![]() I also realized I didn’t give you all the background so you can help me better. I took your advice and now have an additional question. I simply find it really interesting and would like to understand a bit more.Ĭharles. It doesnt really matter that much for the dissertation, because the study has a lot of limitations. the biggest difference of BF% was seen in the groupes that had measurements between 2-3 years after started therapy. the difference of BF% before and after HT was the greatest in the group of bigger fat percentage.Ģ. I also played around with different diagrams, and could see thatġ. Ist in meaningful to use multiple regression in this case? Therapy duration in months( ranging from 24- 67 ) So I wanted to see how characteristics influenced the change of BF.ġ. Now I want to look at how inital bodyfat % and duration of hormone therapy (months) affect the measured bodyfat % after at least 24 months of therapy.įirst I did a paired t-test for the BF % and Bone density (which was also measured before and after therapy at the same time as BF) for the study population. I have 20 people in my study (How does Hormone therapy affect Bone density and Body Fat%). The only advice I could give is to try to map the regression model into one or more ANOVA (or t test) models (at least for the cases that you most care about) and see whether the sample size of 13 for the homosexual group is sufficient. If you want to compare results based on sexual orientation, then I would imagine that a small sample of homosexuals could be a problem. The sample size required depends on your objective. With an additional continuous predictor, you have ANCOVA. If you have an additional categorical predictor, you can consider the regression to be two-factor ANOVA. In general, the sample size that matters the most is the size of the smaller group. In this case, there are two samples size estimates, one for each of the two groups. If you are doing regression and there is only one independent variable and it is sexual orientation, then regression is equivalent to a t-test (or ANOVA when there are more than two possible values). 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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