On the Practical Interpretability of Cross-Lagged Panel Models: Rethinking a Developmental Workhorse

Abstract

Reciprocal feedback processes between experience and development are central to contemporary developmental theory. Autoregressive cross-lagged panel (ARCL) models represent a common analytic approach intended to test such dynamics. The authors demonstrate that-despite the ARCL model's intuitive appeal-it typically (a) fails to align with the theoretical processes that it is intended to test and (b) yields estimates that are difficult to interpret meaningfully. Specifically, using a Monte Carlo simulation and two empirical examples concerning the reciprocal relation between spanking and child aggression, it is shown that the cross-lagged estimates derived from the ARCL model reflect a weighted-and typically uninterpretable-amalgam of between- and within-person associations. The authors highlight one readily implemented respecification that better addresses these multiple levels of inference.