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Correspondence Analysis

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Modern Psychometrics with R

Part of the book series: Use R! ((USE R))

Abstract

Correspondence analysis (CA) aims to scale the row and column categories of a contingency table in a low-dimensional space. In this space basic structures and associations among the row categories and among the column categories are represented. The basic distinction we make in CA is simple CA vs. multiple CA. Simple CA involves two categorical variables only, whereas multiple CA is used for higher-dimensional frequency tables. In this chapter we present the French approach to CA which tackles the CA problem analytically, whereas in the next chapter, we use a numerical approach. At the end of this chapter, we present a method called configural frequency analysis which can be used to investigate CA outputs in more detail.

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Notes

  1. 1.

    We have to be careful with the term “variance” within a CA context since we are dealing with categorical data.

  2. 2.

    The reader can try this out by saying anacor( fit_chisq$expected) .

  3. 3.

    See http://psychology.fas.harvard.edu/faculty

  4. 4.

    Only the most frequent keywords are considered for this analysis.

  5. 5.

    Note that in order to avoid confusion with confirmatory factor analysis (CFA), we use the acronym KFA introduced by its inventor Gustav Lienert who called his baby by the catchy name of Konfigurationsfrequenzanalyse.

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Authors and Affiliations

  1. Department of Psychology, Harvard University, Cambridge, MA, USA

    Patrick Mair

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  1. Patrick Mair
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Mair, P. (2018). Correspondence Analysis. In: Modern Psychometrics with R. Use R!. Springer, Cham. https://doi.org/10.1007/978-3-319-93177-7_7

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