1. TAIR 2008 Quick and Painless Strategies for Evaluating Faculty Salaries Maureen Grimes Croft, Ph.D. Office of Institutional Research

2. The Law and Order Disclaimer The following data is fictional and does not depict any actual person or event. Any resemblance to UH faculty, implied or otherwise, is purely a coincidental.

3. Objectives Demonstrate how to plot salaries and interpret their relative placement using OLS regression. Demonstrate how to benchmark salaries against OSU data using the Compa-Ratio. Demonstrate how to combine the techniques to create and interpret a scatterplot of Compa-Ratios which have the ability to transcend rank and discipline. Demonstrate how to take advantage of the Compa- Ratio’s interval level qualities to learn more about the data.

4. Common Concerns for Faculty and Administration Salary Compression:  An unusually small salary differential between faculty with different levels of experience.  When the salary differential between junior and senior faculty is smaller than it should be. Salary Inversion:  When salary junior faculty salaries are greater than senior faculty salaries. Disparity Among Groups Reflecting Potential Bias

5. Using Scatterplots to Diagnose Potential Problems 1. Obtain Years Since Terminal Degree (IV). 2. Normalized Salary from CBM008 (DV). 3. Obtain Y-Intercept and Slope through Ordinary Least Squares (OLS) Regression. 4. Fit the Regression Line to a Plot of IV and DV.

6. Salary Inversion?

7. Male-Dominated Field?

8. Inequality with Recent Hires?

9. Influential Outlier? (Hint: Run a Cook’s D)

10. Salary Compression Can Be Difficult to Diagnose

11. Identifying Salary Differentials with the Compa-Ratio Compa-Ratio: The ratio of an employee's actual salary (the numerator) to the midpoint of the applicable salary range (the denominator).

12. About the Compa-Ratio The Compa-Ratio is an effective tool for benchmarking how an individual, department or organization is paid relative to the market for comparable jobs. A Compa-Ratio of less than 1 indicates that an individual is paid less than market rate. A Compa- Ratio of greater than 1 indicates that an individual is paid more than market rate. Bereman and Scott (1991) first proposed using the Compa-Ratio in higher education to detect bias in faculty salaries.

13. Compa-ratio Expressed as a Formula

14. Snapshot of OSU Data

15. Combining the Techniques 1. Compute a Compa-Ratio for all faculty. (Hint: Normalized Salary/OSU Average) 2. Plot X = Years Since Degree Y = Compa-Ratio 3. Additional information can be obtained by taking advantage of the interval level qualities of the Compa-Ratio.

16. Plotting the Compa-ratio Mean = 1.08 Median = 1.05 SD =.24

17. Advantages to this Approach The Compa-Ratio already has controlled for rank and discipline, and this makes faculty salaries across ranks/disciplines comparable. Compa-Ratio plots allow one to distinguish over time whether bias has been present in salaries while keeping the influence of rank and discipline constant. Treating the Compa-Ratio as an interval level variable allows one to obtain a mean and a standard deviation. Outliers can be easily detected across ranks and disciplines.

18. Considerations Attend to OLS assumptions of independence, linearity, normality and homogeneity of variance when fitting salaries to a line. The definition of compression and inversion may vary between colleges/departments and should be considered in light of the market.  Example: Under the General Business CIP code 52.0000, the average salaries for Assistant, Associate and Full professors in Carnegie II institutions are $95,775, $95,493 and $121,685.

19. Considerations One can glean information about market trends by attending to new professor salaries. Data begins to look curvilinear when professors past retirement age are included in the scatterplots. Compression is present when inversion is present in the data. The Compa-Ratio can be computed using other sources of data.

20. Demonstration