![]() Let me look at the problem in a different way. However, I don’t know how to translate this observation into a specific effect size. If this split between Homosexuals and Heterosexuals directly impacts the effect size value, then the reported minimum sample size should be accurate. from G*Power or R) the driver is the effect size (e.g. If I look at the usual sample size analysis (e.g. Sorry for the long message, i hope i am making sense – if not, please do ask me to clarify. It seems that this individual had the same issue: but unfortunately did not get a response. So i was hoping you could help me find out how many Homosexual participants i would need in my sex_orient predictor variable for my multiple regression to obtain a power of 80%. I doubt it is the latter as this would not specify the ratio of Homosexuals to Heterosexuals. Therefore, I used the pwr.f2.test() function in Rstudio to calculate the required sample size to produce a power of 80% (as 80% is the minimum acceptable power).Įssentially, it stated that i needed a sample size of 37 participants for my multiple regression to have a power of 80%…BUT my issue is…is this 37 participants in EACH sexual orientation group OR just a sample of 37 participants, comprising of Homosexuals and Heterosexuals. However, I had over 300 heterosexual respondents, but only 15 homosexual respondents, and thus concluded that my multiple regression was underpowered as a result of this low Homosexual sample size. Importantly, this sex_orient variable was comprised of homosexuals and heterosexuals – it had two levels. Something like this: lm(swls ~ sex_orient + additional_predictor2 + additional_predictor3). In short, I ran a multiple regression to observe whether an individual’s sexual orientation (sex_orient) significantly impacted their life satisfaction (swls). I really hope you can help me as I am pretty confused about how I should go about calculating the required sample size needed for my multiple regression to obtain a power of 80%.
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