Three Extensions of the Random Intercept Cross-Lagged Panel Model
2020/08/11 by Jeroen D. Mulder, Ellen L. Hamaker · 930 citations
Economics, Econometrics and Finance · #Regional Economics and Spatial Analysis #Spatial and Panel Data Analysis
paper · pdf · doi:10.1080/10705511.2020.1784738
published in Structural Equation Modeling: A Multidisciplinary Journal 28(4), 638-648 (Informa UK Limited)
crossref issued 2020/08/11 · crossref published 2020/08/11 · crossref published-online 2020/08/11 · openalex publication_date 2020/08/11 · crossref created 2020/08/12 · crossref published-print 2021/07/04 · crossref deposited 2021/07/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/02 · crossref indexed 2026/08/08
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.
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