![]() These conversions can only be performed for single, focused contrasts ( e.g., cases with a single degree of freedom), but otherwise follow simple equations. The correlation coefficient is widely used, easily interpretable, and has the added bonus of being easily determinable from other commonly used statistics such as z-scores, t-tests, F-statistics, and χ 2 statistics. While a number of alternate metrics have been suggested for measuring effect size, including standardized mean differences and odds ratios, , historically, one of the more popular measures of effect has been the correlation coefficient, ,, . The formula for converting an odds ratio to a relative risk is straightforward 4 5 (fig, table 1 ): Relative riskodds ratio/ (1p 0 + (p 0 ×odds ratio)) (Where p 0 is the baseline risk. All effect size measures are a means of representing the results of primary research in a common way so that the results from individual studies can be compared and evaluated. In experimental studies, the effect size is a measurement of the response of the subjects to an experimental treatment relative to a control group. An effect size is a statistical parameter that can be used to compare, on the same scale, the results of different studies in which a common effect of interest has been measured. One of the fundamental concepts in systematic and comparative reviews such as meta-analysis is that of the effect size. ![]()
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