Read through this scenario and look at the data that was collected. State the null and all possible research hypotheses. Review the results below (I used SPSS) and answer the questions that follow. Scenario: 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. Below is the data set followed by the results: Gender: 1=female 2=male Income per year: 1=less than $15,000 2=$15,000 to $25,000 3=$25,000-$50,000 4=over $50,000 Between-Subjects Factors Value Label N gender 1 FEMALE 10 2 MALE 10 income 1 <15,000 1 2 <25,000 3 3 <50,000 5 4 >50,000 11 Tests of Between-Subjects Effects Dependent Variable: to help Source Type III Sum of Squares df Mean Square F Sig. Corrected Model 5.496a 6 .916 3.212 .037 Intercept .158 1 .158 .553 .470 gender .057 1 .057 .199 .663 income 4.142 3 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 What is the null hypothesis? What is one research hypothesis (there are three possible hypotheses here – name them all if you can but naming at least one is required)? Is there significance for either gender or income? Is there significance for the interaction of gender and income? What do the results of this study mean to you?
Read through this scenario and look at the data that was collected. State the null and all possible research hypotheses. Review the results below (I used SPSS) and answer the questions that follow.
Scenario:
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.
Below is the data set followed by the results:
Gender:
1=female
2=male
Income per year:
1=less than $15,000
2=$15,000 to $25,000
3=$25,000-$50,000
4=over $50,000
|
Between-Subjects Factors |
|||
|
|
Value Label |
N |
|
|
gender |
1 |
FEMALE |
10 |
|
2 |
MALE |
10 |
|
|
income |
1 |
<15,000 |
1 |
|
2 |
<25,000 |
3 |
|
|
3 |
<50,000 |
5 |
|
|
4 |
>50,000 |
11 |
|
Tests of Between-Subjects Effects |
|||||
|
Dependent Variable: to help |
|||||
|
Source |
Type III Sum of Squares |
df |
Mean Square |
F |
Sig. |
|
Corrected Model |
5.496a |
6 |
.916 |
3.212 |
.037 |
|
Intercept |
.158 |
1 |
.158 |
.553 |
.470 |
|
gender |
.057 |
1 |
.057 |
.199 |
.663 |
|
income |
4.142 |
3 |
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 |
|
|
|
What is the null hypothesis?
What is one research hypothesis (there are three possible hypotheses here – name them all if you can but naming at least one is required)?
Is there significance for either gender or income?
Is there significance for the interaction of gender and income?
What do the results of this study mean to you?
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