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 5 5 5 1 2 3 0 1 2 T=12 SS=26 T=16 SS=30 T=20 SS=38     a. 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 a=.05 to test for significant differences among the three treatments.     b.Assume that the data are from a repeated-measures study using one sample of n=4 participants and use a repeated-measures ANOVA with a=.05 to test for significant differences among the three treatments. c. Compare your results from part A and part B.

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  1. 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

5

5

5

1

2

3

0

1

2

T=12

SS=26

T=16

SS=30

T=20

SS=38

 

 

a. 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 a=.05 to test for significant differences among the three treatments.

 

 

b.Assume that the data are from a repeated-measures study using one sample of n=4 participants and use a repeated-measures ANOVA with a=.05 to test for significant differences among the three treatments.

c. Compare your results from part A and part B.

 

 

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