Modeling Predictors of Latent Classes in Regression Mixture Models
Modeling Predictors of Latent Classes in Regression Mixture Models
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DOI：
10.1080/10705511.2016.1158655
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发表时间：
2016-07-01
影响因子：
6
通讯作者：
Van Horn, M. Lee
中科院分区：
文献类型：
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作者：
Kim, Minjung;Vermunt, Jeroen;Van Horn, M. Lee
本研究的目的是提供指导的过程中，包括潜在的类预测回归混合模型。我们首先检查使用1步和3步方法的当前实践的性能，其中忽略了对结果的直接协变量影响。没有一种方法显示出模型参数的充分估计。鉴于3步方法的第1步在类枚举中显示了足够的结果，我们建议使用替代方法：（a）在没有潜在类预测因子的情况下决定潜在类的数量，以及（B）将潜在类预测因子纳入模型中，并包含假设的直接协变量效应。我们的模拟表明，这种方法导致所有模型参数的良好估计。本研究以实证资料验证家庭资源对学生学业成就的差异影响。研究的意义进行了讨论。
The purpose of this study is to provide guidance on a process for including latent class predictors in regression mixture models. We first examine the performance of current practice for using the 1-step and 3-step approaches where the direct covariate effect on the outcome is omitted. None of the approaches show adequate estimates of model parameters. Given that Step 1 of the 3-step approach shows adequate results in class enumeration, we suggest using an alternative approach: (a) decide the number of latent classes without predictors of latent classes, and (b) bring the latent class predictors into the model with the inclusion of hypothesized direct covariate effects. Our simulations show that this approach leads to good estimates for all model parameters. The proposed approach is demonstrated by using empirical data to examine the differential effects of family resources on students' academic achievement outcome. Implications of the study are discussed.