Finite Mixtures for Simultaneously Modeling Differential Effects and Nonnormal Distributions

Melissa R.W. George, Na Yang, Thomas Jaki, Daniel J. Feaster, Andrea E. Lamont, Dawn K. Wilson, M. Lee van Horn

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Regression mixture models have been increasingly applied in the social and behavioral sciences as a method for identifying differential effects of predictors on outcomes. Although the typical specification of this approach is sensitive to violations of distributional assumptions, alternative methods for capturing the number of differential effects have been shown to be robust. Yet, there is still a need to better describe differential effects that exist when using regression mixture models. This study tests a new approach that uses sets of classes (called differential effects sets) to simultaneously model differential effects and account for nonnormal error distributions. Monte Carlo simulations are used to examine the performance of the approach. The number of classes needed to represent departures from normality is shown to be dependent on the degree of skew. The use of differential effects sets reduced bias in parameter estimates. Applied analyses demonstrated the implementation of the approach for describing differential effects of parental health problems on adolescent body mass index using differential effects sets approach. Findings support the usefulness of the approach, which overcomes the limitations of previous approaches for handling nonnormal errors.

Original languageEnglish (US)
Pages (from-to)816-844
Number of pages29
JournalMultivariate Behavioral Research
Issue number6
StatePublished - 2013

ASJC Scopus subject areas

  • Statistics and Probability
  • Experimental and Cognitive Psychology
  • Arts and Humanities (miscellaneous)


Dive into the research topics of 'Finite Mixtures for Simultaneously Modeling Differential Effects and Nonnormal Distributions'. Together they form a unique fingerprint.

Cite this