Part Two: We owe it to our clients and users to do UX research well
Part Two (of Three)
Research starts before responding to the brief
Research starts before we write a response to the brief because a proper response requires us to know what we’re talking about.
It’s a beautiful thing when a brief can give you everything you need to write a Proposal, project and research plan. But often that’s not the case. Research starts before we write a response to the brief because a proper response requires us to know what we’re talking about. And we can do this by immersing ourselves in the perceived problem space using both primary (our own) and secondary (others’) research. This shows the client we’re willing to understand not only their problem, but the problem faced by their customers. It helps separate us from other practitioners and agencies not only for the lengths we’ll go to, but also in showcasing our unique skill as practitioners in developing a solid and innovative research plan and proposal.
Research your plan, and plan your research
A thorough and well informed research plan will be your source of inspiration for finding problems, ideation, and creating solutions. Using multiple methods across a range of data sources (eg in primary and secondary research) will enable greater insights, triangulation of data, and lead to solutions which are more likely to succeed. Triangulation when applied to qualitative data (the foundation of behavioural research) is when findings or insights are repeatedly identified across multiple channels thereby providing reliability and validity.
An interviewer’s skill is potentially the greatest research tool
The true depth and quality of information gathered from an interview, however, rests on the skills of an interviewer — if it weren’t then I think more of us would use qualitative surveys and wait for the data to come in.
In UX we most commonly conduct interviews with users. The true depth and quality of information gathered from an interview, however, rests on the skills of an interviewer — if it weren’t then I think more of us would use qualitative surveys and wait for the data to come in. As interviewers we need to have a well developed interview style, communication skills (verbal, nonverbal, and paraverbal), an understanding of how emotion affects behaviour, be non-judgemental, and be able to explore with the user what they are/were thinking/feeling/ experiencing/sensing. I’ve written about conducting interviews here.
“Rubbish in, rubbish out” Standardise your qualitative data!
Having standardised note taking means data is more reliable, which means more accurate insights and solutions. In academia we would transcribe audio-recorded interviews and use all data because it gave deeper context, we could use different analytic methods such as narrative analysis which helps identify how people create meaning in their life. I still prefer to record all quotes, then throw away post-it notes with less relevant data when doing synthesis. Others tend to only write down what they consider important, but I would argue that this creates a bias in data as the note-taker is filtering content. When multiple note takers each decide by themselves which participant quotes to record or not, there becomes inconsistency in the data, findings, and results. And this impacts on your recommendations. You will struggle to provide accurate weight to each customer need or pain point.
When multiple note takers each decide by themselves which participant quotes to record or not, there becomes inconsistency in the data, findings, and results. And this impacts on your recommendations. You will struggle to provide accurate weight to each customer need or pain point.
An easy solution is to decide at the beginning of the project: how data will be recorded, whether you want to quantify your qualitative data, and how this will be reported and used in making recommendations for solutions.
- Introduce standardisation in data collection
- Plan in advance what data you want and how it will be used
- Develop a system that enables replicability during data analysis and reporting
Part One covers “Adding to the knowledge base and advocating for the user”
Part Three covers “Your research doesn’t prove anything, and 8 isn’t the magic number”, and “The irony of not being transparent or handing over the data”.
Annie has been a researcher since 2004. She started in clinical psychology research before moving to social research, and finally translational research with mental health users. She loves to challenge the status quo of research and practice. She’s passionate about improving the way we work because she has seen the effects that valid and reliable methodologies, and best practice can have on creating change.
