交叉分类多隶属度数据的三水平潜变量回归模型估计

IF 2 3区 心理学 Q2 PSYCHOLOGY, MATHEMATICAL
Audrey J. Leroux, S. Beretvas
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引用次数: 6

摘要

目前的研究提出了一种新的模型,称为交叉分类多成员潜在变量回归(CCMM-LVR)模型,该模型为三级潜在变量回归模型(HM3-LVR)提供了扩展,该模型可用于交叉分类的多成员数据,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,并处理更高级别(3级)单元中参与者的纯聚类。然而,HM3-LVR模型涉及学生在感兴趣的整个时间段内都留在同一集群(学校)的假设。CCMM-LVR模型适当地对参与者随时间变化的集群进行建模。通过比较模型(CCMM-LVR)的参数估计、标准误差估计和模型拟合指数,研究了忽略真实数据中移动性的影响,该模型适当地对交叉分类的多成员结构进行了建模,并与忽略该结构时的结果(HM3-LVR)进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimating a Three-Level Latent Variable Regression Model With Cross-Classified Multiple Membership Data
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).
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来源期刊
CiteScore
2.70
自引率
6.50%
发文量
16
审稿时长
36 weeks
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