A researcher wants to see if gender and/or income affect the total amount of help given to a stranger who is sitting on the side of a busy road with a sign asking for help.  The independent variables are gender, income, and the interaction of gender and income.  The dependent variable is total help. He wants to know if one or both factors – or the interaction of the two - affect the

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A researcher wants to see if gender and/or income affect the total amount of help given to a stranger who is sitting on the side of a busy road with a sign asking for help. 

The independent variables are gender, income, and the interaction of gender and income. 
The dependent variable is total help.

He wants to know if one or both factors – or the interaction of the two - affect the total amount of help offered.  Because he is analyzing two independent variables (gender and income), he used a factorial ANOVA.  His results show the main effect of each of the independent variables on the dependent variable (total help) and the interaction effect.  The researcher is using a 95% confidence interval which means that he wants to be at least 95% sure that his independent variables affected total help if he rejects the null hypothesis.

Is there significance for either gender or income?

gender
age
marital
ethnic
income
tothelp
hseveret
problem
1
1
22
2
4
4
-45
4.0
2
24
1
3
43
5.0
3
1
39
2
1
3
.63
6.7
1
4.
2
42
1
4
-98
4.0
3
1
29
1
1
4
.87
4.5
3
6
1
22
1
1
-20
3.5
1
7
26
1
4
-.06
2.0
1
8
2
60
2
1
4
-.98
5.0
3
9
2
28
2
2
1.59
7.0
1
10
1
37
1
1
1.01
4.0
2
11
1
43
1
1
32
6.0
12
1
39
1
3
.12
7.0
2
13
2
27
2
2
4
.15
5.0
2
14
2
45
1
1
4
.39
6.5
2
15
1
54
1
1
4
.65
5.5
3
16
77
1
1
-.11
7.0
1
17
1
33
2
1
2
.16
7.0
2
18
1
33
1
1
47
5.0
19
2
25
1
1
-1.25
2.5
1
20
2
38
1
4
.16
5.0
21
2.
3.
4.
3.
3.
2.
24
3.
2.
2.
2.
2.
2.
5.
CO
Transcribed Image Text:gender age marital ethnic income tothelp hseveret problem 1 1 22 2 4 4 -45 4.0 2 24 1 3 43 5.0 3 1 39 2 1 3 .63 6.7 1 4. 2 42 1 4 -98 4.0 3 1 29 1 1 4 .87 4.5 3 6 1 22 1 1 -20 3.5 1 7 26 1 4 -.06 2.0 1 8 2 60 2 1 4 -.98 5.0 3 9 2 28 2 2 1.59 7.0 1 10 1 37 1 1 1.01 4.0 2 11 1 43 1 1 32 6.0 12 1 39 1 3 .12 7.0 2 13 2 27 2 2 4 .15 5.0 2 14 2 45 1 1 4 .39 6.5 2 15 1 54 1 1 4 .65 5.5 3 16 77 1 1 -.11 7.0 1 17 1 33 2 1 2 .16 7.0 2 18 1 33 1 1 47 5.0 19 2 25 1 1 -1.25 2.5 1 20 2 38 1 4 .16 5.0 21 2. 3. 4. 3. 3. 2. 24 3. 2. 2. 2. 2. 2. 5. CO
Gender:
Income per year:
1=less than $15,000
2=$15,000 to $25,000
3-$25,000-$50,00o|
4=over $50,000
1=female
2=male
Between-Subjects Factors
Value Label
N
gender
1
FEMALE
10
MALE
10
income
1
<15.000
1
<25,000
3
3
<50.000
5
4
>50,000
11
Tests of Between-Subjects Effects
Dependent Variable: to help
Type III Sum of
Source
Squares
df
Mean Square
F
Sig.
Corrected Model
5.496
.916
3.212
.037
Intercept
.158
1
.158
.553
470
gender
.057
1
.057
.199
663
income
4.142
1.381
4.842
018
gender * income
1.421
2
.710
2.491
121
Error
3.707
13
.285
Total
9.639
20
Corrected Total
9.203
19
CO
3.
Transcribed Image Text:Gender: Income per year: 1=less than $15,000 2=$15,000 to $25,000 3-$25,000-$50,00o| 4=over $50,000 1=female 2=male Between-Subjects Factors Value Label N gender 1 FEMALE 10 MALE 10 income 1 <15.000 1 <25,000 3 3 <50.000 5 4 >50,000 11 Tests of Between-Subjects Effects Dependent Variable: to help Type III Sum of Source Squares df Mean Square F Sig. Corrected Model 5.496 .916 3.212 .037 Intercept .158 1 .158 .553 470 gender .057 1 .057 .199 663 income 4.142 1.381 4.842 018 gender * income 1.421 2 .710 2.491 121 Error 3.707 13 .285 Total 9.639 20 Corrected Total 9.203 19 CO 3.
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