Assessing Measurement Invariance in Multiple-Group Latent Profile Analysis

Abstract

The study of measurement invariance in latent profile analysis (LPA) indicates whether the latent profiles differ across known subgroups (e.g., gender). The purpose of the present study was to examine the impact of noninvariance on the relative bias of LPA parameter estimates and on the ability of the likelihood ratio test (LRT) and information criteria statistics to reject the hypothesis of invariance. A Monte Carlo simulation study was conducted in which noninvariance was defined as known group differences in the indicator means in each profile. Results indicated that parameter estimates were biased in conditions with medium and large noninvariance. The LRT and AIC detected noninvariance in most conditions with small sample sizes, while the BIC and adjusted BIC needed larger sample sizes to detect noninvariance. Implications of the results are discussed along with recommendations for future research.

Keywords:

• latent profile analysis
• measurement invariance
• multiple-group analysis

ACKNOWLEDGMENTS

All statements expressed in this article are the authors’ own and do not reflect the official opinions or policies of Education Testing Service. This article is based in part on a presentation given at the 77th Annual Meeting of the National Council on Measurement in Education, April 2015.