Effect Sizes in Marketing Research: Why Cohen's Local f^2 Belongs in the Toolkit

TL;DR

This paper advocates for including Cohen's local f^2 effect size in marketing research to better assess the contribution of individual predictors within models, emphasizing its advantages in large samples and potential extensions.

Contribution

It highlights the importance of Cohen's local f^2 as an effect size measure, filling a gap in current statistical reporting practices in marketing research.

Findings

Local Cohen's f^2 is based on R-squared, making it suitable for large samples.

It can be extended to multilevel models and machine learning models.

Including local effect sizes improves substantive interpretation of predictors.

Abstract

In an editorial in the Journal of Marketing, Steenkamp et al. (2026) make a valuable and timely intervention by urging marketing scholars to move beyond dichotomous significance testing and to report effect sizes that speak to substantive significance. Their editorial is especially strong in its insistence on exact p-values, richer statistical reporting, and closer alignment between rigor and relevance. Yet, their framework omits the local form of Cohen's f^2, that is f(B)^2 as an effect-size measure for the contribution of an individual predictor or predictor block B within a multivariable model. That omission matters because much of marketing research relies on regression-type models in which the central theoretical question is not merely whether a model fits globally, but whether a focal construct adds meaningful explanatory power beyond competing predictors and controls. This commentary argues that the R-squared foundation of local Cohen's f(B)^2 is a strength, especially in large-sample settings. Moreover, f-squared-type local effect sizes can be extended beyond ordinary least squares to multilevel models and, more tentatively, to neural networks and other machine-learning models.