Too many cooks in the kitchen? How large teams can work together effectively to deliver qualitative studies of complex policy problems

Dr Geoff Bates is a Research Associate at the University of Bath Institute for Policy Research (IPR). Dr Anna Le Gouais is a Senior Research Associate at Bristol Medical School, University of Bristol.

Many of the big societal challenges that policy makers face today, like addressing climate change, reducing widespread economic and social inequalities, and tackling rising non-communicable diseases such as heart disease and diabetes, are highly complex problems. There is often disagreement about what causes these problems across the large and messy systems they exist within, and what policy solutions are possible, desirable and effective.

Large teams of researchers from different disciplines are ideally placed to investigate complex problem. Bringing together different perspectives and experiences can help to look at long-standing and complex issues in new and innovative ways to inform policy debates. Many of these large teams will want to use qualitative methods in their research. However, there are significant challenges facing large teams of qualitative researchers who hope to engage with complex real-world problems.

Our experience working in a large disciplinary diverse team

We have written about our recent experience delivering a large-scale qualitative study in a new article published in the International Journal of Qualitative Methods. We find that to deliver high quality research, teams need to balance researcher autonomy with team collaboration and centralised decision-making, with one taking priority over the other at different points during a large-scale qualitative study. We also identify important factors required to maintain team cohesion and support the needs of individual researchers.

We published our critical reflections on delivering a large-scale qualitative study as part of the ‘Tackling the Root causes of Unhealthy Urban Development’ (TRUUD) project. TRUUD aims to develop ways to prioritise health in the decision-making processes that shape the urban environment.

In the first phase of the project, the team conducted a study that involved 132 in-depth qualitative interviews with influential actors in urban development to explore how decisions are made across this complex system and how to increase the extent that health outcomes are considered in policy making. The study was designed and delivered by nine sub-teams of researchers across five universities with expertise in public policy and administration, public health, urban planning, transport, sustainability, real estate, law, management, and public involvement.

Delivering high quality research while ensuring team cohesion in large-scale qualitative research

A key challenge for large qualitative teams, particularly when involving multiple disciplines, is that they will likely include a range of perspectives and preferences on methods and approaches that the study should be based on. These can be strongly held and reflect the different epistemologies of team members and the often long-established norms and values of the disciplines that they come from.

This presents difficulties at different stages of a qualitative study for (i) following best practice for conducting high quality research, because what constitutes best practice can vary across disciplines, and (ii) maintaining good team cohesion and individual motivation, because individuals in the team must consider their own careers and institutional requirements alongside the needs of the team.

This table, adapted from table 1 in the article, summarises some of the additional challenges facing large teams compared to smaller or single disciplinary qualitative research teams at key common stages of a study:

Process	Characteristics of single disciplinary teams	Additional challenges for large, disciplinary-diverse teams
Identifying research questions	Based on common aims, theories, and interests.	Interests and expectations vary across disciplines, presenting a greater challenge to identify a shared aim and vision.
Participant sampling	Teams are likely to have clear and relatively narrow participant populations. This may simplify sampling decisions.	Covering multiple types of stakeholder groups creates challenges in identifying a robust and complimentary sample. Different sampling methods may be suitable. Checking coverage across teams with anonymous samples may be challenging.
Developing interview questions	Single set of interview questions likely to be developed. Aligned interests and expectations across research team.	Multiple interview schedules developed for different participant groups; differences in interview styles, expectations, and language. Differing interests and knowledge across the team. Researchers have varying needs and expectations for how data can be used.
Coding data	Supported by common terminology, relatively narrow sample, shared preferences.	Variation in preferences on coding approaches further complicated by lack of shared language, more diverse sample, variation in interview scope, large numbers of interviews.
Analysis/ interpretation of data	Based on clear understanding of purpose and expectations for analysis. Data is likely to be focussed on a relatively small number of issues.	Complexity through the size and breadth of the dataset, and researcher analysis preferences. Collaborative analysis may need to be carried out alongside interrogation of data by individuals for disciplinary-specific purposes. Challenges of integrating knowledge from across the discipline-specific findings.

How can large-scale qualitative teams balance researcher autonomy and team collaboration?

To deliver a high-quality study and maintain team cohesion, some tasks in a large qualitative study require high levels of freedom for researchers within the team (e.g., sampling, interview question design, and developing inductive codes). At other times, teams should prioritise collaboration based on centralised decision making (e.g., setting research questions, data management, and developing deductive codes). In addition to ensuring some autonomy, it is also important to give individuals a voice in centralised decision-making so that collaboration reflects thinking across the whole team and considers different individual as well as institutional needs (e.g., publishing and standards for best practice).

We developed eight recommendations to help other large qualitative teams work together in this way, to effectively explore complex policy issues:

1. Make time to engage in ongoing reflexivity to understand differences
2. Acknowledge no single ‘right’ way
3. Create inclusive environments for regular team discussions
4. Empower researchers to make choices
5. Allow time to trial approaches and be prepared to change direction where necessary
6. Understand variation in publication requirements across disciplines
7. Provide clarity and guidance on procedures in working protocols
8. Agree terminology and create clear definitions to understand a shared language

Creating a team culture that is open-minded and accepting of different views and practices associated with other epistemological and disciplinary perspectives is fundamental to this approach. Understanding differences can take time and undertaking reflexive practice during a project may be challenging within limited time frames, especially when facing competing pressures. However, building in substantial time for discussion and reflection on different perspectives and preferences is critical if teams are to collaborate using shared qualitative methods and processes.

In our experience, this will not just help deliver high-quality and policy-relevant research on complex problems, but also ensure the experience is inclusive, enjoyable, and useful for the whole team.

As multi-, inter- and transdisciplinary research becomes more sought after, it is important that those managing and funding large and disciplinary diverse research projects prioritise developing the right team culture from the outset. This requires that significant time is built in to develop shared understandings and trust across teams. This will assist qualitative researchers from different disciplines to work together to effectively engage with complex real-world problems.