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 i...
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| Veröffentlicht in: | Social science research 2016-03, Vol.56, p.108-116 |
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| creator | Taylor, Joanna Moon, Graham Twigg, Liz |
| description | 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.
•The data requirements for multilevel synthetic estimation are manifold and restrictive.•Having greater choice of area level covariates can improve the accuracy of synthetic estimates.•Attaching area data from an external source rather than aggregating survey responses can improve small area estimates.•Geocoded surveys are preferable when selecting a dataset to generate small area synthetic estimates. |
| doi_str_mv | 10.1016/j.ssresearch.2015.12.006 |
| format | Article |
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•The data requirements for multilevel synthetic estimation are manifold and restrictive.•Having greater choice of area level covariates can improve the accuracy of synthetic estimates.•Attaching area data from an external source rather than aggregating survey responses can improve small area estimates.•Geocoded surveys are preferable when selecting a dataset to generate small area synthetic estimates.</description><identifier>ISSN: 0049-089X</identifier><identifier>EISSN: 1096-0317</identifier><identifier>DOI: 10.1016/j.ssresearch.2015.12.006</identifier><identifier>PMID: 26857175</identifier><identifier>CODEN: SSREBG</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Accuracy ; Adult ; Censuses ; Chronic Disease - epidemiology ; Comparative analysis ; Data ; Epidemiologic Methods ; Epidemiology ; Estimating techniques ; Female ; Geocodes ; History of medicine and histology ; Humans ; Illness ; Knowledge ; Limiting long term illness ; Male ; Methodology (Data analysis) ; Middle Aged ; Models, Statistical ; Multilevel ; Prevalence ; Residence Characteristics ; Respondents ; Social Class ; Social sciences ; spatial identifiers ; Surveys and Questionnaires ; Synthetic estimation ; UK census</subject><ispartof>Social science research, 2016-03, Vol.56, p.108-116</ispartof><rights>2016 The Authors</rights><rights>Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.</rights><rights>Copyright Academic Press Mar 2016</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c555t-967cd10f7b14a320775758f4f446c57447667a591507fbb3c2e31fafdd2bb6913</citedby><cites>FETCH-LOGICAL-c555t-967cd10f7b14a320775758f4f446c57447667a591507fbb3c2e31fafdd2bb6913</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.ssresearch.2015.12.006$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,33774,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26857175$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Taylor, Joanna</creatorcontrib><creatorcontrib>Moon, Graham</creatorcontrib><creatorcontrib>Twigg, Liz</creatorcontrib><title>Using geocoded survey data to improve the accuracy of multilevel small area synthetic estimates</title><title>Social science research</title><addtitle>Soc Sci Res</addtitle><description>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.
•The data requirements for multilevel synthetic estimation are manifold and restrictive.•Having greater choice of area level covariates can improve the accuracy of synthetic estimates.•Attaching area data from an external source rather than aggregating survey responses can improve small area estimates.•Geocoded surveys are preferable when selecting a dataset to generate small area synthetic estimates.</description><subject>Accuracy</subject><subject>Adult</subject><subject>Censuses</subject><subject>Chronic Disease - epidemiology</subject><subject>Comparative analysis</subject><subject>Data</subject><subject>Epidemiologic Methods</subject><subject>Epidemiology</subject><subject>Estimating techniques</subject><subject>Female</subject><subject>Geocodes</subject><subject>History of medicine and histology</subject><subject>Humans</subject><subject>Illness</subject><subject>Knowledge</subject><subject>Limiting long term illness</subject><subject>Male</subject><subject>Methodology (Data analysis)</subject><subject>Middle Aged</subject><subject>Models, Statistical</subject><subject>Multilevel</subject><subject>Prevalence</subject><subject>Residence Characteristics</subject><subject>Respondents</subject><subject>Social Class</subject><subject>Social sciences</subject><subject>spatial identifiers</subject><subject>Surveys and Questionnaires</subject><subject>Synthetic estimation</subject><subject>UK census</subject><issn>0049-089X</issn><issn>1096-0317</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BHHNA</sourceid><recordid>eNqNkU1v1DAQhi0EokvhLyBLXLgkzMRfyRGq8iFV4kIlbpbjTFqvkrjYyUr77_FqC0hc4DSXZ-admYcxjlAjoH63r3NOlMklf183gKrGpgbQT9gOodMVCDRP2Q5AdhW03fcL9iLnPQCihvY5u2h0qwwatWP2Nofljt9R9HGggectHejIB7c6vkYe5ocUD8TXe-LO-y05f-Rx5PM2rWGiA008z26auEvkeD4uBVyD55TXMLuV8kv2bHRTpleP9ZLdfrz-dvW5uvn66cvV-5vKK6XWqtPGDwij6VE60YAxyqh2lKOU2isjpdHaONWhAjP2vfANCRzdOAxN3-sOxSV7e55b9v2xlXg7h-xpmtxCccsWTQsoJXTyP1AthWiFaAr65i90H7e0lEMKZaBrWgWn7PZM-RRPXkb7kMr16WgR7MmX3ds_vuzJl8XGFl-l9fVjwNbPNPxu_CWoAB_OAJXnHQIlm32gxdMQEvnVDjH8O-Un7nyrhQ</recordid><startdate>20160301</startdate><enddate>20160301</enddate><creator>Taylor, Joanna</creator><creator>Moon, Graham</creator><creator>Twigg, Liz</creator><general>Elsevier Inc</general><general>Academic Press</general><scope>6I.</scope><scope>AAFTH</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7U4</scope><scope>8BJ</scope><scope>BHHNA</scope><scope>DWI</scope><scope>FQK</scope><scope>JBE</scope><scope>WZK</scope><scope>7X8</scope></search><sort><creationdate>20160301</creationdate><title>Using geocoded survey data to improve the accuracy of multilevel small area synthetic estimates</title><author>Taylor, Joanna ; Moon, Graham ; Twigg, Liz</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c555t-967cd10f7b14a320775758f4f446c57447667a591507fbb3c2e31fafdd2bb6913</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Accuracy</topic><topic>Adult</topic><topic>Censuses</topic><topic>Chronic Disease - epidemiology</topic><topic>Comparative analysis</topic><topic>Data</topic><topic>Epidemiologic Methods</topic><topic>Epidemiology</topic><topic>Estimating techniques</topic><topic>Female</topic><topic>Geocodes</topic><topic>History of medicine and histology</topic><topic>Humans</topic><topic>Illness</topic><topic>Knowledge</topic><topic>Limiting long term illness</topic><topic>Male</topic><topic>Methodology (Data analysis)</topic><topic>Middle Aged</topic><topic>Models, Statistical</topic><topic>Multilevel</topic><topic>Prevalence</topic><topic>Residence Characteristics</topic><topic>Respondents</topic><topic>Social Class</topic><topic>Social sciences</topic><topic>spatial identifiers</topic><topic>Surveys and Questionnaires</topic><topic>Synthetic estimation</topic><topic>UK census</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Taylor, Joanna</creatorcontrib><creatorcontrib>Moon, Graham</creatorcontrib><creatorcontrib>Twigg, Liz</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Sociological Abstracts (pre-2017)</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>Sociological Abstracts</collection><collection>Sociological Abstracts</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><collection>Sociological Abstracts (Ovid)</collection><collection>MEDLINE - Academic</collection><jtitle>Social science research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Taylor, Joanna</au><au>Moon, Graham</au><au>Twigg, Liz</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Using geocoded survey data to improve the accuracy of multilevel small area synthetic estimates</atitle><jtitle>Social science research</jtitle><addtitle>Soc Sci Res</addtitle><date>2016-03-01</date><risdate>2016</risdate><volume>56</volume><spage>108</spage><epage>116</epage><pages>108-116</pages><issn>0049-089X</issn><eissn>1096-0317</eissn><coden>SSREBG</coden><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.
•The data requirements for multilevel synthetic estimation are manifold and restrictive.•Having greater choice of area level covariates can improve the accuracy of synthetic estimates.•Attaching area data from an external source rather than aggregating survey responses can improve small area estimates.•Geocoded surveys are preferable when selecting a dataset to generate small area synthetic estimates.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>26857175</pmid><doi>10.1016/j.ssresearch.2015.12.006</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Accuracy Adult Censuses Chronic Disease - epidemiology Comparative analysis Data Epidemiologic Methods Epidemiology Estimating techniques Female Geocodes History of medicine and histology Humans Illness Knowledge Limiting long term illness Male Methodology (Data analysis) Middle Aged Models, Statistical Multilevel Prevalence Residence Characteristics Respondents Social Class Social sciences spatial identifiers Surveys and Questionnaires Synthetic estimation UK census |
| title | Using geocoded survey data to improve the accuracy of multilevel small area synthetic estimates |
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