Effect Size for Power Analysis

Quantitative Results

When conducting a power analysis a priori, there are typically three parameters. Hence a researcher will need to know to calculate an appropriate sample size to achieve empirical validity.  Those parameters are the alpha value, the power, and the effect size.  The alpha value is the level at which you determine to reject the null hypothesis.  Researchers typically use an alpha level of .05 when conducting statistical analysis in the social sciences field. Power refers to the probability that researchers will correctly reject the null hypothesis. And according to Howell (2010), a generally accepted power is 80.

Regarding effect size, researchers often accept using a medium effect in the sample size calculation. They can determine an effect size that more accurately reflects what previous studies have found. And to obtain a more precise measure.

To obtain a more realistic effect, the researcher needs to review the literature from previous studies. And find measures of effect sizes reported in that literature. The researcher should draw effect sizes from studies that focus on the same or a similar topic to the proposed research. The previous studies could have used any type of statistical analysis, but keep in mind that different analyses report different measures of effect sizes.  The researcher will need to collect effect sizes from several articles. And typically, not all of the articles will report the same measure of effect.

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Once the researcher has obtained multiple measures of effect sizes from previous studies, the effect sizes will need to be converted to a common measurement, such as Pearson’s r, an odds ratio, or Cohen’s d.  Then the researcher converts all effect sizes to a common measurement, they should average the effect sizes to determine the mean effect size of the studies. The researcher should then use the calculated mean effect size in the power analysis.

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