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  • RegMixSampleSizeFinalJune252018_with_Authors

    Rights statement: 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/

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

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<mark>Journal publication date</mark>1/04/2019
<mark>Journal</mark>Educational and Psychological Measurement
Issue number2
Volume79
Number of pages27
Pages (from-to)358-384
Publication statusPublished
Early online date10/08/18
Original languageEnglish

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.

Bibliographic note

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/