Process: After the field study
In this chapter, you will learn how to work with your field findings and how to conclude your study. Collaborative data analysis is the way to go!
Chapter summary: what to focus on when you are back from the field
The aftermath of a field study is about the analysis of the field findings, and their use in the design process that is informed by the field study. We use a collaborative data analysis workshop to manage the analysis process. This workshop is the central activity in the post-field study phase, and the other activities are structured around the workshop: recruiting participants, prepping them, preparing workshop material, testing the workshop. And then, documenting and analysing the workshop findings.
The goal of the workshop is to present, discuss and critique the observations and innovation potential you have captured in the field, together with relevant team members and external participants. Working with a panel of participants helps removing your inherent personal observation bias. In addition, it anchors the field findings in the design process informed by the field study, by reviewing them alongside design stakeholders who have not participated in the field study itself, but who have contributed in establishing the objectives of the field study.
Before starting the workshop preparations: engage with your data
The goal of this step is to transform the hand-written notes and media collected during the field study into a structured, digital data set. Consolidating the data is critical to ensure that the field study findings can be used after the field study. The data set might even be used to support another design process later on. When working with the workshop, you will create additional findings, which you can treat the same way as your field findings, and add to your data set.
Start with the data entry. Write-up your hand-written observations into structured, clear, short (think tweet size), text-based observations. Write these observations in an objective way. Then review your photo and video material and select the ones that best communicate your observations. Make sure to anonymise both the text and media: remove all evidence of personal and private data in the text notes and media selection, such as names and faces.
When going through your data, you will naturally start thinking about the meaning of your data. Start the data analysis by writing a list of topics that describe your main findings. To write the list, you can start with the field study objectives you had in your field study plan, and the summaries of findings produced by your debriefings during the field study. Then go back to your observations and sort them under each topic. You can use a tagging or coding system.
The data analysis starts to take shape while you sort your observations. Write the reflections that come up during the sorting process: for instance ideas, problem descriptions, list of tasks or requirements, new questions. It is the same analysis process as the one you used during your debriefs while in the field.
To move forward with the analysis, prepare a draft presentation that you will share with the rest of the team. The presentation will include the field study objectives, your current summary of findings and observed problems, illustrated by a selection of observations. Present the main ideas and concepts, always in the context of a documented problem (or several problems at the same time).
Keep your analysis and your data structured
To guide our students in the course we teach at the Architecture and Design school, we ask them to produce a map of their field study objectives and findings. Start with the overall objective on top, and work your way down to specific focus areas, and then the problems you have identified. Use your observations as evidence and illustration of the identified problems. The observations can take the form of your own short observations, or a citation from an informant, a sketch, an image.
Use this map to connect your ideas and concepts. You should be able to connect each idea or concept to a specific problem, or a focus area that encompasses several problems. Or some form of combination. This will be useful when you work with the evaluation of your ideas and concepts: how does each concept address the identified problem(s)?
If you find it helpful, you can keep this type of structure throughout the rest of the analysis. Think about it as a map to navigate your findings, and to onboard other team members on the status of the study.
Preparing the collaborative data analysis workshop
This type of workshop was introduced by David Millen (see the Further reading section at the bottom). It has two main functions:
- To transfer your experience and knowledge from the field to people to the rest of the team, and the stakeholders sponsoring the field study
- To use the knowledge of the workshop participants to improve your understanding of the field data, critique your ideas and generate new ones.
As a result, the workshop is built upon two main components: a presentation of the field study findings (to enable function #1) and participatory, generative activities (to enable function #2).
Recruiting and prepping participants
Reach out to individuals that have the expertise to contribute to the workshop and the mandate to champion the innovations produced by the field study.
Talk with a sample of the participants before the workshop to check the relevance of the workshop objectives, secure their participation and build up their motivation.
Example of workshop activities
Here are a few examples of activities we might use in a workshop:
- Present and review a scenario observed on the field to check the quality and accuracy of observations
- Analyse a workplace observed on the field: who works there, what work tasks do they have, what systems do they use, what are their experiences
- After you have given a good contextual introduction to the participants, give them a short design brief to work with for a short time period
- Collaboratively evaluate and improve concepts
Testing the workshop
In the field study course we hold for design students at the Oslo school of architecture and design, we have documented the following challenges experienced by our students: “Preparing for the workshop forced us to discuss our findings with each other before presenting them to the rest of the team”; “We should have done a dry-test of our workshop beforehand….We interrupted each other often during our presentation of the findings”.
So we recommend to prepare well. For example, make sure to define roles: agree upon who will present the field study, take notes, keep the time, facilitate participatory work. Ideally you can perform a dry run of the workshop, or a sample of it, with volunteers in order to test the workshop plan, material, timing and location.
Conclusion
To complete the field study with an efficient workshop, you will need to:
- Use your design skills to support findings presentation and collaborative/participatory design activities
- For instance, remember to present field data in ways that allows people to respond
- Bring the design problems you have observed into the workshop, and allow for analysis, critique, ideation
- Document the new insights generated by the workshop, and add the key outcomes into the field study documentation
Further reading
The idea of a collaborative data analysis workshop was introduced originally by David Millen in this paper.
For an overview of how we work with field studies at the Ocean Industries Concept Lab, we recommend published work by Lurås and Nordby, and by Gernez and Nordby:
- Field studies informing ship’s bridge design at the Ocean Industries Concept Lab
- Shaping designers’ sea sense: A guide for design-driven field research at sea
- Human-centered, collaborative, field-driven design-a case study
- Implementing field research in ship design
Credits
This chapter was written with contributions from Sigrun Lurås, Kjetil Nordby and Etienne Gernez.
