Measuring similarity of concentration between different informetric distributions: Two new approaches.
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| Title: | Measuring similarity of concentration between different informetric distributions: Two new approaches. |
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| Authors: | Burrell, Quentin L.1 q.burrell@lbs.ac.im |
| Source: | Journal of the American Society for Information Science & Technology. May2005, Vol. 56 Issue 7, p704-714. 11p. |
| Subjects: | Theory of distributions (Functional analysis), Classification, Mathematical statistics, Regression analysis, Functional analysis, Statistical correlation |
| Abstract: | From its earliest days, much investigative work in informetrics has been concerned with inequality aspects. Beginning with the well-known Gini coefficient as a measure of the concentration/inequality of productivity within a single data set, in this study we look at the problem of measuring relative inequality of productivity between two data sets. A measure originally proposed by Dagum (1987), analogous to the Gini coefficient, is discussed and developed with both theoretical and empirical illustrations. From this we derive a standardized measure—the relative concentration coefficient—based on the notion of “relative economic affluence” also introduced by Dagum (1987). Finally, a new standardized measure—the co-concentration coefficient, in some ways analogous to the correlation coefficient—is defined. The merits and drawbacks of these two measures are discussed and illustrated. Their value will be most readily appreciated in comparative empirical studies. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of the American Society for Information Science & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 17073850 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Measuring similarity of concentration between different informetric distributions: Two new approaches. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Burrell%2C+Quentin+L%2E%22">Burrell, Quentin L.</searchLink><relatesTo>1</relatesTo><i> q.burrell@lbs.ac.im</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+the+American+Society+for+Information+Science+%26+Technology%22">Journal of the American Society for Information Science & Technology</searchLink>. May2005, Vol. 56 Issue 7, p704-714. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Theory+of+distributions+%28Functional+analysis%29%22">Theory of distributions (Functional analysis)</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+statistics%22">Mathematical statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Functional+analysis%22">Functional analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+correlation%22">Statistical correlation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: From its earliest days, much investigative work in informetrics has been concerned with inequality aspects. Beginning with the well-known Gini coefficient as a measure of the concentration/inequality of productivity within a single data set, in this study we look at the problem of measuring relative inequality of productivity between two data sets. A measure originally proposed by Dagum (1987), analogous to the Gini coefficient, is discussed and developed with both theoretical and empirical illustrations. From this we derive a standardized measure—the relative concentration coefficient—based on the notion of “relative economic affluence” also introduced by Dagum (1987). Finally, a new standardized measure—the co-concentration coefficient, in some ways analogous to the correlation coefficient—is defined. The merits and drawbacks of these two measures are discussed and illustrated. Their value will be most readily appreciated in comparative empirical studies. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of the American Society for Information Science & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/asi.20160 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 704 Subjects: – SubjectFull: Theory of distributions (Functional analysis) Type: general – SubjectFull: Classification Type: general – SubjectFull: Mathematical statistics Type: general – SubjectFull: Regression analysis Type: general – SubjectFull: Functional analysis Type: general – SubjectFull: Statistical correlation Type: general Titles: – TitleFull: Measuring similarity of concentration between different informetric distributions: Two new approaches. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Burrell, Quentin L. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2005 Type: published Y: 2005 Identifiers: – Type: issn-print Value: 15322882 Numbering: – Type: volume Value: 56 – Type: issue Value: 7 Titles: – TitleFull: Journal of the American Society for Information Science & Technology Type: main |
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