Research Synthesis Template (Affinity Map, Insights)

Research synthesis: sources, atomic observations, affinity clusters, insights with evidence links and confidence, opportunities, gaps

डिज़ाइन और UXमानकresearch synthesisअपडेट किया गया 29 अक्टू॰ 2026

एडिटर में खोलें.md डाउनलोड करें

उपयोग कैसे करें: Use it after interviews or tests to turn raw notes into insights. Fill the observations table first, then clusters, then insights with linked evidence and a confidence level. The main pitfall is writing insights first and then hunting for supporting quotes.

प्रीव्यू

Research Synthesis: [Study title]

A record of how raw research data became insights: the sources analysed, atomic observations, the affinity clusters they formed, insights with links to their evidence and a stated confidence, and the opportunities and open questions that follow. It follows common qualitative analysis practice (affinity diagramming and thematic analysis). Replace every [bracketed] value and every row marked Example. Worked examples continue the Quillmere Libraries interviews from design/research-interview-guide (Quillmere Libraries is a fictional city library service).

Document control

Field	Value
Document ID	[RSY-001]
Version	[1.0]
Status	[In analysis / In review / Final]
Owner (lead analyst)	[@researcher]
Second analyst	[@name]
Reviewers	[@product-owner], [@designer]
Last updated	YYYY-MM-DD
Classification	[Internal; pseudonymised]
Studies synthesised	[Example: RIG-001 interviews, 10 participants, YYYY-MM-DD to YYYY-MM-DD]

Revision history

Version	Date	Author	Change
0.1	YYYY-MM-DD	[@name]	Observations coded
0.2	YYYY-MM-DD	[@name]	Clusters and draft insights after team workshop
1.0	YYYY-MM-DD	[@name]	Reviewed by second analyst

1. Inputs

Source ID	Type	Participants	Date	Used	Notes
SRC-01 (Example)	Interviews (RIG-001)	P01 to P10	YYYY-MM-DD	Yes	Transcripts and notes
SRC-02 (Example)	Support tickets, last 90 days	n/a	YYYY-MM-DD	Yes	214 tickets, filtered to reservations
SRC-03 (Example)	Usability test UTR-001 debrief	U01 to U08 (different people, own codes)	YYYY-MM-DD	Yes	Debrief answers only
SRC-04 (Example)	Pilot interview	Colleague	YYYY-MM-DD	No	Pilot only, not a participant

2. Observations

ID	Participant	Source and time	Observation
OBS-001 (Example)	P03	SRC-01, 12:30	Saves screenshots of book posts and rarely returns to them
OBS-002 (Example)	P07	SRC-01, 09:15	Keeps a paper list on the fridge of books to look for
OBS-003 (Example)	P01	SRC-01, 18:40	Gave up on a reservation when the wait was 9 weeks and bought the e-book
OBS-004 (Example)	P05	SRC-01, 21:05	Did not know reservations could be collected at any branch
OBS-005 (Example)	P09	SRC-01, 15:20	Trusts a friend's recommendation more than any list
OBS-006 (Example)	n/a	SRC-02	38 of 214 tickets ask where a reserved book will arrive

The example shows one or two observations per cluster for brevity; in a real synthesis this table holds every coded observation, and the participant counts in section 3 come from it.

Coding: [Example: two analysts coded transcripts P01 to P04 independently, compared codes, agreed a codebook, then split the remaining six].

3. Affinity clusters

Cluster ID	Cluster statement	Observations	Participants
CL-1 (Example)	Book ideas are captured in many places and then lost	OBS-001, OBS-002	7 of 10
CL-2 (Example)	Long or unknown waits push people to buy instead	OBS-003	5 of 10
CL-3 (Example)	Collection options are poorly understood	OBS-004, OBS-006	4 of 10, plus tickets
CL-4 (Example)	Personal recommendations outweigh lists and algorithms	OBS-005	6 of 10

4. Insights

ID	Insight	Evidence	Confidence	What would change it
INS-01 (Example)	People do not lack ideas for what to read; they lack one place that holds them until they are ready to borrow	CL-1; OBS-001, OBS-002	Medium	A survey showing most borrowers already use a wishlist
INS-02 (Example)	Uncertainty about when and where a book will arrive drives abandonment more than the wait itself	CL-2, CL-3; OBS-003, OBS-004, OBS-006	Medium	Abandonment rates that do not change when wait and branch are shown
INS-03 (Example)	Recommendations from people the borrower knows carry more weight than library lists	CL-4; OBS-005	Low	More interviews across age groups; only urban participants so far

5. Confidence rubric

Level	Criteria
High	Seen across most participants and at least two independent sources or methods; no strong contrary evidence
Medium	Seen across several participants in one method, or supported by a second source with gaps
Low	Seen in a few participants, or in one segment only, or contradicted by some evidence

6. Opportunities

ID	Opportunity	From insight	Next step
OPP-01 (Example)	How might we help borrowers keep book ideas in one place until they are ready?	INS-01	Concept test of a "Want to read" list
OPP-02 (Example)	How might we show when and where a reserved book will arrive before people commit?	INS-02	Add to the reservation redesign; check in a usability test
OPP-03 (Example)	How might we bring personal recommendations into the app?	INS-03	Further interviews first, confidence is low

7. Gaps and next questions

Example: no participants under 25 or over 70; no screen reader users in this round.
Example: how many borrowers already keep wishlists elsewhere (needs a survey).
Example: whether showing estimated wait reduces abandonment (needs an experiment, see product/ab-test-plan).

Standard and licence note

Based on common qualitative analysis practice: affinity diagramming (the KJ method attributed to Kawakita Jiro) and thematic analysis as described by Braun and Clarke (2006, "Using thematic analysis in psychology", Qualitative Research in Psychology, 3(2), 77 to 101). The text is original. Related: design/research-interview-guide, design/usability-test-report, product/persona, product/journey-map. Template licence: MIT.