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

DOI: 10.4236/ojs.2015.53022   PDF   HTML   XML   3,130 Downloads   4,139 Views   Citations


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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Finch, H. (2015) A Comparison of Statistics for Assessing Model Invariance in Latent Class Analysis. Open Journal of Statistics, 5, 191-210. doi: 10.4236/ojs.2015.53022.

Conflicts of Interest

The authors declare no conflicts of interest.


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