Long-Run Effects in Dynamic Systems: New Tools for Cross-Lagged Panel Models

IF 8.9 2区 管理学 Q1 MANAGEMENT
A. Shamsollahi, M. Zyphur, Ozlem Ozkok
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引用次数: 6

Abstract

Cross-lagged panel models (CLPMs) are common, but their applications often focus on “short-run” effects among temporally proximal observations. This addresses questions about how dynamic systems may immediately respond to interventions, but fails to show how systems evolve over longer timeframes. We explore three types of “long-run” effects in dynamic systems that extend recent work on “impulse responses,” which reflect potential long-run effects of one-time interventions. Going beyond these, we first treat evaluations of system (in)stability by testing for “permanent effects,” which are important because in unstable systems even a one-time intervention may have enduring effects. Second, we explore classic econometric long-run effects that show how dynamic systems may respond to interventions that are sustained over time. Third, we treat “accumulated responses” to model how systems may respond to repeated interventions over time. We illustrate tests of each long-run effect in a simulated dataset and we provide all materials online including user-friendly R code that automates estimating, testing, reporting, and plotting all effects (see https://doi.org/10.26188/13506861). We conclude by emphasizing the value of aligning specific longitudinal hypotheses with quantitative methods.
动态系统的长期效应:交叉滞后面板模型的新工具
交叉滞后面板模型(clpm)是常见的,但它们的应用往往侧重于“短期”影响的时间近端观测。这解决了动态系统如何立即对干预作出反应的问题,但未能显示系统如何在更长的时间框架内进化。我们探索了动态系统中三种类型的“长期”效应,这些效应扩展了最近对“冲动反应”的研究,反映了一次性干预的潜在长期效应。除此之外,我们首先通过测试“永久影响”来评估系统的稳定性,这很重要,因为在不稳定的系统中,即使是一次性的干预也可能产生持久的影响。其次,我们探索了经典的计量经济学长期效应,展示了动态系统如何对持续一段时间的干预做出反应。第三,我们处理“累积反应”来模拟系统如何随着时间的推移对重复干预作出反应。我们在模拟数据集中演示了每个长期效果的测试,并在线提供了所有材料,包括用户友好的R代码,可自动评估、测试、报告和绘制所有效果(参见https://doi.org/10.26188/13506861)。最后,我们强调将具体的纵向假设与定量方法结合起来的价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
23.20
自引率
3.20%
发文量
17
期刊介绍: Organizational Research Methods (ORM) was founded with the aim of introducing pertinent methodological advancements to researchers in organizational sciences. The objective of ORM is to promote the application of current and emerging methodologies to advance both theory and research practices. Articles are expected to be comprehensible to readers with a background consistent with the methodological and statistical training provided in contemporary organizational sciences doctoral programs. The text should be presented in a manner that facilitates accessibility. For instance, highly technical content should be placed in appendices, and authors are encouraged to include example data and computer code when relevant. Additionally, authors should explicitly outline how their contribution has the potential to advance organizational theory and research practice.
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