Three Extensions of the Random Intercept Cross-Lagged Panel Model
Three Extensions of the Random Intercept Cross-Lagged Panel Model
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DOI：
10.1080/10705511.2020.1784738
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发表时间：
2020-08-12
影响因子：
6
通讯作者：
Hamaker, Ellen L.
中科院分区：
文献类型：
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作者：
Mulder, Jeroen D.;Hamaker, Ellen L.
随机截距交叉滞后面板模型（RI-CLPM）作为纵向数据的结构方程模型（SEM）方法，在心理学及相关领域迅速流行。它将观察到的分数分解为单元内动态和稳定的单元间差异。本文讨论了研究人员可能感兴趣，但不确定如何实现的RI-CLPM的三个扩展：（a）包括稳定的，个人水平的特征作为预测因子和/或结果;（B）指定一个多组版本;（c）包括多个指标。对于每个扩展，我们讨论了哪些模型需要运行，以调查基本的假设，我们演示了各种建模选项使用一个激励的例子。我们在附带的网站上提供了lavaan（R-package）和Mplus的完整注释代码。
The random intercept cross-lagged panel model (RI-CLPM) is rapidly gaining popularity in psychology and related fields as a structural equation modeling (SEM) approach to longitudinal data. It decomposes observed scores into within-unit dynamics and stable, between-unit differences. This paper discusses three extensions of the RI-CLPM that researchers may be interested in, but are unsure of how to accomplish: (a) including stable, person-level characteristics as predictors and/or outcomes; (b) specifying a multiple-group version; and (c) including multiple indicators. For each extension, we discuss which models need to be run in order to investigate underlying assumptions, and we demonstrate the various modeling options using a motivating example. We provide fully annotated code forlavaan(R-package) and Mplus on an accompanying website.