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Estimation Techniques for Ordinal Data in Multiple Frame Surveys with Complex Sampling Designs

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  • Maria del Mar Rueda
  • Antonio Arcos
  • David Molina
  • Maria Giovanna Ranalli

Abstract

Surveys usually include questions where individuals must select one in a series of possible options that can be sorted. On the other hand, multiple frame surveys are becoming a widely used method to decrease bias due to undercoverage of the target population. In this work, we propose statistical techniques for handling ordinal data coming from a multiple frame survey using complex sampling designs and auxiliary information. Our aim is to estimate proportions when the variable of interest has ordinal outcomes. Two estimators are constructed following model†assisted generalised regression and model calibration techniques. Theoretical properties are investigated for these estimators. Simulation studies with different sampling procedures are considered to evaluate the performance of the proposed estimators in finite size samples. An application to a real survey on opinions towards immigration is also included.

Suggested Citation

  • Maria del Mar Rueda & Antonio Arcos & David Molina & Maria Giovanna Ranalli, 2018. "Estimation Techniques for Ordinal Data in Multiple Frame Surveys with Complex Sampling Designs," International Statistical Review, International Statistical Institute, vol. 86(1), pages 51-67, April.
    • Handle: RePEc:bla:istatr:v:86:y:2018:i:1:p:51-67
    DOI: 10.1111/insr.12218
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    Cited by:

    1. Maria Mar Rueda & Maria Giovanna Ranalli & Antonio Arcos & David Molina, 2021. "Population empirical likelihood estimation in dual frame surveys," Statistical Papers, Springer, vol. 62(5), pages 2473-2490, October.
    2. Daniela Cocchi & Lorenzo Marchi & Riccardo Ievoli, 2022. "Bayesian Bootstrap in Multiple Frames," Stats, MDPI, vol. 5(2), pages 1-11, June.

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