{"id":"https://openalex.org/W4281652257","doi":"https://doi.org/10.48550/arxiv.2205.12478","title":"Visualising Multilevel Regression and Poststratification: Alternatives to the Current Practice","display_name":"Visualising Multilevel Regression and Poststratification: Alternatives to the Current Practice","publication_year":2022,"publication_date":"2022-05-25","ids":{"openalex":"https://openalex.org/W4281652257","doi":"https://doi.org/10.48550/arxiv.2205.12478"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2205.12478","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2205.12478","pdf_url":"https://arxiv.org/pdf/2205.12478","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":null,"raw_type":"text"},"parent_works":[],"lineage":["https://openalex.org/W4281652257"],"child_works_count":0,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2205.12478","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5052609132","display_name":"Dewi Amaliah","orcid":"https://orcid.org/0000-0001-6903-6237","observed_orcids":["https://orcid.org/0000-0001-6903-6237"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Amaliah, Dewi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5052609132"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13030","display_name":"Survey Sampling and Estimation Techniques","score":0.5673999786376953,"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"}},"topics":[{"id":"https://openalex.org/T13030","display_name":"Survey Sampling and Estimation Techniques","score":0.5673999786376953,"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/T10799","display_name":"Data Visualization and Analytics","score":0.2093999981880188,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11539","display_name":"Survey Methodology and Nonresponse","score":0.10930000245571136,"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/uncertainty-visualization","display_name":"uncertainty visualization","score":0.5899999737739563},{"id":"https://openalex.org/keywords/choropleth-maps","display_name":"choropleth maps","score":0.9879999756813049},{"id":"https://openalex.org/keywords/bias-variance-trade-off","display_name":"bias-variance trade-off","score":0.8370000123977661},{"id":"https://openalex.org/keywords/2016-us-presidential-election","display_name":"2016 US presidential election","score":0.8799999952316284},{"id":"https://openalex.org/keywords/cooperative-congressional-election-study","display_name":"Cooperative Congressional Election Study","score":0.9789999723434448},{"id":"https://openalex.org/keywords/survey-inference","display_name":"survey inference","score":0.6959999799728394}],"concepts":[{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.7429718971252441},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6436834931373596},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.6382583379745483},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.598362386226654},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.5393685102462769},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5121331214904785},{"id":"https://openalex.org/C172367668","wikidata":"https://www.wikidata.org/wiki/Q6504956","display_name":"Data visualization","level":3,"score":0.48793908953666687},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.48280394077301025},{"id":"https://openalex.org/C41426520","wikidata":"https://www.wikidata.org/wiki/Q1192065","display_name":"Point estimation","level":2,"score":0.4811890721321106},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.43517494201660156},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4102402627468109},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.39216315746307373},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3685905933380127},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.33232587575912476},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2821803689002991},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.12774384021759033},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10176348686218262},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0920870304107666},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2205.12478","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2205.12478","pdf_url":"https://arxiv.org/pdf/2205.12478","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2205.12478","doi":"https://doi.org/10.48550/arxiv.2205.12478","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2205.12478","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2205.12478","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2205.12478","pdf_url":"https://arxiv.org/pdf/2205.12478","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[{"display_name":"Systematic Review","id":"https://openalex.org/study-designs/systematic-review"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4281652257.pdf","grobid_xml":"https://content.openalex.org/works/W4281652257.grobid-xml"},"referenced_works_count":4,"referenced_works":["https://openalex.org/W2039122016","https://openalex.org/W2965374073","https://openalex.org/W4205636518","https://openalex.org/W4398308202"],"related_works":["https://openalex.org/W7097322865","https://openalex.org/W2026438978","https://openalex.org/W4381380008","https://openalex.org/W4416337629","https://openalex.org/W4388689002","https://openalex.org/W3048575279","https://openalex.org/W4320488637","https://openalex.org/W7133065862","https://openalex.org/W4240815490","https://openalex.org/W2098656313"],"abstract_inverted_index":{"Surveys":[0],"provide":[1,18,36],"important":[2],"evidence":[3],"for":[4,39,49,131,148],"policymaking,":[5],"decision-making,":[6],"and":[7,24,28,61,128,170],"understanding":[8],"of":[9,46,87,124,190],"society.":[10],"However,":[11,65],"conducting":[12],"the":[13,44,84,102,107,113,122,142,155,173,188,195,210],"large":[14],"surveys":[15,42],"required":[16],"to":[17,35,59,100,120,159,207],"subpopulation":[19],"level":[20],"estimates":[21,38,162,169],"is":[22,31,137,154],"expensive":[23],"time-consuming.":[25],"Multilevel":[26],"Regression":[27],"Poststratification":[29],"(MRP)":[30],"a":[32,91,204],"promising":[33],"method":[34],"reliable":[37,50],"subpopulations":[40],"from":[41],"without":[43],"amount":[45],"data":[47,119],"needed":[48],"direct":[51],"estimates.":[52,64],"Graphical":[53],"displays":[54],"have":[55,67],"been":[56,68],"widely":[57],"used":[58,158],"communicate":[60],"diagnose":[62],"MRP":[63,88,99,161,217],"there":[66],"few":[69],"studies":[70],"on":[71,194],"how":[72],"visualisation":[73,89,126,183,205],"should":[74],"be":[75],"performed":[76],"in":[77,106,141],"this":[78,81,200],"field.":[79],"Accordingly,":[80],"study":[82,96,201],"examines":[83],"current":[85,125,143],"practice":[86],"using":[90,112],"systematic":[92],"literature":[93],"review.":[94],"This":[95],"also":[97,202,208],"applies":[98],"estimate":[101],"Trump":[103],"vote":[104],"share":[105],"U.S.":[108],"2016":[109],"presidential":[110],"election":[111],"Cooperative":[114],"Congressional":[115],"Election":[116],"Study":[117],"(CCES)":[118],"illustrate":[121,187],"implication":[123],"practices":[127],"explore":[129],"alternatives":[130],"improvement.":[132],"We":[133],"find":[134],"that":[135,182],"uncertainty":[136,185],"not":[138],"often":[139],"displayed":[140],"practice,":[144],"despite":[145],"its":[146],"importance":[147],"survey":[149],"inference.":[150],"The":[151],"choropleth":[152],"map":[153],"most":[156],"frequently":[157],"display":[160],"even":[163],"though":[164],"it":[165],"only":[166],"shows":[167],"point":[168],"could":[171],"hinder":[172],"information":[174],"conveyed.":[175],"Using":[176],"various":[177],"graphical":[178],"representations,":[179],"we":[180],"show":[181],"with":[184],"can":[186],"effect":[189],"different":[191],"model":[192],"specifications":[193],"estimation":[196],"result.":[197],"In":[198],"addition,":[199],"proposes":[203],"strategy":[206],"take":[209],"bias-variance":[211],"trade-off":[212],"into":[213],"account":[214],"when":[215],"evaluating":[216],"models.":[218]},"counts_by_year":[],"updated_date":"2026-11-14T11:04:58.165337","created_date":"2022-06-13T00:00:00"}
