Making sense together
Contributing to analysis without losing the community
Coding, themes, cultural interpretation, member checking, and how to disagree with the team.
Analysis is where community knowledge makes the largest difference, and where it is easiest to be sidelined. You may not run the coding software. You will read the material, name what you see, and challenge a reading that would misrepresent your community.
What analysis is for
Analysis turns records into findings. A finding is a statement about a pattern, supported by evidence, that answers part of the research question. It is not a list of what people said, and it is not a summary of the most memorable line.
The route, in five steps
Five steps you can join at any point
- Familiarisation. Read or listen to everything, and note what strikes you. No labels yet.
- Coding. Give short labels to pieces of data. "Fear of being judged", "the form was impossible", "my daughter rang for me".
- Grouping. Put related codes together. Name the group.
- Reviewing. Test the groups against the whole data set. Look for the cases that do not fit.
- Reporting. Write each theme with evidence, and say how common it was and who it came from.
Contributing cultural interpretation
Some data cannot be read without knowledge of the community. The IBISES work shows what that looks like: a decision to discuss prostate cancer at a funeral, an interpretation of why a service feels unwelcoming, a recognition that a phrase means something different in a community than in a clinic.
Four questions to ask of any draft finding
- Would a member of this community recognise themselves in this statement?
- Does the wording import a clinical assumption the community would reject?
- Is the theme built on the most articulate participants only?
- What is missing from the sample, and does this finding depend on that gap?
Contesting a finding
You will disagree with the team. Do it early, in writing, with a reason.
A workable form of words
"I think this theme is not quite right, because [what you observed]. The quotation in box 2 reads to me as [your reading], not [the team's reading], because [reason]. Can we look at the four cases that do not fit before we finalise?"
That is specific, it separates observation from interpretation, and it asks for a check rather than a reversal. Teams accept it. Vague discomfort is easier to dismiss.
Member checking
Member checking means taking findings back to the people who provided them. It is not a vote, and it does not make a finding true. It tests whether the account is recognisable, and it often adds the explanation.
- Take three or four draft themes, in plain language.
- Ask: does this match your experience? What is missing? What is wrong?
- Record the response, including disagreement.
- Report what changed, if anything.
Quantitative contributions
Where the study counts, you can still contribute. Sense-check a survey question for wording and assumptions. Explain why a response rate differs. Notice when a category does not match how people describe themselves. Review a table for a claim it cannot support.
Writing up, as a link worker
Three routes to credit
- Co-author. Where you contributed to design, data collection and interpretation, authorship is defensible. Say so early, in writing.
- Named contribution. An acknowledgement that names the specific contribution, not "the community".
- Your own voice. A presentation, a report section, or a community-facing summary in your name.
Ask which is available at the start of the study. It is harder to negotiate after the paper is drafted.
What good analysis avoids
- Quotation shopping: choosing the line that proves the point.
- The single voice promoted to a theme.
- Theory language that the community would not use.
- Losing the dissent, because it was inconvenient.
- Reporting a finding about a community without a member of that community reading the wording.
The test of understanding
The team drafts a theme: "Participants were reluctant to attend appointments because of low health literacy." What is wrong, and what would you say?
It attributes a cause to people rather than describing what happened, and "low health literacy" is a team judgement, not the participants' account. You would ask what participants actually said: what the letter looked like, who they could ask, what happened when they rang. A better theme describes the barrier and the mechanism: "Appointment letters arrived in a form people could not use, and nobody was named to help."