DRE 1010 Qualitative Methods: Data and Analysis

DRE 1010 Qualitative Methods: Data and Analysis

Course code: 
DRE 1010
Department: 
Leadership and Organizational Behaviour
Credits: 
6
Course coordinator: 
Svein S Andersen
Course name in Norwegian: 
Qualitative Methods: Data and Analysis
Product category: 
PhD
Portfolio: 
PhD Leadership and Organisation Courses
Semester: 
2022 Spring
Active status: 
Active
Level of study: 
PhD
Teaching language: 
English
Course type: 
One semester
Introduction

Qualitative studies usually focus on phenomena and processes that are subjected to intense, in-depth studies and analysis. Examples are organizations (or parts of them), decision processes, 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 approaches and techniques, including the art and craft of doing observations, interviews and document analysis. Data gathering can be viewed as implementation of research strategy, where case selection, categories for description and analysis are closely connected. Various approaches and techniques must be linked to an overall understanding of the research process as an iterative process of moving between the empirical field and the world of theory.

The course emphasizes the relationship between formulation of research problem, design, data gathering, analysis, reporting and publishing. This is particularly important for successful application or development of theory. During the course, the participants will get practical training in developing tools for data gathering, practice both interviewing and observations, and get experience with analyzing qualitative empirics. They will also learn more about the publishing process and about what responding to demanding reviews may entail. In addition to lectures and discussion, the participants will present and receive feedback on how to apply qualitative methods in their own projects – related to planned or ongoing data gathering and/or analysis.

Learning outcomes - Knowledge

After completion of the course, the students should have obtained in depth knowledge and understanding of key issues in qualitative methods, including:

  • Important aspects to consider when planning qualitative research
  • Standards, pitfalls and strategies to conduct observations, interviews and document analysis
  • The process of analyzing qualitative empirics

The role of theorizing, how to position research, make a contribution, and how to publish

Learning outcomes - Skills

After completion of the course, the students should have foundations for applying qualitative methods. This includes being able to;  

  • identify appropriate design for a qualitative study
  • apply various means of data collection and analysis, in particular when seeing both as a learned and embodied craft rather than generalized and isolated techniques
  • reflect upon practical challenges with conducting a qualitative study
  • get a first taste of what it might mean to respond well to reviews
General Competence

After completing the course, the students should have:

  • Developed the ability to critically reflect upon possibilities and limitations with different ways of doing qualitative research

Understanding of ethical aspects involved in all phases of doing qualitative research

Course content

The course takes place over 5 intensive days (36 teaching hours).

In addition, one day for individual feed back

The course starts with a one-day session – to introduce overall perspectives and plan for individual preparations and presentations as part of requirement for the course.

The second and third session are held in the subsequent weeks.

In addition to lectures and discussion, the participants will present and receive feed- back on how to apply qualitative methods in their own projects – related to planned or ongoing data gathering or analysis.

The exam will be a 15 page paper – where course participant will be asked to identify and discuss one or a few main options/ challenges related to design, data collection and/or data analysis.

A compendium is available on the net.

An additional reading list for each session will be made available. Articles on google scholar.

Teaching and learning activities

Session 1 –Introduction, overview

Day 1 Qualitative studies – what is it about?

  • Qualitative versus quantitative studies
  • Different types of studies, designs, identifying important challenges in the research process
  • Practical exercises
  • Guest – doing qualitative research
  • Planning for exercises and presentations during the course

Session 2 – Major perspectives and types of data collection

Day 2 Major approaches

  • Inductive, deductive and abductive studies
  • Grounded theory
  • Process studies
  • Theoretically informed studies
  • Student group  discussion -strengths and limitations

Day 3 – Types of data collection

  • Doing qualitative interviews
  • Organizational ethnographies
  • document analysis
  • Student presentations pilot exercises
  • Group discussion
  • For each technique consider  choice of site, choice of informants, access, roles played, documentation, ethics and so forth

Session 3 – Analysis, generalizations and publication strategies

Day 4 - Qualitative analysis - assumptions, strategies and tools

  • Coding
  • Data matrixes
  • Interpretation
  • Analysis
  • Presentations student exercises
  • Exemplary articles

Day 5 Theorizing, explanations and publishing

  • Theorizing contribution from qualitative research
  • Explanations
  • Positioning and generalization
  • Publishing qualitative research
  • Student presentations exemplary articles
  • Guest: Publishing - against the grain?
  • Summing up - Paper format and requirements
Software tools
No specified computer-based tools are required.
Additional information

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Qualifications

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.

Covid-19

Due to the Covid-19 pandemic, there may be deviations in teaching and learning activities as well as exams, compared with what is described in this course description.

Teaching 

Information about what is taught on campus and other digital forms will be presented with the lecture plan before the start of the course each semester.

Required prerequisite knowledge

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Assessments
Assessments
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
Type of Assessment: 
Ordinary examination
Total weight: 
100
Student workload
ActivityDurationComment
Student's own work with learning resources
140 Hour(s)
Specified learning activities (including reading).
Teaching
30 Hour(s)
Sum workload: 
170

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.