Abstract: Effect sizes are important supplement of the null hypothesis significance testing. More and more academic journals request authors provide the effect sizes of their researches. Our purpose is to provide a guideline on how to compute the appropriate effect sizes of different researches and data types. We classified the effect sizes into three types, including difference-type, correlation-type and group-overlap. For each type of the effect sizes, there are different approaches of calculations and applications under different research designs (e.g., single-factor/multifactor between-subjects, single-factor/multifactor within-subjects) and data conditions (e.g., small sample size, heterogeneity of variance). Many effect sizes, however, can be transformed from one type to another. We summarized a table that may help readers to choose appropriate effect sizes for their researches based on the research purposes and research designs.

Key words: effect size, difference-type, correlation-type, group overlap-type