清晰思考交叉滞后面板模型中的时变混杂因素:从因果推论角度选择统计模型指南》。

IF 7.6 1区 心理学 Q1 PSYCHOLOGY, MULTIDISCIPLINARY
Kou Murayama, Thomas Gfrörer
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引用次数: 0

摘要

人们提出了许多统计模型来研究面板数据的互惠跨滞后因果效应。本文旨在阐明这些不同的统计模型如何控制未测量的时间不变混杂因素,帮助研究人员从因果推断的角度理解统计模型的差异。假设真实的数据生成模型(即因果模型)有未测量的时变型混杂因素,我们比较了不同统计模型(如动态面板模型和随机截距交叉滞后面板模型)在何种条件下能对目标因果估计值提供相对准确的估计。基于这些条件的比较和现实合理性,我们为研究人员在进行因果推断时选择统计模型提出了一些实用建议。(PsycInfo Database Record (c) 2024 APA, 版权所有)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Thinking clearly about time-invariant confounders in cross-lagged panel models: A guide for choosing a statistical model from a causal inference perspective.

Many statistical models have been proposed to examine reciprocal cross-lagged causal effects from panel data. The present article aims to clarify how these various statistical models control for unmeasured time-invariant confounders, helping researchers understand the differences in the statistical models from a causal inference perspective. Assuming that the true data generation model (i.e., causal model) has time-invariant confounders that were not measured, we compared different statistical models (e.g., dynamic panel model and random-intercept cross-lagged panel model) in terms of the conditions under which they can provide a relatively accurate estimate of the target causal estimand. Based on the comparisons and realistic plausibility of these conditions, we made some practical suggestions for researchers to select a statistical model when they are interested in causal inference. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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来源期刊
Psychological methods
Psychological methods PSYCHOLOGY, MULTIDISCIPLINARY-
CiteScore
13.10
自引率
7.10%
发文量
159
期刊介绍: Psychological Methods is devoted to the development and dissemination of methods for collecting, analyzing, understanding, and interpreting psychological data. Its purpose is the dissemination of innovations in research design, measurement, methodology, and quantitative and qualitative analysis to the psychological community; its further purpose is to promote effective communication about related substantive and methodological issues. The audience is expected to be diverse and to include those who develop new procedures, those who are responsible for undergraduate and graduate training in design, measurement, and statistics, as well as those who employ those procedures in research.
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