Analyzing Repeated Measures Data on Individuals Nested Within Groups: Accounting for Dynamic Group Effects
Analyzing Repeated Measures Data on Individuals Nested Within Groups: Accounting for Dynamic Group Effects
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DOI：
10.1037/a0030639
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发表时间：
2013-03-01
影响因子：
7
通讯作者：
Zucker, Robert A.
中科院分区：
文献类型：
--
作者：
Bauer, Daniel J.;Gottfredson, Nisha C.;Zucker, Robert A.
研究人员通常收集重复测量的个人嵌套在群体中，如学生在学校，治疗组内的患者，或家庭中的兄弟姐妹。通常情况下，最合适的概念，这些群体作为动态的实体，可能会经历随机的结构和/或功能的变化随着时间的推移。例如，随着学生在学校的进步，更多的高年级学生入学，而更多的低年级学生入学，管理人员和教师可能会移交，课程可能会发生变化。这意味着什么是一个学生在这所学校可能因此不同，从1年到下一个。本文演示了在分析嵌套在随时间演变的组中的个体的重复测量数据时，如何使用多水平线性模型来恢复随时间变化的组效应。提供了两个例子。第一个例子考察了学校对学生科学成就轨迹的影响，允许学校影响随着时间的推移而变化。第二个例子关注动态家庭对个体外化行为和抑郁轨迹的影响。
Researchers commonly collect repeated measures on individuals nested within groups such as students within schools, patients within treatment groups, or siblings within families. Often, it is most appropriate to conceptualize such groups as dynamic entities, potentially undergoing stochastic structural and/or functional changes over time. For instance, as a student progresses through school, more senior students matriculate while more junior students enroll, administrators and teachers may turn over, and curricular changes may be introduced. What it means to be a student within that school may thus differ from 1 year to the next. This article demonstrates how to use multilevel linear models to recover time-varying group effects when analyzing repeated measures data on individuals nested within groups that evolve over time. Two examples are provided. The 1st example examines school effects on the science achievement trajectories of students, allowing for changes in school effects over time. The 2nd example concerns dynamic family effects on individual trajectories of externalizing behavior and depression.