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Statistics in a Nutshell, 2nd Edition by Sarah Boslaugh

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Chapter 12. Factor Analysis, Cluster Analysis, and Discriminant Function Analysis

There are more statistical techniques in use today than could possibly be covered in a single book. In fact, there are more types of statistics out there than anyone could hope to master in a lifetime. However, it’s often useful to be familiar with a technique, even if you don’t know how to perform it yourself. You might need to read articles using techniques you have not mastered yourself, for instance, or you might decide you need to learn a technique or hire a consultant familiar with the technique after reading about how it was used in someone else’s research. This chapter introduces several advanced statistical techniques by providing some specific examples of how they have been used; the techniques themselves will not be taught because the intent is to help the reader identify when one of these techniques is appropriate for a given research question. Methodologies covered in this chapter include factor analysis, cluster analysis, and discriminant function analysis.

Factor Analysis

Factor analysis (FA) uses standardized variables to reduce data sets by using principal components analysis (PCA), the most widely used data reduction technique. It is based on an orthogonal decomposition of an input matrix to yield an output matrix that consists of a set of orthogonal components (or factors) that maximize the amount of variation in the variables from the input matrix. This process usually produces a ...

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