title: Using geocoded survey data to improve the accuracy of multilevel small area synthetic estimates.
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.
authors: Geography and Environment, University of Southampton, University Road, Southampton, SO17 1BJ, UK. Electronic address: j.l.taylor@soton.ac.uk.
authors: Joanna
authors: J
authors: Taylor
authors: Geography and Environment, University of Southampton, University Road, Southampton, SO17 1BJ, UK. Electronic address: g.moon@soton.ac.uk.
authors: Graham
authors: G
authors: Moon
authors: Department of Geography, University of Portsmouth, Buckingham Building, Lion Terrace, Portsmouth, PO1 3HE, UK. Electronic address: liz.twigg@port.ac.uk.
authors: Liz
authors: L
authors: Twigg
mesh_terms: D000328:Adult; D019352:Censuses; D002908:Chronic Disease / Q000453:epidemiology; D004812:Epidemiologic Methods; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D015995:Prevalence; D012111:Residence Characteristics; D012923:Social Class; D011795:Surveys and Questionnaires
keywords: Geocodes; Limiting long term illness; Multilevel; Synthetic estimation; UK census; spatial identifiers