Zero Risk Enrollment: Receive a full refund through the second week of the course, no questions asked.
Course features
Level: Foundations
2 hours/week
6 weeks
Class Size: 15 Students
Dates: Wednesday, January 21-February 25, 2026, 12-2pm EST
Format: Live Online via Google Meet
Course Description
Learn how to create a logical, tight connection between your survey data and the outcomes that matter to your organization, while also insuring you gather data your stakeholders can truly rely on.
This course teaches an end-to-end, scientifically grounded process—from defining constructs and planning measurement strategies, to writing items, testing validity, analyzing data, and translating results into confident insights.
You’ll learn how professional survey methodologists design surveys that capture meaningful, reliable data, not just surface-level responses. We’ll emphasize a part of the process almost always missing from UX research: construct development and measurement specification. By learning how to define what you actually intend to measure, you’ll avoid common pitfalls such as ambiguous concepts, invalid scales, and misleading results, and you'll forge a very strong logical connection between your survey data and business objectives.
By the end, you will be equipped to design surveys that hold up under scrutiny, support high-stakes product decisions, and provide clear, defensible insights.
Why take a quantitative UX research course on surveys?
Surveys are an essential tool in any UX researcher's toolbox. Yet they are one of the most deceptively difficult research methods. There are innumerable "invisible" mistakes you might be making, and no survey tool will tell you what's wrong.
Subtle wording choices, question ordering, or response format decisions could be systematically skewing your results. The insights drawn from flawed data could be driving major product decisions. If there's no one at your org conversant in survey methodology, no one will be able to say what's going wrong.
Survey methodology has decades of established best practices that most UX researchers never learn. We pick up survey writing through trial and error on the job. If we're lucky, we absorb some theory for self-study resources or from peers.
Course Format
Most survey courses teach abstract theory or academic examples that don’t translate into UX practice. At the UXR Institute, we take a different approach. Every concept is applied immediately to realistic product scenarios so you can practice new skills in context.
Each week focuses on a different stage of the end-to-end survey process: from sampling design, to defining constructs and measurable variables, to writing and evaluating questions, validating interpretation with cognitive interviews, and finally running significance tests and correlational analyses to uncover patterns and insights.
You’ll work in small groups throughout the course, reviewing real survey examples, building your own instruments, and practicing the workflows professional survey scientists use every day.
Weekly progression:
Week 1: Translate business goals into research questions, constructs, and a full measurement plan.
Week 2: Write and evaluate high-quality survey questions.
Week 3: Validate items using cognitive interviewing and refine them for accuracy.
Week 4: Design an appropriate sampling strategy and target population.
Week 5: Apply statistical significance testing to interpret differences in survey data.
Week 6: Analyze survey data using correlation analysis and generate actionable insights.
Tools needed: Google Meet for live sessions and basic spreadsheet tools (Google Sheets or Excel). No prior stats experience required.
What new skills will I gain from this quantitative UX research course?
Core Competencies
Research Strategy and Measurement Planning
Forge a tight connection between business objectives and your survey data
Define research goals by mapping constructs to measurable variables.
Develop measurement plans that reduce ambiguity and strengthen validity.
Choose modes and sampling strategies that minimize error for your context.
Question Engineering
Write items that accurately capture defined constructs.
Design response scales that provide interpretable, actionable data.
Sequence and structure surveys to maximize clarity and engagement.
Quality Assurance
Apply professional frameworks (e.g., QAS) to catch flaws before launch.
Conduct cognitive interviews to identify interpretation errors.
Reduce measurement error by refining constructs and item wording.
Sample Design
Choose sampling frames and recruitment strategies that match research goals.
Identify biases, coverage issues, and tradeoffs in real-world sampling decisions.
Understand how sampling choices affect data quality and interpretability.
Data Analysis and Communication
Conduct significance testing to evaluate meaningful differences.
Use correlation analysis to understand relationships between variables.
Translate quantitative patterns into clear, actionable insights for stakeholders.
How will this course help my career?
Develop end-to-end quantitative UX research competency
UX and product teams increasingly expect researchers to plan, design, and analyze surveys with methodological rigor. You’ll learn the complete pipeline—not just item writing—so you can confidently lead survey projects from concept to insights.
Gain increased stakeholder trust
Product leaders increasingly demand quantified evidence when making decisions. When your survey methodology is bulletproof, leaders will feel more comfortable relying on your insights for big investments and strategic decisions.
Achieve methodological authority
Become the go-to person for evaluating survey quality, training junior researchers, and establishing research standards across your organization.
Enable stronger mixed methods research
Understanding constructs and measurement allows you to design mixed-methods studies that integrate qualitative depth with quantitative validation.
Who is this course for?
UX researchers who regularly use surveys but want to be able to speak confidently about their process and defend the validity and reliability of their data. Qualitative UX researchers looking to transition to mixed-methods or quantitative UX research roles.
Research leaders and managers who need to quality-check team members' survey work but lack expertise to spot subtle methodological problems.
Product managers who commission user research and want to distinguish good surveys from poor ones, enabling better collaboration with research teams and more informed interpretation of findings.
This course is especially critical if you...
Support product decisions where small measurement errors translate into major strategic mistakes
Work at organizations where user data directly influences significant business decisions
Want to advance into senior research roles where quantitative competency is increasingly non-negotiable
Prerequisites
None. No prior experience with survey methodology, statistics, or advanced mathematics required. We start from first principles and build systematically.
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HarmoniJoie Noel, PhD
Bio
Dr. HarmoniJoie Noel is a Senior Mixed Methods Researcher with a PhD in Sociology and Survey Research Methodology, bringing 15 years of expertise in healthcare research and patient experience studies. She has held distinguished roles at major organizations including RTI International, CDC's National Center for Health Statistics, and Booz Allen Hamilton, conducting groundbreaking research on health insurance literacy and patient experiences that has directly informed healthcare policy.
Free Advisory Session
Have questions about the course? Want to chat about your learning goals to see if they align with the course approach? Book a free call with the instructor.
Learning Outcomes
By course completion, you will confidently:
Create strong connections between survey data and business objectives by defining constructs and measurements
Write high-quality survey items and response scales using established methodological best practices.
Evaluate question quality using frameworks like QAS and cognitive interviewing.
Design appropriate sampling strategies and identify the right target population.
Test for statistical significance and understand when group differences are real vs. noise.
Analyze survey data—including correlations and patterns—to generate clear, defensible insights for stakeholders.
Overview
Course Syllabus
Week 1: Survey Planning: Research Questions, Constructs, and Measurement
Learning Objective: Translate business goals into precise research questions, constructs, and measurable variables.
This week focuses on the part of survey methodology most often skipped in UX research: measurement development. You’ll learn how to map broad business questions into actionable research questions, break them into conceptual domains, define constructs, and specify how those constructs will be measured. We’ll explore how tight alignment at this stage prevents ambiguous data, helps stakeholders interpret findings, and ensures every survey item has a clear purpose.
Workshop: Draft a full survey measurement plan for a product scenario—identifying research questions, constructs, sub-constructs, and desired measurement approaches for each.
Week 2: Question Design and Evaluation
Learning Objective: Design high-quality survey items and evaluate them using professional frameworks.
This combined week walks through the full lifecycle of question creation—from selecting the right item type to evaluating its validity. You’ll learn major question formats (multiple select, yes/no, scale types, behavioral vs. attitudinal), common design pitfalls, and how to match item types to constructs defined in Week 2. We then use the Questionnaire Appraisal System (QAS) to systematically evaluate item quality, covering clarity, assumptions, memory demands, bias, sensitivity, and response categories.
Workshop: Draft items for your measurement plan, review good and bad examples, and use QAS in small groups to refine and strengthen your questions.
Week 3: Cognitive Interviewing and Measurement Refinement
Learning Objective: Validate whether questions measure the intended constructs and refine them based on participant interpretations.
You’ll practice running cognitive interviews, using “think-aloud” techniques and probing to uncover hidden assumptions, misinterpretations, and unintended meanings. You’ll connect these findings back to constructs and measurement plans, learning how to iteratively strengthen validity and reduce measurement error.
Workshop: Conduct paired cognitive interviews on draft items, evaluate interpretation issues, and revise items for clarity and alignment with constructs.
Week 4: Sampling Strategy and Target Audience Definition
Learning Objective: Design an appropriate sampling approach that aligns with research goals, constraints, and data quality needs.
We begin with the foundation most UX surveys overlook: how to define and recruit the right sample. You’ll learn the principles of sampling frames, inclusion/exclusion criteria, segmentation, and representativeness. We’ll discuss convenience samples vs. purposive samples, when probability sampling matters, and how sampling decisions impact validity. You’ll also examine common failure modes—undercoverage, biased samples, overly narrow or broad targeting—and practice making tradeoffs under real-world constraints.
Workshop: Build a sampling plan for your project: define the target population, sampling approach, recruitment channels, and sample size considerations based on your research and business goals.
Week 5: Statistical Significance Testing
Learning Objective: Understand and apply statistical significance testing to evaluate survey results.
This session covers the practical statistical tools UX researchers need to interpret differences in survey data. You’ll learn how to run and interpret crosstabs, chi-square tests, p-values, confidence levels, and understand Type I and Type II errors. The emphasis is on practical decision-making, not advanced math.
Workshop: Work with your project dataset to run significance tests and determine which differences are meaningful.
Week 6: Analysis, Correlation, and Insight Generation
Learning Objective: Analyze survey data to identify patterns, relationships, and actionable insights.
In the final session, you’ll learn how to interpret correlations, identify patterns in data, and connect results back to the constructs and business questions defined in Week 2. We’ll cover practical workflows for cleaning data, generating early findings, and translating statistical patterns into intuitive, stakeholder-ready insights.
Workshop: Analyze your project dataset to identify correlations, interpret patterns, and formulate clear recommendations tied to business objectives.
