Correcting the Bias Correction for the Bootstrap Confidence Interval in Mediation Analysis.

ABSTRACT: The bias-corrected bootstrap confidence interval (BCBCI) was once the method of choice for conducting inference on the indirect effect in mediation analysis due to its high power in small samples, but now it is criticized by methodologists for its inflated type I error rates. In its place, the percentile bootstrap confidence interval (PBCI), which does not adjust for bias, is currently the recommended inferential method for indirect effects. This study proposes two alternative bias-corrected bootstrap methods for creating confidence intervals around the indirect effect: one originally used by Stine (1989) with the correlation coefficient, and a novel method that implements a reduced version of the BCBCI's bias correction. Using a Monte Carlo simulation, these methods were compared to the B

SUBMITTER: Tibbe TD 

PROVIDER: S-EPMC9197131 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature