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Introduction to Qualitative Data Analysis with Atlas.ti

Johannes Starkbaum
johannes.starkbaum@univie.ac.at

University of Vienna

Johannes Starkbaum studied sociology at the Universities of Vienna (2003–2009) and Copenhagen (2005). From 2004 to 2007 he worked on different projects on knowledge- and learning technologies, and from 2008–2009 on the sociology of families and fatherhood.

Johannes has taught qualitative methods and computer-assisted analysis since 2009, and works on several international projects doing comparative, qualitative research using Atlas.ti.

His current research focuses on the governance of medical technologies.


Course Dates and Times

Course Dates and Times

Friday 3 March: 14:00-16:00 and 16:30-18:00
Saturday 4 March: 09:30-12:00 and 13:00-14:30
7.5 hours over two days

Prerequisite Knowledge

Participants are expected to be well grounded in Microsoft Windows and PCs in general. It is expected that students bring prior experiences with qualitative methods and analysis. At best, knowledge about Qualitative Content Analysis is existent. Otherwise it is strongly recommended to read the mandatory literature for the first unit and complement it with the additional readings. Former experiences with Atlas.ti are not required.


Short Outline

The main purpose of this course is to learn about computer assisted qualitative data analysis with Atlas.ti. Qualitative Content Analysis (QCA) will be introduced exemplarily to demonstrate practical ways of using Atlas.ti. The focus will be mainly on textual data; however, visual and audio data will be included. After reflecting the assets and drawbacks of computer assisted analysis, a brief introduction to QCA will be provided. Then, the process from the decision for doing computer assisted analysis, to producing output, will be retraced. Students will build up their own project with a given data set. Major steps are: data management, creation and usage of quotes and codes, usage of comments and memos, splitting and comparing data, retrieving data pieces by different search functions, semantic networks. Throughout all of these steps, QCA will be applied to provide a practical example for using Atlas.ti. Lecturing, plenary sessions and independent work will regularly take turns. If available, we will discuss some existing projects from students.


Long Course Outline

The main purpose of this course is to learn about computer assisted qualitative data analysis with Atlas.ti. Qualitative Content Analysis (QCA) will be applied exemplarily to demonstrate practical ways of using Atlas.ti. The focus will be mainly on textual data; however, visual and audio data will be included as well. The whole process, from the decision for using computer assisted analysis, to producing text material for dissemination, will be retraced. Lecturing, plenary sessions and independent work will thereby regularly take turns.

First of all, computer assisted data analysis will be discussed on a general level, critically reflecting the assets and drawbacks of these programs. It will be discussed where possible areas of applications are, and where the use of such programs is less effective or even problematic.

A brief introduction to QCA will be provided to point out the particularity of this particular approach. This overview will include the general logics, as well as concrete techniques and application. Further information will be given on a regular basis according to the different stages of the student’s projects.

Students will then build up their own projects with a given data set. Data management and the up-building of an efficient project folder are essential for the whole further project and will be discussed in detail. In a next step, the creation and usage of quotes and codes will be practised, while deconstructing the logic assembling of Atlas.ti. Different logics of defining secluded patterns of meaning and linking them with codes are going to be introduced and practiced. Both, inductive and deductive coding will be applied. While the coding and working with textual data lies at the heart of this course, some images and audio/video material will be included in the analysis, i.e. assembled with codes.

Parallel to that, the usage and importance of comments and memos will be discussed. Specific attention will be given to its importance for reflecting and bringing forward the project, developing concepts, team work, and for preparing context for further dissemination of results.

Then, different options of splitting and merging, and therewith comparing the own data, are going to be introduced. Students will learn how to group their primary documents as well as the so far created codes and categories. Constant comparison is one of the major logics of many methodologies, and important not only for quality management, but also for producing rich results. These functions may also be used for building categories within the coding system.

Code-and-retrieve is the immanent logic of most qualitative data analysis packages. Atlas.ti offers not only the possibility to retrieve coded data segments and provide its output, but to operate complex inquiries using Boolean, proximity or semantic operators (query tool). Different ways of retrieving already coded data will be shown and their practical usage for qualitative research will be discussed. These functions will be amended by search operations based on the text documents that may also include GREP operators. The option of auto-coding these search results advances the areas of application and plays out its strength the most by further combination with the query tool. Then, almost any entity within the project may be defined as unit and compared or brought in context with other material.

Finally, the usage of networks has several functions within Atlas.ti. Networks are particularly helpful for structured content analyses in QCA. Throughout the semantic networks one can up-build, Atlas.ti supports researcher in understanding the larger logics within the field. With structured content analysis, comparison and contrasting is the major goal. Furthermore, networks are also useful for building categories and hierarchies between codes.

With the experience of retracing a complete project of qualitative data analysis, and the parallel documentation via memos, students will learn about how knowledge is created in such a process and how this knowledge can then be extracted and transformed into a written paper. Here the constant swapping between textual and conceptional level are of significance. The aim of this course is to provide knowledge about the whole process of computer assisted data analysis and to practice these technical steps with a common data set. The implication of QCA will provide concrete examples of how an analysis may be performed, and how Atlas.ti can be used for that. Students will have the opportunity to practice all steps and techniques on their own and consult the Instructor. They will gain insight in theoretical and practical issues with computer assisted data analysis with Atlas.ti. Furthermore there will be the option for some students to present own data/projects to demonstrate the scope of possibilities of Atlas.ti and to gain feedback.

Day Topic Details
Friday - General discussion of computer assisted analysis - Brief introduction to Qualitative Content Analysis - Data management and project up-build - Quotes and coding Lecture and plenary sessions as well as individual work.
Saturday morning - Memos and comments - Data splitting and comparison: document- and code families - Retrieval: text search, auto-coding, query tool, co-occurrence Lecture and plenary sessions as well as individual work.
Saturday afternoon - Networks and linkage - Discussion of existing projects - Teamwork and data management

Lecture and plenary sessions as well as individual work.

Saturday morning - Memos and comments - Data splitting and comparison: document- and code families - Retrieval: text search, auto-coding, query tool, co-occurrence

Lecture and plenary sessions as well as individual work.

Day Readings
Friday

Clive Seale (2013): Using Computers to Analyse Qualitative Data. In: Silvermann, David (Ed.) Doing Qualitative Research. Sage: London, 264-278

Mayring P (2014) Qualitative Content Analysis. theoretical foundation, basic

procedures and software solution. SSOAR Open Access: http://www.psychopen.eu/fileadmin/user_upload/books/mayring/ssoar-2014-mayring-Qualitative_content_analysis_theoretical_foundation.pdf

Friese S (2014) Qualitative Data Analysis with Atlas.ti. Thousand Oaks, Sage.

Konopasek, Z (2008) Making Things Visible With Atlas.ti: Computer Assisted Analysis as Textual Practice. Forum Qulitative Sozialforschung 9(2). http://www.qualitative-research.net/index.php/fqs/article/view/420/910

Software Requirements

Atlas.ti 7.x

Literature

To be confirmed


Additional Information

Disclaimer

This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc). Registered participants will be informed in due time.

Note from the Academic Conveners

By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, contact the instructor before registering.