So, more variance is explained by terms independent of your variable of interest in the second than the first model. Power for ANOVA and ANCOVA is available in Excel using the XLSTAT statistical software. BUT, this isn't the case in your between-factor model. Ensure optimal power or sample size using power analysis. The only way this could happen is if there's a large within-subject effect of the repeated measurement that is independent of all other effects in the model (i.e., the random slope explains a lot of variance). You're getting a smaller N in the second model because the way GPower runs these analyses implies the presence of large effects (outside of your control) that are independent of your effects of interest.įor your interaction, try to imagine a situation where there is no main effect of group, there is no main effect of your repeated measurement, but there is an interaction and the repeated measurements are highly correlated within-subjects. In essence, GPower assumes that all other effects are null except when the model implies that they are not - it has no way of specifying how much variance is explained by other parameters in your model. Why is this? The key is understanding how power analyses in GPower work (and why I hate GPower for complex models). In your case, you have specified the same effect size for two parameters in a regression model, yet you get different estimates of the necessary sample size. So the standard is to plan on a small effect size for an interaction. GPower Single-sample t-test Paired-sample t-test Independent-sample t-test Two independent proportions One-way ANOVA Multiple Regression. This is not because interactions are special statistically (they are just variables in a regression model after all), rather in practice interactions tend to be very small across fields and domains. Now, you mentioned that interactions often require larger samples. You might also want to think about determining what your smallest effect size of interest is before running the power analysis. Enhance your theoretical performance If you want to enhance your academic performance, you need to be willing to put in the work. Under the Do math problem I cant do math equations. Under the Test family drop-down menu, select F tests. Instead, I would look into the literature to determine what effect sizes are reasonable for the kind of data I'm working with. The steps for calculating sample size for an ANOVA in GPower 1. ![]() As significance level and power are given. ![]() As we are searching for sample size, an ‘A Priori’ power analysis is appropriate. Approaching Example 1, first we set GPower to a t-test involving the difference between two independent means. Based on your comment, it seems your running a power analysis using the default GPower values (except for Power parameter). In GPower, it is fairly straightforward to perform power analysis for comparing means.
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