Teaching Computational Social Science for All.
Computational methods have become an integral part of political science research. However, helping students to acquire these new skills is challenging because programming proficiency is necessary, and most political science students have little coding experience. This article presents pedagogical st...
| Publicado en: | PS: Political Science & Politics Vol. 55; no. 3; pp. 605 - 610 |
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| Autores principales: | , |
| Formato: | Artículo |
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Cambridge University Press
Jul2022
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| 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=157408018&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 157408018 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: Jul2022 vid: 55 iid: 3 pid: 15979 pub: Cambridge University Press artinfo: ui: 157408018 10.1017/S1049096521001815 ppf: 605 ppct: 5 formats: tig: atl: Teaching Computational Social Science for All. aug: au: Kim, Jae Yeon Ng, Yee Man Margaret affil: KDI School of Public Policy and Management, Republic of Korea University of Illinois Urbana–Champaign, USA su: Social sciences education Computer programming sug: subj: Social sciences education Custom Computer Programming Services Computer systems design and related services (except video game design and development) Other Computer Related Services Research and Development in the Social Sciences and Humanities Computer programming ab: Computational methods have become an integral part of political science research. However, helping students to acquire these new skills is challenging because programming proficiency is necessary, and most political science students have little coding experience. This article presents pedagogical strategies to make transitioning from Excel, SPSS, or Stata to R or Python for data analytics less challenging and more exciting. First, it discusses two approaches for making computational methods accessible: showing the big picture and walking through the workflow. Second, a step-by-step guide for a typical course is provided using three examples: learning programming fundamentals, wrangling messy data, and communicating data analysis. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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