Slide 1

Mixed ANOVA (GLM 5)

Chapter 15

Slide 2

Mixed ANOVA

• Mixed:– 1 or more Independent variable uses the

same participants– 1 or more Independent variable uses

different participants

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

SPSS

• Analyze > General Linear Model > Repeated Measures

SPSS

• Enter repeated factor

SPSS

• Move over the RM variables in the within subjects variables

• Move over the between subjects variables

SPSS

• Plots– Move over the variables and hit add– (remember you can get two of them to see the

interaction if it exists)

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).

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

SPSS

• Hit ok!

Output

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

Output

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

Output

Box’s = multivariate homogeneity, akin to Levene’s. You will ignore this box if you arenot doing a multivariate test.

Output

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

Output

Sphericity for your repeated measures variables only.

Output

Output

• Main effect of charisma: • F(2, 36) = 328.25, p <.001, partial n2 = .95

• Interaction of charisma & gender:• F(2, 36) = 62.45, p <.001, partial n2 = .78

• The within subjects box will only have repeated measures factors or interactions with repeated measures factors.

Output

Contrasts if you wanted them.

Output

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

Output

Now you need to use the between subjects box.Main effect of Gender: F(1, 18) = .01, p = .95, partial n2 <.01

Output

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

Output

Output

Output

Output

Output

• 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

OutputHigh Average Low

Male Repeated Measures

Female Repeated Measures

Between Subjects

Between Subjects

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.

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.

Output

Output

Output

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

happened.

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.

Effect Size

• ANOVAs = partial eta squared, R squared, omega squared

• Post hoc tests = Cohen’s d, Hedges g, Glass’ delta

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