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User Research Fellowship | Shivani Gupta
Summary
Materials from a fellowship I taught for Ownpath’s User Research Fellowship (Nov 2022) that walks through a practical research practice (behavioral-science-informed) for designers and practitioners: from framing the problem to sampling, methods, bias checks, tooling, and planning. The core idea is rigor without rigidity: stay clear on the objective, build redundancy into your inputs, and set guardrails so the work doesn’t get derailed by bias or logistics.
What this covers
- Problem identification and framing (guiding principles + mapping exercises)
- Sourcing inputs and defining context (stakeholders, influence, and research redundancy)
- Hypothesis building + sacrificial concepts (learn fast through disposable prompts)
- Methods overview (what to keep sacred, what to adapt)
- Table stakes planning (effort, logistics, contingencies, roles)
- Cultural context + bias management (with emphasis on India)
- Sampling (representative and realistic)
- Tooling (research, documentation, synthesis)
- Tips, traps, watch-outs (participant dynamics, fatigue, scheduling, debriefs)
Key frameworks and ideas
1) Problem framing principles: move from business → human → behavioral/emotional; solution-first → problem-first; generic → contextual; surface-level → fundamental; negative → positive (where possible); open-ended → directional.
2) Context + input redundancy: use past research, stakeholder interviews, audits, quantitative sources, analogous inspiration, and credible frameworks; define the objective before “going hunting.”
3) Hypotheses + sacrificial concepts
- Descriptive hypotheses explain behavior.
- Prescriptive hypotheses aim to influence behavior.
- Sacrificial concepts are intentionally rough and discardable; they’re used to provoke conversation and surface assumptions early.
4) Cultural context + bias acknowledgment: bias can enter via the researcher, intent, tools, participants, and context. Common traps include confirmatory framing, leading/closed-ended questions, and “lies honestly told” in hot states.
5) Sampling (often overlooked): avoid self-selection and purely demographic segments; prioritize behavioral segments, extreme users, and positive deviants. Rule of thumb: qualitative 5–7 per type (until saturation), quantitative 50+; plan backups and screen carefully.
Credits
Prepared by Shivani Gupta for ownpath