Understanding people: A course on qualitative and quantitative HCI research methods

Duncan P. Brumby, Ann Blandford, Anna L. Cox, Sandy J.J. Gould, Paul Marshall

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

2 Citations (Scopus)

Abstract

This course will provide an introduction to methods used in Human-Computer Interaction (HCI) research. An equal focus will be given to both the quantitative and qualitative research traditions used to understand people and interactional contexts. We shall discuss these major research traditions along with their contemporary framings (e.g., in-the-wild research and Interaction Science). By the end of the course attendees will have a detailed understanding of how to select and apply methods to address a range of problems that are of concern to contemporary HCI researchers.

Original languageEnglish
Title of host publicationCHI 2017 Extended Abstracts - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems
Subtitle of host publicationExplore, Innovate, Inspire
PublisherAssociation for Computing Machinery (ACM)
Pages1170-1173
Number of pages4
VolumePart F127655
ISBN (Electronic)9781450346566
DOIs
Publication statusPublished - 6 May 2017
Event2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017 - Denver, United States
Duration: 6 May 201711 May 2017

Conference

Conference2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017
CountryUnited States
CityDenver
Period6/05/1711/05/17

Keywords

  • Empirical research
  • Hci research methods
  • Qualitative
  • Quantitative

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  • Cite this

    Brumby, D. P., Blandford, A., Cox, A. L., Gould, S. J. J., & Marshall, P. (2017). Understanding people: A course on qualitative and quantitative HCI research methods. In CHI 2017 Extended Abstracts - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems: Explore, Innovate, Inspire (Vol. Part F127655, pp. 1170-1173). Association for Computing Machinery (ACM). https://doi.org/10.1145/3027063.3027103