{"id":"https://openalex.org/W3163947212","doi":"https://doi.org/10.1080/03610918.2021.1919707","title":"Bayesian multilevel multidimensional item response modeling approach for multiple latent variables in a hierarchical structure","display_name":"Bayesian multilevel multidimensional item response modeling approach for multiple latent variables in a hierarchical structure","publication_year":2021,"publication_date":"2021-05-09","ids":{"openalex":"https://openalex.org/W3163947212","doi":"https://doi.org/10.1080/03610918.2021.1919707","mag":"3163947212"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2021.1919707","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2021.1919707","pdf_url":null,"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":null,"license_id":null,"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":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100353050","display_name":"Jiwei Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiwei Zhang","raw_affiliation_strings":["Key Lab of Statistical Modeling and Data Analysis of Yunnan Province, School of Mathematics and Statistics, Yunnan University, Kunming, Yunnan, China"],"affiliations":[{"raw_affiliation_string":"Key Lab of Statistical Modeling and Data Analysis of Yunnan Province, School of Mathematics and Statistics, Yunnan University, Kunming, Yunnan, China","institution_ids":["https://openalex.org/I189210763"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101948865","display_name":"Jing L\u00fc","orcid":"https://orcid.org/0000-0001-8333-9146"},"institutions":[{"id":"https://openalex.org/I184983240","display_name":"Northeast Normal University","ror":"https://ror.org/02rkvz144","country_code":"CN","type":"education","lineage":["https://openalex.org/I184983240"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jing Lu","raw_affiliation_strings":["Key Laboratory of Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, Changchun, Jilin, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, Changchun, Jilin, China","institution_ids":["https://openalex.org/I184983240"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020214478","display_name":"Xin Xu","orcid":"https://orcid.org/0000-0002-0791-111X"},"institutions":[{"id":"https://openalex.org/I184983240","display_name":"Northeast Normal University","ror":"https://ror.org/02rkvz144","country_code":"CN","type":"education","lineage":["https://openalex.org/I184983240"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Xu","raw_affiliation_strings":["Key Laboratory of Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, Changchun, Jilin, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, Changchun, Jilin, China","institution_ids":["https://openalex.org/I184983240"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014261970","display_name":"Jian Tao","orcid":"https://orcid.org/0000-0002-0343-1426"},"institutions":[{"id":"https://openalex.org/I184983240","display_name":"Northeast Normal University","ror":"https://ror.org/02rkvz144","country_code":"CN","type":"education","lineage":["https://openalex.org/I184983240"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Tao","raw_affiliation_strings":["Key Laboratory of Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, Changchun, Jilin, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, Changchun, Jilin, China","institution_ids":["https://openalex.org/I184983240"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101948865"],"corresponding_institution_ids":["https://openalex.org/I184983240"],"apc_list":null,"apc_paid":null,"fwci":0.2365,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.57065244,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"52","issue":"7","first_page":"2822","last_page":"2842"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9970999956130981,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9970999956130981,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9797999858856201,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9697999954223633,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.6851223707199097},{"id":"https://openalex.org/keywords/multilevel-model","display_name":"Multilevel model","score":0.6651127934455872},{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.6598338484764099},{"id":"https://openalex.org/keywords/deviance-information-criterion","display_name":"Deviance information criterion","score":0.6269391179084778},{"id":"https://openalex.org/keywords/hierarchical-database-model","display_name":"Hierarchical database model","score":0.6052215695381165},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.5900596380233765},{"id":"https://openalex.org/keywords/deviance","display_name":"Deviance (statistics)","score":0.5888221859931946},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5421529412269592},{"id":"https://openalex.org/keywords/structural-equation-modeling","display_name":"Structural equation modeling","score":0.48519256711006165},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.45640599727630615},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4539685845375061},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.36256909370422363},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3383557200431824},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.334800124168396},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.33120763301849365},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3106176257133484},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2835174798965454}],"concepts":[{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.6851223707199097},{"id":"https://openalex.org/C53059260","wikidata":"https://www.wikidata.org/wiki/Q374758","display_name":"Multilevel model","level":2,"score":0.6651127934455872},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.6598338484764099},{"id":"https://openalex.org/C17634605","wikidata":"https://www.wikidata.org/wiki/Q972431","display_name":"Deviance information criterion","level":4,"score":0.6269391179084778},{"id":"https://openalex.org/C144986985","wikidata":"https://www.wikidata.org/wiki/Q871236","display_name":"Hierarchical database model","level":2,"score":0.6052215695381165},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.5900596380233765},{"id":"https://openalex.org/C177599991","wikidata":"https://www.wikidata.org/wiki/Q3706279","display_name":"Deviance (statistics)","level":2,"score":0.5888221859931946},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5421529412269592},{"id":"https://openalex.org/C71104824","wikidata":"https://www.wikidata.org/wiki/Q1476639","display_name":"Structural equation modeling","level":2,"score":0.48519256711006165},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.45640599727630615},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4539685845375061},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.36256909370422363},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3383557200431824},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.334800124168396},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.33120763301849365},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3106176257133484},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2835174798965454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610918.2021.1919707","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2021.1919707","pdf_url":null,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.8799999952316284}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":59,"referenced_works":["https://openalex.org/W52094030","https://openalex.org/W128740895","https://openalex.org/W1487482136","https://openalex.org/W1535520578","https://openalex.org/W1595860368","https://openalex.org/W1604380904","https://openalex.org/W1648888400","https://openalex.org/W1968346944","https://openalex.org/W1990430331","https://openalex.org/W2002091574","https://openalex.org/W2005736442","https://openalex.org/W2006338519","https://openalex.org/W2015558203","https://openalex.org/W2016570224","https://openalex.org/W2017966270","https://openalex.org/W2020999234","https://openalex.org/W2031792658","https://openalex.org/W2034518286","https://openalex.org/W2040228963","https://openalex.org/W2049376076","https://openalex.org/W2057765075","https://openalex.org/W2063439372","https://openalex.org/W2079101498","https://openalex.org/W2079235780","https://openalex.org/W2084398551","https://openalex.org/W2090643063","https://openalex.org/W2093223772","https://openalex.org/W2098300789","https://openalex.org/W2099368747","https://openalex.org/W2104716902","https://openalex.org/W2108130864","https://openalex.org/W2113338391","https://openalex.org/W2119159447","https://openalex.org/W2124607502","https://openalex.org/W2135934118","https://openalex.org/W2138703513","https://openalex.org/W2148534890","https://openalex.org/W2152977846","https://openalex.org/W2154372487","https://openalex.org/W2157852156","https://openalex.org/W2158672897","https://openalex.org/W2162929365","https://openalex.org/W2164868033","https://openalex.org/W2168003017","https://openalex.org/W2168175751","https://openalex.org/W2170454983","https://openalex.org/W2283850105","https://openalex.org/W2327332664","https://openalex.org/W2500845915","https://openalex.org/W2798264002","https://openalex.org/W2921407539","https://openalex.org/W3098349105","https://openalex.org/W4214744654","https://openalex.org/W4229669534","https://openalex.org/W4234508882","https://openalex.org/W4240166507","https://openalex.org/W4241635929","https://openalex.org/W4246435226","https://openalex.org/W6674661097"],"related_works":["https://openalex.org/W2067456216","https://openalex.org/W2902559081","https://openalex.org/W4362554097","https://openalex.org/W3121270766","https://openalex.org/W2017600768","https://openalex.org/W2139746560","https://openalex.org/W4248004587","https://openalex.org/W2094078243","https://openalex.org/W4382918797","https://openalex.org/W3122570117"],"abstract_inverted_index":{"In":[0],"this":[1,28],"paper,":[2],"we":[3],"propose":[4],"a":[5,21,39,114],"multilevel":[6],"multidimensional":[7,42,71],"item":[8],"response":[9],"model":[10,65,115,131],"for":[11,99],"studying":[12],"the":[13,35,68,75,90,106,123],"relations":[14],"among":[15,70],"multiple":[16],"abilities":[17],"and":[18,50,53,79,82],"covariates":[19,81],"in":[20,37,54,128],"hierarchical":[22],"data":[23],"structure.":[24],"As":[25],"an":[26],"example,":[27],"study":[29],"is":[30,97,111,126],"well":[31],"suited":[32],"to":[33],"examining":[34],"scenario":[36],"which":[38,55],"test":[40],"measures":[41],"latent":[43],"traits":[44],"(e.g.,":[45],"reading":[46],"ability,":[47,49],"cognitive":[48],"computing":[51],"ability)":[52],"students":[56],"are":[57],"nested":[58],"within":[59,89],"classes":[60],"or":[61],"schools.":[62],"The":[63],"new":[64],"can":[66],"recover":[67],"correlations":[69],"abilities,":[72],"along":[73],"with":[74],"correlation":[76],"between":[77],"person-":[78],"school-level":[80],"abilities.":[83],"A":[84,102],"fully":[85],"Gibbs":[86],"sampling":[87],"algorithm":[88],"Markov":[91],"chain":[92],"Monte":[93],"Carlo":[94],"(MCMC)":[95],"framework":[96],"proposed":[98],"parameter":[100],"estimation.":[101],"unique":[103],"form":[104],"of":[105],"deviance":[107],"information":[108],"criterion":[109],"(DIC)":[110],"used":[112],"as":[113],"comparison":[116],"index.":[117],"Two":[118],"simulation":[119],"studies":[120],"show":[121],"that":[122],"estimation":[124],"method":[125],"suitable":[127],"recovering":[129],"all":[130],"parameters.":[132]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}