Digital analysis of qualitative data

Faculty
Associate professor Torkil Clemmensen, Guest professors (to come)

Course Coordinator
Associate professor Torkil Clemmensen

This Ph.D.-course enables the participants to understand and apply digital tools to plan, carry out and report the qualitative parts of different research designs. The course offers participants experience with contemporary qualitative and mixed research designs, which often make use of both quantitative and qualitative data. Examples include experiments (verbal protocols), surveys (open questions), qualitative research interviews, literature reviews (qualitative content analysis), observational studies (video analysis) and more. The course raises participants’ awareness of the quality of qualitative data analysis, such as the transparency of the research design, and the adherence to established rules for presenting qualitative data.

The course offers participants practical experience in analysing and presenting qualitative data from a research project in a multidisciplinary context. Best cases and worst cases of digital qualitative analysis will be presented. Participants are expected to be willing to use data from their own research to analyze during the course. Each we will have a “write notes for your method section” exercise.

The course will help facilitate the participant’s qualitative data analysis by presenting and discussing some of the questions that researchers ask other researchers when they look for help and advice in how to plan and execute their qualitative data analysis, including technical issues of how to format data for analysis and philosophical discussions of the meaning of reliability and validity in the context of qualitative data analysis.

Students are expected to master basic research design before enrollment and have been introduced to qualitative research methods. Depending on the level of experience with digital analysis of qualitative data among the participants, different kind of qualitative analysis software will be demonstrated (see below for more detail on this).

The plan is to allow participants to during the course to work with digital analysis of their own data. The aim is that the course will enable the participant to go through with digital analysis of own data after the course has ended. Appropriate time for coffee breaks has been allocated. The tentative course program is:

By participation

We will have a limited number of licences of the software Atlas.ti single user educational version running on machines at CBS DIT lab, and these will be available to course participant during the course to try out analysis of their own data. Both Atlas.ti and Nvivo have downloadable demo versions from the software websites. Also take a look at Maxqda and possible other software suitable for qualitative data analysis, including MS excel, Word, Onenote and more. Other qualitative analysis software will also be discussed when relevant. We encourage you to bring your own project with your own software, if relevant, and to do analysis work with this during the course.

There will be no traditional demonstrations of any software during the course, but there will be plenty of opportunities for hands-on experience connected with reflection in class. You are therefore required to familiarize yourself with different kind of software packages for qualitative analysis before the course (as good as you can J).

There will be examples of complex setups of research projects, best cases as well as worst cases.

Alexa, M., & Zuell, C. (2000). Text analysis software: Commonalities, differences and limitations: The results of a review. Quality & Quantity, 34(3), 299-321.

Barata, P. C., Gucciardi, E., Ahmad, F., & Stewart, D. E. (2006). Cross-cultural perspectives on research participation and informed consent. Social Science & Medicine, 62(2), 479-490.

Barry, C. A. (1998). Choosing qualitative data analysis software: Atlas/ti and nudist compared. Sociological Research Online, 3(3), http://www.socresonline.org.uk/socresonline/3/3/4.html .

Creswell, J. W. (2003). Research design - qualitative, quantitative and mixed method approaches. London: SAGE. (in particular chapter 1 and chapter 11)

Brett, J. A., Heimendinger, J., Boender, C., Morin, C., & Marshall, J. A. (2002). Using ethnography to improve intervention design. American Journal Of Health Promotion, 16(6), 331-340.

Buston, K. (1997). Nud*ist in action: Its use and its usefulness in a study of chronic illness in young people. Sociological Research Online, 2(3), U77-U89.

Calloway, L. J., & Ariav, G. (1995). Designing with dialogue charts - a qualitative content-analysis of end-user designers experiences with a software engineering design tool. Information Systems Journal, 5(2), 75-103.

Carroll, J. M., & Swatman, P. A. (2000). Structured-case: A methodological framework for building theory in information systems research. European Journal of Information Systems, 9(4), 235-242.

Clemmensen, T. (2004). Four approaches to user modelling - a qualitative research interview study of hci professionals' practice. Interacting With Computers, 16(4), 799-829.

Dahler-Larsen, P. (2002). At fremstille kvalitative data: Odense Universitetsforlag.

Dainty, A. R. J., Bagihole, B. M., & Neale, R. H. (2000). Computer aided analysis of qualitative data in construction management research. Building Research And Information, 28(4), 226-233.

Gibson, W., Callery, P., Campbell, M., Hall, A., & Richards, D. (2005). The digital revolution in qualitative research: Working with digital audio data through atlas.Ti. Sociological Research Online, 10(1).

Guerrier, Y. (1996). Atlas-ti. Management Learning, 27(2), 252-254.

Hoshmand, L. (1989). Alternate research paradigms - a review and teaching proposal. Counseling Psychologist, 17(1), 3-79.

Loxley, W. (2001). Drowning in words? Using nudist to assist in the analysis of long interview transcripts from young injecting drug users. Addiction Research & Theory, 9(6), 557-573.

MacMillan, K., & McLachlan, S. (1999). Theory-building with nud.Ist: Using computer assisted qualitative analysis in a media case study. Sociological Research Online, 4(2), U135-U151.

Marsh, E. (2001). Atlas.Ti, the knowledge workbench. Library & Information Science Research, 23(1), 93-95.

Murray, P. J., & Muhr, T. (2000). Atlas.Ti the knowledge workbench - software for visual qualitative data analysis, management and model building in education, research and business. Journal Of Advanced Nursing, 31(1), 245-245.

Olsen, H. (2002). An evaluation of Danish qualitative interview investigations. Nordisk Psykologi, 54(2), 145-172.

Patrizi, P. (2005). Deviant action and self-narration: A qualitative survey through atlas.Ti. Journal For The Theory Of Social Behaviour, 35(2), 171-+.

Preece, J. (2001). Sociability and usability in online communities: Determining and measuring success. Behaviour & Information Technology, 20(5), 347.

Richards, T., & Richards, L. (1992). Database organization for qualitative-analysis - the nudist(tm) system. Lecture Notes In Artificial Intelligence, 611, 116-133.

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Yerbury, H., & Parker, J. (1998). Novice searchers' use of familiar structures in searching bibliographic information retrieval systems. Journal Of Information Science, 24(4), 207-214.

Zhang, Z., Lee, M. K. O., Huang, P., Zhang, L., & Huang, X. Y. (2005). A framework of erp systems implementation success in china: An empirical study. International Journal Of Production Economics, 98(1), 56-80.

A few texts will be in Danish only. Replacements of these will be sought.

(Dahler-Larsen, 2002; Olsen, 2002)

Application form: By e-mail to ao.inf@cbs.dk. Please state the name of your department and university.