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How to Obtain Valid Inference under Unit Nonresponse?

Author

Listed:
  • Boeschoten Laura

    (Tilburg School of Social and Behavioral Sciences, Tilburg University, Warandelaan 2, 5037 AB Tilburg, The Netherlands.)

  • Vink Gerko

    (Department of Methodology&Statistics, Utrecht University, Padualaan 14, 3584 CH Utrecht, The Netherlands.)

  • Hox Joop J.C.M.

    (Department of Methodology&Statistics, Utrecht University, Padualaan 14, 3584 CH Utrecht, The Netherlands.)

Abstract

Weighting methods are commonly used in situations of unit nonresponse with linked register data. However, several arguments in terms of valid inference and practical usability can be made against the use of weighting methods in these situations. Imputation methods such as sample and mass imputation may be suitable alternatives, as they lead to valid inference in situations of item nonresponse and have some practical advantages. In a simulation study, sample and mass imputation were compared to traditional weighting when dealing with unit nonresponse in linked register data. Methods were compared on their bias and coverage in different scenarios. Both, sample and mass imputation, had better coverage than traditional weighting in all scenarios.Imputation methods can therefore be recommended over weighting as they also have practical advantages, such as that estimates outside the observed data distribution can be created and that many auxiliary variables can be taken into account. The use of sample or mass imputation depends on the specific data structure.

Suggested Citation

  • Boeschoten Laura & Vink Gerko & Hox Joop J.C.M., 2017. "How to Obtain Valid Inference under Unit Nonresponse?," Journal of Official Statistics, Sciendo, vol. 33(4), pages 963-978, December.
    • Handle: RePEc:vrs:offsta:v:33:y:2017:i:4:p:963-978:n:6
    DOI: 10.1515/jos-2017-0045
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    References listed on IDEAS

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    3. Li‐Chun Zhang, 2012. "Topics of statistical theory for register‐based statistics and data integration," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 66(1), pages 41-63, February.
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