On the Practical Interpretability of Cross-Lagged PanelModels: 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 thatdespite the ARCL model's intuitive appealit 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 weightedand typically uninterpretableamalgam of between- and within-person associations. The authors highlight one readily implemented respecification that better addresses these multiple levels of inference.