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Multivariate Analysis of Ordinal Variables

Bibliographic Data

ID7620994
AuthorsJae-On Kim (corresponding author)
Year1975
Volume81
Issue2
Pages261-298
Publication date1975-09-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueAmerican Journal of Sociology (JOURNAL)
Journal identifiersISSN: 0002-9602 • E-ISSN: 1537-5390
PublisherUniversity of Chicago Press (PUBLISHER • US)
DOI10.1086/226074
OpenAlexW2054239797
LanguageEN
Citations received62
References cited38

This article examines the assumptions underlying two multivariate strategies commonly used in analyzing ordinal data. Both strategies employ as a descriptive tool the ordinary multiple regression algorithms; the crucial difference between the two is that the first, ordinal strategy, uses the matrix of Kendall's 's as the building block of multivariate analysis, while the second, parametric strategy, uses the matrix of Pearson's 's. These two strategies are evaluated and constrasted in terms of their usefulness in answering basic research questions that arise in multivariate analysis. One overriding conclusion is that, contrary to the claims of its proponents, the ordinal strategy is no better than the parametric strategy at meeting some of the basic requirements of multivariate analysis. It is argued that parametric strategy, when accompanied by careful evaluation of the validity of the implict quantification of ordinal variables, is more amenable to one of the goals of scientific research: successive approximation and refinement

Econometrics · Multivariate analysis · Multivariate statistics · Ordinal data · Ordinal regression · Ordinal Scale · Parametric statistics · Statistics · Advanced Statistical Methods and Models · Computer Science · Mathematics · Statistical and Computational Modeling · Statistical Methods and Applications

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Unique citing works62
Citations per year1,27
Citation span1977 - 2022 (46)
Citation velocityhistorical
Highly citedNo
Citation typesNeutral: 54

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