Department of Psychology, University of North Carolina, Chapel Hill, NC 27599-3270, USA.
Psychol Methods. 2013 Mar;18(1):1-14. doi: 10.1037/a0030639. Epub 2012 Nov 12.
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.
研究人员通常会在嵌套在群体中的个体上收集重复测量数据，例如学校内的学生、治疗组内的患者或家庭内的兄弟姐妹。通常，将此类群组概念化为动态实体最为合适，它们可能随着时间的推移经历随机的结构和/或功能变化。例如，随着学生在学校的进步，高年级学生入学，而低年级学生入学，管理人员和教师可能会更替，课程可能会发生变化。因此，在那个学校里作为一名学生的意义可能会与下一年有所不同。本文展示了如何在分析随时间演变的嵌套在群组中的个体的重复测量数据时，使用多层次线性模型来恢复时变的群组效应。提供了两个示例。第一个示例研究了学校对学生科学成绩轨迹的影响，允许学校效应随时间变化。第二个示例涉及动态家庭效应对个体外化行为和抑郁轨迹的影响。
