Introduction
In this assignment, the randomly sampled students from across campus dataset has been used and the project deliverables are from the fact that the Vice-President has to find out whether working on campus or off campus is associated with the number of hours worked.
Compute the expected cell counts for all cells, reported the expected counts to two decimal places.
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Working on campus or off campus * On average, how many hours do one work each week Crosstabulation |
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On average, how many hours do one work each week |
Total |
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1 – 10 Hours |
11 – 20 Hours |
21 – 30 Hours |
31 – 40 Hours |
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| Working on campus or off campus | On-Campus | Count |
14 |
11 |
9 |
7 |
41 |
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| % within Working on campus or off campus |
34.15% |
26.83% |
21.95% |
17.07% |
100.00% |
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| % within On average, how many hours do one work each week |
58.30% |
61.10% |
60.00% |
43.80% |
56.20% |
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| % of Total |
19.20% |
15.10% |
12.30% |
9.60% |
56.20% |
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| Off-Campus | Count |
10 |
7 |
6 |
9 |
32 |
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| % within Working on campus or off campus |
31.25% |
21.88% |
18.75% |
28.13% |
100.00% |
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| % within On average, how many hours do one work each week |
41.70% |
38.90% |
40.00% |
56.20% |
43.80% |
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| % of Total |
13.70% |
9.60% |
8.20% |
12.30% |
43.80% |
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| Total | Count |
24 |
18 |
15 |
16 |
73 |
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| % within Working on campus or off campus |
32.88% |
24.66% |
20.55% |
21.92% |
100.00% |
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| % within On average, how many hours do one work each week |
100.00% |
100.00% |
100.00% |
100.00% |
100.00% |
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| % of Total |
32.88% |
24.66% |
20.55% |
21.92% |
100.00% |
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Determine whether a Chi-Square test is appropriate in this situation. Defend your choice.
The quantitative data were analyzed using both descriptive and inferential statistics. The descriptive statistics were used to describe and summarize the data in form of tables, frequencies, and percentages. The inferential statistics were used to help make inferences and draw conclusions. Statistical tests including bivariate categorical tests (Chi-Square test) were used to test the hypotheses. The bivariate table was designed to organize the significant relationship between two variables. Therefore, given that Chi-Square test is useful in a categorical data, there is no manipulation of the independent variable because the event of interest, or the dependent variable, has already occurred.
Calculate the Chi-Square test statistic.
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Chi-Square Tests |
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Value |
df |
Asymp. Sig. (2-sided) |
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| Pearson Chi-Square |
1.316 a |
3 |
.725 |
| Likelihood Ratio |
1.308 |
3 |
.727 |
| Linear-by-Linear Association |
.642 |
1 |
.423 |
| N of Valid Cases |
73 |
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| a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 6.58. | |||
From the cross-tabulation analysis, it is evidenced that the percentages significantly vary between the two variables, but the relationship is not identified. The Chi-Square test indicates a value of 1.316 with an associated p (true) value of .725.
Using the Chi-Square critical value, determine whether working on campus or off campus is associated with the number of hours worked.
Because the test is not significant and above the .05 threshold, there is a need to accept the null hypothesis that there is no relationship. In other words, there is no relationship between working on campus or off campus with the number of hours worked.
