The Utility of Latent Class Analysis to Understand Heterogeneity in Youth Coping Strategies - A Methodological Introduction

Oct 15, 2026

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Latent class analysis (LCA) provides a statistical framework for understanding heterogeneity within a population, using observed response patterns to form latent classes. The article offers a pedagogical introduction to LCA modeling, including how to select the number of classes and how to integrate the LCA variable into structural equation models, particularly a latent class moderation design. An applied example shows how coping strategy class membership moderates links between social stress and mood outcomes. Supplementary online materials provide R and auxiliary-variable procedures to automate enumeration and figure generation for LCA results.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Abstract

Overview of Latent Class Analysis

Empirical Creation of Groups

Practical Considerations and Limitations of Cutoff Scores

Misclassification Risks

Need for Empirical Justification

The Utility of Latent Class Analysis to Understand Heterogeneity in Youth Coping Strategies: A Methodological Introduction Behavioral Disorders 2023, Vol. 48(2) 106–120 © Hammill Institute on Disabilities 2022 Article reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)[DOI: 10.1177/01987429211067214](DOI: 10.1177/01987429211067214)[ ](DOI: 10.1177/01987429211067214)[journals.sagepub.com/home/bhd](journals.sagepub.com/home/bhd) Karen Nylund-Gibson, PhD 1, Adam C. Garber, MA 1, Jay Singh, BS 1, Melissa R. Witkow, PhD2, Adrienne Nishina, PhD3, and Amy Bellmore, PhD4 Abstract Latent class analysis (LCA) is a useful statistical approach for understanding heterogeneity in a population. This article provides a pedagogical introduction to LCA modeling and provides an example of its use to understand youths’ daily coping strategies. The analytic procedures are outlined for choosing the number of classes and integration of the LCA variable within a structural equation model framework, specifically a latent class moderation model, and a detailed table provides a summary of relevant modeling steps. This applied example demonstrates the modeling context when the LCA variable is moderating the association between a covariate and two outcome variables. Results indicate that students’ coping strategies moderate the association between social stress and negative mood; however, they do not moderate the social stresspositive mood association. Online supplemental materials include R (MplusAutomation) code to automate the enumeration procedure, ML three-step auxiliary variable integration, and the generation of figures for visually depicting LCA results. Keywords latent class analysis (LCA), moderation, student coping, auxiliary variables, mixture modeling, structural equation modeling Latent class analysis (LCA) is becoming a widely used statistical technique for a range of social scientists. This analytic approach allows researchers to address questions that involve grouping individuals based on who respond similarly to a set of observed items. This modeling approach provides a way for researchers to better understand patterns of responses to a set of observed measures, allowing a deeper and more parsimonious understanding of unmeasured groups or types within the data that alternative modeling approaches are not able to reveal. Applications of LCA are found across a wide range of research fields and have been widely used within the social sciences. For example, LCA has been used in epidemiology to assess the effectiveness of a bullying and victimization prevention program (Tsiantis et al., 2013) and in behavioral disorder research to investigate the correspondence between teacher management practices and elementary school students’ disruptive behavior (Gage et al., 2018), among other applications. A recent study by Kavčič et al. (2021), aimed at understanding coping strategies during the COVID 19 pandemic, found three coping strategy profiles that aligned with previous work in which LCA was used to investigate coping. This study found that individuals in the engaged coping profile group had the best mental health outcomes. Common among these fields is a trend toward valuing modeling approaches that are capable of revealing heterogeneity within populations and go beyond descriptions of samples based on standard regression-type estimates (i.e., unconditional means) . Empirical Creation of Groups The general appeal of LCA is that it is a statistical method that can be used to create groups, or latent classes, to describe a population. These latent classes are thought of as unobserved “subgroups” or “typologies” that capture heterogeneity in a population with respect to a specific set of measured items. That is, LCA uses patterns in the set of observed items to identify groups of individuals who have 1 University of California, Santa Barbara, USA 2W

FAQs

What is latent class analysis used for in youth research?

LCA is used to capture heterogeneity by grouping individuals into latent classes based on similar response patterns, helping researchers understand unobserved subgroups within the data.

How does the article use LCA within a structural equation modeling framework?

It integrates the LCA variable within a structural equation model, specifically a latent class moderation model, to test whether class membership moderates associations between covariates and outcomes.

What did the applied example conclude about coping strategies as a moderator?

Students’ coping strategies moderated the association between social stress and negative mood, but did not moderate the association between social stress and positive mood.

Oct 15, 2026

Uploaded by Bulrr