.png){kind=icon}
.png){kind=icon}
Return
A Robust Bootstrap Test for Mediation Analysis
.png){kind=icon}
OA
AI
DOI：10.1177/1094428121999096.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Mediation analysis is central to theory building and testing in organizational sciences. Scholars often use linear regression analysis based on normal-theory maximum likelihood estimators to test mediation. However, these estimators are very sensitive to deviations from normality assumptions, such as outliers, heavy tails, or skewness of the observed distribution. This sensitivity seriously threatens the empirical testing of theory about mediation mechanisms. To overcome this threat, we develop a robust mediation method that yields reliable results even when the data deviate from normality assumptions. We demonstrate the mechanics of our proposed method in an illustrative case, while simulation studies show that our method is both superior in estimating the effect size and more reliable in assessing its significance than the existing methods. Furthermore, we provide freely available software in R and SPSS to enhance its accessibility and adoption by empirical researchers.
Keywords:
mediation analysis
robust statistics
linear regression
bootstrap
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.6
Papers:
883
Citations:
1.4W