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Analysing large volumes of complex qualitative data - Reflections from a group of international experts
Get PDFThis working paper brings together the reflections of a wide range of international researchers to explore, showcase and reflect critically on the potentials and challenges of analysing large volumes of complex qualitative, and qualitative longitudinal (QLR) data, including archived material. Big Qual analysis is a new area for qualitative work and there is little guidance on how best to work with masses of qualitative material. The working paper comprises a set of blogs housed in the ‘Big Qual Analysis Resource Hub’ (http://bigqlr.ncrm.ac.uk/). We created this website to map the progress of our ESRC National Centre for Research Methods research project ‘Working across qualitative longitudinal studies: a feasibility study looking at care and intimacy’ (2015-2019). As part of the project we developed procedures for working with multiple sets of in-depth temporal qualitative data (see Davidson et al. 2019; Edwards et al. 2019 for discussion of our methodological findings).
We have gathered together and made available the 27 blog post reflections from 32 authors in this working paper form because accounts of data management and analysis in qualitative research are often sanitised by the time they reach academic journals. Here, our contributors document and share publicly the trials and tribulations, intellectual commitments, contingencies and decision-making processes underlying such analysis, contributing to debates around good practice. We hope that this collection of reflections will promote further conversations about analysis/secondary analysis across large scale and/or multiple qualitative data sets. With guest posts from international scholars, from early career through to established researchers, on topics as varied as the ethics of using Big Qual data, using secondary qualitative material and computer-assisted qualitative data analysis software, this collection of reflections profiles the diversity of work taking place internationally
Multilevel Models: Introducing multilevel modelling
No full textThis video provides a general overview of multilevel modelling, covering what it is, what it can be used for, and the general data structures that are suitable for multilevel models.
This video is part of an Online Learnign Resource created by NCRM. This is video 1 of 3
Methods on the Move: walking, sensing, belonging
No full textPresentation from the NCRM Summer School 2019.
Building quality in inclusive, particpatory and emancipatory research, NCRM Summer School: 3-5 July 201
Multilevel Models: Random Coefficient Models
No full textThis video introduces random coefficient models and cross-level interactions
AI and privacy - problem or opportunity?
No full textThis talk was delivered by Professor Mark Elliot from the National Centre for Research Methods and the University of Manchester.
Mark was speaking at an NCRM 'Ahead of the Curve' event on 'Social Science Methods and Automated Data Algorithms', held on 1 November 2018 in London
Dr Olga Maslovskaya: What is Data Survey Quality?
No full textThis video introduces survey data quality concept in general and dimensions of total survey quality in particular
Understanding predictive privacy harms - Orwellian when accurate, Kafkaesque when flawed
No full textThis talk was delivered by Frederike Kaltheuner, Data Exploitation Programme Lead at Privacy International.
Frederike was speaking at an NCRM 'Ahead of the Curve' event on 'Social Science Methods and Automated Data Algorithms', held on 1 November 2018 in London
The Good the bad and the ugly – lessons from participatory research
No full textPresentation from the NCRM Summer School 2019.
Building quality in inclusive, particpatory and emancipatory research, NCRM Summer School: 3-5 July 201
A review of new technologies and data sources for measuring household finances: Implications for total survey error
Get PDFWe review process generated data sources and new technologies that could be used to improve the measurement of household finances. The sources include data generated by financial aggregator services, customer loyalty programmes, credit/debit card transactions, and credit rating agencies. The technologies include scanning of barcodes or shopping receipts and smartphone applications. For each of the data sources and technologies, we review what, if anything, is known about (i) the content of what can be measured, (ii) examples of research for which these data have been used, (iii) whether the data have been used as free-standing data sources or linked to probability sample surveys, and (iv) the quality of the data regarding representativeness and measurement quality. The review is structured around an adapted version of the Total Survey Error framework we have developed for evaluating these new data sources, and concludes with a discussion of implications for survey practice and research needs
Indigenous and Non-Indigenous Research Partnerships
No full textWhat do researchers need to consider when looking to achieve effective Indigenous and Non-Indigenous Partnerships?
Illustrated thoughts from researchers Ros Edwards, Helen Moewaka Barnes, Deborah McGregor and Tula Brannelly who have been collaborating in a UKRI funded project aimed at Transforming Indigenous and Non Indigenous research.
Video slides come from a comic produced as part of the project.
https://www.indigenous.ncrm.ac.u