Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study

Autor/inn/en	Nylund, Karen L.; Asparouhov, Tihomir; Muthen, Bengt O.
Titel	Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study
Quelle	In: Structural Equation Modeling: A Multidisciplinary Journal, 14 (2007) 4, S.535-569 (35 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1070-5511
Schlagwörter	Test Items; Monte Carlo Methods; Program Effectiveness; Data Analysis; Simulation; Sample Size; Goodness of Fit; Statistical Analysis; Probability + Suchen Sie Ihr Suchwort? Test content; Testaufgabe; Monte-Carlo-Methode; Auswertung; Simulation program; Simulationsprogramm; Statistische Analyse; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie
Abstract	Mixture modeling is a widely applied data analysis technique used to identify unobserved heterogeneity in a population. Despite mixture models' usefulness in practice, one unresolved issue in the application of mixture models is that there is not one commonly accepted statistical indicator for deciding on the number of classes in a study population. This article presents the results of a simulation study that examines the performance of likelihood-based tests and the traditionally used Information Criterion (ICs) used for determining the number of classes in mixture modeling. We look at the performance of these tests and indexes for 3 types of mixture models: latent class analysis (LCA), a factor mixture model (FMA), and a growth mixture models (GMM). We evaluate the ability of the tests and indexes to correctly identify the number of classes at three different sample sizes (n = 200, 500, 1,000). Whereas the Bayesian Information Criterion performed the best of the ICs, the bootstrap likelihood ratio test proved to be a very consistent indicator of classes across all of the models considered. (Contains 4 footnotes, 4 figures and 9 tables. A description of the sequential stopping rule approach used in M "plus" is appended.) (Author).
Anmerkungen	Lawrence Erlbaum. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals/default.html
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10