Method

Collaborative Analysis

InformParticipateHave a sayFinalisation & ImpactReflection & EvaluationStructuring & PrioritisingAnalogueDigital1 - 3 Hours1 DayDirectly AffectedOrganised ActorsSmall Group up to 15SimpleStructured

Collaborative Analysis is a method in which results are not interpreted solely by experts or project leaders but together with the people who participated in the data collection or engagement process. The aim is to incorporate different perspectives into the interpretation and ensure that findings are understood not only about participants but with them.

Goal

The aim is to interpret results collaboratively and make different perspectives visible. The method helps avoid blind spots in the analysis and ensures that participants' experiences are directly incorporated into the interpretation.

When useful?

results should be interpreted as closely as possible to participants' lived experiences.
different perspectives on the findings are important.
trust and transparency within the participation process should be strengthened.
underrepresented or hard-to-reach groups should be actively involved.
interpretations should not be based solely on the perspective of public administration or project staff.

When not useful?

confidential or sensitive data are involved.
very large amounts of data need to be analysed.
there is insufficient time for joint reflection.
the results have already been finalised and are no longer open for discussion.

Process

Present the findings in a way that is easy for everyone to understand.

Possible materials include:

interview quotations
thematic clusters
photographs
maps
statements from workshops
results of a Thematic Analysis

The materials should be clearly organised and easy to interpret.

Consider which questions should be explored together.

Examples include:

What stands out to you in these results?
Which findings surprise you?
Which aspects are missing?
What do you consider most important?

Where possible, invite people who:

participated in the data collection,
are directly affected by the topic,
can contribute different perspectives.

A diverse group improves the quality of the interpretation.

Begin by presenting the findings without offering interpretations.

For example:

thematic clusters
quotations
maps
observations
charts or diagrams

Avoid drawing conclusions at this stage.

Ask participants to first describe what they notice.

Helpful questions include:

"What catches your attention first?"
"Which themes do you recognise?"
"Which findings seem particularly important?"

Avoid questions such as:

"Are these results correct?"
"Do we all agree?"

At this stage, the focus is on observations rather than evaluation. Record participants' first impressions.

The group then discusses the findings collaboratively.

Helpful questions include:

"Why do you think this topic was mentioned so often?"
"What experiences might lie behind these findings?"
"What connections do you see?"
"Which perspectives might be missing?"

Different interpretations should be allowed to coexist without forcing agreement.

Participants can:

add missing aspects,
clarify or refine themes,
correct misunderstandings,
explain relationships between findings.

This stage often generates valuable insights that were not apparent in the original data.

Finally, document the group's most important conclusions.

Examples include:

key themes,
surprising findings,
open questions,
possible priorities for action.

The result is not only an analysis of the data but also a shared interpretation of what the findings mean.

Possible outcomes include:

validated findings,
additional perspectives,
a deeper understanding of the results,
greater acceptance of the analysis,
shared priorities for future action,
stronger ownership of the overall process.

The findings are often more robust because they have been interpreted not only by project staff or experts but also together with the people whose experiences form the basis of the data.

These insights can then be used to identify appropriate next steps, responsible organisations, and relevant stakeholders.

After the analysis, participants should be informed about how the results will be used and what the next steps in the process will be. Transparent communication strengthens trust and increases people's willingness to participate in future engagement activities.

Source: Kerr, J., Cheers, J., Gallegos, D., Blackler, A. & Kelly, N. (2023): The Art of Co-Design: A Guide Through Creative Collaboration. BIS Publishers.

Notes

Tips

Use clear and accessible materials.
Include original quotations and concrete examples whenever possible.
Ensure that everyone has sufficient opportunity to contribute.
Document differing perspectives as well as areas of agreement.
Be open to contradictions.
Avoid drawing conclusions too quickly.
Allow enough time for discussion and reflection.

Not suitable for

confidential data cannot be shared.
a rapid analysis is required.
there is no willingness to engage in joint reflection.
only statistical or quantitative results need to be analysed.

Afterwards

Afterwards, methods that organise, deepen, or translate the gathered content into concrete steps are useful. Depending on the goal, the following formats can follow, for example:

Sensemaking

Sensemaking is a collaborative method for analysing and interpreting information, observations, and participation results. Unlike scientific analysis, it does not focus on systematically coding data or conducting statistical evaluations. Instead, it aims to identify patterns, relationships, themes, and insights in order to develop a shared understanding of a situation.

SimpleStructured

Content Analysis

Content Analysis is a structured method for analysing qualitative and quantitative data. It helps examine, categorise, and evaluate large amounts of information in a systematic way. All collected material is analysed according to predefined categories. The aim is to identify patterns, relationships, differences, and recurring statements.