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Factor Analysis

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Data Mining with SPSS Modeler

Abstract

Factor analysis is used to reduce the number of variables in a dataset, identify pattern, and reveal hidden variables. Various types of factor analysis are similar, in terms of calculating the final results. The steps are generally the same but the assumptions, and therefore the interpretation of the results, are different. This chapter presents the key idea of factor analysis. Statistical terms are discussed if they are necessary for understanding the calculation and helpful to interpret the results.

After finishing this chapter, the reader is able to …

  1. 1.

    Evaluate data using more complex statistical techniques such as factor analysis

  2. 2.

    Explain the difference between factor and cluster analysis

  3. 3.

    Describe the characteristics of principal component analysis and principal factor analysis as well as

  4. 4.

    Apply especially the principal component analysis and explain the results

Ultimately, the reader will be called upon to propose well thoughtout and practical business actions from the statistical results.

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Author information

Authors and Affiliations

  1. HTW Berlin, University of Applied Sciences, Berlin, Germany

    Tilo Wendler

  2. Stuttgart, Germany

    Sören Gröttrup

Authors
  1. Tilo Wendler
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  2. Sören Gröttrup
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Wendler, T., Gröttrup, S. (2016). Factor Analysis. In: Data Mining with SPSS Modeler. Springer, Cham. https://doi.org/10.1007/978-3-319-28709-6_6

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