Reevaluating R²_med as an Effect Size Measure for Indirect Effects

R²_med quantifies the size of the indirect effect using a combination of R² values from multiple regression equations. While it exhibits many characteristics as an effect measure, prior research has identified potential limitations. Specifically, it has been argued that R²_med may yield nonzero values even when no indirect effect exists and that it can produce negative values in certain cases. In response, alternative R² measures have been proposed. This article clarifies that, contrary to previous critiques, R²_med consistently returns zero when the indirect effect is truly absent. Additionally, R²_med distinguishes between mediation and suppression effects, assigning positive values to mediation effects and negative values to suppression effects. Therefore, rather than being a limitation, this distinguishing property is a strength. Overlooking this distinction may lead researchers to misinterpret unintended indirect effects (“flipped indirect effects”) or unexpected direct effects (“mysterious direct effects”) when suppression effects are present. Translational Abstract Understanding the size and impact of indirect effects is essential in mediation analysis, which examines how one variable influences another through an intermediary (mediator). A common statistical measure used for this purpose is R²_med, which helps researchers quantify the strength of these indirect effects. However, previous studies have raised concerns about its accuracy, suggesting that it may sometimes indicate an indirect effect when none exists or produce negative values in certain situations. This article reassesses these concerns and finds that R²_med, reliably returns a value of zero when there is no indirect effect, countering previous critiques. Additionally, this measure differentiates between mediation effects (where an indirect effect is present) and suppression effects (where the relationship between variables changes unexpectedly). Specifically, positive values of R²_med indicate mediation, while negative values indicate suppression. Rather than being a flaw, this ability to distinguish between mediation and suppression is a valuable feature of R²_med. Ignoring this distinction may lead researchers to misinterpret results, potentially overlooking hidden relationships or drawing incorrect conclusions about cause-and-effect processes. By clarifying how R²_med works, this study provides researchers with a more reliable tool for analyzing indirect effects in psychological and social science research.