Relative importance analysis in multiple mediator models.

IF 7.6 1区心理学 Q1 PSYCHOLOGY, MULTIDISCIPLINARY

Psychological methods Pub Date : 2025-02-13 DOI:10.1037/met0000725

Xun Zhu, Xin Gu

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引用次数: 0

Abstract

Mediation analysis is widely used in psychological research to identify the relationship between independent and dependent variables through mediators. Assessing the relative importance of mediators in parallel mediator models can help researchers better understand mediation effects and guide interventions. The traditional coefficient-based measures of indirect effect merely focus on the partial effect of each mediator, which may reach undesirable results of importance assessment. This study develops a new method of measuring the importance of multiple mediators. Three R² measures of indirect effect proposed by MacKinnon (2008) are extended to parallel mediator models. Dominance analysis, a popular method of evaluating relative importance, is applied to decompose the R² indirect effect and attribute it to each mediator. This offers new measures of indirect effect in terms of relative importance. Both frequentist and Bayesian methods are used to make statistical inference for the dominance measures. Simulation studies investigate the performance of the dominance measures and their inference. A real data example illustrates how the relative importance can be assessed in multiple mediator models. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

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来源期刊

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.