A Comparison of Different Approaches for Estimating Cross-Lagged Effects from a Causal Inference Perspective. Issue 6 (2nd November 2022)

• Record Type: Journal Article
• Authors: Lüdtke, Oliver; Robitzsch, Alexander
• Is Part Of: Structural equation modeling. Volume 29:Issue 6(2022)
• Page Start: 888
• Page End: 907
• Publication Date: 2022-11-02
• Subjects: Causal inference -- cross-lagged effect -- cross-lagged panel model -- longitudinal data -- random intercept cross-lagged panel model; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2022.2065278
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract: This article compares different approaches for estimating cross-lagged effects with a cross-lagged panel design under a causal inference perspective. We distinguish between models that rely on no unmeasured confounding (i.e., observed covariates are sufficient to remove confounding) and latent variable-type models (e.g., random intercept cross-lagged panel model) that use parametric assumptions to adjust for unmeasured time-invariant confounding by including additional latent variables. Simulation studies confirm that the cross-lagged panel model provides biased estimates of the cross-lagged effect in the presence of unmeasured confounding. However, the simulations also show that the latent variable-type approaches strongly depend on the specific parametric assumptions, and produce biased estimates under different data-generating scenarios. Finally, we discuss the role of the longitudinal design and the limitations of assessing model fit for estimating cross-lagged effects.