By the end of this topic, you should be able to...
apply a variety of user-centred research methods (field research, user observation, interviews, questionnaires and focus groups) and analyse data to establish users’ characteristics, behaviours, and the wants and needs of the target population defined by their demographics.
Guiding Question
How does understanding user needs directly impact the design of products and services?
Why Research Methods Are the Foundation of UCD
The IBO Design Technology Guide (First Assessment 2025) is unambiguous: user-centred design is only as valid as the research that drives it. Research methods are the instruments of inquiry through which designers move from assumption to evidence. Without rigorous, appropriately selected methods, a UCD process is simply designer preference dressed in user-centred language.
As Norman states in The Design of Everyday Things:
"The design is for people, but people are complex, contradictory, and context-dependent. Only systematic research reveals what users actually need — not what they say they need, and certainly not what designers assume they need."
The five methods specified in the IBO Design Technology Guide (First Assessment 2025) each serve distinct research purposes and generate different types of data. A skilled designer selects methods deliberately, matching each to a specific research question.
Understanding the Research Landscape
Before examining each method, it is essential to understand the two fundamental types of data that UCD research generates:
Data Type | Description | Answers |
Qualitative | Rich, descriptive, narrative — words, observations, feelings | Why? How? What does it mean? |
Quantitative | Numerical, measurable, statistical | How many? How often? What percentage? |
Critical insight for IB students: The most powerful UCD research plans triangulate across both data types and multiple methods. A finding supported by interview data and observational data and questionnaire statistics is significantly more credible than one supported by a single method alone.
Method 1: Field Research
What It Is
Field research involves the designer entering the user's real environment to gather data. Rather than bringing users into a controlled setting, the designer goes to where the product will actually be used — a home, workplace, hospital, public space, or vehicle.
Field research encompasses:
Ethnographic study — immersive, extended observation within a community
Contextual inquiry — structured observation combined with ongoing interview
Cultural probes — leaving users with tools (cameras, diaries, maps) to self-document their experience
Shadowing — following a user through their daily tasks without intervention
Why It Matters
Field research reveals the gap between what users say they do and what they actually do — one of the most important insights in UCD. Users consistently misreport their own behaviour when asked in artificial settings; observation in context captures truth.
Data Generated
Behavioural patterns and workarounds
Environmental constraints on product use
Social and cultural context of use
Unarticulated needs — problems users have normalised
Real-World Example: When Intel wanted to understand how personal computers were being used in developing economies, they sent anthropologists into homes in rural India, Egypt, and Brazil. Field research revealed that multiple family members shared a single device — a usage pattern completely absent from Intel's existing product assumptions. This directly influenced the design of the Classmate PC — a rugged, shared-use computer built around field research insights that no interview or questionnaire in a Western lab would have uncovered.
Real-World Example: Procter & Gamble used ethnographic field research to develop the Swiffer cleaning system. Researchers observed that people were using conventional mops but found them cumbersome, ineffective, and hygienically questionable. The insight — that users wanted to dispose of dirt rather than redistribute it — came entirely from watching people clean in their own homes, not from asking them about cleaning products.
Method 2: User Observation
What It Is
User observation is the systematic watching and recording of users as they interact with a product, space, or service. It is distinct from broader field research in its sharper focus on specific tasks, interactions, and behaviours rather than the broader cultural context.
Types of Observation
Type | Description | When to Use |
Direct observation | Designer watches user perform task in real time | When behaviour in natural context is critical |
Indirect observation | Video recording reviewed after the fact | When observer presence would alter behaviour |
Participant observation | Designer joins users in their activity | When insider perspective is needed |
Non-participant observation | Designer watches without joining | When objectivity must be maintained |
Think-aloud protocol | User narrates thoughts while performing task | When cognitive process is the focus |
Recording Tools
Observation grids / structured coding sheets
Video and audio recording
Eye-tracking technology
Heat maps of spatial movement
Data Generated
Actual (not reported) task sequences
Points of confusion, hesitation, or error
Physical interaction patterns — grip, posture, reach
Emotional responses — frustration, satisfaction, delight
Real-World Example: Microsoft used extensive user observation studies when developing the Xbox Adaptive Controller (2018). Designers observed gamers with limited mobility attempting to use standard controllers — watching precisely where physical limitations created barriers. The resulting product — a large, flat controller with programmable ports — was designed from observed behaviour data, not from a survey asking "what would you like in a controller?"
Real-World Example: The London Underground used passenger observation studies to redesign their ticketing hall layouts. Observers recorded where queues formed, where passengers hesitated, and where collisions occurred. The redesigned Oyster card barrier systems were positioned and angled based on observed pedestrian flow data — not theoretical traffic modelling.
Method 3: Interviews
What It Is
User interviews are structured or semi-structured conversations between a designer and individual users, designed to gather deep qualitative insight into user experiences, attitudes, motivations, and needs.
Interview Structures
Structure | Description | Best For |
Structured | Fixed questions, fixed order, consistent across all participants | Comparative data across multiple users |
Semi-structured | Core questions with freedom to explore unexpected responses | Balance of consistency and depth |
Unstructured | Conversational, exploratory — researcher follows user's lead | Early-stage discovery, new territory |
Principles of Effective UCD Interviewing
Ask open-ended questions — "Tell me about the last time you used..." not "Did you find it easy to use?"
Pursue the unexpected — when a user says something surprising, follow it
Avoid leading questions — "What frustrated you?" assumes frustration
Probe for specifics — "Can you give me an example?" is one of the most powerful interview tools
Interview in context — where possible, interview users in the environment of use
The 5 Whys Technique
A particularly powerful interview tool within UCD, the 5 Whys involves iteratively asking "why" in response to each user answer to drill down to the root cause of a behaviour or need:
"Why did you stop using the app?""Because it was too slow.""Why was speed important in that situation?""Because I was trying to find information while standing in a queue.""Why couldn't you find it quickly?""Because the search function required too many steps."
The surface answer ("it was slow") and the root insight ("users need single-gesture access to core functions in time-pressured, standing contexts") are entirely different design briefs.
Data Generated
User motivations and mental models
Attitudes and values around the design context
Detailed narratives of past experience
Unarticulated needs surfaced through skilled questioning
Real-World Example: When Spotify was developing its Discover Weekly feature, researchers conducted in-depth interviews with users about their music discovery habits. Users consistently described a paradox: they wanted to discover new music but felt anxious about investing time in unfamiliar artists. This emotional insight — not a feature request — drove the algorithm design to surface familiar-sounding new artists, reducing discovery anxiety. Interview data produced the design logic; no other method would have uncovered the emotional dimension.
Method 4: Questionnaires
What It Is
Questionnaires are structured sets of written questions distributed to a sample of users to gather quantitative (and some qualitative) data about attitudes, behaviours, demographics, and experiences. They are the primary tool for collecting data across large user samples.
Question Types
Question Type | Example | Data Output |
Closed / Binary | "Do you use this product daily? Yes / No" | Frequency counts |
Multiple choice | "How often do you use this product?" | Distribution data |
Likert scale | "Rate your satisfaction: 1–5" | Attitude measurement |
Semantic differential | "Complex — — — — — Simple" | Bipolar attitude data |
Ranking | "Rank these features 1–5 in order of importance" | Priority data |
Open-ended | "Describe your experience using this product" | Qualitative narrative |
Designing Effective Questionnaires
Pilot test before distribution — ambiguous questions will produce unusable data
Order questions logically — begin with easy, non-threatening questions
Avoid double-barrelled questions — "Is this product fast and reliable?" cannot be answered precisely
Match scale to purpose — a 5-point Likert scale is appropriate for attitude measurement; a 3-point scale loses nuance
Sample size matters — a questionnaire completed by 5 users is anecdotal; 50+ begins to reveal patterns
Demographic Data Collection
Questionnaires are the primary method for establishing demographic profiles of your target population. Key demographic variables include:
Age range → Shapes physical, cognitive, and digital capability assumptions
Gender → May influence use context, preference, and accessibility needs
Occupation → Indicates use context and technical literacy
Location → Cultural, climatic, and infrastructural context
Income level → Affordability constraints and purchasing behaviour
Education level → Literacy assumptions, instruction complexity
Disability/ability → Accessibility requirements
Real-World Example: Nike uses large-scale questionnaire research as part of their Consumer Insights programme to profile the athletic behaviour and purchasing motivations of distinct demographic segments. Their discovery — through questionnaire data — that a significant segment of Nike+ running app users were recreational runners aged 35–55 (not competitive athletes) directly influenced the app's redesign toward social sharing and achievement milestone features rather than performance analytics.
Real-World Example: The NHS (National Health Service, UK) deployed questionnaires across thousands of patients to understand barriers to digital health service adoption among elderly demographics. Questionnaire data revealed that the barrier was not unwillingness to use digital services but lack of confidence — a critical distinction that redirected design efforts from simplifying interfaces to developing guided onboarding experiences.
Method 5: Focus Groups
What It Is
A focus group is a facilitated discussion involving 6–12 participants who share relevant characteristics, guided by a moderator through structured discussion topics. Focus groups generate qualitative data through group interaction — ideas build on each other, disagreements reveal range of opinion, and consensus in
Structure of a Focus Group Session
Graphic coming soon...
Strengths and Limitations
Strengths | Limitations |
Generates rich qualitative data quickly | Dominant participants can skew group opinion |
Group dynamics surface ideas individuals would not raise alone | Social desirability bias — users say what seems acceptable |
Reveals range and diversity of opinion within a demographic | Not statistically representative |
Efficient — multiple users simultaneously | Requires skilled moderation |
Ideal for concept and prototype feedback | Groupthink can suppress minority opinions |
Data Generated
Shared attitudes and values within a demographic
Range of opinion on design concepts
Vocabulary users use to describe their experience
Social and cultural norms around product use
Real-World Example: LEGO used focus groups extensively during the development of LEGO Mindstorms to test the concept with their core demographic — children aged 8–14 — as well as with parents. Critically, focus group discussions with children revealed that the programming interface was perceived as "homework" rather than "play" — a fatal framing that required a complete redesign of the interaction model before launch.
Real-World Example: Heinz used focus groups to test consumer responses to their EZ Squirt coloured ketchup range (purple, green, blue). Initial focus group data showed strong enthusiasm from children. However, analysis of the cross-demographic focus group data — including parents — revealed a pattern: parents expressed willingness to purchase once for novelty, but not repeatedly. This usage pattern prediction proved accurate in market performance, and the product was discontinued after initial success. The focus group data was correct — it was the analysis of that data that determined the product's commercial limitations.
Analysing UCD Research Data
Gathering data is only half the task. The learning objective requires students to analyse that data to establish user characteristics, behaviours, and needs. Key analysis methods include:
Affinity Diagramming
A collaborative synthesis tool where all qualitative data points (from interviews, observations, focus groups) are written on individual notes and grouped by theme. Patterns emerge from the data rather than being imposed upon it.
Thematic Analysis
Applied to qualitative interview and focus group transcripts — identifying recurring themes, phrases, and concepts across multiple participants. A theme appearing across 3 or more participants begins to indicate a genuine user need rather than individual preference.
Statistical Analysis of Questionnaire Data
Frequency distributions — what percentage of users reported each response?
Mean and median — central tendency in attitude scales
Cross-tabulation — does response pattern differ between demographic groups?
Correlation — do two variables move together? (e.g. age and feature preference)
Persona Construction from Data
Analysed data is synthesised into user personas — rich, composite portraits of the target user that consolidate demographic, behavioural, and motivational data into a usable design reference tool.
Matching Methods to Research Questions
A critical skill tested in IB assessment is the ability to justify method selection. Not all methods suit all questions:
Research Question | Most Appropriate Method | Why |
How many users in our demographic own a smartphone? | Questionnaire | Quantitative, distributable to large sample |
Why do elderly users avoid self-checkout kiosks? | Interviews | Deep qualitative insight into attitudes and fears |
What physical difficulties do users encounter with the packaging? | User observation | Behaviour is directly visible; users cannot accurately self-report |
How do teenagers respond to different visual concepts? | Focus group | Group dynamics and peer comparison generate richer response data |
How does this product get used in a factory environment? | Field research | Context and environment cannot be replicated in a lab |
Key Takeaway
Research methods are not interchangeable tools — each is precision-engineered for a specific type of insight. The skill of a UCD researcher lies not in using all methods simultaneously, but in selecting the right method for each specific research question, then analysing the data rigorously to extract genuine user understanding.
The IBO Design Technology Guide (First Assessment 2025) requires students to demonstrate application of multiple methods — this signals that triangulation across methods produces more credible findings than reliance on a single approach.
The analytical hierarchy — from data to design:
Raw data — interview transcripts, observation notes, questionnaire responses
Organised data — coded themes, frequency tables, affinity clusters
Interpreted data — user characteristics, identified behaviours, established needs
Applied data — design decisions justified by analytical findings
Practical Application
Where B1.1.2 Appears in Your IA
IA Section | Application |
Criterion A — Research | Primary research using minimum two UCD methods generates your user evidence base |
Criterion A — Analysis | Data from methods must be analysed — themes extracted, patterns identified, needs stated |
Criterion A — Design Brief | Your design brief specifications must be traceable to research method findings |
Criterion D — Evaluation | Test phase re-applies observation and interview methods to evaluate against original user needs |
What Examiners Are Looking For
Evidence of multiple methods applied — not a single questionnaire
Analysis of data — not just presentation of raw results
Clear connection between demographic data and design decisions
Justified method selection — why was this method appropriate for this question?
Distinction between what users said and what they did (observation vs. self-report)
Common IA Errors to Avoid
Error | Consequence |
Single-method research (questionnaire only) | Insufficient triangulation — low Criterion A score |
Presenting raw data without analysis | Data collection without insight — does not meet "analyse" command term |
Leading questions in interviews/questionnaires | Biased data that does not represent genuine user needs |
Sample too small or demographically narrow | Cannot establish target population characteristics |
No link between research findings and design decisions | Research and design appear |
💡Student Tip
Your questionnaire is not your research plan — it is one tool within it. The most common IA weakness at this sub-topic level is students who conduct a questionnaire with 10 friends, present the pie charts, and consider their user research complete.
Design a mixed-method research plan before you begin. A strong approach for IB IA level:
Questionnaire (25–40 respondents) → establish demographic profile and identify priority issues
Interviews (3–5 participants from target demographic) → explore the priority issues in depth
Observation (2–3 users performing relevant tasks) → validate or challenge what interview participants reported
Then analyse across all three — where findings align, you have strong evidence. Where they conflict, you have your most interesting design insight.
Write this sentence for every key finding: "Both [Method A] and [Method B] data indicate that [user characteristic/behaviour/need], because [specific evidence]." If you can write that sentence, your analysis meets the command term requirement.
Sources
International Baccalaureate Organization. Design Technology Guide. International Baccalaureate Organization, 2023. First Assessment 2025.
Brown, Tim. Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation. HarperBusiness, 2009.
Cross, Nigel. Design Thinking: Understanding How Designers Think and Work. Berg Publishers, 2011.
Kumar, Vijay. 101 Design Methods: A Structured Approach for Driving Innovation in Your Organization. John Wiley and Sons, 2013.
Martin, Bella, and Bruce Hanington. Universal Methods of Design: 100 Ways to Research Complex Problems, Develop Innovative Ideas, and Design Effective Solutions. Rockport Publishers, 2012.
Morgan, David L. Focus Groups as Qualitative Research. 2nd ed., SAGE Publications, 1997.
Norman, Donald A. The Design of Everyday Things. Rev. ed., Basic Books, 2013.
Portigal, Steve. Interviewing Users: How to Uncover Compelling Insights. Rosenfeld Media, 2013.
Pruitt, John, and Tamara Adlin. The Persona Lifecycle: Keeping People in Mind Throughout Product Design. Morgan Kaufmann, 2006.
Rubin, Jeffrey, and Dana Chisnell. Handbook of Usability Testing: How to Plan, Design, and Conduct Effective Tests. 2nd ed., Wiley Publishing, 2008.
Sanders, Elizabeth B.-N., and Pieter Jan Stappers. "Co-creation and the New Landscapes of Design." CoDesign, vol. 4, no. 1, 2008, pp. 5–18, doi:10.1080/15710880701875068.
Seidman, Irving. Interviewing as Qualitative Research: A Guide for Researchers in Education and the Social Sciences. 4th ed., Teachers College Press, 2013.
Linking Questions
To what extent does UCD rely on a strong foundation of ergonomics? (A1.1)
How important is a good understanding of user-centred research methods to ensure effective UCD? (A2.1)
To what extent can the UCD process be influenced by the quality of modelling and prototyping of potential design solutions? (B2.2)
To what extent should a UCD process focus on ensuring inclusive design? (C1.2)
What influence can product analysis and evaluation have on the effectiveness of UCD? (C3.1).