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CHAPTER 11: Data Analysis and Interpretation: Part I. Describing Data, Confidence Intervals, Correlation 359between means will reflect a larger effect size. Because effect sizes are presentedin standard deviation units, they can be used to make meaningfulcomparisons of effect sizes across experiments using different dependentvariables. For example, an effect size from a study of vocabulary knowledgethat compared college students and older adults on tests emphasizing discriminationof word meanings (i.e., multiple-choice tests) and an effect sizefrom a study contrasting performance of two similar groups using recall ofword definitions could be directly compared. Such comparisons form thebases of meta-analyses, which seek to summarize the effect of a particular independentvariable across many different studies (see Chapter 6).There are some guidelines to help us interpret d ratios. J. Cohen (1992)provided a useful classification of effect sizes with three values—small, medium,and large. He describes the rationale for his classification of effect size(ES) as follows:My intent was that medium ES represent an effect likely to be visible to the nakedeye of a careful observer. (It has since been noted in effect-size surveys that it approximatesthe average size of observed effects in various fields.) I set small ESto be noticeably smaller than medium but not so small as to be trivial, and I setlarge ES to be the same distance above medium as small was below it. Althoughthe definitions were made subjectively, with some minor adjustments, these conventions. . . have come into general use. (p. 156)Each of the classes of effect size can be expressed in quantitative terms; for example,a medium effect for a two-group experiment is a d of .50; a small and largeeffect are ds of .20 and .80, respectively. These expressions of effect magnitudeare especially useful when comparing results from similar studies.It is important to note that researchers define the standardized differencebetween means in slightly different ways (see, for example, Cohen, 1988; Kirk,1996; Rosenthal, 1991). Which measure of effect size to use is a decision leftup to the investigator. But, given the differences in measures appearing in thepsychology literature, it is very important to identify in a research report preciselyhow a measure of effect size was calculated.An effect size for the vocabulary study using Cohen’s d is__X __ d _______ 1 X 2 _________________________________64.04 45.58 1.65_______________________________(26 1)(150.04) (26 1)(109.45)52To interpret the value of 1.65, we can use J. Cohen’s (1992) classification of effectsizes of d .20 for a small effect size, d .50 for a medium effect size, and d .80for a large effect size. Because our value is larger than .80, we can conclude that“age” had a large effect on vocabulary knowledge.Conclusion In the second, summary stage of data analysis, we should identify(a) the central tendency (e.g., mean) of each condition or group in the study;(b) measures of dispersion (variability), such as the range and standarddeviation, for each condition of the study;