1. TWO-WAY BETWEEN-SUBJECTS ANOVA What is the Purpose? What are the Assumptions? How Does it Work?

2. What is the Purpose? Determine whether there are significant main effects and a significant interaction in a factorial design. Use for a between-subjects factorial design.

3. What are the Assumptions? Independent observations Interval or ratio level data Normal distribution of DV Homogeneity of variance (or proportional cell sizes)

4. How Does it Work? As in any ANOVA, variance is divided into parts and then the parts are compared. Three F-tests are computed: – Main effect of Factor A – Main effect of Factor B – A x B interaction

5. Dividing the Variance Total variance is divided into Between Groups and Within Groups Between Groups variance is subdivided into three parts: – Factor A – Factor B – A x B interaction

6. Dividing the Variance Each part of the Between Groups variance can be affected by: – Effect (A, B, or A x B): systematic – Individual differences: non-systematic – Measurement error: non-systematic Within Groups variance can be affected by: – Individual differences: non-systematic – Measurement error: non-systematic

7. Comparing the Variance Each F-test is the ratio between variance for the effect (numerator) and Within Groups variance (denominator)

8. Comparing the Variance