Data reduction techniques and hypothesis testing for analysis of benchmarking data

This paper proposes a data reduction and hypothesis testing methodology that can be used to perform hypothesis testing with data commonly collected in benchmarking studies. A reduced-form performance vector and reduced-form set of decision variables are constructed using the multivariate data reduction techniques of principal component analysis and exploratory factor analysis. Reductions in dependent and exogenous variables increase the available degrees of freedom, thereby facilitating the use of standard regression techniques. We demonstrate the methodology with data from a semiconductor production benchmarking study.