Impact of an equality constraint on the class-specific residual variances in regression mixtures: A Monte Carlo simulation study

Minjung Kim, Andrea E. Lamont, Thomas Jaki, Daniel J Feaster, George Howe, M. Lee van Horn

Research output: Contribution to journalArticle

6 Scopus citations


Regression mixture models are a novel approach to modeling the heterogeneous effects of predictors on an outcome. In the model-building process, often residual variances are disregarded and simplifying assumptions are made without thorough examination of the consequences. In this simulation study, we investigated the impact of an equality constraint on the residual variances across latent classes. We examined the consequences of constraining the residual variances on class enumeration (finding the true number of latent classes) and on the parameter estimates, under a number of different simulation conditions meant to reflect the types of heterogeneity likely to exist in applied analyses. The results showed that bias in class enumeration increased as the difference in residual variances between the classes increased. Also, an inappropriate equality constraint on the residual variances greatly impacted on the estimated class sizes and showed the potential to greatly affect the parameter estimates in each class. These results suggest that it is important to make assumptions about residual variances with care and to carefully report what assumptions are made.

Original languageEnglish (US)
JournalBehavior Research Methods
StateAccepted/In press - Jul 3 2015



  • Differential effects
  • Effect heterogeneity
  • Regression mixture
  • Residual variances

ASJC Scopus subject areas

  • Psychology(all)
  • Psychology (miscellaneous)
  • Experimental and Cognitive Psychology
  • Arts and Humanities (miscellaneous)
  • Developmental and Educational Psychology

Cite this