DRE 1010 Qualitative Methods: Data and Analysis
DRE 1010 Qualitative Methods: Data and Analysis
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Participants will be familiar with various perspectives on qualitative methods, how it relates to different views on the nature of reality and social science, as well as practical procedures and tools for organizing and analyzing data.
Presentations and class discussions focus on points of methodological controversy and consensus. Through examples of classical contributions as well as discussions of participants projects they gain understanding of the practical challenges of qualitative research.
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Qualitative studies are usually case studies that focus on phenomenon that are subjected to intense studies and analysis. Examples are organizations (or parts of them), decisions, negotiations, a discourse, an event, an action, a procedure, a statement etc. Qualitative data and qualitative analysis usually play a major role in such studies.
The course provides an overview over types of qualitative studies (use of written sources, interviews, field work), how to document, interpret, analyse and explain using different types of data. Data gathering can be viewed as implementation of research strategy, where case selection, categories for description and analysis are closely connected. Various procedures and techniques must be linked to an overall understanding of the research process.
The course emphasizes the relationship between formulation of research problem, design, data gathering and analysis. This is particularly important for successful application or development of theory. In addition to lectures and discussion, the participants will present and get feed back on how to apply qualitative methods in their own projects related to planned or ongoing data gathering or analysis.
The format is four intensive days (24 teaching hours) – and one day indivual preparation for seminar presentation, with a 15 page exam paper.
Day 1: Overview and perspectives
- Short presentation of participants and their projects
- Formulation of research problem, design, data gathering and analysis.
- Research strategies and various designs – single case versus comparative case.
- Qualitative studies, qualitative data, qualitative analysis
- Qualitative studies, qualitative data, qualitative analysis
- Description, interpretation, explanation - using and generating theory.
- Major challenges – some examples – discussion
- Plan for participant presentations - articles, individual projects
Day 2: Data gathering
There are three major topics:
Data gathering. Preparing a study, based on existing empirical and theoretical knowledge. Operationalization and coding. Looking for patterns – coding – adjusting research plans based on new knowledge. A key concern is the practical application of methods in the research process.
What are major types of data – strength and weaknesses, how to combine them (data triangulation)?
Informant interviews: strategies for conversations, types of data generated, criteria for interpretation, relevance and reliability.
Use of documents, and other sources.
Contacts and relationships in the research field, how to handle them and implications for research process.
Presentation of individual projects
Day 3: Data analysis
Data analysis is partly about identifying structures in data, partly to establish relationship between data and analytical assumptions and hypothesis. Techniques and procedures must reflect underlying logic. Central topics are: grounded theory, hermeneutics, discourse analysis, narratives and content analysis.
Presentation individual projects
Day 4: Interpretation and explanation – summary and discussion
Qualitative analysis emphasizes interpretation, but also explanation and theoretical generalizations. Major challenges in qualitative analysis are discussed with a focus on pattern matching and process tracing. Single case versus comparative case. How qualitative studies can contribute to theory development
Presentation individual projects
Summary and discussion
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Enrollment in a PhD Programme is a general requirement for participation in PhD courses at BI Norwegian Business School.
External candidates are kindly asked to attach confirmation of enrollment in a PhD programme when signing up for a course. Other candidates may be allowed to sit in on courses by approval of the courseleader. Sitting in on a course does not permit registration for the course, handing in exams or gaining credits for the course. Course certificates or confirmation letters will not be issued for sitting in on courses.
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| Assessments |
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Exam category: Submission Form of assessment: Written submission Weight: 100 Grouping: Individual Duration: 2 Month(s) Comment: A paper (15 pages) should be original work, and be written specifically for this course. Exam code: DRE10101 Grading scale: Pass/fail Resit: Examination when next scheduled course |
| Activity | Duration | Comment |
|---|---|---|
Student's own work with learning resources | 140 Hour(s) | Specified learning activities (including reading). |
Teaching | 30 Hour(s) |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.