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ERIC Number: EJ1209028
Record Type: Journal
Publication Date: 2019-Apr
Pages: 27
Abstractor: As Provided
ISSN: ISSN-0013-1644
The Effects of Sample Size on the Estimation of Regression Mixture Models
Jaki, Thomas; Kim, Minjung; Lamont, Andrea; George, Melissa; Chang, Chi; Feaster, Daniel; Van Horn, M. Lee
Educational and Psychological Measurement, v79 n2 p358-384 Apr 2019
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.
SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail:; Web site:
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) (NIH)
Authoring Institution: N/A
Grant or Contract Numbers: R01HD054736