Data Reduction Techniques and Hypothesis Testing

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 a 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. * John M. Olin School of Business, Washington University in St. Louis, Box 1133, One Brookings Drive, St. Louis MO 63130, USA. E-mail: [email protected]. † Department of Management, School of Business Administration, University of Miami, Coral Gables, FL 331249145, USA. E-mail: [email protected], Tel: 305/284-1086, Fax: 305/284-3655. Please direct correspondence to this author.