The Effects of Sample Size on the Estimation of Regression Mixture Models

Autor/inn/en	Jaki, Thomas; Kim, Minjung; Lamont, Andrea; George, Melissa; Chang, Chi; Feaster, Daniel; Van Horn, M. Lee
Titel	The Effects of Sample Size on the Estimation of Regression Mixture Models
Quelle	In: Educational and Psychological Measurement, 79 (2019) 2, S.358-384 (27 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164418791673
Schlagwörter	Sample Size; Computation; Regression (Statistics); Reliability; Research Problems; Multivariate Analysis; Classification + Suchen Sie Ihr Suchwort? Regression; Regressionsanalyse; Reliabilität; Forschungskritik; Multivariate Analyse; Classification system; Klassifikation; Klassifikationssystem
Abstract	Regression mixture models are a statistical approach used for estimating heterogeneity in effects. This study investigates the impact of sample size on regression mixture's ability to produce "stable" results. Monte Carlo simulations and analysis of resamples from an application data set were used to illustrate the types of problems that may occur with small samples in real data sets. The results suggest that (a) when class separation is low, very large sample sizes may be needed to obtain stable results; (b) it may often be necessary to consider a preponderance of evidence in latent class enumeration; (c) regression mixtures with ordinal outcomes result in even more instability; and (d) with small samples, it is possible to obtain spurious results without any clear indication of there being a problem. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01