Analyzing cross‐lag effects: A comparison of different cross‐lag modeling approaches

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Abstract
Developmental researchers often have research questions about cross‐lag effects—the effect of one variable predicting a second variable at a subsequent time point. The cross‐lag panel model (CLPM) is often fit to longitudinal panel data to examine cross‐lag effects; however, its utility has recently been called into question because of its inability to distinguish between‐person effects from within‐person effects. This has led to alternative forms of the CLPM to be proposed to address these limitations, including the random‐intercept CLPM and the latent curve model with structured residuals. We describe these models focusing on the interpretation of their model parameters, and apply them to examine cross‐lag associations between reading and mathematics. The results from the various models suggest reading and mathematics are reciprocally related; however, the strength of these lagged associations was model dependent. We highlight the strengths and limitations of each approach and make recommendations regarding modeling choice.

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