The effects of sample size on the estimation of regression mixture models

Jaki, Thomas Friedrich and Kim, Minjung and Lamont, Andrea E. and George, M. and Feaster, Daniel and Van Horn, M. Lee (2019) The effects of sample size on the estimation of regression mixture models. Educational and Psychological Measurement, 79 (2). pp. 358-384. ISSN 0013-1644

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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.

Item Type:
Journal Article
Journal or Publication Title:
Educational and Psychological Measurement
Additional Information:
The final, definitive version of this article has been published in the Journal, Educational and Psychological Measurement, 79 (2), 2019, © SAGE Publications Ltd, 2019 by SAGE Publications Ltd at the Educational and Psychological Measurement page: https://journals.sagepub.com/home/epm on SAGE Journals Online: http://online.sagepub.com/
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/3200/3204
Subjects:
ID Code:
126408
Deposited By:
Deposited On:
13 Aug 2018 08:36
Refereed?:
Yes
Published?:
Published
Last Modified:
24 Sep 2020 04:05