Jeroen K Vermunt
Dados Biográficos
| ID | 123840 |
|---|---|
| NOME | Jeroen K Vermunt |
| PRENOMES | Jeroen K |
| SOBRENOME | Vermunt |
| ASSINATURA | VERMUNT J K |
| AFILIAÇÕES | Tilburg University |
| ORCID | 0000-0001-9053-9330 |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 73 |
| TOTAL DE CITAÇÕES | 819 |
| TOTAL COMO AUTOR | 73 |
| TOTAL COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 1996 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2026 |
| ÍNDICE H | 11 |
Mixture multigroup structural equation modelling for ordinal data
Social scientists often compare groups in terms of relations between latent variables (LV) (i.e. structural relations) using Structural Equation Modelling (SEM). LVs are measured indirectly by questionnaires; thus, measurement invariance must be evaluated before comparisons can be made. To efficiently compare many groups, the recently proposed Mixture Multigroup SEM (MMG‐SEM) clusters groups based on their structural relations while accounting fo…
Bivariate Associations in Multilevel Cross-Classified Latent Class Models
Multilevel cross-classified latent class models (MCCLC) extend standard latent class analysis to account for data nested within multiple groupings. These models often rely on assumptions of local independence, which require robust validation tools. Traditional global fit measures are inadequate for detecting local violations, leading to the use of bivariate residuals (BVRs). However, BVRs are computationally intensive, especially in multilevel co…
Using Mixture Multigroup Structural Equation Modeling to Compare Structural Relations Across Many Groups
The growing availability of large-scale survey data has increased interest in comparing relations among latent variables (often called structural relations) across many groups (e.g., countries or schools) using Structural Equation Modeling (SEM). Traditional multigroup or multilevel SEM become impractical in these settings, as they require pairwise comparisons to identify specific differences between groups. These pairwise comparisons become daun…
Revealing cross-national differences and similarities in relations between human values and climate policy support by Mixture Multigroup Structural Equation Modeling
With growing availability of large-scale international surveys, social scientists are increasingly interested in comparing relations among latent variables, such as values and attitudes, across countries using Multigroup or Multilevel Structural Equation Modeling. However, these two methods require numerous pairwise comparisons to pinpoint which groups differ and which groups share similar structural relations. Mixture Multigroup Structural Equat…
Distribution of gender and labour force participation and filial support types in Europe and Israel
Informal care-giving studies have largely ignored how gender and labour force participation intersect to shape filial support across diverse national contexts over time. In particular, comparative longitudinal research that explores care-giving intensity in relation to adult children’s employment status and gender remains scarce. This study addresses this gap by developing a typology of filial support and examining how care-giving patterns vary b…
Causal Latent Class Analysis with Distal Outcomes
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 use of propensity scores to estimate the causal effect of LC membership on a distal outcome variable.…
Big Five Personality Traits and Trajectories of Fertility Expectations Across the Reproductive Age Period
OBJECTIVE: In recent decades, increased freedom of choice and advancements in fertility regulation have allowed individuals to follow different fertility paths. This greater autonomy provides room for personality traits to shape long-term fertility expectations, which in turn can be predictive of fertility outcomes. The present study investigates how Big Five personality traits are related to fertility expectations trajectories and outcomes. METH…
Causal Inference for Latent Markov Models Using the Parametric G-Formula
The parametric g-formula can be used to estimate causal effects of time-varying exposures on observable outcomes. It resolves intermediate confounding in such settings by specifying several parametric models, one each for every time-varying variable, and by performing micro-simulations. However, its restriction to applications with observable outcomes limits its usability for social sciences where variables of interest are often unobservable cons…
Does Acquiescence Disagree with Measurement Invariance Testing
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 worry about scale inequivalences but model the ARS instead. In a simulation study, we investigated …
An empirically based typology of temporary alcohol abstinence challenge participants using latent class analysis
Correcting for Extreme Response Style
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 models are however rare in the literature, especially in the context of cross-cultural comparisons, wher…
Transgressive incidents targeted on staff in forensic psychiatric healthcare
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 transgressive incidents based on incident characteristics such as impact, severity, (presumed) cause, t…
Linear Logistic Scoring Equations for Latent Class and Latent Profile Models
Researchers are often interested in using latent class or latent profile parameter estimates to obtain posterior class membership probabilities for observations other than those of the original sample. In this paper, we demonstrate that these probabilities typically take on the form of linear logistic equations with coefficients which are functions of the original model parameters. In other words, the posterior class membership probabilities can …
Latent Vector Autoregressive Modeling
Researchers often study dynamic processes of latent variables in everyday life, such as the interplay of positive and negative affect over time. An intuitive approach is to first estimate the measurement model of the latent variables, then compute factor scores, and finally use these factor scores as observed scores in vector autoregressive modeling. However, this approach neglects the uncertainty in the factor scores, leading to biased parameter…
Three-Step Latent Class Analysis with Inverse Propensity Weighting in the Presence of Differential Item Functioning
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 item function (DIF) caused by the treatment or exposure variable. Following the approach by Vermunt a…
Awareness Is Bliss
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 psychometric properties, where the number of measured constructs (i.e., factors) is assessed, and, afterward,…
Evaluating Covariate Effects on ESM Measurement Model Changes with Latent Markov Factor Analysis
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 mixture factor analysis: The Markov model captures changes in MMs over time by clustering subjects' o…
Stepwise Latent Class Analysis in the Presence of Missing Values on the Class Indicators
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. We apply three-step LC analysis with both modal and proportional class assignments, as well as the…
Comorbidity patterns, family history and breast cancer risk
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 health conditions/diseases. A multiple logistic regression model was used to derive ORs and the corres…
How to Perform Three-Step Latent Class Analysis in the Presence of Measurement Non-Invariance or Differential Item Functioning
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 represents subgroups showing measurement non-invariance (MNI) or differential item functioning (DIF). …
Dietary patterns and oesophageal cancer
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 (ESCC) carried out between 1992 and 2009 in three Italian areas and in the Canton of Vaud, Switzerland, …
One is not the other
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 for the number of minor, moderate, and severe offences after discharge from secure residential care. The sam…
Subgroups of Dutch homeless young adults based on risk- and protective factors for quality of life
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 factors for quality of life. In addition, differences between subgroups were examined regarding the socio…
Deciding on the Starting Number of Classes of a Latent Class Tree
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 sequentially splitting classes into two subclasses. The resulting tree structure gives a clear insight in…
The GRoLTS-Checklist
Estimating models within the mixture model framework, like latent growth mixture modeling (LGMM) or latent class growth analysis (LCGA), involves making various decisions throughout the estimation process. This has led to a wide variety in how results of latent trajectory analysis are reported. To overcome this issue, using a 4-round Delphi study, we developed Guidelines for Reporting on Latent Trajectory Studies (GRoLTS). The purpose of GRoLTS i…
Latent Class Modeling with Covariates
Researchers using latent class (LC) analysis often proceed using the following three steps: (1) an LC model is built for a set of response variables, (2) subjects are assigned to LCs based on their posterior class membership probabilities, and (3) the association between the assigned class membership and external variables is investigated using simple cross-tabulations or multinomial logistic regression analysis. Bolck, Croon, and Hagenaars (2004…
Estimating the Association between Latent Class Membership and External Variables Using Bias-adjusted Three-step Approaches
Latent class analysis is a clustering method that is nowadays widely used in social science research. Researchers applying latent class analysis will typically not only construct a typology based on a set of observed variables but also investigate how the encountered clusters are related to other, external variables. Although it is possible to incorporate such external variables into the latent class model itself, researchers usually prefer using…
Multilevel Latent Class Models
The latent class (LC) models that have been developed so far assume that observations are independent. Parametric and non-parametric random-coefficient LC models are proposed here, which will make it possible to modify this assumption. For example, the models can be used for the analysis of data collected with complex sampling designs, data with a multilevel structure, and multiple-group data for more than a few groups. An adapted EM algorithm is…
Latent Class Factor and Cluster Models, Bi-Plots, and Related Graphical Displays
We propose an alternative method of conducting exploratory latent class analysis that utilizes latent class factor models, and compare it to the more traditional approach based on latent class cluster models. We show that when formulated in terms of R mutually independent, dichotomous latent factors, the LC factor model has the same number of distinct parameters as an LC cluster model with R+1 clusters. Analyses over several data sets suggest tha…
Cultural classifications under discussion latent class analysis of highbrow and lowbrow reading
Love, necessity and opportunity
This article examines long-term trends in the pattern of age homogamy among first marriages, using vital registration data on all first marriages contracted between 1850 and 1993 in the Netherlands. After discussing the main mechanisms that could account for trends in age differences, we show that age differences between spouses have narrowed considerably between 1850 and 1970. After 1970 the trend becomes less clearcut
Event history analysis of authors' reputation
Memory Bias in Retrospectively Collected Employment Careers
Event history data constitute a valuable source to analyze life courses, although the reliance of such data on autobiographical memory raises many concerns over their reliability. In this paper, we use Swedish survey data to investigate bias in retrospective reports of employment biographies, applying a novel model-based latent Markov method. A descriptive comparison of the biographies as reconstructed by the same respondents at two interviews ca…
The Simultaneous Decision(s) about the Number of Lower- and Higher-Level Classes in Multilevel Latent Class Analysis
Recently, several types of extensions of the latent class (LC) model have been developed for the analysis of data sets having a multilevel structure. The most popular variant is the multilevel LC model with finite mixture distributions at multiple levels of a hierarchical structure; that is, with LCs for both lower-level units (e.g. individuals, citizens, or patients) and higher-level units (e.g. groups, regions, or hospitals). A problem in the a…
Measurement Equivalence of Ordinal Items
Three distinctive methods of assessing measurement equivalence of ordinal items, namely, confirmatory factor analysis, differential item functioning using item response theory, and latent class factor analysis, make different modeling assumptions and adopt different procedures. Simulation data are used to compare the performance of these three approaches in detecting the sources of measurement inequivalence. For this purpose, the authors simulate…
Heterogeneity in Post-materialist Value Priorities. Evidence from a Latent Class Discrete Choice Approach
Protagonists of values theory such as Inglehart-among others-have argued that values should be conceived of as relative priorities rather than absolute preferences. As such they insist on using ranking techniques of measurement which generates choice data. In this study, we aim at validating the measurement of Inglehart's (post-)materialism by means of a latent class discrete choice model. We argue that from a statistical point of view this is th…
Relating Latent Class Assignments to External Variables
Latent class analysis is used in the political science literature in both substantive applications and as a tool to estimate measurement error. Many studies in the social and political sciences relate estimated class assignments from a latent class model to external variables. Although common, such a “three-step” procedure effectively ignores classification error in the class assignments; Vermunt (2010, “Latent class modeling with covariates: Two…
On the Development of Harmony, Turbulence, and Independence in Parent–Adolescent Relationships
The separation-individuation, evolutionary, maturational, and expectancy violation-realignment perspectives propose that the relationship between parents and adolescents deteriorate as adolescents become independent. This study examines the extent to which the development of adolescents' perceived relationship with their parents is consistent with the four perspectives. A latent transition analysis was performed in a two-cohort five-wave longitud…
The Effect of Labeling and Numbering of Response Scales on the Likelihood of Response Bias
Extreme response style (ERS) and acquiescence response style (ARS) are among the most encountered problems in attitudinal research. The authors investigate whether the response bias caused by these response styles varies with following three aspects of question format: full versus end labeling, numbering answer categories, and bipolar versus agreement response scales. A questionnaire was distributed to a random sample of 5,351 respondents from th…
Dealing with Extreme Response Style in Cross-Cultural Research
Cross-cultural comparison of attitudes using rating scales may be seriously biased by response styles. This paper deals with statistical methods for detection of and correction for extreme response style (ERS), which is one of the well-documented response styles. After providing an overview of available statistical methods for dealing with ERS, we argue that the latent class factor analysis (LCFA) approach proposed by Moors (2003) has several adv…
Multiple Imputation of Incomplete Categorical Data Using Latent Class Analysis
We propose using latent class analysis as an alternative to log-linear analysis for the multiple imputation of incomplete categorical data. Similar to log-linear models, latent class models can be used to describe complex association structures between the variables used in the imputation model. However, unlike log-linear models, latent class models can be used to build large imputation models containing more than a few categorical variables. To …
Latent Class Analysis With Sampling Weights
The authors illustrate how to perform maximum-likelihood estimation in latent class (LC) analysis when there are sampling weights. The methods are natural extensions of the approaches proposed by Clogg and Eliason (1987) and Magidson (1987) for dealing with sampling weights in the log-linear analysis of frequency tables. For the log-linear form of the LC model, the approach corresponds to a special case of Haberman's (1979) log-linear LC model wi…
Estimating True Changes when Categorical Panel Data are Affected by Uncorrelated and Correlated Classification Errors
Conclusions about changes in categorical characteristics based on observed panel data can be incorrect when (even a small amount of) measurement error is present. Random measurement errors, referred to as independent classification errors, usually lead to over-estimation of the total amount of gross change, whereas systematic, correlated errors usually cause underestimation of the transitions. Furthermore, the patterns of true change may be serio…
Micro-Macro Multilevel Analysis for Discrete Data
A multilevel regression model is proposed in which discrete individual-level variables are used as predictors of discrete group-level outcomes. It generalizes the model proposed by Croon and van Veldhoven for analyzing micro-macro relations with continuous variables by making use of a specific type of latent class model. A first simulation study shows that this approach performs better than more traditional aggregation and disaggreagtion procedur…
Response Strategies and Response Styles in Cross-Cultural Surveys
This article addresses the following research questions: Do respondents participating in cross-cultural surveys differ in their response style when responding to attitude statements? If so, are characteristics of the response process associated with their ethnicity and generation of immigration? To answer these questions we conducted a mixed method study. Quantitative analysis of a large representative sample of minorities in the Netherlands show…
Evaluating Measurement Invariance in Categorical Data Latent Variable Models with the EPC-Interest
Many variables crucial to the social sciences are not directly observed but instead are latent and measured indirectly. When an external variable of interest affects this measurement, estimates of its relationship with the latent variable will then be biased. Such violations of “measurement invariance” may, for example, confound true differences across countries in postmaterialism with measurement differences. To deal with this problem, researche…
Identifying dietary patterns using a normal mixture model
BACKGROUND: Finite mixture models posit the existence of a latent categorical variable and can be used for probabilistic classification. The authors illustrate the use of mixture models for dietary pattern analysis. An advantage of this approach is taking classification uncertainty into account. METHODS: Participants were a random sample of women from the European Prospective Investigation into Cancer. Food consumption was measured using dietary …
Age differences in the prevalence of physical aggression among 5–11‐year‐old Canadian boys and girls
It has been proven extremely difficult in the past to estimate the prevalence of physical aggression in children for two main reasons: (a) a heterogeneous sampling of behaviors (i.e., mix between physically aggressive and non‐physically aggressive antisocial behaviors), and (b) a lack of a “gold standard” to identify children who exhibit physically aggressive behaviors on a frequent basis. The goal of this study was to test for age differences in…
Modeling Joint and Marginal Distributions in the Analysis of Categorical Panel Data
This article presents a unifying approach to the analysis of repeated univariate categorical (ordered) responses based on the application of the generalized log-linear modeling framework proposed by Lang and Agresti. It is shown that three important research questions in longitudinal studies can be addressed simultaneously. These questions are the following: What is the overall dependence structure of the repeated responses? What is the structure…
Acquisition patterns of financial products
Event history analysis of authors' reputation
A General Class of Nonparametric Models for Ordinal Categorical Data
This paper presents a general class of models for ordinal categorical data that can be specified by means of linear and/or log-linear equality and/or inequality restrictions on the (conditional) probabilities of a multiway contingency table. Some special cases are models with ordered local odds ratios, models with ordered cumulative response probabilities, order-restricted row association and column association models, and models for stochastical…
Cultural classifications under discussion latent class analysis of highbrow and lowbrow reading
Log-Multiplicative Association Models as Latent Variable Models for Nominal and/or Ordinal Data
Associations between multiple discrete measures are often due to collapsing over other variables. When the variables collapsed over are unobserved and continuous, log-multiplicative association models, including log-linear models with linear-by-linear interactions for ordinal categorical data and extensions of Goodman's (1979, 1985) RC(M) association model for multiple nominal and/or ordinal categorical variables, can be used to study the relatio…
Estimating True Changes when Categorical Panel Data are Affected by Uncorrelated and Correlated Classification Errors
Conclusions about changes in categorical characteristics based on observed panel data can be incorrect when (even a small amount of) measurement error is present. Random measurement errors, referred to as independent classification errors, usually lead to over-estimation of the total amount of gross change, whereas systematic, correlated errors usually cause underestimation of the transitions. Furthermore, the patterns of true change may be serio…
Latent Class Factor and Cluster Models, Bi-Plots, and Related Graphical Displays
We propose an alternative method of conducting exploratory latent class analysis that utilizes latent class factor models, and compare it to the more traditional approach based on latent class cluster models. We show that when formulated in terms of R mutually independent, dichotomous latent factors, the LC factor model has the same number of distinct parameters as an LC cluster model with R+1 clusters. Analyses over several data sets suggest tha…
Love, necessity and opportunity
This article examines long-term trends in the pattern of age homogamy among first marriages, using vital registration data on all first marriages contracted between 1850 and 1993 in the Netherlands. After discussing the main mechanisms that could account for trends in age differences, we show that age differences between spouses have narrowed considerably between 1850 and 1970. After 1970 the trend becomes less clearcut
Modeling Joint and Marginal Distributions in the Analysis of Categorical Panel Data
This article presents a unifying approach to the analysis of repeated univariate categorical (ordered) responses based on the application of the generalized log-linear modeling framework proposed by Lang and Agresti. It is shown that three important research questions in longitudinal studies can be addressed simultaneously. These questions are the following: What is the overall dependence structure of the repeated responses? What is the structure…
Latent Class Cluster Analysis
INTRODUCTION Kaufman and Rousseeuw (1990) define cluster analysis as the classification of similar objects into groups, in which the number of groups as well as their forms are unknown. The form of a group refers to the parameters of cluster; that is, to its cluster-specific means, variances, and covariances that also have a geometrical interpretation. A similar definition is given by Everitt (1993), who speaks about deriving a useful division in…
Multilevel Latent Class Models
The latent class (LC) models that have been developed so far assume that observations are independent. Parametric and non-parametric random-coefficient LC models are proposed here, which will make it possible to modify this assumption. For example, the models can be used for the analysis of data collected with complex sampling designs, data with a multilevel structure, and multiple-group data for more than a few groups. An adapted EM algorithm is…
Latent Class Models
Bayesian Posterior Estimation of Logit Parameters with Small Samples
When the sample size is small compared to the number of cells in a contingency table, maximum likelihood estimates of logit parameters and their associated standard errors may not exist or may be biased. This problem is usually solved by 'smoothing' the estimates, assuming a certain prior distribution for the parameters. This article investigates the performance of point and interval estimates obtained by assuming various prior distributions. The…
Mixed-Effects Logistic Regression Models for Indirectly Observed Discrete Outcome Variables
A well-established approach to modeling clustered data introduces random effects in the model of interest. Mixed-effects logistic regression models can be used to predict discrete outcome variables when observations are correlated. An extension of the mixed-effects logistic regression model is presented in which the dependent variable is a latent class variable. This method makes it possible to deal simultaneously with the problems of correlated …
Random Effects Models for Personal Networks
We propose analyzing personal or ego-centered network data by means of two-level generalized linear models. The approach is illustrated with an example in which we assess whether personal networks are homogenous with respect to marital status after controlling for age homogeneity. In this example, the outcome variable is a bivariate categorical response variable (alter’s marital status and age category). We apply both factor-analytic parametric a…
Development and individual differences in transitive reasoning
Homogeneity of social networks by age and marital status
Acquisition patterns of financial products
Age differences in the prevalence of physical aggression among 5–11‐year‐old Canadian boys and girls
It has been proven extremely difficult in the past to estimate the prevalence of physical aggression in children for two main reasons: (a) a heterogeneous sampling of behaviors (i.e., mix between physically aggressive and non‐physically aggressive antisocial behaviors), and (b) a lack of a “gold standard” to identify children who exhibit physically aggressive behaviors on a frequent basis. The goal of this study was to test for age differences in…
Latent Class Analysis With Sampling Weights
The authors illustrate how to perform maximum-likelihood estimation in latent class (LC) analysis when there are sampling weights. The methods are natural extensions of the approaches proposed by Clogg and Eliason (1987) and Magidson (1987) for dealing with sampling weights in the log-linear analysis of frequency tables. For the log-linear form of the LC model, the approach corresponds to a special case of Haberman's (1979) log-linear LC model wi…
Heterogeneity in Post-materialist Value Priorities. Evidence from a Latent Class Discrete Choice Approach
Protagonists of values theory such as Inglehart-among others-have argued that values should be conceived of as relative priorities rather than absolute preferences. As such they insist on using ranking techniques of measurement which generates choice data. In this study, we aim at validating the measurement of Inglehart's (post-)materialism by means of a latent class discrete choice model. We argue that from a statistical point of view this is th…
Latent class and finite mixture models for multilevel data sets
An extension of latent class (LC) and finite mixture models is described for the analysis of hierarchical data sets. As is typical in multilevel analysis, the dependence between lower-level units within higher-level units is dealt with by assuming that certain model parameters differ randomly across higher-level observations. One of the special cases is an LC model in which group-level differences in the logit of belonging to a particular LC are …
Multiple Imputation of Incomplete Categorical Data Using Latent Class Analysis
We propose using latent class analysis as an alternative to log-linear analysis for the multiple imputation of incomplete categorical data. Similar to log-linear models, latent class models can be used to describe complex association structures between the variables used in the imputation model. However, unlike log-linear models, latent class models can be used to build large imputation models containing more than a few categorical variables. To …
Memory Bias in Retrospectively Collected Employment Careers
Event history data constitute a valuable source to analyze life courses, although the reliance of such data on autobiographical memory raises many concerns over their reliability. In this paper, we use Swedish survey data to investigate bias in retrospective reports of employment biographies, applying a novel model-based latent Markov method. A descriptive comparison of the biographies as reconstructed by the same respondents at two interviews ca…
Latent Class Modeling with Covariates
Researchers using latent class (LC) analysis often proceed using the following three steps: (1) an LC model is built for a set of response variables, (2) subjects are assigned to LCs based on their posterior class membership probabilities, and (3) the association between the assigned class membership and external variables is investigated using simple cross-tabulations or multinomial logistic regression analysis. Bolck, Croon, and Hagenaars (2004…
The Simultaneous Decision(s) about the Number of Lower- and Higher-Level Classes in Multilevel Latent Class Analysis
Recently, several types of extensions of the latent class (LC) model have been developed for the analysis of data sets having a multilevel structure. The most popular variant is the multilevel LC model with finite mixture distributions at multiple levels of a hierarchical structure; that is, with LCs for both lower-level units (e.g. individuals, citizens, or patients) and higher-level units (e.g. groups, regions, or hospitals). A problem in the a…
Statistics (53 obras) · Mathematics (52 obras) · Computer Science (45 obras) · Latent class model (40 obras) · Econometrics (35 obras) · Psychology (31 obras) · Artificial Intelligence (25 obras) · Latent variable (24 obras) · Artificial Intelligence (22 obras) · Statistical Methods and Bayesian Inference (18 obras)