Methods for quantifying ordinal variables: a comparative study

Methods for quantifying ordinal variables: a comparative study The solution to the problem of ‘quantification’ or scoring, i.e., assigning real numbers to the qualitative modalities (categories) of an ordinal variable, is of primary relevance in data analysis. The literature offers a wide variety of quantification methods, all with their pros and cons. In this work, we present a comparison between an univariate and a multivariate approach. The univariate approach allows to estimate the category values of an ordinal variable from the observed frequencies on the basis of a distributional assumption. The multivariate approach simultaneously transforms a set of observed qualitative variables into interval scales through a process called optimal scaling. As an example of application, we consider the Bank of Italy data coming from the “Survey on Household Income and Wealth” in order to ‘quantify’ a self-rating item of happiness. A simulation study to compare the performance of the two approaches is also presented. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

Methods for quantifying ordinal variables: a comparative study

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Publisher
Springer Netherlands
Copyright
Copyright © 2014 by Springer Science+Business Media Dordrecht
Subject
Social Sciences, general; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-014-0063-2
Publisher site
See Article on Publisher Site

Abstract

The solution to the problem of ‘quantification’ or scoring, i.e., assigning real numbers to the qualitative modalities (categories) of an ordinal variable, is of primary relevance in data analysis. The literature offers a wide variety of quantification methods, all with their pros and cons. In this work, we present a comparison between an univariate and a multivariate approach. The univariate approach allows to estimate the category values of an ordinal variable from the observed frequencies on the basis of a distributional assumption. The multivariate approach simultaneously transforms a set of observed qualitative variables into interval scales through a process called optimal scaling. As an example of application, we consider the Bank of Italy data coming from the “Survey on Household Income and Wealth” in order to ‘quantify’ a self-rating item of happiness. A simulation study to compare the performance of the two approaches is also presented.

Journal

Quality & QuantitySpringer Journals

Published: Jul 25, 2014

References

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