UX Workshops

The session will cover the key components of SEM, including measurement models, structural models, and latent variables. You will learn how to translate abstract UX concepts such as trust, usability, and satisfaction into measurable constructs, design multi-item scales, and connect them to behavioral metrics. The workshop will also guide you through building and interpreting causal paths between latent variables, testing mediation models such as usability leading to satisfaction and retention, and evaluating model fit using indices like CFI, RMSEA, and SRMR. Through a hands-on example, you will practice building and testing a UX model using R (lavaan) or SmartPLS, interpreting the results, and transforming them into actionable design insights. Finally, we will discuss common pitfalls such as overfitting, missing data, and weak construct design, and explore how to integrate SEM into iterative UX research and stakeholder reporting.

​Quantitative UX (From Basics to Advanced) is a hands-on course for designers and researchers who want to bring more evidence and structure into their work, without getting lost in statistics. Across five practical sessions, we’ll start with the basics of understanding and describing data, then move into real-world methods like t-tests, regression, Bayesian thinking, and modeling. You’ll work with provided datasets, follow clear examples, and see how each method connects to everyday UX questions. By the end, you’ll know how to design and analyze studies that feel rigorous, not rigid, and use data to make design decisions with confidence.

In UX research, small samples are common and often unavoidable, especially when testing early designs or working with limited time and resources. This workshop focuses on how to make sound, defensible decisions with small datasets using rigorous, science-based methods. We will discuss why small samples are risky, how to identify unstable results, and how to correct them using practical approaches. You will also learn when and how to use nonparametric tests, simple but powerful methods for small-sample analysis, and leave with tools and examples you can apply right away in your own studies.​​

Bayesian thinking transforms how you approach small-sample UX research. Instead of stopping at whether results are significant, it encourages you to ask how much evidence you have and how certain that evidence is. In small UX studies, this shift can mean the difference between uncertainty and confident, defensible insights. In our Bayesian Thinking for UX Research workshop, we go beyond p-values to show how to extract meaningful conclusions from every data point, even when your sample is tiny. You will learn the essentials of Bayesian reasoning, how to apply it to A/B testing, when to use Bayesian versus frequentist methods, how to interpret Bayes Factors and credible intervals, and how to communicate Bayesian results clearly and effectively.​

This 2-hour online workshop is a hands-on session designed to help you confidently analyze and interpret UX survey data using scientific and practical methods. You will work with real datasets, learn how to summarize responses with descriptive statistics and cross-tabs, test reliability with Cronbach’s Alpha and Factor Analysis, and apply methods such as Conjoint, MaxDiff, t-tests, ANOVA, Chi-square, regression, and clustering. By the end, you will know when to use each technique and how to turn raw survey data into clear, defensible, and evidence-based insights.​

This online workshop offers a practical introduction to regression analysis in UX research. You will learn how and when to use models such as linear, logistic, multinomial, ordinal, and Poisson, and gain hands-on experience applying them in R and JAMOVI with real UX datasets. The session also focuses on how to interpret and report regression results clearly for both academic and industry audiences. Whether you are new to regression or looking to strengthen your applied skills, this workshop provides a structured, evidence-based foundation for using regression methods effectively in UX studies.​

Learning statistics and R can feel overwhelming without the right approach, which is why this course is designed to be short, focused, and practical for UX researchers who want to apply what they learn immediately. Across three interactive 2-hour sessions, you will move from opening R for the first time to confidently running the analyses needed to answer research questions and communicate insights effectively. You will start with setting up R and RStudio, then work hands-on with real UX data to practice descriptive analysis, data visualization, and key inferential methods such as t-tests, ANOVA, nonparametric tests, and simple regression. By the end, you will understand both the “how” and the “why” behind these methods, gaining the skills to design, analyze, and explain studies with clarity and confidence.​​

Determining how many participants a UX study really needs is one of the most common and confusing challenges in research. This one-hour workshop makes the process clear and practical, showing you how to make confident sample size decisions and interpret power analyses without heavy math or coding. You will learn to plan studies that are both rigorous and efficient using accessible tools like G*Power, and understand how to avoid common statistical mistakes that undermine results. Designed for researchers, designers, and students alike, this session helps you turn small or uncertain samples into reliable, evidence-based insights.​