Scaling and Cluster Analysis

the classification of objects into categories (Bailey, 1994). To classify an object as a member of a category, we must have explicit or implicit understanding about how the objects can be similar or different. For example, some persuasive strategies use references to authority (e.g., experts, parental figures), whereas others may reference friends. Are these similar kinds of strategies? How would a researcher know how a person classifies persuasive strategies? As one might imagine, knowing how we classify is more complex than knowing that we classify. This chapter covers how to use scaling and clustering statistical methods—to address questions of interest to communication researchers. Borg and Groenen (1997) identified several goals of scaling and clustering procedures (see also Aldenderfer & Blashfield, 1986; Bailey, 1994; Kruskal & Wish, 1978). One goal is to simplify and describe data. All scaling and clustering methods help researchers reduce the complexity of a data set by identifying the underlying structure within a set of data. As an example, Wish (1976) wanted to know how people perceive different kinds of interpersonal situations. Using multidimensional scaling, he discovered four dimensions along which interpersonal situations varied: (1) cooperative and friendly versus competitive and hostile, (2) equal versus unequal, (3) socioemotional and informal versus task-oriented and formal, and (4) intense versus superficial. Another example is a study by Hamilton and Nowak (2005) tracing the development of scholarship within the Information Systems Division (Division 1) of the International Communication

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