U.S. News Rankings: An Analytical Approach With Multivariate Calculus

Session Abstract: This session presents a new analytical model using multivariate calculus to examine the complexities of U.S. News rankings. Recent changes in ranking formulas have caused universities to experience shifts in standings without corresponding institutional changes. Traditional reverse engineering methods to analyze the ranking face challenges like incomplete data, unclear processes, and model instability. Our model focuses on key indicators that are accessible and reliable, using partial derivatives to analyze their impacts on overall rankings. Statistical techniques are applied to enhance the accuracy of the model based on the distribution of the data. By bypassing unreliable indicators, the model improves stability and effectiveness and offers more clarity and actionable insights for institutional planning. The presentation reviews traditional methods and demonstrates the model construction, ranking sensitivity analysis and discussion of how it can be adapted to other ranking systems.

Keywords: U.S. News ranking, sensitivity analysis, multivariate calculus, institutional planning