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Investigating the impact of the time interval selection on autoregressive mediation modeling: Result interpretations, effect reporting, and temporal designs.
Psychological Methods ( IF 7.6 ) Pub Date : 2020-06-01 , DOI: 10.1037/met0000235
Lijuan Wang 1 , Qian Zhang 2
Affiliation  

This study investigates the impact of the time interval (the time passed between 2 consecutive measurements) selection on autoregressive mediation modeling (AMM). For a widely used autoregressive mediation model, via analytical derivations, we explained why and how the conventionally reported time-specific coefficient estimates (e.g., âb̂ and ĉ' ) and inference results in AMM provide limited information and can arrive in even misleading conclusions about direct and indirect effects over time. Furthermore, under the stationarity assumption, we proposed an approach to calculate the overall direct and indirect effect estimates over time and the time lag lengths at which they reach maxima, using AMM results. The derivation results revealed that the overall direct and indirect effect curves are asymptotically invariant to the time interval selection, under stationarity. With finite samples and thus sampling errors and potential computing problems, however, our simulation results revealed that the overall indirect effect curves were better recovered when the time interval is selected to be closer to half of the time lag length at which the overall indirect effect reaches its maximum. An R function and an R Shiny app were developed to obtain the overall direct and indirect effect curves over time and facilitate the time interval selection using AMM results. Our findings provide another look at the connections between AMM and continuous time mediation modeling and the connections are discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

中文翻译:


研究时间间隔选择对自回归中介模型的影响:结果解释、效果报告和时间设计。



本研究调查了时间间隔(两次连续测量之间经过的时间)选择对自回归中介模型 (AMM) 的影响。对于广泛使用的自回归中介模型,通过分析推导,我们解释了为什么以及如何传统报告的特定时间系数估计(例如 âb̂ 和 ĉ' )和 AMM 中的推理结果提供的信息有限,甚至可能得出关于直接的误导性结论。以及随着时间的推移产生的间接影响。此外,在平稳性假设下,我们提出了一种使用 AMM 结果来计算随时间变化的总体直接和间接效应估计以及它们达到最大值的时滞长度的方法。推导结果表明,在平稳性下,整体直接和间接效应曲线对时间间隔选择渐近不变。然而,由于样本有限,因此存在采样误差和潜在的计算问题,我们的模拟结果表明,当选择的时间间隔接近总体间接效应达到的时滞长度的一半时,总体间接效应曲线可以更好地恢复。其最大值。开发了 R 函数和 R Shiny 应用程序,以获得随时间变化的整体直接和间接效应曲线,并方便使用 AMM 结果选择时间间隔。我们的研究结果提供了对 AMM 和连续时间中介模型之间联系的另一种看法,并讨论了这种联系。 (PsycINFO 数据库记录 (c) 2019 APA,保留所有权利)。
更新日期:2020-06-01
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