Understanding and Interpreting Effect Size Measures

Statistical methods are the hallmark of quantitative research. Examining whether a result is statistically significant is standard content in social work research statistics courses. In all of social science, statistical significance testing has been the accepted procedure for examining results. Despite ongoing efforts aimed at encouraging researchers to report some index of magnitude that is not directly affected by sample size--for example, effect size statistical significance testing appears to remain the standard. In 1994, the APA Publication Manual provided encouragement to authors to report effect sizes with little impact (see for example Keselman et al., 1998; Kirk, 1996; Thompson Snyder, 2000; Trusty, Thompson, Engelhardt, Toseland, Goa, Padgett, Gulcur, Perry, 2006). There do not appear to be any social work journals that require effect size reporting. Whereas many researchers are familiar with the use of effect size measures for power estimation and sample size determination, they are less familiar with using such measures to interpret their findings. Many researchers fail to understand that there are different types of effect size measures with different methods of calculation within each type and that the interpretation of effect size varies depending on the measure used. The purpose of this column is to further the basic understanding of effect size measures: what they mean, how they differ, and to suggest one way of presenting outcome data for easier interpretation. It is because effect sizes are becoming part and parcel of the social science research enterprise that they must be clearly understood. In particular, this column is focused on the use of effect size measures when presenting the results of intervention research. THE BASICS Measures of effect size provide critically different information than alpha levels. This is because effect size addresses the practical importance of the results as assessed by the magnitude of the effect. It is well known that one can obtain a statistically significant result that is not practically significant. One basic misunderstanding in statistical analysis is thinking that an observed p value that is considered highly significant, for example p = .0001, also reflects a large effect. The p value simply represents the likelihood that a finding is due to chance or sampling error and reveals nothing about the size of the effect. One important reason to use effect size measures is that they can help the researcher consider the importance of his or her findings apart from statistical significance. This degree of analysis is too often neglected, as reflected in comments by Abelson (1995), who noted that statistical significance tests should be used for guidance rather than sanctification (p. 9). Rosenthal and colleagues (2000) demonstrated how effect sizes can be used in conjunction with significance levels to arrive at an inference. …