{"id":"https://openalex.org/W2010438366","doi":"https://doi.org/10.1080/00273171.2014.900431","title":"Simultaneous Decision on the Number of Latent Clusters and Classes for Multilevel Latent Class Models","display_name":"Simultaneous Decision on the Number of Latent Clusters and Classes for Multilevel Latent Class Models","publication_year":2014,"publication_date":"2014-05-04","ids":{"openalex":"https://openalex.org/W2010438366","doi":"https://doi.org/10.1080/00273171.2014.900431","mag":"2010438366","pmid":"https://pubmed.ncbi.nlm.nih.gov/26735190"},"language":"en","primary_location":{"id":"doi:10.1080/00273171.2014.900431","doi":"https://doi.org/10.1080/00273171.2014.900431","is_oa":false,"landing_page_url":"https://doi.org/10.1080/00273171.2014.900431","pdf_url":null,"source":{"id":"https://openalex.org/S64250036","display_name":"Multivariate Behavioral Research","issn_l":"0027-3171","issn":["0027-3171","1532-7906"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","dongbi-b","erih-plus","jufo-1","ki-jl-1","medline","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Multivariate Behavioral Research","raw_type":"journal-article"},"parent_works":[],"lineage":["https://openalex.org/W2010438366"],"child_works_count":0,"type":"article","is_traditional":true,"indexed_in":["crossref","pubmed"],"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/A5007705046","display_name":"Hsiu\u2010Ting Yu","orcid":"https://orcid.org/0000-0002-3668-8033","observed_orcids":["https://orcid.org/0000-0002-3668-8033"]},"institutions":[{"id":"https://openalex.org/I5023651","display_name":"McGill University","ror":"https://ror.org/01pxwe438","country_code":"CA","type":"education","lineage":["https://openalex.org/I5023651"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Hsiu-Ting Yu","raw_affiliation_strings":["Department of Psychology, McGill University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Psychology, McGill University","institution_ids":["https://openalex.org/I5023651"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086834547","display_name":"Jungkyu Park","orcid":"https://orcid.org/0000-0002-8320-576X","observed_orcids":["https://orcid.org/0000-0002-8320-576X"]},"institutions":[{"id":"https://openalex.org/I5023651","display_name":"McGill University","ror":"https://ror.org/01pxwe438","country_code":"CA","type":"education","lineage":["https://openalex.org/I5023651"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jungkyu Park","raw_affiliation_strings":["Department of Psychology, McGill University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Psychology, McGill University","institution_ids":["https://openalex.org/I5023651"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5007705046"],"corresponding_institution_ids":["https://openalex.org/I5023651"],"apc_list":null,"apc_paid":null,"fwci":2.504,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.88296968,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":99},"biblio":{"volume":"49","issue":"3","first_page":"232","last_page":"244"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10467","display_name":"Psychometric Methodologies and Testing","score":0.9199000000953674,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10467","display_name":"Psychometric Methodologies and Testing","score":0.9199000000953674,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.032600000500679016,"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"}},{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.020800000056624413,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/model-selection","display_name":"model selection","score":0.8859999775886536},{"id":"https://openalex.org/keywords/information-criteria","display_name":"information criteria","score":0.9440000057220459},{"id":"https://openalex.org/keywords/nested-data","display_name":"nested data","score":0.9760000109672546},{"id":"https://openalex.org/keywords/sample-size-effects","display_name":"sample size effects","score":0.7509999871253967}],"concepts":[{"id":"https://openalex.org/C70727504","wikidata":"https://www.wikidata.org/wiki/Q1806878","display_name":"Latent class model","level":2,"score":0.8906068801879883},{"id":"https://openalex.org/C65965080","wikidata":"https://www.wikidata.org/wiki/Q1806885","display_name":"Latent variable model","level":3,"score":0.6072763204574585},{"id":"https://openalex.org/C112933361","wikidata":"https://www.wikidata.org/wiki/Q2845258","display_name":"Probabilistic latent semantic analysis","level":2,"score":0.5252922177314758},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5069077610969543},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.48219338059425354},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.4650949239730835},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.43673864006996155},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.42576050758361816},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.425611287355423},{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.4150041937828064},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.38986364006996155},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37417662143707275},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.35009974241256714},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3152950406074524},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28805604577064514},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2629625201225281},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.11063629388809204}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/00273171.2014.900431","doi":"https://doi.org/10.1080/00273171.2014.900431","is_oa":false,"landing_page_url":"https://doi.org/10.1080/00273171.2014.900431","pdf_url":null,"source":{"id":"https://openalex.org/S64250036","display_name":"Multivariate Behavioral Research","issn_l":"0027-3171","issn":["0027-3171","1532-7906"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","dongbi-b","erih-plus","jufo-1","ki-jl-1","medline","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Multivariate Behavioral Research","raw_type":"journal-article"},{"id":"pmid:26735190","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/26735190","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Multivariate behavioral research","raw_type":"Journal Article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W26112474","https://openalex.org/W42845060","https://openalex.org/W138052739","https://openalex.org/W168645371","https://openalex.org/W626100886","https://openalex.org/W1506422675","https://openalex.org/W1527291206","https://openalex.org/W1571139513","https://openalex.org/W1572134371","https://openalex.org/W1579271636","https://openalex.org/W1595369573","https://openalex.org/W1604544535","https://openalex.org/W1796738135","https://openalex.org/W1830719945","https://openalex.org/W1973443859","https://openalex.org/W1974510822","https://openalex.org/W1980862128","https://openalex.org/W1981903823","https://openalex.org/W1985321835","https://openalex.org/W2006005104","https://openalex.org/W2006756398","https://openalex.org/W2015307819","https://openalex.org/W2016359636","https://openalex.org/W2019657225","https://openalex.org/W2020862928","https://openalex.org/W2022306341","https://openalex.org/W2033029828","https://openalex.org/W2044579390","https://openalex.org/W2051974258","https://openalex.org/W2052417735","https://openalex.org/W2055241620","https://openalex.org/W2057964179","https://openalex.org/W2058815839","https://openalex.org/W2070411279","https://openalex.org/W2079817125","https://openalex.org/W2081985429","https://openalex.org/W2090330784","https://openalex.org/W2096353520","https://openalex.org/W2118265605","https://openalex.org/W2118360516","https://openalex.org/W2136209445","https://openalex.org/W2146855196","https://openalex.org/W2155377619","https://openalex.org/W2169988609","https://openalex.org/W2171460817","https://openalex.org/W2242071559","https://openalex.org/W2554987453","https://openalex.org/W3121499295","https://openalex.org/W4256589769","https://openalex.org/W4294841944","https://openalex.org/W4301284037"],"related_works":["https://openalex.org/W2758319537","https://openalex.org/W2155377619","https://openalex.org/W2329897787","https://openalex.org/W7001791267","https://openalex.org/W2047431113","https://openalex.org/W4415396382","https://openalex.org/W7171751258","https://openalex.org/W980567460","https://openalex.org/W2090330784","https://openalex.org/W2036006420"],"abstract_inverted_index":{"The":[0,27,56,136],"Multilevel":[1],"Latent":[2,86],"Class":[3,87],"Model":[4,46],"(MLCM)":[5],"proposed":[6],"by":[7],"Vermunt":[8],"(2003)":[9],"has":[10],"been":[11],"shown":[12],"to":[13,39,64,96,159],"be":[14],"an":[15,49],"excellent":[16],"framework":[17],"for":[18,61],"analyzing":[19],"nested":[20],"data":[21],"with":[22],"assumed":[23],"discrete":[24,36,71],"latent":[25,37,72,110,125,152],"constructs.":[26],"nonparametric":[28],"version":[29],"of":[30,35,58,70,101,109,117,128,151],"MLCM":[31,62],"assumes":[32],"2":[33],"levels":[34,79],"components":[38,73],"describe":[40],"the":[41,65,68,99,107,115,123,129,140,145,149],"dependency":[42],"observed":[43],"in":[44,52,121],"data.":[45,162],"selection":[47,60],"is":[48,81],"important":[50],"step":[51],"any":[53],"statistical":[54],"modeling.":[55],"task":[57],"model":[59],"amounts":[63],"decision":[66,147],"on":[67,114],"number":[69,108,150],"at":[74],"both":[75],"higher":[76],"and":[77,80,106,112,134,142,154],"lower":[78],"more":[82],"challenging":[83],"than":[84],"standard":[85],"Models.":[88],"In":[89],"this":[90],"article,":[91],"simulation":[92,130],"studies":[93,131],"were":[94],"conducted":[95],"systematically":[97],"examine":[98],"effects":[100],"sample":[102],"sizes,":[103],"clusters/classes":[104],"distinctness,":[105],"clusters":[111,155],"classes":[113,153],"performance":[116],"various":[118],"information":[119],"criteria":[120],"recovering":[122],"true":[124],"structure.":[126],"Results":[127],"are":[132],"summarized":[133],"presented.":[135],"final":[137],"section":[138],"presents":[139],"remarks":[141],"recommendations":[143],"about":[144],"simultaneous":[146],"regarding":[148],"when":[156],"applying":[157],"MLCMs":[158],"analyze":[160],"empirical":[161]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":1}],"updated_date":"2026-11-11T12:16:26.678958","created_date":"2016-06-24T00:00:00"}
