User research Study Guide

📖 Core Concepts User Research – Systematic study of users’ behaviors, needs, and motivations using interviews, surveys, usability tests, etc. Iterative Nature – Research → insights → design proposals → prototype → test → repeat; continues even after launch. Human‑Centred Perspective – Empathy‑driven; data are “humanized” to keep real people at the centre of decisions. Research Types Generative (Exploratory) – Discover what problems exist; early‑stage, open‑ended methods (interviews, observations). Descriptive (Explanatory) – Define who has the problem and how it looks; characterises known issues. Evaluative – Test whether a solution works; usability testing, task performance, subjective ratings. Causal – Explain why a behavior occurs after launch; A/B testing, usage analytics. Nielsen Norman Three‑Dimensional Framework Attitudinal vs. Behavioral – What users say they think vs. what they actually do. Qualitative vs. Quantitative – Open‑ended, rich data vs. numerically coded data for statistics. Context of Use – Natural (real‑world) setting vs. scripted laboratory setting. Common Deliverables – Research reports, personas, journey maps, mental‑model diagrams, low‑fidelity wireframes, storyboards. Business Benefits – Saves redesign costs, provides evidence for stakeholder decisions, creates desirable experiences. --- 📌 Must Remember User research is core to user‑centered design and can occur at any development stage. Generative → Descriptive → Evaluative → Causal is the typical progression. Attitudinal data → beliefs/opinions; Behavioral data → actual actions. Qualitative = depth; Quantitative = breadth & statistical power. Natural context = high ecological validity, limited clarification; Lab context = high control, can ask follow‑ups. Personas are archetypes derived from data, not real individuals. A/B testing is a causal method, not an evaluative usability test. --- 🔄 Key Processes Define Problem – Use generative methods (interviews, observations) to uncover pain points. Synthesize Insights – Identify target users, rank problem importance. Create Prototypes – Low‑fi sketches or wireframes based on insights. Evaluative Testing – Conduct usability tests with representative users; iterate on design. Launch & Observe – Deploy solution; collect behavioral data (analytics, click‑rates). Causal Analysis – Run A/B tests or usage studies to explain adoption/rejection patterns. --- 🔍 Key Comparisons Generative vs. Descriptive Goal: discover problems vs. define characteristics of known problems. Methods: open interviews/observations vs. surveys/secondary data. Evaluative vs. Causal Goal: test solution usability vs. explain why users behave a certain way post‑launch. Methods: usability testing vs. A/B testing, analytics. Attitudinal vs. Behavioral Attitudinal: “I find this confusing.” Behavioral: Click‑through logs show users avoid the feature. Qualitative vs. Quantitative Qualitative: rich narratives, themes. Quantitative: numeric ratings, statistical significance. Natural Context vs. Lab Context Natural: users in their own environment, minimal researcher interference. Lab: controlled tasks, ability to probe in real time. --- ⚠️ Common Misunderstandings “Research only happens before design.” – It’s iterative; research continues after launch (causal). “Surveys = quantitative.” – Open‑ended survey responses are qualitative. “A/B testing is just UI tweaking.” – It’s a causal method to uncover why a change works or not. “Personas are actual users.” – Personas are fictional composites built from real data. “Qualitative data isn’t trustworthy.” – It provides essential context that numbers alone can’t capture. --- 🧠 Mental Models / Intuition Compass Model – Think of research as a compass: generative points the direction, descriptive maps the terrain, evaluative checks the route, causal explains detours. 3‑Axis Graph – Visualize the Nielsen Norman framework as a 3‑dimensional cube (Attitudinal ↔ Behavioral, Qualitative ↔ Quantitative, Natural ↔ Lab). Each research method lands in a specific quadrant. Funnel Analogy – Start wide with generative (many questions), narrow through descriptive, focus on specific solutions in evaluative, and finally pinpoint cause‑effect in causal. --- 🚩 Exceptions & Edge Cases Time‑boxed projects may skip full generative research and rely on secondary data, but still need at least a quick descriptive scan. Small user populations → mixed‑methods (qualitative depth + limited quantitative) to compensate for low statistical power. Remote usability → still evaluative, but context of use shifts toward “natural” despite being virtual. Highly regulated domains (e.g., medical devices) may require stricter ethical consent even for informal interviews. --- 📍 When to Use Which | Situation | Best Research Type | Reason | |-----------|-------------------|--------| | You don’t know what problems exist | Generative | Open‑ended methods uncover hidden needs. | | You have a known problem & need to profile users | Descriptive | Quantifies who, how many, and context. | | You have a prototype to validate | Evaluative | Directly measures task success & usability. | | Product is live & you need to explain adoption trends | Causal (A/B, analytics) | Isolates variables that cause behavior. | | Need rich insights (why users feel a way) | Qualitative (interviews, diary) | Captures attitudes, motivations. | | Need statistical confidence (how many) | Quantitative (surveys, click‑rates) | Allows hypothesis testing, generalisation. | | Testing in real environment is critical | Natural context methods | High ecological validity. | | Testing specific interactions under control | Lab context methods | Precise measurement, ability to probe. | --- 👀 Patterns to Recognize Iterative language (“repeat”, “refine”, “loop”) signals the need for another research cycle. “Attitudinal” paired with surveys → expect Likert‑scale or open comments. “Behavioral” paired with analytics → look for click‑rates, time‑on‑task metrics. “Context of Use” often precedes a decision about remote vs. in‑lab testing. “Evidence‑based” in business cases signals a quantitative deliverable (report, dashboard). --- 🗂️ Exam Traps Choosing A/B testing for early‑stage concept validation – A/B is causal; early ideas need generative or evaluative methods. Assuming personas equal user interviews – Personas are synthesized artifacts, not raw data. Selecting “lab context” because it’s easier – May lose ecological validity; exam may ask which context is appropriate for “real‑world adoption”. Mixing up attitudinal vs. behavioral – A statement like “Users say they love the feature” is attitudinal; actual usage statistics are behavioral. Thinking qualitative data can’t be reported – Deliverables like journey maps and mental models are qualitative outputs. ---