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Random probability vs quota sampling

Random probability vs quota sampling
Random probability vs quota sampling
Executive Summary

- Probability sampling has a well-developed, relatively straightforward, design-based estimation framework providing the best approach to making inference about a population.
- Non-probability sampling includes a diverse range of methods that are not easily described under a single framework, however model-based methods are required when making inference from a non-probability sample to adjust for differences between the sample and known population information. Inference from the nonprobability sampling method is only as good as the model and assumptions that are used.
- Sampling in longitudinal studies requires a precise definition of the target population, which may not merely be a finite population, but instead a dynamic population or superpopulation.
- Problems with non-response, attrition and under-coverage should be anticipated and factored into the design of the longitudinal study using model-based methods, rather than addressed post hoc.
- Having a representative sample is an important aim for a national-level longitudinal study, as this ensures that the data will have a wider potential to be used in the distant future across different disciplines. A probability sample is the best starting point to ensure this.
- Non-probability sampling for longitudinal studies may be useful to supplement the main sample for specific populations which it is impractical to reach with a probability sample, but the ways to analyse such combinations of data from different sample types needs more research.
University of Southampton
Smith, Paul A.
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Dawber, James
85c7c036-2ae3-4c57-a8b3-9f5223cd4da6
Smith, Paul A.
a2548525-4f99-4baf-a4d0-2b216cce059c
Dawber, James
85c7c036-2ae3-4c57-a8b3-9f5223cd4da6

Smith, Paul A. and Dawber, James (2019) Random probability vs quota sampling University of Southampton 30pp.

Record type: Monograph (Project Report)

Abstract

Executive Summary

- Probability sampling has a well-developed, relatively straightforward, design-based estimation framework providing the best approach to making inference about a population.
- Non-probability sampling includes a diverse range of methods that are not easily described under a single framework, however model-based methods are required when making inference from a non-probability sample to adjust for differences between the sample and known population information. Inference from the nonprobability sampling method is only as good as the model and assumptions that are used.
- Sampling in longitudinal studies requires a precise definition of the target population, which may not merely be a finite population, but instead a dynamic population or superpopulation.
- Problems with non-response, attrition and under-coverage should be anticipated and factored into the design of the longitudinal study using model-based methods, rather than addressed post hoc.
- Having a representative sample is an important aim for a national-level longitudinal study, as this ensures that the data will have a wider potential to be used in the distant future across different disciplines. A probability sample is the best starting point to ensure this.
- Non-probability sampling for longitudinal studies may be useful to supplement the main sample for specific populations which it is impractical to reach with a probability sample, but the ways to analyse such combinations of data from different sample types needs more research.

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Published date: July 2019

Identifiers

Local EPrints ID: 435300
URI: http://eprints.soton.ac.uk/id/eprint/435300
PURE UUID: 395a6fb9-101d-495d-b63c-e0cba9f73dda
ORCID for Paul A. Smith: ORCID iD orcid.org/0000-0001-5337-2746

Catalogue record

Date deposited: 30 Oct 2019 17:30
Last modified: 14 Aug 2020 01:41

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