The analysis of structured qualitative data

Carlo Lauro and Simona Balbi

Applied Stochastic Models and Data Analysis, 1999, vol. 15, issue 1, 1-27

Abstract: The aim of this paper is to give an overview of the methodological contribution given by Italian researchers in introducing a priori information into multidimensional data analysis techniques, paying special attention to categorical variables. The basic method is Non‐Symmetrical Correspondence Analysis, which enables the analysis of a contingency table when the behaviour of one variable is supposed to be dependent on the other cross‐classified variable. As usual correspondence analysis decomposes an association index (Pearson's Φ2), in a principal component sense, the proposed method is based on a decomposition of a predictability index (Goodman and Kruskal's τb). Non‐symmetrical correspondence analysis has been extended to more than one dependent/explanatory variable(s), by means of proper flattening procedures, i.e. by the use of multiple tables, and the decomposition of Gray and Williams' multiple and partial τb's. In doing so multiple and partial versions have been proposed. A forward selection procedure for choosing the variables with higher predictive power is presented. After a brief review of non‐symmetrical correspondence analysis confirmatory approach, the problem of validating results in terms of analytical stability and replication stability is faced by means of influence functions and resampling techniques. Copyright © 1999 John Wiley & Sons, Ltd.

Date: 1999
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https://doi.org/10.1002/(SICI)1099-0747(199903)15:13.0.CO;2-F

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