Like factor analysis (Chapter 19),cluster analysis examines an entire set of interdependent relationships. Cluster analysis makes no distinction between dependent and independent variables. Rather, interdependent relationships between the whole set of variables are examined. The primary objective of cluster analysis is to classify objects into relatively homogeneous groups based on the set of variables considered. Objects in a group are relatively similar in terms of these variables and different from objects in other groups. When used in this manner, cluster analysis is the obverse of factor analysis, in that it reduces the number of objects, not the number of variables, by grouping them into a much smaller number of clusters.
This chapter describes the basic concept of cluster analysis. The steps involved in conducting cluster analysisare discussed and illustrated in the context of hierarchical clustering by using a popular computer program. Then an application of nonhierarchical clustering is presented, followed by the TwoStep procedure and a discussion of clustering of variables
Finally, we discuss the use of software in cluster analysis. Help for running the SPSSand SAS Learning Edition programs used in this chapter is provided in four ways: (1) detailed step-by-step instructions are given later in the chapter, (2)you can download (from the Web site for this book) computerized demonstration movies illustrating these step-by-step instructions, (3)you can download screen captures with notes illustrating these step-by-step instructions, and (4)you can refer to the Study Guide and Technology Manual, a supplement that accompanies this book
To begin, we provide some examples to illustrate the usefulness of cluster analysis.