What is Triangulation of Method

This chapter explains the variations of DEA with visual examples and test data in a way that can be understood by business practitioners, students, and stakeholders who are not mathematicians. DEA input and output-focused approaches are discussed along with constant returns to scale, variable returns to scale, and increasing and decreasing returns to scale. Radial movements and slack estimates are explained to achieve the efficient frontier. A small example dataset is used, and a larger real-world secondary higher education dataset is processed from the U.S. Department of Education. DEA is compared with nonparametric statistics. This chapter also exposes the problems of using DEA and how to use methods and data triangulation to check reliability and validity. The most surprising finding was that the research questions could not be answered, and independent data revealed some paradoxically contrasting results for some of the sample data cases. The chapter will interest managers, decision-makers in any industry, students, government regulators, and researchers.