用有序分类指标识别潜在类
Identifying latent classes with ordered categorical indicators
摘要：
使用蒙特卡罗模拟确定当响应变量具有5个有序类别时，有序分类数据（连续性与离散性类别）的哪些假设最常识别潜在因子结构。还审查了不经常核可的反应类别的影响，这一情况尚未得到充分探讨。在应用研究中，克服不经常被认可的类别的典型方法是将响应选项与相邻类别合并，从而导致更频繁被认可的响应类别更少，但这种方法不一定提供有用的信息。反应类别背书问题已在项目反应理论研究，但这个问题还没有得到解决，在分类分析，也没有合适的措施在这些条件下的性能进行了检查。我们发现，常用的拟合统计量的性能，以确定真正的潜在类的数量取决于是否假设连续性，样本量，和收敛。当假设五个响应选项为分类时，拟合统计量表现最佳。然而，在样本量较低的情况下，当收敛是一个问题时，假设连续性和使用调整后的Lo-Mendell-Rubin似然比检验可能是有用的。
英文摘要：
A Monte Carlo simulation was used to determine which assumptions for ordered categorical data, continuity vs. discrete categories, most frequently identifies the underlying factor structure when a response variable has five ordered categories. The impact of infrequently endorsed response categories was also examined, a condition that has not been fully explored. The typical method for overcoming infrequently endorsed categories in applied research is to collapse response options with adjacent categories resulting in less response categories that are endorsed more frequently, but this approach may not necessarily provide useful information. Response category endorsement issues have been studied in Item Response Theory, but this issue has not been addressed in classification analyses nor has fit measure performance been examined under these conditions. We found that the performance of commonly used fit statistics to identify the true number of latent class depends on the whether continuity is assumed, sample size, and convergence. Fit statistics performed best when the five response options are assumed to be categorical. However, in situations with lower sample sizes and when convergence is an issue, assuming continuity and using the adjusted Lo-Mendell-Rubin likelihood ratio test may be useful.
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