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Correlational Statistics for Characterising Relationships

In: Illustrating Statistical Procedures: Finding Meaning in Quantitative Data

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  • Ray W. Cooksey

    (University of New England, UNE Business School)

Abstract

This chapter discusses and illustrates correlational statistics for characterising relationships. The purposes of the procedures and fundamental concepts reviewed in this chapter are quite varied ranging from providing a simple summary of the relationship between two variables to facilitating an understanding of complex relationships among many variables. A statistical relationship is a pattern or an association which exists between two or more variables. We employ the statistical concept of correlation to summarise, in a single number, the nature of this patterned relationship or association between two variables. No matter how many variables are involved or how sophisticated the analysis is, all correlational procedures depend upon measuring and then analysing the relationships between pairs of variables. In this chapter, you will explore various procedures (e.g. contingency tables, correlation; partial and semi-partial correlation, simple and multiple regression, exploratory factor analysis, cluster analysis, multidimensional scaling and canonical correlation) that can be employed to answer simple or complex relational or associational questions about data like those posed above. In addition, you will find a more detailed discussion of the fundamental concepts of correlation and partial and semi-partial correlation which will provide necessary foundation material for understanding the discussions to come later in the chapter.

Suggested Citation

  • Ray W. Cooksey, 2020. "Correlational Statistics for Characterising Relationships," Springer Books, in: Illustrating Statistical Procedures: Finding Meaning in Quantitative Data, edition 3, chapter 0, pages 141-239, Springer.
    • Handle: RePEc:spr:sprchp:978-981-15-2537-7_6
    DOI: 10.1007/978-981-15-2537-7_6
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