Grady Matthew W, Beretvas S Natasha
a The University of Texas at Austin.
Multivariate Behav Res. 2010 May 28;45(3):393-419. doi: 10.1080/00273171.2010.483390.
Multiple membership random effects models (MMREMs) have been developed for use in situations where individuals are members of multiple higher level organizational units. Despite their availability and the frequency with which multiple membership structures are encountered, no studies have extended the MMREM approach to hierarchical growth curve modeling (GCM). This study introduces a cross-classified multiple membership growth curve model (CCMM-GCM) for modeling, for example, academic achievement trajectories in the presence of student mobility. Real data are used to demonstrate and compare growth curve model estimates using the CCMM-GCM and a conventional GCM that ignores student mobility. Results indicate that the CCMM-GCM represents a promising option for modeling growth for multiple membership data structures.
多重成员随机效应模型（MMREMs）已被开发用于个体属于多个更高级组织单位的情况。尽管它们可用且多重成员结构经常出现，但尚无研究将MMREM方法扩展到分层生长曲线建模（GCM）。本研究引入了一种交叉分类多重成员生长曲线模型（CCMM-GCM），用于在存在学生流动的情况下对学业成就轨迹等进行建模。使用真实数据来演示和比较使用CCMM-GCM和忽略学生流动的传统GCM的生长曲线模型估计。结果表明，CCMM-GCM是对多重成员数据结构的生长进行建模的一个有前景的选择。
