# Where are the t-values in my pairwise comparisons?

I have data from a study with three conditions. The independant variable was within-subjects and within-items.

I did an F1 and F2 analysis (= twice a repeated measures anova) on the averages, by subject (the participants) and by item (the words). I checked/corrected for sphericity and the means do differ significantly.

Now I want to know how the three conditions differ from one another. I have a "Pairwise Comparisons" box and p-values (I checked "Bonferroni" when doing the analyses). But I do not have t-values.

How can I know my corresponding t-values? Or should I do some paired-samples t-tests? If so, what does this "Pairwise Comparisons" box tell me?

Screenshot of the "PC" box.

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Could you clarify your 2nd sentence, and also what you mean by "item." Also, for people to interpret your software program's output, it would be best if you could show it. –  rolando2 May 3 '12 at 13:01
I didn't want to go in too deep, because I think it's irrelevant. But is it better now? I added a screenshot as well. –  Mien May 3 '12 at 13:07
I am not familiar with the software but if Std. Error ist the standard error of the estimate of the differences in means as I expect, the t-value is just the difference divided through the std. error (about 3.08 in the first row). –  Erik May 3 '12 at 13:31
Bonferroni is a bound on the familywise error rate (FWER). It just involves the p-values of the individual tests. There is no test statistic for it. Basically if you are doing $k$ tests and all the $p$-values are less than $p$ Bonferroni gives $k\cdot p$ as the upper bound on FWER. It has the advantage of being general (not requiring any specific assumptions). But the disadvantage is that it is a conservative upper bound and will not be useful unless $p$ is small enough to make $k\cdot p$ relatively small. It is possible for $k\cdot p$ to be greater than 1. There are many other procedures that can be used that are not so conservative and some just involve the individual $p$-values. Examples are bootstrap and permutation adjusted p-values. You can find this in the resampling book for multiple comparisons by Westfall and Young.