Research on Resilient Adaptation

To illustrate this point, consider the key finding from research on adaptation to stressful life

conditions that protective factors (e. g. cognitive, social or emotional competencies) interact with

risk factors, such that the presence of protective factors in resilient individuals diminishes the

effects of risk factors (Masten, 2001). Within a variable-centered perspective, such protective

effects require the study of risk by protective factor interactions (or stated differently, the

moderation of risk effects by protective factors). If the risk and the protective factors are traits, a

study of trait interactions is required where adaptation is predicted from the individual


configuration of two traits.

This move from studying two isolated traits to the configuration of two traits is a first

step toward a person-centered approach to personality effects on adaptation. However, it is

seriously limited because only one risk factor and one protective factor are considered. Adding

many such factors and their interactions quickly leads to the problem that the design becomes

extremely complex, so very large samples are required for a reliable estimation of the

parameters.

Therefore, there is a long tradition in research on resilient adaptation to identify patterns

of protective factors. Within a high-risk group, a poorly adapted subgroup is compared with a

well-adapted subgroup in terms of many individual characteristics that might differentiate these

two subgroups (e.g., Werner & Smith, 1982). If subgroups of a low-risk group varying in

adaptation are included (e.g., Luthar, 1991), protective factors can be distinguished from factors

that generally promote adaptation. Such "person-focused" designs move the study of resilience

closer to a person-centered perspective (Masten, 2001).

However, the configuration of protective factors detectable with such designs is only a

configuration for an average resilient person and thus still far off a true person-centered

perspective. For example, if social competence and IQ are both identified as protective factors

for school conduct, but the protective effect of social competence is stronger than the effect of

IQ, this difference may generalize to all or at least most high-risk persons, or it may apply only

to a minority of high-risk individuals that show a particularly strong difference whereas for

others the difference is small or even reversed. Therefore, “person-focused” studies of resilience

are at best a halfway approach to the individual protective patterns.

Another step further toward person-centered approaches are studies based on cluster


analyses of individual profiles of scores on risk, protective factors and adaptation (e.g., Masten,

Hubbard, Gest, Tellegen, Garmezy, & Ramirez, 1999). They classify participants into groups

characterized by a similar profile rather than by a priori defined cut scores for high/low risk and

adaptation. Masten et al. (1999) found that a three-cluster solution largely replicated the a priori

defined groups of resilient (adequate adaptation, high risk), maladaptive (poor adaptation, high

risk) and competent (adequate adaptation, low risk) individuals. It should be noted though that

the profiles obtained by such cluster analyses strongly depend on the relative contribution of risk,

protective, and adaptation variables. For example, if clusters are derived for one risk, one

adaptation outcome and five independent protective factors, the clusters will likely reflect

different types of protection, whereas clustering based on five independent risk factors, one

protective factor and one adaptation outcome will likely reflect different types of risk.

Despite these complications in studies that mix different types of variables in varying

proportions, clustering of trait profiles is the most frequently chosen person-centered approach to

personality. But it is important to recognize that clustering profiles is only one of many person-

centered approaches. Therefore, a broader and historically informed look at personality from

Allport's (1937) perspective is in order.

Emerging Complexity of Person-Centered Views

At the end of the 19th

century, early experimental psychologists considered individual

peculiarities mainly as a source of error in the formulation of general laws of human behavior.

Louis William Stern, a student of the eminent experimental psychologist Ebbinghaus at Berlin

University, not only coined the term IQ in 1912 but also recognized in his first book (Stern,

1900) the scattered nature of early research on inter-individual differences and the importance of

studying individual persons seriously. In his second book (Stern, 1911), he outlined for the first


time a systematic framework for the study of what he called "differentielle Psychologie"

(differential psychology). This framework was based on an attribute by individual matrix (see

Fig. 1).

- Fig. 1 –

Within this framework, Stern distinguished between four "disciplines of differential

psychology". Two disciplines focus on attributes. "Variation Research" is concerned with the

distribution of a particular attribute across individuals, whereas "Correlational Research" is

concerned with the similarity of variation between two attributes across the same individuals.

Today we would refer here to the variable-centered approaches of studying the distribution of a

variable and the correlation of two variables. Two additional disciplines in Stern's framework

focus on individuals. "Psychography" is concerned with the distribution of attributes within a

particular individual, whereas "Comparative Research" is concerned with the similarity of two

individuals in terms of the same attributes. Today we would refer here to the person-centered

approaches of studying individual profiles and the similarity of such profiles.

Stern (1911) had a broad concept of an attribute. He classified attributes according to

their degree of observability: phenomena (directly observable behaviors, states or physical

features, e.g., behaves aggressively), acts (phenomena related to a common goal, e.g., tries to get

a toy used by peers), traits (dispositions for showing particular phenomena or acts in particular

situations, e.g., aggressiveness), and "Anlagen" (dispositions for particular developmental

outcomes, e.g. childhood aggressiveness as an "Anlage" for later criminality). His framework

can be applied to personality psychology in a broad sense, including abilities such as intelligence

or musical ability, physical attractiveness, and neuroanatomical, neurophysiological and

(epi)genetic characteristics related to behavioral individuality.


Figure 1 indicates that Stern was fully aware that variable- and person-centered

approaches deal with the same set of data from two complementary perspectives. His approach

can be reconstructed as three research programs (Asendorpf, 1991): (a) difference-centered

psychology that examines differences between individuals and groups in a population, (b) group-

centered psychology that examines attributes characterizing particular groups in a population

such as gifted students, criminals, or females (examples used by Stern), and (c) individual-

centered psychology that examines attributes of one particular individual. Although the

narrowing focus, from populations to groups and from groups to individuals, seems systematic, it

does not make sense from a methodological point of view to use the term "differential" for the

study of groups (without comparison groups) or the study of an individual (without comparison

to other individuals). Instead, using the label "differential" for all three research programs blurs

the distinction between inter-individual and intra-individual differences that Stern himself had

drawn so clearly in his 1911 framework. Indeed, confusing inter-individual and intra-individual

differences and the use of the ambiguous term "individual differences" abounds in the

psychological literature until today.

Allport (1937) was quite familiar with Stern's work. In fact, after receiving his PhD from

Harvard in 1922, he spent two years at German universities, including some months at the

University of Hamburg that was co-founded by Stern in 1919. He even rented a room in Stern's

house (see Lamiell, 2003). However, Allport was skeptical about Stern's framework mainly

because he questioned the tacit assumption underlying the framework that different individuals

can be sufficiently compared by using a common set of attributes (see also the later section on

idiographic and nomothetic approaches).

Cattell (1946) significantly expanded Stern's (1911) framework by adding to his own


"covariation chart" a third dimension of "occasions" that he interpreted sometimes as time points

(adding a short-term temporal perspective) or as situations (adding a cross-situational

perspective). Based on this scheme, Cattell (1957) later proposed a taxonomy for correlational

techniques, particularly R-correlations between two variables on one occasion (corresponding to

Stern's Correlational Research), Q-correlations between two profiles of individuals on one

occasion (corresponding to Stern's Comparative Research), and P correlations between two

variables within one individual (measuring the extent to which two different states covary over

time within one individual).

Ozer (1986) expanded Cattell's covariation chart once more by adding behavioral

indicators of the same trait for a systematic discussion of various kinds of consistency, and

Fahrenberg (1986) based his sophisticated multivariate psychophysiological studies on a similar

model, decomposing the observed variance in physiological reactions into effects of persons,

reactions, situations, time points, and their interactions. All these models offer a person-centered

view that became more and more complex over the century following Stern (1911), owing to the

realization that persons can be described not only by a profile of attributes but by a profile of

attributes that can vary over time, across situations, and across different reactions.

Clarifying Two Consistency Debates from a Person-Centered View

This multivariate perspective on personality profiles is also helpful for clarifying two consistency

debates in personality psychology. The person-situation debate started with the finding by

Hartshorne and May (1928) that inter-individual differences in honesty showed only a low

consistency across different situations, and was stirred up again by Mischel (1968) who reported

in his review of the relevant literature of his time that cross-situational consistency correlations

based on behavioral observations as well as predictions of behavior from judged personality


traits rarely exceeded .30. He interpreted this finding as indicating a low cross-situational

consistency "of behavior" and that situations are more important than persons in determining

"behavior". Following from Mischel’s work, others concluded that the very notion of a

consistent personality is a fiction.

Apart from arguments that .30 underestimated true consistency due to the unreliability of

the behavioral measures in most of the studies reviewed by Mischel (1968) such that a more

realistic limit is .40 or .50, the whole argumentation by Mischel (1968) is unimpressive from a

person-centered point of view. What is relevant for the concept of personality characterizing the

behavioral individuality of a person is the consistency of this person's behavioral profile over

time and across situations, and these types of consistency were studied by neither Hartshorne and

May (1928) nor Mischel (1968). Probably the vague term "consistency of behavior" used by

Mischel (1968) contributed to this conceptual confusion.

I hasten to add that Mischel himself was involved in the later clarification of this

confusion. Shoda, Mischel, and Wright (1994) showed in an extensive behavioral observation

study of 53 children (each observed for an average of 163 hours over 6 weeks) that inter-

individual differences in aggressive behavior showed a low cross-situational consistency, but that

at the same time the cross-situational profiles of most of the children showed a high temporal

stability. It is this latter person-centered finding of a stable "situational signature" of personality

that informs us about the personality of these children.

Less noticed by personality psychologists was a second, parallel person-response debate

in psychophysiology about the extremely low cross-response coherence of physiological

measures indicating the same state. In stress research for example, situational means (computed

over persons) of physiological measures of stress such as heart rate, systolic and diastolic blood


pressure, skin conductance responses, and muscle tension show high correlations across different

measures such that the stressful nature of each situation is similarly captured by each such

measure. However, when the same data are averaged across situations for each person,

separately for each physiological response measure, the cross-response correlation of these

measures is close to zero, even when baseline differences are controlled (e. g., Stemmler, 1992).

Thus, rank-orders of persons in terms of how strongly they generally react under stress are

inconsistent across different measures, and this inconsistency is even larger than the cross-

situational inconsistency of these measures. People cannot be ordered consistently across the

various components of their physiological stress response.

The low cross-response coherence of inter-individual differences in physiological

responding was first observed by Lacey (1950) who used the term "individual response

hierarchy" for describing the hypothesis that each person is characterized by a hierarchy of

responses to stress (the "response signature" of personality). One person may strongly react with

heart rate but not with muscle tension, another not with heart rate but with muscle tension, and so

on. Foerster, Schneider, and Walschburger (1983) studied multiple physiological responses in

multiple stress situations. Of the 125 participants, 57% showed a coherent response profile

across the situations. Moving beyond psychophysiology, Asendorpf (1988) adopted this

approach to the study of response profiles for shy behavior observed in multiple shyness-

inducing situations. Of the 66 participants, again a majority showed a consistent response profile.

Note that the results for individual cross-situational profiles by Shoda et al. (1994) and

the results for individual cross-response profiles by Foerster et al. (1983) and Asendorpf (1988)

are structurally similar; replacing situations by responses or vice versa translates one into the

other. That not all participants displayed consistent profiles in these studies may be due to the


low reliability of the profiles of some participants, or due to erratic and ultimately unpredictable

behavior of some participants.

To summarize, both consistency debates can be resolved by taking a person-centered

perspective on personality data that is congenial with the definition of personality. Or stated

more critically, both debates were pseudo-debates based on an incorrect application of a

variable-centered perspective to a question that concerns persons, not variables.

Personality Profiles: Average Versus Unique

Individual profiles are not necessarily informative about a person's unique personality (how it is

different from the personality of others) because the profiles often contain information about the

average profile in a reference population such as age-mates (see Furr, 2008, for a detailed

discussion). Consider the two profiles depicted in Figure 2a that are based on diary data obtained

from a Berlin undergraduate student (Asendorpf & Wilpers, 1998). The student was asked to

report over a period of three weeks any lengthy or emotionally significant social interaction and

to rate each interaction on various scales including a scale for reporting the degree of

interpersonal conflict with the interaction partner(s). Ratings were averaged for interactions with

the same type of interaction partners, separately for odd and even days of the diary. The resulting

cross-situational profiles suggest stable tendencies to have more conflict in interactions with the

father and the romantic partner than with the mother, the siblings, and peers. Because of its

consistency, the profile describes a reliable part of the situational signature of this student's

personality.

- Fig. 2a,b –

In terms of Stern's (1911) framework, the profile was generated by Psychography. What

does it tell us about the uniqueness of the student in terms of interpersonal conflict? Nothing -


unless we know what the profiles of other students look like. In fact, the mean of the two profiles

in Fig. 2a is identical with the average profiles of all participants in the Asendorpf and Wilpers

(1998) study. Therefore, the profiles of the student in Fig. 2a can be interpreted as average in

every respect. If we were to guess how the student's profile might look without having observed

this student (but a sufficiently large sample of other students), the average profile in Fig. 2a is the

best bet because it maximizes accuracy by relying on stereotype accuracy (Cronbach, 1955), that

is, on knowledge about the average profile in the sample.

Therefore, it is necessary to contrast an individual profile with the average profile in

order to identify the individual's unique personality. Three different methods of doing so can be

distinguished: comparison of profiles based on an absolute scale, standardized variables, and the

Q-sort method.

Profiles Based on an Absolute Scale

If the profiles are based on variables that are measured on the same absolute scale such as

frequency, duration, speed, size, or weight, or ratings of frequency or intensity of behavior, a

straightforward method for contrasting individual profiles with the average profile first

determines the average profile for a reference population (e. g., age-mates). In a second step, the

uniqueness of the profile of any member of the population is determined by the profile based on

the differences between the individual profile and the average profile. In the example of Fig. 2a,

the differences were close to zero. In Figure 2b, Student 2 also shows a profile close to the

average profile whereas Student 1 reported consistently less interpersonal conflict for each type

of interaction partner. This student clearly deviates from the average profile in terms of the

elevation of the profile (the intra-individual mean of the profile) but not in the shape (the intra-

individual between-variable differences) or the scatter of the profile (the intra-individual


standard deviation).

It is tempting to use a Q-correlation (the within-person Pearson correlation) in order to

describe the similarity of profiles. However, the Q-correlation is not an adequate measure of

profile similarity in this case because it ignores both elevation and scatter and would therefore

treat Student 1 and Student 2 as identical (see Fig. 2b). A more adequate measure of profile

similarity is the Euclidean distance (a measure of dissimilarity rather than similarity). It is zero if

the profiles are identical and becomes larger to the extent that the profiles differ in elevation,

scatter, or shape. Using Pearson correlations or ipsatized scores (standardizing the scores within

persons, e. g. by applying a within-person z-transformation such that the profile elevation equals

zero and the profile scatter equals one) are inadequate methods for personality profiles based on

absolute scales unless profile differences in elevation and scatter are identical for all persons.

For the same reason, Q-factor analysis (also called inverted factor analysis) is an

inadequate method of deriving prototypic personality profiles that describe personality types if

the profiles are based on absolute measures. Q-factor analysis is identical to a traditional R-factor

analysis except that it is applied to the transposed person by variable matrix. Whereas in

traditional factor analysis, factors can be interpreted as new variables (a weighted sum of the

input variables where the weights are the factor loadings), in Q-factor analysis of personality

profiles, the factors can be interpreted as new profiles and the factor loadings as the similarity of

each person's profile with the new profiles. It is again tempting to interpret Q-factors as

prototypic profiles characterizing a personality type, and to group persons into personality types

according to their most similar prototype, but again this is inadequate for profiles based on

absolute measures because factor analysis is based on the Pearson correlation as a measure of

similarity, so the above arguments apply also to Q-factor analysis.


Instead, cluster analysis based on the Euclidean distance of profiles is often used because

it groups profiles into clusters of similar profiles, minimizing the dissimilarities within clusters

and maximizing the dissimilarities between clusters. However, cluster analysis has been

criticized because it is merely descriptive and always yields clusters, even in cases where the

cluster differences are so small that they reflect only random differences due to the specific

sample. Therefore, replicated cluster analysis in which the final clusters are shown to be

replicable across random subsamples has been used to alleviate this problem (e.g., Asendorpf,

Borkenau, Ostendorf, & van Aken, 2001; Caspi & Silva, 1995). Today, more advanced methods

such as latent class analysis (LCA) should be used. LCA is a confirmatory version of cluster

analysis that offers fit indices and statistics to determine the number of classes and assigns class

membership by a procedure that takes uncertainty of membership into account (Fraley &

Raftery, 2002). Monte Carlo studies have shown that LCA is superior to traditional cluster

analysis (e. g., Reinke, Herman, Petras, & Ialongo, 2008).

Personality Profiles Based on Standardized Variables

In most studies of personality profiles, attributes are measured on relative scales that assess

individual deviations from the mean of a population. Sometimes this is facilitated by instruction

when participants are asked to compare target individuals with age-mates, same-sex age-mates,

and so on, and sometimes this is explicitly the case because the profiles are based on

standardized variables such as z-transformed scores (M = 0, SD = 1), T-scores (M = 50, SD = 10)

or IQ-scores (M = 100, SD = 15).

The problems of comparing intra-individual differences and measuring profile similarity

that were discussed in the preceding section apply to relative scales as well. Consider a study of

personality based on the Big Five personality traits Openness, Conscientiousness, Extraversion,


Agreeableness and Neuroticism (OCEAN). Because high scores in OCEA are desirable but high

scores in N are undesirable, the elevation of an OCEAN profile has no clear psychological

meaning. However, if neuroticism scores are inverted such that they assess emotional stability,

profile elevation can be interpreted as the overall desirability of the profile (due to real

desirability and/or response biases), but the scatter of the profiles may still vary across

individuals.

In both cases, ipsatizing scores or using the Pearson correlation as a measure of similarity

is inappropriate because dissimilar profiles are sometimes treated as similar. This problem is

often not acknowledged when authors use ipsatizing in order to control for social desirability

biases (e. g., McCrae, Herbst, & Costa, 2001; Soto, John, Gosling, & Potter, 2008). To the extent

that the profile mean contains information about the real desirability of personality, ipsatizing

leads to distorted findings.

I highlight this point here because some authors seem to believe that ipsatized scores are

a step toward a person-centered analysis because person's scores are ranked according to their

salience within each person. But ipsatizing may ignore psychologically meaningful between-

person information and in this case distorts the description of unique personality. Person-

centered does not mean, or should not mean, that one can ignore all between-person differences.

The bottom line is that ipsatizing makes no sense from a person-centered point of view, and Q-

correlations and Q-factor analysis should only be applied to person by variable data when all

profiles have identical means and standard deviations due to the method of assessment. The Q-

sort method (discussed in the next section) is one such method.

Q-Sort Profiles

The Q-sort method (Stephenson, 1953) was discussed in detail for the assessment of personality


by J. Block (1961, 2008). Each individual is described by a knowledgeable informant who sorts

attributes such as trait descriptions according to how well they fit the individual's personality.

The resulting Q-sort describes the relative salience of the attributes for that individual and thus a

person-centered personality profile. Based on the assumption that salience is a purely within-

person concept in this method of assessment (see however a note of caution later in this chapter),

the elevation and scatter of the Q-sort profiles have no meaning and thus can be standardized by

instructing the Q-sort judges to assign for each individual the same number of attributes to each

category of salience (equal within-person distribution), or to produce another fixed within-person

distribution. For example, in the California Child Q-set (CCQ; J. H. Block & J. Block, 1980),

100 brief descriptions of traits on which children may vary are sorted into categories of

increasing salience such that each category contains the same number of traits. Note that such an

equal distribution maximizes the within-individual variance which is desirable from a person-

centered perspective because it leads to maximally differentiated personality descriptions. A

similar Q-sort is available for adults (California Adult Q-set CAQ; J. Block, 1961, 2008).

Another example is the Riverside Behavioral Q-set (RBQ; Funder, Furr, & Colvin, 2000)

where 67 descriptions of behavioral traits are sorted into nine categories of increasing salience

such that a quasi-normal distribution results (increasingly more traits toward the center of the

distribution). The downside of forced distributions is that raters are forced to follow a prescribed

distributional form that requires a sound justification by the researcher. In the case of Q-sorts

with forced distributions, Q-correlations are an adequate measure of profile similarity, and Q-

factor analysis can be applied to search for sets of relatively independent prototypical profiles

that together efficiently describe the inter-individual variation of these patterns (J. Block, 1961).

In the Q-sort method, the responsibility for justifying ipsatization is shifted from data analysis to


data assessment - but it remains a responsibility in any case.

Applications of the Q-method to personality Q-sorts typically yield three main Q-factors

that can be replicated within samples, across ages, across different types of judges, and across

different languages (Asendorpf & van Aken, 1999; Hart, Hofmann, Edelstein, & Keller, 1997;

Robins, John, Caspi, Moffitt, & Stouthamer-Loeber, 1996). The first Q-factor represents the

most frequently occurring personality profile. It is therefore strongly related to the average

profile in the sample and to the profile that results when judges are asked to describe a desirable

personality. The next two Q-factors describe less frequently occurring profiles and are

uncorrelated with already-extracted factors by definition.

Robins et al. (1996) related the three Q-factors to the theory of ego-control and ego-

resiliency by J. H. Block and J. Block (1980). In their variable-centered model of personality in

childhood, ego-resiliency refers to the tendency to respond flexibly rather than rigidly to

changing situational demands, particularly stressful situations. Ego-control refers to the tendency

to contain versus express emotional and motivational impulses (overcontrol vs. undercontrol). J.

H. Block and J. Block (1980) operationalized children's ego-control and ego-resiliency by

prototypicality scores (the Q-correlation between an individual child's Q-sort profile and

prototypic profiles for an ego-undercontrolled child and an ego- resilient child). Note that these

continuous measures of ego-control and ego-resiliency are not based on Q-factor analysis, but on

theoretical considerations.

The Q-sort method is designed to assess personality from a person-centered perspective,

and therefore caution should be exercised if variable-centered methods are applied to Q-sort data.

Somewhat problematic are variable-centered analyses using R-correlations or R-factor analyses

if only a few attributes are sorted according to a fixed distribution because the saliencies are


intrinsically negatively correlated. However, for large Q-sets such as the CAQ or the CCQ, these

correlations are negligibly small such that R-correlations and R-factor analysis can be applied to

Q-sort data (see, e. g., McCrae, Costa, & Bush, 1986; Asendorpf & van Aken, 2003).

Really problematic however are person-centered analyses via Q-correlations or Q-factor

analyses based on inter-individually standardized saliency scores (see McCrae, Terracciano,

Costa, & Ozer, 2006, for an example). Standardizing the saliencies for each Q-sort item

transforms the saliency scores into relative saliencies compared to other individuals of the

sample, which implies that the elevation and scatter of the personality profiles are not identical

anymore across individuals. Application of Q-correlations or Q-factor analysis therefore biases

the results if these individual means and standard deviations are psychologically meaningful. For

example, a person with extremely deviant scores for a set of traits is not distinguished from

another person that shows the same deviations but only to a small degree (see Asendorpf, 2006a,

for a detailed discussion).

Personality Types: Q-Types and Clusters

The idea that persons can be grouped into discrete types according to their personality dates back

to ancient Greece where Hippocrates (460 - 377 BC) distinguished temperamental types

according to an assumed dominance of bodily humors (e. g., melancholic temperament due to a

dominance of black bile), and Theophrastus (371 - 287 BC) described 30 types of characters

mainly in moral terms. More importantly, discrete types characterize everyday reasoning about

personality, prescientific classifications such as the Myers-Briggs types as well as DSM-IV

classifications of personality disorders. In empirical personality psychology, discrete personality

types are sometimes forced on the data (e.g., when plotting an interaction effect).

Such classifications have been used and defended mainly in terms of their convenience


for communication with lay people, clinicians, personnel managers, lawyers, or teachers who

seem to use the same person-centered concept both in their daily and professional life. Criticism

of the discrete type concept concerns mainly the strong information reduction of continuous

univariate or multivariate distributions to a few discrete classes. Although continuous

distributions of inter-individual differences often deviate from normality, multimodal

distributions that would offer visible clues as to where cut points between types should be set are

very rare, and multimodality in multivariate distributions is difficult to detect (Meehl, 1992).

Instead, discrete personality types are empirically derived mainly with two different methods.

For Q-sort data with fixed profile elevation and scatter, Q-types are identified on the basis of

prototypic Q-sort profiles. For profiles that vary across individuals in elevation or scatter, cluster

analysis or latent cluster analysis (LCA) is used.

Q-Types are derived in two steps. First, a small set of rather independent prototypic Q-

sort profiles is identified, either by Q-factor analysis where each factor describes a prototypic Q-

sort profile (see preceding section), or by asking experts to provide prototypic Q-sorts for certain

types of personality. For example, in the case of children, teachers might be asked to describe a

desirable child, an undercontrolled child, or an overcontrolled child (J. H. Block & J. Block,

1980; Asendorpf, 1991). These prototypic Q-sort profiles describe Q-types: groups of individuals

with a similar Q-sort profile. Second, each member of a sample of persons is assigned to the

most similar prototypic Q-sort profile.

Such Q-types were mainly derived from child or adult versions of the California Q-Set.

For example, Block (1971) used the 100-item California Adult Q-Set, Robins et al. (1996) the

100-item California Child Q-Set (CCQ), and Asendorpf and van Aken (1999) a 54-item German

short version of the CCQ. The number of Q-factors can be determined by the replicability of the


factors if the sample is split into two random halves. Using this criterion, Robins et al. (1996),

Hart et al. (1997), and Asendorpf and van Aken (1999) found that three, but not more, Q-factors

were replicable in their studies of children.

Robins et al. (1996) confirmed the theoretically expected result that the type based on the

first Q-factor had higher ego-resiliency scores than the types based on the second and third Q-

factor, and that the types based on the second and third Q-factor were characterized by high ego-

overcontrol and high ego-undercontrol, respectively (see Fig. 3). This pattern was also confirmed

by Asendorpf and van Aken (1999).

- Fig. 3 -

Early studies based on cluster analysis also revealed three similar personality types

among both children and adults. Caspi and Silva (1995) obtained 22 behavioral ratings from

examiners who observed a full birth cohort of Dunedin, New Zealand, at age three in various

testing situations. These ratings were reduced by factor analysis to three factors, and the resulting

profiles of factor scores were clustered. van Lieshout, Haselager, Riksen-Walraven, and van

Aken (1995) clustered Big Five scales that were derived from the California Child Q-Set, and

Asendorpf et al. (2001) clustered parental ratings of 12-year-olds on Big Five scales as well as

self-ratings of adults on two different Big Five questionnaires.

Because of the striking similarity of the early findings for both Q-types and cluster-based

types independent of the type of judge, age of the person sample, and other specifics of the many

studies, Caspi (1998) proposed that the three types may constitute a core set of types for any

generalizable personality typology. Asendorpf et al. (2001) confirmed this conjecture in four

studies based on both children and adults and on both Q-types and cluster analysis. Figure 4

presents the average Big Five profiles of the three types across these four studies. Resilients were


characterized by desirable scores (low N, above-average OCEA), overcontrollers by high N and

low E, and undercontrollers by low AC. Because the three types were initially confirmed by

Caspi and Silva (1995), Robins et al. (1996), and Asendorpf et al. (2001), they are sometimes

called the ARC (Asendorpf-Robins-Caspi) types (Chapman & Goldberg, 2011; Costa, Herbst,

McCrae, Samuels, & Ozer, 2002). Perhaps more appropriate is the label RUO types (resilient,

undercontrolled, overcontrolled types; Denissen, Asendorpf, & van Aken, 2007).

- Fig. 4 -

Asendorpf, Caspi, and Hofstee (2002) attempted to confirm the three types once more

across different ages, judges, and languages by inviting various colleagues to analyze non-

clinical data sets based on similar Big Five questionnaires with the clustering method used by

Asendorpf et al. (2001). The results only partially confirmed the RUO types. Only four of the

seven samples reproduced them. Nevertheless, the RUO types have been replicated in so many

studies over the last decade (see Chapman & Goldberg, 2011; Meeus, Van de Schoot, Klimstra,

& Branje, 2011, for recent examples) that it can be concluded that the RUO types can be

recovered from most personality data sets if one wants to confirm them, although the number and

psychological meaning of the types derived from any specific sample does vary across samples

due to unique characteristics in terms of sampling, instruments, ages, and judges.

The RUO types show substantial validity particularly in terms of long-term real-life

outcomes (e.g., Asendorpf & Denissen, 2006; Caspi, Moffitt, Newman, & Silva, 1996; Chapman

& Goldberg, 2011; Denissen et al. (2007); Hart, Atkins, Fegley, 2003; Meeus et al., 2011). In a

nutshell, resilients show desirable traits, overcontrollers show internalizing tendencies such as

low social self-esteem, high anxieties, and depressive tendencies, and undercontrollers show

externalizing tendencies such as high aggressiveness and antisocial behavior.


Costa et al. (2002) challenged these findings by suggesting that the validity of the

variables on which the types were based (e. g., the Big Five) is higher than the validity of the

differences between the types. Asendorpf (2003) pointed out some methodological problems in

Costa et al.'s (2002) approach but nevertheless agreed on the basis of his reanalyses of RUO

types that discrete types rarely show incremental validity over variables on which they are based,

whereas the opposite is more often true. Chapman and Goldberg (2011) compared Big Five

variables and RUO type predictors in terms of the Bayesian information criteria (BIC) that

penalizes additional predictors. They found that the RUO types slightly outperformed the Big

Five variables for six of seven midlife health outcomes predicted from childhood, whereas the

opposite was true for probabilities of classification that did not penalize additional predictors.

Thus, both the RUO types and the variables on which they are based capture important

personality information for real-life outcomes.

However, it is important to notice that the person-centered approach is not confined to the

study of discrete types. Asendorpf and van Aken (1999) studied continuous prototypicality

scores for Q-types (the extent to which a Q-sort profile is similar to the prototypic profile

describing a Q-type), and Asendorpf (2006b) and Chapman and Goldberg (2011) studied

continuous prototypicality scores for RUO types based on the Big Five. The advantage of such

approaches is that the between-profile information is better preserved than in discrete typologies,

which often results in somewhat higher validities. The disadvantage is that the ease with which

researchers are able to communicate their person-centered results to a broader audience is lost.

Thus, discrete types and continuous prototypicality scores are both viable approaches to

personality differences, with different (dis)advantages.

Individual Development from a Person-Centered Perspective


Until this point, the discussion has focused on only one part of Allport's (1937) definition of

personality, namely the organizational nature of the personality system at any point in time. In

order to characterize a person's individuality (and not a fleeting pattern of fluctuating states), this

organization should show a high degree of stability over short time periods such as days or

weeks. Of course, this does not exclude the possibility of slower, long-term changes in

personality.

Allport (1961) (more than Allport, 1937, and Stern, 1911) discussed long-term changes in

personality only in theoretical terms. Early longitudinal studies of personality differences chose a

variable-centered perspective that focused on the long-term stability of inter-individual

differences or the prediction of later life outcomes from inter-individual differences at earlier

ages. This approach still dominates the literature on personality development, yet it misses two

key points about personality development.

First, this approach misses the very concept of personality: There is no personality pattern

in development that changes, only a trait score that changes. Second, and perhaps less obvious to

personality psychologists, the focus on stability treats instability as an error, not as an interesting

source of data on change. From a stability point of view, it is irrelevant whether an observed trait

score at a later age is higher or lower than the score expected when stability is assumed. But

development is inherently directed change, and it is of course important, for example, whether a

child's IQ test score increases or decreases with increasing age.

J. Block (1971) in his seminal study on personality development was aware of both

limitations in the earlier stability-oriented studies. He used for the first time the terms "variable-

centered" and "person-centered" to contrast his person-centered approach from the earlier

variable-centered longitudinal studies (J. Block, 1971, p.12-13) and took personality seriously by


using Q-sort profiles for the study of long-term personality change. Also, he distinguished

between seven different types of continuity and change in the salience of single Q-sort items, and

factored courses of personality development by a Q-factor analysis of the individual profiles

across a repeated Q-sort (adding the Q-sort saliencies of the second assessment to those of the

first assessment). Elevation and scatter of these profiles are constant across participants, so that

this requirement for Q-factor analysis was met although the method did not distinguish between

within-person differences at a particular time point and within-person changes over time.

Nevertheless, this study was important because it suggested that individual continuity and

change in personality patterns might be captured by studying types of individual developmental

trajectories.

Magnusson (1988) and Morizon and Le Blanc (1995) followed this idea although they

used cluster analysis in order to identify types of individual developmental pathways (see

Bergman, 2000, for a discussion of this approach). Alternatively, configural frequency analysis

(CFA) can be used to analyze patterns of change (see, e. g., von Eye & Bergman, 2003).

Recently, Cattell's P-factor approach where individual trajectories in multiple variables

are factored within each individual has seen a revival (see Borkenau & Ostendorf, 1998, for an

application to Big Five data). Molenaar, Sinclair, Rovine, Ram, and Corneal (2009) presented a

multivariate non-stationary time series model for the study of interrelated change within an

individual that can be considered an extension of the P-factor approach. Although research on

modeling non-stationary time series flourishes among methodologists today, it is seriously

limited for the analysis of personality development because these models require many time

points to begin with, a condition rarely met in studies of personality development.

Instead of searching for types of individual trajectories, a more natural approach for


personality psychologists is studying the stability and change of personality profiles and

personality types. Discrete types can be separately derived for each age using cluster analysis or

Q-factor analysis, and subsequently related to each other across time in terms of the similarity of

the prototypic profiles or the cross-age agreement of individuals' classification (e.g., Asendorpf

& van Aken, 1991, 1999; Hart et al., 2003; Meeus et al., 2011).

Caution must be exercised when personality profiles are compared across ages using Q-

correlations or Euclidean distances because the stability of the profile is partly due to a particular

version of stereotype accuracy (Cronbach, 1955) that can be called stereotype stability. That is,

the stability of personality profiles is partly due to the continuity of the average profile and thus

inflated as far as the stability of the unique personality profile is concerned.

More subtle is a second bias that is based on the regression to the mean effect. Consider a

normally distributed variable measured with some error. Scores close to the mean are more likely

than scores far away from the mean such that the probability for a change toward the mean is a

priori higher than for a change away from the mean if measurement error is not highly correlated

between the assessments.

Consequently, profiles based on variables that show the regression to the mean effect will

accumulate all these effects, resulting in a tendency to move toward the average profile over

time; this tendency may be called the regression to the mean profile effect. In Q-sort studies, the

mean profile is strongly correlated with the first Q-factor and with the prototypicality for the

resilient type. Therefore, due to measurement error alone individuals with unlikely, undesirable

or non-resilient profiles tend to change to a more average, desirable, and resilient personality.

For example, Asendorpf and van Aken (1991) studied Q-sort profiles between the ages of

4 and 6 years. They found a moderately high stability for the individual Q-sort profiles (median


Q-correlation .43, interquartile range .24 - .58) and a surprisingly high correlation of .57 between

the individual stability of the profile (the child's Q-correlation between ages 4 and 6) and the

child's ego-resiliency at age 4. Thus, resilient children did not change much whereas non-

resilient children changed more. Part of this correlation may be due to the regression to the mean

profile effect based on measurement error. However, in a second sample studied between ages 10

and 12 with Q-sort judgments from teachers and mothers they found substantial correlations

between resiliency as determined by one type of judge and stability as determined by the other

type of judge, which implies that this relation cannot be explained only by a regression to the

mean profile effect based on measurement error.

In terms of the prototypical profiles of the three Q-factors, Asendorpf and van Aken

(1999) found a high continuity between 4-6 years (average of 3 Q-sorts at ages 4, 5, and 6 years)

and 10 years, ranging from .78 to .88. This is a high continuity particularly because the early

personality was judged by teachers, but the age 10 personality was judged by parents. In contrast,

the individual prototypicalities for the three Q-factors (the Q-factor loadings) showed only a low

to moderate stability between 4-6 and 10 years, ranging from .22 to .44, and the dummy-coded

type membership showed a similarly low stability of .30 for all three Q-types. Asendorpf and van

Aken (1999) concluded that the prototypic profiles describing personality types were highly

continuous over childhood, supporting a high continuity of the personality types, but that many

children changed their membership for the types.

This conclusion may have been premature because a recent longitudinal study of two

large Dutch cohorts showed that membership for the RUO types based on self-rated Big Five

profiles was quite stable over a 4-year-period including adolescence (74% of the participants

were identically classified across all 5 assessments) if the prototypical profiles defining the types


are forced to be identical across age and latent class analysis is used to identify the profiles

(Meeus et al., 2011). Forcing the types to be identical in terms of their profile does not seem to

be a problem because not much discontinuity can be expected for an age interval of only four

years. The main reason for the much higher stability, compared to the stability found by

Asendorpf and van Aken (1999), seems to be that the types were identically defined across age

such that small discontinuities of the types could not affect stability of type membership; also,

the type of judge was identical in this study whereas different types of judges were involved in

Asendorpf and van Aken's (1999) study.

Forty years after Block (1971), the important study by Meeus et al. (2011) shows for the

first time convincingly that personality types, if adequately defined and measured, show high

stability over many years. Future studies should attempt to replicate this finding for different

types of judges and age groups such that the person-centered view on personality stability

becomes more commensurate with the variable-centered finding of moderate to high stability of

single traits (Roberts & del Vecchio, 2000).

In addition, Meeus et al. (2011) studied directed change in the unstable participants, using

latent transition analysis, a longitudinal extension of latent class analysis. They confirmed the

expectation that transitions between the RUO types were not randomly distributed but that the

two non-resilient types tended to become resilient rather than the opposite non-resilient type. For

example, 21% of the initial overcontrollers became resilient, but only 3% became

undercontrollers four years later. This finding can be partly attributed to the regression to the

mean profile effect because the resilient type is closer to the average profile than the two non-

resilient profiles. However, the asymmetric transition pattern favoring the resilient type was

stronger over 4 years than over 1 year, which supports interpretations in terms of real personality


change.

Idiographic and Nomothetic Approaches to Personality

Stern (1911) related his two perspectives on the attribute by individual matrix to the distinction

between nomothetic and idiographic sciences made earlier by the German philosopher and

historian Windelband (1894). With that distinction, Windelband wanted to stress the fact that

natural sciences such as physics focus mainly on general laws (Greek: nomos), whereas

humanities such as history focus mainly on uniqueness as an outcome of a historical process and

therefore need different methods such as biography.

Stern (1911) believed that idiographic methods can be applied in science and that

nomothetic methods can be applied in the humanities, and that an idiographic approach focusing

on a unique person might be fruitfully combined with general laws from nomothetic inquiries. In

that respect, Allport (1937) overly simplified matters with his equation of idiographic with

"history, art, or biography", and nomothetic with "science", setting the stage for an unfortunate

dichotomy that survives until today in North-American psychology. This simplification is

surprising because Allport must have been fully aware of Stern's view.

Although Stern (1911) was aware that nomothetic methods can be applied to single case

studies (he was a student of Ebbinghaus who formulated quantitative laws of forgetting based on

his own memory), he believed that such approaches are not sufficient for capturing a person's

individuality (individual personality). They may become close to it but will never fully capture

its uniqueness: "...individuality [is] the asymptote of law-searching science" (Stern, 1911, p. 4,

own translation).

Allport (1961) later took up this view in his description of the limits of Stern's (1911)

discipline of psychography based on profiles: "A profile brings us near, but not very near, to our


goal of individuality" because "a profile tells us nothing about the organization of the qualities in

question.” Therefore, psychography is a "halfway approach to individuality" (Allport, 1961, p.

16). Another issue for Allport (1937, 1961) was that profiles are based on "common traits" that

can be used to describe every person. Allport (1937) proposed that this is not necessarily true

because the "individual relevance" of such traits varies across persons. Even if personality

description is restricted to the relevant traits from a common set of traits, it may miss the most

characteristic "personal dispositions" or "foci of organization" that best capture a person's

uniqueness.

However, Allport never proposed a method for operationalizing personal dispositions; it

was Kelly (1953) who first proposed such an operationalization by the repertory grid test. More

importantly, the whole discussion of individuality (including the more recent discussion by

Lamiell, 2003) seems a bit misplaced from a broader perspective on science. For astrophysics,

the uniqueness of the earth's moon is not a methodological problem. There are stars, there are

moons of stars, and the earth's moon is a unique entity, but this is not a motivation for

astrophysics to adopt methods from history for the study of the moon.

According to Nobel Prize winner Gell-Mann (1994) who searches for general laws of

complex systems, everything in this world is the result of "simple rules" and "frozen accidents".

The simple rules are formulated by nomothetic inquiry that can be used to approach the single

case asymptotically, but because of the frozen accidents, an unexplained residual remains as part

of measurement error.

From that perspective, there is nothing mystical about individuality. In single case

studies, nomothetic laws can be formulated and statistically evaluated at the level of individuals

(see Simonton, 1998, for a nice example of the lagged influence of personal and political stress


on the mental and physical health of British King George III). Different individuals can then be

compared in terms of parameters in these laws (e. g., the strength of the influence of stress on

health for different time lags), or grouped into discrete types. In this case, between-person

comparisons are based on rich idiographic information that is generated "bottom-up".

Alternatively, personality profiles can be based "top-down" on rich nomothetic

information, and subsequently sorted into clusters of similar profiles. An example would be

clustering profiles of intelligence subtest scores where each score is standardized in terms of an

IQ metric (M = 100, SD = 15). In order to derive the IQ scores of one individual, the distribution

of subtest results for a large normative sample of age-mates is required. But should we consider

such an intelligence profile idiographic? Perhaps yes, because it describes a single person that

may even show a unique profile that is different from all other profiles of age-mates of the same

culture. Perhaps no, because the profile is based on nomothetic knowledge about the

distributions of the test results in all subtests.

A closer look at the Q-sort methodology also reveals problems with the common

assumption that Q-sorts are idiographic descriptions. If a judge is asked to assign traits to

categories of salience for one individual, how does the judge accomplish this task? How will

Jack decide whether Jeanne is more anxious than aggressive? Jack will decide this by an implicit

rule that Jeanne is more anxious than aggressive if Jeanne's anxiety relative to her age-mates is

higher than her aggressiveness relative to her age-mates. Will Jack use sex-specific norms for

this decision? Probably Jack Block would have done this, but perhaps not every Jack would. All

these considerations require at least implicit nomothetic knowledge about the distribution of

anxiety and aggressiveness among (female) age-mates. From this perspective, Q-sorts are

ultimately both idiographic and nomothetic, just as intelligence profiles based on IQ scores.


A similar argument applies to "absolute ratings" in terms of frequency or intensity, such

as the ratings of interpersonal conflict on which the profiles depicted in Figure 2 are based.

When students are asked to provide ratings of interpersonal conflict, they may use knowledge

about intra-individual differences in their conflict with different interaction partners but also

knowledge about inter-individual differences in conflict frequency or intensity for their ratings

for each type of interaction partner. Here too, intra-individual differences are somewhat

confounded with inter-individual differences.

At this point, the distinction between idiographic and nomothetic approaches to

personality becomes questionable. Whereas person- and variable-centered can be clearly defined

in terms of the unit of analysis in a particular step of an often multi-step analysis of personality

patterns, idiographic and nomothetic remain rather fuzzy concepts.

A Multilevel Perspective on Personality

Beyond distinguishing person- and variable-centered approaches, and beyond acknowledging

that both can be fruitfully combined in the personality psychology enterprise, many (but

probably not all) research questions about personality can be studied within a framework that

systematically combines both person- and variable-centered approaches to the same data. Such a

framework is a multilevel approach to personality data (see West, Ryu, Kwok, & Cham, 2011).

This approach is increasingly used particularly in longitudinal studies of personality change and

in diary studies of emotional states and social behavior although its potential for the person-

centered perspective has been rarely fully recognized.

The approach is based on a multilevel regression model (see e. g., Hox, 2010). I focus

here on the conceptual advantages of multilevel models, ignoring many other advantages in

terms of parameter estimation and flexibility in dealing with missing data. At level 1 of the


analysis, the intra-individual variation of an individual outcome is predicted by one or multiple

intra-individual predictors. For a concrete application, consider a 10-year longitudinal study with

yearly assessments and a statistical model in which the life satisfaction of each individual at each

assessment is predicted by duration of participation in the study measured in years such that year

is zero at the first assessment and 10 at the last assessment. The unstandardized regression

coefficient bj of each individual j describes, from a person-centered point of view, the linear

change of the individual's life satisfaction. If we assume more complex changes, we can simply

add nonlinear functions of year such as year squared which would predict quadratic change over

and above linear change:

LEVEL 1: life satisfactionj = b0j + b1j·year + b2j·year² + errorj

The intercept b0j is the estimated life satisfaction at the first assessment, b1j is the

estimated increase in life satisfaction over one year, b2j is the estimated quadratic change over

one year, and errorj is the individual deviation from the predicted life satisfaction at each

assessment. Note that these coefficients describe the change of a particular individual whereas

the model for individual change (linear plus quadratic components of change) is constant for all

individuals. This already provides rich information on individual change ("idiographic"

information based on a "nomothetic" model of change).

In a second step, we can then predict these individual parameters by other individual

characteristics that are assumed to be constant over the course of the study such as AGE at the

first assessment and NEUROTICISM (written in capital letters in order to distinguish them from

the level 1 variables). This approach results in three regression equations at level 2 of the model

where each individual parameter is predicted by constant individual characteristics:

LEVEL 2: b0j = b00 + b01·AGEj + b02·NEUROTICISMj + ERROR0j


b1j = b10 + b11·AGEj + b12·NEUROTICISMj + ERROR1j

b2j = b20 + b21·AGEj + b22·NEUROTICISMj + ERROR2j

Note that year at level 1 captures intra-individual age-related changes whereas AGE at level 2

captures inter-individual age differences at baseline, and that error at level 1 captures residual

error of within-person predictions whereas ERROR at level 2 captures residual error of between-

person predictions. The coefficients at level 2 can be interpreted as follows:

b00 = average life satisfaction across all assessments of all individuals

b10 = average linear change of life satisfaction in the sample over age

b20 = average quadratic change of life satisfaction in the sample over age

b01 = dependency of average life satisfaction on AGE at the first assessment

b11 = dependency of linear change of average life satisfaction on AGE at the first assessment

b21 = dependency of quadratic change of average life satisfaction on AGE at the first assessment

b02 = dependency of average life satisfaction on NEUROTICISM

etc.

Together, this two-level regression model describes both intra-individual change from a

person-centered perspective at level 1 and inter-individual differences in intra-individual change

at level 2 from a variable-centered perspective. The regression coefficients at level 2 can be

interpreted as moderator effects: Constant individual characteristics moderate intra-individual

changes. However, these are cross-level interactions, not ordinary within-level interactions.

Whereas the multilevel approach discussed so far explicitly models time, a second

approach does this implicitly by predicting at level 1 intra-individual change in an outcome by

intra-individual change in another individual characteristic. For example, we can drop year² in

the first model and replace year by repeated assessments of neuroticism centered at the


individual mean across all assessments (in this case neuroticism is written with small letters

because it is a level 1 predictor). The coefficient b0j informs us about the typical level of life

satisfaction based on the typical level of the individual's neuroticism whereas b1j informs us

whether an intra-individual change in neuroticism is accompanied by an intra-individual change

in life satisfaction for this individual.

Very similar is the approach of treating repeated assessments of neuroticism as the

outcome and life satisfaction as the predictor. In this case, we would predict personality change

based on changes in life satisfaction, which is similarly plausible. Finally, a multivariate

extension of the traditional univariate multilevel model (see Hox, 2010, chapter 10) allows for

modeling different subtraits, or facets, of the same outcome trait. For example, changes in

different facets of neuroticism could be predicted from changes in life satisfaction.

Although I am not aware of applications of the multivariate multilevel approach to

personality research, the multilevel approach provides interesting options for combining person-

and variable-centered approaches to personality change and its moderation by constant inter-

individual differences. This approach takes Allport's (1937) definition of personality as "the

dynamic organization within the individual" seriously.

As a note of caution, I should add that multilevel regression models rely on linear

functions of within-person differences. Therefore these linear models cannot readily be applied

to questions concerning the similarity of personality patterns based on Euclidean distances (the

discussion of the dangers of within-person standardization applies here again). In any case, I

expect that multilevel models will greatly expand the scope of personality research in the next

decade.

Conclusion


Whereas Stern (1911) and Cattell (1946) offered models of intra- and inter-individual differences

that allow for an analysis of the same multivariate personality data from both a person- and a

variable-centered perspective, empirical personality research has been dominated by a variable-

centered view, and person-centered approaches have been mainly offered as an alternative rather

than a complimentary perspective on personality, both in terms of assessment (Q-sort) and in

terms of analysis (Q-factor analysis). Also, the person-centered perspective has often been

identified with the study of discrete personality types (Q-types, clusters of profiles) although the

person-centered approach also offers methods of studying personality patterns based on

continuous information such as prototypicality scores for discrete types, studying the consistency

of personality patterns across time, situations and judges, or fitting within-person regression

models of behavior or developmental change. Many of these approaches allow for the

simultaneous analysis of both person- and variable-centered questions within a multilevel

framework, overcoming the traditional dichotomy between these two perspectives.


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Author notes

I am indebted to Jack Block, David Magnusson, Lars Bergman, Avshalom Caspi, Marcel van

Aken, Ivan Mervielde, David Funder, Dan Ozer, and Jaap Denissen for many fruitful discussions

about person-centered approaches to personality. This chapter is in memoriam of Jack Block

who for a long time was a lonely swimmer against the mainstream of exclusively variable-

centered approaches to personality.


Table 1

Terms Used in Person-centered Approaches and Their Definition

Term Definition

idiographic research approach focused on understanding individuality (the unique

personality of an individual and its development)a

nomothetic research approach focused on general laws (e.g., the correlation between

two traits in a population)

person-centered research approach focusing on individuals who are described by many

data points (e.g., by many personality traits, in many situations, at many

different ages)

variable-centered research approach focusing on variables that distinguish individuals

with regard to 1 personality trait

Euclidean distance measure of profile similarity (square root of the sum of the squared

differences between the traits); it is sensitive to differences across

persons in the mean (elevation) and standard deviation (scatter) of the

profiles

individual consistency similarity of the standardized scores of 2 traits for 1 individual

ipsatized scores scores that are standardized within a person (e.g., within-person

z-scores)

personality profile the unique pattern of trait scores that describe 1 individual

personality type group of individuals sharing a similar personality profile

prototypic profile profile describing a personality type, can be average profile of all

members of the type, theory-derived by experts, or by Q-sort procedures

Q-correlation correlation between 2 variables that assess intra-individual differences

(e.g., between two personality profiles or 2 developmental trajectories

of 1 individual); it is not sensitive to differences across persons in the

mean and standard deviation of profiles or trajectories

Q-factor analysis factor analysis based on Q-correlations; factor loadings are Q-

correlations between individual profiles and a Q-factor

Q-factor factor resulting from a Q-factor analysis (e.g., describing a prototypic

profile or a prototypic developmental trajectory)

Q-type group of individuals with high factor loadings on a Q-factor

stereotype accuracy/

stability

similarity/stability of personality profiles based only on the

similarity/stability of the mean profiles in a population

Q-sort assessment method where 1 individual is described by sorting many

traits according to their salience for this individual

Q-sort profile personality profile of 1 individual resulting from a Q-sort

Q-sort prototype,

prototypic Q-sort,

Q-sort template

Q-sort profile describing a personality type, either derived from theory

by experts or sorted by informants (e.g., teachers describing a typical

resilient child) a from Greek "idios"; the widespread usage of "ideographic" instead of "idiographic" in the

personality literature is incorrect.


Figure captions

Figure 1. Four disciplines of differential psychology (adapted from Stern, 1911, p. 18). Note that

– contrary to today's convention for statistical software – rows represent attributes and columns

represent individuals.

Figure 2. Profiles of conflict reported by one student on odd and even days in a diary (Panel A)

and profiles of conflict of two students differing in elevation but not shape or scatter (Panel B).

Figure 3. Ego-resilient, overcontrolled, and undercontrolled personality types as a function of

ego-control and ego-resiliency.

Figure 4. Average Big Five prototypic profiles for personality types based on four studies in

childhood and adulthood (adapted from Asendorpf et al., 2001, Fig. 2E). N = Neuroticism, E =

Extraversion, O = Openness, A = Agreeableness, C = Conscientiousness.


Fig.1


Fig.2

Panel A

Panel B


Fig.3


Fig 4.

PANEL E: AGGREGATE

Undercontrolled

Overcontrolled

Resilient

Z S

core

1.0

.5

0.0

-.5

-1.0

N

E

O

A

C

Documents

Person-centered approaches 1 Person-centered Approaches to Personality Jens B. Asendorpf