Data from psychotherapy groups are nested by nature. Ignoring this nesting when performing statistical tests can result in inflated Type I error rates and spurious significant results. This presents a serious problem for interpreting research that does not account for members nested within a specific group. Factor analytic methods are not immune to the negative effects of nesting, and when it is ignored, a poorly fitting factor structure can be hidden if one does not examine model fit at both the between or within-group level. Multilevel confirmatory factor analysis allows the researcher to account for nesting by separately examining both the betweenand within-group structures of measures used in group research. This article presents an overview of methods for evaluating the level of group dependency using the intraclass correlation coefficient (ICC) and a comparison of 2 methods for calculating ICCs. It then provides an overview and an example of multilevel factor analysis as a method for testing the model fit at the between and within levels separately by using partially saturated models. The authors end by reviewing common problems and offering guidelines for interpreting differences in withinand between-level fit in group research.

中文翻译：

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评估小组研究中措施的因素结构：在内部和内部进行观察。

来自心理治疗组的数据本质上是嵌套的。在执行统计测试时忽略这种嵌套会导致 I 类错误率过高和虚假的重要结果。这给解释研究带来了一个严重的问题，因为它没有考虑嵌套在特定群体中的成员。因子分析方法不能避免嵌套的负面影响，当它被忽略时，如果不检查组间或组内级别的模型拟合，就会隐藏拟合不佳的因子结构。多层次验证性因素分析允许研究人员通过分别检查组间研究中使用的测量的组间和组内结构来解释嵌套。本文概述了使用类内相关系数 (ICC) 评估组依赖性水平的方法，并比较了两种计算 ICC 的方法。然后，它提供了多水平因子分析的概述和示例，作为使用部分饱和模型分别测试模型在水平之间和水平内的拟合度的方法。作者最后回顾了常见问题，并提供了解释群体研究中层级内和层间匹配差异的指南。