DRE 1010 Qualitative Methods: Data and Analysis
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APPLIES TO ACADEMIC YEAR 2012/2013
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DRE 1010 Qualitative Methods: Data and Analysis Responsible for the course Svein S Andersen Department Department of Leadership and Organizational Behaviour Term According to study plan ECTS Credits 6 Language of instruction English Introduction Learning outcome Learning outcome 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’ project they gain understanding of the practical challenges of qualitative research. Prerequisites Admission to 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 admission to a PhD programme when signing up for a course with the doctoral administration. Other candidates may be allowed to sit in on courses by approval of the courseleader. Sitting in on courses does not permit registration for courses, handing in exams or gaining credits for the course. Course certificates or conformation letters will not be issued for sitting in on courses Please note that there is a limitation for the total number of participants in the course, usually no more than 10 students are admitted. Compulsory reading Collection of articles: A compendium will be made available to the PhD candidates taking the course. Recommended reading Course outline 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 (30 teaching hours) – with a 15 page exam paper. Day 1: Overview and perspectives Short presentation of participants and their projects The relationships between the 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 Description, interpretation, explanation – using and generating theory Major challenges – some examples – discussion Plan for participant presentations - articles, individual projects Day2: – time for preparations - no lectures. Day 3: 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 4: 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 5: 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 Computer-based tools Not applicable Learning process and workload Workload (6 ECTS) Lectures 30 hours Specified learning activities (including reading) 140 hours Total 170 hours Examination Evaluation: Paper (15 pages) The paper should be original work, and be written specifically for this course. Graded pass/fail Examination code(s) DRE 10101 counts for 100% of the grade in the course Examination support materials Not applicable Re-sit examination Next time the course is offered Additional information Honour Code Academic honesty and trust are important to all of us as individuals, and represent values that are encouraged and promoted by the honour code system. This is a most significant university tradition. Students are responsible for familiarizing themselves with the ideals of the honour code system, to which the faculty are also deeply committed. Any violation of the honour code will be dealt with in accordance with BI’s procedures for cheating. These issues are a serious matter to everyone associated with the programs at BI and are at the heart of the honour code and academic integrity. If you have any questions about your responsibilities under the honour code, please ask. |
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