The following data are from a two-factor study examining the effects of two treatment conditions on males and females.
a. Use an ANOVA with α = .05 for all tests to evaluate the significance of the main effects and the interaction.
b. Compute η2 to measure the size of the effect for each main effect and the interaction.
Factor B: Treatment
B1 | B2 | B3 | ||
Male | 3 | 1 | 10 | |
1 | 4 | 10 | ||
1 | 8 | 14 | ||
6 | 6 | 7 | TROW1 =90 ROWI |
|
4 | 6 | 9 | ||
M= 3 | M = 5 | M = 10 | ||
T = 15 | T=25 | T= 50 | ||
SS = 18 | SS =28 | SS = 26 | N = 30 | |
Factor A: | 0 | 2 | 1 | G = 120 |
Gender | 2 | 7 | I | ΣX2 = 860 |
0 | 2 | 1 | ||
0 | 2 | 6 | TROW2 = 30 | |
Female | 3 | 2 | 1 | |
M = 1 | M = 3 | M = 2 | ||
T =5 | T =15 | T =10 | ||
SS =8 | SS =20 | SS = 20 |
TCO4.1 = 20 TCO4.2 = 40 TCO4.3 = 60
a.
Answer to Problem 25P
For factor A treatment has significant effect means there is difference in performance of male and female. For factor B treatment has significant effect means there is difference in treatment with
Explanation of Solution
Given info:
The following data are given in the question:
Factor B | |||||
|
|
|
|||
Factor A | Male | 3 | 1 | 10 |
|
1 | 4 | 10 | |||
1 | 8 | 14 | |||
6 | 6 | 7 | |||
4 | 6 | 9 | |||
|
|
|
| ||
Female | 0 | 2 | 1 |
|
|
2 | 7 | 1 | |||
0 | 2 | 1 | |||
0 | 2 | 6 | |||
3 | 2 | 1 | |||
|
|
|
|||
Total = 20 | Total = 40 | Total = 60 |
Calculation:
Total variability:
Total sum of squareis calculated as:
Total degree of freedomis calculated as:
Within treatment:
Sum of square within treatment is calculated as:
Degree of freedom within treatmentis calculated as:
Between treatment variability:
Sum of square between treatmentsis calculated as:
Degree of freedom between treatments is calculated as:
For factor A, the row totals are 90 and 30, and each total was obtained by adding nothing.
Degree of freedom for factor Ais calculated as:
For factor B, sum of square is calculated as:
Degree of freedom for factor B is calculated as:
Interaction between factors A and B denoted as
Degree of freedom for interaction between factors is calculated as:
Two factor ANOVA consists of three separate hypothesis test with 3 separate F-ratios.
This value is same for all three F-ratios.
The numerator of the 3 F-ratios factor A, Factor B,
Variances for factor Bis calculated as:
Variance for
Now F statistic for factor A is calculated as:
F statistic for factor B is calculated as:
F statistic for
Decision rule:
If the absolute value of the F-ratio is greater than the table value then there is sufficient evidence to reject the null hypothesis.
F-ratio for factor A has
Thus, the absolute F-ratio forfactor A is 24 which is greater than 4.26. Thus, there is enough evidence to reject that there is no mean difference for factor A. Hence, factor A has a significant effect.
Thus, the absolute F-ratio for factor B is * which is greater than 3.40. Thus, there is enough evidence to reject that there is no mean difference for factor A. Hence, factor B has a significant effect.
Thus, the absolute F-ratio forinteraction effect is 6 which is greater than 3.40. Thus, there is enough evidence to reject that there is no mean difference for factor A. Hence, factor B has a significant effect. For these data, treatment of interaction has significant effect.
b.
Compute: The value of
Answer to Problem 25P
The value of
Explanation of Solution
Given info:
The following data are given in the question:
Factor B | |||||
|
|
|
|||
Factor A | Male | 3 | 1 | 10 |
|
1 | 4 | 10 | |||
1 | 8 | 14 | |||
6 | 6 | 7 | |||
4 | 6 | 9 | |||
|
|
|
| ||
Female | 0 | 2 | 1 |
|
|
2 | 7 | 1 | |||
0 | 2 | 1 | |||
0 | 2 | 6 | |||
3 | 2 | 1 | |||
|
|
|
|||
Total = 20 | Total = 40 | Total = 60 |
Calculation:
The size of the effect for factor A is calculated as:
Now substitute the values calculated in part A then:
The size of the effect for factor B is calculated as:
Now substitute the values calculated in part A then:
The size of the effect for interaction
Now substitute the values calculated in part A then:
Conclusion:
The value of
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Chapter 13 Solutions
Essentials of Statistics for the Behavioral Sciences
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