Latent transition analysis (LTA), also referred to as latent Markov modeling, is an extension of latent class/profile analysis (LCA/LPA) used to model the interrelations of multiple latent class variables. LTA methods have become increasingly accessible and in-turn are being utilized in applied research. The current article provides an introduction to LTA by answering 10 questions commonly asked by applied researchers. Topics discussed include: (1) an overview of LTA; (2) a comparison of LTA to other longitudinal models; (3) software used to run LTA; (4) sample size suggestions; (5) modeling steps in LTA; (6) measurement invariance; (7) the inclusion of auxiliary variables; (8) interpreting results of an LTA; (9) the nature of data (e.g., longitudinal, cross-sectional); and (10) extensions of LTA. An applied example of LTA is included to help understand how to build an LTA and interpret results. Finally, the article suggests future areas of research for LTA. This article provides an overview of LTA, highlighting key decisions researchers need to make to navigate and implement an LTA analysis from start to finish.

中文翻译：

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有关潜在转换分析的十个常见问题。

潜在转移分析 (LTA)，也称为潜在马尔可夫建模，是潜在类/概况分析 (LCA/LPA) 的扩展，用于对多个潜在类变量的相互关系进行建模。LTA 方法越来越容易获得，反过来又被用于应用研究。当前文章通过回答应用研究人员常问的 10 个问题来介绍 LTA。讨论的主题包括：(1) LTA 概述；(2) LTA 与其他纵向模型的比较；(3) 用于运行 LTA 的软件；(4) 样本量建议；(5) LTA 中的建模步骤；(6) 测量不变性；(7) 辅助变量的加入；(8) 长期协议结果的解释；(9) 数据的性质（例如，纵向的、横截面的）；(10) LTA 的扩展。LTA 的应用示例包括在内，以帮助理解如何构建 LTA 和解释结果。最后，文章提出了 LTA 未来的研究领域。本文概述了 LTA，强调了研究人员需要做出的关键决策，以便从头到尾导航和实施 LTA 分析。