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. Treatments Person I II III Totals A 6 8 10 P = 24 B 5 5 5 P = 15 C 1 2 3 P = 6 D 0 1 2 P = 3 M = 3 M = 4 M = 5 N = 12 T = 12 T = 16 T = 20 G = 48 SS = 26 SS = 30 SS = 38 ∑X² = 294 A. Now assume that the data are from a repeated-measures study using the same sample of n = 4 participants in all three treatment conditions. Use a repeated-measures ANOVA with α = .05 to test the significance of the mean differences. Source SS df MS F Between treatments Within treatments Between subjects Error Total B. F critical = C. Explain why the two analyses lead to different conclusions. The repeated-measures ANOVA ___ the error variance by removing individual differences. This ___ the likelihood that the
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. Treatments Person I II III Totals A 6 8 10 P = 24 B 5 5 5 P = 15 C 1 2 3 P = 6 D 0 1 2 P = 3 M = 3 M = 4 M = 5 N = 12 T = 12 T = 16 T = 20 G = 48 SS = 26 SS = 30 SS = 38 ∑X² = 294 A. Now assume that the data are from a repeated-measures study using the same sample of n = 4 participants in all three treatment conditions. Use a repeated-measures ANOVA with α = .05 to test the significance of the mean differences. Source SS df MS F Between treatments Within treatments Between subjects Error Total B. F critical = C. Explain why the two analyses lead to different conclusions. The repeated-measures ANOVA ___ the error variance by removing individual differences. This ___ the likelihood that the
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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.
Treatments
Person
|
I
|
II
|
III
|
Totals
|
---|---|---|---|---|
A | 6 | 8 | 10 | P = 24 |
B | 5 | 5 | 5 | P = 15 |
C | 1 | 2 | 3 | P = 6 |
D | 0 | 1 | 2 | P = 3 |
M = 3 | M = 4 | M = 5 | N = 12 | |
T = 12 | T = 16 | T = 20 | G = 48 | |
SS = 26 | SS = 30 | SS = 38 | ∑X² = 294 |
A. Now assume that the data are from a repeated-measures study using the same sample of n = 4 participants in all three treatment conditions. Use a repeated-measures ANOVA with α = .05 to test the significance of the mean differences.
Source
|
SS
|
df
|
MS
|
F
|
---|---|---|---|---|
Between treatments |
|
|
|
|
Within treatments |
|
|
||
Between subjects |
|
|
||
Error |
|
|
|
|
Total |
|
|
B. F critical =
C. Explain why the two analyses lead to different conclusions.
The repeated-measures ANOVA ___ the error variance by removing individual differences. This ___ the likelihood that the
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