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Comparative Study of Alternatives Analysis of Incomplete Disjunctive Tables

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  • Zárraga Castro, María Amaya
  • Goitisolo Lezama, Beatriz

Abstract

[EN] Multiple Correspondence Analysis (MCA) studies the relationship between several categorical variables defined with respect to a certain population. However, one of the main sources of information are those surveys in which it is usual to find a certain number of absent data and conditioned questions that do not need to be answered by the whole population. In these cases, the data codification in a complete disjunctive table requires the inclusion of non-answer categories that can alter the results.

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Paper provided by Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística) in its series BILTOKI with number 2000-08.

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Date of creation: May 2000
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Handle: RePEc:ehu:biltok:200008

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Postal: Dpto. de Econometría y Estadística, Facultad de CC. Económicas y Empresariales, Universidad del País Vasco, Avda. Lehendakari Aguirre 83, 48015 Bilbao, Spain
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Related research

Keywords: multiple correspondence analysis; valores propios; incomplete disjunctive table; independence between categorical variables; eigenvalues; percentages of inertia; análisis de correspondencias múltiples; tabla disyuntiva incompleta; independencia entre variables cualitativas; tasas de inercia;

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  1. Michael Greenacre, 2008. "Correspondence analysis of raw data," Economics Working Papers 1112, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2009.
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