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Qualitative Research Methods Part 2
Provider: Science

Activity no.: 5227-25-03-32There are 30 available seats 
Enrollment deadline: 08/09/2025
Tilmelding : Qualitative Research Methods Part 2
PlaceVon Langen F111
Rolighedsvej 23, 1958 Frederiksberg C
Date and time29.09.2025, at: 09:00 -
01.10.2025, at: 16:00
 
Tilmelding : Qualitative Research Methods Part 2 2. del
PlaceVon Langen F111
Rolighedsvej 23, 1958 Frederiksberg C
Date and time21.10.2025, at: 09:00 - 16:00
 
Tilmelding : Qualitative Research Methods Part 2 3. del
PlaceVon Langen F111
Rolighedsvej 23, 1958 Frederiksberg C
Date and time04.11.2025, at: 09:00 - 16:00
Regular seats30
ECTS credits2.50
Contact personCharlotte Bukdahl Jacobsen    E-mail address: cja@ifro.ku.dk
Teaching languageEnglish
Semester/BlockAutumn
Course workload
Course workload categoryHours
Preparation18.50
Lectures15.00
Class Instruction5.25
Practical exercises30.00
Sum68.75
Aim and content
This toolbox course provides PhD students with skills to analyse and report qualitative data.
The course is organized so that it runs approximately six months after the course Qualitative Research Methods Part 1. In that way PhD students have time to collect data after taking part 1, so they have their own data available for analysis.
The course will provide PhD students with skills relevant to carrying out qualitative analysis of semi-structured interviews and text documents, observations, and visual data. The students will be presented to the main phases of qualitative data analysis: interview transcription, qualitative analysis software, coding, and data interpretation. The focus will be on providing students with a set of generic coding/analysis procedures that can be used irrespective of the research tradition that their own data collection instrument draws on (e.g. hermeneutical, discourse, phenomenological, social-practice theory). There will be practical exercises where students are to code and analyze qualitative data (handed out by lecturers). Students will predominantly be presented with, and get exercises in, first-cycle and second-cycle coding, but more exploratory coding/analysis procedures (e.g. the constant comparison method) will also be introduced. Exercises will also include procedures to address the credibility of a qualitative data analysis , including how to calculate inter-rater agreement coefficients. A mixture of the qualitative software program NVivo and Excel will be used for the above-mentioned exercises. Further, the four general criteria to obtain trustworthiness in qualitative data analysis (credibility, transferability, dependability, confirmability) are presented. Here, focus will be on how PhD students in practice can meet the criteria in their own PhD project. On Day 4 and 5, The participants will also be working on their individual qualitative data (if possible). GDPR considerations, and an overview of the FAIR data principles pertinent to qualitative data will be outlined.
There are five course days that take place over five weeks. See the timeline and an overview of the five course days below

Course content:
- Day 1, 2 and 3 (week 40): Sessions on Research paradigms; Research quality and generalization in qualitative analysis; Ethics; Phases in Qualitative analyses; Different forms of analysis.
- Day 4 (week 43): Data analysis - own empirical material.
- Day 5 (week 45): Student presentations of analysis and peer feedback

Learning outcome
Knowledge:
• The students can identify the features, strengths and weaknesses of qualitative research analyses
• The students can discuss qualitative analysis and its validity claims
• The students possess knowledge about the four criteria of trustworthiness
• The students have knowledge about different forms of analysis used in qualitative research


Skills:
• The students can analyze their qualitative data using the first-cycle and second-cycle coding method in NVivo
• The students can account for the credibility of findings at the practical level of coding, interpretation, and analysis
• The students can calculate inter-rater agreement coefficients and use them as a tool to improve the credibility of the data interpretation
• The students possess tools to activate peers, supervisors and co-authors to achieve conformability (one of the trustworthiness criteria).
• The students know how to document the main research procedures and data analysis processes to ensure the research is transparent and traceable (the dependability criterion).

Competences:
• The students can critically assess the quality of qualitative analyses
• The students can translate knowledge about qualitative analysis to their own work

Target group
The course is aimed at PhD students at the Faculty of Science who have limited to moderate experience with qualitative research methods. Applicants should have completed the course Qualitative Research Methods Part 1.

It is recommended that applicants have completed the obligatory course PHD course Responsible Conduct of Research before taking this course.

It is recommended that PhD students follow part 2 of the course when they have carried out qualitative data collection.

Teaching and learning methods
The course will consist of a mix of lectures and class exercises. The exercises will involve choosing the appropriate qualitative theorical framework, method, and analytical approach. The qualitative data analysis software NVivo will be presented as a tool for data coding and management. Excel will be presented as a tool for analysis of inter-rater agreement. Many exercises are conducted in peer groups and with peer-review.

Participants are required to actively participate in course sessions to complete the course. In addition, the participants must present their project work to pass the course.

Lecturers
Guest lectures with expertise in qualitative methods will be used as needed. Guest lecturers will be announced later.

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