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Behaviormetrika
Article . 1976 . Peer-reviewed
License: Springer TDM
Data sources: Crossref
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The Analysis of Categorical Data

Application of Three Methods to Political Participation
Authors: Oishi, Koichiro;

The Analysis of Categorical Data

Abstract

Some aspect of plitical participation in Japan is the subject of this paper. Three methods are used to analyse categorical data : D-systems analysis, path analysis using coded “values”, and odds ratios with hierarchical modeling. The first two construct causal systems and pretend no interaction. The last one does not assume causal order or does not mind interactions at all. Women in Japan have disadvantage in socio-economic status, which increases the inherently negative attitude toward political interest. Therefore, political participation is lower on the part of women. The interrelationship among political interest, electoral activities, and party support is shown. That questions posed and answered are of multivariate nature implies the effectiveness of the methods.

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Keywords

D-systems analysis, multivariate, path analysis, odds ratio, categorical data, hierarchical modeling, political participation

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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!
0
Average
Average
Average
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