Devlin. The Research Experience. by Ann Sloan Devlin

Author:Ann Sloan Devlin
Language: eng
Format: epub
Published: 2017-02-20T14:37:04.064536+00:00

More on Power, Sample Size, and Power Calculations

Of the variables that affect power, you can see that sample size gives you the most flexibility, as people are not in the habit of changing either the alpha level of .05, nor can you switch from a two-tailed to a one-tailed test (the use of a one-tailed test has to be stated in your hypotheses), nor can they change the effect size reported in the literature. Recommendations for calculating sample size include performing a power analysis before conducting the study.

The real challenge in determining how many participants you need is obtaining the effect size. David Howell (2013, pp. 234–235) pointed to three routes: (1) existing research, (2) the researcher’s decision about the magnitude of difference between µ1 (the mean under H0) and µ2 (the mean under H1) that would be considered important, and (3) conventions accepted in the literature, such as those by Jacob Cohen (1988; see Chapter 3). In other words, the third route (values from Cohen or others) is used when you have no other way to make an estimate of the parameters you need but is not preferred (Howell, 2013). Howell (2013) mentioned that there are several online software packages (many of them free) for calculating power and pointed to G*Power (Faul, Erdfelder, Lang, & Buchner, 2007) as one such option (http://www.gpower.hhu.de/en.html). Howell (2013) reminded us that “power = 1−β” (p. 230) and that β is the probability of making a Type II error. Thus, if power is set at .80, the probability is 20% that a Type II error will occur. To decrease that probability, in the same experiment, we would have to increase the number of participants.

G*Power: free online software that calculates power for a given design.

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