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Ignoring a Level in a Multilevel Model: Evidence from UK Census Data

Author

Listed:
  • Mark Tranmer

    (Centre for Census and Survey Research, University of Manchester, Manchester M13 9PL, England)

  • David G Steel

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

Abstract

Because of the inherent multilevel nature of census data, it is often appropriate to use multilevel models to investigate relationships between census variables. For a local population, the data available from the census allow a three-level nested model to be assumed, with an individual level (level 1), an enumeration district (ED) level (level 2), and a ward level (level 3). The consequences of ignoring one of the three levels in this model are assessed here theoretically. Empirical results, based on 1991 UK Census data, are also provided, comparing the variance components estimated from the three-level model with analyses based on models where the ED or ward level are ignored. The results show how the variation that occurs at the level not included in the models is redistributed to the other levels that the models do include.

Suggested Citation

  • Mark Tranmer & David G Steel, 2001. "Ignoring a Level in a Multilevel Model: Evidence from UK Census Data," Environment and Planning A, , vol. 33(5), pages 941-948, May.
    • Handle: RePEc:sae:envira:v:33:y:2001:i:5:p:941-948
    DOI: 10.1068/a3317
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    References listed on IDEAS

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    1. C G Amrhein, 1995. "Searching for the Elusive Aggregation Effect: Evidence from Statistical Simulations," Environment and Planning A, , vol. 27(1), pages 105-119, January.
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    2. Szeles, Monica Răileanu, 2018. "New insights from a multilevel approach to the regional digital divide in the European Union," Telecommunications Policy, Elsevier, vol. 42(6), pages 452-463.

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