ABSTRACT

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 for lavaan (R-package) and Mplus on an accompanying website.

摘要

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无法确定如何完成的RI-CLPM的三个扩展：（a）包括稳定的个人水平特征作为预测因子和/或结果；（b）指定多组版本；（c）包括多个指标。对于每个扩展，我们讨论了需要运行哪些模型以调查基本假设，并使用一个激励性的示例演示了各种建模选项。我们为lavaan提供完整注释的代码 （R-package）和Mplus在随附的网站上。