I conducted a 2 x 4 within-subjects design experiment. I presented participants with unpleasant and neutral pictures and asked them to cope with their emotions in four different ways. Therefore, the first independent variable refers to picture valence (unpleasant vs. neutral pictures) and has two levels while the other one refers to emotion regulation (ER) and has four levels (1, 2, 3, 4, where 1 is a control condition). I measured various things which resulted in a few dependent variables. My a priori hypotheses where that for every dependent variable I will find differences between a control condition and any other condition (1 vs. 2; 1 vs. 3; 1 vs. 4) only for unpleasant images. That is why I decided to run planned contrasts for these comparisons.The overall ANOVA is not significant (p = .081) but one of the contrasts is (p = .049). I know that I can find significant differences when comparing two conditions at a time even if the overall ANOVA is not significant. However, there seems to be no agreement on whether it is ok to look at the contrasts when ANOVA is not significant. What is more, there seems to be a disagreement on whether a correction for multiple comparisons should be applied when running planned contrasts. My question is if I should say that I found a significant result between the two conditions? I have doubts about it because the overall ANOVA is not significant and the difference between the two conditions is found only when no correction for multiple comparisons is applied. On the other hand, I am not running any other comparisons and I predicted this difference in advance. Thanks
If you pre-specify something then that is what you should do. Otherwise why pre-specify it? In this case if you have a strong theoretical justification for looking at just those contrasts then do it. Ideally this would all have been recorded somewhere in advance like your ethical approval document or your published protocol so nobody can accuse you have being post-hoc.
If that would be my research question and I knew all my hypothesis most likely I'd look at 95% CI (for both means and contrast coef.) and see how they plot all together (looking for overlaps and band widths).