Methods, Guides, and Strategic Insights. Evidence-based resources to elevate your research practice.
The biggest mistake teams make with AI is treating it like a magic black box. Here is a complete, reliable workflow for using LLMs as research assistants while maintaining critical human oversight.
Transform interview transcripts and observation notes into actionable themes through systematic coding. The difference between an opinion and a finding is whether two people agree.
Tools promising 'adaptive AI interviews' often deliver a data nightmare. Why inconsistent probing creates a patchwork of anecdotes instead of a dataset.
Four pillars that protect your data: verified participants, respectful experience, study review before launch, and continuous quality monitoring.
Validity is not one thing. It is a family of concepts, each addressing a different way your research can go wrong. A comprehensive guide to study design validity, measurement validity, qualitative trustworthiness, and modern unified frameworks.
An interactive calculator that uses decision theory to estimate whether a research study is worth the investment — before you run it.
An interactive sample size calculator for UX research, with the statistical foundations explained — from binomial problem discovery to power analysis.
An interactive tool that guides you to the right UX research method based on your goals, constraints, and context.
Standardized measurement instruments provide benchmarks and comparability. But using them effectively requires understanding what each one actually measures, and what it does not.
Before you begin any study, you must define its scope. This involves identifying the Layer of Experience you will focus on, from broad customer journey down to individual task steps.
AI is transforming what researchers do daily, but it amplifies rather than replaces the core value researchers provide. Understanding which skills remain essential and how to grow them is critical for career development in this changing landscape.
The AI landscape changes weekly. Rather than chasing specific tools, you need a durable framework for evaluating any platform against principles that will not change: privacy, transparency, portability, and reproducibility.
Everything we know about running research that actually changes products — packed into one honest, practical guide.
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