UX Research Language Exercises

User interview vocabulary includes "open-ended questions," "probing," "follow-up," and "think-aloud protocol." Researchers say they "explore" a participant's experience, "probe for specifics" with phrases like "Can you walk me through that?" and avoid "leading questions" that suggest a desired answer. Key terms also include "participant recruitment," "screener," "interview guide," and "rapport-building."

Usability testing is described as observing "real users" attempting to complete "representative tasks" with a product to identify "pain points," "confusion," and "breakdowns." Researchers distinguish "moderated testing" (researcher present) from "unmoderated testing" (remote, no facilitator). Key metrics discussed are "task completion rate," "error rate," "time on task," and "satisfaction score" (e.g., SUS — System Usability Scale).

Affinity mapping (also called affinity diagramming) is a synthesis method where research observations are written on individual notes and grouped by theme or pattern. Researchers say they "cluster insights," "identify themes," and "surface patterns" from qualitative data. The output is an "affinity diagram" that organises raw observations into a hierarchy of insights, making it easier to communicate findings to stakeholders and identify design priorities.

Jobs-to-be-Done frames user behaviour around the "job" a person is trying to accomplish, not the product features they use. Researchers say users "hire" a product to do a job, and the goal is to understand "functional," "emotional," and "social" dimensions of that job. The standard phrasing is "When [situation], I want to [motivation], so I can [outcome]." This framework helps teams communicate user needs without prescribing solutions.

Research synthesis reports use language like "key findings," "themes," "patterns," "implications," and "recommendations." Researchers present "evidence-backed insights" (tied to specific quotes or observations) and avoid overstating certainty by using hedging language: "participants tended to," "several users reported," rather than "users always." Deliverables include "research readouts," "insight repositories," and "opportunity areas."

Participant recruitment is described using terms like "screener survey," "recruitment criteria," "target demographic," and "representative sample." Researchers aim to recruit "users who match the target persona" and balance "novice" and "expert" participants. Phrases include "recruiting through a panel," "incentivising participation," and "achieving saturation" — the point where new interviews yield no new themes.

Journey mapping visualises the end-to-end experience of a user achieving a goal, showing "touchpoints," "emotions," "pain points," and "opportunities" at each stage. Researchers describe "mapping the current-state journey" (how things are today) versus "future-state journey" (the desired experience). Common terms include "moments of truth" (high-impact touchpoints), "emotional arc," "friction points," and "drop-off stages."

Triangulation means combining multiple research methods or data sources to increase confidence in findings. Researchers say they "triangulate qualitative and quantitative data" — for example, pairing interview insights with analytics data to confirm a pattern. This term signals rigour: a finding "supported by triangulation" is more credible than one from a single source. Methods triangulated include "interviews," "surveys," "usability tests," and "usage logs."

Generative research (also called "discovery research") explores "what problems exist" and "what users need" — it "generates" hypotheses and opportunities. Evaluative research tests whether a solution "works as intended" — validating a prototype or feature. Researchers say they do generative work "early in the design cycle" and evaluative work "before and after launch." Common generative methods include "contextual inquiry" and "diary studies"; evaluative methods include "usability testing" and "A/B testing."

Presenting research findings involves "storytelling with data," where researchers frame insights around "user needs," "business impact," and "design implications." Effective deliverables are described as "actionable," meaning they lead to clear next steps. Researchers use "top-line summary," "evidence," and "recommendations" structure, and distinguish "must-fix" issues (critical to usability) from "nice-to-have" improvements. The goal is to "build empathy" and "drive informed decisions."