ABSTRACT

Latent class models can exhibit poor parameter recovery and low convergence rates under the traditional frequentist estimation approach. Bayesian estimation may be a viable alternative for estimating latent class models–especially when categorical items are present and priors can be placed directly on the categorical item-thresholds. We present a simulation study involving Bayesian latent class analysis (LCA) with categorical items. We demonstrate that the frequentist framework and the Bayesian framework with diffuse (non-informative) priors are unable to properly recover parameters (e.g., latent class item-thresholds); a substantive interpretation of the obtained results would lead to improper conclusions under these estimation conditions. However, specifying (weakly) informative priors within the Bayesian framework generally produced accurate parameter recovery, indicating that this may be a more viable estimation approach for LCA models with categorical indicators. The paper concludes with a general discussion surrounding the advantages of Bayesian estimation for LCA models.

摘要

在传统的频率估计方法下，潜在类模型可能表现出较差的参数恢复和低收敛速度。贝叶斯估计可能是估计潜在类别模型的可行替代方案——特别是当存在分类项目并且先验可以直接放在分类项目阈值上时。我们提出了一项涉及贝叶斯潜在类别分析 (LCA) 和分类项目的模拟研究。我们证明了具有扩散（非信息）先验的频率论框架和贝叶斯框架无法正确恢复参数（例如，潜在类项目阈值）；在这些估计条件下，对所得结果的实质性解释将导致得出不正确的结论。然而，在贝叶斯框架内指定（弱）信息先验通常会产生准确的参数恢复，这表明对于具有分类指标的 LCA 模型，这可能是一种更可行的估计方法。本文最后围绕 LCA 模型的贝叶斯估计优势进行了一般性讨论。