FIT5152 Chap.2 Understanding Users: Data Gathering and Analysis
Understanding Users: Data Gathering and Analysis
This chapter follows the first stage of the FIT5152 design project, where the group learns who its users are before drawing any screen. The Week 2 seminar challenges the idea of an average user and offers four ways to classify people instead: by the roles they play, by demographics, by skill level with the interface or the domain, and by the need state that drives them at a given moment.
It also sets expectations for sample size, noting that a handful of participants can yield good qualitative insight while quantitative claims need considerably more, and compares probability, purposive and convenience sampling.
Data collection gets equal attention.
Week 1 covers questionnaire design, from closed nominal and ordinal questions to open questions and balanced Likert scales, and Week 2 adds structured, semi-structured and unstructured interviews, three styles of observation, contextual inquiry and focus groups. The chapter then moves from data to people. Cross tabulation shows how different groups answer the same question, which turns a summary into an analysis.
Archetypes give way to personas built from evidence, empathy maps record what a persona says, thinks, does and feels, and one-sentence user stories naming a persona, a goal and a benefit are ranked with MoSCoW. These are the building blocks of Submission 1.
What this chapter covers
- 01
Customer versus user thinking
- 02
Four ways to classify users
- 03
Sampling approaches and sample size
- 04
Closed, open and Likert questions
- 05
Interviews, observation and focus groups
- 06
Cross tabulation and analysis
- 07
Personas, user stories and MoSCoW
Worked example · free
From a cross-tabulated finding to a prioritised user story
- 1Analysis: daily commuters, not riders in general, are the group for whom repair turnaround decides whether the service is usable, because their bike is their transport.
- 1Persona: Tomas, 29, who rides to a hospital shift every day and has no car.
- 1Story: “As Tomas, I want to know before booking how soon my bike will be ready so that I can arrange other transport for the gap.”
- 1Priority: Must, since the data show turnaround is a deciding factor for the core commuter group.
Key terms
- Need State
- The motivation driving a user at a particular moment, such as exploring, problem-solving or learning, which can change for the same person.
- Purposive Sampling
- Selecting participants who meet predefined criteria for the user group being studied.
- Cross Tabulation
- An analysis that compares how different groups of respondents answer the same question.
- Persona
- A fictional profile of one representative user built from research findings, with goals, pain points, behaviours and context.
- Empathy Map
- A four-part canvas recording what a persona says, thinks, does and feels.
- MoSCoW Prioritisation
- A method that labels requirements as Must, Should, Could or Won’t have for the current release.
- Hawthorne Effect
- The tendency of people to change their behaviour when they know they are being observed.
Understanding Users: Data Gathering and Analysis FAQ
How many participants does a usability study need?
The seminar gives no single number, but it suggests good qualitative insight can come from as few as five participants, while good quantitative insight should include at least thirty, balanced against time and cost.
What makes a data finding an analysis rather than a summary?
A summary restates figures already visible in the data. An analysis uses the figures as evidence for a new idea, for example by comparing groups, testing earlier assumptions or inferring what the pattern means for users.
Why should user stories avoid naming interface features?
A story that names a button describes the developer’s solution. Keeping the goal and benefit in the user’s terms leaves the design team free to find the best way to meet that need.
When is an archetype not enough?
Archetypes describe broad groups and remain open to interpretation. A persona narrows the group to one specific, evidence-based person, which reduces ambiguity and helps the whole team share the same picture.
Assessment move
Work through this material in the same order as the project. First, take any app you use and classify its users four ways, by role, demographics, skill and need state, noting where one person moves between need states. Second, draft five questionnaire items and test each against the Week 1 tips: no leading wording, one idea per question, balanced scales and no requests to predict future behaviour.
Third, invent a small table of responses and practise one cross tabulation, then write one sentence that summarises it and one that analyses it, and compare the two. Finally, build a persona from that analysis, write two user stories in the standard template and assign MoSCoW labels with a one-sentence reason each. Doing the chain end to end on a toy example makes the real Submission 1 much faster.
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