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.