Decomposition of the main effects and interaction term by using orthogonal polynomials in multiple non symmetrical correspondence analysis

Antonello D'Ambra, Pietro Amenta and Anna Crisci

Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 20, 10179-10188

Abstract: The multiple non symmetric correspondence analysis (MNSCA) is a useful technique for analyzing a two-way contingency table. In more complex cases, the predictor variables are more than one. In this paper, the MNSCA, along with the decomposition of the Gray–Williams Tau index, in main effects and interaction term, is used to analyze a contingency table with two predictor categorical variables and an ordinal response variable. The Multiple-Tau index is a measure of association that contains both main effects and interaction term. The main effects represent the change in the response variables due to the change in the level/categories of the predictor variables, considering the effects of their addition, while the interaction effect represents the combined effect of predictor categorical variables on the ordinal response variable. Moreover, for ordinal scale variables, we propose a further decomposition in order to check the existence of power components by using Emerson's orthogonal polynomials.

Date: 2017
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DOI: 10.1080/03610926.2016.1231817

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