This is an online course, which uses Microsoft Teams.

Practitioners and researchers increasingly use qualitative research methods in Marketing Research to understand issues that are becoming increasingly complex. They often get harder to address, e.g. in new topic areas. Consequently, qualitative market research in general and qualitative computing in particular has become widely accepted.

The research process includes several stages, i.e. the formulating of research questions, the framing and design of the work, the methodology and methods; the data analysis; and the final conclusions and recommendations. The course suggests a CAQDAS approach for the analysis of qualitative data.

This course covers theoretical and practical aspects on: epistemology and research designs, data collection methods, data analysis methods (including qualitative computing) and reporting results.

The purpose of this course is to provide Master Students with deep understanding of qualitative research methodology and to familiarize them with specific techniques for qualitative data analysis, including hands-on application of techniques using the NVIVO software.

After the course, students understand qualitative research approaches and related techniques as an analytical tool to investigate and solve marketing management problems. They understand philosophy of science behind the techniques as well as acquire information, understanding and skills to solve problems and build up theories in the field of marketing. The course assignments aim to improve students’ ability to use, analyze and document qualitative research and to demonstrate their knowledge of the methods literature and its application in practice. After completing the course, students will also be familiar and able to work with a particular software package (NVivo), which integrates a wide range of tools and enables researchers to analyze and visualize qualitative data.

After completing the course students are able to:

• define, describe, and select qualitative methods
• collect qualitative data such as interviews, observations, photos, videos, and narratives and apply these methods to their own work
• analyze and interpret qualitative raw data
• formulate conclusions based on the scientific results
• evaluate, criticize, and judge the scientific results and research ethics
• understand the basics of philosophy of science
• communicate and present the selected research design and scientific results
• understand implications of research for society

Overall there is a mandatory attendance rate of 80%.

The course covers several topics in qualitative research. A mixture of readings, online presentation, discussion, assignments and software training will be used.

The course literature gives introductory information of the sessions. It also deepens the information of the sessions.

The theme sessions introduce the assignments and discuss different elements of a qualitative study.

Through the assignments, students train and learn how to draw a sample, plan and conduct an interview and observation, transcribe, analyse, present and evaluate qualitative data.

1. Participation (15%): being physically present and participate in class and individual discussions

2. Individual assignments and readings (25%): tbd in first session

3. Participants are required to work on their own projects (40%): each participant should

• collect qualitative data: conduct interviews, run a focus group discussion, collect social media data (from twitter, facebook, etc.,) collect observation material (photos, videos, audio, website content, field notes, brochures, etc.);
• apply a coding technique according to the chosen methodology (NODE SYSTEM);
• prepare transcripts (FILES), manage material (SETS, CLASSIFICATION) and write a protocol (MEMO) for each interview, focusgroup, observation, etc.

The data will be analyzed (including above mentioned steps, plus QUERY) using NVivo. The project will be handed in as NVivo project (*.nvp), which includes all above mentioned elements (homework).

Weighting criteria for the homework are:

• 10% - data collection and preparing data in NVivo (SETS, CLASSIFICATION)
• 20% - coding (NODE SYSTEM)
• 10% - analytical steps (QUERY), write a protocol (MEMO)

4. Presentation (online): 20%