Introduction to Empirical Data Analysis

Klaus Backhaus (), Bernd Erichson (), Sonja Gensler (), Rolf Weiber () and Thomas Weiber
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Klaus Backhaus: University of Münster
Bernd Erichson: Otto-von-Guericke-University Magdeburg
Sonja Gensler: University of Münster
Rolf Weiber: University of Trier

Chapter Chapter 1 in Multivariate Analysis, 2021, pp 1-54 from Springer

Abstract: Abstract This chapter introduces, characterizes and classifies the eight methods of multivariate data analysis (MVA) covered in this book. When using MVA, several variables are considered simultaneously and their relationship is analyzed quantitatively. MVA aims to describe and explain these relationships or to predict future developments. Bivariate analyses that consider just two variables at a time are a special case of MVA. However, reality is usually much more complex and requires the consideration of more than just two variables. Furthermore, this chapter presents the fundamentals of empirical data analysis that are relevant to all methods discussed in the book. Since most readers will be familiar with these basics, these presentations serve primarily as a repetition or as an opportunity to look up important aspects of quantitative data analysis, such as basic statistical concepts (e.g. mean, standard deviation, covariance), the difference between correlation and causality, and the basics of statistical testing. Finally, the handling of outliers and missing values is discussed and the statistical package IBM SPSS Statistics, which is used in this book, is briefly introduced.

Date: 2021
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DOI: 10.1007/978-3-658-32589-3_1

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