Summary#

Michelle James, James Copestake and Rachel Forrester-Jones describe the methods behind Michelle's PhD research with asylum seekers in a dispersal city in South-West England, in a 2026 article in Social Sciences. Peer researchers from the refugee community ran Qualitative Impact Protocol (QuIP) interviews in the interviewees' mother tongue. Together with a Photovoice project, a survey of members of local support organisations and feedback data, all of the text was coded in a single Causal Map project. The article sets out what this causal analysis showed that a general thematic analysis would not, and what was hard about the peer-researcher model.

Its starting point will be familiar to anyone who reports qualitative findings to government: policymakers ask for simple, top-level findings, while the hundreds of pieces of evidence behind them usually stay in a "black box" that only the analyst can see. Here, each count on a causal map leads back to the quotations behind it.

This is a companion to the substantive findings from the same study, in Exploring the Role of Social Protection in UK Asylum-Seeker Wellbeing.

Abstract#

This case study draws on innovative, collaborative research to demonstrate the benefits and limitations of using mixed methods data collection, including peer-led Qualitative Impact Protocol (QuIP) interviews and Photovoice, and causal analysis. We illustrate how the methodological design addressed the needs of research participants, policymakers, and wider stakeholders and was more incisive than general thematic analysis in assessing how different forms of social protection were perceived to mediate or hinder UK asylum seekers' wellbeing. Despite challenges in adopting a peer-researcher model, the QuIP was effective in drawing out detailed stories of change in participants' lives while mitigating potential response bias. The use of peer researchers from the refugee community encouraged open, frank conversations and empathic listening and supported both interviewee and peer researcher wellbeing. The Causal Map (v2) software successfully analysed and visualised complex causal relationships in the mixed, textual data, producing top-level quantitative visualisations linked clearly to underlying qualitative evidence.

Keywords: causal analysis; wellbeing; causal map; peer researcher; asylum seekers; social protection

How Causal Map was used#

• The QuIP transcripts, Photovoice image descriptions, Photovoice workshop transcript, survey data and feedback statements were all coded inductively in one Causal Map project.
• Keeping the data types separable within that project let the team analyse a core group of 27 key informants (the QuIP interviewees and Photovoice participants) on its own, and bring in the narrower data only where it answered a specific question.
• The article's figures are causal maps in which the number on each arrow is the number of respondents who mentioned that link. Some trace causal chains from community-based social protection, and others from government social protection, to their reported consequences.
• After the inductive coding and outputs were complete, the team ran a secondary analysis of the same data using Max-Neef's human scale development theory.
• The authors write that linking these top-level visualisations to the underlying text supported outputs for academics, policymakers, practitioners and participants, and that dissemination events with peer researchers and Photovoice participants produced agreement with the findings.

Access the article here (open access)

Reference (APA): James, M., Copestake, J., & Forrester-Jones, R. (2026). Moving from Thematic to Causal Analysis: Research into How Asylum Seekers Perceive the Impact of Social Protection on Their Wellbeing. Social Sciences, 15(9), 642.