A One-Stage Approach for Estimating a Meta-Analytic Continuous-Time Random-Intercept Cross-Lagged Panel Model
Abstract
This article describes, evaluates, and applies a meta-analytic random-intercept cross-lagged panel model (RI-CLPM). The model enables meta-analytic study of temporal relations between two variables while separating stable between-person differences from within-person fluctuations. The RI-CLPM has been proposed as an alternative to the cross-lagged panel model and has recently motivated many reanalyzes of longitudinal datasets. To support flexible, integrative reanalysis across multiple datasets, we introduce a Bayesian one-stage approach for fitting a meta-analytic RI-CLPM using correlation matrices from longitudinal primary studies. The method combines Bayesian meta-analytic structural equation modeling with continuous-time modeling. The continuous-time framework allows us to account for different time lags across studies, a common challenge in longitudinal meta-analysis. We propose four model variants that differ in their random-effects structures for capturing between-study heterogeneity. A simulation study compares their performance, and an empirical example from educational psychology examines reciprocal relations between academic self-concept and achievement, illustrating their application.
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BibTeX
@misc{lohmann2026onestage,
title = {{A One-Stage Approach for Estimating a Meta-Analytic Continuous-Time Random-Intercept Cross-Lagged Panel Model}},
author = {Julian F Lohmann and Oliver Lüdtke and Alexander Robitzsch},
howpublished = {PsyArXiv},
year = {2026},
month = sep,
doi = {10.31234/osf.io/yfn58_v3},
url = {https://osf.io/preprints/psyarxiv/yfn58_v3/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - A One-Stage Approach for Estimating a Meta-Analytic Continuous-Time Random-Intercept Cross-Lagged Panel Model
AU - Julian F Lohmann
AU - Oliver Lüdtke
AU - Alexander Robitzsch
PY - 2026
DA - 2026/09/06
PB - PsyArXiv
DO - 10.31234/osf.io/yfn58_v3
UR - https://osf.io/preprints/psyarxiv/yfn58_v3/
AB - This article describes, evaluates, and applies a meta-analytic random-intercept cross-lagged panel model (RI-CLPM). The model enables meta-analytic study of temporal relations between two variables while separating stable between-person differences from within-person fluctuations. The RI-CLPM has been proposed as an alternative to the cross-lagged panel model and has recently motivated many reanalyzes of longitudinal datasets. To support flexible, integrative reanalysis across multiple datasets, we introduce a Bayesian one-stage approach for fitting a meta-analytic RI-CLPM using correlation matrices from longitudinal primary studies. The method combines Bayesian meta-analytic structural equation modeling with continuous-time modeling. The continuous-time framework allows us to account for different time lags across studies, a common challenge in longitudinal meta-analysis. We propose four model variants that differ in their random-effects structures for capturing between-study heterogeneity. A simulation study compares their performance, and an empirical example from educational psychology examines reciprocal relations between academic self-concept and achievement, illustrating their application.
ER -