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On valid descriptive inference from non-probability sample

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  • Li-Chun Zhang

Abstract

We examine the conditions under which descriptive inference can be based directly on the observed distribution in a non-probability sample, under both the super-population and quasi-randomisation modelling approaches. Review of existing estimation methods reveals that the traditional formulation of these conditions may be inadequate due to potential issues of under-coverage or heterogeneous mean beyond the assumed model. We formulate unifying conditions that are applicable to both types of modelling approaches. The difficulties of empirically validating the required conditions are discussed, as well as valid inference approaches using supplementary probability sampling. The key message is that probability sampling may still be necessary in some situations, in order to ensure the validity of descriptive inference, but it can be much less resource-demanding given the presence of a big non-probability sample.

Suggested Citation

  • Li-Chun Zhang, 2019. "On valid descriptive inference from non-probability sample," Statistical Theory and Related Fields, Taylor & Francis Journals, vol. 3(2), pages 103-113, July.
    • Handle: RePEc:taf:tstfxx:v:3:y:2019:i:2:p:103-113
    DOI: 10.1080/24754269.2019.1666241
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    Cited by:

    1. Li‐Chun Zhang, 2021. "Proxy expenditure weights for Consumer Price Index: Audit sampling inference for big‐data statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(2), pages 571-588, April.
    2. Paul A. Smith, 2021. "Estimating Sampling Errors in Consumer Price Indices," International Statistical Review, International Statistical Institute, vol. 89(3), pages 481-504, December.
    3. Martina Patone & Li‐Chun Zhang, 2021. "On Two Existing Approaches to Statistical Analysis of Social Media Data," International Statistical Review, International Statistical Institute, vol. 89(1), pages 54-71, April.
    4. Yingli Pan & Wen Cai & Zhan Liu, 2022. "Inference for non-probability samples under high-dimensional covariate-adjusted superpopulation model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(4), pages 955-979, October.

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