I will defend my Master's thesis on Thursday, and I have a doubt about an analysis that I have to present. In my experiment, I had two independent variables:
- Age category (SU = senior unemployed subjects and YU = young unemployed subjects)
- Experimental condition (ST = stereotype threat; NST = No threat condition). My dependent variable is the performance achieved by subjects on a memory test.
My hypothesis was that there would be an effect of Experimental condition only on SU subjects. Consequently, I performed a two-way 2x2 ANCOVA and expected the interaction between Age category and Experimental condition to be significant. Unfortunately, the interaction turned out to be non significant (p=0.615).
However, when I looked at pairwise comparisons, only SU subjects' performance varied (almost) significantly from one experimental condition to another (p=0.058), whereas YU did not vary significantly across experimental conditions (p=0.213). This helped me confirm my hypothesis and conclude that there was an effect of the experimental condition only on SU participants.
However, I'm not quite sure about the conditions under which one is allowed to look directly at pairwise comparisons (as I did) and overlook the global interaction is not significant. I don't have any textbook at hand, so if anyone could indicate me a stat published article arguing in favor of this method and indicating the conditions under which it is doable, I'd be very grateful!