Evaluating the Quality of Classification in Mixture Model Simulations. (April 2023)

• Record Type: Journal Article
• Authors: Jang, Yoona; Hong, Sehee
• Is Part Of: Educational and psychological measurement. Volume 83:Number 2(2023)
• Page Start: 351
• Page End: 374
• Publication Date: 2023-04
• Subjects: latent class analysis -- Monte Carlo simulation -- sample size -- quality of classification -- effects of covariates; Educational tests and measurements -- Periodicals; Psychological tests -- Periodicals; 151.205
• DOI: 10.1177/00131644221093619
• Languages: English
• ISSNs: 0013-1644

Abstract

The purpose of this study was to evaluate the degree of classification quality in the basic latent class model when covariates are either included or are not included in the model. To accomplish this task, Monte Carlo simulations were conducted in which the results of models with and without a covariate were compared. Based on these simulations, it was determined that models without a covariate better predicted the number of classes. These findings in general supported the use of the popular three-step approach; with its quality of classification determined to be more than 70% under various conditions of covariate effect, sample size, and quality of indicators. In light of these findings, the practical utility of evaluating classification quality is discussed relative to issues that applied researchers need to carefully consider when applying latent class models.