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- Title
The Effects of Sample Size on the Estimation of Regression Mixture Models.
- Authors
Jaki, Thomas; Kim, Minjung; Lamont, Andrea; George, Melissa; Chang, Chi; Feaster, Daniel; Van Horn, M. Lee
- 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.
- Subjects
REGRESSION analysis; RESEARCH funding; SAMPLE size (Statistics); STRUCTURAL equation modeling; DATA analysis software; STATISTICAL models
- Publication
Educational & Psychological Measurement, 2019, Vol 79, Issue 2, p358
- ISSN
0013-1644
- Publication type
Article
- DOI
10.1177/0013164418791673