A Company Hires You to Help Them Understand Turnover in Their Organization

PROJECT #2

A company hires you to help them understand who advances in their organization. Over the past several years they have been maintaining records to try and determine which new Associates are promoted to Senior Associate within one year of working for their organization. Use the data they have collected to predict whether employees will be promoted or not using the following predictor variables: job satisfaction, need for achievement, extraversion, age and sex. You should run the analyses and write it up using both binary logistic regression and discriminant function analysis, and hand in two separate short write-ups for each analysis as follows. After you have completed that, please answer the questions in section #3 comparing the two techniques.

1. Logistic Regression. Write up the results of this analysis according to convention for logistic regression (refer to your examples for ideas). In your write-up make sure to answer the following questions: a. What percent of cases would be classified correctly (promote/no promote) using the null model? How much improvement is there in the classification with the addition of the four predictors? b. Which of the five predictors uniquely predict group membership? How do you know this? (Note: Discuss Wald test). How much do these contribute to the prediction of group membership? (Note: Discuss odds ratio) c. What is the estimated percent of variance that the four predictors account for in the promotion process?

2. Discriminant Analysis. Write up the results of this analysis according to convention for discriminant analysis (refer to your examples for ideas). In your write-up make sure to answer the following questions: a. Are the assumptions of discriminant analysis met? (Hint: Box’s M test). If not, what are the implications of this? b. When using discriminant analysis, how well can you classify group membership (promoted/not promoted) based on the five predictors? c. Is the discriminant function that was extracted useful for prediction of group membership? How do you know? (Hint: Wilks lamba) d. Use discriminant loadings and weights to describe the discriminant function that was extracted. Which variables make up the discriminant function? e. Plot the group centroids on the discriminant function. Interpret what this graph means in terms of the prediction of group membership.

3. Logistic Regression vs. Discriminant Analysis. a. Compare and contrast the results you got with logistic regression and those you got with discriminant analysis. How are these results similar or different? b. If you had to choose one of these techniques to report the results of this data analysis and submit for publication, which would it be and why?