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 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.
#Gpower software software
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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