PHDPR9500 Qualitative Data Analysis Course description

Qualitative data analysis is a notoriously difficult and often under-estimated aspect of qualitative research. This course aims to provide the participants with a solid understanding of the basics of qualitative data analysis, so as to enable them to carry out their own analyses more efficiently and confidently.

The course's focus will be on thematic analysis as the most widely used method of qualitative analysis. Day one starts with a general introduction to the logic of qualitative research and the specificities and varieties of qualitative data analysis. It then provides a first theoretical and practical introduction to thematic analysis. Days two to four will be devoted to increasingly complex exercises in thematic analysis (its various stages, from the elaboration of a coding scheme to the coding process, the aggregation of coded elements into a coherent "story" and its writing up). These exercises will be accompanied by lectures and discussions on key issues related to the analysis as such, such as the role of theory in qualitative data analysis (inductive, deductive and hybrid coding logics, and the logic of working with "sensitising concepts"), epistemological issues (validity, reliability, triangulation, etc.), ethical issues (when dealing with interview data for example) and various practical issues (how to compose a data body, how to deal with research design, etc.).

Part of the exercises will be done on data produced by the participants itself, but we will also look into other kinds of data (for example interview data, media data etc.).

The last day of the course (day five) consists in a short introduction to discourse analysis that can be useful either as a complement or as an alternative to thematic analysis. Through some sensitising exercises, the participants will be made aware of the existence of layers of meaning of the data that cannot be captured with thematic analysis.

On successful completion of the course, the student has the following learning outcome defined as knowledge, skills and general competence:

Knowledge

The student has

• a solid understanding/knowledge of thematic analysis: its logic, assumptions, practical steps and limitations
• a solid understanding and capacity for autonomous thinking on key issues inherent in qualitative data analysis: how to make the best use of "theory" for a given analysis; how to compose a meaningful data body and accurately assess its limitations; how to think critically about the validity and reliability of qualitative research; how to deal with ethical issues accurately

Skills

The student is

• able to prepare and carry out thematic analysis confidently from beginning to end
• able to turn his/her findings into well documented argumentative text

General competence

The student

• be able to see how the acquired knowledge and skills transfer to their own work
• have sharpened their ability to assess other people's qualitative analyses critically
• have acquired a good understanding of qualitative data analysis to be able to navigate through the literature and identify and use additional tools

The course focuses mainly on thematic analysis in all its aspects (preparation, composition of a data body, elaboration of a coding scheme, coding and successive layers of aggregation of the findings, writing-up). A short introduction to discourse analysis will be given towards the end of the course.

A short introduction to software assisted qualitative data analysis may be provided as part of the course if desired.

The course is organised as an intensive one-week full-time course running from 9-4pm (with an one hour lunch break). It will consist in a variety of activities: lectures, discussions of readings, practical exercises, general discussions of methodological issues and/or of the participants' own research. If necessary, one-to-one meetings can be scheduled outside the usual class hours to discuss the participants' own research projects. The course is taught in English.

The number of participants is limited to max. 20. Each participant is expected to play an active part in the workshop (please refer to section ¿Work Requirements¿). The course literature is expected to be read before the course.

In preparation of the course, the participants will be requested to

• fill in and return a short questionnaire with some questions regarding their background and interests
• send a two to three page thesis proposal (including a methodology section)
• send a max. Two pages "free" text on a topic that will be communicated a bit ahead of the course a bit ahead of the course; (this text will be used in analysis assignments during the course)
• active and continuous presence throughout the course

Work requirement must be met within fixed deadlines. Work requirements are evaluated Approved or Not Approved.

The assessment will be based on the following:

A final paper (in English) consisting of two parts:

• An empirical analysis of qualitative data (the students own data, or data provided by the course).
• A short methodological essay on a set topic in connection to the course readings.

Upon fulfilment of the work requirements and approval of the final paper, the students are granted 10 ECTS. The paper is assessed and approved by the course responsible. The paper is assessed on the basis of the stated learning outcome for the course. Deadline for the completion of the final exam is six weeks after the end of the course.

Legitimate absence based on e.g. a medical certificate does not exempt students from the work requirements. Students who due to illness or any other documented legitimate absence do not meet the coursework requirements before the deadline, should be given a new deadline. An agreement with the course responsible must be made in each case.

The paper must be of about 6000-7000 words (in English), one and a half spaced, with ample margins.

Grading Scale

Pass or Fail .

Appeal

Provisions about exams and cheating in 'Regulations Relating to Studies and Examinations at Oslo and Akershus University College of Applied Sciences' applies for academic works which is part of the training component in PhD programmes.

Target Group

The course is aimed at PhD students.

Admission

Applicants must be enrolled in a PhD program. Applicants who meet the formal requirements for admittance to PhD programs may be considered. The decisions on acceptance will be taken by the course's responsible.

Applications must include an approximately one-page description about the applicants' background, education, PhD program and a short description of their project.

Pre-requisites

To benefit from this course, the participants should have a general understanding of social science methodology and, if possible, some experience with qualitative research.