In: Statistics and Probability
One of the primary advantages of a repeated-measures design, compared to an independent-measures design, is that it reduces the overall variability by removing variance caused by individual differences. The following data are from a research study comparing three treatment conditions.
treatment |
|||
A |
B |
C |
P |
6 |
9 |
12 |
27 |
8 |
8 |
8 |
24 |
5 |
7 |
9 |
21 |
0 |
4 |
8 |
12 |
2 |
3 |
4 |
9 |
3 |
5 |
7 |
15 |
N=18, G=108, SUM=108
Treatment A: -
M=4
T=24
SS=42
Treatment B:-
M=6
T=36
SS=28
Treatment c:-
M=8
T=48
SS=34
a) Assume that the data are from an independent-measures study using three separate samples, each with n = 6 participants. Ignore the column of P totals and use an independent-measures ANOVA with alpha = .05 to test the significance of the mean differences.
b) Now assume that the data are from a repeated-measures study using the same sample of n = 6 participants in all three treatment conditions. Use a repeated-measures ANOVA with alpha = .05 to test the significance of the mean differences.
c) Explain why the two analyses lead to different conclusions.