title: Finite Mixtures for Simultaneously Modelling Differential Effects and Non-Normal Distributions.
abstract: Regression mixture models have been increasingly applied in the social and behavioral sciences as a method for identifying differential effects of predictors on outcomes. While 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. The current study tests a new approach that uses sets of classes (called differential effects sets) to simultaneously model differential effects and account for non-normal 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 non-normal errors.
authors.affiliation: Department of Psychology, University of South Carolina, Columbia, South Carolina, USA.
authors.forename: Melissa R W
authors.initials: MR
authors.lastname: George
authors.affiliation: AdvanceMed Corporation, Nashville, TN, USA.
authors.forename: Na
authors.initials: N
authors.lastname: Yang
authors.affiliation: Department of Mathematics and Statistics, Lancaster University, Lancaster, UK.
authors.forename: Thomas
authors.initials: T
authors.lastname: Jaki
authors.affiliation: Department of Epidemiology and Public Health, University of Miami, Miami, FL, USA.
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Feaster
authors.affiliation: Department of Psychology, University of South Carolina, Columbia, South Carolina, USA.
authors.forename: Andrea E
authors.initials: AE
authors.lastname: Lamont
authors.affiliation: Department of Psychology, University of South Carolina, Columbia, South Carolina, USA.
authors.forename: Dawn K
authors.initials: DK
authors.lastname: Wilson
authors.affiliation: Department of Psychology, University of South Carolina, Columbia, South Carolina, USA.
authors.forename: M Lee Van
authors.initials: ML
authors.lastname: Horn
keywords: Regression mixture models; differential effects; non-normal errors