Course description
This 3-day PhD course is targeted PhD students at any stage of their study that may be entirely qualitative or include a qualitative component. The course is relevant for students who are at an introductory level on using NVivo software in qualitative research. The student must bring own dataset including transcripts of data to work with AND have access to NVivo14 (earlier versions also welcome).
NVivo is a software that allows researchers to import, organize, explore, analyse and visualize any unstructured data to reveal more significant insights from their qualitative data. The course aims for the student to gain practical knowledge on how to use the software NVivo to enhance and qualify the analysis of data from qualitative interviews and other types of unstructured, qualitative data. We wish to provide the student with knowledge and abilities to use the different tools in the software. The potential for insights from qualitative research data is almost unlimited. With a software like NVivo, researchers can work smarter with their qualitative data and discover more significant insights sooner. Use of NVivo is not restricted to a specific analytic approach and any research method can be applied, including systematic review and mixed-method approaches.
Students are encouraged to work on their own projectdata and share questions, troubleshooting and solving of problems in the classroom with peers, to increase the learning experience.
Intended learning outcome:
Knowledge– the student does
1. Understand how the software is constructed and its various functions, and how it can support the research process from writing the project protocol to finishing the manuscript or research report.
Skills – the student can
1. Use the different facilities and tools in the software to plan and perform the qualitative analysis based on their own data.
2. Explain and demonstrate the various analytic tools used in the data analysis
3. Establishing an audit trail in own analysis to ensure quality of own research – e.g credibility, transferability, dependability and confirmability
Competences – the student is able to
1. Critically reflect on the process of using NVivo software in analysis of own data
2. Evaluate the quality of the qualitative analysis
3. Apply and discuss the limitations and possibilities of use of NVivo software on different types of qualitative data.
The course follows the project trail, departs from creating a project in NVivo, importing different kind of data sources and query them for initial familiarizing with data, followed by coding and data analysis, discussing use of different analytic approaches in NVivo. Finally, the course will teach and train the students in how to use NVivo to present the results of a project.
Lecture plan – tentative
Day 1
NVivo's user interface and basic functions are introduced. During the day, the different elements in the software are demonstrated leaving time for the students to train them on own project data, following the logic process of the progression of analysis.
Day 2
The students present their preliminary and ongoing analyses and current learning experience. Difficulties are presented and problems are solved using in group- and lecturer feedback. Lecturer teaches issues of interest and more advanced functions in the software, according to the more mature phase of the analysis, e.g case classification, and presentation of results in maps or diagrams.
In the period between day 1 and day 2, the students will work with their own analysis and it is mandatory to reserve a considerable time for this between the days.
Teaching metods
The course will teach and train the student in the most important functions of NVivo software for qualitative analysis of textual and audiovisual data sources, including but not limited to:
- Create a project in NVivo
- Import text, audio, video, emails, images, spreadsheets, that is relevant for own PhD-project
- Search for emerging terms or topics using specific queries to familiarize with the dataset and identify themes
- Add interpretations and notes, using maps, Query and search data
- Code data to conduct the most in-depth analysis possible, based on the method chosen to own phd-project.
- Organize the coding structure for flexibility and rigor using iterations of coding and case classification.
- Create cases (units of analysis) for own project, and add attribute values (demographics) to allow for comparison of different groups in data, according to relevance In own project.
- Visualize data with word frequency charts, word clouds, comparison diagrams, maps to explain and present own analysis
- Quality assessment by coder comparison query and establishing audit trail
The students will be placed in small groups and will meet - online or face-to face - at least once in the period between day 1 and 2, to share difficulties and experience.
Or
An open, two-hour lecturer-lead supervision is planned between the days, to discuss the progression of analysis, share difficulties and current learning experience.
Student tasks
1. The student submit a research proposal prior to the course (max 5 pages). The student will present and discuss the proposal with specific focus on the qualitative analysis
2. Prepare for 2. Course day by working with their own analysis between course days
3. Participate in group meeting / open supervision between day 1 and 2 and present learning experience and persisting difficulties in class.
4. Present ongoing analysis, highlighting at last one issue to share and discuss in class. The chose issue / focus must be uploaded on Itslearning 1 week before 2.nd course day.
Student evaluation: The student will be graded “passed” when uploading preparations for 2nd course day and presenting the issue in class
Lecturer: Lene Bjerregaard, Associate professor, OPEN, eventually assisted by Signe Bech Titlestad, project coordinator, OPEN
Readings, literature will be advertised on It’slearning
Course fee
The course is free of charge for PhD students enrolled in Universities that have joined the "Open market agreement" or are members of NorDoc
For all other participants the course fee is:
DKK 4500
EURO 603