This paper presents the details of an effort by Indiana State University to apply multiple regression analysis to the problem of eliminating faculty salary inequities. The services of a consultant were used to develop a multiple regression model to predict an individual's salary while excluding any independent variable addressing race or gender. Residuals were then computed by gender, gender by rank, race, and race by rank. The results showed no evidence of a consistent pattern of salary bias due to gender or race. The consultant's analysis appeared somewhat contradictory when considering salary compression in that a variable representing the number of internal promotions (a previously undocumented measure of compression) was statistically significant in the model, even though the analysis stated that no evidence of significant compression existed. After much discussion, the internal promotions variable was dropped. The revised model was run and residuals analysis was used to verify the presence of significant salary compression. The residuals became an important consideration in the adjustment of salaries to address the compression problems. The use of the model demonstrates how quantitative models can aid in addressing salary inequities and improve faculty salary allocation decisiorw by combining a data-driven approach with more traditional peer evaluation methods.

Manufacturing and service supervisors share many similarities in their day‐to‐day activities, their beliefs about employees and their concern for doing a good job. However some fundamental differences in attitudes are revealed by the National Survey of Supervisory Management Practices which surveyed over 8,000 supervisors in 564 different organisational units in 37 states in the USA. Service supervisors perceive themselves as having more authority, stronger management support and a broader and better developed range of skills. They also have stronger career aspirations. Manufacturing supervisors are less positive. The majority believe that people gain most of their satisfaction from non‐work activity. Few believe that good work results in promotion. They are inclined to accept traditional ways of doing things and less inclined to be confident with newer ways of management. The implications for manufacturing companies in the future are that they must invest in increasing the skills and confidence of their current supervisors, provide more attractive career paths in manufacturing management and recruit new talent from universities and in‐house.

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