1. Statistics for the Behavioral Sciences (5th ed.) Gravetter & Wallnau
Chapter 14 Repeated-Measures ANOVA
University of Guelph Psychology 3320 — Dr. K. Hennig Winter 2003 Term

3. Table (p. 444) - Notation Two sets of data representing typical examples of single-factor, repeated measures research designs. Example see text p. 449
number of scores in each Tx
“Person totals” = A1 + A2 …An
total number of scores
Grand total=T1+ T2…

4. Step 1: (recall s2 = SS/df) Calculate the SS for each of the partitions:.

5. Calculate:
within treatments …
between treatments
between subjects

6. Step 2 Determine the df.

7. Step 3 Calculation of the variances (MS values)
MSbetween treatments = SSbetween treatments/dfbetween treatments
=50/3 = 16.67
MSerror = SSerror/dferror= 8/12 = 0.67
F = MSbetween treatments/ MSerror = 16.67/ 0.67 = 24.88
F(2, 12) = ?? for  = .05; What about .01?

8. Hypothesis testing
E.g., Example 14.1 effectiveness of classroom control technique on unruly outbursts
H0: before =  1 + 1 month + 6months
H1: at least one mean is different

9. Advantages For independent measures ANOVA: For repeated measures ANOVA
F= Tx effect + (individual differences + error) (individual differences + error)
F = = 1011 / 1001 = 1.01
For repeated measures ANOVA
F= Tx effect + error (- individual differences) error (-individual differences )
F = 10 +1/1 = 11/1 = 11
Lets suppose:
Tx effect = 10 units ind. differences = 1000 units
other error = 1 unit

10. Figure 14-5 (p. 464) The effect of amount of reward on running speed
Figure (p. 464) The effect of amount of reward on running speed. Treatment means are depicted by the broken line. Individual scores for each subject at each level of reward are shown by solid lines.

11. Figure 14-6 (p. 464) The effect of amount of reward on running speed
Figure (p. 464) The effect of amount of reward on running speed. The treatment means are depicted by the broken line. Individual scores for each subject at each level of reward are shown by solid lines.