Multidimensional Scaling

This chapter introduces the critical MDS statistical models, and then illustrates them with actual applications, and concludes that further improvements are needed in the technique itself and in the application of the method.

Abstract

Multidimensional scaling (MDS) is a versatile technique for understanding and displaying the structure of multivariate data. This technique has seen wide application in the behavioral sciences and has led to increased understanding of complex psychological phenomena. MDS has been used to assess cognitive developmental theories, study interracial relations among children, determine consumer preferences, and evaluate the dimensional structure and content validity of tests and questionnaires. This chapter begins by describing the types of data to which MDS is applied and then MDS itself. Illustrative applications to direct and derived proximity data are then presented. This is followed by a discussion of the relationships between MDS and other multivariate techniques. Moreover, the statistical aspects of MDS are often difficult to grasp because several features critical to understanding MDS are not typically covered in traditional statistics courses. This chapter introduces the critical MDS statistical models, and then illustrates them with actual applications. Finally, it concludes that further improvements are needed in the technique itself and in the application of the method.