Convenience sampling is quick and cost-effective for UX research, but probability sampling is better when you need representative and generalizable data.
Use the EAS framework — Eliminate first, Automate where possible, and Simplify what remains — to minimize user effort and improve form completion rates.
Well-designed questions related to age, gender, race, income and other demographic characteristics help UX researchers screen participants, recruit a diverse participant pool, and segment data. These questions are sensitive and should put research participants at ease.
Maintaining participants’ data privacy and security before, during, and after data collection is critical to the user-research process. It protects participants from data breaches and cyber threats.
To keep your stakeholders and team members engaged, incorporate storytelling techniques such as writing for your audience, adding anecdotes, and using analogies in your asynchronous research deliverables.
Uncover the story within extensive UX-research data by following a process of revisiting original research objectives and organizing findings into themes.
User-related questions and assumptions are not tracked throughout a product’s lifecycle, causing misalignment and overconfidence. Documenting these questions and assumptions in a knowledge board differentiates them from real facts.
A treemap is a complex, area-based data visualization for hierarchical data that can be hard to interpret precisely. In many cases, simpler visualizations such as bar charts are preferable.
Focus on UX goals to drive analytics measurement plans, rather than tracking superficial metrics. Identify the core goal of a design to meaningfully measure it.