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Iterative Approaches to R × C Ecological Inference Problems: Where They Can Go Wrong and One Quick Fix

Published online by Cambridge University Press:  04 January 2017

Karen E. Ferree
Karen E. Ferree*
Affiliation:
Department of Political Science, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0521. e-mail: [email protected]
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Abstract

This article argues that a key step in King's iterative approach to R × C ecological inference problems—the aggregation of groups into broad conglomerate categories—can introduce problems of aggregation bias and multimodality into data, inducing model violations. As a result, iterative EI estimates can be considerably biased, even when the original data conform to the assumptions of the model. I demonstrate this problem intuitively and through simulations, show the conditions under which it is likely to arise, and illustrate it with the example of Coloured voting during the 1994 elections in South Africa. I then propose an easy fix to the problem, demonstrating the usefulness of the fix both through simulations and in the specific South African context.

Type
Research Article
Information
Political Analysis , Volume 12 , Issue 2 , Spring 2004 , pp. 143 - 159
Copyright
Copyright © Society for Political Methodology 2004 

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