The last section of Part II (see “BSF #6 Guidelines” on the left side of this site) has you analyze the potential for employment discrimination at IBM using hypothetical employee data and an SPSS procedure that produces a multiple regression model based on the data. We have been reviewing the relationship between correlation (r and r2) and regression (R, R2 and 1-R2) in class, through lectures, blog .pdfs and a PowerPoint presentation (also found on the left side of the site), and have integrated SPSS procedures into the discussion. Though an important part of the discussion has centered on what to do when the numbers are in, I thought I’d continue this part of our in-class dialogue here to help sharpen your data analytic skills.
Specifically, you are to test and analyze the following regression equation and include the implications in the recommendations section written for your boss at IBM:
From this equation, the level of education was found to be the only independent variable with a significant impact on employee salary (b=.673, p<.000) when all of the variables were entered into the regression equation. We found that the higher the level of employee education at IBM, the greater the salary level. The employee’s age, previous experience in months, and time on the job since being hired did not meet the necessary criteria to significantly impact employee salary, so they played no role at this stage of the analysis. We should note that an employee’s previous experience (measured in months) did approach significance (b=.107, p=.06).
I hope this helps!