{"id":"https://openalex.org/W6960109024","doi":"https://doi.org/10.13016/luya-bjt2","title":"Construct measurement error in latent social network relationship: An item response theory based latent space model","display_name":"Construct measurement error in latent social network relationship: An item response theory based latent space model","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W6960109024","doi":"https://doi.org/10.13016/luya-bjt2"},"language":"en","primary_location":{"id":"pmh:oai:drum.lib.umd.edu:1903/31726","is_oa":true,"landing_page_url":"http://hdl.handle.net/1903/31726","pdf_url":"https://drum.lib.umd.edu/bitstreams/902aa3d4-5775-4266-8893-12f77e086e17/download","source":{"id":"https://openalex.org/S4306401518","display_name":"University Libraries (University of Maryland)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66946132","host_organization_name":"University of Maryland, College Park","host_organization_lineage":["https://openalex.org/I66946132"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dissertation"},"type":"other","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://drum.lib.umd.edu/bitstreams/902aa3d4-5775-4266-8893-12f77e086e17/download","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Ding, Yishan","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ding, Yishan","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":[],"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":true,"primary_topic":{"id":"https://openalex.org/T11468","display_name":"Genetic Mapping and Diversity in Plants and Animals","score":0.4959999918937683,"subfield":{"id":"https://openalex.org/subfields/1311","display_name":"Genetics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T11468","display_name":"Genetic Mapping and Diversity in Plants and Animals","score":0.4959999918937683,"subfield":{"id":"https://openalex.org/subfields/1311","display_name":"Genetics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12045","display_name":"Rice Cultivation and Yield Improvement","score":0.1551000028848648,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12472","display_name":"Plant responses to water stress","score":0.05649999901652336,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.8120999932289124},{"id":"https://openalex.org/keywords/item-response-theory","display_name":"Item response theory","score":0.5741999745368958},{"id":"https://openalex.org/keywords/social-network","display_name":"Social network (sociolinguistics)","score":0.5551000237464905},{"id":"https://openalex.org/keywords/proxy","display_name":"Proxy (statistics)","score":0.5217999815940857},{"id":"https://openalex.org/keywords/observational-error","display_name":"Observational error","score":0.4562000036239624},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.45570001006126404},{"id":"https://openalex.org/keywords/latent-variable-model","display_name":"Latent variable model","score":0.4377000033855438},{"id":"https://openalex.org/keywords/social-network-analysis","display_name":"Social network analysis","score":0.420199990272522},{"id":"https://openalex.org/keywords/level-of-measurement","display_name":"Level of measurement","score":0.35420000553131104},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.35359999537467957}],"concepts":[{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.8120999932289124},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5924999713897705},{"id":"https://openalex.org/C19875794","wikidata":"https://www.wikidata.org/wiki/Q1207340","display_name":"Item response theory","level":3,"score":0.5741999745368958},{"id":"https://openalex.org/C4727928","wikidata":"https://www.wikidata.org/wiki/Q17164759","display_name":"Social network (sociolinguistics)","level":3,"score":0.5551000237464905},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.5217999815940857},{"id":"https://openalex.org/C19619285","wikidata":"https://www.wikidata.org/wiki/Q196372","display_name":"Observational error","level":2,"score":0.4562000036239624},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.45570001006126404},{"id":"https://openalex.org/C65965080","wikidata":"https://www.wikidata.org/wiki/Q1806885","display_name":"Latent variable model","level":3,"score":0.4377000033855438},{"id":"https://openalex.org/C114713312","wikidata":"https://www.wikidata.org/wiki/Q7551269","display_name":"Social network analysis","level":3,"score":0.420199990272522},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3783999979496002},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36660000681877136},{"id":"https://openalex.org/C55786151","wikidata":"https://www.wikidata.org/wiki/Q951437","display_name":"Level of measurement","level":2,"score":0.35420000553131104},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.35359999537467957},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34700000286102295},{"id":"https://openalex.org/C131158328","wikidata":"https://www.wikidata.org/wiki/Q1307337","display_name":"Social influence","level":2,"score":0.3312000036239624},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.32839998602867126},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.32089999318122864},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3172000050544739},{"id":"https://openalex.org/C71104824","wikidata":"https://www.wikidata.org/wiki/Q1476639","display_name":"Structural equation modeling","level":2,"score":0.3084999918937683},{"id":"https://openalex.org/C156216132","wikidata":"https://www.wikidata.org/wiki/Q17099642","display_name":"Local independence","level":4,"score":0.3068000078201294},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.30320000648498535},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.29989999532699585},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.2962999939918518},{"id":"https://openalex.org/C13606891","wikidata":"https://www.wikidata.org/wiki/Q2623243","display_name":"Conceptual model","level":2,"score":0.29330000281333923},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.28929999470710754},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.28850001096725464},{"id":"https://openalex.org/C49453240","wikidata":"https://www.wikidata.org/wiki/Q1592163","display_name":"Construct validity","level":3,"score":0.273499995470047},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.27219998836517334},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.2685999870300293},{"id":"https://openalex.org/C62886766","wikidata":"https://www.wikidata.org/wiki/Q17013138","display_name":"Organizational network analysis","level":3,"score":0.2669000029563904},{"id":"https://openalex.org/C13540734","wikidata":"https://www.wikidata.org/wiki/Q5318996","display_name":"Dynamic network analysis","level":2,"score":0.2639999985694885},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.2621999979019165},{"id":"https://openalex.org/C171606756","wikidata":"https://www.wikidata.org/wiki/Q506132","display_name":"Psychometrics","level":2,"score":0.26159998774528503},{"id":"https://openalex.org/C38180746","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate analysis","level":2,"score":0.26100000739097595},{"id":"https://openalex.org/C137753397","wikidata":"https://www.wikidata.org/wiki/Q2434424","display_name":"Network science","level":3,"score":0.257099986076355},{"id":"https://openalex.org/C70727504","wikidata":"https://www.wikidata.org/wiki/Q1806878","display_name":"Latent class model","level":2,"score":0.2549000084400177},{"id":"https://openalex.org/C32946077","wikidata":"https://www.wikidata.org/wiki/Q618079","display_name":"Network analysis","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:drum.lib.umd.edu:1903/31726","is_oa":true,"landing_page_url":"http://hdl.handle.net/1903/31726","pdf_url":"https://drum.lib.umd.edu/bitstreams/902aa3d4-5775-4266-8893-12f77e086e17/download","source":{"id":"https://openalex.org/S4306401518","display_name":"University Libraries (University of Maryland)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66946132","host_organization_name":"University of Maryland, College Park","host_organization_lineage":["https://openalex.org/I66946132"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dissertation"},{"id":"doi:10.13016/luya-bjt2","is_oa":true,"landing_page_url":"https://doi.org/10.13016/luya-bjt2","pdf_url":null,"source":{"id":"https://openalex.org/S4306402644","display_name":"Digital Repository at the University of Maryland (University of Maryland College Park)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66946132","host_organization_name":"University of Maryland, College Park","host_organization_lineage":["https://openalex.org/I66946132"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"thesis"}],"best_oa_location":{"id":"pmh:oai:drum.lib.umd.edu:1903/31726","is_oa":true,"landing_page_url":"http://hdl.handle.net/1903/31726","pdf_url":"https://drum.lib.umd.edu/bitstreams/902aa3d4-5775-4266-8893-12f77e086e17/download","source":{"id":"https://openalex.org/S4306401518","display_name":"University Libraries (University of Maryland)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66946132","host_organization_name":"University of Maryland, College Park","host_organization_lineage":["https://openalex.org/I66946132"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dissertation"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.6682124137878418,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W6960109024.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Research":[0],"on":[1,11,113,203],"measurement":[2,13,30,48,90,141,187,230,274,304],"error":[3,49,91,142],"in":[4,35,43,143,260,271,310],"social":[5,26,44,54,85,104,114,144,190,198],"network":[6,45,55,105,115,145],"analysis":[7,106,252],"has":[8,39],"primarily":[9],"focused":[10],"proxy":[12,23],"error,":[14,31,231,305],"which":[15,68],"refers":[16],"to":[17,62,80,87,177,184,201,307],"inadequate":[18],"or":[19,60,130],"inaccurate":[20],"observations":[21],"of":[22,25,99,180,188,197,211,228,244,257,268],"measurements":[24],"relationships.":[27],"However,":[28,94],"construct":[29,89,140,229,273,303],"a":[32,97,127,135,150,165,181,193,204,232,239],"key":[33],"concern":[34],"modern":[36],"psychometric":[37],"studies,":[38],"received":[40],"less":[41],"attention":[42],"studies.":[46],"Construct":[47],"is":[50,96,117],"particularly":[51],"relevant":[52],"for":[53,84,102],"relationships":[56,86,199],"that":[57,156,301],"are":[58,69],"difficult":[59],"impossible":[61],"observe":[63],"explicitly,":[64],"such":[65],"as":[66,72,134,154],"friendships,":[67],"better":[70],"conceptualized":[71],"latent":[73,166,189],"constructs.":[74],"Historically,":[75],"researchers":[76],"have":[77],"long":[78],"advocated":[79],"use":[81],"multi-item":[82,108,182],"scales":[83],"address":[88],"(Marsden,":[92],"1990).":[93],"there":[95],"lack":[98],"methods":[100],"tailored":[101],"multivariate":[103],"using":[107],"measurements.":[109],"Commonly,":[110],"when":[111],"data":[112,251,313],"ties":[116],"collected":[118],"from":[119],"multiple":[120],"items,":[121],"prevalent":[122],"strategies":[123],"involve":[124],"either":[125],"choosing":[126],"representative":[128],"item":[129,133,159,245],"analyzing":[131],"each":[132],"distinct":[136],"network.":[137],"To":[138,207],"accommodate":[139],"analysis,":[146],"this":[147,212],"study":[148],"proposes":[149],"new":[151],"model,":[152],"termed":[153],"IRT-LSM,":[155],"integrates":[157],"an":[158,249],"response":[160],"theory":[161],"(IRT)":[162],"model":[163,168,176,223,277],"into":[164],"space":[167],"(LSM).":[169],"The":[170,263,276],"proposed":[171],"method":[172],"leverages":[173],"the":[174,186,209,215,226,242,254,258,266,269,292,296,311],"IRT":[175],"take":[178],"advantage":[179],"scale":[183],"enhance":[185],"relationships,":[191],"providing":[192],"more":[194],"comprehensive":[195],"understanding":[196],"compared":[200],"relying":[202],"single":[205],"item.":[206],"evaluate":[208],"efficacy":[210],"novel":[213],"approach,":[214],"dissertation":[216],"comprises":[217],"three":[218],"simulation":[219],"studies:":[220],"One":[221],"assessing":[222],"feasibility":[224],"and":[225,238,282],"impact":[227],"second":[233],"exploring":[234],"various":[235,286],"misspecification":[236],"models,":[237],"third":[240],"investigating":[241],"effects":[243],"parameter":[246],"distributions.":[247],"Additionally,":[248],"empirical":[250,312],"demonstrates":[253,283],"practical":[255],"application":[256],"IRT-LSM":[259,270,297],"real-world":[261],"settings.":[262],"results":[264],"underscore":[265],"effectiveness":[267],"addressing":[272],"error.":[275],"consistently":[278],"yields":[279],"unbiased":[280],"estimates":[281],"robustness":[284],"against":[285],"factors":[287],"influencing":[288],"its":[289],"performance":[290],"across":[291],"simulated":[293],"conditions.":[294],"Notably,":[295],"outperforms":[298],"naive":[299],"approaches":[300],"neglect":[302],"leading":[306],"divergent":[308],"conclusions":[309],"analyses.":[314]},"counts_by_year":[],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}