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

Thomas Jaki, Minjung Kim, Andrea Lamont, Melissa George, Chi Chang, Daniel Feaster, M. Lee Van Horn

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)358-384
Number of pages27
JournalEducational and Psychological Measurement
Issue number2
StatePublished - Apr 1 2019


  • heterogeneous effects
  • regression mixture models
  • sample size

ASJC Scopus subject areas

  • Education
  • Developmental and Educational Psychology
  • Applied Psychology
  • Applied Mathematics


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