Quantitative UX Research Workshop

Visualizing and Presenting Survey Data for Impact

Course features
  • Level: Intermediate
  • 90 minutes
  • Wednesday, November 19, 2:30-4pm EST US
  • Format: Live Online via Google Meet

Workshop Description

Clear and credible presentation of data is essential for research impact. The complexity of survey data makes it especially challenging to choose the right chart, structure data tables, and translate statistical testing results into clear, meaningful visual insights. 

This workshop equips you to plan, design, and communicate survey findings with clarity and precision.

You’ll learn how to match your survey variables and analyses to the right visualization forms, build a compelling data story, and apply best practices for data preparation and transformation. By the end of this course, you’ll confidently create visualizations that not only look professional but also convey valid, interpretable insights to stakeholders.

Workshop Format

Typical visualization tutorials focus on tools and aesthetics, but this session focuses on analytical accuracy and storytelling clarity.


In this live, 2-hour workshop, you’ll learn how different variable types (nominal, ordinal, interval, continuous) drive chart selection, and how to plan visualizations that reflect both your data and your story.


We’ll end with a live critique and group discussion of visualization examples—reinforcing best practices you can immediately apply to your own research reporting.

What new skills will I gain from this workshop?

You’ll develop the ability to translate raw survey data into visuals that make analytical sense: charts that reflect real relationships, not just look appealing. You’ll gain fluency in aligning data types with the correct visualization form, planning your analytical story before charting, and reporting results that withstand scrutiny from both researchers and stakeholders.

How will this workshop help my career?

Strong data visualization skills elevate your credibility as a UX researcher. By demonstrating mastery of analytical rigor and visual clarity, you’ll be able to communicate insights that drive design and business decisions. Whether you’re preparing a deck, dashboard, or report,, you’ll know how to make your data tell a clear and defensible story.

Who is this workshop for?

This workshop is ideal for UX researchers, analysts, and research ops professionals who work with survey data and want to improve how they communicate quantitative findings. It’s also suitable for researchers transitioning from qualitative to mixed methods, or for anyone who wants to refine their data storytelling craft.

Y. Patrick Hsieh, PhD

Bio
Y. Patrick Hsieh is a senior mixed methods researcher with over 12 years of experience bridging user-centered design, survey methodology, and business intelligence across academic and applied settings. With a Ph.D. in Media, Technology, and Society from Northwestern University, he began his career at RTI International before becoming Principal Research Scientist at ReconMR, where he specializes in developing fit-for-purpose research strategies that integrate qualitative and quantitative approaches to engage diverse populations—from general audiences to hard-to-reach groups—through innovative recruitment methods and rigorous survey design.

Patrick's expertise spans UX research, large-scale social surveys, and actionable data analytics, and he is passionate about exploring how emerging technologies and design thinking can expand the reach and relevance of research methodology. In addition to his applied work, he teaches graduate-level survey methods at the Institute of Design at Illinois Institute of Technology, where he equips Master of Design students with practical skills for crafting effective survey instruments within product design and development cycles.

Learning Outcomes

By course completion, you will confidently:
  • Identify the correct variable types (nominal, ordinal, interval, continuous) and their visualization implications
  • Match analytical methods (univariate, bivariate, multivariate) to appropriate tables and charts
  • Select the right visual form—bar, line, pie, or scatter plot—based on data properties and analytical goals
  • Plan visual narratives that align with research questions and expected patterns
  • Apply best practices for data cleaning and transformation before visualization
  • Integrate statistical testing results effectively into tables and graphs
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