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.

中文翻译：

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分析交叉滞后效应：不同交叉滞后建模方法的比较

发展研究人员经常有关于交叉滞后效应的研究问题——一个变量在随后的时间点预测第二个变量的影响。交叉滞后面板模型 (CLPM) 通常适用于纵向面板数据以检查交叉滞后效应；然而，由于它无法区分人与人之间的影响，它的效用最近受到了质疑。这导致提出了 CLPM 的替代形式来解决这些限制，包括随机截距 CLPM 和具有结构化残差的潜在曲线模型。我们描述这些模型的重点是对其模型参数的解释，并将它们应用于检查阅读和数学之间的跨滞后关联。各种模型的结果表明阅读和数学是相互关联的；然而，这些滞后关联的强度取决于模型。我们强调了每种方法的优点和局限性，并就建模选择提出建议。