1. Slide 1 Mixed ANOVA (GLM 5) Chapter 15

2. Slide 2 Mixed ANOVA Mixed: – 1 or more Independent variable uses the same participants – 1 or more Independent variable uses different participants

3. Slide 3 An Example: Speed Dating Is personality or looks more important? – IV 1 (Personality): High Charisma, Some Charisma, Dullard – IV 2 (Gender): Male or Female? Dependent Variable (DV): P’s rating of the date – 100% = The prospective date was perfect! – 0% = I’d rather date my own mother

4. SPSS Analyze > General Linear Model > Repeated Measures

5. SPSS Enter repeated factor

6. SPSS Move over the RM variables in the within subjects variables Move over the between subjects variables

9. SPSS Plots – Move over the variables and hit add – (remember you can get two of them to see the interaction if it exists)

11. SPSS Click post hoc – Move over the variable – Click your favorite post hoc – This analysis will only give you the main effect for the between subjects (and here we don’t actually need it because we only have two levels, but you would normally).

13. SPSS Click options – Move the variables over – Click compare main effects (pick an option) Remember LSD is no correction This section gives us the main effect analysis for the repeated factor – Click descriptives, effect size, homogeneity

15. SPSS Hit ok!

16. Output Gives you the order of your levels for both types of variables.

17. Output Gives you the means for each repeated and between subjects variables.

18. Output Box’s = multivariate homogeneity, akin to Levene’s. You will ignore this box if you are not doing a multivariate test.

19. Output Gives you the MANOVA, again you’ll ignore it if you are not doing multivariate test.

20. Output Sphericity for your repeated measures variables only.

21. Output

22. Main effect of charisma: F(2, 36) = 328.25, p <.001, partial n 2 =.95 Interaction of charisma & gender: F(2, 36) = 62.45, p <.001, partial n 2 =.78 The within subjects box will only have repeated measures factors or interactions with repeated measures factors.

23. Output Contrasts if you wanted them.

24. Output Levene’s test for your between subjects variable – you’ll get one for each RM level.

25. Output Now you need to use the between subjects box. Main effect of Gender: F(1, 18) =.01, p =.95, partial n 2 <.01

26. Output You may get pairwise comparisons box but remember only two levels = no post hoc.

27. Output

31. Simple effect analysis – Post hoc test now depends on the direction you decide to analyze. – Same basic rules: Go with the hypothesis Or the smaller number of levels

32. Output HighAverageLow MaleRepeated Measures FemaleRepeated Measures Between Subjects Since gender has a smaller number of levels, we can see if gender affects ratings for each type of charisma That’s going to be independent t because we are comparing the between subjects levels.

33. Output Analyze > compare means > independent t- test Move over the between subjects IV into the grouping box. Move over all the levels of the RM factor into the test variables box.

34. Output

36. High and low charisma are significant. – Look at the means, so you can tell what happened.

37. Output Still want to correct for multiple comparisons Bonferroni =.05 / 3 =.0167 – Since it’s Bonferroni – look at sig to determine if it’s significant after correction.

38. Effect Size ANOVAs = partial eta squared, R squared, omega squared Post hoc tests = Cohen’s d, Hedges g, Glass’ delta

39. Write ups Need to include – Type of ANOVA – Main effect F values (2 of them) – Interaction F values – Type of post hoc and correction – Post hoc values – Figure / means and SD