This article considers identification, estimation, and model fit issues for models with contemporaneous and reciprocal effects. It explores how well the models work in practice using Monte Carlo studies as well as real-data examples. Furthermore, by using models that allow contemporaneous and reciprocal effects, the paper raises a fundamental question about current practice for cross-lagged panel modeling using models such as cross-lagged panel model (CLPM) or random intercept cross-lagged panel model (RI-CLPM): Can cross-lagged panel modeling be relied on to establish cross-lagged effects? The article concludes that the answer is no, a finding that has important ramifications for current practice. It is suggested that analysts should use additional models to probe the temporalities of the CLPM and RI-CLPM effects to see if these could be considered contemporaneous rather than lagged. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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可以依靠交叉滞后面板模型来建立交叉滞后效应吗？同时发生和相互影响的情况。


本文考虑具有同期效应和相互效应的模型的识别、估计和模型拟合问题。它使用蒙特卡罗研究和真实数据示例探讨了模型在实践中的运行情况。此外，通过使用允许同时和相互效应的模型，本文提出了一个关于当前使用交叉滞后面板模型（CLPM）或随机截距交叉滞后面板模型（RI- CLPM）：可以依靠交叉滞后面板模型来建立交叉滞后效应吗？文章的结论是答案是否定的，这一发现对当前的实践具有重要影响。建议分析师使用额外的模型来探究 CLPM 和 RI-CLPM 效应的时间性，看看这些效应是否可以被视为同时发生而不是滞后的。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。