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2017年8月11日 星期五

effect size

Effect size (ES) is a name given to a family of indices that measure the magnitude of a treatment effect. Unlike significance tests, these indices are independent of sample size.

ES measures are the common currency of meta-analysis studies that summarize the findings from a specific area of research. See, for example, the influential meta-analysis of psychological, educational, and behavioral treatments by Lipsey and Wilson (1993).
SPSS does not report any effect size statistics within the output for a t test. The most commonly used effect size estimates are Cohen's d (Cohen, 1988) and the effect size correlation. Cohen's d is found by dividing the mean difference by the pooled standard deviation. The effect size correlation for a t test is computed as the Pearson's product moment correlation between the independent variable with two groups and the dependent variable. By convention, effect size measures are positive if the mean difference supports the hypothesis, and negative if the mean difference is opposite to that predicted by the hypothesis. Cohen (1988) hesitantly defined effect sizes as "small, d = .2," "medium, d = .5," and "large, d = .8", stating that "there is a certain risk in inherent in offering conventional operational definitions for those terms for use in power analysis in as diverse a field of inquiry as behavioral science"