The purpose of the current 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 the step-1 of the three-step approach shows adequate results in class enumeration, we suggest using an alternative approach: 1) decide the number of latent classes without predictors of latent classes and 2) bring the latent class predictors into the model with the inclusion of hypothesized direct covariates 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.

中文翻译：

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回归混合模型中潜在类别的建模预测

当前研究的目的是为在回归混合模型中包含潜在类别预测变量的过程提供指导。我们首先检查当前使用 1 步和 3 步方法的性能，其中忽略了对结果的直接协变量影响。没有一种方法显示对模型参数的充分估计。鉴于三步法的第 1 步在类枚举中显示出足够的结果，我们建议使用另一种方法：1) 确定没有潜在类预测变量的潜在类的数量，以及 2) 将潜在类预测变量引入模型包括假设的直接协变量效应。我们的模拟表明，这种方法可以对所有模型参数进行良好的估计。通过使用实证数据来检验家庭资源对学生学业成绩的不同影响，证明了所提出的方法。讨论了研究的意义。