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Multilevel modelling of survey data: impact of the two-level weights used in the pseudolikelihood

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  • Jean-Paul Lucas
  • V�ronique S�bille
  • Alain Le Tertre
  • Yann Le Strat
  • Lise Bellanger

Abstract

Approaches that use the pseudolikelihood to perform multilevel modelling on survey data have been presented in the literature. To avoid biased estimates due to unequal selection probabilities, conditional weights can be introduced at each level. Less-biased estimators can also be obtained in a two-level linear model if the level-1 weights are scaled. In this paper, we studied several level-2 weights that can be introduced into the pseudolikelihood when the sampling design and the hierarchical structure of the multilevel model do not match. Two-level and three-level models were studied. The present work was motivated by a study that aims to estimate the contributions of lead sources to polluting the interior floor dust of the rooms within dwellings. We performed a simulation study using the real data collected from a French survey to achieve our objective. We conclude that it is preferable to use unweighted analyses or, at the most, to use conditional level-2 weights in a two-level or a three-level model. We state some warnings and make some recommendations.

Suggested Citation

  • Jean-Paul Lucas & V�ronique S�bille & Alain Le Tertre & Yann Le Strat & Lise Bellanger, 2014. "Multilevel modelling of survey data: impact of the two-level weights used in the pseudolikelihood," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(4), pages 716-732, April.
    • Handle: RePEc:taf:japsta:v:41:y:2014:i:4:p:716-732
    DOI: 10.1080/02664763.2013.847404
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    References listed on IDEAS

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    1. Sophia Rabe‐Hesketh & Anders Skrondal, 2006. "Multilevel modelling of complex survey data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 805-827, October.
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