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

Kim, Minjung and Lamont, Andrea E. and Jaki, Thomas and Feaster, Daniel and Howe, George and Van Horn, M. Lee (2016) Impact of an equality constraint on the class-specific residual variances in regression mixtures:a Monte Carlo simulation study. Behavior Research Methods, 48 (2). pp. 813-826. ISSN 1554-351X

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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.

Item Type:
Journal Article
Journal or Publication Title:
Behavior Research Methods
Additional Information:
The final publication is available at Springer via http://dx.doi.org/10.3758/s13428-015-0618-8
Subjects:
ID Code:
74061
Deposited By:
Deposited On:
24 Jun 2015 15:26
Refereed?:
Yes
Published?:
Published
Last Modified:
16 Jul 2020 04:06