Intervention studies in psychology often focus on identifying mechanisms that explain change over time. Cross-lagged panel models (CLPMs) are well suited to study mechanisms, but there is a controversy regarding the importance of detrending-defined here as separating longer-term time trends from cross-lagged effects-when modeling these change processes. The aim of this study was to present and test the arguments for and against detrending CLPMs in the presence of an intervention effect. We conducted Monte Carlo simulations to examine the impact of trends on estimates of cross-lagged effects from several longitudinal structural equation models. Our simulations suggested that ignoring time trends led to biased estimates of auto- and cross-lagged effects in some conditions, while detrending did not introduce bias in any of the models. We used real data from an intervention study to illustrate how detrending may affect results. This example showed that models that separated trends from cross-lagged effects fit better to the data and showed nonsignificant effect of the mechanism on outcome, while models that ignored trends showed significant effects. We conclude that ignoring trends increases the risk of bias in estimates of auto- and cross-lagged parameters and may lead to spurious findings. Researchers can test for the presence of trends by comparing model fit of models that take into account individual differences in trends (e.g., autoregressive latent trajectory model, the latent curve model with structured residuals, or the general cross-lagged model). (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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去趋势，还是不去趋势，这是一个问题？去趋势对面板模型中交叉滞后效应的影响。


心理学的干预研究通常侧重于识别解释随时间变化的机制。交叉滞后面板模型（CLPM）非常适合研究机制，但在对这些变化过程进行建模时，关于去趋势（此处定义为将长期时间趋势与交叉滞后效应分开）的重要性存在争议。本研究的目的是提出并检验在存在干预效应的情况下支持和反对 CLPM 去趋势的论据。我们进行了蒙特卡罗模拟，以检查趋势对几个纵向结构方程模型的交叉滞后效应估计的影响。我们的模拟表明，忽略时间趋势会导致在某些情况下对自动滞后效应和交叉滞后效应的估计出现偏差，而消除趋势不会在任何模型中引入偏差。我们使用干预研究的真实数据来说明趋势消除可能如何影响结果。这个例子表明，将趋势与交叉滞后效应分开的模型更适合数据，并且显示该机制对结果的影响不显着，而忽略趋势的模型则显示出显着的影响。我们的结论是，忽略趋势会增加自动滞后参数和交叉滞后参数估计中的偏差风险，并可能导致虚假结果。研究人员可以通过比较考虑趋势个体差异的模型的模型拟合度来测试趋势的存在（例如，自回归潜在轨迹模型、具有结构化残差的潜在曲线模型或一般交叉滞后模型）。 （PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。