An Introduction to Latent Class Analysis/Mixture Modeling

Abstract: Are you interested in learning about so-called ‘person-centered’ analyses? Would you like to know how to identify unobserved (i.e., latent) subgroups of individuals within your data? Sign up for this introductory workshop on Latent Class Analysis (LCA) and mixture modeling! 

This workshop is designed for researchers with little to no prior knowledge of/experience in Latent Class Analysis or mixture modeling and will address the following key questions: 

– What is Latent Class Analysis/mixture modeling? 

– What analytical steps and decisions are involved? 

– How to interpret and report results? 

The primary focus will be on cross-sectional applications of Latent Class Analysis/mixture modeling, with a brief introduction to the longitudinal extensions Growth Mixture Modeling (GMM), Latent Class Growth Analysis (LCGA), and Latent Transition Analysis (LTA). The Mplus software program will be used for the demonstration and interpretation of results. While prior experience with basic Structural Equation Modeling (SEM) and Mplus is beneficial for this workshop, it is not required. Basic R code for Latent Class Analysis will also be provided. 

This workshop is divided into two parts: Participants may choose to attend only PART I (conceptual) or both PART I (conceptual) and PART II (applied). 

PART I – Conceptual Overview 

· Introduction to the methodology and core principles of Latent Class Analysis/mixture modeling; 

· An outline of the key analytical steps and decisions involved in conducting Latent Class Analysis/mixture modeling; 

· Brief introduction of Latent Class Analysis/mixture modeling in the context of longitudinal data; 

· Short empirical illustration. 

PART II – Applied Analysis (maximum 20 participants) 

· Step-by-step practical demonstration of Latent Class Analysis using Mplus. 

· Empirical exercises using example data: For this part of the workshop, participants should have Mplus installed on their computer. 

Bio: Stefanie Nelemans is Associate Professor at the department of Youth and Family, Utrecht University, The Netherlands. Her research applies a biopsychosocial ecological systems approach to understand how biological, intrapersonal, interpersonal, and contextual factors are associated with the development of mental health—particularly internalizing symptoms—and aspects of the self (e.g., identity, personality, self-view, self-esteem) throughout adolescence. She focuses on etiological risk and protective factors, explanatory mechanisms, and developmental consequences. Her current work concentrates on the development of mental health and self in the context of adolescents’ online video gaming experiences.