One of the advantages of a repeated-measures design is that it removes the individual differences from the error variance and increases the likelihood of rejecting the null hypothesis. The following data were obtained from a research study comparing three treatment conditions. Treatment 1 Treatment 2 Treatment 3 6 8 10 G=48 ∑? 2 = 294 5 5 5 1 2 3 0 1 2 T=12 SS=26 T=16 SS=30 T=20 SS=38 1. Assume that the data are from an independent-measures study (like in Chapter 12 normal ANOVA) using three separate samples, each with n=4 participants, and use an independent-measures ANOVA with =.05 to test for significant differences among the three treatments. 2. Assume that the data are from a repeated-measures study using one sample of n=4 participants and use a repeated-measures ANOVA with =.05 to test for significant differences among the three treatments. 3. Compare your results from part A and part B.
One of the advantages of a repeated-measures design is that it removes the individual differences from the error variance and increases the likelihood of rejecting the null hypothesis. The following data were obtained from a research study comparing three treatment conditions.
Treatment 1 Treatment 2 Treatment 3
6 8 10 G=48
∑?
2 = 294
5 5 5
1 2 3
0 1 2
T=12
SS=26 T=16
SS=30 T=20
SS=38
1. Assume that the data are from an independent-measures study (like in Chapter 12 normal ANOVA) using three separate samples, each with n=4 participants, and use an independent-measures ANOVA with =.05 to test for significant differences among the three treatments.
2. Assume that the data are from a repeated-measures study using one sample of n=4 participants and use a repeated-measures ANOVA with =.05 to test for significant differences among the three treatments.
3. Compare your results from part A and part B.
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