06. Determining Adequate Sample Sizes for Latent Class Analysis through Simulation Investigation

Latent Class Analysis (LCA) is a statistical method used to identify distinct subgroups and uncover patterns within observed categorical data. It has broad and practical implications in social, behavioral, and health sciences. However, explicit guidelines for determining the optimal sample size remain limited, despite prior simulation research underscoring the significance of a sufficient sample size to ensure reliable class identification in LCA. This study aims to enhance the utilization of LCA by offering insights into sample size determination and performance assessment across diverse scenarios through a simulation involving over 500 scenarios encompassing different class counts, observed indicators, and sample sizes. The study examines the probabilities of identifying latent classes. The results reveal that, among the considered models, the two-class model consistently performs well, particularly at a sample size of 100.

My presentation is targeted to an audience either with little exposure to the topic (introductory) or assumes a base level of knowledge (intermediate).

Latent Class Analysis

LCA

Sample Size

Simulation

Subgroup