ANOVA
1. Look at the Descriptive statistics – equal variance
2. Look at Levene’s test – p > 0.05 – homogenous
3. Look at ANOVA p- value < 0.05: There is a statistically significant difference between at least two of the groups.
4. We decide which is the better model by looking at the R2 you can make a conclusion when comparing it to the R2 of another model
5. If R2 is on its own, we can say that the result of R2 (example: 16,1%) “16 % of the variability in partner effectiveness can be explained by the
consumed alcohol” (OV-PV)
Follow up / Post hoc
1. Look at Multiple comparison table, p-value
2. If significant look at mean difference to determine direction – increase / decrease
Factorial ANOVA
1. Look at Significance Main model: P-value < 0.05
2. Look at Significance individual variables
3. Look at Partial Eta Square: SS gender/ SS gender + SS residual
Effect size = Rule of thumb:
0.01= small 0.06 medium Above 0.06 large
4. R square = Corrected model/corrected total (Sum of squares) (quality check)
Follow up/ Post Hoc
A. Main effects
5. Look at Pairwise comparison table, p-value
6. If significant, look at mean difference to determine direction and order of main effects
B. Interaction (SIMPLE EFFECTS TEST)
7. Look at Pairwise comparison table, p-value
8. If significant, look at mean difference to determine direction and compare all different levels of PV with each level of the moderator.
1. Look at the Descriptive statistics – equal variance
2. Look at Levene’s test – p > 0.05 – homogenous
3. Look at ANOVA p- value < 0.05: There is a statistically significant difference between at least two of the groups.
4. We decide which is the better model by looking at the R2 you can make a conclusion when comparing it to the R2 of another model
5. If R2 is on its own, we can say that the result of R2 (example: 16,1%) “16 % of the variability in partner effectiveness can be explained by the
consumed alcohol” (OV-PV)
Follow up / Post hoc
1. Look at Multiple comparison table, p-value
2. If significant look at mean difference to determine direction – increase / decrease
Factorial ANOVA
1. Look at Significance Main model: P-value < 0.05
2. Look at Significance individual variables
3. Look at Partial Eta Square: SS gender/ SS gender + SS residual
Effect size = Rule of thumb:
0.01= small 0.06 medium Above 0.06 large
4. R square = Corrected model/corrected total (Sum of squares) (quality check)
Follow up/ Post Hoc
A. Main effects
5. Look at Pairwise comparison table, p-value
6. If significant, look at mean difference to determine direction and order of main effects
B. Interaction (SIMPLE EFFECTS TEST)
7. Look at Pairwise comparison table, p-value
8. If significant, look at mean difference to determine direction and compare all different levels of PV with each level of the moderator.
