The University of Southampton
University of Southampton Institutional Repository

Using geocoded survey data to improve the accuracy of multilevel small area synthetic estimates

Using geocoded survey data to improve the accuracy of multilevel small area synthetic estimates
Using geocoded survey data to improve the accuracy of multilevel small area synthetic estimates
This paper examines the secondary data requirements for multilevel small area synthetic estimation (ML-SASE). This research method uses secondary survey data sets as source data for statistical models. The parameters of these models are used to generate data for small areas. The paper assesses the impact of knowing the geographical location of survey respondents on the accuracy of estimates, moving beyond debating the generic merits of geocoded social survey datasets to examine quantitatively the hypothesis that knowing the approximate location of respondents can improve the accuracy of the resultant estimates. Four sets of synthetic estimates are generated to predict expected levels of limiting long term illnesses using different levels of knowledge about respondent location. The estimates were compared to comprehensive census data on limiting long term illness (LLTI). Estimates based on fully geocoded data were more accurate than estimates based on data that did not include geocodes.
multilevel, synthetic estimation, uk census, geocodes, spatial identifiers, limiting long term illness
0049-089X
1-9
Taylor, Joanna
a39b190f-02da-42a7-b993-c7b77a706ec5
Moon, Graham
68cffc4d-72c1-41e9-b1fa-1570c5f3a0b4
Twigg, Liz
41a8c6df-488f-4c0f-b38d-e83b8b41728c
Taylor, Joanna
a39b190f-02da-42a7-b993-c7b77a706ec5
Moon, Graham
68cffc4d-72c1-41e9-b1fa-1570c5f3a0b4
Twigg, Liz
41a8c6df-488f-4c0f-b38d-e83b8b41728c

Taylor, Joanna, Moon, Graham and Twigg, Liz (2016) Using geocoded survey data to improve the accuracy of multilevel small area synthetic estimates. Social Science Research, 1-9. (doi:10.1016/j.ssresearch.2015.12.006).

Record type: Article

Abstract

This paper examines the secondary data requirements for multilevel small area synthetic estimation (ML-SASE). This research method uses secondary survey data sets as source data for statistical models. The parameters of these models are used to generate data for small areas. The paper assesses the impact of knowing the geographical location of survey respondents on the accuracy of estimates, moving beyond debating the generic merits of geocoded social survey datasets to examine quantitatively the hypothesis that knowing the approximate location of respondents can improve the accuracy of the resultant estimates. Four sets of synthetic estimates are generated to predict expected levels of limiting long term illnesses using different levels of knowledge about respondent location. The estimates were compared to comprehensive census data on limiting long term illness (LLTI). Estimates based on fully geocoded data were more accurate than estimates based on data that did not include geocodes.

Text
__soton.ac.uk_ude_personalfiles_users_gm1d06_mydocuments_SSRpapernoendnotedec15.pdf - Accepted Manuscript
Available under License Creative Commons Attribution.
Download (518kB)

More information

Accepted/In Press date: 31 December 2015
e-pub ahead of print date: 8 January 2016
Keywords: multilevel, synthetic estimation, uk census, geocodes, spatial identifiers, limiting long term illness
Organisations: Population, Health & Wellbeing (PHeW)

Identifiers

Local EPrints ID: 385490
URI: https://eprints.soton.ac.uk/id/eprint/385490
ISSN: 0049-089X
PURE UUID: 45f751a2-3c34-418e-91d6-ce5498ac886c
ORCID for Graham Moon: ORCID iD orcid.org/0000-0002-7256-8397

Catalogue record

Date deposited: 20 Jan 2016 10:36
Last modified: 15 Aug 2019 00:42

Export record

Altmetrics

Contributors

Author: Joanna Taylor
Author: Graham Moon ORCID iD
Author: Liz Twigg

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×