1. ANOVA:
Analysis of Variance

2. What Is It?
Analysis of Variance (ANOVA): allows for the simultaneous comparison of the difference between two or more means
Partition: a statistical procedure in which the total variance is divided into separate components
Partitioning of variance is what gives the ANOVA its name
One-Way ANOVA: compares more than two levels of a single IV

3. General Linear Model Factor: the term used for an IV in an ANOVA
Factors have several Levels (values or conditions)
Major Assumptions:
The only difference in means is due to the levels of the IV
The variances of the groups are equivalent (homogeneity of variance)

4. Assumptions of ANOVA
Data meet the criteria for parametric statistics (interval/ratio level data).
The data is normally distributed in each group.
There is homogeneity of variance
The observations in each sample are independent of one another.

5. Components of Variance
Total Variance: the variance of all scores in the data set regardless of experimental group
Comprised of:
Between-Groups Variance
Within-Groups Variance

6. Within-Groups Variance
Within-Groups Variance: estimate of the average variance within each group
Homogeneity of Variance: σ12 = σ22 = σ32 = … σj2

7. Between-Groups Variance
Between-Groups Variance: estimate of variance between group means
Two Sources:
Error Variance: uncontrolled and unpredicted differences among individual scores; the within-groups variance estimates the error variance
Treatment Variance: the variance among group means that is due to the effects of the IV

8. The F-Ratio
F-Ratio: the ratio of the between-groups variance divided by the within-groups variance
Can be expressed as: Or

9. The F-Ratio
No Treatment Effect
Treatment Effect Present

10. Is it Significant?
Same Concept: distributions represent the probability of various F-ratios when the null hypothesis is true
Two types of degrees of freedom determine the shape of the distribution.
Between-Groups
Within-Groups or
If computed F > critical F (or if the computer tells you it is), the F statistic is significant.

11. Post What?
The F-ratio does not specify which means are different from other means.
It only implies that the difference between the means (at least two) is great enough to be statistically significant.
Post hoc tests utilize pairwise comparisons to determine which groups are statistically different.

12. Using SPSS to Compute a One-Way ANOVA
Analyze  General Linear Model  Univariate
Move the independent variable to the Fixed Factor(s) box  Move the dependent variable to the Dependent Variable box
Click Options  highlight the independent variable in the Factor(s) box and move it to the Display Means for box  Under Display, check descriptive statistics, estimates of effect size, and homogeneity tests  Note that the significance level is already set at 0.05  Click Continue
Click Post Hoc highlight the independent variable in the Factor(s) box and move it to the Post Hoc Tests for box  Under Equal Variances Assumed, check Tukey (not Tukey’s-b)  Click continue.

13. What Does It All Mean?
The descriptive statistics box provides the mean, standard deviation, and number for each group.
Levene’s test is designed to compare the error variance of the dependent variable across groups. We do not want this result to be significant.

14. Understanding the Output
The row you are interested in is the row which has the name of your variable in it. The between df appear in this row; the within degrees of freedom appear in the error row. F is your test statistic, and Sig is its probability.
Estimated marginal means (the next box), I did not put here. It merely provides the 95% confidence intervals for each of the means.

15. Post Hoc Analysis
Multiple Comparisons provide the mean difference between each level of the IV and its significance. The numbers in this box repeat themselves. It is only necessary to interpret one of each comparison… which one depends on the hypothesis.