Faster, Stronger, together: Introduction to Collaborative Matrix Analysis
Norha Vera San Juan gave the QHRN seminar on the 12th April 2022.
Title: Faster, stronger, together: Introduction to Collaborative Matrix Analysis
Date: 12th April 2022
Time: 12:00-13:00 (UK time)
Collaborative Matrix Analysis is a method that enables multidisciplinary teams, including team members with no experience of conducting qualitative research, to conduct rapid analysis of large qualitative datasets.
Reflexivity and methodological rigour are essential to produce insightful and accurate findings, however, traditional qualitative analysis methods rely on lengthy processes conducted by skilled researchers that are not always possible in emergency response or low-resource settings. Combining elements of table-based qualitative analysis with team-based reflexivity, we increased the efficiency and applicability of our research, while also promoting the active involvement of lived experience researchers and other stakeholders. In this seminar, I will go through the steps to conduct Collaborative Matrix Analysis, two case-examples, and discuss the opportunities and challenges that this new method presents.
Norha has a mixed academic background in political science, clinical and experimental psychology, and population research. She completed a PhD in Social Epidemiology at King's College London, and currently works as a Research fellow at the Institute for Global Health (UCL) and the NIHR Mental Health Policy Research Unit, informing mental health service and policy development.
She is a member of the Rapid Research Evaluation and Appraisal Lab (RREAL), the Qualitative Applied Health Research Centre (QUAHRC), and the Lancet Psychiatry Commission on Psychoses. She is also co-founder of the Social Research Platform on Mental Health in Latin America (PLASMA), and lead of the Recovery Chapter of the Latin American Consortium for Early Psychosis Research (ANDES).
Norha's work focuses on applying participatory research methods that promote stakeholder involvement; it challenges the traditional focus on clinical views and rather advocates for co-construction of knowledge to promote the sustainable development of health services. She is interested in innovative research methods that allow for collaborative data collection and analysis of large qualitative datasets in a timely manner. This includes qualitative methods such as rapid appraisal, framework analysis, and combining thematic analysis with health informatics methods like text network analysis.