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Time Varying Grouping Variables in Markov Latent Class Analysis: Some Problems and Solutions

机译：Markov潜在类分析中的时间变化分组变量：一些问题和解决方案

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摘要

Markov latent class analysis (MLCA) is a modeling technique for panel or longitudinal data that can be used to estimate the classification error rates (e.g., false positive and false negative rates for dichotomous items) for discrete outcomes with categorical predictors when gold-standard measurements are not available. Because panel surveys collect data at multiple time points, the grouping variables in the model may either be time varying or time invariant (static). Time varying grouping variables may be more correlated with either the latent construct or the measurement errors because they are measured simultaneously with the construct during the measurement process. However, they generate a large number of model parameters that can cause problems with data sparseness, model diagnostic validity, and model convergence. In this paper we investigate whether more parsimonious grouping variables that either summarize the variation of the time varying grouping variable or assume a structure that lacks memory of previous values of the grouping variables can be used instead, without sacrificing model fit or validity. We propose a simple diagnostic approach for comparing the validity of models that use time-invariant summary variables with their time-varying counterparts. To illustrate the methodology, this approach is applied to data from the National Crime Victimization Survey (NCVS) where greater parsimony and a reduction in data sparseness were achieved with no appreciable loss in model validity for the outcome variables considered. The approach is generalized for application to essentially any MLCA using time varying group variables and its advantages and disadvantages are discussed.
机译：马尔可夫潜在阶级分析（MLCA）是面板或纵向数据的建模技术，可用于估计金标准测量时与分类预测因子的离散结果估计分类误差率（例如，二分法物品的假负率）不可用。因为面板调查在多个时间点收集数据，所以模型中的分组变量可以是时变或不变（静态）。与潜在构造或测量误差更加不同的分组变量，因为它们在测量过程期间与构造同时测量它们。但是，它们会产生大量模型参数，可能导致数据稀疏性，模型诊断有效性和模型融合出现问题。在本文中，我们调查了是否可以在不牺牲模型适合或有效性的情况下总结时间变化变量的变化或假设时间变化变量的变化或假设缺少分组变量的先前值的内存的结构的更加分组的分组变量。我们提出了一种简单的诊断方法，用于比较使用时间不变摘要变量的模型的有效性与其时变的对应物。为了说明该方法，这种方法适用于来自国家犯罪受害调查（NCV）的数据，其中达到了更大的数据稀疏和减少数据稀疏，对于考虑的结果变量没有明显的模型有效性损失。该方法广泛地用于应用于基本上使用时间变化的群体变量的任何MLCA，并且讨论了其优点和缺点。

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