An Examination of Effect Sizes and Statistical Power in Speech, Language, and Hearing Research

<jats:sec> <jats:title>Purpose</jats:title> <jats:p>The purpose was to examine and determine effect size distributions reported in published audiology and speech-language pathology research in order to provide researchers and clinicians with more relevant guidelines for the interpretation of potentially clinically meaningful findings.</jats:p> </jats:sec> <jats:sec> <jats:title>Method</jats:title> <jats:p> Cohen's <jats:italic>d,</jats:italic> Hedges' <jats:italic>g,</jats:italic> Pearson <jats:italic>r,</jats:italic> and sample sizes ( <jats:italic>n</jats:italic> = 1,387) were extracted from 32 meta-analyses in journals in speech-language pathology and audiology. Percentile ranks (25th, 50th, 75th) were calculated to determine estimates for small, medium, and large effect sizes, respectively. The median sample size was also used to explore statistical power for small, medium, and large effect sizes. </jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p> For individual differences research, effect sizes of Pearson <jats:italic>r</jats:italic> = .24, .41, and .64 were found. For group differences, Cohen's <jats:italic>d</jats:italic> /Hedges' <jats:italic>g</jats:italic> = 0.25, 0.55, and 0.93. These values can be interpreted as small, medium, and large effect sizes in speech-language pathology and audiology. The majority of published research was inadequately powered to detect a medium effect size. </jats:p> </jats:sec> <jats:sec> <jats:title>Conclusions</jats:title> <jats:p> Effect size interpretations from published research in audiology and speech-language pathology were found to be underestimated based on Cohen's (1988, 1992) guidelines. Researchers in the field should consider using Pearson <jats:italic>r</jats:italic> = .25, .40, and .65 and Cohen's <jats:italic>d</jats:italic> /Hedges' <jats:italic>g</jats:italic> = 0.25, 0.55, and 0.95 as small, medium, and large effect sizes, respectively, and collect larger sample sizes to ensure that both significant and nonsignificant findings are robust and replicable. </jats:p> </jats:sec>