Ashford 4: - Week 3 - Discussion 1 Your initial discussion thread is due on Day 3 (Thursday) and you have until Day 7 (Monday) to respond to your classmates. Your grade will reflect both the quality of your initial post and the depth of your responses. Reference the Discussion Forum Grading Rubric for guidance on how your discussion will be evaluated. ANOVA In many ways, comparing multiple sample means is simply an extension of what we covered last week. Just as we had 3 versions of the t-test (1 sample, 2 sample (with and without equal variance), and paired; we have several versions of ANOVA – single factor, factorial (called 2-factor with replication in Excel), and within-subjects (2-factor without replication in Excel). What examples (professional, personal, social) can you provide on when we might use each type? What would be the appropriate hypotheses statements for each example? Guided Response: Review several of your classmates’ posts. Respond to at least two classmates by commenting on why you agree or disagree with the statistical test that your peers have described as appropriate in this scenario. Ashford 4: - Week 3 - Discussion 2 Your initial discussion thread is due on Day 3 (Thursday) and you have until Day 7 (Monday) to respond to your classmates. Your grade will reflect both the quality of your initial post and the depth of your responses. Reference the Discussion Forum Grading Rubric for guidance on how your discussion will be evaluated. Effect Size Several statistical tests have a way to measure effect size. What is this, and when might you want to use it in looking at results from these tests on job related data? Ashford 4: - Week 3 - Assignment Problem Set Week Three Complete the problems included in the resources below and submit your work in an Excel document. Be sure to show all of your work and clearly label all calculations. All statistical calculations will use the Employee Salary Data Set and the Week 3 assignment sheet. Carefully review the Grading Rubric for the criteria that will be used to evaluate your assignment. See comments at the right of the data set. ID Salary Compa Midpoint Age Performance Rating Service Gender Raise Degree Gender1 Grade 8 23 1.000 23 32 90 9 1 5.8 0 F A The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 10 22 0.956 23 30 80 7 1 4.7 0 F A Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work. 11 23 1.000 23 41 100 19 1 4.8 0 F A 14 24 1.043 23 32 90 12 1 6 0 F A The column labels in the table mean: 15 24 1.043 23 32 80 8 1 4.9 0 F A ID – Employee sample number Salary – Salary in thousands 23 23 1.000 23 36 65 6 1 3.3 1 F A Age – Age in years Performance Rating – Appraisal rating (Employee evaluation score) 26 24 1.043 23 22 95 2 1 6.2 1 F A Service – Years of service (rounded) Gender: ...

1. Ashford 4: - Week 3 - Discussion 1
Your initial discussion thread is due on Day 3 (Thursday) and
you have until Day 7 (Monday) to respond to your classmates.
Your grade will reflect both the quality of your initial post and
the depth of your responses. Reference the Discussion Forum
Grading Rubric for guidance on how your discussion will be
evaluated.
ANOVA
In many ways, comparing multiple sample means is simply an
extension of what we covered last week. Just as we had 3
versions of the t-test (1 sample, 2 sample (with and without
equal variance), and paired; we have several versions of
ANOVA – single factor, factorial (called 2-factor with
replication in Excel), and within-subjects (2-factor without
replication in Excel). What examples (professional, personal,
social) can you provide on when we might use each type? What
would be the appropriate hypotheses statements for each
example?
Guided Response: Review several of your classmates’ posts.
Respond to at least two classmates by commenting on why you
agree or disagree with the statistical test that your peers have
described as appropriate in this scenario.
Ashford 4: - Week 3 - Discussion 2

2. Your initial discussion thread is due on Day 3 (Thursday) and
you have until Day 7 (Monday) to respond to your classmates.
Your grade will reflect both the quality of your initial post and
the depth of your responses. Reference the Discussion Forum
Grading Rubric for guidance on how your discussion will be
evaluated.
Effect Size
Several statistical tests have a way to measure effect size. What
is this, and when might you want to use it in looking at results
from these tests on job related data?
Ashford 4: - Week 3 - Assignment
Problem Set Week Three
Complete the problems included in the resources below and
submit your work in an Excel document. Be sure to show all of
your work and clearly label all calculations.
All statistical calculations will use the Employee Salary Data
Set and the Week 3 assignment sheet.
Carefully review the Grading Rubric for the criteria that will be
used to evaluate your assignment.
See comments at the right of the data set.

3. ID
Salary
Compa
Midpoint
Age
Performance Rating
Service
Gender
Raise
Degree
Gender1
Grade
8
23
1.000
23
32
90
9
1
5.8

4. 0
F
A
The ongoing question that the weekly assignments will focus on
is: Are males and females paid the same for equal work (under
the Equal Pay Act)?
10
22
0.956
23
30
80
7
1
4.7
0
F
A
Note: to simplfy the analysis, we will assume that jobs within
each grade comprise equal work.
11
23
1.000
23
41
100
19
1
4.8
0
F
A

5. 14
24
1.043
23
32
90
12
1
6
0
F
A
The column labels in the table mean:
15
24
1.043
23
32
80
8
1
4.9
0
F
A

6. ID – Employee sample number
Salary – Salary in thousands
23
23
1.000
23
36
65
6
1
3.3
1
F
A
Age – Age in years
Performance Rating – Appraisal rating (Employee evaluation
score)
26
24
1.043
23
22
95
2
1
6.2
1
F
A
Service – Years of service (rounded)
Gender: 0 = male, 1 = female
31

7. 24
1.043
23
29
60
4
1
3.9
0
F
A
Midpoint – salary grade midpoint
Raise – percent of last raise
35
24
1.043
23
23
90
4
1
5.3
1
F
A
Grade – job/pay grade
Degree (0= BSBA 1 = MS)
36
23
1.000
23
27

8. 75
3
1
4.3
1
F
A
Gender1 (Male or Female)
Compa - salary divided by midpoint
37
22
0.956
23
22
95
2
1
6.2
1
F
A
42
24
1.043
23
32
100

9. 8
1
5.7
0
F
A
3
34
1.096
31
30
75
5
1
3.6
0
F
B
18
36

10. 1.161
31
31
80
11
1
5.6
1
F
B
20
34
1.096
31
44
70
16
1
4.8
1
F
B

11. 39
35
1.129
31
27
90
6
1
5.5
1
F
B
7
41
1.025
40
32
100
8
1
5.7
0
F
C

12. 13
42
1.050
40
30
100
2
1
4.7
1
F
C
22
57
1.187
48
48
65
6
1
3.8
0

13. F
D
24
50
1.041
48
30
75
9
1
3.8
1
F
D
45
55
1.145
48
36
95

14. 8
1
5.2
0
F
D
17
69
1.210
57
27
55
3
1
3
0
F
E
48
65

15. 1.140
57
34
90
11
1
5.3
1
F
E
28
75
1.119
67
44
95
9
1
4.4
1
F
F

16. 43
77
1.149
67
42
95
20
1
5.5
1
F
F
19
24
1.043
23
32
85
1
0
4.6
1
M
A

17. 25
24
1.043
23
41
70
4
0
4
0
M
A
40
25
1.086
23
24
90
2
0
6.3
0

18. M
A
2
27
0.870
31
52
80
7
0
3.9
0
M
B
32
28
0.903
31
25
95

19. 4
0
5.6
0
M
B
34
28
0.903
31
26
80
2
0
4.9
1
M
B
16
47

20. 1.175
40
44
90
4
0
5.7
0
M
C
27
40
1.000
40
35
80
7
0
3.9
1
M
C

21. 41
43
1.075
40
25
80
5
0
4.3
0
M
C
5
47
0.979
48
36
90
16
0
5.7
1
M
D

22. 30
49
1.020
48
45
90
18
0
4.3
0
M
D
1
58
1.017
57
34
85
8
0
5.7
0

23. M
E
4
66
1.157
57
42
100
16
0
5.5
1
M
E
12
60
1.052
57
52
95

24. 22
0
4.5
0
M
E
33
64
1.122
57
35
90
9
0
5.5
1
M
E
38
56

25. 0.982
57
45
95
11
0
4.5
0
M
E
44
60
1.052
57
45
90
16
0
5.2
1
M
E

26. 46
65
1.140
57
39
75
20
0
3.9
1
M
E
47
62
1.087
57
37
95
5
0
5.5
1
M
E

27. 49
60
1.052
57
41
95
21
0
6.6
0
M
E
50
66
1.157
57
38
80
12
0
4.6
0

28. M
E
6
76
1.134
67
36
70
12
0
4.5
1
M
F
9
77
1.149
67
49
100

29. 10
0
4
1
M
F
21
76
1.134
67
43
95
13
0
6.3
1
M
F
29
72

30. 1.074
67
52
95
5
0
5.4
0
M
F
Score:
Week 3
ANOVA and Paired T-test

31. At this point we know the following about male and female
salaries.
a.
Male and female overall average salaries are not equal in the
population.

32. b.
Male and female overall average compas are equal in the
population, but males are a bit more spread out.
c.
The male and female salary range are almost the same, as is
their age and service.
d.
Average performance ratings per gender are equal.

33. Let's look at some other factors that might influence pay -
education(degree) and performance ratings.
<1 point>
1
Last week, we found that average performance ratings do not
differ between males and females in the population.

34. Now we need to see if they differ among the grades. Is the
average performace rating the same for all grades?
(Assume variances are equal across the grades for this
ANOVA.)
You can use these columns to place grade Perf Ratings if
desired.
A
B
C
D

35. E
F
Null Hypothesis:
Alt. Hypothesis:

36. Place B17 in Outcome range box.

45. Interpretation:

46. What is the p-value:
Is P-value < 0.05?

47. Do we REJ or Not reject the null?
was rejected, what is the effect size value (eta squared):
If the null hypothesis
Meaning of effect size measure:

48. What does that decision mean in terms of our equal pay
question:

50. <1 point>
2
While it appears that average salaries per each grade differ, we
need to test this assumption.
Is the average salary the same for each of the grade levels?
ANOVA.)
(Assume equal variance, and use the analysis toolpak function
Use the input table to the right to list salaries under each grade
level.

51. Null Hypothesis:
If desired, place salaries per grade in these columns
Alt. Hypothesis:

52. A
B
C
D
E
F

53. Place B55 in Outcome range box.

62. What is the p-value:

63. Is P-value < 0.05?
Do you reject or not reject the null hypothesis:

64. If the null hypothesis was rejected

65. Interpretation:

66. <1 point>
3
The table and analysis below demonstrate a 2-way ANOVA.
with replication. Please interpret the results
BA

67. MA
Ho: Average compas by gender are equal
Male
1.017
1.157
Ha: Average compas by gender are not equal
0.870
0.979
Ho: Average compas are equal for each degree

68. 1.052
1.134
Ha: Average compas are not equal for each degree
1.175
1.149
Ho: Interaction is not significant
1.043
1.043
Ha: Interaction is significant

69. 1.074
1.134
1.020
1.000
Perform analysis:

70. 0.903
1.122
0.982
0.903
Anova: Two-Factor With Replication

71. 1.086
1.052
1.075
1.140
SUMMARY
BA
MA
Total

72. 1.052
1.087
Male
Female
1.096
1.050
Count
12
12
24
1.025
1.161

73. Sum
12.349
12.9
25.249
1.000
1.096
Average
1.02908333
1.075
1.052042
0.956
1.000
Variance

74. 0.00668645
0.00652
0.006866
1.000
1.041
1.043
1.043
Female

75. 1.043
1.119
Count
12
12
24
1.210
1.043
Sum
12.791
12.787
25.578

76. 1.187
1.000
Average
1.06591667
1.065583
1.06575
1.043
0.956
Variance
0.00610245
0.004213
0.004933

77. 1.043
1.129
1.145
1.149
Total

78. Count
24
24
Sum
25.14
25.687

79. Average
1.0475
1.070292
Variance
0.00647035
0.005156

80. ANOVA

81. Source of Variation
SS
df
MS
F
P-value
F crit

82. 0.00225502
1
0.002255
0.383482
0.538939
4.061706
(This is the row variable or gender.)
Columns
0.00623352
1
0.006234
1.060054
0.30883
4.061706
(This is the column variable or Degree.)
Interaction
0.00641719
1
0.006417
1.091288
0.301892
4.061706

83. Within
0.25873675
44
0.00588
Columns

84. Total
0.27364248
47

85. Interpretation:

86. If the null hypothesis was rejected, what
What is the p-value:
Is P-value < 0.05?

87. Do you reject or not reject the null hypothesis:
is the effect size value (eta squared):

88. Meaning of effect size measure:

89. For Ho: Average compas are equal for all degrees Ha: Average
compas are not equal for all grades
What is the p-value:
Is P-value < 0.05?

90. Do you reject or not reject the null hypothesis:
s the effect size value (eta squared):

91. If the null hypothesis was rejected, what i
Meaning of effect size measure:
For: Ho: Interaction is not significant

92. Ha: Interaction is significant
What is the p-value:
Is P-value < 0.05?

93. Do you reject or not reject the null hypothesis:
ect size value (eta squared):

94. If the null hypothesis was rejected, what is the eff
Meaning of effect size measure:

95. What do these decisions mean in terms of our equal pay
n in terms of our equal pay question:

96. Place data values in these columns
<1 point>
4
Salary
Midpoint

97. Many companies consider the grade midpoint to be the "market
rate" - what is needed to hire a new employee.
Does the company, on average, pay its existing employees at or
above the market rate?

98. Null Hypothesis:
Alt. Hypothesis:

99. Statistical test to use:

100. Place the cursor in B160 for test.

109. What is the p-value:
Is P-value < 0.05?

110. What else needs to be checked on a 1-tail in order to reject the
null?
Do we REJ or Not reject the null?
If the null hypothesis was rejected, what is the effect size

111. value:
NA
Meaning of effect size measure:
NA
Interpretation:

113. <2 points>
5.
Using the results up thru this week, what are your conclusions
about gender t?
equal pay for equal work at this poin