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Not Quite Normal: Consequences of Violating the Assumption of Normality in Regression Mixture Models
Peer reviewed
Direct linkVan Horn, M. Lee; Smith, Jessalyn; Fagan, Abigail A.; Jaki, Thomas; Feaster, Daniel J.; Masyn, Katherine; Hawkins, J. David; Howe, George – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Regression mixture models, which have only recently begun to be used in applied research, are a new approach for finding differential effects. This approach comes at the cost of the assumption that error terms are normally distributed within classes. This study uses Monte Carlo simulations to explore the effects of relatively minor violations of…
Descriptors: Structural Equation Models, Home Management, Drug Abuse, Research Methodology
