Cross-classified models accommodate data structures that have more than one cluster variable, which are not nested in each other but overlap. They simultaneously consider all clustering variables. This allows one to study effects on several levels at once. Cross-classified data structures are common in various field of applied research (e.g., research on teams, career paths, interventions). The present article demonstrates modeling options and specifications for cross-classified data to be used within these different research strands. For more specific demonstration purposes, we use a data set on rater variance in assessment centers. Commonly, raters observe only a subset of participants and hence ratings are both nested in participants and raters, but participants and raters are not nested in each other. Using cross-classified models allows studying sources of rater variance (e.g., professional expertise) and interactions between cluster level variables, for example, interactions between participants' and raters' personality and sociodemographic characteristics (e.g., gender). From a practical research point of view and to ease application, we deliver a step-by-step overview of modeling procedures and power analysis including software code.

交叉分类模型可容纳具有多个群集变量的数据结构，这些群集变量彼此之间不是嵌套的而是重叠的。他们同时考虑所有聚类变量。这样一来，就可以一次研究多个级别的效果。交叉分类的数据结构在应用研究的各个领域（例如，团队研究，职业发展道路，干预措施）很常见。本文演示了在这些不同研究链中使用的交叉分类数据的建模选项和规范。为了更具体的演示目的，我们使用评估中心评估者差异的数据集。通常，评估者仅观察参与者的一个子集，因此评估都嵌套在参与者和评估者中，但参与者和评估者并不相互嵌套。使用交叉分类的模型可以研究评估者方差（例如专业知识）的来源以及集群级别变量之间的相互作用，例如，参与者和评估者的性格与社会人口统计学特征（例如性别）之间的相互作用。从实践研究的角度并简化应用程序，我们逐步提供了建模过程和功耗分析（包括软件代码）的概述。