{"id":"https://openalex.org/W3014330132","doi":null,"title":"Best Practices for Constructing Confidence Intervals for the General Linear Model Under Non-Normality","display_name":"Best Practices for Constructing Confidence Intervals for the General Linear Model Under Non-Normality","publication_year":2017,"publication_date":"2017-11-14","ids":{"openalex":"https://openalex.org/W3014330132","mag":"3014330132"},"language":"en","primary_location":{"id":"pmh:oai:yorkspace.library.yorku.ca:10315/34511","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/10315/34511","pdf_url":"http://hdl.handle.net/10315/34511","source":{"id":"https://openalex.org/S4306400473","display_name":"YorkSpace (York University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I192455969","host_organization_name":"York University","host_organization_lineage":["https://openalex.org/I192455969"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Electronic Thesis or Dissertation"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://hdl.handle.net/10315/34511","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5004362342","display_name":"Mark Christopher Adkins","orcid":"https://orcid.org/0000-0002-3173-9479","observed_orcids":["https://orcid.org/0000-0002-3173-9479"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Mark Christopher Adkins","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-3173-9479","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5004362342"],"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.524399995803833,"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":0.524399995803833,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.23839999735355377,"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/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.06400000303983688,"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/confidence-intervals","display_name":"confidence intervals","score":0.996999979019165},{"id":"https://openalex.org/keywords/general-linear-model","display_name":"general linear model","score":0.9539999961853027},{"id":"https://openalex.org/keywords/non-normality","display_name":"non-normality","score":0.9049999713897705},{"id":"https://openalex.org/keywords/effect-size","display_name":"effect size","score":0.6890000104904175},{"id":"https://openalex.org/keywords/replication-crisis","display_name":"replication crisis","score":0.7950000166893005},{"id":"https://openalex.org/keywords/null-hypothesis-significance-testing","display_name":"null hypothesis significance testing","score":0.800000011920929},{"id":"https://openalex.org/keywords/data-transformation","display_name":"data transformation","score":0.6650000214576721},{"id":"https://openalex.org/keywords/percentile-bootstrap","display_name":"percentile bootstrap","score":0.8420000076293945},{"id":"https://openalex.org/keywords/coverage-accuracy","display_name":"coverage accuracy","score":0.6769999861717224},{"id":"https://openalex.org/keywords/monte-carlo-simulation","display_name":"Monte Carlo simulation","score":0.699999988079071},{"id":"https://openalex.org/keywords/non-normal-error-distributions","display_name":"non-normal error distributions","score":0.6579999923706055}],"concepts":[{"id":"https://openalex.org/C2776157432","wikidata":"https://www.wikidata.org/wiki/Q1375683","display_name":"Normality","level":2,"score":0.743486225605011},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.6027290225028992},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.49601826071739197},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4631364643573761},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.37708693742752075}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:yorkspace.library.yorku.ca:10315/34511","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/10315/34511","pdf_url":"http://hdl.handle.net/10315/34511","source":{"id":"https://openalex.org/S4306400473","display_name":"YorkSpace (York University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I192455969","host_organization_name":"York University","host_organization_lineage":["https://openalex.org/I192455969"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Electronic Thesis or Dissertation"},{"id":"mag:3014330132","doi":null,"is_oa":false,"landing_page_url":"https://yorkspace.library.yorku.ca/xmlui/handle/10315/34511","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:yorkspace.library.yorku.ca:10315/34511","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/10315/34511","pdf_url":"http://hdl.handle.net/10315/34511","source":{"id":"https://openalex.org/S4306400473","display_name":"YorkSpace (York University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I192455969","host_organization_name":"York University","host_organization_lineage":["https://openalex.org/I192455969"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Electronic Thesis or Dissertation"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3014330132.pdf","grobid_xml":"https://content.openalex.org/works/W3014330132.grobid-xml"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W1507985183","https://openalex.org/W1513618424","https://openalex.org/W2000295531","https://openalex.org/W2032167823","https://openalex.org/W2055367468","https://openalex.org/W2058806401","https://openalex.org/W2102493402","https://openalex.org/W2111669400","https://openalex.org/W2112688502","https://openalex.org/W2115050143","https://openalex.org/W2127875809","https://openalex.org/W2154516913","https://openalex.org/W2155982020","https://openalex.org/W2307358936","https://openalex.org/W2538623560","https://openalex.org/W2559671103","https://openalex.org/W2597353595","https://openalex.org/W2622376831","https://openalex.org/W2796901959","https://openalex.org/W2797152899","https://openalex.org/W3035751571","https://openalex.org/W4297397521"],"related_works":["https://openalex.org/W2108232773","https://openalex.org/W4391430467","https://openalex.org/W2620123033","https://openalex.org/W2335128785","https://openalex.org/W4389286253","https://openalex.org/W7082447568","https://openalex.org/W2160937459","https://openalex.org/W2069288005","https://openalex.org/W1998081412","https://openalex.org/W2761740783"],"abstract_inverted_index":{"Given":[0],"the":[1,5,22,47,54,63,121,143],"current":[2],"climate":[3],"surrounding":[4],"replication":[6],"crisis":[7],"facing":[8],"scientific":[9],"research,":[10],"a":[11,25,86],"subsequent":[12],"call":[13],"for":[14,24,60,142],"methodological":[15],"reform":[16],"has":[17],"been":[18],"issued":[19],"which":[20,58],"explicates":[21],"need":[23],"shift":[26],"from":[27,85],"null":[28],"hypothesis":[29],"significance":[30],"testing":[31],"to":[32,91],"reporting":[33],"of":[34,50,56,72,101,105,110,123,127],"effect":[35],"sizes":[36],"and":[37,79,82,112,130],"their":[38],"confidence":[39],"intervals":[40],"(CI).":[41],"However,":[42],"little":[43],"is":[44],"known":[45],"about":[46,139],"relative":[48],"performance":[49,94,122],"CIs":[51,124,141],"constructed":[52],"following":[53],"application":[55],"techniques":[57,71],"accommodate":[59],"non-normality":[61],"under":[62,145],"general":[64,132],"linear":[65],"model":[66],"(GLM).":[67],"We":[68],"review":[69],"these":[70,97],"normalizing":[73],"data":[74],"transformations,":[75],"percentile":[76],"bootstrapping,":[77,81],"bias-corrected":[78],"accelerated":[80],"present":[83],"results":[84],"Monte":[87],"Carlo":[88],"simulation":[89],"designed":[90],"evaluate":[92],"CI":[93],"based":[95],"on":[96,120],"techniques.":[98],"The":[99],"effects":[100],"sample":[102],"size,":[103],"degree":[104],"association":[106],"among":[107],"predictors,":[108,111],"number":[109],"different":[113],"non-normal":[114],"error":[115],"distributions":[116],"were":[117],"examined.":[118],"Based":[119],"in":[125],"terms":[126],"coverage,":[128],"accuracy,":[129],"efficiency,":[131],"recommendations":[133],"are":[134],"made":[135],"regarding":[136],"best":[137],"practice":[138],"constructing":[140],"GLM":[144],"non-normality.":[146]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2020-04-10T00:00:00"}