Document Type

Article

Publication Date

11-7-2019

Abstract

Purpose

The objective of this study was to develop and implement a simple and flexible mathematical model to generate merit-based salary increases as a percentage of the faculty base salaries, with the flexibility to choose the range of merit raises.

Methods

Annual faculty performance scores, faculty base salaries, and available salary increase pool were used in a relatively simple linear model to determine the individual faculty merit raises as a percentage of their base salary. The core model allows the selection of a slope value that determines how steeply the merit raise changes with a change in the performance score. The application of the method to different scenarios, including random and non-random distribution of salaries and performance scores, was also tested. More advanced versions of the core model, where the slope value is calculated based on various criteria, are presented in an appendix. The models were incorporated into spreadsheets, which automatically calculate percent merit raises for different input scenarios.

Results

The developed method successfully estimates percent merit raises for individual faculty to precisely match the available merit pool fund. Additionally, merit raises simulated for scenarios with different slopes indicate that the range of distribution of percent merit raise is directly proportional to the slope, i.e., doubling the slope doubles the difference in the percent merit raises for the faculty with the lowest and highest performance scores. The application of the method to different scenarios indicates that the method is robust and independent of the available merit raise pool or distribution patterns of the salaries and performance scores among faculty.

Conclusion

Faculty merit raises may be easily calculated using a relatively simple model, which may be applied to a variety of cases where flexibility in the degree of distribution of raises is desired.

Comments

NOTICE: this is the author’s version of a work that was accepted for publication in Computers in Biology and Medicine. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers in Biology and Medicine, volume 116, in 2020. https://doi.org/10.1016/j.compbiomed.2019.103533

The Creative Commons license below applies only to this version of the article.

Copyright

Elsevier

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Available for download on Saturday, November 07, 2020

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