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

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  • Ferree, Karen E.

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.

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  • Ferree, Karen E., 2004. "Iterative Approaches to R × C Ecological Inference Problems: Where They Can Go Wrong and One Quick Fix," Political Analysis, Cambridge University Press, vol. 12(2), pages 143-159, April.
    • Handle: RePEc:cup:polals:v:12:y:2004:i:02:p:143-159_00
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    Cited by:

    1. Olga Orlanski & Günther G. Schulze, 2017. "The Determinants of Islamophobia - An Empirical Analysis of the Swiss Minaret Referendum," CESifo Working Paper Series 6741, CESifo.
    2. D. James Greiner & Kevin M. Quinn, 2009. "R×C ecological inference: bounds, correlations, flexibility and transparency of assumptions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 67-81, January.
    3. Matt Barreto & Loren Collingwood & Sergio Garcia-Rios & Kassra AR Oskooii, 2022. "Estimating Candidate Support in Voting Rights Act Cases: Comparing Iterative EI and EI-R×C Methods," Sociological Methods & Research, , vol. 51(1), pages 271-304, February.

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