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Psychometrika
Article
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Psychometrika
Article . 1985
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Psychometrika
Article . 1985 . Peer-reviewed
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zbMATH Open
Article . 1985
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Correspondence Analysis used Complementary to Loglinear Analysis

Correspondence analysis used complementary to loglinear analysis
Authors: Van der Heijden, P.G.M.; De Leeuw, J.;

Correspondence Analysis used Complementary to Loglinear Analysis

Abstract

Loglinear analysis and correspondence analysis provide us with two different methods for the decomposition of contingency tables. In this paper we will show that there are cases in which these two techniques can be used complementary to each other. More specifically, we will show that often correspondence analysis can be viewed as providing a decomposition of the difference between two matrices, each following a specific loglinear model. Therefore, in these cases the correspondence analysis solution can be interpreted in terms of the difference between these loglinear models. A generalization of correspondence analysis, recently proposed by Escofier, will also be discussed. With this decomposition, which includes classical correspondence analysis as a special case, it is possible to use correspondence analysis complementary to loglinear analysis in more instances than those described for classical correspondence analysis. In this context correspondence analysis is used for the decomposition of the residuals of specific restricted loglinear models.

Country
Netherlands
Related Organizations
Keywords

multidimensional scaling, correspondence analysis, Factor analysis and principal components; correspondence analysis, Contingency tables, decomposition of contingency tables, loglinear analysis

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    selected citations
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    93
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
93
Top 10%
Top 1%
Top 10%
Green
bronze
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