Psychological data are often clustered within organizational units, which violates the independence assumption in standard regression models. Clustered errors, multilevel models, and fixed-effects models all address this issue, but in different ways. Disciplinary preferences for approaching clustered data are strong, which can restrict questions researchers ask because certain approaches are better equipped to handle particular types of questions. Resources comparing approaches to facilitate broader understanding of clustered data approaches exist for economists, political scientists, and biostatisticians. These existing resources use concepts and terminology consistent with statistical training in other disciplines, so this article provides a resource using language and principles familiar to psychologists. The article starts by walking through the origin and importance of the independence assumption to motivate the problem and emergence of different solutions in different fields. Then, information on clustered errors, multilevel models, and fixed-effect models is provided, including (a) how each approach addresses independence violations, (b) research questions ideally suited for each approach, and (c) example analyses highlighting advantages and disadvantages. The article then discusses how these approaches are not mutually exclusive but instead can be blended together to create tailor-made models that flexibly accommodate idiosyncrasies in research questions and are robust to nuances of a particular data set. The broader theme is that there is no one-size-fits-all approach to clustered data. The research question-not disciplinary preferences-should inform the statistical approach. Wider appreciation of the landscape of clustered data approaches can expand the questions researchers ask and improve the theoretical foundation of statistical models. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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选择和混合聚类数据方法的实用指南：聚类错误、多级模型和固定效应模型。

心理数据通常聚集在组织单位内，这违反了标准回归模型中的独立性假设。聚类错误、多级模型和固定效应模型都解决了这个问题，但方式不同。处理聚类数据的学科偏好很强，这可能会限制研究人员提出的问题，因为某些方法更适合处理特定类型的问题。为经济学家、政治科学家和生物统计学家提供了比较方法的资源，以促进对聚类数据方法的更广泛理解。这些现有资源使用与其他学科的统计培训一致的概念和术语，因此本文提供了使用心理学家熟悉的语言和原则的资源。本文首先介绍了独立性假设的起源和重要性，以激发该问题以及不同领域中不同解决方案的出现。然后，提供有关聚类错误、多级模型和固定效应模型的信息，包括（a）每种方法如何解决独立性违规问题，（b）非常适合每种方法的研究问题，以及（c）突出优点和缺点的示例分析。然后，本文讨论了这些方法如何不是相互排斥的，而是可以混合在一起创建定制模型，灵活地适应研究问题的特质，并对特定数据集的细微差别具有鲁棒性。更广泛的主题是，没有一种通用的方法来处理集群数据。研究问题——而不是学科偏好——应该为统计方法提供信息。对集群数据方法的更广泛的了解可以扩大研究人员提出的问题并改善统计模型的理论基础。（PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。