Simple effects analysis is used to examine what?

Simple effects analysis is used to examine what?

• A. It estimates the interaction effect across all levels of the moderator.
• B. It examines the effect of one independent variable at individual levels of another variable.
• C. It computes the main effect of both variables by averaging across levels.
• D. It tests the equality of variances across groups.

Answer: (B)

Simple effects analysis focuses on the effect of one independent variable at specific levels of another variable. This is used when there’s an interaction between factors, so the impact of one factor can depend on the level of the other. By probing the effect at particular levels (or values) of the other variable, you can see exactly where the influence lies and how the relationship changes across levels. For example, if you’re looking at how study time affects a test score and you have a moderator like teaching method (traditional vs. modern), simple effects would tell you how study time influences scores within each teaching method separately. This helps interpret an interaction: you might find that more study time helps more with one method than the other, or only at certain levels of study time. The other options aren’t about conditioning the effect on another variable. Estimating the interaction across all levels describes the interaction term itself, not the conditional effects. Computing main effects by averaging across levels ignores the possibility that the effect changes depending on the other factor. Testing equality of variances across groups deals with homogeneity of variance, not the pattern of effects across levels.

Simple effects analysis focuses on the effect of one independent variable at specific levels of another variable. This is used when there’s an interaction between factors, so the impact of one factor can depend on the level of the other. By probing the effect at particular levels (or values) of the other variable, you can see exactly where the influence lies and how the relationship changes across levels. For example, if you’re looking at how study time affects a test score and you have a moderator like teaching method (traditional vs. modern), simple effects would tell you how study time influences scores within each teaching method separately. This helps interpret an interaction: you might find that more study time helps more with one method than the other, or only at certain levels of study time.

The other options aren’t about conditioning the effect on another variable. Estimating the interaction across all levels describes the interaction term itself, not the conditional effects. Computing main effects by averaging across levels ignores the possibility that the effect changes depending on the other factor. Testing equality of variances across groups deals with homogeneity of variance, not the pattern of effects across levels.