Editorial
Special issue on correspondence analysis and related methods

https://doi.org/10.1016/j.csda.2008.11.010Get rights and content

Section snippets

Correspondence analysis

In “Power transformations in correspondence analysis”, Greenacre (2009) shows two different ways of introducing power transformations into correspondence analysis. Either the original data are powered and then regular CA is performed (with margins that change depending on the transformed data), or the so-called contingency ratios (i.e., observed divided by expected values) are power-transformed and the CA algorithm applied while maintaining the original row and column margins. In the limit as

Methods related to correspondence analysis and principal components analysis

In “Better biplots”, Blasius et al. (2009) discuss different new possibilities of visualizing biplots stemming from principal component analysis. Thereby, the elements of a biplot are a set of axes representing variables that are usually concurrent at the centroid of a set of points representing cases. The axes are approximations to conventional coordinate axes that can be labeled and calibrated. When there are many points, say several thousands, the whole effect can be very confusing. As the

Multidimensional scaling and unfolding methods

In “Trend vector models for the analysis of change in continuous time for multiple groups”, de Rooij (2009) looks at multinomial response data over time, from a modeling perspective. The probability that an individual at a particular time is in a particular group is related inversely to the distance between an individual point and a category point, where these points lie in a low dimensional space. Furthermore, explanatory variables may be available for the individuals at all time points, and

Book reviews

Correspondence analysis and related methods now constitute a wide area of statistical theory and practice. Over the last few years, several books have appeared dealing with these topics. We found it useful to include short reviews of the five most recent books on correspondence analysis and related methods. These reviews may help readers interested in correspondence analysis as regards further reading, be it more application oriented or more statistical and computational. These books, their

Acknowledgements

We would like to thank all of the expert reviewers who helped with selecting the papers in the present issue of CSDA. The initiative to have this special issue arose during the conference Correspondence Analysis and Related Methods 2007, Rotterdam, The Netherlands.

References (20)

There are more references available in the full text version of this article.

Cited by (15)

  • Sustainable industries: Production planning and control as an ally to implement strategy

    2021, Journal of Cleaner Production
    Citation Excerpt :

    For each variable, the distance between its categories is reflected in a graph, so that it is possible to analyze the joint graphical representation of a large number of variables and identify similar categories located next to each other; the graphical axles identify the dimensions found in the data (Blasius et al., 2009; Pestana and Gageiro, 2008). The results come from the analysis of visualization of columns and rows of the data table in a usually two-dimensional graphics generated, called map (Blasius and Schmitz, 2015; Blasius et al., 2009; Carvalho, 2004). Due to the standardization used in MCA, the chi-square distance, it can be used to analyze small to large samples (Blasius et al., 2009).

  • Technology engagement and privacy: A cluster analysis of reported social network use among transport survey respondents

    2016, Transportation Research Part C: Emerging Technologies
    Citation Excerpt :

    Once any groups were identified using HCA, Multi-criteria Analysis (MCA) (Greenacre, 2007) was used to create composite variables for use in the cluster analysis. Several ways of considering MCA exist in the literature (Blasius et al., 2009; Greenacre, 2007). The approach used by SPSS is referred to as homogeneity analysis, and is based on the correlation of the categories chosen for each variable by each respondent (IBM Corporation, 2011; Greenacre, 2007).

  • Correspondence analysis in practice, third edition

    2017, Correspondence Analysis in Practice, Third Edition
View all citing articles on Scopus
View full text