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Høst 2020

HEL-8043 Analytical strategies in qualitative data analysis - 5 stp

The course is administrated by

Faculty of Health Sciences

Type of course

The course is part of the PhD-program of the Faculty of Health Sciences, University of Tromsø - the Arctic University of Norway. The course will be held every second year from 2020 It is available as a singular course.

Course contents

The course will provide in-depth knowledge of various approaches to qualitative data analysis and their connection to theoretical perspectives and methodology. It is a specialised and advanced course relevant for students in medicine, health sciences and social sciences. The course is relevant for those who work with qualitative or mixed methods, and others who want to obtain these qualifications.

The course is about how to perform data analysis in qualitative research. Students will obtain knowledge, skills and competence that enable them to judge, select, design and perform qualitative analyses of a wide range of qualitative data based on sound and consistent methodologies. Methodologies include epistemological perspectives, theoretical framing, analytical strategies and modes of interpretation. The phase of data-analysis is the main component, but the course also entails training in critically evaluating qualitative research, how to write up results, and how to write for international journals.

We will also cover how to carry out qualitative data analysis with computer software for qualitative research involving text-based or multimedia data (ie. NVivo).

Application deadline

Applicants from Nordic countries: 1 June

Exchange students and Fulbright students: 15 April

Admission requirements

The course is available as a singular course.

Participants must be enrolled in a PhD program or a Student Research Program at UiT or another Norwegian/Nordic university. PhD students from other universities may also apply.

Participants are assumed to have basic skills in qualitative methodology; theory of science, research design and research ethics. We recommend students to start with or fulfil HEL-8040 (or an equivalent course) before taking this course.

The course will be held only with a minimum of 6 participants.

Application code: 9301

Objective of the course


Skills: After the course, the student is expected to be able to

Competence: After the course, the student is expected to be able to

Language of instruction


Teaching methods

The course will run through six days, divided into two workshops during one semester. Both workshops contain didactic teaching through lectures, group-working sessions, and interactive discussion and feedback. Individual reading toward knowledge assimilation is a requirement. It includes pree-course work in the form of reading the literature connected to the course.

There will be a mixture of teacher-led and student-led activities throughout each day of the course. Students will be expected to take an active and dynamic part of all coursework. We encourage students to put their knowledge into practice through group sessions that aim to improve practical skills and general competence.

Lectures and group-sessions will cover interpretations of various kinds of qualitative data sources, such as interview data collected solo or in groups; experiential autobiographical texts and images; observational data and dialogical conversation-data from naturally occurring situations. We cover theories, perspectives and methods from a wide range of research traditions.


Mandatory work requirements:


An individually written home exam over two weeks on a given topic, length 2000 - 3000 words (written in English). Graded as pass or fail.

A re-sit exam will be arranged for this course

Date for examination

Take-home assignment hand out date 05.11.2020 hand in date 19.11.2020

The date for the exam can be changed. The final date will be announced at your faculty early in May and early in November.

Course overlap

HEL-8028 Analysing Qualitative Data 2 stp
Lectures Autumn 2020
Lectures prof. Berit Støre Brinchmann
prof. Olaug Synnøve Lian
prof. Mette Bech Risør
prof. Sarah Joan Nettleton