Where are the t-values in my pairwise comparisons? I have data from a study with three conditions. The independent 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:

 A: 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.
A: I hope this quote helps you. 
"As you see in the output below, the table titled "Contrast Coefficients (L' Matrix)" shows the coding scheme that was used for each comparison.  The table entitled "Contrast Results (K Matrix)" shows the results of the various contrasts.  In our example, the difference between level 1 of race and level 4 of race is statistically significant.  You will notice that the contrast estimate is the difference between the mean for the dependent variable for the first level minus the mean of the dependent variable for the omitted level.  In other words, the mean for level 1 minus the mean for level 4 which is 46.4583 - 54.0552 = -7.597.  The row labeled "Sig." is .000, indicating that this difference is significant, and this is followed by a confidence interval for the difference. The next part of the table compares level 2 of race and level 4 of race and shows that this difference is not statistically significant and the next part of the table shows the difference between level 3 of race and level 4 of race is statistically significant. You might note that while the significance ("Sig.") is given for each of these tests, there is no "t" value, but you could obtain this by dividing the "Contrast Estimate" by the "Std. Error", i.e., -7.597 / 1.989.". 
Thus, if you have values for contrast estimate and std erro, you can calculate t value yourself. 
A: The p value in the output is for the associated z statistic: mean difference divided by the standard error. So, it's a z value, not a t value. If you look up the p value associated with the z value, that's what you'll see in the SPSS output. Here are the steps if you want to verify what I'm saying:

*

*In the SPSS output for pairwise comparisons, take the mean difference and divide it by its standard error. That is your z value

*Go here (or somewhere similar): https://www.socscistatistics.com/pvalues/normaldistribution.aspx

*Enter the z value in the calculator to get your associated p value. That should be the p value you see in the SPSS output. For the life of me I don't know why they don't just display the value of the z statistic.

