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Ecological inference techniques: an empirical evaluation using data describing gender and voter turnout at New Zealand elections, 1893–1919.

Authors :
Hudson, Irene L.
Moore, Linda
Beh, Eric J.
Steel, David G.
Source :
Journal of the Royal Statistical Society: Series A (Statistics in Society); Jan2010, Vol. 173 Issue 1, p185-213, 29p, 7 Charts, 8 Graphs
Publication Year :
2010

Abstract

The difference, if any, between men's and women's voting patterns is of particular interest to historians of gender and politics. For elections that were held before the introduction of opinion surveying in the 1940s, little data are available with which to estimate such differences. We apply six methods for ecological inference to estimate men's and women's voting rates in New Zealand (NZ), 1893–1919. NZ is an interesting case-study, since it was the first self-governing country where women could vote. Furthermore, NZ officials recorded the voting rates of men and women at elections, making it possible to compare estimates produced by methods for ecological inference with known true values, thus testing the efficacy of different methods for ecological inference for this data set. We find that the most popular methods for ecological inference, namely Goodman's ecological regression and King's parametric method, give poor estimates, as does the much debated neighbourhood method. However, King's non-parametric method, Chambers and Steel's semiparametric method and the Steel, Beh and Chambers homogeneous approach all gave good estimates that were close to the known values, with the homogeneous approach performing best overall. The success of these methods in this example suggests that ecological inference may be a viable option when investigating gender and voting. Moreover, researchers using ecological inference in other fields may do well to consider a range of statistical methods. This work is a significant NZ contribution to historical politics and the first quantitative contribution, in the area of NZ gender and politics. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09641998
Volume :
173
Issue :
1
Database :
Complementary Index
Journal :
Journal of the Royal Statistical Society: Series A (Statistics in Society)
Publication Type :
Academic Journal
Accession number :
45671063
Full Text :
https://doi.org/10.1111/j.1467-985X.2009.00609.x