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The vast majority of vocational research adopts a variable-centered approach. Implicit in this approach is the assumption that the population under study is homogeneous, and that therefore we can use a set of “averaged” parameters to describe it. Person-centered methods are a family of methods that relax this assumption of population homogeneity, viewing the individual as holistic and paying more attention to how specific configurations of variables, present in different subgroups of the population, act in concert to shape behavior. Despite the potential advantages of person-centered research, the adoption of this approach by vocational researchers has been relatively slow for both conceptual (e.g., What exactly is person-centered research?) and methodological (e.g., Which methods?) reasons. In response to these issues, the goal of the present article is to showcase the role and relevance of person-centered methods for vocational research. Having discussed different conceptualizations of the term “person-centered” we present a structured overview of the most relevant person-centered techniques. This overview includes a description of the formal characteristics of each technique, as well as an overview of existing applications of these techniques in the literature. Finally, we provide a balanced discussion of both the advantages and challenges associated with the person-centered approach.
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Person-centered methods in vocational research
Joeri Hofmans1, Bart Wille2, Bert Schreurs1
1 Vrije Universiteit Brussel, Belgium
2 Ghent University, Belgium
Accepted version of paper in press at Journal of Vocational Behavior. This paper is not the copy of
record and may not exactly replicate the final, authoritative version of the article. Date of acceptance:
February 6, 2020
Correspondence concerning this article should be addressed to Joeri Hofmans, Research group of
Work and Organizational Psychology, Vrije Universiteit Brussel, Belgium, Pleinlaan 2, 1050 Brussel
Email: joeri.hofmans@vub.be
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Abstract
The vast majority of vocational research adopts a variable-centered approach. Implicit in this
approach is the assumption that the population under study is homogeneous, and that therefore we
can use a set of “averaged” parameters to describe it. Person-centered methods are a family of
methods that relax this assumption of population homogeneity, viewing the individual as holistic and
paying more attention to how specific configurations of variables, present in different subgroups of
the population, act in concert to shape behavior. Despite the potential advantages of person-centered
research, the adoption of this approach by vocational researchers has been relatively slow for both
conceptual (e.g., What exactly is person-centered research?) and methodological (e.g., Which
methods?) reasons. In response to these issues, the goal of the present article is to showcase the role
and relevance of person-centered methods for vocational research. Having discussed different
conceptualizations of the term “person-centered” we present a structured overview of the most
relevant person-centered techniques. This overview includes a description of the formal
characteristics of each technique, as well as an overview of existing applications of these techniques
in the literature. Finally, we provide a balanced discussion of both the advantages and challenges
associated with the person-centered approach.
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In vocational research, the vast majority of studies examines relationships among variables
across individuals. Such studies have, for example, shown that charismatic personality relates
positively to career outcomes 15 years later (Vergauwe, Wille, Hofmans, & De Fruyt, 2017), or that
employment self-efficacy is positively related to job search intensity at the between-person level,
while the relation is negative at the within-person level (da Motta Veiga & Turban, 2018). Implicit in
such studies is the assumption that the population can be described by a single set of “averaged”
parameters (Morin, Bujacz, & Gagné, 2018).
Despite the prevalence of this assumption, career theories suggest that describing an entire
population using a single set of parameter estimates most likely oversimplifies reality. For example,
vocational researchers are increasingly recognizing that contemporary careers and career orientations
cannot simply be categorized as either boundaryless or protean, but that people differ in the extent to
which they hold unique combinations of different career orientations. As a result, there have been
repeated calls for the integration rather than the separation of different career orientations (Kuron,
Schweitzer, Lyons, & Ng, 2016). Also in the counseling field the idea of population homogeneity is
called into question by for example research that shows that problems associated with career
indecision are manifested in very different ways for different groups of people (e.g., Cohen,
Chartrand, & Jowdy, 1995). Such findings have led to the awareness in the counseling literature that
phenomena can only rarely be explained by a universal relationship between a small number of
variables (Frankfurt, Frazier, Syed, & Jung, 2016). Although variable-centered methods do allow
studying the interplay of variables through the inclusion of interaction terms, this quickly becomes
impractical when the number of interacting variables increases. Because of the awareness that a
single set of parameter estimates cannot be assumed to hold for a whole population and because of
the limitations of variable-centered methods to capture complex patterns of interactions,
researchers called for supplementing the body of variable-centered research with person-centered
research (e.g., Morin et al., 2018).
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In person-centered research, the focus is no longer (exclusively) on relations between
variables, but (also) on relations among people (Zyphur, 2009). To fulfill this task, person-centered
methods do not assume population homogeneity, but model unobserved heterogeneity within the
population (Woo, Jebb, Tay, & Parrigon, 2018). Thus, person-centered methods shift the attention
away from a focus on variables to a focus on individuals by allowing the study population to be
heterogeneous (Weiss & Rupp, 2011). By doing so, person-centered methods pay more attention to
how specific configurations of variables act in concert to shape behavior (Bergman & Trost, 2006).
Despite the potential advantages of person-centered research, vocational behavior and
counseling researchers have been rather slow in adopting this approach due to conceptual (e.g., What
exactly is person-centered research?) and methodological reasons (e.g., Which methods can be used
when performing person-centered research? ). In response to these issues, the goal of the present
article is to showcase the role and relevance of person-centered research for vocational behavior and
career counseling (hereafter referred to as vocational research). More specifically, the aims of this
article are fourfold. First, we aim to improve the understanding of person-centered research and how
it is applied in vocational research. To this end, we first discuss different conceptualizations of the
term “person-centered” and differentiate it from related, yet different approaches. Based on this
outline, we then present an overview of the most relevant techniques within this approach, including
k-means and hierarchical clustering, latent profile and latent class analysis, factor mixture analysis,
mixture regression analysis, configural frequency analysis, Davison and Davenport’s (2002)
criterion-based method, latent class growth modeling (and growth mixture modeling), and latent
transition analysis. As a second contribution, we describe the state-of-the art of person-centered
approaches to vocational research by taking stock of the literature in this area. Our literature review
covers seven of the most well-established international peer-reviewed journals listed in Web of
Science that in their objectives specifically focus on careers, career counseling, and vocational
behavior: Journal of Vocational Behavior, Career Development International, Career Development
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Quarterly, Journal of Career Development, Journal of Career Assessment, Journal of Counseling
Psychology, and The Counseling Psychologist. The results of this literature search are presented
alongside the formal description of each technique, highlighting the most important trends in
vocational research using those methods. Third, apart from describing the methods themselves and
from summarizing existing research with those methods, we highlight how those person-centered
techniques can potentially be used for advancing vocational research. Finally, a fourth objective of
this article is to provide a balanced discussion of both the advantages and challenges associated with
person-centered research.
Person-Centered Methods: Different Conceptualizations and Approaches
Although the ability of person-centered methods to account for unobserved population
heterogeneity is a key feature that distinguishes them from variable-centered methods, it is important
to note that traditional variable-centered techniques can also deal with observed heterogeneity.
Observed heterogeneity occurs when different subpopulations can be differentiated based on an
observed variable (e.g., age, occupational category). In this case, the subpopulations are referred to
as groups and traditional multi-group analytic techniques such as t-tests, ANOVA, MANOVA and
multi-group SEM can be used to test between-group differences (or heterogeneity) on the outcomes
of interest. Often, however, the variables that cause population heterogeneity are not known
beforehand and/or not observed. If this happens, heterogeneity is due to unknown reasons, which is
why this type of heterogeneity is referred to as unobserved heterogeneity and why we speak about
latent classes rather than groups. Because in this scenario it not possible to a priori divide the sample
into groups, traditional analytic techniques are of little use. It is in this particular situation that
person-centered methods, with their ability to infer subpopulation membership from the data, are
particularly useful.
Although the differentiation of variable- and person-centered methods in terms of their
treatment of observed versus unobserved heterogeneity is relatively straightforward, the term
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person-centered has led to some confusion in previous writings (see Woo et al., 2018 for an
excellent treatment of these issues). According to Woo and colleagues (2018), three
conceptualizations of the term “person-centered” can be discerned in the scientific literature. First,
some researchers refer to person-centered studies as research on the characteristics of individuals (as
opposed to research on the characteristics of situations). According to this perspective, a study is
person-centered when it focuses on characteristics of people, such as personality, skills, or ability.
Second, others have used the term person-centered to refer to research that focuses on the
subjectivity of worker experiences, as opposed to research that focuses on more objective
characteristics of individuals (Weiss & Rupp, 2011). Finally, according to the third
conceptualization, the term person-centered is used to refer to a collection of methods that classify
individuals on the basis of the similarity in their scores on a set of variables (Howard & Hoffman,
2018). The approach aligns well with the idea of studying persons based on certain profiles across
multiple variables or characteristics (see further). This third conceptualization is the one that
“maximizes the level of precision in methodological discussions …” (Woo et al., 2018; p. 816).
Because of this reason, we delve a bit deeper into this conceptualization.
Using the conceptualization of person-centered research as research that clusters individuals,
such methods have been argued to be characterized by three features (Morin et al., 2018). The first
feature is that they are typological in the sense that they use a classification system that categorizes
individuals into qualitatively and quantitatively distinct subpopulations, with each of the
subpopulations being characterized by different sets of model parameters. The typological nature of
such methods is very appealing to vocational researchers since the classification system implied by
person-centered models corresponds to a way of thinking often used by managers (i.e., thinking
about employees by categorizing them in types of employees) (Morin et al., 2018) and by counseling
psychologists, who tailor their treatment based on the type of employee they have in front of them
(Cohen et al., 1995).
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Second, person-centered research is often said to be prototypical. This means that each
individual in the sample belongs to each of the estimated profiles with a certain probability. This
probability is based on the extent to which the individual’s unique configuration of scores on the
study variables resembles the profile’s specific configuration of scores. In such probabilistic
scenario, individuals are not assigned to one of the profiles, but are assessed as being more or less
similar to each of the prototypical profiles. Accounting for the uncertainty in assignment by using
probabilistic memberships offers a way to account for the fact that the classification of individuals
into unobserved subpopulations is not without error. Although prototypicality is undisputedly a key
feature of most person-centered methods, some methods use ‘definite’ (or hard) assignment,
implying that each individual is assigned to one and only one profile. Such ‘hard clustering’ happens
in the large majority of the cluster analytic models, some of which will be discussed below.
Third, person-centered models are exploratory. Because of a lack of goodness-of-fit
information that allows for a direct assessment of the adequacy of the tested model(s), the ‘final’
model is typically obtained by comparing solutions with different numbers of profiles or clusters.
Moreover, and similar to what happens in exploratory factor analysis, in person-centered methods
the relations between the profiles and indicators are typically freely estimated (Morin, McLarnon, &
Litalien, in press). It is important to note that these methodological peculiarities do not imply that
person-centered models cannot be used for confirmatory purposes, an issue that will be elaborated on
in the Discussion section.
It is important to note that there are a number of methods that, while they are strictly
speaking not encompassed by this definition, fit the goal of person-centered approaches because they
are aimed at studying profiles or patterns of scores (e.g., Asendorpf, 2006; Davison & Davenport,
2002; von Eye, 2002). That is, whereas these methods are not classification-based, they do focus on
the pattern of scores of individuals and therefore they do consider the person as an “organized
whole” (Bergman & Magnusson, 1997, p. 291). Because of this reason, some authors consider them
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to be person-centered (e.g., Asendorpf, 2006). Because the goal of non-typological methods closely
relates to the central goal of the “typical” person-centered approaches (i.e., moving the focus away
from studying relations between variables to studying relations between people on the study
variables), we will include two of such methods in our overview (i.e., configural frequency analysis
and Davison and Davenport’s (2002) criterion-based method).
As we mentioned above, person-centered methods relax the assumption of population
homogeneity by clustering individuals in subgroups or subpopulations. One might relax this
assumption even further, in which case inferences are made for each individual separately. Such an
approach, which in the context of Cattell’s (1952) data box has been described as the P-technique, is
referred to as an idiographic or person-specific approach. In person-specific analyses, the goal is to
build a model for each individual separately, drawing on the idea that each individual can best be
described and understood using an individualized model (Howard & Hoffman, 2018). The
philosophical differences underlying the person-specific and the person-centered approach clearly
show in the data they use as input. Unlike person-specific methods, which operate on occasions ×
variables matrices, person-centered methods operate on persons × variables matrices (except for
longitudinal person-centered methods, which use the full persons × variables × occasions data box).
In other words, whereas person-specific methods by definition analyze intra-individual variation
1
,
person-centered methods can work with both inter-individual and/or intra-individual variation (Woo
et al., 2018). In that sense, person-centered analyses offer a compromise between the parsimony of
the variable-centered approach, yielding a single set of parameters, and the person-specific approach,
yielding a set of parameters for each individual in the sample.
Following this discussion of definitional issues, in the next section, we offer an overview of
several methods that move the focus away from studying relations between variables to studying
1
Some person-specific techniques, such as dynamic factor analysis (Molenaar, 1985) and dynamic structural equation
modeling (Asparouhov, Hamaker, & Muthén, 2018) allow for the consideration of between-person variation. Those
techniques thus also operate on the full persons × variables × occasions data box.
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relations between people. Three types of methods are discussed. First, we review methods that seek
to identify subpopulations based on their profile of scores (i.e., cluster analysis, latent class and latent
profile analysis, and factor mixture analysis). In all of those methods the profiles are based on scores
on a set of variables, without taking into account potential outcome variable(s). The second category
of models addresses the issue of a lack of criterion variables by making explicit reference to such
variable. That is, in this category of models, subpopulations are either made based on differential
relationships between a set of predictors and an outcome variable (i.e., mixture regression analysis),
or the models identify specific patterns of predictors that are associated with the criterion variable
(e.g., Davison & Davenport, 2002). Finally, and in line with recent calls for more longitudinal
studies in organizational research in general (e.g., Vantilborgh, Hofmans, & Judge, 2018), and
vocational research in particular (e.g., Zacher, Rudolph, Todorovic, & Ammann, 2019), we also pay
attention to longitudinal person-centered models, being growth mixture modeling and latent
transition analysis. This last category of models is particularly interesting as such methods allow
studying interindividual differences in intraindividual change processes (Ram & Grimm, 2007).
From Studying Variables to Studying People: An Overview of Person-Centered Methods
Modeling profiles of scores
We will review four internal person-centered methods (i.e., cluster analysis, latent class
analysis, latent profile analysis, and factor mixture analysis). The preposition internal refers to the
fact that in those analyses, only “internal variables” matter for profile estimation. “External
variables”—including predictors, outcomes and/or covariates of the profilescan be included in the
analysis, but those variables do not directly contribute to the definition of the profiles. They are
instead used to provide validity evidence for the profiles obtained
2
.
2
Note that for LCA and LPA, direct inclusion of predictors, outcomes and/or covariates of profile membership can be
done within the Generalized Structural Equations Modeling framework (GSEM) (see Morin et al., 2019).
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Cluster Analysis. Cluster analysis pertains to a family of methods aimed at dividing objects
into a limited number of mutually exclusive groups (or clusters), in such way that objects belonging
to one cluster are more similar to each other than objects belonging to another cluster
3
. In general,
there are two broad cluster analytic approaches: hierarchical clustering and nonhierarchical
clustering.
In hierarchical clustering, one seeks to build a hierarchy of clusters, and this hierarchy can be
either built bottom-up (the agglomerative approach) or top-down (the divisive approach). In the
bottom-up approach, one starts from a cluster solution in which each object has its own cluster, after
which pairs of clusters are merged when moving up the hierarchy. In the top-down approach, the
initial cluster solution is one in which all objects are grouped into one cluster, after which splits are
performed while moving down the hierarchy. Thus, the idea in the bottom-up approach is to merge
pairs of clusters that are most similar to one another, while in the top-down approach one splits those
clusters that are most dissimilar to one another. An important question in hierarchical clustering
pertains to the measurement of (dis)similarity of clusters. There are several ways to measure
(dis)similarity, including the nearest neighbor (or single linkage) method, the furthest neighbor (or
complete linkage) method, and the average linkage method.
Nonhierarchical clustering, as opposed to hierarchical clustering, is not aimed at building a
hierarchy of clusters. Instead, its aim is to cluster objects into a pre-defined number of clusters by
maximizing or minimizing some criterion. Arguably the most popular type of nonhierarchical
clustering is the K-means method (Hofmans, Ceulemans, Steinley, & Van Mechelen, 2015). In K-
means clustering, the algorithm searches for a combination of a binary partitioning matrix 
(containing the memberships of the I objects to the K clusters) and a centroids matrix 
(containing the centroids for the K clusters) that minimizes the following least squares loss function
(with  being the data matrix in which I objects are measured on J variables):
3
Note that cluster analysis can also cluster variables into groups based on their values on a set of objects.
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
    (1)
Formula 1 implies that the K-means algorithm assigns objects to the cluster for which the distance to
the cluster centroid (i.e., the center of the cluster) is minimal. Readers interested in learning more
about K-means clustering can consult the overview paper of Steinley (2006), while Kaufman and
Rousseeuw (2005) provide an excellent treatment of cluster analysis in general.
Our literature review reveals that cluster analysis has a relatively long history in vocational
research, with the first studies using this technique already dating back more than half of a century
(e.g., Matthews & Tiedeman, 1964). Today, the technique is still commonly used to group
individuals and/or career events based on shared/similar features (see Table A1 for an overview) .
Latent Class Analysis (LCA) and Latent Profile Analysis (LPA). The goal of LCA and
LPA is to identify subpopulations of people, with those subpopulations being characterized by
distinct configurations of scores on a set of variables (see Figure 1). In that sense, LCA and LPA are
similar to cluster analysis. However, unlike cluster analysis, LCA and LPA (1) are model-based (i.e.,
they are based on a formal model instead of (dis)similarity measures) and (2) are prototypical, which
means that they yield probabilistic, rather than hard, assignment [note that some clustering methods,
such as fuzzy clustering (see Tan, Steinbach, Karpatne, & Kumar, 2019) also yield probabilistic
memberships].
Although the terms LCA and LPA are often used interchangeably, the difference is that LCA
uses categorical indicators, while in LPA the indicators are continuous. More formally, the
traditional latent class analysis (LCA) model can be expressed as follows:
  
   


 (2)
In this formula, represents a specific response pattern (or a pattern of scores on J categorical
variables). The chance of observing this particular response profile is a function of the probability of
membership to the k latent classes (the ’s), and the probability of observing each response
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conditional on latent class membership (the ’s). The indicator function  
equals 1 when
the response to variable j equals
. If not, this indicator function is 0 (see Collins & Lanza, 2010 for
an in-depth overview of the technicalities of LCA).
As opposed to LCA, in LPA the latent variable indicators are continuous. Assuming that
these indicators are normally distributed within each latent profile, that the indicators are unrelated
within each latent profile (i.e., local independence), and that the indicator variances are equivalent
across the latent profiles (i.e., homogeneity), LPA models the distribution of observed scores on a set
of indicators (     ) as a function of the probability of membership to the K latent classes
(the ’s) and each class’ normal density
(with each class having a class-specific mean
vector and covariance matrix  ):
 (3)
In a more generic form, the LPA model decomposes the variance of each indicator i into two
components (see Formula 4): a between-profile component that captures how far the profile-specific
means  are from the general mean (i.e.,   
 ) and a within-profile component
containing the profile-specific variances 
(i.e., 
 ). In both the between- and the within-
profile component, denotes the density parameter, or the probability of membership to profile k.
  

 (4)
Although LPA can thus be used to estimate profiles differing in both means and variances,
more constrained versions in which only the means are profile-specific can also be tested (i.e., 
; Peugh & Fan, 2013). This assumption of homogeneity of variances is shared with methods such
as K-means clustering and is the default parameterization in some statistical packages, such as Mplus
(Muthén, & Muthén, 2017). Readers interested in a more in-depth discussion of LPA can consult the
book chapter by Masyn (2013) or the paper by Sterba (2013).
Our review indicates that the application of LCA and LPA in vocational research took off
around 2012-2013, with the first studies using these techniques to identify subgroups of people based
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on their commitment profiles (e.g., Meyer, Stanley, & Parfyonova, 2012). Since then, LCA and LPA
have been adopted widely with the aim to study among other things interest profiles, motivation
profiles, and profiles of work characteristics (see tables A2 and A3 for LCA and LPA, respectively).
Factor Mixture Analysis (FMA). Whereas in LCA and LPA unobserved heterogeneity is
modeled through the inclusion of a categorical latent variable, FMA simultaneously includes a latent
categorical and one or multiple latent continuous variables within the same model (see Figure 2).
The latent categorical variable allows for the classification of individuals in groups, whereas the
latent dimensional variable(s) allow for heterogeneity within groups by modeling covariation
between observed variables within each class. Hence, FMA relaxes the conditional independence
assumption of classical LPA analyses (Lubke & Muthén, 2005). This is of particular importance to
vocational research, where the assumption of conditional independence is often unlikely due to a
global factor underlying the different indicators (e.g., in commitment research; Morin, Morizot,
Boudrias, & Madore, 2011). Moreover, because in FMA the continuous latent variable controls for
variance shared across all indicators when estimating latent profiles, FMA may result in profiles with
clearer shape differences (Morin & Marsh, 2015). Finally, by combining latent continuous and latent
categorical variables within the same model, FMA can tell us something about the underlying
continuous and categorical nature of psychological constructs (Clark et al., 2013). Formally, the
FMA model is expressed as follows (see Lubke & Muthén, 2005 for a thorough treatment of FMA):
      (5)
    (6)
Scores on indicator variable  are expressed as a function of the regression intercept , the
regression slope or factor loading  and the residual . Factor scores are denoted as . All
parameters in Formula 5 have subscript k, implying that they may vary across classes. Formula 6
shows that the factor scores are a function of the latent class variable , an intercept vector A, and
the residual factor scores .
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Our review demonstrates that FMA has been applied scarcely in careers research. The studies
that were identified adopted FMA for the categorization of reward patterns, or for creating subgroups
based on stereotype sensitivity, or vocational interests (see Table A4).
The potential of modeling profiles of scores for vocational research. Cluster analysis,
LPA, LCA and FMA can be used to address a wide variety of questions in vocational research. First
of all, many of the constructs being studied in this domain are multidimensional. This for example
holds true for predictor variables such as personality and interests, but also for outcomes such as
performance, career success or commitment. Person-centered techniques such as cluster analysis,
LPA, LCA or FMA allow studying how those different characteristics combine into profiles. Such
insight is important because, by showing which profiles emerge and how frequent those profiles are,
these methods contribute to a better understanding of the psychological makeup of individuals. This
is crucial for vocational research, where a basic principle is that vocational behavior and attitudes
result from the unique interplay or patterning of a broad set of different characteristics. Moreover, it
aligns well with the increasing individualization of career development (Vondracek & Porfeli, 2002)
and the person-focused approach used in career counseling.
An important remark is that the techniques that allow for the modeling of scores do not
require the different scores to tap into one overall dimension (Morin et al., 2019). They can also be
used when studying profiles across a collection of variables of interest. For example, Haines, Doray-
Demers, and Martin (2018) performed LCA with the goal to develop a typology of part-time
employment on the basis of work characteristics and role occupancy. To this end, they included a
wide range of variables into their LCA, including having a partner or not, having children or not,
household income distribution, educational requirements of the part-time position and work hours.
Or in the counseling domain, Hirschi and Valero (2017) used LPA to identify five differing profiles
according to levels of perceived chance events and career decidedness.
Such endeavors have the potential to advance the career and counseling field in various ways
15
(Borgen & Barnett, 1987). First, they allow exploring the identification and structure of subgroups,
which might help in understanding the research problem better. For example, drawing on the idea
that the traditional career is declining, Gerber, Wittekind, Grote, and Staffelbach (2009) used LCA to
explore the nature and prevalence of different types of career orientation. Second, these techniques
can be used to challenge or confirm existing classifications. For example, the four-class solution by
Haines and colleagues (2018) revealed that qualifying part-time work in good and bad is too
reductionist, and that a more complex classification is warranted. Third, these techniques allow
simplifying complex datasets. For example, Ferguson and Hull (2019) identified profiles of science
career interests based on scores on science motivation, attitude, interest, and academic experiences.
Modeling predictor-outcome profiles
The methods we have reviewed up until now are all internal techniques, meaning that the
profiles are derived without taking into consideration their predictive value for outcome variable(s)
(Davison & Davenport, 2002). The second category of models addresses this issue. That is, in this
category, some models use subpopulations to capture differential relations between a set of
predictors and an outcome variable (i.e., mixture regression analysis), while others look for specific
patterns of predictors that are uniquely associated to the criterion variable [i.e., configural frequency
analysis and Davison and Davenport’s (2002) criterion-based method].
Mixture Regression Analysis (MRM). The subpopulations in mixture regression analysis
(MRM) differ from each other in the relationships between the constructs of interest. Similar to
traditional multiple regression, in MRM a criterion variable is regressed on a set of predictors. The
major difference, however, is that subpopulations are identified for whom the predictor(s)criterion
relationship is different (Brusco, Cradit, Steinley, & Fox, 2008). In that sense, the latent categorical
variable in MRM can be thought of as an unobserved moderator of the relation between the predictor
and the criterion (see Figure 3). For example, in their study on reward satisfaction, Hofmans, De
Gieter, and Pepermans (2013) found two subpopulations with a different pattern of job rewardjob
16
satisfaction relationships. For the first type, job satisfaction related to financial and psychological
reward satisfaction, whereas for the second type it related to psychological reward satisfaction only.
Formally, the MRM model can be expressed as follows:
      (7)
In Formula 7, the latent subpopulations are represented by a latent categorical variable C, where C =
1,2,3,…K. Hence,  represents the intercept for subpopulation (or class) k, while and 
represent the regression coefficient and error term for this subpopulation. Similar to the traditional
regression model, more than one predictor variable can be included, in which case each of the
predictor variables has a class-specific regression coefficient. Moreover, as in traditional regression
models, the errors are assumed to be multivariate normal with a mean of zero and a class-specific
variance (i.e.,  
). Readers interested in a more technical treatment of MRM can consult
Wedel and DeSarbo (1995).
Our literature review demonstrated that MRM has only seldom been used in vocational
behavior research (see Table A5).
Configural Frequency Analysis (CFA). The aim of Configural Frequency Analysis (CFA)
is to identify whether specific configurations or response patterns are more likely to be associated
with specific criterion groups (von Eye, 1990). This method is developed for the analysis of
categorical predictors and outcomes and draws on an analysis of frequencies in multi-way
contingency tables. In such multi-way contingency tables, individuals are categorized in disjunct
categories based on their unique profile (or configuration of scores) on the study variables. For
example, when one has two dichotomous predictor variables and one trichotomous outcome,
participants can belong to one of 2 × 2 × 3 = 12 unique profiles. After having tabulated those unique
profiles and their frequencies, the crucial test is in the comparison of the observed with the expected
frequencies of those configurations. In case n individuals are being measured on i = 1, 2, …, m
17
dichotomous variables, and assuming that all variables are independent, the expected frequencies of
a specific configuration c can be calculated as follows:
 
 (8)
with  being the probability for a member of the population to have a value of 0 on variable i,
and  being the probability for a member of the population to have a value of 1 on variable i. To
test whether these expected frequencies (assuming independence of all variables) differ significantly
from the observed frequencies (denoted by ), the following -statistic can be calculated:

(9)
In case a configuration is significantly more often observed than expected, it is referred to as
a type, whereas an antitype refers to the case where a configuration is significantly less often
observed than expected. Although we have presented the default version of CFA, the -test can be
replaced by other tests (e.g., von Eye, 2002) and the expected frequencies can be calculated using
another model than the independence model (e.g., von Eye, 1990). Finally, because in CFA one
performs a (-)test to each profile or configuration, a Bonferroni-correction is often used to control
for Type I error inflation. An in-depth discussion of the technicalities of CFA can be found in the
books by von Eye (1990; 2002).
Our literature review revealed two empirical studies which used CFA in the context of
vocational research (see Table A6). First, Reitzle and Vondracek (2000) illustrated the usefulness of
this technique by identifying patterns of (categorical) career and family characteristics, including
marital status, completion of training, history of unemployment, etc. More recently, Moeller and
colleagues (2018) investigated the relationship between demands-resources profiles and engagement-
burnout profiles. For this purpose, they compared the proportions of three demands-resources
profiles in one of four engagement-burnout profiles, testing whether each profile combination was
more (or less) frequent than would be expected if the types of profiles were unrelated.
18
Davison and Davenport’s (2002) criterion-based method. Similar to mixture regression
analysis (MRM), Davison and Davenport’s (2002) criterion-based method draws on a multiple
regression-based model. Unlike MRM, however, it does not look for subpopulations with different
predictorcriterion relationships but tries to capture profile similarity as a continuous measure.
Moreover, the method has an even stronger focus on the criterion variable as its explicit goal is to
maximize the predictive value of the profile.
When performing Davison and Davenport’s (2002) criterion-based method, four steps are
taken. First, for each individual, a level score, or an average score across the predictor variables, is
calculated. Second, the criterion-related profile is identified. This is done by (1) predicting the
criterion variable from the predictor variables using multiple regression analysis, (2) calculating the
average unstandardized regression coefficient across all predictors, and (3) ipsatizing each regression
coefficient around the average unstandardized regression coefficient. This yields the criterion-related
profile, or a set of deviations around the average unstandardized regression coefficient. After having
calculated this criterion-related profile, one can calculate the profile fit score for each individual as
the average covariance between the individual’s predictor profile and the criterion-related profile.
Thus, rather than identifying different subgroups of profile scores, Davison and Davenport’s (2002)
criterion-based method treats the different profiles in a continuous manner, ranging from fit (i.e.,
high average covariance with the criterion-related profile) to misfit (i.e., low average covariance with
the criterion-related profile). Third, the level and (mis)fit scores are related to the criterion variable
using multiple regression analysis. This allows testing what percentage of the variance in the
criterion can be accounted for by level and profile effects. Finally, because the criterion-related
profile is obtained using multiple regression, and because the regression weights tend to capitalize on
the characteristics of the sample at hand, a crucial test is evaluating whether the level and profile
effects cross-validate. This can be done by splitting the data in half, after which one can estimate the
criterion-related profile using the first half of the data after which the level and (mis)fit scores can be
19
related to the criterion variable in the second half. Note that, although Davison and Davenport’s
(2002) criterion-based method is explicitly criterion-focused, it does not explain additional variance
in the criterion above and beyond a traditional multiple regression analysis. Instead, it separates the
predictor variance into level and profile effects, thereby providing insight into the usefulness of using
profiles or patterns in applied prediction.
Our literature review identified two articles using criterion profile/pattern analysis in the
context of vocational research (see Table A7). In a first application of this technique, Perry (2008)
investigated the effects of vocational exploration and racial identity on behavioral (attendance,
attention, time spent on class work) and psychological (identification with school) factors of school
engagement among urban youth of color. The criterion-based method revealed a predictive profile
marked by high levels of positive racial internalization and career planning combined with low levels
of racial dissonance. As a second application, Wiernik (2016) identified patterns in the predictive
relationships between personality traits and Realistic vocational interests. In two studies, he
demonstrated that one’s personality profile pattern, rather than the absolute levels of those traits,
drove the validity of personality traits in explaining Realistic vocational interest.
The potential of modeling predictor-outcome profiles for vocational research. The
usefulness of MRM for vocational research lies in the fact that in this domain people’s behaviors and
attitudes are thought to result from the unique interplay of a broad set of characteristics. For
example, in their overview of 100 years of research on career management and retirement, Wang and
Wanberg (2017) noted that career choices are impacted by among other things ability, personality
characteristics and biographical data such as socioeconomic status and parental involvement.
Similarly, De Vos, Van der Heijden, and Akkermans (in press) note that careers form a complex
mosaic of objective experiences and subjective evaluations, resulting in an enormous diversity in
terms of how careers can take shape” and that “different levels of influential factors have to be taken
into account” to understand the nature of contemporary careers. Importantly, those factors are not
20
only assumed to have unique effects, but they interact in complicated manners. For example,
Chlosta, Patzelt, Klein, and Dormann (2012) demonstrated that the likelihood to become self-
employed depends on the unique interplay of the presence of parental role models and the person’s
score on trait openness. In such situation, where behaviors and attitudes are believed to result from
the unique interplay of multiple determinants, assuming that predictors relate to outcomes in the
same way for everyone is counterintuitive at best. Considering this complexity, methods for
modeling predictor-outcome profiles are very useful, particularly because those predictor-outcome
relations are likely to be affected by not one variable, but by a wide range of variables (and their
unique interplay), some of which are not known a priori.
The goal of configural frequency analysis and Davison and Davenport’s (2002) criterion-
based method are somewhat different from that of MRM in the sense that the former focuses on
capturing unobserved heterogeneity in predictor-outcome relations, whereas the latter techniques are
explicitly designed to test the predictive validity of patterns or profiles of predictor variables. Hence,
CFA and Davison and Davenport’s (2002) criterion-based method are well suited to test the idea that
people develop interests for jobs that align with their relative strengths (i.e., the peaks in their profile
of trait scores), rather than their absolute trait levels (Weirnik, 2016), or the hypothesis that it is the
particular patterning of specific job demands and resources that is predictive of burnout and
engagement, rather than the demands and resources as such (Moeller et al., 2018). In sum, those
techniques can be particularly helpful in expanding our knowledge on how specific patterns of
variables matter for vocational outcomes.
Modeling profiles of intraindividual change processes
In this last category of models, we review two longitudinal person-centered models, being
growth mixture modeling and latent transition analysis. Those models are well suited to model
stability and change over time, allowing for example for an assessment of the impact of important
transitions in employees’ lives (e.g., starting a job, promotion, retirement; see Solinger, van Olffen,
21
Roe, & Hofmans, 2013), or for analyzing the impact of the occurrence of critical events in
organizations (e.g., organizational change). Because of their ability to examine inter-individual
differences in intra-individual processes, these models are ideally placed as the analytical solution to
calls for more longitudinal, within-person research in vocational research (e.g., Zacher et al., 2019).
Growth Mixture Modeling (GMM). Growth Mixture Modeling (GMM) aims at identifying
subpopulations that follow different longitudinal growth trajectories over time, thereby being a
mixture extension of latent growth or latent curve models (see Bollen & Curran, 2006)
4
. In such
latent growth models, one or more variables is measured repeatedly and growth in the level of these
variables across time is estimated via random intercept and slope(s) factors. The random intercept
factor(s) capture each individual’s initial level on the repeated measures, while the random slope
factor(s) capture each individual’s change in those repeated measures as a function of time. At its
simplest, growth is characterized by a random intercept and a random linear slope factor, although
more complicated growth trajectories can be modeled by adding additional, higher-order slope
factors (e.g., quadratic, cubic, ….).
GMM, being a mixture extension of the latent growth model, aims to identify subpopulations
following different growth trajectories over time (see Figure 4). In this sense, GMM is similar to
multi-group growth curve modeling, where different growth models are tested for each group.
However, unlike in multi-group growth curve modeling, where the groups are observed, in GMM the
grouping variable is latent or unobserved (Ram & Grimm, 2009). In its simplest form, the latent
subpopulations are only allowed to differ regarding their average level on the growth factors.
However, more complex GMMs can also be estimated, with the subpopulations being allowed to
vary not only on intercept and slope(s) averages, but also intercept and slope(s) variances and
4
Latent Class Growth Analysis (LCGA) is a special case of GMM in which the variances and covariances of the growth
factors in each latent class are fixed to zero (see e.g., Jung & Wickrama, 2008).
22
covariances, and even time-specific residuals. Moreover, the subpopulations can also be allowed to
follow a different functional form. Formally, a general linear GMM can be expressed as:

     (10)
     (11)
     (12)
In Formula 10, or the level of variable y for person i at time t is a function of (1) the
profile-specific random intercepts , linear slopes , and error terms  (with k = 1, 2, …, K
being the latent profiles), and (2) the probability of belonging to each of the latent subpopulation or
profiles, (with all   and
 =1). In other words, the raw repeated measures data for
each individual are conceived of as a mixture (i.e., a weighted sum) of the K different latent growth
profiles. Time in formula 10 is represented by , being the factor loading matrix relating the
repeated measures of y to the slope factor. In GMM, should be coded in such way that it reflects
the interval between measurement occasions (for example λ1 = 0, λ2 = 1, λ3 = 2, λ4 = 3 with four
equally spaced measures or λ1 = 0, λ2 = 1, λ3 = 1.5, λ4 = 2 in case the measurement one and two are
separated by a period double the period separating measurement two and three and three and four).
More information on the technicalities involved in defining the time codes can be found in Biesanz,
Deeb-Sossa, Papadakis, Bollen, and Curran (2004). The random intercepts  in Formula 10 can
further be decomposed into , or the average intercept for each profile, and , or the deviation
of each person’s profile intercept from this average intercept (see Formula 11). Similarly, the random
linear slope  is decomposed into , or the average slope for each profile and , or the
deviation from this average slope for each person I (see Formula 12). Interestingly, because  and
 capture deviations from the average intercept and slope, respectively, they represent the
variability of the intercepts and slopes across cases within profiles. Of particular importance is that,
because all terms in formulas 11 and 12 have a subscript k, each of the profiles can have a unique
growth function. Although the GMM in formulas 10-12 is a linear GMM, other functional forms can
23
be tested as well, such as a quadratic or cubic GMM. Readers interested in a more thorough
treatment of GMM can consult the papers by Jung and Wickrama (2009) or Ram and Grimm (2009).
Our literature review revealed a modest number of studies applying this longitudinal person-
centered technique in vocational research, with the first studies using this technique being published
around the year 2010 (see Table A8). For instance, Hirschi (2011c) used LCGA to identify different
developmental trajectories of career-choice readiness: (1) “high increasing” describes a class of
people with high initial readiness and a linear increase of readiness over time; (2) “high decreasing”
is characterized by a very high initial level of readiness followed by a decline in readiness over time;
(3) “moderate increasing” showed a moderate initial level of readiness and a linear subsequent
increase in readiness; and finally (4) “low stable” showed a low initial level of readiness and almost
no increase in readiness over time.
Latent Transition Analysis (LTA). Latent Transition Analysis (LTA) is a longitudinal
extension of LCA/LPA (Collins & Lanza, 2010). That is, in LTA people can transition from one
latent class to another over time (see Figure 5). Because the latent classes in LTA refer to subgroup
memberships at that particular point in time, they are referred to as latent statuses, rather than latent
classes. A good illustration of this technique comes from research conducted by Mäkikangas (2018),
who studied latent profiles of job crafting strategies across time. Using latent profile analysis, she
first demonstrated that in a sample of Finnish rehabilitation center employees a distinction can be
made between ‘active’ and ‘passive’ job crafters, with the latter only trying to decrease their
hindering job demands to some extent, without trying to increase their job resources or challenging
job demands. In a next step, she used LTA to investigate the stayer-mover patterns across job
crafting profiles over time. In this specific example, the latent transition probabilities were zero,
indicating that no transitions occurred across a one-week interval.
In a LTA for categorical indicators, three sets of parameters are estimated. First, at each time
point the proportion of individuals that is expected to belong to each latent status is estimated. This is
24
referred to as the latent status membership probabilities ( in Formula 13). Second, the transition
probabilities capture the probability of transitioning from a specific latent status at time t to another
latent status at time t+1 ( in Formula 13). Third, item-response probabilities tap into the connection
between latent status membership and the observed categorical indicators at each time point (the ’s
in Formula 13). By doing so, item-response probabilities provide information on the differentiation
of the latent statuses. Formally, a LTA model for two measurement occasions (i.e., t and t+1),   
   latent statuses (with a denoting a latent status at measurement occasion t, and b denoting a
latent status at measurement occasion t+1), and four indicators at each measurement occasion (i.e.,
    ,     ,     ,     ) can be expressed as follows:
   
 (13)
In Formula 13, y represents a specific response pattern on the categorical indicators across both
measurement occasions (i.e.,        ), represents the proportion of
individuals in latent status a at time t, and  is the probability of membership in latent status b at
measurement occasion t+1, conditional on membership in latent status a at measurement occasion t.
Finally, is the probability of response i to the first item at measurement occasion t, conditional
on membership in latent status a at measurement occasion t. Readers interested in a more thorough
treatment of LTA can consult the book by Collins and Lanza (2010).
Our literature review identified only three studies that used LTA in vocational research so far
(see Table A9). In addition to Mäkikangas (2018; see above), Kunst, van Woerkom, van Kollenburg
and Poell (2018) used LTA to identify trajectories of goal orientation profiles in a teacher sample.
Although the majority of teachers remained in the same goal orientation profile over the one-year
interval (i.e., success-oriented, diffuse, low-performance, or high-avoidance), a small percentage of
teachers shifted towards a different profile, and this shift was supported by a specific type of
managerial coaching. Rice, Ray, Davis, DeBlaere and Ashby (2015) used LTA to study the stress
25
trajectories of different types of perfectionists, showing that maladaptive perfectionists never
transitioned to low stress whereas only 4% of the adaptive perfectionists transitioned to high stress.
The potential of modeling profiles of intraindividual change processes for vocational
research. Methods for detecting profiles of intraindividual change have direct relevance to
vocational research because careers by definition develop and evolve over time (De Vos et al., in
press; Hall, 2002). Traditional longitudinal models, such as the latent growth model, however, make
the strong assumption that change can be described using the same functional form (e.g., linear or
quadratic) for everyone. Whereas this might be true in very specific circumstances, the awareness
that careers and career choices are driven by the complex interplay of a wide set of person
characteristics, as well as the situations one encounters, suggests that heterogeneity in change might
be the rule rather than the exception. In response to this awareness, GMM is particularly interesting
because it allows the change over time to be qualitatively different for different groups of
individuals. Using GMM, Hirschi (2011) for example identified distinct developmental trajectories
of career-choice readiness in adolescents and demonstrated that students in those trajectories differed
on core-self evaluations, occupational knowledge and barriers. Also for the counseling field GMM
shows a lot of promise because it for example allows studying patterns of responses to treatment,
showing “what works or does not work for whom?” (Frankfurt et al., 2016; p. 624). Such insights
gained through GMM might help counseling psychologists tailoring their treatments and intervene
more effectively.
LTA, being a longitudinal extension of LCA/LPA, is a method holding a lot of promise for
the careers field because it aligns well with the definition of careers as “the individually perceived
sequence of work-related experiences and activities over the span of a person’s life” (Hall, 2002, p.
12). In LTAand LPA and LCA more broadlythose work-related experiences are not studied in
isolation, rather the combined profile states of those work-related experiences are the unit of
analysis. Moreover, because of its ability to model transitions between those profile states, the
26
treatment of careers in LTA closely resembles our theoretical conceptualization of it. This is not only
important from a substantive-methodological fit perspective, but studying careers in this way might
also provide novel and unique information. For example, Xu and Payne (2018) used LTA for
studying changes (or transitions) in organizational commitment profiles over time, and they
demonstrated that those transitions themselves (e.g., from a value-based commitment profile to a
weak commitment profile) were predictive of turnover hazards. Future studies could for example use
LTA to investigate transitions in career profiles (e.g., from “protean career architects” to “solid
citizen”, Briscoe & Hall, 2006) or in work versus family commitment profiles (e.g., from “work
profile” to “family profile”, Cinamon & Rich, 2002).
Important issues in person-centered research
There are a number of issues that are fundamental and practical to many contemporary
person-centered analyses, including class enumeration, profile labeling, inclusion of covariates, and
multi-group invariance testing. Because these issues apply to most methods discussed above, we
review them in a separate section.
Class enumeration
Selecting the optimal number of latent profiles is a thorny issue. Typically, models with an
increasing number of latent profiles are tested after which the most optimal one is selected based on
interpretability and theoretical conformity of the solution, statistical adequacy (e.g., no negative
residual variances), and statistical indicators. Regarding the latter, several indicators are available,
with simulation research showing that the Bayesian Information Criterion (BIC), the sample-adjusted
BIC (SABIC), the Consistent Akaike Information Criterion (CAIC), and the Bootstrap Likelihood
Ratio Test (BLRT) are among the most effective ones (e.g., Henson, Reise, & Kim, 2007; Nylund,
Asparouhov, & Muthén, 2007). However, because of the sample size dependency of those indicators,
they might suggest keeping on adding profiles in case one’s sample size is large. If this happens,
27
Morin and colleagues (2011) suggest looking at additional gains in fit when adding more latent
profiles using so-called “elbow plots”.
Labeling of profiles
The latent profiles in a profile solution can differ in many ways, including differences in the
unique pattern of high and low mean scores on the indicators (i.e., shape differences), differences in
the mean score across all indicators (i.e., level differences), and differences in the degree of
differentiation among indicators within a profile (i.e., scatter differences) (Meyer & Morin, 2016).
When it comes to labeling of the profiles, any of these differences can be referred to, with different
labeling schemes being used in different research fields. For example, in the commitment literature,
researchers have predominantly focused on shape differences, with the most common labeling
scheme being one in which the commitment component with the highest score is referred to as
“dominant” (e.g., affective commitment dominant or continuance commitment dominant). The
advantage of focusing on only one of the differences is simplicity. The downside, however, is that it
comes with decreased accuracy because other between-profile differences are not taken into account.
One solution adopted by Meyer and Morin (2016) is to add level and scatter information whenever
relevant (i.e., whenever level or scatter are either high or low).
Incorporating covariates
When engaging in person-centered research, one is often interested in learning how profile
membership relates to covariates. Research generally shows that covariates should only be included
once the optimal unconditional profile solution (i.e., the profile solution based on only those
variables making up the profile) is selected (Morin et al., 2019; Nylund-Gibson & Masyn, 2016).
Moreover, inclusion of covariates in the model should not change the nature of the profiles as this
causes the latent categorical variable to “lose its meaning” (Asparouhov & Muthén, 2014; p. 329).
Looking at different ways in which covariates can be included in the analysis, a first way to
test predictors and/or outcomes is to directly include them in the final solution. For example, one
28
might include profile outcomes by specifying them as additional profile indicators. Whereas direct
inclusion of covariates might help to reduce biases in the estimation of the profile-covariate relations
and although this helps limiting Type 1 errors (Diallo & Lu, 2017), one needs to make sure that
including the covariates does not change the optimal unconditional profile solution (see above). In
case the profile solution is modified by the inclusion of the covariates, a different approach can be
taken. This approach, referred to as the automated auxiliary approach, is specifically designed to
prevent this from happening. In fact, there is not one but different automated auxiliary approaches,
with Morin and colleagues (2019) suggesting that the preferred automated auxiliary approach
depends on whether you look at predictors, outcomes or correlates of profile membership. In case
one is interested in predictors, the “three-step” approach seems to perform well (see Asparouhov &
Muthén, 2014 for more information). For outcomes, either the three-step approach, the approach by
Lanza, Tan, and Bray (2013), or the BCH approach (see Asparouhov & Muthén, 2014 for more
information) can be used. According to Meyer and Morin (2016), correlates can best be tested using
the E function in Mplus because this approach does not assume directionality of the associations.
Finally, McLarnon and O’Neill (2018) discuss how one can manually implement the BCH and three-
step approach when one wants to test more complex mediation and moderation models or models
that look at the effect on an outcome after accounting for control variables.
Multi-group invariance testing
An important issue in person-centered research is whether profiles found in one sample
generalize across known subpopulations (Morin, Meyer, Creusier, & Biétry, 2016). For LCA, multi-
group invariance has typically been tested using a three-step approach in which one tests whether (1)
the same numbers of latent classes are extracted within each group, (2) the response probabilities are
the same across groups, and (3) the relative size of the profiles is the same across groups (see Eid,
Langeheine, & Diener, 2003).
29
Recently, Morin and colleagues (2016) extended this approach by revising the second step for
LPA rather than LCA and by including tests of similarity between the profiles, antecedents and
outcomes across subpopulations. This approach consists of six steps that test (1) whether the same
number of latent profiles is found in each group (i.e., configural similarity), (2) whether the
indicator’s levels are equal across groups (i.e., structural similarity), (3) whether the indicator’s
variability is equal across groups (i.e., dispersion similarity), (4) whether the relative size of the
profiles is the same across groups (i.e., distributional similarity), (5) whether the predictor-profile
relations are the same across groups (i.e., predictive similarity), and (6) whether the profile-outcome
relations are the same across groups (i.e., explanatory similarity). Morin and Wang (2016) extended
this approach to MRM, which essentially requires one additional step between the first and second
one in which the invariance of regression coefficients is tested across groups. Readers interested in
learning more about multi-group invariance testing in the context of LPA can consult the paper and
accompanying Mplus code by Morin and colleagues (2016), while the chapter by Morin and Wang
(2016) shows how to perform multi-group invariance testing for MRM.
Finally, as argued by Morin and colleagues (2019), the six-step multi-group profile similarity
framework can also be used to test for longitudinal invariance in LTA, although in the presence of
distributional similarity (i.e., the profiles account for equal proportions of the sample over time) one
cannot directly impose equality constraints on the relative size of the profiles over time. In this case,
the approach described by Morin and Litalien (2017) is needed.
Discussion: Critical Reflections on the Use of Person-Centered Methods
Despite their promise to vocational research, as evidenced by our literature review, some
researchers remain reluctant to adopt person-centered methods because of their exploratory nature
and their choice for a categorical rather than a continuous latent variable. In what follows, we aim to
offer a balanced discussion of these issues, hoping that this helps researchers to take a stance and
make informed decisions when designing their studies and plans of analysis.
30
The exploratory nature of person-centered methods
As mentioned earlier, person-centered models are exploratory in the sense that a ‘final’
model is typically obtained by comparing solutions with different numbers of profiles (or
subpopulations) after which the ‘optimal’ one is selected. One concern is that such an exploratory
procedure is highly sample-dependent, thus limiting the generalizability of one’s findings.
First, it is important to note that balancing model fit and model parsimony does not preclude
the generation of expectations regarding the number and/or the structure of the profiles (Morin et al.,
2018). For example, in case one would study how people’s job satisfaction develops after starting a
new job, it would be good practice to build on previous research that has demonstrated that job
satisfaction generally shows a trend of steady decline after entering a new job (e.g., Boswell,
Boudreau, & Tichy, 2005). Hence, in that particular case one expects a hypothesis that at least one of
the subpopulations follows such a hangover-pattern (see Solinger et al., 2013). Thus, although the
exact number of profiles can often not be predicted when performing person-centered analyses, one
might still have expectations concerning the nature of some of the profiles. Morin and colleagues
(2018) make exactly the same point, using the analogy of fishing. Whereas a fully a-theoretical
undertaking (which they refer to as dustbowl empiricism) corresponds to dynamite fishing, in which
one throws sticks of dynamite into the water and catches whatever floats to the surface, valuable
exploratory research is like fly fishing. In fly fishing, one starts by carefully selecting the appropriate
bait and fishing location, anticipating catching a particular type of fish. While in the fly fishing
scenario the number of fish, their size and even the type of fish is not known in advance, the
difference with dynamite fishing is that one goes well prepared to the expedition, knowing that
something valuable will come out of it (Morin et al., 2018).
Even though exploratory research, when well-planned, often leads to interesting findings, the
lack of a comprehensive theory that serves as a basis for clear hypotheses makes replication of one’s
findings increasingly important. This is particularly true provided that in some casesfor example
31
when the model’s distributional assumptions are violated—spurious profiles can emerge (Bauer &
Curran, 2003). Therefore, construct validation of one’s solution is essential (Morin et al., 2018).
According to Morin and colleagues (2018), such construct validation involves the following steps:
(1) demonstrating that the profiles have theoretical value, (2) demonstrating that the profiles relate in
a meaningful way to key covariates, and (3) demonstrating that the profiles generalize to new
samples or are (at least somewhat) stable across time.
Finally, we believe that person-centered methods are useful for inductive theorizing
(Hofmans, Vantilborgh, & Solinger, 2018). In case suitable theory is scarce or even non-existing,
restricting oneself to deductive logic in which one draws on a theory to build a general rule, after
which one tests whether the rule also applies to one’s data, might be too limiting (Ketokivi &
Mantere, 2010). In the scenario where there is little theory, person-centered methods are particularly
interesting because they offer a way to discover new aspects of phenomena through inductive
thinking. This inductive thinking might take the form of contextual or theoretical induction (Ketokivi
& Mantere, 2010). With contextual induction, the reason for existence of (some of) the
subpopulations is looked for in the research context. In theory-based induction, the subpopulations
are not assumed to be the result of a particular sample setting; instead one tries to achieve a
theoretical understanding of the subject matter. Apart from contextual and theoretical induction,
researchers can also look for counter-factuals in their findings. Such counter-factuals are findings
that are counter to one’s set of theoretical assumptions, and typically give rise to imaginative and
innovative research because they are followed by a problematization of assumptions from the part of
the researcher and the presentation of an alternative (Cornelissen & Durand, 2014).
A categorical versus dimensional approach to latent variables
As we have argued above, person-centered methods are typically classification-based. This
means that, in case the method is performed in a latent variable framework, it posits a categorical
latent variable. A logical question then is whether the choice for a categorical latent variable, rather
32
than a continuous one, makes sense. This issue is particularly important because, as Molenaar and
von Eye (1994) demonstrated, under certain conditions, an m-factor common factor model can be
perfectly reproduced with a K = m +1 class latent profile model (see also Bauer & Curran, 2003).
This equivalence is important because it places great onus on researchers to argue for the tenability
of one representation versus the other. We feel that there are two possible ways to go about this (see
also Collins & Lanza, 2010).
First, one may have a strong belief that the latent variable is categorical, and that it therefore
should be modeled in a person-centered, categorical way. Although such discussions are not very
prominent in vocational research, there are a number of research domains in which the issue of
dimensionality versus categoricity is a key question. Psychopathology research is such a domain,
with the crucial question being whether a dimensional or categorical classification of personality
disorders should be used (Ruscio, Ruscio, & Carney, 2011). An important criterion that is used to
argue for the meaningfulness of a categorical (or person-centered) rather than a dimensional (or
variable-centered) solution is the presence of qualitative, rather than quantitative differences between
profiles (Chen, Morin, Parker, & Marsh, 2015; De Boeck, Wilson, & Acton, 2005). The rationale
behind this idea is that quantitative differences, or ordered profiles that differ only in level, can be
well accommodated by a model with a continuous latent variable. Qualitative differences, or profiles
that differ in shape, instead, support the meaningfulness of a categorical, person-centered approach
because such differences cannot be captured well using the traditional, variable centered approach.
Although the issue of testing for dimensionality versus categoricity goes beyond the scope of the
present article, it is important to know that empirical tests have been developed to evaluate whether a
construct is categorical versus dimensional. Readers interested in this issue can consult the
taxometric method developed by Meehl (1992) or the article of Ruscio and colleagues (2011).
Second, rather than debating whether a construct is continuous or categorical, one might
consider that both the continuous as well as the categorical approach provide separate, but equally
33
useful information (Collins & Lanza, 2010). When such an agnostic perspective on the nature of
constructs is adopted, the choice for a continuous or a categorical latent variable is dictated by the
research question at hand. For example, although few people would dispute a continuous treatment
of personality traits, according to which individual differences in personality traits are expressed as
the degree to which the trait is characteristic of those individuals, most people also see value in a
categorical, profile-perspective on traits. The reason is that those two approaches to personality traits
address different questions. While the dimensional perspective is well suited to study the effect of
individual differences in one or more traits, thereby taking those traits as the focal point of analysis,
the categorical perspective looks at the effects of specific combinations of trait scores, which implies
shifting the focus from the traits to the individual. This example also illustrates a broader point.
Despite our plea for more person-centered vocational research, it is important to realize that person-
and variable-centered approaches are not conflicting but rather complementary. Ultimately, person-
and variable-centered approaches can even be used in tandem to provide a more comprehensive view
of the same phenomena (e.g., Morin, Boudrias, Marsh, Madore, & Desrumaux, 2016).
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Figure 1: Latent Profile Analysis (LPA). Scores on the continuous (LPA) indicators (y’s) are caused
by the categorical latent variable C (with k latent classes). In the most constrained model, the latent
classes differ in mean scores on the indicators only, but in alternative formulations, indicator
variances can be class-specific and correlated residuals can be added. In case the indicators are not
continuous but categorical, LPA becomes Latent Class Analysis (LCA).
43
Figure 2: Factor Mixture Analysis (FMA). The indicators (y’s) are caused by both a continuous
latent variable f and a categorical latent variable C (with k latent classes). The dashed lines indicate
that the factor structure can be different in each latent class. In FMA, factor loadings, factor means,
the factor covariance matrix and item intercepts/thresholds can be class-specific.
44
Figure 3: Mixture Regression Analysis (MRM). The latent variable C (with k latent classes)
moderates the relation between X and Y. In the basic MRM, the means and variances of the
outcome(s) are class-specific, while in a more flexible representation the means and variances of the
predictor(s) can also be class-specific.
45
Figure 4: Growth Mixture Modeling (GMM). k latent classes are estimated, each having class-
specific growth parameters. In GMM, any part of the model can be class-specific (including the
means and variances of the latent growth parameters, the indicator variances, etc.).
46
Figure 5: Latent Transition Analysis (LTA). The LTA model estimates on both measurement
occasions k latent classes (from repeated measures of the same four items at t1 and t2), as well as the
probabilities to transition from classes in Ck1 to classes in Ck2 over time. The number and structure
of profiles can be different on both measurement occasions, and indicators can be categorical
(implying that the latent transition model is an extension of Latent Class Analysis) or continuous
(implying that the latent transition model is an extension of Latent Profile Analysis).
47
Table A1: Overview of studies in research on careers, career counseling and/or vocational
behavior that used cluster analysis, along with the substantive question they wanted to address
Clustering dimensions of vocational identity and career beliefs
Hechtlinger, Levin, & Gati
(2019)
Investigating the factor structure and psychometric properties of the
Dysfunctional Career Decision-Making Beliefs questionnaire.
Shimizu, Dik, & Conner
(2019)
Investigating characteristics of subgroups of individuals who
identified as having a calling.
Capitano, DiRenzo, Aten, &
Greenhaus (2017)
Identifying role identity by means of profiles that reflect the
salience of three roles: work, home, and military service.
Rhee, Lee, Kim, Ha, & Lee
(2016)
Identifying statuses of vocational identity and how these are related
to planned happenstance skills.
Sestito, Sica, Ragozini, Porfeli,
Weisblat, & Di Palma (2015)
Exploring the configuration of vocational and overall identity
domains in young adults.
Vilhjalmsdottir & Arnkelsson
(2013)
Examining the relationship between habitus (i.e., a cognitive
structure based on configurations of cultural and leisure activities)
and career choice.
Kossek, Ruderman, Braddy, &
Hannum, (2012)
Exploring how boundary management profiles, reflecting
interruption behaviors, identity centralities, and boundary control,
relate to key work-family outcomes.
Zhou, Leung, & Li (2012)
Examining the meaning of work among Chinese university
students.
Santos & Ferreira (2012)
Identifying groupings that underly the concept of career indecision
based on a battery of instruments designed to assess career and
personality dimensions.
48
Hirschi (2011a)
Identifying essential and optional components of a presence of
calling.
Hirschi (2011b)
Identifying groups of students based on the dimensions of career
exploration and career commitment; investigating whether these
different identity statuses relate to differences in interest structure
in terms of differentiation, coherence, elevation, and interest-
aspiration congruence.
Luyckx, Duriez, Klimstra, &
De Witte (2010)
Identifying identity clusters or statuses and investigating concurrent
and prospective relations with work engagement and burnout.
Moen, Kelly, & Huang (2008)
Identifying job and home ‘ecologies’ based on work-family and
demands-control variables.
Argyropoulou, Sidiropoulou-
Makakou, & Besevegis (2007)
Classifying students based on their career decision status;
investigating the relationship between career decision status groups
and generalized self-efficacy, coping strategies, and vocational
interests.
Akos, Konold, & Niles (2004)
Exploring a career readiness typology of 8th-graders using the
Career Factors Inventory.
Kelly & Lee (2002)
Exploring the structure of career indecision based on six factors:
lack of information, need for information, trait indecision,
disagreement with others, identity diffusion, and choice anxiety.
Tracey & Darcy (2002)
Examining the relationships between career decidedness and
vocational interest.
Cinamon & Rich (2002)
Identifying profiles of attribution of importance to life roles and
examining their implications for the work-family conflict.
49
Larson & Majors (1998)
Identifying subtypes of undecided students based on the clustering
of a variety of career planning measures.
Meldahl & Muchinsky (1997)
Identifying different clusters of career indecision based on
measures of career indecision and neuroticism.
Gati, Krausz, & Osipow (1996)
Investigating the internal structure of a career decision-making
difficulties model and questionnaire.
Cohen & Chartrand (1995)
Investigating the relationships between career indecision subtypes
and ego identity development.
Rojewski (1994)
Identifying different career indecision types in a group of
adolescents from rural areas.
Wanberg & Muchinsky (1992)
Identifying groups of college students on the basis of their scores
on a range of personality and vocational indecision constructs.
Savickas & Jarjoura (1991)
Identifying groups of college students on the basis of their
responses to career decision scale items.
Lucas & Epperson (1990)
Identifying types of vocational undecidedness based on a battery of
personality and work orientation questionnaires.
Larson, Heppner, Ham, &
Dugan (1988)
Investigating multiple subtypes of career indecision.
Hamilton (1977)
Identifying different types of professional identity in doctorate
level programs in clinical and counseling psychology.
Matthews & Tiedeman (1964)
Investigating attitudes toward career and marriage and the
development of life style in young women.
Clustering career events and occupational characteristics
Smith & Campbell (2006)
Investigating the structure of ONET occupational values.
50
Armstrong, Smith, Donnay, &
Rounds (2004)
Creating a classification system and spatial map of occupations
using the Basic Interest Scale profiles of occupational incumbent
samples.
Bruce & Scott (1994)
Identifying types of inter-role transitions based on desirability and
magnitude ratings of 15 career events. Investigating whether
transition outcomes (strain, role ambiguity, adjustment difficulty,
transition eagerness, perceived gains and losses) differ across
transition types.
Claes & Quintanilla (1994)
Constructing career patterns based on self-reported activities taking
place (i.e., employment, educational preparations, unemployment,
military or civil service), personal and work-related variables, as
well as by means of work indices (work centrality, intrinsic versus
extrinsic work orientations, and societal norms about working).
Pickering & Galvin-Schaefers
(1988)
Comparing reentry women with career women on a set of
demographic and personality variables.
Clustering attitude dimensions (commitment motivation balance)
Vieira, Matias, Lopez, &
Matos (2018)
Identifying couple-level profiles of conflictual and enriching
dimension of work-family balance and investigating their
associations with individuals’ work- and family-related satisfaction.
Paixão & Gamboa (2017)
Identifying distinct motivational profiles in a sample of high school
students and investigating differences between and among these
profiles across career exploration and career indecision levels.
Kuron, Schweitzer, Lyons, &
Ng (2016)
Identifying career profiles based on protean and boundaryless
career attitudes and to examine differences between profiles in
51
terms of agency (i.e. career commitment, self-efficacy, and work
locus of control) and career attitudes (i.e. salience and satisfaction).
Moran, Diefendorff, Kim, &
Liu (2012)
Exploring how different combinations or patterns of motivations
(based on self-determination theory) relate to organizational
factors.
Tsoumbris & Xenikou (2010)
Creating profiles of commitment, based on the three components
(i.e., affective, continuance, normative) of organizational and
occupational commitment.
Segers, Inceoglu, Vloeberghs,
Bartram, & Henderickx (2008)
Identifying motivational groups based on a combination of work
motives and protean and boundaryless career attitudes.
Wasti (2005)
Exploring how affective, continuance, and normative commitment
combine to create distinct profiles of commitment; investigating
how commitment profiles relate to desirable job behaviors.
Clustering behaviors and problems
Maher, Gallagher, Rossi,
Ferris, & Perrewe (2018)
Investigating configurations of impression management tactics; and
to test political skill and political will as predictors of impression
management configurations.
Poynton, Lapan, & Marcotte
(2015)
Identifying distinct financial planning groups in a sample of 12th
graders and investigating how these groups differ on various
college and career readiness characteristics.
Solberg, Carlstrom, Howard, &
Jones (2007)
Classifying high school youth into varying academic at-risk
profiles using self-reported levels of academic confidence,
motivation to attend school, perceived family support, connections
with teachers and peers, and exposure to violence.
52
Multon, Wood, & Gysbers
(2007)
Identifying different types of career counseling clients based on a
variety of career-related variables (e.g., vocational identity) and
psychological issues that may affect career concerns (e.g., level of
psychological distress).
Gore, Bobek, Robbins, &
Shayne (2006)
Identifying a typology of computerized career guidance users,
based on a clustering of career exploratory behavior.
Rochlen, Milburn, & Hill
(2004)
Identifying different types of career counseling clients based on
(personal and career-related) distress, discomfort, uncertainty,
concerns, and stigma about career counseling.
Niles, Anderson, &
Goodnough (1998)
Identifying different ways in which adults use exploratory behavior
to cope with career development tasks.
Clustering interests and preferences
Einarsdottir, Eyjolfsdottir, &
Rounds (2013)
Clustering vocational interest items into basic interest scales to
describe the vocational interest landscape in Iceland.
Tay, Su, & Rounds (2011)
Investigating the structure and meaning of the peoplethings and
dataideas interest dimensions.
Armstrong & Vogel (2009)
Investigating interest-efficacy associations from a RIASEC
perspective.
Armstrong, Rounds, & Hubert
(2008)
Exploring whether specific interest measures are best clustered
according to Holland’s higher order RIASEC types.
Stratton, Witzke, Elam, &
Cheever (2005)
Generating instructional profiles, reflecting participants’
comparative preferences for self-study/lecture versus group
discussion/computers.
Hansen & Scullard (2002)
Investigating the structure of leisure interests.
53
Shivy, Rounds, & Jones (1999)
Examining the structure of naturally occurring occupational
perceptions.
Clustering personality variables
Viola, Musso, Inguglia, & Lo
Coco (2016)
Identifying profiles of hardiness and to explore the moderating role
of hardiness in the association between psychological well-being
and career indecision.
Oztemel (2013)
Examining the validity of the classification system of the emotional
and personality-related career decision-making difficulties model
and questionnaire.
De Fruyt (2002)
Demonstrating that clusters of individuals, based on their FFM
personality scores (i.e., internalizers/externalizers and resilients),
show different positions on the labor market and demonstrate
differential initial career outcomes.
Gustafson & Mumford (1995)
Identifying clusters of ‘personal style’ based on seven job-relevant
personality variables. Identifying environmental constraints and
opportunities based on nine workgroup characteristics.
Note: Search terms were “cluster”, “hierarchical cluster”, and “k-means”
54
Table A2: Overview of studies in research on careers, career counseling and/or vocational
behavior that used latent class analysis, along with the substantive question they wanted to address
Classifying career preferences
Johnson & Bouchard (2009)
Investigating links between general intelligence and eight occupational
interest dimensions.
Gerber, Wittekind, Grote, &
Staffelbach (2009)
Identifying types of career orientation; to explore their prevalence; and
investigating differences in work attitudes and sociodemographic
variables between types.
Classifying employment types and outcomes
Haines, Doray-Demers, &
Martin (2018)
Developing a typology of part-time employment on the basis of role
occupancy and work characteristics; investigating attitudinal and
health-related outcomes associated with different employment
forms.
Hyvönen, Räikkönen, Feldt,
Mauno, Dragano, & Matthewman
(2017)
Forming reward patterns on the basis of perceived and objective
career rewards (i.e., career stability and promotions) and
investigating the impact of these patterns on personal work goals.
Van Aerden, Moors, Levecque,
& Vanroelen (2015)
Creating a typology of employment arrangements; investigating
differences between employment arrangements in terms of work-
related well-being indicators.
Classifying mobility patterns
Majeed, Forder, Mishra,
Kendig, & Byles (2015)
Identifying workforce participation patterns across the adult life
course, and exploring the influences of various early and adult life
socio-demographic circumstances.
55
Woo (2011)
To validate Ghiselli’s “hobo syndrome” as a career pattern
characterized by frequent job movement behavior and positive
attitudes about such behavior. To explore the dispositional roots of
hobo syndrome and its work-related outcomes.
Note: The search term was “latent class”
56
Table A3: Overview of studies in research on careers, career counseling and/or vocational
behavior that used latent profile analysis, along with the substantive question they wanted to address
Profiling interests
Ferguson & Hull (2019)
Identifying distinct profiles of science interest; investigating gender,
vocabulary ability, and personality as predictors of profile
membership.
Perera & McIlveen (2018)
Identifying distinct profiles of interests; examining the likelihood of
STEM degree choice as a function of profile membership; and
investigating personality predictors of interest profile membership.
Profiling attitudes (motivation engagement burnout commitment)
Moeller, Ivcevic, White,
Menges, & Brackett (2018)
Investigating intra-individual engagement-burnout profiles, and
demands-resources profiles.
Gillet, Morin, Sandrin, &
Houle (2018)
Exploring combinations of work engagement and workaholism
levels; and investigate their relations with negative outcomes.
Valero & Hirschi (2016)
Identifying profiles of work-related motivation among adolescents
(i.e., autonomous goals, positive affect, and occupational self-
efficacy). Investigating whether motivational profiles predict
changes in desirable work outcomes.
Howard, Gagne, Morin, & Van
den Broeck (2016)
Identifying the simultaneous occurrence of multiple motivation
types within individual workers. Investigating the relationship
between motivation profile and work performance.
Graves, Cullen, Lester,
Ruderman, & Gentry (2015)
Identifying managers' motivational profiles based on four
motivational types delineated by self-determination theory (i.e.,
external, introjected, identified, intrinsic). To test a model of the
57
antecedents (i.e., perceived supervisor support and organizational
politics) and consequences (i.e., work attitudes and promotability)
of these profiles.
Meyer, Morin, &
Vandenberghe (2015)
Investigating profiles of (affective, normative, continuance)
commitment to two interrelated targets, the organization and
supervisor.
Lopez, McDermott, & Fons-
Scheyd (2014)
Identifying clusters of participants with distinct profiles of multiple
role planning attitudes; and investigating the well-being outcomes
of profile-membership.
Stanley, Vandenberghe,
Vandenberg, & Bentein (2013)
Identifying profiles of commitment based on combinations of
affective, normative, perceived sacrifice, and few alternative
commitments. Investigating how these profiles determine turnover
intention and turnover.
Meyer, Stanley, & Parfyonova
(2012)
Identifying distinct profiles of (affective, normative, continuance)
commitment and how these relate to a range of outcomes (i.e., need
satisfaction, regulation, affect, engagement, organizational
citizenship behavior, and well-being).
Profiling personality (traits adaptability)
Barbaranelli, Fida, Paciello, &
Tramontano (2018)
Combining work self-efficacy dimensions into different patterns;
investigating whether self-efficacy profiles associate with different
levels of adjustment.
Perera & McIlveen (2017)
Identifying distinct profiles of adaptivity based on combinations of
the Big-Five personality dimensions. Linking these adaptivity
profiles with adapting and adaptation outcomes.
58
Hirschi & Valero (2015)
Identifying subgroups with distinct adaptability profiles in terms of
concern, control, curiosity and confidence. Exploring the
relationship between the various adaptability profiles and adapting
(career planning, career decision-making difficulties, career
exploration, and occupational self-efficacy beliefs) and adaptivity
(core self-evaluations and proactivity).
Rice, Ray, Davis, DeBlaere, &
Ashby (2015)
Identifying different types of perfectionism (i.e., adaptive,
maladaptive, and nonperfectionist).
Rice, Lopez, & Richardson
(2013)
Creating profiles based on measures of perfectionism and
personality (i.e., conscientiousness, neuroticism) and investigate
how this relates to STEM performance.
Profiling behaviors
Mäkikangas (2018)
Examining whether discernable profiles can be identified based on
scores on four job crafting behaviors, and if so, whether such
profiles differ in relation to work engagement.
Profiling the (perceived) environment
Hirschi & Valero (2017)
Identifying qualitatively differing profiles according to levels of
perceived chance events and career decidedness. Investigating
whether these groups differ in work motivation (i.e., occupational
self-efficacy beliefs, perceived person-job fit, and work
engagement).
Dahling, Gabriel, &
MacGowan (2017)
Identifying profiles of feedback environment perceptions; link these
profiles to antecedents grounded in social exchange theory; and test
59
the relations of these profiles with important feedback environment
criteria.
Note: The search term was “latent profile”
60
Table A4: Overview of studies in research on careers, career counseling and/or vocational
behavior that used factor mixture analysis, along with the substantive question they wanted to
address
Hyvönen, Räikkönen, Feldt,
Mauno, Dragano, & Matthewman
(2017)
Identifying different reward patterns on the basis of perceived and
objective career rewards (i.e., career stability and promotions)
across four measurements and investigating the impact of long-term
rewards patterns on contents of personal work goals.
Leuty, Hansen, & Speaks (2016)
Identifying groups of college students with similar profiles of vocational
and leisure interests.
McLarnon, Carswell, &
Schneider (2015)
Identifying qualitatively and quantitatively distinct subgroups or
types of individuals differentiated on the basis of interests in the
RIASEC variables.
Deemer, Lin, Graham, & Soto
(2009)
Identifying subgroups of STEM-students on the base of their
vulnerabilities and affective responses to threatening stereotypes.
Note: The search term was factor mixture
61
Table A5: Overview of studies in research on careers, career counseling and/or vocational
behavior that used mixture regression analysis, along with the substantive question they wanted to
address
Gillet, Morin, Sandrin, &
Houle (2018)
Exploring combinations of work engagement and workaholism
levels; and investigating their relations with negative outcomes.
Chénard-Poirier, Morin, &
Boudrias (2017)
Exploring patterns of relations among three leadership
empowerment practices (i.e., delegation, coaching, and recognition)
and five indicators of behavioral empowerment
Hofmans, De Gieter &
Pepermans (2013)
Identifying types of individuals based on different job reward-job
satisfaction relationships. Investigating whether these different
person types have differential associations with turnover intention
and organizational commitment.
Note: Search terms were “mixture regression”, “clusterwise regression”, and “latent class regression”
62
Table A6: Overview of studies in research on careers, career counseling and/or vocational
behavior that used configural frequency analysis, along with the substantive question they wanted to
address
Moeller, Ivcevic, White,
Menges, & Brackett (2018)
Examining associations between demands-resources profiles and
engagement-burnout profiles.
Reitzle & Vondracek (2000)
Identifying patterns of (categorical) career and family
characteristics (e.g., marital status, completion of training, history of
unemployment, etc.).
Note: The search term was “configural frequency”
63
Table A7: Overview of studies in research on careers, career counseling and/or vocational
behavior that used Davison and Davenport’s (2002) criterion-based method, along with the
substantive question they wanted to address
Wiernik (2016)
Identifying patterns in the predictive relationships between
personality traits and Realistic vocational interests
Perry (2008)
Identifying a predictive profile of vocational exploration and racial
identity for behavioral and psychological factors of school
engagement.
Note: We searched for articles that referred to the Davison and Davenport (2002) article and also looked for the terms
“criterion profile” and “criterion pattern”
64
Table A8: Overview of studies in research on careers, career counseling and/or vocational
behavior that used growth mixture modeling, along with the substantive question they wanted to
address
Modeling career trajectories and success
Huang, Evans, Hara, Weiss, &
Hser (2011)
Identifying employment trajectory groups and the impact of gender
and drug use.
Zwaan, ter Bogt, &
Raaijmakers (2010)
Identifying groups of musicians with different career patterns; and
investigating how career success was influenced by social support,
professional attitude and professional network.
Modeling vocational identity trajectories
Hirschi (2011c)
Identifying distinct developmental trajectories of career-choice
readiness in adolescents; and investigating the impact of
environmental demands and individual differences on these
developmental trends.
Modeling performance trajectories
Miraglia, Alessandri, &
Borgogni (2015)
Identifying trajectory classes of job performance based on repeated
supervisory ratings and data on employees’ organizational tenure
and self-efficacy.
Modeling attitude trajectories (motivation engagement)
Gillet, Morin, Huart, Odry,
Chevalier, Coillot, &
Fouquereau (2018)
Identifying distinct trajectories of self-determined motivation for a
vocational training, and investigating the implications of these
65
trajectories for a variety of outcomes (i.e., positive and negative
affect, and performance).
Upadyaya & Salmela-Aro
(2015)
Identifying latent trajectory groups based on repeated assessments
of career engagement and satisfaction among young adults.
Rice, Ray, Davis, DeBlaere, &
Ashby (2015)
Identifying distinctly low, moderate, and high patterns of academic
stress over the year.
Note: Search terms were “growth mixture”, and “latent class growth
66
Table A9: Overview of studies in research on careers, career counseling and/or vocational
behavior that used latent transition analysis, along with the substantive question they wanted to
address
Mäkikangas (2018)
Identifying profiles of job crafters based on self-report crafting
behaviors; and investigating whether employees maintain their
profile membership over time.
Kunst, van Woerkom, van
Kollenburg & Poell (2018)
Identifying goal orientation profiles; evaluate their stability over
time; and assessing the impact of managerial coaching behavior
change in employees’ goal orientation profiles.
Rice, Ray, Davis, DeBlaere, &
Ashby (2015)
Examining the relationships between different types of
perfectionism and the experience of stress levels across time.
Note: The search term was “latent transition”
67
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73
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... Trait complex theory is a variable-centered model of individual differences because it focuses on how levels of specific traits are related to each other. In contrast, a personcentered model of individual differences is concerned with profile shape, or how the levels of a person's traits relate to each other (Hofmans et al. 2020;Woo et al. 2018). Evaluating the profile shape of abilities and interests is critical because it provides information that is unavailable when only comparing traits between persons (Lubinski 2020). ...
... The aim of this study is to investigate profiles of interests and abilities and the extent to which these profiles are associated with strengths and weaknesses in knowledge. To accomplish this, we employ latent profile analysis (LPA), a person-centered method that groups individuals with similar levels of traits into different profiles rather than grouping items or variables based on their similarity (Hofmans et al. 2020). By producing such profiles, LPA allows us to investigate how different patterns of interests and abilities lead to different kinds of knowledge. ...
... LPA has become an increasingly valuable tool for assessing combinations of individual differences, both in vocational (Howard et al. 2016;Meyer et al. 2015) and personality (Gerlach et al. 2018;Merz and Roesch 2011) research. LPA has three main advantages over simpler clustering techniques, such as centroid-based clustering (Hofmans et al. 2020). First, LPA uses "soft clustering", which means that it calculates the probability that individuals belong to each class rather than directly assigning them to one class. ...
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Cognitive abilities and interests both play an important role in guiding knowledge acquisition, but most previous studies have examined them separately. The current study used a large and representative dataset to integrate interests and abilities using a person-centered approach that examines how distinct profiles of interests and abilities relate to individual strengths and weaknesses in knowledge. Two key findings emerged. First, eight interest–ability profiles were generated from Latent Profile Analysis (LPA), which replicated and extended the interrelations of interests and abilities found in previous studies using variable-centered approaches. Second, each profile’s strongest knowledge scores corresponded to their strongest abilities and interests, highlighting the importance of interest–ability profiles for guiding the development of knowledge. Importantly, in some domains, the lower ability profiles were actually more knowledgeable than higher ability profiles. Overall, these findings suggest that people learn best when given opportunities to acquire knowledge relevant to both their interests and abilities. We discuss how interest–ability profiles inform integrative theories of psychological development and present implications for education and career development.
... In response, scholars have started to explore profile-based approaches. Results suggest the existence of distinct subpopulations with unique configurations of the four adaptability resources (e.g., Hirschi & Valero, 2015), thus highlighting the relevance of person-centered approaches in vocational behavior research (Hofmans et al., 2020;Spurk et al., 2020). In summary, despite widespread acceptance of the multidimensional and malleable nature of career adaptability, scholars have only recently begun to utilize methods that capture either its malleability (e.g., Koen et al., 2012) or multidimensionality (e.g., Hirschi & Valero, 2015). ...
... Also, career development and career adaptability entail social phenomena shaped by a combination of variables/factors, which means that person-centered research has the potential in improving our understanding of interdependencies between variables that shape career adaptability (Hofmans et al., 2020;Spurk et al., 2020;Vondracek & Porfeli, 2002). The focus of the present research is on identifying groups of individuals with unique combinations of curiosity, control, confidence, and concern within a given population (cf., Rudolph et al., 2019;Spurk et al., 2020). ...
... The focus of the present research is on identifying groups of individuals with unique combinations of curiosity, control, confidence, and concern within a given population (cf., Rudolph et al., 2019;Spurk et al., 2020). This emphasis also aligns with a recent surge in profile-based research in organizational behavior (e.g., Bouckenooghe et al., 2019;Gabriel et al., 2015;Kam et al., 2016) and in career-related research in particular (e.g., Hirschi & Valero, 2015;Hofmans et al., 2020;Perera & McIlVeen, 2017;Vondracek & Porfeli, 2002). In short, the person-centered approach offers a uniquely informative perspective to organizational research by focusing on configurations that differ in level (i. ...
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Research on career adaptability has mainly relied on a variable-centered approach, focusing on the average effects of its four resource dimensions (i.e., concern, control, confidence, and curiosity) in relation to antecedents and outcomes within a given sample. A complementary approach is person-centered research (i.e., mixture models). Following 93 university students across an 11-week service-learning project, we collected data about students' career adaptability resources in three waves (at the beginning, middle, and completion of the project). Our analyses identified unique subgroups with distinct profiles of career adaptability resources that differed in level (i.e., low career adaptability, average career adaptability and high career adaptability), but not in shape. We then explored the patterns of movement and stability in these three profiles over time using latent transition analysis. Data about personal (i.e., regulatory focus: prevention and promotion focus) and situational (i.e., challenge and hindrance stressors) factors were collected to help explain the transitional probabilities for stability and change in profiles over time. The negative valence predictors (i.e., prevention focus and hindrance stressors) did not play a role in explaining the transitional probabilities between profiles, whereas the positive valence predictors (i.e., promotion focus and challenge stressors) did – a finding also relevant to the literature on developmental tasks as instigators of young people developing their career adaptability. Overall, these findings suggest that the person-centered approach can be a useful method to analyze change and stability in career adaptability profiles.
... This study explores the impact of team processes on individuals' proactive motivation longitudinally with a person-centred approach. By modelling proactive motivational states as latent profiles, the effects of team processes on the three facets of proactive motivation can be investigated simultaneously (Hofmans et al., 2020;Moran et al., 2012;Valero and Hirschi, 2016). For this purpose, individuals are classified into profiles characterised by patterns of proactive motivation (Moran et al., 2012). ...
... Person-centred methods can add valuable insights in that regard as they generate knowledge on the joint impact of different dimensions (Hofmans et al., 2020). In contrast to Proactive motivation in teams variable-centred methods, which study variables at the unit level, person-centred methods aim to identify subgroups that share configural profiles of personal attributes (Bergman and Trost, 2006;Spurk et al., 2020). ...
... Thus, investigating proactive motivational profiles can reveal unique insights into the co-occurrence of the three states and their relations to predictors and outcomes (Moran et al., 2012). Albeit the incremental value for the investigation of integrative theories, person-centred studies are rare in vocational research (Hofmans et al., 2020;Spurk et al., 2020) and have only been used once for investigating proactive motivation (Valero and Hirschi, 2016). This study adds to the proactive motivation research domain by investigating proactive motivational profiles longitudinally. ...
Article
Purpose This study aims to investigate cross-level influences of team cohesion, trust and conflicts on team member’s proactive motivational profiles and outcomes of profile membership over time. Design/methodology/approach Data was collected in a four-month longitudinal field study with 47 teams (N = 202). Findings Latent profile analysis derived four proactive motivational profiles. The higher motivated profiles reported better study outcomes, higher levels of team trust and cohesion and fewer conflicts over time. Team trust and interpersonal conflicts emerged as significant predictors of profile membership. Practical implications Recommendations are derived on how to best manage teams and the members comprising it when trust in teams is low or interpersonal conflicts are high. Originality/value Applying a person-centred approach in a team context advances multi-level theories of team motivation by mapping the cross-level effects of team processes on different kinds of motivational states.
... The study aimed at investigating students' diversity in their preparation to HE using latent profile analysis. Complementary to variable-centred approaches, person-centred approaches aim to identify clusters of individuals-called profiles-who are distinct on a set of indicators (Hofmans, Wille, and Schreurs 2020). Based on the previous taxonomy of entrance profiles (De Clercq, Galand, and Frenay 2017; De Clercq, Van Meenen, and Frenay 2020), we first aimed at replicating students' patterns based on past performance, informed choice, self-efficacy beliefs, and SES. ...
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The transition into higher education (HE) is a particularly challenging process for students due to a large variety of difficulties and requirements. Moreover, increasing student numbers and diversity in European HE have complexified the issue of the successful transition to university. Consequently, it is important to further develop our understanding of the heterogeneity of students and the specific challenges that impact their successful and less stressful transitions into higher education. This paper contributes to this scientific endeavour. More precisely, a study was carried out among 1,048 first-year students from a French-speaking Belgian university. Using latent profile analysis, our results yielded five profiles representing different combinations of achievement pre-dictors (high school grade, socioeconomic status, informed-choice, and self-efficacy beliefs). When comparing the profiles, our results further highlighted key differences in the way students experienced the specific challenges associated with the transition and succeeded at the end of the first year. The discussion of the results allowed us to provide practical implications and future perspectives on the thorny issue of diversity into the transition to HE.
... Improving our programmatic understanding of job search requires a broader, more integrated assessment of multiplex motivations. One approach to studying multiplex motivations is a configurational or person-based perspective (Hofmans et al., 2020) that considers individuals may be interested in optimizing across wages, incentives, personal or family time, social identity, and values expression. This approach considers that different sub-populations of job seekers with similar motivational configurations or behaviors may exist within a larger population. ...
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A growing body of research across multiple disciplines has aimed to better understand the phenomenon of job search. However, little empirical research has examined the combined content and structure of the job search literature to accumulate programmatic knowledge. Unfortunately, this has resulted in redundancies and isolated advances that harm our ability to make concrete practical recommendations to aid policy makers, organizations, and broader society. Using bibliometric analysis of 3,197 articles on job search, the present article identifies and describes 10 distinct communities of thought and assesses patterns of integration between these communities. Assessment of community relationships confirms disciplinary divides, but reveals insights into patterns of thought within disciplines, and structural and conceptual relationships between them. Based on these findings, we offer a multilevel conceptual framework to organize the job search literature and suggest possible ways to improve its integration to build a more programmatic understanding of the job search phenomenon.
... Moreover, higher values on entropy (range = 0-1) are a sign of better fit and class precision. We primarily relied on the LMR, BIC, SSABIC, and BLRT and aimed for a parsimonious and theoretically meaningful model that can be interpreted with ease (for an overview, see Hofmans et al., 2020;Spurk et al., 2020). ...
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Work design plays an important role in workers' job-related well-being, but not every employee responds to work design in the same way. Given trends toward longer working lives and higher age diversity in the workforce, worker age is an important factor to consider. However, knowledge about the interplay between worker age and work design is limited, especially when considering the multitude of job characteristics that people experience at the same time. Integrating the work design and lifespan/career development literatures and adopting a person-centered approach, we investigated how worker age affects membership in work design profiles and the relationship between work design profiles and occupational well-being. Using two independent samples (N = 989; 980), we conducted latent profile analysis to group workers into work design profiles based on 6 age-relevant job characteristics (autonomy, information-processing, workload, social support, emotional demands, and social conflicts). We identified 3 profiles and linked them to well-being: motivating (most favorable), moderately stimulating, and socially taxing (least favorable). Older workers were more likely to be in, and responded better to motivating work design profiles, and less likely to be in, and responded worse to socially taxing profiles. Meanwhile, younger workers seemed more tolerant of socially taxing work design profiles than older workers. Most age-contingent effects were robust after adding organizational tenure as a covariate. Findings qualify lifespan development theories and shed light on workers' nuanced responses to work design profiles.
... Latent profile analysis (LPA) is a person-centered approach that identifies 'profiles' of latent group membership based on a set of continuous indicator variables (Hofmans et al., 2020;Howard & Hoffman, 2018). In the current study, we sought to identify profiles that differentially characterized the nature and extent of disruption experienced by participants due to COVID-19. ...
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The disruptive effects of the COVID pandemic on vulnerable and/or minority demographic groups among 1) student populations and 2) persons employed in low wage sectors are well-established. This study examined whether disparity in the disruptive effects of the pandemic extend to adult learners employed in "bright prospect" sectors (e.g., computing and information technology). Survey results from a sample of 989 employees enrolled in an online Masters of Science in Computer Science program during the onset of COVID-19 revealed significant disparate impacts to work and learning as a function of age, race, and psycho-social factors (e.g., social support). The findings show that disparity in the effects of the pandemic transcend wage to affect the education and professional development of persons engaged in knowledge-based occupations. While results are based on the experiences of the COVID-19 pandemic, they provide observations and implications for navigating ongoing and future disruptive events. Specifically, results highlight the value of a 'whole-person' approach to more precisely identify the pathways by which these disruptive effects occur, particularly in the context of career development. At the institutional level, interventions to support adult learners through disruption should incorporate such an approach. Because continuous professional learning is critical for career advancement in knowledge-based sectors, the findings have implications for improving participation and mobility of underrepresented groups in computing and related fields.
... Therefore, in this study work engagement is studied at a total of four-time points between April 2020 and February 2021 in order to achieve a longer time perspective on remote work. We adopted a person-centered approach (Hofmans et al., 2020) which enabled us to identify possible differing work engagement experiences and changes therein over time. It is plausible that among some employees work engagement may have decreased at some point of the pandemic (Oksa et al., 2021;Syrek et al., 2022), but an opposite development may also be possible because due, for example, to increased autonomy (Syrek et al., 2022) and the opportunity to concentrate on individual tasks due to the absence of office distractions and commuting (Kelliher & Anderson, 2010). ...
Article
The aim of this study was to investigate characteristics associated with employees’ ability to cope with the challenges of remote working as flexible work arrangements are predicted to constitute an increasingly pervasive model of work. More specifically, we investigated job resources specific to remote work and employees’ strengths and behaviours that may be crucial for enhancing work engagement when working outside a traditional office environment. The present study adopted a person-centered approach to investigate work engagement and its antecedents. A sample of 455 employees completed a questionnaire four times across a ten-month period during the enforced remote work occasioned in response to the corona pandemic. The results revealed four distinct work engagement profiles. Most employees (75%) belong to profiles with either average or high levels of work engagement, which remained stable after a slight initial increase. A decrease was observed in 25% of those employees whose work engagement was already low at the study baseline. High levels of organisational support, the functionality of home as a work environment, job-related self-efficacy, and job crafting characterised the profile in which work engagement remained at a high level during the remote work. Implications for practice concerning well-being protective multi-locational work are presented.
... It is particularly appropriate for studying commitment as a system because it allows for all possible interactions between commitment dimensions in the analysis, whereas the variable-centred approach is restricted to a two-way or three-way interaction (Howard and Hofman, 2018). Furthermore, the personcentred approach identifies underlying prototypical subpopulations, which enables both inductive and abductive theorizing (Hofmans, Wille and Schreurs, 2020). Previous work has analysed commitments as profiles consisting of (1) multiple targets of commitment (e.g., Morin et al., 2011;2015); (2) multiple types of commitment (e.g., Kabins et al., 2016); (3) both types and targets (Loscher, Ruhle and Kaiser, 2020;Meyer, Morin and Vandenberghe, 2015;Morin et al., 2015); and (4) commitment in combination with other constructs that are inherent or inter-related to commitment, such as Gouldner's (1958) latent 'Cosmopolitans' and 'Locals'. ...
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Work increasingly takes place across organizational boundaries. This has implications for workers’ commitments to a plethora of targets, including the organization and the client, as well as for the conflicting nature of the interrelation between these commitments. In this paper, we draw on commitment system theory (CST), which views commitment as a malleable and interconnected system. Using a pragmatic abductive and quantitative discovery approach, we distinguish and develop commitment systems to consist of strength, including the multiple targets and types of commitment, as well as interaction between commitments, including coupling and nature. In particular, we develop a theoretical understanding of the nature dimension by identifying two target‐neutral types of conflict and one context‐ and target‐specific type of conflict. Three commitment systems are identified: ‘balanced system’, ‘conflicting system’, and ‘detached system’. Furthermore, we provide insights into how job demands resources and contextual job aspects influence membership of these commitment systems. This paper is the first to empirically explore commitment as a system, and it advances the theoretical understanding of commitment systems in a cross‐boundary space. Our study discusses the theoretical, methodological and practical implications for cross‐boundary organizations that compete with their clients for the commitment of their employees.
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Purpose Past research on the motivational processes underpinning knowledge sharing has assumed that the sharing processes are similar for all individuals. Yet, sharing is a fundamental affiliative behavior, and the sharing processes can differ between people. This study aims to propose and test a model of the moderating influence that employee attachment patterns have on the theory of reasoned action (TRA)-defined knowledge sharing processes. Design/methodology/approach The authors administered a questionnaire to 1,103 employees from a range of industries who participated in an online Qualtrics survey. Advanced forms for structural equation modeling and latent profile analysis were used to assess the proposed model. Findings The results revealed that participants in the study exhibited the latent profiles corresponding to secure, dismissive, preoccupied and fearful patterns. The preoccupied cohort had the lowest knowledge sharing behavior, yet the strongest links within the sharing process. Secure, dismissive and fearful had similar sharing levels, but the strength of the TRA-defined processes differed. These findings underscore equifinality: although sharing may be approximately equal across different attachment patterns, the fundamental processes underpinning sharing differ. Research limitations/implications The authors used self-report data, given that sharing attitudes, norms and intentions may not be overly amenable to ratings even from well-acquainted others. Further, the use of advanced analytical methods helps to minimize common method concerns. Additionally, causal mechanisms underscoring the TRA have been demonstrated (Ajzen and Fishbein, 2005), allowing us to explore the moderating role of attachment patterns. Practical implications This study speaks to the importance of considering employees’ attachment patterns, and developing comprehensive intra-organizational norms, policies and systems that support and encourage knowledge sharing from employees with a variety of attachment patterns. Originality/value This study uniquely contributes to knowledge sharing literatures by incorporating attachment patterns as moderators within the TRA-defined sharing processes. The authors provide important insights on the role of individuals’ attachment patterns have for knowledge sharing behaviors, but also highlight how structure of knowledge sharing differed across subgroups of employees, determined based on their dispositional attachment pattern.
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The concept of habitus designates a cognitive structure that is socially embedded and can give valuable insight into the social influences on career choice. A study investigating the relationship of habitus to career choice is presented in a sample of Icelandic youth aged 19–22 (N = 476). Measures of habitus were developed on the basis of reported cultural and leisure activities. The analysis of leisure and cultural items progressed in two steps: factor analysis and cluster analysis. The clusters constitute the four different habitus groups: Pop and fashion, Sports and rock, Music, and Literature. Correspondence factor analysis showed that the habitus groups (clusters) were related to other social variables, such as gender and class; validating habitus theory. Additionally, habitus measures were strongly linked to career variables, such as occupational perception and preferred future occupation. The results support previous research in showing that habitus theory is relevant to career counseling theory and can be a basis of understanding the relationship between social structure and career choice.
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Academic career development refers to the process by which employers as well as scholars working in research, teaching, and/or administrative roles in academic and higher education contexts manage various tasks, behaviors, and experiences within and across jobs and organizations over time, with implications for scholars' work-related identity. In this review article, we address the question: to what extent has conceptual and empirical research on academic career development captured central constructs and processes outlined by two important and comprehensive career development theories? Using social cognitive career theory and life-span, life-space theory as guiding frameworks, we categorized relevant articles published in academic journals into five thematic clusters: (a) individual characteristics, (b) contextual factors, (c) active regulation of behavior, (d) career stages, and (e) work and nonwork roles. Within these thematic clusters, major topics in the existing literature on academic career development include gender differences and women's experiences, mentoring and other career development interventions, and career development in the field of medicine. In contrast, social and cognitive processes, action regulation, later career stages, and the work-nonwork interface have been neglected in the literature on academic career development. We conclude by outlining an agenda for future research, including theoretical and methodological considerations.
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The 2011 Organizational Research Methods Feature Topic on latent class procedures has helped to establish person-centered analyses as a method of choice in the organizational sciences. This establishment has contributed to the generation of substantive-methodological synergies leading to a better understanding of a variety of organizational phenomena and to an improvement in research methodologies. The present Feature Topic aims to provide a user-friendly introduction to these new methodological developments for applied organizational researchers. Organized around a presentation of the typological, prototypical, and methodologically exploratory nature of person-centered analyses, this introductory article introduces seven contributions aiming to: (a) clarify the meaning, advantages, and applications of person-centered analyses; (b) illustrate emerging prototypical and longitudinal cluster analytic approaches; (c) introduce researchers to multilevel person-centered analyses as well as to auxiliary approaches that will drastically increase the scope of application of these methods; and (d) describe the application of these methods for confirmatory purposes.
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Most, if not all, workplace phenomena are dynamic, meaning that they emerge, evolve, and dissolve over time. Yet, the role of time is commonly overlooked in OB literature. This special issue showcases how a temporal process-oriented lens can be used to study dynamics of workplace phenomena. In this editorial, we define the term dynamics, arguing that research on workplace dynamics focuses on how within-person (or more broadly, within-unit) processes unfold over time. Moreover , we zoom in on the diverse roles of time, illustrating the rich diversity in research on workplace dynamics, and we highlight three specific challenges for scholars wanting to pursue this line of research. We conclude that the time has come to move from a differential to a temporal and process-oriented perspective, allowing us to understand what happens, how things happen, and why things happen at the workplace.
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Research on the simultaneous and interactive effects of both conflictual and enriching dimen-sions on work-family balance is scarce, and still scarcer are studies using a typological (person-oriented) approach to establish links between couple-level profiles of work-family balance ex-periences and individual's work- and family-related satisfaction. To address these gaps, thepresent study explored whether groups of dual-earner couples could be distinguished with re-spect to their specific combinations of work-to-family and family-to-work conflict and enrich-ment dynamics. Using cluster analysis procedures within a sample of 525 dual-earner couples, weidentified four couple-level profiles in the work-to-family balance direction (WFB) and four in thefamily-to-work balance direction (FWB): Harmful WFB and FWB groups (i.e., high conflict, lowenrichment), Beneficial WFB and FWB groups (i.e., low conflict, high enrichment), Active WFBgroup (i.e., high conflict, high enrichment), Passive FWB group (i.e., low conflict, low enrich-ment) and two other groups, one with men reporting a Harmful WFB and women a BeneficialWFB, and another with men reporting a Beneficial FWB and women a Harmful FWB. A subsequentmixed model ANOVA also found Harmful types were associated with lower individual satisfac-tions with work, family, and partner participation. Moreover, belonging to profiles where hus-band and wife have dissimilar WFB experiences seems to imply detrimental effects to bothpartners
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The present series of three independent studies examines how workaholism and work engagement combine relying on a variety of distinct methodologies: interaction effects (Study 1, n = 160), a person-centered approach (Study 2, including two samples of n = 321 and 332), and a hybrid mixture regression approach (Study 3, n = 283). This research also documents the relations between workaholism, work engagement, and work outcomes (i.e., work-family conflicts, work performance, sleeping difficulties, and burnout). Furthermore, this research investigates the role of workload (Studies 2 and 3) and perceived social support (Study 2) in the prediction of profile membership. Studies 1 and 2 showed that the combination of high levels of work engagement with high levels of workaholism was associated with a variety of negative outcomes. In Study 3, the highest levels of sleeping difficulties and work-family conflicts were associated with the workaholic profile, followed by the engaged-workaholic profile, and finally the engaged profile. Finally, in Studies 2 and 3, workload showed strong associations with an increased likelihood of membership into the profiles characterized by higher levels of workaholism.
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