One of the primary advantages of a repeated-measures design, compared to independent measures, 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 1 Treatment 2 Treatment 3 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 M = 4 M = 6 M = 8 N = 18 T = 24 T = 36 T = 48 G = 108 SS = 42 SS = 28 SS = 34 SX2 = 800 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 a = .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 conditions. Use a repeated-measures ANOVA with a = .05 to test the significance of the mean differences. C) Explain why the two analyses lead to different conclusions.
One of the primary advantages of a repeated-measures design, compared to independent measures, 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 1 Treatment 2 Treatment 3 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
M = 4 M = 6 M = 8 N = 18
T = 24 T = 36 T = 48 G = 108
SS = 42 SS = 28 SS = 34 SX2 = 800
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 a = .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 conditions. Use a repeated-measures ANOVA with a = .05 to test the significance of the mean differences. C) Explain why the two analyses lead to different conclusions.
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