![]() Still, n=64 per cell should mean that you need N=256 people in the 2 x 2 experiment, not 128, right? What’s the real story? Now, the power analysis of the first experiment shows that you need over three times that number. In days of old, the conventional wisdom said n=20 was just fine. Your overall N should grow, not stay the same, as the design gets more complex. Heather, however, always heard in graduate school that you should base factorial designs on a certain n per cell.Because this power test of the interaction uses the same numerator df and other inputs as for the main effect, it gives the same result for N. GPower tells her that again, she needs only a total of 128, but now divided among 4 cells, for 32 people per cell. Using GPower, she tests the N needed to achieve 80% power of an interaction in this 2 x 2 ANOVA design, with 1 degree of freedom and f =. Heather assumes that the interaction effect is not known, even if the main effect is, so she goes with a medium effect again, d =.And each is telling Heather different things. So, she crosses the tempo manipulation with whether the music is mildly pleasant or intensely pleasant.īut there are three different authorities out there for this power analysis. Heather now wants to expand into a 2 x 2 between-subjects design that tests the effect’s moderation by the intensity of the music. People are significantly happier after listening to the speeded-up music. The result actually shows a slightly larger effect size, d =. Using GPower software, for a between-subjects t-test, this requires n = 64 in each condition, or N = 128 total. ![]() ![]() She wants to give her experiment 80% power to detect a medium effect, d =. In an Experiment 1, she has participants listen either to a speeded-up or normal-tempo piece of mildly pleasant music. Meet our example social psychologist, Heather. The bad news is: you’re usually going to need a much bigger sample to get decent power than GPower alone will suggest. But for novel effects that are built upon existing ones, a little reasoning can let you guess the likely size of the new one. I often hear that power analysis is impossible to carry out for a novel effect, because you don’t know the effect size ahead of time. Most authors simply open up GPower software and plug in the numerator degrees of freedom of the interaction effect, which gives a very generous estimate. In particular, the standards for power analysis of interaction effects are not clear. With all the manuscripts I see, as editor-in-chief of Journal of Experimental Social Psychology, it’s clear that authors are following a wide variety of standards for statistical power analysis.
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