# Effect Size Estimation in Linear Mixed Models

## TL;DR

This paper revisits Cohen's effect size measure $f^2$ within linear mixed models, demonstrating its calculation using R's lme4 package on simulated data, simplifying effect size estimation without needing a coefficient of determination.

## Contribution

It introduces a method to compute Cohen's $f^2$ effect size in linear mixed models using standard software, avoiding complex calculations.

## Key findings

- $f^2$ can be effectively computed in linear mixed models using R.
- The method simplifies effect size estimation without requiring a coefficient of determination.
- Application demonstrated on artificially generated data.

## Abstract

In this note, we reconsider Cohen's effect size measure $f^2$ under linear mixed models and demonstrate its application by employing an artificially generated data set. It is shown how $f^2$ can be computed with the statistical software environment R using lme4 without the need for specification and computation of a coefficient of determination.