However, these frequently used measures of effect size are inappropriate for more complex data structures such as hierarchical data, which may consist of observations nested within subjects (e.g., repeated observations of the same participants) or observations nested within groups (e.g., observational studies of students within different classrooms). Additionally, many effect sizes are unable to address research questions which may involve competing effect sizes of different variables within the same multivariate model, rather than a variable’s effect size for the overall model; this distinction has been described as local vs. global effect sizes (Peugh, 2010). A researcher developing an intervention program with limited resources, for example, may wish to know which of many simultaneous risk factors has the largest effect on the outcome to determine which intervention strategy may be most effective.
