Mixed Mode Latent Class Analysis: An Examination of Fit Index Performance for Classification

• Published in: Structural equation modeling Vol. 22; no. 1; pp. 76 - 86
• Main Author: Morgan, Grant B.
• Format: Journal Article
• Language: English
• Published: Hove Routledge 02.01.2015; Psychology Press
• Subjects: Approximation; Bayesian analysis; Classification; fit indices; latent class analysis; Mathematical models; mixed mode data; model selection; Monte Carlo simulation; Sample size
• ISSN: 1070-5511; 1532-8007
• DOI: 10.1080/10705511.2014.935751

This Monte Carlo study examines the performance of fit indices commonly used by applied researchers interested in finite mixture modeling for the purposes of classification. Conditions for the simulation study were selected to reflect conditions found in applied educational and psychological research. The factors included in the investigation were metric level of indicators, sample size, and class prevalence. All models contained a combination of categorical and continuous indicators. All categorical indicators were dichotomous, and continuous indicators were normally distributed. The fit indices examined were Akaike's information criterion, Bayesian information criterion (BIC), sample size-adjusted Bayesian information criterion (SSBIC), integrated classification likelihood criterion with Bayesian-type approximation, and Lo-Mendell-Rubin likelihood ratio test. Overall, SSBIC tended to identify the correct solution with higher frequency than other indices. BIC tended to identify the correct solution with higher frequency than the other indices in models with more continuous than categorical indicators, or when rare classes were absent.