In the context of a two-way ANOVA, which concept helps understand the effect of one independent variable at different levels of another?

In a two-way ANOVA, the interaction effect is critical for determining how the effect of one independent variable varies at different levels of another independent variable. This means that rather than just looking at the main effects of each factor independently, the interaction effect provides insight into whether the combination of these factors leads to different results than would be expected from simply adding their individual effects.

For instance, if one independent variable is 'treatment type' and the other is 'time of measurement', an interaction effect would show whether the impact of treatment differs depending on the time measured. This interaction can reveal significant relationships and dependencies that may not be apparent when only examining the main effects of each variable. Understanding this relationship is vital in psychology research, as it allows for a more nuanced understanding of how variables work together to influence outcomes.