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The Uses of Entropy in the Multivariate Analysis of Categorical Variables

Bibliographic Data

ID7519280
AuthorsR Darcy, Hans Aigner
Year1980
Volume24
Issue1
Pages155-155
Publication date1980-02-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueAmerican Journal of Political Science (JOURNAL)
Journal identifiersISSN: 0092-5853 • E-ISSN: 1540-5907
PublisherWiley (PUBLISHER • GB)
DOI10.2307/2110930
OpenAlexW2325546830
LanguageEN
Citations received8
References cited30

Entropy analysis is suggested as a useful technique for the examination of multivariate relationships among categorical variables. It makes no assumptions about the numerical properties of variables nor about the nature of the interrelationships among sets of variables. The technique is able to describe completely the explanatory power of all the constraints within a set of variables and to partition them into the following categories: distributional constraints, internal constraints, external constraints and conditional entropy (unconstrained). After discussing the entropy concept and outlining the statistics, we perform an entropy analysis of Converse's Belief Systems of Mass Publics data, controlling for social groups. The results show that the responses of the mass sample to the several political issue items are as constrained as those of the candidate sample

Categorical variable · Conditional entropy · Converse · Econometrics · Entropy (arrow of time · Multivariate analysis · Multivariate statistics · Principle of maximum entropy · Statistics · Complex Systems and Time Series Analysis · Mathematics · Opinion Dynamics and Social Influence · Statistical Mechanics and Entropy

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Unique citing works8
Citations per year0,17
Citation span1979 - 2023 (45)
Citation velocityhistorical
Highly citedNo
Citation typesNeutral: 8

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