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Analyzing Second-Stage Ecological Regressions: Comment on Herron and Shotts

Published online by Cambridge University Press:  04 January 2017

Christopher Adolph  and
Gary King
Christopher Adolph
Affiliation:
Department of Government, Harvard University, Cambridge, MA 02138. e-mail: cadolph@fas.harvard.edu
Gary King
Affiliation:
Department of Government, Harvard University, Cambridge, MA 02138. e-mail: king@harvard.edu
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Extract

We take this opportunity to comment on Herron and Shotts (2003; hereinafter HS) because of its interesting and productive ideas and because of the potential to affect the way a considerable body of practical research is conducted. This article, and the literature referenced therein, is based on the suggestions in three paragraphs in King (1997, pp. 289–290). Because these paragraphs were not summarized in HS, we thought they might be a useful place to start.

Type
Using EI in Second-Stage Regressions
Information
Political Analysis , Volume 11 , Issue 1 , Winter 2003 , pp. 65 - 76
Copyright
Copyright © Political Methodology Section of the American Political Science Association 2003 

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References

Duncan Otis, Dudley, and Davis, Beverly. 1953. “An Alternative to Ecological Correlation.” American Sociological Review 18:665–666.CrossRefGoogle Scholar
Goodman, Leo. 1959. “Some Alternatives to Ecological Correlation.” American Journal of Sociology 64:610–624.Google Scholar
Herron, Michael C., and Shotts, Kenneth W. 2003. “Using Ecological Inference Point Estimates as Dependent Variables in Second-Stage Linear Regressions.” Political Analysis 11:44–64.Google Scholar
Imai, Kosuke, and King, Gary. 2002. “Did Illegally Counted Overseas Absentee Ballots Decide the 2000 U.S. Presidential Election?” (Available from http://gking.harvard.edu/preprints.shtml#ballots.) Google Scholar
King, Gary. 1997. A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data. Princeton, NJ: Princeton University Press.Google Scholar
Meng, X. L. 1994. “Multiple-Imputation Inferences with Uncongenial Sources of Input.” Statistical Science 9:538–573.Google Scholar