Regression Mixture Models: Does Modeling the Covariance Between Independent Variables and Latent Classes Improve the Results?
Regression Mixture Models: Does Modeling the Covariance Between Independent Variables and Latent Classes Improve the Results?
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DOI：
10.1080/00273171.2015.1095063
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发表时间：
2016
影响因子：
3.8
通讯作者：
Van Horn ML
中科院分区：
文献类型：
--
作者：
Lamont AE;Vermunt JK;Van Horn ML
Regression mixture models are increasingly used as an exploratory approach to identify heterogeneity in the effects of a predictor on an outcome. In this simulation study, we test the effects of violating an implicit assumption often made in these models – i.e., independent variables in the model are not directly related to latent classes. Results indicated that the major risk of failing to model the relationship between predictor and latent class was an increase in the probability of selecting additional latent classes and biased class proportions. Additionally, this study tests whether regression mixture models can detect a piecewise relationship between a predictor and outcome. Results suggest that these models are able to detect piecewise relations, but only when the relationship between the latent class and the predictor is included in model estimation. We illustrate the implications of making this assumption through a re-analysis of applied data examining heterogeneity in the effects of family resources on academic achievement. We compare previous results (which assumed no relation between independent variables and latent class) to the model where this assumption is lifted. Implications and analytic suggestions for conducting regression mixture based on these findings are noted.
