In your output Mauchley’s test will indicate a non-significant violation of sphericity for both variables, but I have argued that you should ignore this test and routinely apply the Greenhouse-Geisser correction, so that’s what we’ll do. Note that the correction does not exist for factors with only 2 variables (i.e., `Lighting`), so be sure to also keep None ticked under Assumption Checks. All effects are significant at p < .001. We’ll look at each effect in turn.
The main effect of lighting shows that the attractiveness ratings of photos was significantly lower when the lighting was dim compared to when it was bright, F(1, 25) = 23.42, p < .001, = 0.25. The main effect of alcohol shows that the attractiveness ratings of photos of faces was significantly affected by how much alcohol was consumed, F(2.62, 65.47) = 104.39, p < 0.001, = 0.75. However, both of these effects are superseded by the interaction, which shows that the effect that alcohol had on ratings of attractiveness was significantly moderated by the brightness of the lighting, F(2.81, 70.23) = 22.22, p < 0.001, = 0.35. To interpret this effect let’s move onto the next task.
