Mark R. Leary Quote

In DepthTypes of Effect Size IndicatorsResearchers use several different statistics to indicate effect size depending on the nature of their data. Roughlyspeaking, these effect size statistics fall into three broad categories. Some effect size indices, sometimes called dbased effect sizes, are based on the size of the difference between the means of two groups, such as the difference between the average scores of men and women on some measure or the differences in the average scoresthat participants obtained in two experimental conditions. The larger the difference between the means, relativeto the total variability of the data, the stronger the effect and the larger the effect size statistic.The r-based effect size indices are based on the size of the correlation between two variables. The larger thecorrelation, the more strongly two variables are related and the more of the total variance in one variable is systematic variance related to the other variable.A third category of effect sizes index involves the odds-ratio, which tells us the ratio of the odds of anevent occurring in one group to the odds of the event occurring in another group. If the event is equally likely inboth groups, the odds ratio is 1.0. An odds ratio greater than 1.0 shows that the odds of the event is greater inone group than in another, and the larger the odds ratio, the stronger the effect. The odds ratio is used when thevariable being measured has only two levels. For example, imagine doing research in which first-year students incollege are either assigned to attend a special course on how to study or not assigned to attend the study skillscourse, and we wish to know whether the course reduces the likelihood that students will drop out of college.We could use the odds ratio to see how much of an effect the course had on the odds of students dropping out.You do not need to understand the statistical differences among these effect size indices, but you willfind it useful in reading journal articles to know what some of the most commonly used effect sizes are called.These are all ways of expressing how strongly variables are related to one another—that is, the effect size.Symbol Named Cohen’s dg Hedge’s gh2 eta squaredv2omega squaredr or r2 correlation effect sizeOR odds ratioThe strength of the relationships betweenvariables varies a great deal across studies. In somestudies, as little as 1% of the total variance may besystematic variance, whereas in other contexts,the proportion of the total variance that is systematicvariance may be quite large,