Achieving Transparency, Traceability, and Readability with Human-Coded Data.
Many important questions in political science require the use of human-coded data or information that has been systematically ordered and quantified by a human being from qualitative sources. This article discusses challenges and recent innovations in collecting and documenting human-coded data. We...
| Publicado en: | PS: Political Science & Politics Vol. 58; no. 2; pp. 346 - 352 |
|---|---|
| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Cambridge University Press
Apr2025
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=184796776&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 184796776 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10490965 PSP jtl: PS: Political Science & Politics issn: 10490965 maglogo: N pubinfo: dt: Apr2025 vid: 58 iid: 2 pid: 15979 pub: Cambridge University Press artinfo: ui: 184796776 10.1017/S1049096524000714 ppf: 346 ppct: 6 formats: tig: atl: Achieving Transparency, Traceability, and Readability with Human-Coded Data. aug: au: Edgell, Amanda B. Lachapelle, Jean Maerz, Seraphine F. affil: University of Alabama, USA Université de Montréal, Canada University of Melbourne, Australia su: Technological innovations Political science Computer programming sug: subj: Technological innovations Political science Custom Computer Programming Services Computer systems design and related services (except video game design and development) Other Computer Related Services Computer programming ab: Many important questions in political science require the use of human-coded data or information that has been systematically ordered and quantified by a human being from qualitative sources. This article discusses challenges and recent innovations in collecting and documenting human-coded data. We review five datasets produced within the past 10 years and also reflect on our experiences in collecting a quarterly dataset that tracked state responses to the COVID-19 pandemic. We argue that scholars can deliberately produce and publish theoretically grounded human-coded data in an accessible format that promotes transparency, traceability, and readability. We highlight several ways that scholars are already doing this, including narratives, source lists, and coding justifications that enhance the quality of their human-coded datasets. We also discuss common issues during coding and how technological innovation through interactive web-based platforms can improve the documentation of coding decisions. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|