You might supplement these examples with examples from recent research, such as the finding that the use of artificial bright lights for treating winter depression works well for patients that sleep a lot, have increased appetite, carbohydrate cravings, and who tend to feel worst in the evening, but not as well for patients who have insomnia, anxiety, suicidal tendencies, and who feel worst in the morning.
To help students understand interactions, you might want to discuss interactions involving drugs (such examples also emphasize the importance of interactions--it can be a matter of life or death). This link
can help you find specific examples of interactions involving prescription
Once students understand interactions at the conceptual level, they will still need some practice before they are able to recognize and interpret interactions in data. Specifically, they will have trouble in two areas:
1. interpreting tables of means; andTo give students the necessary practice with tables of means, go over tables 12.5 - 12.11. (If you haven't already, have them go through the advanced factorial design worksheet). If students still have trouble, have them graph the table's results (this may take some time). Then, show them that with interactions, the lines are not parallel. They either cross or, if they were extended, they would cross. Students rapidly grasp this idea. However, make sure that they understand that there is more to interactions than just lines crossing. Reiterate the points made in tables 12.2 - 12.3.
2. confusing main effects with interactions when interpreting ANOVA summary tables.
An excellent article that explains the concept of interactions extremely well is
Astin, A. W. (1993). An empirical typology of college students. Journal of College Student
Development, 34, 36-44.
We often have students get in groups of 2-3 and read discuss the first two pages of this article.
For a more detailed presentation of discussing interactions, see
Schaefer, V. H. (1988). Teaching the concept of interaction and sensitizing students to its
implications. In Ware, M. E. & Brewer, C. L. (Eds.), Handbook for teaching
statistics and research methods. Hillsdale, NJ: Lawrence Erlbaum Associates.
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