On valid descriptive inference from non-probability sample
On valid descriptive inference from non-probability sample
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.
Non-informative selection, prediction model, calibration, inverse propensity weighting, sample matching, model misspecification
103-113
Zhang, Li-Chun
a5d48518-7f71-4ed9-bdcb-6585c2da3649
2019
Zhang, Li-Chun
a5d48518-7f71-4ed9-bdcb-6585c2da3649
Zhang, Li-Chun
(2019)
On valid descriptive inference from non-probability sample.
Statistical Theory and Related Fields, 3 (2), .
(doi:10.1080/24754269.2019.1666241).
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.
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TSTF-2018-0039_R3 (002)
- Accepted Manuscript
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On valid descriptive inference from non probability sample
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Accepted/In Press date: 7 September 2018
e-pub ahead of print date: 13 September 2019
Published date: 2019
Keywords:
Non-informative selection, prediction model, calibration, inverse propensity weighting, sample matching, model misspecification
Identifiers
Local EPrints ID: 434333
URI: http://eprints.soton.ac.uk/id/eprint/434333
ISSN: 2475-4269
PURE UUID: 65448a88-5f80-4705-9670-47b3388990d4
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Date deposited: 19 Sep 2019 16:30
Last modified: 16 Mar 2024 08:12
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