This page compiles the resources that we, as a team, have designed and produced.
The resources for teaching and learning were produced in partnership with Prof Melanie Nind and Dr Sarah Lewthwaite as part of a National Centre for Research Methods collaborative project: Big Qual Analysis: Innovation in method and pedagogy
Edwards, R., Davidson, E., Jamieson, L. and Weller, S. (2020) Theory and the breadth-and-depth method of analysing large amounts of qualitative data: a research note, Quality & Quantity.
Edwards, R., Weller, S., Jamieson, L. and Davidson, E., (2020) Search strategies: Analytic searching across multiple data sets and with combined sources, in K. Hughes and A. Tarrant (eds) Qualitative Secondary Analysis, Sage: London, Ch.5.
Weller, S., Davidson, E., Edwards, R. and Jamieson, L. (2019) Analysing large volumes of complex qualitative data: Reflections from international experts, NCRM Working Paper.
Lewthwaite, S., Weller, S., Jamieson, L., Edwards, R. and Nind, M. (2019) Developing pedagogy for Big Qual Methods: Teaching how to analyse large volumes of secondary qualitative data, NCRM Working Paper.
Davidson, E., Edwards, R. Jamieson, L. and Weller, S. (2019) Big data, qualitative style: a breadth-and-depth method for working with large amounts of secondary qualitative data, Quality & Quantity, 53(1), 363-376. Open access
Edwards, R. and Weller, S (2015) Ethical dilemmas around anonymity and confidentiality in longitudinal research data sharing: The case of Dan, in Tolich, M. Qualitative Ethics in Practice, Left Coast.
Listen to our NCRM audio podcast mini-series to find out more about our approach to big qual analysis:
- Digging deep! The archaeological metaphor helping researchers get into big qual
- A short introduction to the Timescapes Archive
- Making space for big qual: New ideas in research methods and teaching
- Teaching big qual: Benefits and challenges for students and teachers
View our films of the papers presented at our NCRM seminar on Approaches to Analysing Qualitative Data
- Computer-assisted text analysis by Professor Emeritus Clive Seale.
- Archaeology of knowledge and working in the archives by Professor Maria Tamboukou.
- A layered archaeological approach to analysis across multiple sets of qualitative longitudinal data by Dr Emma Davidson and Dr Susie Weller (presented by Emma Davidson).
Interactive Timescapes timeline
Click on our Tiki Toki timeline (web-based software used for creating interactive timelines) depicting ‘time in Timescapes’ which captures when participants within each study were born, the epoch in which the study was conducted, the duration of each project and the different ‘waves’ of research.
Guest blog series
- Reflections from the Men, Poverty and Lifetimes of Care study by Anna Tarrant
- Visual approaches in QLR by Fiona Shirani
- Case histories in QLR by Rachel Thomson
- Data from the past and for the future – Qualitative longitudinal data available at the UK Data Service by Libby Bishop
- The challenges of multiple perspectival QL analysis by Sue Bellass
- Revisiting yesterday’s data today by Nick Emmel
- Computer-assisted text analysis beyond words by Gregor Wiedemann
- Using qualitative secondary analysis as a tool of critical reflexivity by Sarah Wilson
- The ethics of secondary data analysis by Ginny Morrow
- Working with qualitative longitudinal data by Georgia Philip
- The challenges of computer assisted data analysis for distributed research teams working on large qualitative projects by Rebecca Taylor
- Facebook timelines: Young people’s growing up narratives online by Sian Lincoln and Brady Robards
- Research Data as Documents of Life by Bren Neale
- Following families by Jane Millar and Tess Ridge
- Analytic strategies for working within and across cases in qualitative longitudinal research by Ruth Patrick
- Time, technology and documentation by Rachel Thomson, Sara Bragg and Liam Berriman
- Can a computer do qualitative analysis? by Daniel Turner
- Seeing the changes that matter: QLR focused on recovery and adaptation by Joanna Fadyl
- The use of Leximancer in relation to working with large volumes of qualitative data by Elena Zaitseva
- Tracing the changes in child feeding notions and practices by Irmak Karademir-Hazir
- Working in collaboration to develop the teaching of big qual analysis by Sarah Lewthwaite
- Analysing young people’s experiences of coping with problems, difficult situations and feelings: An evolving approach to analysing qualitative longitudinal evaluation data by Emily Stapley
- Working backwards and forwards across the data: Bringing together qualitative longitudinal datasets with different temporal gazes by Jane Gray
- Be transparent (and proud) – How can we better describe the practice of qualitative longitudinal analysis? by Åsa Audulv
- Selecting data sets to create new assemblages by Susie Weller, Rosalind Edwards, Lynn Jamieson and Emma Davidson
- Collaborating with original research teams: Some reflections on good secondary analytic practice by Susie Weller
- Computational text analysis using R in Big Qual data: lessons from a feasibility study looking at care and intimacy by Emma Davidson, Justin Chun-ting Ho and Lynn Jamieson
- COVID-19 and ‘Big Qual’ by Lynn Jamieson, Rosalind Edwards, Emma Davidson and Susie Weller
Weller, S., Davidson, E., Edwards, R., Jamieson, L., (2019) Big Qual Analysis: Teaching Dataset. University of Leeds, UK Timescapes Archive. https://doi.org/10.23635/14.
Jamieson, L. and Lewthwaite, S. (2019) Big Qual – Why we should be thinking big about qualitative data for research, teaching and policy, LSE Impact Blog
Lewthwaite, S., Jamieson, L., Weller, S., Edwards, R., and Nind, M. (2019) Teaching how to analyse large volumes of secondary qualitative data, NCRM Online Learning Resource – comprehensive selection of resources for both students and teachers
Lewthwaite, S. and Nind, M. (2019) Quick guide to teaching big qual analysis
Edwards, R. and Weller, S. (2015) I-Poems as a method of qualitative interview data analysis: Young people’s sense of self, online article, training resources and dataset, Sage Research Methods Dataset