What if an overall ANOVA is not significant, but specific contrasts are? Study Design: I have a 2x3 factorial design, 2 levels of Time (2050 or 2100) by 3 levels of information (None/Control, Moderate, Extreme).
I set up some very specific contrasts when analyzing this design, in particular Control vs. Other, Moderate vs. Extreme, and 2050 vs. 2100. For one DV, the overall ANOVA was not significant but the Control vs. Other contrast was.
Question: What is the best way to interpret a nonsignificant ANOVA but a significant contrast? 
I know that ANOVA is used to lower familywise error rates, and I wouldn't want to fall into the trap of ignoring the overall ANOVA for pairwise comparisons. However, this is one of a select few contrasts that I planned ahead of time. Does that make a difference in interpretation, or should I simply consider the manipulation not significant?
 A: The answer to your question reflects your view of overall (or experimentwise) alpha.  If, before you collected your data, you set forth planned comparisons (and it sounds like you did that), then after you collect the data you do those and only those comparisons, and there is no reason to look at any other comparisons (AKA contrasts) nor at the overall ANOVA.  Why did you look at the overall ANOVA?  If you did both planned comparisons and ad hoc comparisons, then you inflated your experimentwise alpha, which some consider a serious error.  Of course, you might do ad hoc comparisons to help you plan your future research, but not report them.  Also, it seems you have two DVs and did two ANOVAs.  In general, if you do two ANOVAs each with p = .05 then you have inflated your experimentwise alpha.  Therefore, when doing 2 ANOVAs you might reduce the alpha level for each of your two ANOVAs to maintain your experimentwise alpha.  Alternatively, you might do a MANOVA (i.e., a multivariate analysis of variance) which allows you to consider both DVs simultaneously.  Of course, you should also be looking at effect sizes, to help you understand the meaning of your data.
A: Yes you need to adjust alpha, especially if you do planned comparisons that are non-orthogonal. If you have set the contrasts in an ANOVA and tested the orthogonality between them, you can descompose the SC and df of the ANOVA. The planned comparison are always more powerful than traditional post hoc test, but in cases where the contrast are non.orthogonal you have inflated the experimentwise error. you can check for orthogonality among contrast in many textboxs.
However i don´t undestand if you have set that time was factor with two levels, why are you performing a contrast between those times?
