COMPUTER-ASSISTED QUALITATIVE AND MIXED METHODS DATA ANALYSIS: HARNESSING NVIVO - 2019/0
Module code: SOCM056
Qualitative and mixed methods researchers are increasingly making use of advanced computer software packages to enhance the research process and facilitate more in-depth, multi-layered analytic approaches. NVivo is one of the leading Computer Assisted Qualitative Data AnalysiS (CAQDAS) packages designed to facilitate the range of qualitative and mixed-methods analysis. It is widely used to undertake literature reviews, systematise analyses, aid transparency, make projects portable and work in teams. NVivo facilitates the management and analysis of text, spreadsheet, pdf, image, audio and video data, making it a relevant tool for flexible work across disciplines and sectors.
This module provides a comprehensive overview of the role of NVivo in the analytic workflow, it’s architecture and available tools, and instruction in how to harness it powerfully for analytic tasks. This module is framed by the Five-Level QDA® method, a CAQDAS pedagogy designed to enable researchers to develop the expertise they need in NVivo, whatever the research objectives and methodology.
We begin with familiarising with NVivo, by focusing on software components, and the actions that can be taken on them to fulfill analytic tasks. Participants will then plan their analysis and be guided through the process of translating their analytic tasks into software tools. Throughout the course participants will use their own research data, or sample data, and the course tutors will illustrate how to harness NVivo using a range of different types of research projects.
SILVER Christina (Sociology)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
JACs code: L300
Module cap (Maximum number of students): N/A
Prerequisites / Co-requisites
Pre-course reading on principles of using NVivo, the basics of qualitative coding, and the CAQDAS pedagogy used in this course (the Five-Level QDA ® method) will be provided. Participants do not need to bring their own laptops, but they can if they wish. The course will run in a Windows computer lab. Should participants wish to work on a Mac, they must bring their own laptop with the latest version of NVivo pre-installed and tested.
Indicative content includes:
- Differentiating analytic strategies and software tactics
- NVivo features, components and tools
- Analytic planning in the context of NVivo use
- Setting up an NVivo project to reflect initial analytic design
- Representing conceptual frameworks using maps
- Strategies for using NVivo to undertake a literature review
- Exploring data qualitatively and quantitatively: word frequency, text search and annotations
- Conceptualising qualitative data: coding, memoing, linking
- Organising data to factual characteristics: cases, attributes, sets & search folders
- Identifying patterns, relationships and anomalies using query and visualisation tools
- Representing and reporting on findings
|Assessment type||Unit of assessment||Weighting|
|Coursework||Two-part harnessing NVivo assignment||100|
The assessment strategy is designed to provide students with the opportunity to demonstrate:
A detailed understanding of the role and functioning of NVivo and skills in harnessing its tools for a range of analytic tasks.
Thus, the summative assessment for this module consists of:
- A completed series of Analytic Planning Worksheets (approximately 2,500 words) that detail the objectives, analysis plan and translation of analytic tasks into NVivo tools as the project evolved.
- An NVivo project containing the analysis and a compound memo (approximately 1,500 words) providing a transparent roadmap to the elements of the analysis.
Participants will complete a series of workshop exercises during days 1-3 of the course and have the opportunity to discuss their work with each other and the tutors. Feedback During day 5 students will present an overview of their progress to date and receive formative feedback from the tutors and other participants
- • Provide an understanding of the role of CAQDAS packages in qualitative analysis
- • Illustrate the relationship between analytic strategies – what you plan to do - and software tactics – how you plan to do it
- • Provide confidence in the architecture and functioning of NVivo
- • Enable analytic tasks to be appropriately fulfilled using NVivo in the context of participants’ own research projects
|001||Distinguish and separate analytic strategies and software tactics||T|
|002||Have an appropriate mind-set for harnessing NVivo’s tools|
|003||Have a detailed understanding of the architecture, functioning and flexibility of NVivo|
|004||Develop technical and practical skills in the use of software for qualitative analysis|
|005||Be able to translate analytic tasks into NVivo tools using the Five-Level QDA method|
|006||Be able to critically evaluate the strengths and weaknesses of the QDA approach|
|007||Communicate their process and findings|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Overall student workload
Workshop Hours: 22
Independent Study Hours: 128
Methods of Teaching / Learning
The learning and teaching strategy is designed to:
Provide participants with a thorough understanding of and confidence in harnessing NVivo tools for analytic tasks as a project progresses.
The learning and teaching methods include:
- Hands-on workshops
- Small group work
- Independent study
- Group discussion and feedback
- Use of worksheets designed to translate analytic tasks into NVivo tools
This module is taught intensively over one-week. Days 1-3 combine lectures, demonstrations and hands-on practical sessions on harnessing NVivo for qualitative and mixed methods analysis. Day 4 is devoted to independent study. On day 5 students will have the opportunity to receive formative feedback on their initial assignment plans and peer feedback during group discussions. Students will then complete their practical assignment using sample data provided.
Programmes this module appears in
|Social Research Methods MSc||2||Optional||A weighted aggregate mark of 50% is required to pass the module|