Abstract

While latent class (LC) modeling using bias-adjusted stepwise approaches has become widely popular, little is known on how these methods are affected by missing values. Using synthetic data sets, we illustrate under which conditions missing values introduce biases in the estimates of the relationship between class membership and auxiliary variables. We apply three-step LC analysis with both modal and proportional class assignments, as well as the recently proposed two-step LC analysis method.

Our results show that stepwise LC analysis yields unbiased parameter values as long as the MAR assumption holds in the step-one model. When this assumption does not hold because covariates are omitted from the step-one model, each of the stepwise approaches yields some bias, but bias is much larger with modal class assignments. The amount of bias is affected by the amount of deviation from MAR, the proportion of missing values, and the separation between the classes.

摘要

虽然使用偏差调整的逐步方法的潜在类别 (LC) 建模已广受欢迎，但人们对这些方法如何受到缺失值的影响知之甚少。使用合成数据集，我们说明了在哪些条件下缺失值会在类成员和辅助变量之间的关系估计中引入偏差。我们应用了具有模态和比例类分配的三步 LC 分析，以及最近提出的两步 LC 分析方法。

我们的结果表明，只要 MAR 假设在第一步模型中成立，逐步 LC 分析就会产生无偏的参数值。当这个假设不成立时，因为从第一步模型中省略了协变量，每个逐步方法都会产生一些偏差，但是模态类分配的偏差要大得多。偏差量受与 MAR 的偏差量、缺失值的比例以及类之间的分离度的影响。