ERIC Number: EJ780617
Record Type: Journal
Publication Date: 2007-Oct
Reference Count: 36
Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study
Nylund, Karen L.; Asparouhov, Tihomir; Muthen, Bengt O.
Structural Equation Modeling: A Multidisciplinary Journal, v14 n4 p535-569 Oct 2007
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.)
Descriptors: Test Items, Monte Carlo Methods, Program Effectiveness, Data Analysis, Simulation, Sample Size, Goodness of Fit, Statistical Analysis, Probability
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
Publication Type: Journal Articles; Reports - Evaluative
Education Level: N/A
Sponsor: National Inst. on Drug Abuse (DHHS/PHS), Bethesda, MD.; National Inst. on Alcohol Abuse and Alcoholism (DHHS), Rockville, MD.
Authoring Institution: N/A