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

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.