当结果是二元时,为什么不能使用系数差法估计中介效应?

IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Multivariate Behavioral Research Pub Date : 2024-10-29 DOI:10.1080/00273171.2024.2418515
Judith J M Rijnhart, Matthew J Valente, David P MacKinnon
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引用次数: 0

摘要

尽管以前有人警告过,当中介模型中的结果是二元的时候,不要使用系数差法来估计间接效应,但系数差法仍然被广泛应用于各个领域。之所以继续使用这种方法,大概是因为人们没有意识到这种方法混淆了间接效应估计和非可比性。在本文中,我们旨在说明用系数差法估计二元结果中介模型间接效应的相关问题。我们提供了一个公式,将系数差估计值分解为(1)非可比性估计值和(2)间接效应估计值。我们使用一个模拟研究和一个经验数据示例来说明非可比性对间接效应的系数差估计值的影响。此外,我们还演示了几种间接效应估计替代方法的应用,包括系数乘积法和基于回归的因果中介分析。结果强调了选择不受非可比性影响的间接效应估计方法的重要性。
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Why You Should Not Estimate Mediated Effects Using the Difference-in-Coefficients Method When the Outcome is Binary.

Despite previous warnings against the use of the difference-in-coefficients method for estimating the indirect effect when the outcome in the mediation model is binary, the difference-in-coefficients method remains readily used in a variety of fields. The continued use of this method is presumably because of the lack of awareness that this method conflates the indirect effect estimate and non-collapsibility. In this paper, we aim to demonstrate the problems associated with the difference-in-coefficients method for estimating indirect effects for mediation models with binary outcomes. We provide a formula that decomposes the difference-in-coefficients estimate into (1) an estimate of non-collapsibility, and (2) an indirect effect estimate. We use a simulation study and an empirical data example to illustrate the impact of non-collapsibility on the difference-in-coefficients estimate of the indirect effect. Further, we demonstrate the application of several alternative methods for estimating the indirect effect, including the product-of-coefficients method and regression-based causal mediation analysis. The results emphasize the importance of choosing a method for estimating the indirect effect that is not affected by non-collapsibility.

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来源期刊
Multivariate Behavioral Research
Multivariate Behavioral Research 数学-数学跨学科应用
CiteScore
7.60
自引率
2.60%
发文量
49
审稿时长
>12 weeks
期刊介绍: Multivariate Behavioral Research (MBR) publishes a variety of substantive, methodological, and theoretical articles in all areas of the social and behavioral sciences. Most MBR articles fall into one of two categories. Substantive articles report on applications of sophisticated multivariate research methods to study topics of substantive interest in personality, health, intelligence, industrial/organizational, and other behavioral science areas. Methodological articles present and/or evaluate new developments in multivariate methods, or address methodological issues in current research. We also encourage submission of integrative articles related to pedagogy involving multivariate research methods, and to historical treatments of interest and relevance to multivariate research methods.
期刊最新文献
Why You Should Not Estimate Mediated Effects Using the Difference-in-Coefficients Method When the Outcome is Binary. A Causal View on Bias in Missing Data Imputation: The Impact of Evil Auxiliary Variables on Norming of Test Scores. Make Some Noise: Generating Data from Imperfect Factor Models. Exploring Estimation Procedures for Reducing Dimensionality in Psychological Network Modeling. A Review of Some of the History of Factorial Invariance and Differential Item Functioning.
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