My wonderful coauthor Andreas Alfons and I wrote this paper for both statisticians and behavioral scientists. Statisticians get a clear entry point into mediation models; empirical scholars get an accessible guide to the statistical issues that arise when real data violate standard assumptions.
Our simulation study tests many realistic data situations. The results are consistent: OLS-based mediation works well only under ideal conditions. Robust methods stay stable across a much broader range of distributions researchers face in their data.
We review standard mediation methods and show where they struggle—skewness, heavy tails, outliers, rounding, censoring. These issues affect many common tools, including widely used OLS-based approaches like PROCESS.
Bootstrapped CIs only address the distribution of the a*b indirect effect. They do not fix problems in a or b caused by nonnormality or outliers. If those paths are distorted, the indirect effect can be too, no matter how many bootstrap samples you use.
Most mediation models rely on OLS, which is sensitive to skewed data and outliers. Our paper introduces readers to robust statistical tools—like MM-estimation and median regression—and points to R packages that you can use today.
Even if the causal assumptions for mediation hold, statistical assumptions can fail. Features like skewed distributions or outliers can distort standard estimates. Our new paper details which data issues cause trouble and reviews methods built to handle them: dx.doi.org/10.1002/wics...
Yes this! I've been teaching this example in my methods course for years. For instance, there are lots of findings like this showing people underestimate wealth inequality. No, these studies show that people underestimate big things and overestimate small things. www.google.com/url?sa=t&sou...
