{"id":"https://openalex.org/W595511209","doi":"https://doi.org/10.1198/004017005000000300","title":"Combining Data in Small Multiple-Method Studies","display_name":"Combining Data in Small Multiple-Method Studies","publication_year":2006,"publication_date":"2006-04-23","ids":{"openalex":"https://openalex.org/W595511209","doi":"https://doi.org/10.1198/004017005000000300","mag":"595511209"},"language":"en","primary_location":{"id":"doi:10.1198/004017005000000300","is_oa":false,"landing_page_url":"https://doi.org/10.1198/004017005000000300","pdf_url":null,"source":{"id":"https://openalex.org/S985303","display_name":"Technometrics","issn_l":"0040-1706","issn":["0040-1706","1537-2723"],"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","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":"Technometrics","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/A5081744665","display_name":"Charles Hagwood","orcid":"https://orcid.org/0000-0003-1097-9067"},"institutions":[{"id":"https://openalex.org/I1321296531","display_name":"National Institute of Standards and Technology","ror":"https://ror.org/05xpvk416","country_code":"US","type":"funder","lineage":["https://openalex.org/I1321296531","https://openalex.org/I1343035065"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Charles Hagwood","raw_affiliation_strings":["Statistical Engineering Division National Institute of Standards and Technology Gaithersburg, MD 20899"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Statistical Engineering Division National Institute of Standards and Technology Gaithersburg, MD 20899","institution_ids":["https://openalex.org/I1321296531"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111577033","display_name":"William Guthrie","orcid":null},"institutions":[{"id":"https://openalex.org/I1321296531","display_name":"National Institute of Standards and Technology","ror":"https://ror.org/05xpvk416","country_code":"US","type":"funder","lineage":["https://openalex.org/I1321296531","https://openalex.org/I1343035065"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"William Guthrie","raw_affiliation_strings":["Statistical Engineering Division National Institute of Standards and Technology Gaithersburg, MD 20899"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Statistical Engineering Division National Institute of Standards and Technology Gaithersburg, MD 20899","institution_ids":["https://openalex.org/I1321296531"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I1321296531"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.02874175,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"48","issue":"2","first_page":"293","last_page":"302"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11700","display_name":"Hemodynamic Monitoring and Therapy","score":0.9854999780654907,"subfield":{"id":"https://openalex.org/subfields/2746","display_name":"Surgery"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11700","display_name":"Hemodynamic Monitoring and Therapy","score":0.9854999780654907,"subfield":{"id":"https://openalex.org/subfields/2746","display_name":"Surgery"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11443","display_name":"Advanced Statistical Process Monitoring","score":0.9850999712944031,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"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/T11890","display_name":"Scientific Measurement and Uncertainty Evaluation","score":0.9775999784469604,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/credible-interval","display_name":"Credible interval","score":0.8254840970039368},{"id":"https://openalex.org/keywords/confidence-interval","display_name":"Confidence interval","score":0.769128680229187},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.7106298208236694},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.7027002573013306},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.6091309785842896},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5874682068824768},{"id":"https://openalex.org/keywords/standard-deviation","display_name":"Standard deviation","score":0.5274206399917603},{"id":"https://openalex.org/keywords/interval","display_name":"Interval (graph theory)","score":0.5062867403030396},{"id":"https://openalex.org/keywords/tolerance-interval","display_name":"Tolerance interval","score":0.5034226775169373},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.496631920337677},{"id":"https://openalex.org/keywords/bayesian-hierarchical-modeling","display_name":"Bayesian hierarchical modeling","score":0.4933077394962311},{"id":"https://openalex.org/keywords/robust-confidence-intervals","display_name":"Robust confidence intervals","score":0.48544007539749146},{"id":"https://openalex.org/keywords/cdf-based-nonparametric-confidence-interval","display_name":"CDF-based nonparametric confidence interval","score":0.43268340826034546},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.37038150429725647},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3362942337989807},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.32127779722213745}],"concepts":[{"id":"https://openalex.org/C121117317","wikidata":"https://www.wikidata.org/wiki/Q4439221","display_name":"Credible interval","level":3,"score":0.8254840970039368},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.769128680229187},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.7106298208236694},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.7027002573013306},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.6091309785842896},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5874682068824768},{"id":"https://openalex.org/C22679943","wikidata":"https://www.wikidata.org/wiki/Q159375","display_name":"Standard deviation","level":2,"score":0.5274206399917603},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.5062867403030396},{"id":"https://openalex.org/C10590107","wikidata":"https://www.wikidata.org/wiki/Q7814221","display_name":"Tolerance interval","level":3,"score":0.5034226775169373},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.496631920337677},{"id":"https://openalex.org/C191413810","wikidata":"https://www.wikidata.org/wiki/Q17100952","display_name":"Bayesian hierarchical modeling","level":4,"score":0.4933077394962311},{"id":"https://openalex.org/C66520545","wikidata":"https://www.wikidata.org/wiki/Q7353538","display_name":"Robust confidence intervals","level":3,"score":0.48544007539749146},{"id":"https://openalex.org/C42468098","wikidata":"https://www.wikidata.org/wiki/Q5009921","display_name":"CDF-based nonparametric confidence interval","level":3,"score":0.43268340826034546},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.37038150429725647},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3362942337989807},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.32127779722213745},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1198/004017005000000300","is_oa":false,"landing_page_url":"https://doi.org/10.1198/004017005000000300","pdf_url":null,"source":{"id":"https://openalex.org/S985303","display_name":"Technometrics","issn_l":"0040-1706","issn":["0040-1706","1537-2723"],"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","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":"Technometrics","raw_type":"journal-article"},{"id":"mag:595511209","is_oa":false,"landing_page_url":"https://dialnet.unirioja.es/servlet/articulo?codigo=4617956","pdf_url":null,"source":{"id":"https://openalex.org/S13624874","display_name":"Quality Engineering","issn_l":"0898-2112","issn":["0898-2112","1532-4222"],"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","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"Quality Engineering","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1495091607","https://openalex.org/W1969624379","https://openalex.org/W1999147319","https://openalex.org/W2004134002","https://openalex.org/W2016646427","https://openalex.org/W2025318362","https://openalex.org/W2072423759","https://openalex.org/W2088163714","https://openalex.org/W2137480309","https://openalex.org/W2315123323","https://openalex.org/W2413573154","https://openalex.org/W2467562935","https://openalex.org/W2802898185","https://openalex.org/W4240237263","https://openalex.org/W4243132233"],"related_works":["https://openalex.org/W2071514442","https://openalex.org/W2963600133","https://openalex.org/W2115299933","https://openalex.org/W1913823733","https://openalex.org/W2277468085","https://openalex.org/W2014490515","https://openalex.org/W2482336110","https://openalex.org/W3087716616","https://openalex.org/W3098098310","https://openalex.org/W2025903114","https://openalex.org/W2128512291","https://openalex.org/W3122652510","https://openalex.org/W2050078602","https://openalex.org/W2777493566","https://openalex.org/W2271111195","https://openalex.org/W2972079029","https://openalex.org/W2358235643","https://openalex.org/W1992796829","https://openalex.org/W2517221085","https://openalex.org/W3110273989"],"abstract_inverted_index":{"In":[0],"this":[1,81],"article":[2],"an":[3,27,87],"accurate":[4,90],"confidence":[5,49,121],"interval":[6,88,94,100,122],"is":[7,101],"derived":[8],"when":[9,57],"the":[10,24,31,70,74,77,92,96,108,112,120,127],"results":[11],"of":[12,16,26,41,76,132],"a":[13,36,67,133],"small":[14],"number":[15],"possibly":[17],"biased":[18],"experimental":[19,60,85],"methods":[20,64],"are":[21,123],"combined":[22],"for":[23,53],"determination":[25],"unknown":[28],"quantity":[29],"called":[30],"consensus":[32],"mean.":[33],"ANOVA":[34,93],"and":[35,95,111,114,130],"simple":[37,97],"hierarchical":[38,98],"Bayesian":[39,99,135],"analysis":[40],"variance":[42,131],"with":[43,84],"locally":[44],"uniform":[45],"priors":[46],"result":[47],"in":[48,65],"intervals":[50],"too":[51],"wide":[52],"precision":[54],"metrology.":[55],"Often":[56],"deciding":[58],"on":[59],"methods,":[61],"scientists":[62],"choose":[63],"such":[66],"way":[68],"that":[69],"truth":[71],"lies":[72],"between":[73],"extremes":[75],"method":[78],"means.":[79],"Combining":[80],"additional":[82],"information":[83],"data,":[86],"more":[89],"than":[91],"obtained.":[102],"The":[103],"estimate":[104],"obtained":[105],"falls":[106],"within":[107],"ISO":[109],"guidelines,":[110],"mean":[113,129],"standard":[115],"deviation":[116],"used":[117],"to":[118,125],"derive":[119],"shown":[124],"be":[126],"posterior":[128],"fully":[134],"procedure.":[136]},"counts_by_year":[],"updated_date":"2026-08-15T07:11:24.734988","created_date":"2025-10-10T00:00:00"}