Question
Conduct a Forward: LR logistic regression analysis for the following variables: IV- age, educ, hrsl, sibs, rincom91, life2 (categorical) DV- satjob2 Note: The variable life2
Conduct a Forward: LR logistic regression analysis for the following variables:
IV- age, educ, hrsl, sibs, rincom91, life2 (categorical)
DV- satjob2
Note: The variable life2 is categorical such that dull=1, routine/exciting=2, and all other values are systems missing.
1.Develop a research question for the following scenario.
Can job satisfaction be predicted by the respondent's age, education level, number of hours worked last week, number of siblings, income, and perception of life outlook affect job satisfaction?
2.Conduct a preliminary Linear Regression to identify outliers and evaluate multicollinearity among the five continuous variables. Complete the following:
a.Using Chi-Square table in Appendix B near the end of this book, identify the critical value at p<.001 for identifying outliers. Use Explore to determine if there are outliers. Which cases should be eliminated?
Case #s 50, 406, 121, 689, and 1129 can be eliminated.
b.
Descriptive Statistics
Mean
Std. Deviation
N
Job Satisfaction
1.57
.496
571
Highest Year of School Completed
13.92
2.741
571
Age of Respondent
40.77
12.137
571
Number of Hours Worked Last Week
43.10
14.418
571
life2
1.9702
.17011
571
RESPONDENTS INCOME
13.39
5.313
571
NUMBER OF BROTHERS AND SISTERS
3.44
2.784
571
c.
d.Is multicollinearity a problem among the five continuous variables?
None of the values are .1 or below, therefore multicollinearity is not a problem.
3.
Conduct Binary logistic Regression using the Forward: LR method.
IV- age, educ, hrsl, sibs, rincom91, life2 (categorical)
DV- satjob2
Note: Make sure the outliers identified in Exercise 2.a. are removed from data before running the logistic regression. Also, determine life2 as a categorical covariate with the last category as the reference essentially makes routine/exciting=0, and dull= 1, so interpret the results accordingly.
a.Which variables were entered into the model?
b.To what degree does the model fit the data?
c.Is the generated model significantly different from the constant-only model?
d.How accurate is the model in predicting job satisfaction?
e.What are the odds ratios for the model variables? Explain.
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