Jeroen K. Vermunt

• Phd
• Professor (Full) at Tilburg University

Background Mood and anxiety disorders are heterogeneous conditions with variable course. Knowledge on latent classes and transitions between these classes over time based on longitudinal disorder status information provides insight into clustering of meaningful groups with different disease prognosis. Methods Data of all four waves of the Netherla...

Behavioral scientists often examine the relations between two or more latent variables (e.g., how emotions relate to life satisfaction), and structural equation modeling (SEM) is the state-of-the-art for doing so. When comparing these “structural relations” among many groups, they likely differ across the groups. However, it is equally likely that...

Introduction Identifying subgroups of Temporary (alcohol) Abstinence Challenge (TAC) participants may offer opportunities to enhance intervention effectiveness. However, knowledge about such subgroups is missing. This study aimed to (i) describe a TAC population; (ii) identify subgroups of participants based on determinants of changes in drinking b...

Bias-adjusted three-step latent class (LC) analysis is a popular technique for estimating the relationship between LC membership and distal outcomes. Since it is impossible to randomize LC membership, causal inference techniques are needed to estimate causal effects leveraging observational data. This paper proposes two novel strategies that make u...

Behavioral scientists often use Multigroup Structural Equation Modeling (MG-SEM) to compare groups in terms of their relations among latent variables (LV) —also called 'structural relations'. Since LVs are indirectly measured via questionnaire items, one should evaluate to what extent their measurement is invariant before comparing their structural...

Structural equation modeling (SEM) is commonly used to explore relationships between latent variables, such as beliefs and attitudes. However, comparing structural relations across a large number of groups, such as countries, can be challenging. Existing SEM approaches may fall short, especially when measurement non-invariance is present. In this p...

To objectively compare groups on any latent trait using tests, the absence of differential item functioning (DIF) is crucial. While the importance of DIF has been well-established in research, the question of how to identify DIF-free items is still largely open. The fact that item difficulty is not identified from observations may explain this. Rec...

Transgressive incidents directed at staff by forensic patients occur frequently, leading to detrimental psychological and physical harm, underscoring urgency of preventive measures. These incidents, emerging within therapeutic relationships, involve complex interactions between patient and staff behavior. This study aims to identify clusters of tra...

To objectively compare groups on any latent trait using tests, the absence of differential item functioning (DIF) is crucial. While the importance of DIF has been well-established in research, the question of how to identify DIF-free items is still largely open. The fact that item difficulty is not identified from observations may explain this. Rec...

Understanding causality is crucial for social scientific research to develop strong theories and inform practice. However, explicit discussion of causality is often lacking in social science literature due to ambiguous causal language. This paper introduces a text mining model fine-tuned to extract causal sentences from full-text social science pap...

Objectives The therapist‐facilitative interpersonal skills (FIS) has shown to predict therapy outcomes, demonstrating that high FIS therapists are more effective than low FIS therapists. There is a need for more insight into the variability in strengths and weaknesses in therapist skills. This study investigates whether a revised and extended FIS‐s...

Mplus and LatentGOLD implement the Vuong-Lo-Mendell-Rubin test (comparing models with K and K + 1 latent classes) in slightly differ manners. While LatentGOLD uses the formulae from Vuong (1989; https://doi.org/10.2307/1912557), Mplus replaces the standard parameter variance-covariance matrix by its robust version. Our small simulation study showed...

Bias-adjusted three-step latent class (LC) analysis is a popular technique for estimating the relationship between LC membership and distal outcomes. Since it is impossible to randomize LC membership, causal inference techniques are needed to estimate causal effects leveraging observational data. This paper proposes two novel strategies that make u...

We introduce a general method for sample size computations in the context of cross-sectional network models. The method takes the form of an automated Monte Carlo algorithm, designed to find an optimal sample size while iteratively concentrating the computations on the sample sizes that seem most relevant. The method requires three inputs: (1) a hy...

///// This file is a preprint of an already published article: https://www.researchgate.net/publication/383983190_Mixture_Multigroup_Structural_Equation_Modeling_A_Novel_Method_for_Comparing_Structural_Relations_Across_Many_Groups ///// Behavioral scientists often examine the relations between two or more latent variables (e.g., how emotions relat...

Extreme response style (ERS), the tendency of participants to select extreme item categories regardless of the item content, has frequently been found to decrease the validity of Likert-type questionnaire results. For this reason, various item response theory (IRT) models have been proposed to model ERS and correct for it. Comparisons of these mode...

The Antisocial Personality Disorder (ASPD), and antisocial behavior (ASB) in general, is associated with significant impact on individuals themselves, their environment, and society. Although various interventions show promising results, no evidence-based treatments are available for individuals with ASPD. Therefore, making informed choices about w...

The integration of causal inference techniques such as inverse propensity weighting (IPW) with latent class analysis (LCA) allows for estimating the effect of a treatment on class membership even with observational data. In this article, we present an extension of the bias-adjusted three-step LCA with IPW, which allows accounting for differential i...

Extreme response style (ERS), the tendency of participants to select extreme item categories regardless of the item content, has frequently been found to decrease the validity of Likert-type questionnaire results (Moors, 2012). For this reason, various IRT models have been proposed to model ERS and correct for it. Comparisons of these models are ho...

Retrospective Assessment (RA) scores are often found to be higher than the mean of Ecological Momentary Assessment (EMA) scores about a concurrent period. This difference is generally interpreted as bias towards salient experiences in RA. During RA participants are often asked to summarize their experiences in unspecific terms, leaving room for per...

A statistical model can be called a latent class (LC) or mixture model if it assumes that some of its parameters differ across unobserved subgroups, latent classes, or mixture components. This rather general idea has several seemingly unrelated applications, the most important of which are clustering, scaling, density estimation, and random-effects...

Bias-adjusted three-step latent class analysis (LCA) is a popular tool to relate external variables to latent class membership. The integration of causal inference techniques such as inverse propensity weighting (IPW) with LCA allows for addressing causal questions about the relationship between these external variables and the latent classes even...

Measurement invariance (MI) is required for validly comparing latent constructs measured by multiple ordinal self-report items. Non-invariances may occur when disregarding(group differences in) an acquiescence response style (ARS; an agreeing tendency regardless of item content). If non-invariance results solely from neglecting ARS, one should not...

Introduction: Studies investigating latent alcohol use groups and transitions of these groups over time are scarce, while such knowledge could facilitate efficient use of screening and preventive interventions for groups with a high risk of problematic alcohol use. Therefore, the present study examines the characteristics, transitions, and long-te...

In so-called personalized feedback procedures, people report on their experiences through Ecological Momentary Assessment (EMA) and receive a summary of their EMA data afterwards. Although the goal of such a procedure is to promote these peoples’ insight in their moment-to-moment experiences, the extent to which they gain insight from their persona...

Ecological Momentary Assessment (EMA) data about people’s self-reported experiences can be summarized and reported back to them as a so-called personalized feedback report. A popular form of personalized feedback concerns people’s positive affect in different environments and activities. The goal of this type of feedback is to provide participants...

Background Limited evidence exists on how the presence of multiple conditions affects breast cancer (BC) risk. Methods We used data from a network hospital-based case–control study conducted in Italy and Switzerland, including 3034 BC cases and 3392 controls. Comorbidity patterns were identified using latent class analysis on a set of specific hea...

Gaussian mixture models (GMMs) are a popular and versatile tool for exploring heterogeneity in multivariate continuous data. Arguably the most popular way to estimate GMMs is via the expectation–maximization (EM) algorithm combined with model selection using the Bayesian information criterion (BIC). If the GMM is correctly specified, this estimatio...

PurposePatients’ expectations during recovery after a trauma can affect the recovery. The aim of the present study was to identify different physical recovery trajectories based on Latent Markov Models (LMMs) and predict these recovery states based on individual patient characteristics.Methods The data of a cohort of adult trauma patients until the...

The Multiple Imputation of Latent Classes (MILC) method combines multiple imputation and latent class analysis to correct for misclassification in combined datasets. Furthermore, MILC generates a multiply imputed dataset which can be used to estimate different statistics in a straightforward manner, ensuring that uncertainty due to misclassificatio...

Intensive longitudinal data (ILD) have become popular for studying within-person dynamics in psychological constructs (or between-person differences therein). Before investigating the dynamics, it is crucial to examine whether the measurement model (MM) is the same across subjects and time and, thus, whether the measured constructs have the same me...

Assessing the measurement model (MM) of self-report scales is crucial to obtain valid measurements of individuals’ latent psychological constructs. This entails evaluating the number of measured constructs and determining which construct is measured by which item. Exploratory factor analysis (EFA) is the most-used method to evaluate these psychomet...

While latent class (LC) modeling using bias-adjusted stepwise approaches has become widely popular, little is known on how these methods are affected by missing values. Using synthetic data sets, we illustrate under which conditions missing values introduce biases in the estimates of the relationship between class membership and auxiliary variables...

Gaussian Mixture Models (GMMs) are a popular and versatile tool to explore heterogeneity in multivariate continuous data. Arguably the most popular way to estimate GMMs is via the Expectation-Maximization (EM) algorithm combined with model selection using the Bayesian Information Criterion (BIC). If the GMM is correctly specified, this estimation p...

Objective: To identify body mass index (BMI) trajectories in adult life and to examine their association with endometrial cancer (EC) risk, also exploring whether relations differ by HRT use. Design: Pooled analysis of two case control studies. Setting: Italy and Switzerland. Population: A total of 458 EC cases and 782 controls. Methods: W...

In social sciences, the study of group differences concerning latent constructs is ubiquitous. These constructs are generally measured by means of scales composed of ordinal items. In order to compare these constructs across groups, one crucial requirement is that they are measured equivalently or, in technical jargon, that measurement invariance (...

Ecological Momentary Assessment (EMA) in which participants report on their moment-to-moment experiences in their natural environment, is a hot topic. An emerging field in clinical psychology based on either EMA, or what we term Ecological Retrospective Assessment (ERA) as it requires retrospectivity, is the field of personalized feedback. In this...

Retrospective Assessment (RA) scores are often found to be higher than the mean of Ecological Momentary Assessment (EMA) scores about a concurrent period. This difference is generally interpreted as bias towards salient experiences in RA. During RA, participants are often asked to summarize their experiences in unspecific terms, which may indeed fa...

We introduce a general method for sample size computations in the context of cross-sectional network models. The method takes the form of an automated Monte Carlo algorithm, designed to find an optimal sample size while iteratively concentrating the computations on the sample sizes that seem most relevant. The method requires three inputs: 1) a hyp...

Consumer dispersion analysis divides aggregate markets into smaller geographic units that marketers can target with their promotional mix. However, dispersion patterns are not always contiguous. Using survey data from National Football League (NFL) fans, we introduce a new hierarchical expectation-maximization (EM) bi-level clustering model that it...

Invariance of the measurement model (MM) between subjects and within subjects over time is a prerequisite for drawing valid inferences when studying dynamics of psychological factors in intensive longitudinal data. To conveniently evaluate this invariance, latent Markov factor analysis (LMFA) was proposed. LMFA combines a latent Markov model with m...

Bias-adjusted three-step latent class analysis (LCA) is widely popular to relate covariates to class membership. However, if the causal effect of a treatment on class membership is of interest and only observational data is available, causal inference techniques such as inverse propensity weighting (IPW) need to be used. In this article, we extend...

Assessing the measurement model (MM) of self-report scales is crucial to obtain valid measurement of individuals' latent psychological constructs. This entails evaluating the number of measured constructs and determining which construct is measured by which item. Exploratory factor analysis (EFA) is the most-used method to evaluate these psychometr...

Intensive longitudinal data (ILD) have become popular for studying within-person dynamics in psychological constructs (or between-person differences therein). Prior to investigating what the dynamics look like, it is important to examine whether the measurement model (MM) is the same across subjects and time and, thus, whether the measured construc...

Purpose Given the complex association between substance use disorders (SUD), comorbid mental health problems and criminal recidivism in forensic patients, homogenous patient classes can contribute to a refined treatment. This paper aims to construct those classes in forensic patients (N = 286) diagnosed with SUD, unconditionally released between 20...

Ecological Momentary Assessment (EMA), in which participants report on their moment-to-moment experiences in their natural environment, is a hot topic. An emerging field in clinical psychology based on either EMA, or what we term Ecological Retrospective Assessment (ERA) as it requires retrospectivity, is the field of personalized feedback. In this...

Purpose Adolescents who are admitted to secure residential care have a high risk of delinquency after discharge. However, this risk may differ between subgroups in this heterogeneous population of adolescents with severe psychiatric problems and disruptive problem behaviour. In this study, the predictive validity of four risk profiles was examined...

Background: Long-term colon cancer survivors present heterogeneous health-related quality of life (HRQOL) outcomes. We determined unobserved subgroups (classes) of survivors with similar HRQOL patterns and investigated their stability over time and the association of clinical covariates with these classes. Materials and methods: Data from the po...

Psychological research often builds on between-group comparisons of (measurements of) latent variables; for instance, to evaluate cross-cultural differences in neuroticism or mindfulness. A critical assumption in such comparative research is that the same latent variable(s) are measured in exactly the same way across all groups (i.e., measurement i...

Background The considerable differences in food consumption across countries pose major challenges to the research on diet and cancer, due to the difficulty to generalise and reproduce the dietary patterns identified in a specific population. Methods We analysed data from a multicentric case-control study on oesophageal squamous cell carcinoma (ES...

Purpose: Adolescents who are admitted to secure residential care have a high risk of delinquency after discharge. However, this risk may differ between subgroups in this heterogeneous population of adolescents with severe psychiatric problems and disruptive problem behaviour. In this study, the predictive validity of four risk profiles was examined...

Drawing inferences about dynamics of psychological constructs from intensive longitudinal data requires the measurement model (MM)-indicating how items relate to constructs-to be invariant across subjects and time-points. When assessing subjects in their daily life, however, there may be multiple MMs, for instance, because subjects differ in their...

The practice of latent class (LC) modeling using a bias-adjusted three-step approach has become widely popular. However, the current three-step approach has one important drawback – its key assumption of conditional independence between external variables and latent class indicators is often violated in practice, such as when a (nominal) covariate...

In social sciences, the study of group differences concerning latent constructs is ubiquitous. These constructs are generally measured by means of scales composed of ordinal items. In order to compare these constructs across groups, one crucial requirement is that they are measured equivalently or, in technical jargon, that measurement invariance h...

Longitudinal measurement invariance (LMI) of the measurement model (MM) is a prerequisite for drawing valid inferences about daily dynamics of psychological constructs. To efficiently evaluate LMI for multiple subjects simultaneously, latent Markov factor analysis (LMFA) was proposed. LMFA combines a latent Markov model with mixture factor analysis...

Objectives To develop effective return to work (RTW) interventions for employees on sick leave due to mental health problems (MHPs), a better understanding of individual variation in the RTW process is needed. We investigated which RTW trajectories can be identified among employees with MHPs in terms of RTW duration and relapse occurrence during th...

Psychological research often builds on between-group comparisons of (measurements of) latent constructs; for instance, to evaluate cross-cultural differences in neuroticism or mindfulness. A critical assumption in such comparative research is that the same construct(s) are measured in exactly the same way across all groups (i.e., measurement invari...

Standard latent class modeling has recently been shown to provide a flexible tool for the multiple imputation (MI) of missing categorical covariates in cross-sectional studies. This article introduces an analogous tool for longitudinal studies: MI using Bayesian mixture Latent Markov (BMLM) models. Besides retaining the benefits of latent class mod...

The methods traditionally used to identify a posteriori dietary patterns are principal components, factor and cluster analysis. The aim of our study is to assess the relationship between dietary patterns derived with latent class analysis (LCA) and oral/pharyngeal cancer risk (OPC), highlighting the strengths of this method compared to traditional...

This 8-wave person-centered multi-informant study tested whether the quality of parent–adolescent relationships predicted the romantic experiences of young adults and their partners (N = 374; 54.8% girls; Mage = 13.08 years, SDage = 0.48 at the first measurement wave). Perceptions of parent–adolescent relationships were assessed using adolescent, m...

In this paper, we present a novel data-to-text system for cancer patients, providing information on quality of life implications after treatment , which can be embedded in the context of shared decision making. Currently, information on quality of life implications is often not discussed, partly because (until recently) data has been lacking. In ou...

The current study examined trajectories of two indicators of self-control—impulsivity and coping skills—in 317 forensic psychiatric patients, as well as associations with psychopathology, crime, and recidivism. Violent recidivism was positively associated with coping skills at admission to the clinic and with impulsivity at discharge. Only a small...

Regression mixture models are one increasingly utilized approach for developing theories about and exploring the heterogeneity of effects. In this study we aimed to extend the current use of regression mixtures to a repeated regression mixture method when repeated measures, such as diary-type and experience-sampling method, data are available. We h...

Statistics that are published by official agencies are often generated by using population registries, which are likely to contain classification errors and missing values. A method that simultaneously handles classification errors and missing values is multiple imputation of latent classes (MILC). We apply the MILC method to estimate the number of...

Multigroup exploratory factor analysis (EFA) has gained popularity to address measurement invariance for two reasons. Firstly, repeatedly respecifying confirmatory factor analysis (CFA) models strongly capitalizes on chance and using EFA as a precursor works better. Secondly, the fixed zero loadings of CFA are often too restrictive. In multigroup E...

This longitudinal study explored the existence of, and the transition between, latent classes based on risk/need domains of the Structured Assessment of Violence Risk in Youth (SAVRY). The study included 4,267 male and 661 female justice-involved juveniles who had at least one SAVRY assessment completed between 2006 and 2011. A three-step approach...

When time-intensive longitudinal data is used to study daily-life dynamics of psychological constructs (e.g., well-being) within persons over time (e.g., by means of experience sampling methodology), the measurement model (MM)-indicating which constructs are measured by which items-can be affected by time-or situation-specific artefacts (e.g., resp...

Background: Insomnia disorder is the second most prevalent mental disorder, and it is a primary risk factor for depression. Inconsistent clinical and biomarker findings in patients with insomnia disorder suggest that heterogeneity exists and that subtypes of this disease remain unrecognised. Previous top-down proposed subtypes in nosologies have h...

It has become increasingly easy to collect data from individuals over long periods of time. Examples include smart-phone applications used to track movements with GPS, web-log data tracking individuals’ browsing behavior, and longitudinal (cohort) studies where many individuals are monitored over an extensive period of time. All these datasets cove...

It has become increasingly easy to collect data from individuals over long periods of time. Examples include smart-phone applications used to track movements with GPS, web-log data tracking individuals' browsing behavior, and longitudinal (cohort) studies where many individuals are monitored over an extensive period of time. All these datasets cove...

Social scientists are often faced with data that have a nested structure: pupils are nested within schools, employees are nested within companies, or repeated measurements are nested within individuals. Nested data are typically analyzed using multilevel models. However, when data sets are extremely large or when new data continuously augment the d...

Latent class (LC) analysis is widely used in the social and behavioral sciences to find meaningful clusters based on a set of categorical variables. To deal with the common problem that a standard LC analysis may yield a large number classes and thus a solution that is difficult to interpret, recently an alternative approach has been proposed, call...

Drawing valid inferences about daily or long-term dynamics of psychological constructs (e.g., depression) requires the measurement model (indicating which constructs are measured by which items) to be invariant within persons over time. However, it might be affected by time- or situation-specific artifacts (e.g., response styles) or substantive cha...

Developmental changes in adolescents’ relationships with parents and friends intertwine, but individual differences in these relationships are likely to emerge as not all adolescents develop similarly. Generalized anxiety symptoms may underlie these individual differences, as these symptoms have frequently been associated with interpersonal difficu...

In recent studies, latent class tree (LCT) modeling has been proposed as a convenient alternative to standard latent class (LC) analysis. Instead of using an estimation method in which all classes are formed simultaneously given the specified number of classes, in LCT analysis a hierarchical structure of mutually linked classes is obtained by seque...

With this article, we propose using a Bayesian multilevel latent class (BMLC; or mixture) model for the multiple imputation of nested categorical data. Unlike recently developed methods that can only pick up associations between pairs of variables, the multilevel mixture model we propose is flexible enough to automatically deal with complex interac...

Latent class models are often used to assign values to categorical variables that cannot be measured directly. This “imputed” latent variable is then used in further analyses with auxiliary variables. The relationship between the imputed latent variable and auxiliary variables can only be correctly estimated if these auxiliary variables are include...

It is important to gain more insight into specific subgroups of homeless young adults (HYA) to enable the development of tailored interventions that adequately meet their diverse needs and to improve their quality of life. Within a heterogeneous sample of HYA, we investigated whether subgroups are distinguishable based on risk‐ and protective facto...

The latent class model is a powerful unsupervised clustering algorithm for categorical data. Many statistics exist to test the fit of the latent class model. However, traditional methods to evaluate those fit statistics are not always useful. Asymptotic distributions are not always known, and empirical reference distributions can be very time consu...

Binary data latent class models crucially assume local independence, violations of which can seriously bias the results. We present two tools for monitoring local dependence in binary data latent class models: the "Expected Parameter Change" (EPC) and a generalized EPC, estimating the substantive size and direction of possible local dependencies. T...

Latent class analysis has been recently proposed for the multiple imputation (MI) of missing categorical data, using either a standard frequentist approach or a nonparametric Bayesian model called Dirichlet process mixture of multinomial distributions (DPMM). The main advantage of using a latent class model for multiple imputation is that it is ver...

Background The development of delinquent behaviour is largely determined by the presence of (multiple) risk factors. It is essential to focus on the patterns of co-occurring risk factors in different subgroups in order to better understand disruptive behaviour. Aims and hypothesis The aim of this study was to examine whether subgroups could be ide...

Researchers use latent class growth (LCG) analysis to detect meaningful subpopulations that display different growth curves. However, especially when the number of classes required to obtain a good fit is large, interpretation of the encountered class-specific curves might not be straightforward. To overcome this problem, we propose an alternative...