Weighting schemes and incomplete data: A generalized Bayesian framework for chance-corrected interrater agreement
Van Oest (2019) developed a framework to assess interrater agreement for nominal categories and complete data. We generalize this framework to all four situations of nominal or ordinal categories and complete or incomplete data. The mathematical solution yields a chance-corrected agreement coefficie...
Saved in:
| Published in: | Psychological methods Vol. 27; no. 6; p. 1069 |
|---|---|
| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
01.12.2022
|
| Subjects: | |
| ISSN: | 1939-1463, 1939-1463 |
| Online Access: | Get more information |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Van Oest (2019) developed a framework to assess interrater agreement for nominal categories and complete data. We generalize this framework to all four situations of nominal or ordinal categories and complete or incomplete data. The mathematical solution yields a chance-corrected agreement coefficient that accommodates any weighting scheme for penalizing rater disagreements and any number of raters and categories. By incorporating Bayesian estimates of the category proportions, the generalized coefficient also captures situations in which raters classify only subsets of items; that is, incomplete data. Furthermore, this coefficient encompasses existing chance-corrected agreement coefficients: the S-coefficient, Scott's pi, Fleiss' kappa, and Van Oest's uniform prior coefficient, all augmented with a weighting scheme and the option of incomplete data. We use simulation to compare these nested coefficients. The uniform prior coefficient tends to perform best, in particular, if one category has a much larger proportion than others. The gap with Scott's pi and Fleiss' kappa widens if the weighting scheme becomes more lenient to small disagreements and often if more item classifications are missing; missingness biases play a moderating role. The uniform prior coefficient often performs much better than the S-coefficient, but the S-coefficient sometimes performs best for small samples, missing data, and lenient weighting schemes. The generalized framework implies a new interpretation of chance-corrected weighted agreement coefficients: These coefficients estimate the probability that both raters in a pair assign an item to its correct category without guessing. Whereas Van Oest showed this interpretation for unweighted agreement, we generalize to weighted agreement. (PsycInfo Database Record (c) 2023 APA, all rights reserved). |
|---|---|
| AbstractList | Van Oest (2019) developed a framework to assess interrater agreement for nominal categories and complete data. We generalize this framework to all four situations of nominal or ordinal categories and complete or incomplete data. The mathematical solution yields a chance-corrected agreement coefficient that accommodates any weighting scheme for penalizing rater disagreements and any number of raters and categories. By incorporating Bayesian estimates of the category proportions, the generalized coefficient also captures situations in which raters classify only subsets of items; that is, incomplete data. Furthermore, this coefficient encompasses existing chance-corrected agreement coefficients: the S-coefficient, Scott's pi, Fleiss' kappa, and Van Oest's uniform prior coefficient, all augmented with a weighting scheme and the option of incomplete data. We use simulation to compare these nested coefficients. The uniform prior coefficient tends to perform best, in particular, if one category has a much larger proportion than others. The gap with Scott's pi and Fleiss' kappa widens if the weighting scheme becomes more lenient to small disagreements and often if more item classifications are missing; missingness biases play a moderating role. The uniform prior coefficient often performs much better than the S-coefficient, but the S-coefficient sometimes performs best for small samples, missing data, and lenient weighting schemes. The generalized framework implies a new interpretation of chance-corrected weighted agreement coefficients: These coefficients estimate the probability that both raters in a pair assign an item to its correct category without guessing. Whereas Van Oest showed this interpretation for unweighted agreement, we generalize to weighted agreement. (PsycInfo Database Record (c) 2023 APA, all rights reserved).Van Oest (2019) developed a framework to assess interrater agreement for nominal categories and complete data. We generalize this framework to all four situations of nominal or ordinal categories and complete or incomplete data. The mathematical solution yields a chance-corrected agreement coefficient that accommodates any weighting scheme for penalizing rater disagreements and any number of raters and categories. By incorporating Bayesian estimates of the category proportions, the generalized coefficient also captures situations in which raters classify only subsets of items; that is, incomplete data. Furthermore, this coefficient encompasses existing chance-corrected agreement coefficients: the S-coefficient, Scott's pi, Fleiss' kappa, and Van Oest's uniform prior coefficient, all augmented with a weighting scheme and the option of incomplete data. We use simulation to compare these nested coefficients. The uniform prior coefficient tends to perform best, in particular, if one category has a much larger proportion than others. The gap with Scott's pi and Fleiss' kappa widens if the weighting scheme becomes more lenient to small disagreements and often if more item classifications are missing; missingness biases play a moderating role. The uniform prior coefficient often performs much better than the S-coefficient, but the S-coefficient sometimes performs best for small samples, missing data, and lenient weighting schemes. The generalized framework implies a new interpretation of chance-corrected weighted agreement coefficients: These coefficients estimate the probability that both raters in a pair assign an item to its correct category without guessing. Whereas Van Oest showed this interpretation for unweighted agreement, we generalize to weighted agreement. (PsycInfo Database Record (c) 2023 APA, all rights reserved). Van Oest (2019) developed a framework to assess interrater agreement for nominal categories and complete data. We generalize this framework to all four situations of nominal or ordinal categories and complete or incomplete data. The mathematical solution yields a chance-corrected agreement coefficient that accommodates any weighting scheme for penalizing rater disagreements and any number of raters and categories. By incorporating Bayesian estimates of the category proportions, the generalized coefficient also captures situations in which raters classify only subsets of items; that is, incomplete data. Furthermore, this coefficient encompasses existing chance-corrected agreement coefficients: the S-coefficient, Scott's pi, Fleiss' kappa, and Van Oest's uniform prior coefficient, all augmented with a weighting scheme and the option of incomplete data. We use simulation to compare these nested coefficients. The uniform prior coefficient tends to perform best, in particular, if one category has a much larger proportion than others. The gap with Scott's pi and Fleiss' kappa widens if the weighting scheme becomes more lenient to small disagreements and often if more item classifications are missing; missingness biases play a moderating role. The uniform prior coefficient often performs much better than the S-coefficient, but the S-coefficient sometimes performs best for small samples, missing data, and lenient weighting schemes. The generalized framework implies a new interpretation of chance-corrected weighted agreement coefficients: These coefficients estimate the probability that both raters in a pair assign an item to its correct category without guessing. Whereas Van Oest showed this interpretation for unweighted agreement, we generalize to weighted agreement. (PsycInfo Database Record (c) 2023 APA, all rights reserved). |
| Author | Girard, Jeffrey M van Oest, Rutger |
| Author_xml | – sequence: 1 givenname: Rutger orcidid: 0000-0003-0693-0156 surname: van Oest fullname: van Oest, Rutger organization: Department of Marketing, BI Norwegian Business School – sequence: 2 givenname: Jeffrey M orcidid: 0000-0002-7359-3746 surname: Girard fullname: Girard, Jeffrey M organization: Department of Psychology, University of Kansas |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34766799$$D View this record in MEDLINE/PubMed |
| BookMark | eNpNkD1PwzAYhC1URD9g4QcgjywBJ3Zim61UfEmVWECM0RvndRtInNZ2hcqvJ4giccPdDadnuCkZud4hIecpu0oZl9cdRjZIpNkRmaSa6yQVBR_962MyDeGdsVRwJU7ImAtZFFLrCdm-YbNax8ataDBr7DBQcDVtnOm7TYsRaQ0RbuicrtChh7b5wprewh5DA45aDx1-9v6D2t5TswZnMDG992gi_mAieg-DUVh5HPAunpJjC23As0POyOv93cviMVk-Pzwt5ssEuFIxEVJbmwtdK-BC5VAUTPGaAbNSIvK8AqNMypAJo_JKWZWxQmusKxSstjbLZuTyl7vx_XaHIZZdEwy2LTjsd6HMci2FEqnUw_TiMN1VHdblxjcd-H35d1P2DfV_bOs |
| CitedBy_id | crossref_primary_10_1007_s11336_023_09919_4 crossref_primary_10_1007_s11336_022_09881_7 |
| ContentType | Journal Article |
| DBID | CGR CUY CVF ECM EIF NPM 7X8 |
| DOI | 10.1037/met0000412 |
| DatabaseName | Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic |
| DatabaseTitle | MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | no_fulltext_linktorsrc |
| Discipline | Psychology |
| EISSN | 1939-1463 |
| ExternalDocumentID | 34766799 |
| Genre | Journal Article |
| GroupedDBID | --- --Z -~X .-4 07C 0R~ 123 29P 354 53G 5VS 7RZ ABIVO ABNCP ACHQT ACPQG AEHFB ALMA_UNASSIGNED_HOLDINGS AWKKM AZXWR CGNQK CGR CS3 CUY CVF ECM EIF EPA F5P FTD HVGLF HZ~ ISO LW5 NPM O9- OHT OPA OVD P2P ROL SES SPA TEORI TN5 UHS XJT YNT ZPI 3KI 7X8 ABVOZ AFFHD PHGZT |
| ID | FETCH-LOGICAL-a388t-479ff549d8a3485a66083d0a0f77ee35bac8c10e04c85b8f820699edbe40dff22 |
| IEDL.DBID | 7X8 |
| ISICitedReferencesCount | 3 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000733142400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1939-1463 |
| IngestDate | Sun Nov 09 11:20:15 EST 2025 Thu Jan 02 22:53:12 EST 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 6 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-a388t-479ff549d8a3485a66083d0a0f77ee35bac8c10e04c85b8f820699edbe40dff22 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0002-7359-3746 0000-0003-0693-0156 |
| OpenAccessLink | https://hdl.handle.net/11250/2995263 |
| PMID | 34766799 |
| PQID | 2597484179 |
| PQPubID | 23479 |
| ParticipantIDs | proquest_miscellaneous_2597484179 pubmed_primary_34766799 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-12-01 |
| PublicationDateYYYYMMDD | 2022-12-01 |
| PublicationDate_xml | – month: 12 year: 2022 text: 2022-12-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States |
| PublicationTitle | Psychological methods |
| PublicationTitleAlternate | Psychol Methods |
| PublicationYear | 2022 |
| SSID | ssj0014384 |
| Score | 2.4002593 |
| Snippet | Van Oest (2019) developed a framework to assess interrater agreement for nominal categories and complete data. We generalize this framework to all four... |
| SourceID | proquest pubmed |
| SourceType | Aggregation Database Index Database |
| StartPage | 1069 |
| SubjectTerms | Bayes Theorem Humans Observer Variation Reproducibility of Results |
| Title | Weighting schemes and incomplete data: A generalized Bayesian framework for chance-corrected interrater agreement |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/34766799 https://www.proquest.com/docview/2597484179 |
| Volume | 27 |
| WOSCitedRecordID | wos000733142400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV05T8MwFLaAMnThPsolI7FGpLGT2CyoICoGqDpwdKtc-xl1ID0SkMqvx89J6ISExJItUuQ8v_e96_sIubDMBbFYqkAyyQIO7s6phJsg1bGRykolPJnOy0Pa64nBQPargltejVXWPtE7ajPRWCO_jBD5CtTLup7OAlSNwu5qJaGxShrMQRm8mOlg2UXgTFRdZRk4j8BqelLm0n0oPDxGJcrfoKUPMd3N_37cFtmowCXtlNawTVYg2yHNHx-32CWzV18KdfGKurQW3iGnKjMUKRqQJrgAiiOjV7RD30o-6vEXGHqjFoDLltTWo1zUYV2KS8MaAo0CH9ohV4rcE3PknphT5fJ4X3ncI8_du6fb-6BSXQgUE6LAUpu1Lms0QjEuYpUkDqWZUIU2TQFYPFJa6HYIIdciHgmLBPBSghkBD421UbRP1rJJBoeEat1WibBcyAg4ByNSFUUaZGx1bB34aJHz-jiHzqqxVaEymHzkw-WBtshB-U-G05J-Y8h4miSplEd_ePuYNCPcV_DzJyekYd2dhlOyrj-LcT4_8-binr3-4zcma80W |
| linkProvider | ProQuest |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Weighting+schemes+and+incomplete+data%3A+A+generalized+Bayesian+framework+for+chance-corrected+interrater+agreement&rft.jtitle=Psychological+methods&rft.au=van+Oest%2C+Rutger&rft.au=Girard%2C+Jeffrey+M&rft.date=2022-12-01&rft.eissn=1939-1463&rft.volume=27&rft.issue=6&rft.spage=1069&rft_id=info:doi/10.1037%2Fmet0000412&rft_id=info%3Apmid%2F34766799&rft_id=info%3Apmid%2F34766799&rft.externalDocID=34766799 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1939-1463&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1939-1463&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1939-1463&client=summon |