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Estimating a Three-Level Latent Variable Regression Model With Cross-Classified Multiple Membership Data
Methodology ( IF 1.975 ) Pub Date : 2018-01-01 , DOI: 10.1027/1614-2241/a000143
Audrey J. Leroux 1 , S. Natasha Beretvas 2
Affiliation  

The current study proposed a new model, termed the cross-classified multiple membership latent variable regression (CCMM-LVR) model that provides an extension to the three-level latent variable regression (HM3-LVR) model that can be used with cross-classified multiple membership data, for example, in the presence of student mobility across schools. The HM3-LVR model is beneficial for testing more flexible hypotheses about growth trajectory parameters and handles pure clustering of participants within higher-level (level-3) units. However, the HM3-LVR model involves the assumption that students remain in the same cluster (school) throughout the duration of the time period of interest. The CCMM-LVR model appropriately models the participants’ changing clusters over time. The impact of ignoring mobility in the real data was investigated by comparing parameter estimates, standard error estimates, and model fit indices for the model (CCMM-LVR) that appropriately modeled the cross-classified multiple membership structure with results when this structure was ignored (HM3-LVR).

中文翻译:

使用交叉分类的多个成员数据估计三级潜在变量回归模型

当前的研究提出了一种新模型,称为交叉分类多成员潜在隐变量回归(CCMM-LVR)模型,该模型扩展了可与交叉分类一起使用的三级潜在变量回归(HM3-LVR)模型。多个成员资格数据,例如,在学生跨学校流动的情况下。HM3-LVR模型有利于测试关于增长轨迹参数的更灵活的假设,并能处理较高级别(第3级)单位内参与者的纯聚类。但是,HM3-LVR模型假设学生在整个感兴趣的时间段内都处于同一集群(学校)中。CCMM-LVR模型适当地模拟了参与者随时间变化的集群。
更新日期:2018-01-01
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