IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v169y2006i4p681-700.html
   My bibliography  Save this article

Reducing bias in ecological studies: an evaluation of different methodologies

Author

Listed:
  • Gillian A. Lancaster
  • Mick Green
  • Steven Lane

Abstract

Summary. Statistical methods of ecological analysis that attempt to reduce ecological bias are empirically evaluated to determine in which circumstances each method might be practicable. The method that is most successful at reducing ecological bias is stratified ecological regression. It allows individual level covariate information to be incorporated into a stratified ecological analysis, as well as the combination of disease and risk factor information from two separate data sources, e.g. outcomes from a cancer registry and risk factor information from the census sample of anonymized records data set. The aggregated individual level model compares favourably with this model but has convergence problems. In addition, it is shown that the large areas that are covered by local authority districts seem to reduce between‐area variability and may therefore not be as informative as conducting a ward level analysis. This has policy implications because access to ward level data is restricted.

Suggested Citation

  • Gillian A. Lancaster & Mick Green & Steven Lane, 2006. "Reducing bias in ecological studies: an evaluation of different methodologies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 681-700, October.
    • Handle: RePEc:bla:jorssa:v:169:y:2006:i:4:p:681-700
    DOI: 10.1111/j.1467-985X.2006.00418.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-985X.2006.00418.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-985X.2006.00418.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Katherine A. Guthrie & Lianne Sheppard, 2001. "Overcoming biases and misconceptions in ecological studies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 141-154.
    2. Nicky Best & Samantha Cockings & James Bennett & Jon Wakefield & Paul Elliott, 2001. "Ecological regression analysis of environmental benzene exposure and childhood leukaemia: sensitivity to data inaccuracies, geographical scale and ecological bias," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 155-174.
    3. Jon Wakefield, 2003. "Sensitivity Analyses for Ecological Regression," Biometrics, The International Biometric Society, vol. 59(1), pages 9-17, March.
    4. Jonathan Wakefield & Ruth Salway, 2001. "A statistical framework for ecological and aggregate studies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 119-137.
    5. Vinod, Hrishikesh D, 1978. "A Survey of Ridge Regression and Related Techniques for Improvements over Ordinary Least Squares," The Review of Economics and Statistics, MIT Press, vol. 60(1), pages 121-131, February.
    6. Gould, Myles I. & Jones, Kelvyn, 1996. "Analyzing perceived limiting long-term illness using U.K. census microdata," Social Science & Medicine, Elsevier, vol. 42(6), pages 857-869, March.
    7. N. Cleave & P. J. Brown & C. D. Payne, 1995. "Evaluation of Methods for Ecological Inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 158(1), pages 55-72, January.
    8. Katherine A. Guthrie & Lianne Sheppard & Jon Wakefield, 2002. "A Hierarchical Aggregate Data Model with Spatially Correlated Disease Rates," Biometrics, The International Biometric Society, vol. 58(4), pages 898-905, December.
    9. Andrew Gelman & David K. Park & Stephen Ansolabehere & Phillip N. Price & Lorraine C. Minnite, 2001. "Models, assumptions and model checking in ecological regressions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 101-118.
    10. Mark Tranmer & Andrew Pickles & Ed Fieldhouse & Mark Elliot & Angela Dale & Mark Brown & David Martin & David Steel & Chris Gardiner, 2005. "The case for small area microdata," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(1), pages 29-49, January.
    11. Francesca Dominici & Jonathan M. Samet & Scott L. Zeger, 2000. "Combining evidence on air pollution and daily mortality from the 20 largest US cities: a hierarchical modelling strategy," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 163(3), pages 263-302.
    12. Gillian Lancaster & Mick Green, 2002. "Deprivation, ill‐health and the ecological fallacy," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(2), pages 263-278, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hugo Storm & Thomas Heckelei & Ron C. Mittelhammer, 2016. "Bayesian estimation of non-stationary Markov models combining micro and macro data," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 43(2), pages 303-329.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Christopher Jackson & And Nicky Best & Sylvia Richardson, 2008. "Hierarchical related regression for combining aggregate and individual data in studies of socio‐economic disease risk factors," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(1), pages 159-178, January.
    2. Jon Wakefield, 2004. "Ecological inference for 2 × 2 tables (with discussion)," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(3), pages 385-445, July.
    3. Katherine A. Guthrie & Lianne Sheppard & Jon Wakefield, 2002. "A Hierarchical Aggregate Data Model with Spatially Correlated Disease Rates," Biometrics, The International Biometric Society, vol. 58(4), pages 898-905, December.
    4. Yi Liu & Gavin Shaddick & James V. Zidek, 2017. "Incorporating High-Dimensional Exposure Modelling into Studies of Air Pollution and Health," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 559-581, December.
    5. 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.
    6. Francesca Dominici & Lianne Sheppard & Merlise Clyde, 2003. "Health Effects of Air Pollution: A Statistical Review," International Statistical Review, International Statistical Institute, vol. 71(2), pages 243-276, August.
    7. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    8. Jackson, Christopher H. & Richardson, Sylvia & Best, Nicky G., 2008. "Studying place effects on health by synthesising individual and area-level outcomes," Social Science & Medicine, Elsevier, vol. 67(12), pages 1995-2006, December.
    9. Roberto Colombi & Antonio Forcina, 2016. "Latent class models for ecological inference on voters transitions," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(4), pages 501-517, November.
    10. Jon Wakefield, 2003. "Sensitivity Analyses for Ecological Regression," Biometrics, The International Biometric Society, vol. 59(1), pages 9-17, March.
    11. Aiyar, Anaka & Cummins, Joseph R., 2021. "An age profile perspective on two puzzles in global child health: The Indian Enigma & economic growth," Journal of Development Economics, Elsevier, vol. 148(C).
    12. Alaimo, Leonardo Salvatore & Ivaldi, Enrico & Landi, Stefano & Maggino, Filomena, 2022. "Measuring and evaluating socio-economic inequality in small areas: An application to the urban units of the Municipality of Genoa," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
    13. Sabel, Clive Eric & Wilson, Jeff Gaines & Kingham, Simon & Tisch, Catherine & Epton, Mike, 2007. "Spatial implications of covariate adjustment on patterns of risk: Respiratory hospital admissions in Christchurch, New Zealand," Social Science & Medicine, Elsevier, vol. 65(1), pages 43-59, July.
    14. Ishmael Ackah, 2014. "Determinants of natural gas demand in Ghana," OPEC Energy Review, Organization of the Petroleum Exporting Countries, vol. 38(3), pages 272-295, September.
    15. Aleš Urban & Katrin Burkart & Jan Kyselý & Christian Schuster & Eva Plavcová & Hana Hanzlíková & Petr Štěpánek & Tobia Lakes, 2016. "Spatial Patterns of Heat-Related Cardiovascular Mortality in the Czech Republic," IJERPH, MDPI, vol. 13(3), pages 1-19, March.
    16. Burr, Wesley S. & Shin, Hwashin H. & Takahara, Glen, 2019. "Synthetically lagged models," Statistics & Probability Letters, Elsevier, vol. 144(C), pages 37-43.
    17. Ying C. MacNab, 2018. "Some recent work on multivariate Gaussian Markov random fields," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 497-541, September.
    18. Wan Fung Lee & Jeffrey W. Bulcock & Wo Shun Luk, 1984. "The R2 Ridge Trace in 2SLS Regression Estimation," Sociological Methods & Research, , vol. 13(2), pages 219-249, November.
    19. Jeffrey W. Bulcock & Wan Fung Lee, 1983. "Normalization Ridge Regression in Practice," Sociological Methods & Research, , vol. 11(3), pages 259-303, February.
    20. Koop, Gary & Tole, Lise, 2004. "Measuring the health effects of air pollution: to what extent can we really say that people are dying from bad air?," Journal of Environmental Economics and Management, Elsevier, vol. 47(1), pages 30-54, January.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jorssa:v:169:y:2006:i:4:p:681-700. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.