Factor mixture modeling (FMM) incorporates both continuous latent variables and categorical latent variables in a single analytic model clustering items and observations simultaneously. After two decades since the introduction of FMM to psychological and behavioral science research, it is an opportune time to review FMM applications to understand how these applications are utilized in real-world research. We conducted a systematic review of 76 FMM applications. We developed a comprehensive coding scheme based on the current methodological literature of FMM and evaluated common usages and practices of FMM. Based on the review, we identify challenges and issues that applied researchers encounter in the practice of FMM and provide practical suggestions to promote well-informed decision making. Lastly, we discuss future methodological directions and suggest how FMM can be expanded beyond its typical use in applied studies. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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因子混合模型应用的系统回顾与反思


因子混合模型 (FMM) 将连续潜在变量和分类潜在变量合并到单个分析模型中，同时对项目和观察结果进行聚类。自 FMM 引入心理和行为科学研究二十年后，现在是回顾 FMM 应用程序以了解如何在现实世界研究中利用这些应用程序的好时机。我们对 76 份 FMM 申请进行了系统审查。我们根据当前的 FMM 方法论文献开发了一个全面的编码方案，并评估了 FMM 的常见用法和实践。根据回顾，我们确定了应用研究人员在 FMM 实践中遇到的挑战和问题，并提供实用的建议，以促进明智的决策。最后，我们讨论了未来的方法论方向，并建议如何将 FMM 扩展到应用研究中的典型用途之外。 （PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。