Department of Psychology, The Ottawa Hospital-General, Ottawa, Ontario K1H 8L6, Ontario.
Psychother Res. 2009 Jul;19(4-5):453-61. doi: 10.1080/10503300902933188.
Researchers have known for years about the negative impact on Type I error rates caused by dependencies in hierarchically nested and longitudinal data. Despite this, group treatment researchers do not consistently use methods such as multilevel models (MLMs) to assess dependence and appropriately analyse their nested data. The goals of this study are to review some of the study design issues with regard to hierarchically nested and longitudinal data, discuss MLMs for assessing and handling dependence in data, and present a guide for developing a three-level growth MLM that is appropriate for group treatment data, design, and research questions. The authors present an example from group treatment research to illustrate these issues and methods.
多年来，研究人员已经了解到层次嵌套和纵向数据中的相关性对 I 类错误率的负面影响。尽管如此，组治疗研究人员并没有始终如一地使用多层次模型 (MLMs) 等方法来评估相关性并适当地分析他们的嵌套数据。本研究的目的是回顾与层次嵌套和纵向数据有关的一些研究设计问题，讨论用于评估和处理数据相关性的 MLMs，并为适合组治疗数据、设计和研究问题的三级增长 MLM 提供指南。作者提出了一个来自组治疗研究的例子来说明这些问题和方法。
