Digital Analysis of Qualitative Data (Fall 2011)

Faculty
Associate professor Torkil Clemmensen and guest lecturers (tba)
Course Coordinator
Associate professor Torkil Clemmensen
Prerequisite/progression of the course
Students are expected to master basic research design before enrolment and have been introduced to qualitative research methods.
Aim of the course
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.
Course content, structure and teaching
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 participant will have to do 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. 
This course is NOT a course in how to use the software (there are several of such courses available, look for them at the distributors of the software websites and their news and mail lists). Instead, this course is an opportunity to work on the intermediate level between theory and practice of digital qualitative analysis.
Students are expected to master basic research design before enrolment 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. The tentative course program is:
Monday
 
 
13.00
Course start
Torkil Clemmensen
13.15 - 14.15
Examples of the use of digital analysis of qualitative data.
How to do data analysis in groups (teams).
Guests lecturers (to be confirmed)
14.15 - 16.00
The quality of digital analysis of qualitative data
-       transparency of the research design
-       adherence to established rules for presenting qualitative data
Why use software for analysis of qualitative data?
Torkil Clemmensen
16.00 - 18.00
Preparation of data for digital analysis
Torkil Clemmensen
 
 
 
Tuesday
 
 
9.00 - 9.30
Case: Webmasters’ explanation of website quality
Torkil Clemmensen
9.30 - 10.30
Discussion of Wednesday exercises +
“write notes for your method section” session
Torkil Clemmensen
10.30 - 12.30
Next steps in digital analysis:
-       Unit of text
-       Coding scheme
-       Reflection
When to do the digital analysis?
Torkil Clemmensen
12.30 - 14.00
Lunch
 
14.00 - 18.00
Analysis of participants own data
Torkil Clemmensen
Ca. 19.00
Dinner in town
 
 
 
 
Wednesday
 
 
9.00 - 9.30
Discussion of Thursday exercises +
“write notes for your method section” session
Torkil Clemmensen
9.30 - 11.00
Presenting results from digital analysis
-       digital displays
-       use of quotations
Torkil Clemmensen
11.00 - 12.30
Participants work with presentation of their data
Torkil Clemmensen
12.30 - 14.00
Lunch
 
14.00 - 15.30
Challenges, tips & tricks of publishing qualitative research papers.
Guests lecturers (to be confirmed)
15.30 – 16.00
Preparation for email session in December.
Torkil Clemmensen 
 
 
 
December – date to agree on later
Email circulation and discussion of experiences based on the question: “What did you write in your method section”?
Facilitator: Torkil Clemmensen
 
Teaching methods
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.
There will be examples of complex setups of research projects, best cases as well as worst cases.
Course literature
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). 
Note: the literature list may change before course start. A compendium of readings is being planned, and participants will receive this ultimo August).
Recommended literature
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. 
Scales, J., & Lindsay, E. B. (2005). Qualitative assessment of student attitudes toward information literacy. Portal-Libraries And The Academy, 5(4), 513-526. 
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)
Enrolment
You sign up for the course by sending an email to  kht.research@cbs.dk with the following information; your name, email address and telephone, the name of your University and department and if you are not from CBS your department EAN number.
Other
For more information about the course content, please contact Torkil Clemmensen, associate professor at the Department of Informatics, Copenhagen Business School, e-mail: andersen@cbs.dk Phone: +45-3815-2389, email: tc.inf@cbs.dk

Sidst opdateret af Katja Høeg Tingleff 09.09.2011