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Using Census Data to Investigate the Causes of the Ecological Fallacy

Author

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  • M Tranmer

    (Department of Social Statistics, University of Southampton SO17 1BJ)

  • D G Steel

    (School of Mathematics and Applied Statistics, University of Wollongong, NSW 2522, Australia)

Abstract

The authors show how data from the 2% Sample of Anonymised Records (SAR) can be combined with data from the Small Area Statistics (SAS) database to investigate the causes of the ecological fallacy in an Enumeration District (ED) level analysis. A range of census variables are examined in three ‘SAR districts’ (local authority districts with populations of 120 000 or more, or combinations of contiguous districts with smaller populations) in England. Results of comparable analyses from the 1986 Australian census are also given. The ecological fallacy arises when results from an analysis based on area-level aggregate statistics are incorrectly assumed to apply at the individual level. In general the results are different because individuals in the same area tend to have similar characteristics: a phenomenon known as within-area homogeneity. A statistical model is presented which allows for within-area homogeneity. This model may be used to explain the effects of aggregation on variances, covariances, and correlations. A methodology is introduced which allows aggregate-level statistics to be adjusted by using individual-level information on those variables that explain much of the within-area homogeneity. This methodology appears to be effective in adjusting census data analyses, and the results suggest that the SAR is a valuable source of adjustment information for aggregate data analyses from census and other sources.

Suggested Citation

  • M Tranmer & D G Steel, 1998. "Using Census Data to Investigate the Causes of the Ecological Fallacy," Environment and Planning A, , vol. 30(5), pages 817-831, May.
    • Handle: RePEc:sae:envira:v:30:y:1998:i:5:p:817-831
    DOI: 10.1068/a300817
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    Cited by:

    1. Irene L. Hudson & Linda Moore & Eric J. Beh & David G. Steel, 2010. "Ecological inference techniques: an empirical evaluation using data describing gender and voter turnout at New Zealand elections, 1893–1919," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(1), pages 185-213, January.
    2. Sibylle Puntscher & Janette Walde & Gottfried Tappeiner, 2016. "Do methodical traps lead to wrong development strategies for welfare? A multilevel approach considering heterogeneity across industrialized and developing countries," Working Papers 2016-01, Faculty of Economics and Statistics, Universität Innsbruck.
    3. Robert Haining & Jane Law, 2007. "Combining police perceptions with police records of serious crime areas: a modelling approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 1019-1034, October.
    4. D. H. Judson, 2007. "Information integration for constructing social statistics: history, theory and ideas towards a research programme," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(2), pages 483-501, March.
    5. Flowerdew, Robin & Manley, David J. & Sabel, Clive E., 2008. "Neighbourhood effects on health: Does it matter where you draw the boundaries?," Social Science & Medicine, Elsevier, vol. 66(6), pages 1241-1255, March.
    6. Sibylle Puntscher & Christoph Hauser & Janette Walde & Gottfried Tappeiner, 2016. "Measuring Social Capital with Aggregated Indicators: A Case of Ecological Fallacy?," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 125(2), pages 431-449, January.
    7. Gift Dumedah & Nadine Schuurman & Wanhong Yang, 2008. "Minimizing effects of scale distortion for spatially grouped census data using rough sets," Journal of Geographical Systems, Springer, vol. 10(1), pages 47-69, March.
    8. Cockings, Samantha & Martin, David, 2005. "Zone design for environment and health studies using pre-aggregated data," Social Science & Medicine, Elsevier, vol. 60(12), pages 2729-2742, June.
    9. Daisuke Murakami & Morito Tsutsumi, 2015. "Area-to-point parameter estimation with geographically weighted regression," Journal of Geographical Systems, Springer, vol. 17(3), pages 207-225, July.

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