How do researchers interpret interaction effects in ANOVA results?

Researchers interpret interaction effects in ANOVA results as the combined influence of two or more variables on the outcome.

In an Analysis of Variance (ANOVA), researchers are often interested in understanding not just the main effects of individual variables, but also how these variables might interact with each other to affect the outcome. This is known as an interaction effect. When an interaction effect is present, it means that the effect of one variable on the outcome depends on the level of another variable.

For example, let's say researchers are studying the effects of diet and exercise on weight loss. A main effect would be if diet alone or exercise alone significantly affected weight loss. An interaction effect, however, would be if the effect of diet on weight loss depended on the level of exercise. Perhaps diet has a significant effect on weight loss for individuals who exercise regularly, but not for those who don't. This would be an interaction effect between diet and exercise.

When interpreting interaction effects, researchers look at the p-value associated with the interaction term in the ANOVA table. If this p-value is less than the chosen significance level (often 0.05), then the interaction effect is considered statistically significant. This means that there is strong evidence that the effect of one variable on the outcome depends on the level of another variable.

In addition to the p-value, researchers also look at the interaction plot to visually interpret the interaction effect. The interaction plot shows the mean outcome for each combination of levels of the interacting variables. If the lines in the plot are not parallel, this suggests an interaction effect. For a deeper understanding of how these results are used in experimental settings, see our notes on experimental research in psychology.

To ensure the accuracy and consistency of these interpretations, the types of data collected and the methods used to analyse them are crucial. Moreover, understanding the reliability and validity of the measures employed in ANOVA is essential to confirm that the findings are both dependable and meaningful.

IB Psychology Tutor Summary: In ANOVA, interaction effects show how different factors work together to affect results. For example, how diet influences weight loss might depend on exercise levels. These effects are significant if their p-value is below 0.05, meaning they're likely not due to chance. Researchers use interaction plots to visually check these effects, looking for non-parallel lines to indicate an interaction.