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    Rights statement: The final publication is available at Springer via http://dx.doi.org/10.3758/s13428-015-0618-8

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

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  • Minjung Kim
  • Andrea E. Lamont
  • Thomas Jaki
  • Daniel Feaster
  • George Howe
  • M. Lee Van Horn
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<mark>Journal publication date</mark>06/2016
<mark>Journal</mark>Behavior Research Methods
Issue number2
Volume48
Number of pages14
Pages (from-to)813-826
Publication statusPublished
Early online date3/07/15
Original languageEnglish

Abstract

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.

Bibliographic note

The final publication is available at Springer via http://dx.doi.org/10.3758/s13428-015-0618-8