I am familiar with pairwise t-tests, corrections for multiple testing, etc. however I have a problem whose answer I have not found after extensive R-help archive and Google searching.
What I have done in the past: I have N items which are measured, exposed to a condition, and then measured again. I wish to know if the condition changes the items so I can perform a t-test. Better yet I can perform a pairwise t-test because the items are individually identifiable. Sometimes the N items measures do not appear normal and I have used tests other than the t-test (chi-square, etc.) Sometimes the N items are measured after each one of several exposures and I used pairwise.t.test(). I have a situation where N is limited and I cannot increase it, therefore I arranged for M measurements of the N items to be made before and after exposure to the condition. Thus I have N x M measurements before the condition and N x M measurements after the condition. I assumed that performing M measures instead of 1 measure I would be able to provide more statistical power, however I don't know what method to use. I could: (1) average the M measurements to obtain just N measures for the N items and perform pairwise t-testing. This seems to lose statistical weight. (2) make believe that I actually have N x M items and perform pairwise t-testing. This will give me a more powerful result than (1), however I feel like I am losing something by saying I have N x M independent items instead of M measures a piece of N items. Any suggestions how to statistically test whether the condition changed the N items? I am looking for a p-value to indicate whether there was a significant change or not. P.S. Yes, I actually have more than one condition, so the N items are measured M times, exposed to condition 1, measured M times, exposed to condition 2, ... and I plan to set a stringent enough confidence level to avoid Bonferroni problems. I have also tried pairwise.t.test() with the Holm method for p-value adjustment, however I don't see that pairwise.t.test() has a functionality to accommodate my M measurements arrangement. Thank you for any suggestions. Leif S. Kirschenbaum, Ph.D., PMP, CRE Design Reliability Product Reliability Space Systems/Loral 3825 Fabian Way M/S H-21 Palo Alto, CA 94303 650-852-6580 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
