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
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, .
(doi:10.1016/j.ssresearch.2015.12.006).
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
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: http://eprints.soton.ac.uk/id/eprint/385490
ISSN: 0049-089X
PURE UUID: 45f751a2-3c34-418e-91d6-ce5498ac886c
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Date deposited: 20 Jan 2016 10:36
Last modified: 15 Mar 2024 03:27
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Contributors
Author:
Joanna Taylor
Author:
Liz Twigg
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