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Evaluation of Methods for Ecological Inference

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  • N. Cleave
  • P. J. Brown
  • C. D. Payne

Abstract

In ecological inference one uses data which are aggregated by areal units to investigate the behaviour of the individuals comprising those units. Aggregated data are readily available in many fields and within a wide variety of data structures. In the structures considered, the aggregate data are characterized by the absence of available data in the internal cells of a cross‐classification. The aim of the ecological methods is to estimate the expected frequencies of such internal cells, which may be conditional on chosen covariates. Four methods of ecological inference are reviewed and their properties and appropriateness considered. These methods are then applied to data for which the internal cells are known and their performances compared.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jorssa:v:158:y:1995:i:1:p:55-72
    DOI: 10.2307/2983403
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    Cited by:

    1. Carolina Plescia & Lorenzo De Sio, 2018. "An evaluation of the performance and suitability of R × C methods for ecological inference with known true values," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(2), pages 669-683, March.
    2. 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.
    3. 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.
    4. 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.
    5. Robin Lovelace & Mark Birkin & Dimitris Ballas & Eveline van Leeuwen, 2015. "Evaluating the Performance of Iterative Proportional Fitting for Spatial Microsimulation: New Tests for an Established Technique," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(2), pages 1-21.

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