Causal cycles, causal intervals, and measurement in over-time studies

IF 4 2区管理学 Q2 MANAGEMENT Group & Organization Management Pub Date : 2024-08-12 DOI:10.1177/10596011241273216

Charles E. Lance

引用次数: 0

Abstract

If over-time data are used to model XàY causal relationships, the measurement (or “recording”) interval should match (or at least approximate) the actual causal (or “existence”) interval for X’s effect on Y. I discuss this issue in the context of causal cycles of events and give three examples involving hurricanes, job change and adoption and implementation of new technology. I conclude with some considerations and recommendations for matching measurement to causal intervals in over-time research.

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超时空研究中的因果周期、因果间隔和测量方法

如果使用超时空数据来模拟 XàY 因果关系，那么测量（或 "记录"）时间间隔应与 X 对 Y 的影响的实际因果（或 "存在"）时间间隔相匹配（或至少近似）。我将在事件因果周期的背景下讨论这个问题，并给出三个例子，分别涉及飓风、工作变动以及新技术的采用和实施。最后，我将就超时空研究中如何将测量与因果区间相匹配提出一些考虑和建议。

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

Group & Organization Management Multiple-

CiteScore

8.40

自引率

12.50%

发文量

期刊介绍： Group & Organization Management (GOM) publishes the work of scholars and professionals who extend management and organization theory and address the implications of this for practitioners. Innovation, conceptual sophistication, methodological rigor, and cutting-edge scholarship are the driving principles. Topics include teams, group processes, leadership, organizational behavior, organizational theory, strategic management, organizational communication, gender and diversity, cross-cultural analysis, and organizational development and change, but all articles dealing with individual, group, organizational and/or environmental dimensions are appropriate.