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Plate RES·014 · Research methodGrid H2 · Rev. 2026-08-14

Research Synthesis

Turning raw observations into findings, which is where most of the value and most of the bias enters.

Research methodEstablished researchAdvanced

Definition

Synthesis codes raw research data, groups it into themes, and derives findings that can inform decisions. It is the step where quality is won or lost, and the step most often skipped under time pressure.

Established research. Originates in published research and has been replicated. The effect is real. Its size in your product still needs measuring.

On this plate
  1. 1Definition
  2. 2A workable process
  3. 3Keeping the chain back to the data
  4. 4Three levels, kept apart
  5. 5In practice
  6. 6Commonly confused with
  7. 7Local chart
  8. 8Continue from here

Reach for this when

  • After any qualitative research.
  • Combining findings from several methods.
  • Several people took notes and the team is about to argue from memory.

Common mistakes

  • Writing findings from memory.
  • Themes stated as topics rather than findings.
  • No frequency information.
  • Disconfirming evidence omitted.
  • Synthesising alone, then presenting conclusions the rest of the team never saw the data for.

A workable process

  1. 1

    Capture observations atomically

    One observation per note, in the participant's words, with a session identifier.

  2. 2

    Code inductively first

    Let categories emerge from the data before applying your existing framework.

  3. 3

    Group into themes

    Cluster related codes. Name each theme as a statement, not a topic.

  4. 4

    Count

    State how many participants exhibited each theme. 'Three of eight' is meaningfully different from 'users said'.

  5. 5

    Separate observation from interpretation

    Keep what happened distinct from what you think it means, so others can disagree with the second without discarding the first.

  6. 6

    Include disconfirming evidence

    Report what contradicted the theme. Its absence is a signal that the analysis was not neutral.

Keeping the chain back to the data

Do

  • Tag every observation with the session it came from, so a finding can be traced.
  • Write findings as claims that could be wrong, not as themes that cannot.
  • Count how many participants support each claim, and say so in the report.
  • Keep the contradicting observations visible instead of smoothing them out.

Do not

  • Start from the categories you expected to find.
  • Let the most articulate participant become the finding.
  • Report a quote without saying how common the view was.
  • Present a recommendation without the observation that produced it.

Three levels, kept apart

Most disputed research reports have collapsed these into each other. Keeping them separate is what makes a finding arguable rather than personal.

LevelExampleWho can challenge it
ObservationFour of eight participants opened a spreadsheet during the taskOnly the recording
FindingThe product does not support comparing options side by sideAnyone, with other observations
RecommendationAdd a comparison view to the results pageAnyone, on cost, priority or a different solution

In practice

  1. i.

    Two people, one loud

    Weighting

    A single articulate participant can produce more quotable material than the other seven combined. Counting participants per claim is the cheapest available defence against a report that is really one person's opinion.

Commonly confused with

Close neighbours that get substituted for each other. The difference is stated first, then the condition that picks one over the other.

vs.

Affinity Mapping

Research method

Bottom-up clustering of individual observations, with the groups named last.

Key difference

Synthesis is the whole step from raw data to findings a team can act on. Affinity mapping is one technique inside it. Clustering notes on a wall is not synthesis until the themes are interrogated and turned into findings.

Use Research Synthesis when

  • The question is how to get from sessions to conclusions.
  • Rigour, traceability and bias are the concern.
  • The output has to survive being challenged.

Use Affinity Mapping when

  • You have a pile of observations and no structure yet.
  • The session is collaborative and needs a concrete activity.
  • Naming last is the point, so expectations do not drive the sorting.

Learn about Affinity Mapping

Local chart

RES·014 · Region IV · Grid H2

This entry and the entries it is stated to connect with. Each line is labelled with what the connection means.

Availability HeuristicBIAS·011 · PRINCIPLES BEHIND THISConfirmation BiasBIAS·009 · PRINCIPLES BEHIND THISJourney MapTPL·004 · OFTEN USED WITHPersonaTPL·003 · OFTEN USED WITHResearch PlanTPL·001 · OFTEN USED WITHCondensTOOL·015 · OFTEN USED WITHDovetailTOOL·014 · OFTEN USED WITHFigJamTOOL·006 · OFTEN USED WITHMiroTOOL·007 · OFTEN USED WITHMuralTOOL·008 · OFTEN USED WITHNotionTOOL·044 · OFTEN USED WITHAffinity MappingRES·020 · COMMONLY CONFUSED WITH

Continue from here

Each link says what the connection is, so you can tell a principle from an alternative from a thing people mix this up with.

Principles behind this

The reasoning this solution is an application of.

Often used with

These usually appear in the same screen or the same decision.

Short definition

The one-paragraph version, for when that is all you need.

Related concept

Connected closely enough to change how you apply this.