A Comparison of Statistics for Assessing Model Invariance in Latent Class Analysis

Holmes Finch

44 citations0/0 files storedOpen Journal of Statistics

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Psychometric Methodologies and Testing Statistical Methods and Applications Statistical Methods and Bayesian Inference

Abstract

Latent class analysis (LCA) is a widely used statistical technique for identifying subgroups in the population based upon multiple indicator variables. It has a number of advantages over other unsupervised grouping procedures such as cluster analysis, including stronger theoretical underpinnings, more clearly defined measures of model fit, and the ability to conduct confirmatory analyses. In addition, it is possible to ascertain whether an LCA solution is equally applicable to multiple known groups, using invariance assessment techniques. This study compared the effectiveness of multiple statistics for detecting group LCA invariance, including a chi-square difference test, a bootstrap likelihood ratio test, and several information indices. Results of the simulation study found that the bootstrap likelihood ratio test was the optimal invariance assessment statistic. In addition to the simulation, LCA group invariance assessment was demonstrated in an application with the Youth Risk Behavior Survey (YRBS). Implications of the simulation results for practice are discussed.

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2002Evaluating Goodness-of-Fit Indexes for Testing Measurement Invariance 2007Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study 1987Factor Analysis and AIC 1999Gender differences in risk taking: A meta-analysis.2009Latent Class and Latent Transition Analysis: With Applications in the Social, Behavioral, and Health Sciences 2003Latent Class and Latent Transition Analysis 2004Evaluating latent class analysis models in qualitative phenotype identification 2004Applying Multigroup Confirmatory Factor Models for Continuous Outcomes to Likert Scale Data Complicates Meaningful Group Comparisons

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