One practical difficulty for researchers who wish to include effect size in their results is the large number of potential measures available. Kirk (1996) reported 40 different effect size measures, of which several may be appropriate for any given data structure. For example, four effect size measures exist for dichotomous outcomes (Pace, 2011), three for continuous outcomes across groups (Huberty, 2002), and still others for multilevel data, for which there is often no consensus on which is most appropriate (Peugh, 2010). Three commonly used types of effect size are suitable for the majority of relatively simple analyses, and fall into the r family (measures of correlation between continuous variables), the d family (standardized mean differences in a continuous dependent variable across levels of a categorical independent variable), or ratio statistics (measures of comparative risk for dichotomous outcomes; Rosenthal, 1994; Nakagawa and Cuthill, 2007).
