I am currently writing a paper in which I have the pleasure to conduct both between and within subjects comparisons. After discussion with my supervisor we decided to run t-tests and use the pretty simple Holm-Bonferroni method (wikipedia) for correcting for alpha error cumulation. It controls for familwise error rate but has a greater power than the ordinary Bonferroni procedure. Procedure:

1. You run the t-tests for all comparisons you want to do.
2. You order the p-values according to their value.
3. You test the smallest p-value against alpha / k, the second smallest against alpha /( k - 1), and so forth until the first test turns out non-significant in this sequence of tests.

Cite Holm (1979) which can be downloaded via the link in the Wikipedia.

I am currently writing a paper in which I have the pleasure to conduct both between and within subjects comparisons. After discussion with my supervisor we decided to run t-tests and use the pretty simple Holm-Bonferroni method (wikipedia) for correcting for alpha error cumulation. It controls for familwise error rate but has a greater power than the ordinary Bonferroni procedure. Procedure:

1. You run the t-tests for all comparisons you want to do.
2. You order the p-values according to their value.
3. You test the smallest p-value against alpha / k, the second smallest against alpha /( k - 1), and so forth until the first test turns out non-significant in this sequence of tests.

Cite Holm (1979) which can be downloaded via the link at wikipedia.