Hi all,

I have some data like this

Condition, dependent variable, independent variable

A 1,2,3,4,5,5,6... x and y

B 1,2,3,4,5,5,6... x and y

C 1,2,3,4,5,5,6... x and y

D 1,2,3,4,5,5,6... x and y

..... each condition for three times

For this data, I am interested in the difference between to categoreis x and y for each condition testing for each time separately (not repeated measure) . According to my understanding, I can use an independent t-test, one way anova with two factors or linear regression. I used the lm model in R, which is a linear regression method, but tutorials say it can also be called one-way anova. So am I right if I report results as one way anova?

Next, since I have the same condition for three times so for multiple testing correction options I have is either estimated marginal mean (emm) followed by Pairwise comparisons with FDR adjustment. Or direct per condition FDR correction across all time. I get more or less similar results with both, but not sure which one is most reasonable to justify or if I am missing anything major here. My stats knowledge is mostly from tutorials and vidoes so maybe I am completely off track. Would really appreciate any guidance.

Thanks