Quantitative UX Research Course: Statistical Methods for Product Development
Zero Risk Enrollment: Receive a full refund through the second week of the course, no questions asked.
Course features
Level: Foundations
1 hour/week
6 weeks
Class Size: 15 Students
Mondays at 5pm ET US October 13-November 17, 2025
Format: Live Online via Google Meet
Why take a quantitative UX research course?
Quantitative skills are becoming essential in UX research as product teams increasingly demand quantified evidence in order to make strategic decisions. To contribute at a higher level, researchers need to be able to confidently apply statistical methods.
Course Description
This course teaches the descriptive and inferential statistical methods that transform your research from insights to strategic impact. Master Kano analysis for feature prioritization, t-tests and ANOVA for validating design decisions, and factor analysis for uncovering user behavior patterns. Stop relying solely on qualitative findings—add quantitative rigor that stakeholders trust and use to drive product decisions.
Course Format
Most quantitative UX methods courses teach theory in isolation—you memorize concepts but never apply them to real problems. Our course format transforms you into an active practitioner of quantitative UX research, applying concepts in exactly the way you will on the job.
Students will form groups and choose a "Pilot Product" to work on throughout the course. Each method is then aligned to a key point in the product development process, so that students are able to apply it immediately to a realistic scenario.
Week 1: Master the essentials—Kano surveys, t-tests, ANOVA, and factor analysis—in the order you'll actually use them during product development.
Weeks 2-5: Apply these methods immediately. Work in teams of 3 to build a business case for a pilot product idea, using quantitative research to support every major decision.
Week 6: Pitch your product and showcase what you've learned.
You'll need: Google Sheets/Excel + R (free download provided)
What new skills will I gain from this quantitative UX research course?
Core Statistical Methods
Kano Analysis
Kano analysis helps you prioritize features by revealing which ones will delight users, which are basic expectations, and which are performance drivers. Use it during product discovery when you have multiple feature ideas but limited development resources. It transforms subjective feature debates into data-driven roadmap decisions by showing exactly how each feature impacts user satisfaction.
T-tests
T-tests determine whether there's a statistically significant difference between two groups or conditions, making them essential for A/B testing and concept validation. Use them when comparing user performance, satisfaction, or behavior between two design options or user segments. They give you the confidence to say "Version A is genuinely better than Version B" rather than relying on gut feelings or small sample observations.
ANOVA (Analysis of Variance)
ANOVA extends t-test logic to compare three or more groups simultaneously, perfect for testing multiple design variations or user segments at once. Use it when you have several concepts to evaluate or want to understand how different user types respond to your product. It prevents the statistical errors that occur when running multiple t-tests and reveals which variations truly perform differently.
Correlation & Chi-Square
Correlation analysis identifies relationships between user behaviors, preferences, or outcomes, revealing patterns that inform product strategy. Use it to understand connections like whether feature usage correlates with user satisfaction or if certain demographics predict engagement levels. It helps validate assumptions about user behavior and uncover insights that aren't obvious from qualitative research alone.
Factor Analysis
Factor analysis uncovers the underlying dimensions that group user preferences and behaviors together, making it powerful for persona validation and user segmentation. Use it when you suspect users cluster into distinct groups but aren't sure what characteristics define those segments. It transforms hunches about user types into statistically validated segments based on actual behavioral patterns.
How will this course help my career?
Expert guidance when you need it
Stuck interpreting ANOVA results? Unsure about your factor analysis? Get instant feedback from your expert instructor precisely when confusion strikes.
A portfolio-ready case study
Your Pilot Project becomes a polished portfolio piece showcasing your ability to execute complex quantitative research from hypothesis to actionable insights. This real-world case study demonstrates to employers that you can deliver results, not just understand concepts.
Research leadership + strategy skills
Gain skills to champion data-driven decision making and mentor other researchers. Know when and how to apply different statistical approaches to get confidence on product roadmap decisions.
A more robust professional network
Learn alongside UX researchers from all over the world, in a class capped at just 15 students.
Who is this course for?
UX researchers ready to elevate their practice with statistical rigor: Whether you're looking to influence product strategy, validate qualitative research findings with quant data, or stand out in a competitive field—this course builds the quantitative confidence that transforms careers.
Qualitative UX researchers looking to transition to mixed methods: This course teaches qualitative researchers to strategically layer quantitative methods onto their existing skills, creating robust mixed-methods studies. You'll learn when statistical analysis strengthens qualitative insights and how to integrate both approaches for maximum research impact.
Prerequisites: None. No prior knowledge of statistics is required for this course.
Cheryl Abellanoza, PhD
Bio
Cheryl is the Associate Director of User Experience Research at Verizon Connect, where her team integrates quantitative methods into their mixed methods approaches. She is passionate about using quantitative methods that come from behavioral research, particularly with a cognitive psychology lens.
Learning Outcomes
By course completion, you will confidently:
Identify the right quantitative method for any research question
Execute statistical analysis from data collection to interpretation
Communicate findings that influence product and business decisions
Integrate quantitative insights into strategic product decisions
Justify research approaches to skeptical stakeholders
Overview
Course Syllabus
Week 1: Quantitative Methods in UX Research
Begin your transformation from qualitative-only researcher to data-driven practitioner. This opening session provides a comprehensive overview of five powerful statistical methods: Kano analysis, t-tests, ANOVA, correlation, and factor analysis. You'll understand not just what each method does, but when and why to use them in real product scenarios.
The magic begins when you form your project team of 3 researchers and select your Pilot Product idea—the authentic product challenge you'll tackle throughout the entire course. Whether it's a mobile app feature, web platform enhancement, or entirely new product concept, your team will apply every statistical method to this real-world scenario. By session end, you'll have your research foundation and team dynamics set for the intensive work ahead.
Week 2: Quantitative UX Research for Product Discovery
Kano Analysis for Feature Prioritization
Dive deep into the statistical method that revolutionizes feature prioritization. Kano analysis reveals which features will delight users, which are basic expectations, and which are simply nice-to-have. You'll learn to craft Kano surveys that capture nuanced user preferences, then watch as your teammates become your first research participants.
This isn't theoretical—you'll design actual survey questions for your Pilot Product's potential features (maximum 5 features to keep analysis manageable), collect real responses from classmates, and perform live analysis during the session. By the end, you'll have concrete data showing which features your team should prioritize and why. The experience of defending your Kano results to skeptical teammates mirrors the real-world challenge of influencing product roadmaps with data.
Week 3: Quantitative UX Research for Prototype and Concept Testing
Statistical Tests of Difference (T-Tests & ANOVA)
Master the statistical backbone of A/B testing and concept validation. T-tests and ANOVA are your tools for definitively answering "Is Version A really better than Version B?" or "Do these three design concepts perform differently?" You'll learn when to use each method, how small vs. large sample sizes affect your analysis, and how to create surveys that generate the right data format for testing.
Working with your Pilot Product, you'll identify key attributes worth testing—perhaps button placement effectiveness, feature comprehension, or user satisfaction across different approaches. Your team designs the testing framework, collects data from classmates, performs the statistical analysis, and creates visualizations that clearly communicate your findings. This hands-on experience builds the confidence to champion quantitative validation in environments where "gut feelings" typically drive decisions.
Week 4: Quantitative UX Research for Product Engagement - Part 1
Tests of Frequency (Correlations & Chi-Square)
During this session, we will dive deeper into tests of frequency: correlations and chi-square tests. We will discuss how these can be used in the product-market fit phase of a project, especially when actioning insights learned from initial Pilot Product Discovery and Pilot Product Development, thus helping you inform your overall product strategy.
Your Pilot Product work focuses on understanding user engagement patterns. You'll design surveys that capture both user actions and categorizations, then analyze the data to discover relationships that weren't obvious before. Do early adopters behave differently from mainstream users? Are certain feature preferences correlated with usage frequency? These insights help validate your product-market fit assumptions and identify opportunities for user segmentation.
Week 5: Quantitative UX Research for Product Engagement - Part 2
Factor Analysis for User Segmentation
Transform hunches about user types into statistically validated personas. Factor analysis uncovers the underlying dimensions that truly differentiate your users—often revealing segments that don't match your assumptions. This advanced technique helps you identify which user characteristics cluster together and which are independent variables.
Working with your Pilot Product, you'll create surveys that assess multiple user preferences and behaviors simultaneously. The statistical analysis reveals natural user groupings and the key factors that define each segment. You'll learn to interpret elbow graphs that show optimal segmentation points and translate statistical clusters into actionable persona insights. This session bridges advanced statistical analysis with practical UX applications, giving you a powerful tool for validating and refining your understanding of user diversity.
Week 6: Research Readout Day!
Demonstrate your newfound quantitative expertise by delivering a presentation showcasing how you applied all five statistical methods to your Pilot Product challenge. This isn't just about showing results—you'll explain your choices, defend your analytical approaches, and translate statistical findings into actionable business recommendations.
The presentation format mirrors real workplace dynamics where researchers must communicate complex insights to designers, product managers, and executives who need to understand implications, not statistical theory. Your classmates and instructors will question your methods and probe your recommendations—exactly the critical thinking you'll face when advocating for research-driven decisions in your career.
You'll leave this session with presentation-ready materials and the confidence to champion quantitative research in any professional setting.
