Evaluating the Quality of Classification in Mixture Model Simulations

Autor/inn/en	Jang, Yoona; Hong, Sehee
Titel	Evaluating the Quality of Classification in Mixture Model Simulations
Quelle	In: Educational and Psychological Measurement, 83 (2023) 2, S.351-374 (24 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Jang, Yoona)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/00131644221093619
Schlagwörter	Classification; Models; Prediction; Sample Size; Monte Carlo Methods; Evaluation Methods; Comparative Analysis; Goodness of Fit; Evaluation Criteria + Suchen Sie Ihr Suchwort? Classification system; Klassifikation; Klassifikationssystem; Analogiemodell; Vorhersage; Monte-Carlo-Methode
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01