Almost every day, I read assertions about product usability, but evidence in support of these opinions
is much rarer. Crucially, as a psychologist, I am well-versed in the topic of bias in human judgement
and, also important, massive individual differences in people’s evaluations of usability – even amongst professional designers. With opinions often directly contradicting each other, everyone cannot be
right, hence the need for evidence-based design. I have devoted a considerable amount of time to investigating transit map usability. More recently, observing that sometimes usability as determined
in the laboratory may not align with usability as perceived by the users themselves, I have become interested in how this is actually measured. This issue is the crux of user-centred design, and my
recent movement towards UX design is a logical development.
At the end of this page are direct-download links for two short articles from my writing pages which talk about issues directly relevant to research, comparing quantitative and qualitative methods.
I have been conducting user research on map design for almost two decades. Major findings are summarised in this section along with
links to publications.
I was invited by Doug Rose, of FWT Studios – on behalf of the Docklands Light Railway – to evaluate the usability of several train
and station diagrams of the network. Our recommendations were implemented in full.
Despite extensive experience at (mainly quantitative) user research,
I decided to undertake an online course in UX design. My portfolio of assignments from the course is in this section.
Roberts, M.J. (2024).
Quantitative versus qualitative user research.
Part 1: Swings and roundabouts.
Short article first published on LinkedIn.
Many people complain that quantitative research methods have issues
that can be resolved by using qualitative methods, with the added benefit
of richer data that give better insights into behaviour. However, qualitative methods merely camouflage rather than solve the issues, and produce data that are inherently hard to understand, reducing the impact of the richness.
Roberts, M.J. (2024).
Quantitative versus qualitative user research.
Part 2: The paradox of affinity diagrams.
Short article first published on LinkedIn.
Affinity diagram analysis is a popular way of making sense of qualitative data. It is intended to bootstrap conceptual frameworks but, unfortunately,
a fatal logical flaw in the methodology means that the people who need it
the most are least likely to get good results using it.