Predicting the annual salaries of construction educators using multiple regression
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The development of a mathematical model to predict the annual salaries of construction educators is presented. A review of the literature identified a number of factors that are hypothesized to affect the annual salary of construction educators; academic qualifications, longevity, academic rank, parent college of the department, region in which the institution is located and gender. The responses from the annual ASC Faculty Salary Survey were used to develop a multiple regression model that predicts the annual 9-month salary of a construction educator. The stepwise selection method was used to select seven independent qualitative or dummy variables to include in the model. The model developed does not have a very high predictive efficacy as only 51 percent of the variation in the dependent variable (annual 9-month salary) is explained by the variation in the selected independent variables. The variables selected for the model includes levels of academic rank, academic qualifications, region in which the institution is located and parent college of the department. Independent variables representing longevity and gender were not included in the model.