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

Taylor, Joanna and Moon, Graham and Twigg, Liz (2016) Using geocoded survey data to improve the accuracy of multilevel small area synthetic estimates. Social Science Research, 56 (n/a). pp. 108-116. ISSN 0049-089X

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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.

Item Type:Article
Subjects:1. Frameworks for Research and Research Designs > 1.20 Secondary Analysis
1. Frameworks for Research and Research Designs > 1.20 Secondary Analysis > 1.20.4 Analysis of existing survey data
5. Quantitative Data Handling and Data Analysis > 5.3 Small Area Estimation
5. Quantitative Data Handling and Data Analysis > 5.3 Small Area Estimation > 5.3.2 Multilevel models
5. Quantitative Data Handling and Data Analysis > 5.9 Spatial Data Analysis
ID Code:4077
Deposited By: NCRM users
Deposited On:22 Aug 2018 13:56
Last Modified:22 Aug 2018 13:56

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