# Public Contribution in Qualitative Research With Parents of Children Treated for Cancer: Description of and Reflections on a Collaborative Data Analysis Approach

## TL;DR

This paper describes a collaborative data analysis approach involving public contributors in a qualitative study about parents of children treated for cancer.

## Contribution

The study extends the Best Practice Framework for CDA by providing an example of a development and application approach.

## Key findings

- Public contributors added nuanced insights to the analysis, such as highlighting confusion between study procedures and therapy.
- Collaboration led to slightly different data categorizations between public contributors and researchers.
- The CDA approach was deemed successful in managing group dynamics and expectations.

## Abstract

Collaborative data analysis (CDA) in qualitative research is an approach for working with public contributors as co‐researchers in analysing data. Different approaches have been outlined in a Best Practice Framework for CDA: (1) consultation; (2) development; (3) application; and (4) development and application. Four characteristics of successful CDA are also presented; that the CDA process is: (1) co‐produced; (2) realistic within available time and resources; (3) manageable for public contributors; and (4) effective in handling group dynamics and expectations. We adopted a development and application approach to CDA to analyse data from a qualitative study embedded within the single‐arm feasibility trial ENGAGE, exploring the acceptability and feasibility of a cognitive behavioural therapy intervention (EJDeR) for parents of children treated for cancer, and study procedures.

Overall aim: to describe and reflect on the CDA approach used. Specific objectives: to (1) describe the CDA approach used; (2) reflect on the CDA approach used and map reflections onto characteristics of successful CDA; (3) summarise the potential impact of the CDA approach used on findings; and (4) report the costs of the public contribution activities.

Three public contributors collaborated with three research team members to analyse 36 qualitative interviews using the Framework Method. Public contributors received training and supervision, completed all analysis steps, independently developed coding frameworks, applied them to the data and interpreted findings. Together with research team members, they took part in a workshop to reflect on the CDA approach used, with reflections mapped onto four characteristics of successful CDA.

In the reflection workshop, the CDA approach was described as including the characteristics of successful CDA, for example, that public contributors handled demands of the process well, and that relationships and power dynamics were well managed. Potential impact on findings included public contributors bringing in nuances to the analysis overlooked by research team members, for example, their analysis highlighted that participants in ENGAGE experienced difficulties differentiating between EJDeR and ENGAGE study procedures. Public contributors and research team members categorised data in slightly different ways. The total cost for public contribution activities was ≈21,963 EUR.

The CDA approach used extends the Best Practice Framework for CDA by providing an example of CDA adopting a development and application approach. Our description of the CDA approach used may inform other researchers interested in CDA, and contributes to ongoing conversations about embedding ethical and equitable public contribution throughout the research lifecycle.

Public contributors and research team members contributed to the reflections described and writing, editing, and reviewing the manuscript.