Read the article about creating Likert scales. You will be creating 8 different
ID: 3205738 • Letter: R
Question
Read the article about creating Likert scales. You will be creating 8 different scales: work, pay, supervision, co-worker, opportunities, Overall independent, engagement and commitment. Each scale is the average of the items within the dimension. For example, the work scale will be the average of the four items, W1 – W4, for each observation; Overall Independent is the average of the twenty independent variables for each observation (W1 – O4). Cut and paste (you will need to use the paste special option) until you have a clean data set that looks like the following:
Observation
Gender
Age
Education
Level
Work
Pay
Supervision
Co-worker
Opportunities
Overall independent
Engagement
Commitment
Overall
1
M
30
B
N
2.5
3.75
2.5
2.25
3
2.8
3.75
2.5
6
2
F
28
A
H
2.25
3
3
4
2.25
2.9
2.25
2.25
4
I recommend that you keep a “clean” copy of this revised/scaled data set saved separately. You will need to sort the data several times for the following analyses. Begin each analysis with a newly copied version of the scaled data set. Remember, when you sort the data to sort ALL of the data set, not just the variable of interest.
Descriptive Analysis: Describe the data using basic statistics and percentages. For example, 64% of the observations are Male. The average Overall Satisfaction rating (dependent) is 6.58. Etc. Be thorough and use charts, graphs, and tables. Discuss your findings.
Correlation analysis: Determine the correlation between each of the 6 independent measures and the three dependent measures (Work & engagement, work & commitment, work & overall, etc). Also, compute the correlation between age and the three dependent variables. Discuss your results.
Gender analysis: Is there a statistically significant difference between the genders? Use Excel’s t-test function to test for differences in each of the 9 variables. This will be a two-tailed hypothesis test: Ho: Wf - Wm = 0; Ha: Wf – Wm 0. In case you’ve forgotten how to use the Excel function, there’s an illustration below. Discuss your results.
Age analysis: Is there a statistically significant difference between age and the dependent variables? Sort each of the dependent data columns into the following age groups: 29, 30-39, 40-49, 50-59, and 60-69. Use Excel’s Anova-Single Factor to determine if there is a statistically significant difference across ages. Use the example below (for engagement) to set up your data for analysis. Discuss your results.
29
30-39
40-49
50-59
60-69
2.5
3.75
4.25
5
5
2.5
3.75
4.25
5
4.25
2.5
2.25
4.25
5
Etc.
Etc.
Etc.
Etc.
Education analysis: Can we conclude that there is a statistically significant difference between level of education and the dependent variables? Sort and set up the data as you did for the age analysis. Initially, conduct an Anova analysis to determine if there is a difference across education levels. If there is, then test the following null hypotheses (t-test): Es-Eh 0; Ea-Eh 0; Eb-Eh 0; Eb-Ea 0; Eg-Eb 0 (see the t-test in problem 3). Discuss your results. Comment on whether education level appears to be positively or negatively correlated to each dependent variable.
Employment Level analysis: Repeat the analysis above (ANOVA) using level of employment as the categorical variable. Use comparable pairings for the t-tests. Comment on whether employment level appears to be positively or negatively correlated to each dependent variable.
Interdependence: Create a 3 x 2 matrix by further categorizing the variables. For the dependent variable, Engagement, create two categories: unengaged and fully engaged. Code the data as follows: U if the value is < 3.5; E if the value is 3.5. Pick an independent variable (other than overall because it is used below) and code it as follows: L (low) if it is < 3; M (medium)if it is equal to or above 3 but less than 4; H(high) if it is 4. Once you have the data coded, organize it into a contingency table. You can use Excel’s pivot table to do this, but if you are unfamiliar with how the function works, it might be faster to manually count the number of times that you see an M and an E together, for example. The table below illustrates the steps and final table you will need. You are finally ready to do some analysis! Conduct a chi-squared analysis to determine if the dependent variable, Engagement, is independent from the independent variable you have chosen. You will need to repeat this analysis with the other dependent variable, Commitment, but you can use the same independent variable. Code Commitment as follows: N (for not committed) if the value is < 3; C if the value is 3. Discuss your results.
Observation
Overall Indpdnt
Engagement
Overall Indpdnt
Engagement
1
2.8
3.75
H
E
E
U
2
2.9
2.25
H
E
L
10
31
3
3.55
4.25
H
E
M
39
8
4
3.75
5
H
E
H
16
0
Conclusions: Summarize what you learned from the above analyses about Job Satisfaction, its dimensions and its relationship to engagement, commitment, and overall perceived
Observation
Gender
Age
Education
Level
Work
Pay
Supervision
Co-worker
Opportunities
Overall independent
Engagement
Commitment
Overall
1
M
30
B
N
2.5
3.75
2.5
2.25
3
2.8
3.75
2.5
6
2
F
28
A
H
2.25
3
3
4
2.25
2.9
2.25
2.25
4
Explanation / Answer
This is a test answer which is posted only for internal testing purposes. Please ignore it. Sorry for the inconveniencce. This is a test answer. This is a test answer.
Related Questions
drjack9650@gmail.com
Navigate
Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.