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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

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,

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

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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 profiles—can 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

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.

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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

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. 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):

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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

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(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 reward–job

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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

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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.

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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

predictor–criterion 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 LTA—and LPA and LCA more broadly—those 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 cases—for 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 people—things and

data—ideas 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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75

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