On valid descriptive inference from non-probability sample. Issue 2 (3rd July 2019)

• Record Type: Journal Article
• Authors: Zhang, Li-Chun
• Is Part Of: Statistical theory and related fields. Volume 3:Issue 2(2019)
• Page Start: 103
• Page End: 113
• Publication Date: 2019-07-03
• Subjects: Non-informative selection -- prediction model -- calibration -- inverse propensity weighting -- sample matching -- model misspecification; Statistics -- Periodicals; Statistics; Periodicals; Electronic journals; 001.422
• DOI: 10.1080/24754269.2019.1666241
• Languages: English
• ISSNs: 2475-4269

Abstract

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.