Factorials
A factorial has more than one independent variable.
2 Independent variables (gender, education level) with 2 levels each (male, female) and (elementary, secondary). This creates 4 conditions. (Grid)
Mixed Factorial
At least 1 between-subjects IV and 1 within-subjects IV
Mixed design must deal with equivalent groups problems (between) and sequence effects problems (within).
After you collect your data
Check to make sure your statistical assumptions were met
Do you have normal distribution? (bell curve, skewness and curtosis)
Do you have homogeneity of variance? (Have one group vary alot, have another group not)
Are standard deviations similar across conditions?
If assumptions weren’t met, you have to use non-parametric statistics.
P Values
Significance (sig if over threshold, not sig if under)
95% confidence for social science (p , .05%)
The probability that chance will account for the difference less than 5% of the time.
P values are not about effect size. It is simply a threshold question.
Effect Size
Magnitude of difference
P value is a threshold
Effect size measure how big the effect is
For ANOVAs use Partial nsquared (partial eta squared)
“Proportion of total variation attributable to the factor, partialling out (excluding) other factors from the total non-error variation.”




