When conducting a power analysis a priori, there are typically three parameters 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. An alpha level of .05 is typically used when the statistical analysis is conducted in the social sciences field. Power is the probability that the null hypothesis will be correctly rejected. And according to Howell (2010), a generally accepted power is .80.
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Regarding effect size, often times it is acceptable to use a medium effect in the sample size calculation, however, it is possible to determine an effect size that is more true to what has been found in previous studies in order to get a more accurate measure. To obtain a more realistic effect, the researcher will need to review the literature that has been presented for previous studies and find measures of effect sizes that were reported in that literature. The studies that the effect sizes are drawn from should be 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. Effect sizes will need to be collected from several articles and typically all of the articles will not report the same measure of effect.
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. Once all effect sizes have been converted to a common measurement, the researcher should average the effect sizes together to determine the mean effect size of the studies. The calculated mean effect size should be used in the power analysis.
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