{"id":"https://openalex.org/W4207058149","doi":"https://doi.org/10.1080/03610918.2021.1988642","title":"Applying multilevel regression weighting when only population margins are available","display_name":"Applying multilevel regression weighting when only population margins are available","publication_year":2022,"publication_date":"2022-01-25","ids":{"openalex":"https://openalex.org/W4207058149","doi":"https://doi.org/10.1080/03610918.2021.1988642"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2021.1988642","is_oa":true,"landing_page_url":"https://doi.org/10.1080/03610918.2021.1988642","pdf_url":"https://www.tandfonline.com/doi/epdf/10.1080/03610918.2021.1988642?needAccess=true&role=button","source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://www.tandfonline.com/doi/epdf/10.1080/03610918.2021.1988642?needAccess=true&role=button","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071413535","display_name":"Christian Bruch","orcid":"https://orcid.org/0000-0003-0926-6609"},"institutions":[{"id":"https://openalex.org/I4210101898","display_name":"GESIS - Leibniz-Institute for the Social Sciences","ror":"https://ror.org/018afyw53","country_code":"DE","type":"facility","lineage":["https://openalex.org/I315704651","https://openalex.org/I4210101898"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Christian Bruch","raw_affiliation_strings":["GESIS Leibniz Institute for the Social Sciences, Mannheim, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"GESIS Leibniz Institute for the Social Sciences, Mannheim, Germany","institution_ids":["https://openalex.org/I4210101898"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075628676","display_name":"Barbara Felderer","orcid":"https://orcid.org/0000-0002-1717-0415"},"institutions":[{"id":"https://openalex.org/I4210101898","display_name":"GESIS - Leibniz-Institute for the Social Sciences","ror":"https://ror.org/018afyw53","country_code":"DE","type":"facility","lineage":["https://openalex.org/I315704651","https://openalex.org/I4210101898"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Barbara Felderer","raw_affiliation_strings":["GESIS Leibniz Institute for the Social Sciences, Mannheim, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"GESIS Leibniz Institute for the Social Sciences, Mannheim, Germany","institution_ids":["https://openalex.org/I4210101898"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5071413535"],"corresponding_institution_ids":["https://openalex.org/I4210101898"],"apc_list":null,"apc_paid":null,"fwci":1.8618,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.87343614,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"52","issue":"11","first_page":"5401","last_page":"5422"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11539","display_name":"Survey Methodology and Nonresponse","score":0.9919999837875366,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11539","display_name":"Survey Methodology and Nonresponse","score":0.9919999837875366,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9810000061988831,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11645","display_name":"Urban, Neighborhood, and Segregation Studies","score":0.9753999710083008,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.9236667156219482},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.6158468723297119},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.5936495065689087},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5664708614349365},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.5016367435455322},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4833318889141083},{"id":"https://openalex.org/keywords/a-weighting","display_name":"A-weighting","score":0.46201789379119873},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4499706029891968},{"id":"https://openalex.org/keywords/survey-data-collection","display_name":"Survey data collection","score":0.4231681823730469},{"id":"https://openalex.org/keywords/joint-probability-distribution","display_name":"Joint probability distribution","score":0.41651663184165955},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22618368268013}],"concepts":[{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.9236667156219482},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.6158468723297119},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5936495065689087},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5664708614349365},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5016367435455322},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4833318889141083},{"id":"https://openalex.org/C70136482","wikidata":"https://www.wikidata.org/wiki/Q13583781","display_name":"A-weighting","level":3,"score":0.46201789379119873},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4499706029891968},{"id":"https://openalex.org/C198477413","wikidata":"https://www.wikidata.org/wiki/Q7647069","display_name":"Survey data collection","level":2,"score":0.4231681823730469},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.41651663184165955},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22618368268013},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/03610918.2021.1988642","is_oa":true,"landing_page_url":"https://doi.org/10.1080/03610918.2021.1988642","pdf_url":"https://www.tandfonline.com/doi/epdf/10.1080/03610918.2021.1988642?needAccess=true&role=button","source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"},{"id":"pmh:oai:gesis.izsoz.de:document/80477","is_oa":true,"landing_page_url":"https://www.ssoar.info/ssoar/handle/document/80477","pdf_url":null,"source":{"id":"https://openalex.org/S4306401996","display_name":"Social Science Open Access Repository (GESIS \u2013 Leibniz Institute for the Social Sciences)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210101898","host_organization_name":"GESIS - Leibniz Institute for the Social Sciences","host_organization_lineage":["https://openalex.org/I4210101898"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal article"}],"best_oa_location":{"id":"doi:10.1080/03610918.2021.1988642","is_oa":true,"landing_page_url":"https://doi.org/10.1080/03610918.2021.1988642","pdf_url":"https://www.tandfonline.com/doi/epdf/10.1080/03610918.2021.1988642?needAccess=true&role=button","source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.8199999928474426,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4207058149.pdf","grobid_xml":"https://content.openalex.org/works/W4207058149.grobid-xml"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W1590059485","https://openalex.org/W1951724000","https://openalex.org/W1981457167","https://openalex.org/W1996995433","https://openalex.org/W2014908010","https://openalex.org/W2029420412","https://openalex.org/W2061065380","https://openalex.org/W2061408624","https://openalex.org/W2061828456","https://openalex.org/W2085360857","https://openalex.org/W2105602542","https://openalex.org/W2108849070","https://openalex.org/W2117897510","https://openalex.org/W2121909644","https://openalex.org/W2138030815","https://openalex.org/W2147603830","https://openalex.org/W2166730394","https://openalex.org/W2327088997","https://openalex.org/W2582743722","https://openalex.org/W2612145410","https://openalex.org/W2736885773","https://openalex.org/W2890561900","https://openalex.org/W2909629932","https://openalex.org/W2980548856","https://openalex.org/W3000214403","https://openalex.org/W3103098869","https://openalex.org/W3122541118","https://openalex.org/W3124688081","https://openalex.org/W4210675036","https://openalex.org/W4248681815","https://openalex.org/W4399638357"],"related_works":["https://openalex.org/W2092002118","https://openalex.org/W1880952682","https://openalex.org/W1963516324","https://openalex.org/W2014269659","https://openalex.org/W1971836599","https://openalex.org/W2019002543","https://openalex.org/W2619302272","https://openalex.org/W2180954594","https://openalex.org/W2075728349","https://openalex.org/W2033210010"],"abstract_inverted_index":{"Reliable":[0],"survey":[1,10,23,27,55],"data":[2,56],"is":[3,96],"needed":[4],"to":[5,8,12,38,45,57,168],"be":[6],"able":[7],"infer":[9],"findings":[11],"the":[13,22,49,54,73,76,93,147],"general":[14],"population.":[15],"However,":[16],"self-selection":[17],"or":[18,82,104],"panel":[19],"attrition":[20],"of":[21,68,75,101],"respondents":[24],"may":[25],"bias":[26,47],"estimations.":[28],"To":[29],"tackle":[30],"these":[31,126],"challenges,":[32],"weighting":[33,69,94,110,150,153],"adjustments":[34],"have":[35,112],"been":[36,113],"established":[37],"correct":[39],"for":[40,87,115],"different":[41],"inclusion":[42],"probabilities":[43],"and":[44,64,124],"reduce":[46],"in":[48],"survey.":[50],"These":[51],"strategies":[52,70,111],"adjust":[53],"match":[58],"known":[59],"population":[60,90,133,157],"statistics":[61,81],"(e.g.,":[62],"means":[63],"proportions).":[65],"The":[66],"usefulness":[67],"depends":[71],"on":[72,92],"benchmarks":[74],"variables":[77,95,103],"available":[78,97],"from":[79],"official":[80],"other":[83],"highly":[84],"reliable":[85],"sources,":[86],"instance,":[88],"whether":[89],"information":[91],"as":[98,105],"joint":[99,118],"distributions":[100,119,158],"all":[102],"margins":[106,134],"only.":[107,159],"While":[108],"complex":[109],"developed":[114],"poststratification":[116],"using":[117,155],"(for":[120],"example":[121],"multilevel":[122,148],"regression":[123,149],"poststratification),":[125],"methods":[127],"are":[128,135],"not":[129],"applicable":[130],"when":[131],"only":[132],"available.":[136],"In":[137,160],"this":[138],"paper,":[139],"we":[140,164],"propose":[141],"two":[142],"practical":[143],"approaches":[144,167],"that":[145],"combine":[146],"method":[151],"with":[152],"algorithms":[154],"marginal":[156],"a":[161],"simulation":[162],"study,":[163],"applied":[165],"both":[166],"volunteer":[169],"samples.":[170]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
