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 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:
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1.
Evaluate data using more complex statistical techniques such as factor analysis
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2.
Explain the difference between factor and cluster analysis
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3.
Describe the characteristics of principal component analysis and principal factor analysis
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4.
Apply especially the principal component analysis and explain the results
Ultimately, the reader will be called upon to propose well-thought-out and practical business actions from the statistical results.
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5.
Distinguish between feature selection and feature reduction.
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Wendler, T., Gröttrup, S. (2021). Factor Analysis. In: Data Mining with SPSS Modeler. Springer, Cham. https://doi.org/10.1007/978-3-030-54338-9_6
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DOI: https://doi.org/10.1007/978-3-030-54338-9_6
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