Evaluating the Application of Latent Class Analysis in Identifying Unobserved Subpopulations in Survey Data

Posted: Jan 13, 2007

• Mia Lee
• Theodore Rodriguez
• Scarlett Hall

Abstract

Survey research represents a cornerstone of empirical investigation across social sciences, public health, marketing, and policy analysis. Traditional analytical approaches to survey data often rely on observable demographic characteristics or explicit response patterns to segment populations and understand heterogeneity. However, these methods may fail to capture the complex, multidimensional nature of human attitudes, behaviors, and preferences that often manifest as latent structures within response data. The fundamental challenge in survey analysis lies in identifying meaningful subpopulations that share similar response patterns but may not align with conventional demographic or geographic segmentation approaches. Latent Class Analysis (LCA) offers a promising methodological framework for addressing this challenge by identifying unobserved (latent) subgroups based on response patterns to multiple categorical indicators. Unlike traditional cluster analysis, which assigns individuals deterministically to groups, LCA provides a probabilistic framework that acknowledges the uncertainty inherent in classification.