Latent class analysis (LCA) is a statistical procedure used to identify qualitatively different subgroups within populations who often share certain outward characteristics. The assumption underlying LCA is that membership in unobserved groups (or classes) can be explained by patterns of scores across survey questions, assessment indicators, or scales. The application of LCA is an active area of research and continues to evolve. As more researchers begin to apply the approach, detailed information on key considerations in conducting LCA is needed. In the present article, we describe LCA, review key elements to consider when conducting LCA, and provide an example of its application.

中文翻译：

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潜在类别分析：最佳实践指南

潜在类别分析 (LCA) 是一种统计程序，用于在通常具有某些外在特征的人群中识别质量不同的亚组。LCA 的基础假设是未观察组（或类）的成员资格可以通过跨调查问题、评估指标或量表的分数模式来解释。LCA 的应用是一个活跃的研究领域，并且还在不断发展。随着越来越多的研究人员开始应用该方法，需要有关进行 LCA 的关键考虑因素的详细信息。在本文中，我们将描述 LCA，回顾进行 LCA 时要考虑的关键要素，并提供其应用示例。