Structural Equation Modeling (SEM) for UX Researchers

​Most UX research stops at correlations and regressions, but real user experience operates as a system of psychological and behavioral factors. This hands-on workshop introduces you to Structural Equation Modeling (SEM) as a practical framework for modeling that system: understanding why users behave as they do, not just what they do.

​What You’ll Learn

1. ​Foundations of SEM

   • ​What SEM is, and why it fits complex UX data

   • ​Key components: measurement models, structural models, and latent variables

2. ​From Constructs to Models

   • ​Translating fuzzy UX concepts (e.g., trust, usability, satisfaction) into measurable constructs

   • ​Designing multi-item scales and linking them to behavioral metrics

3. ​Path Modeling and Mediation

   • ​Building and interpreting causal paths between latent constructs

   • ​Testing mediation models (e.g., usability → satisfaction → retention)

4. ​Model Fit and Validation

   • ​Understanding model fit indices (CFI, RMSEA, SRMR)

   • ​Assessing reliability and validity of latent constructs

5. ​Hands-on Example

   • ​Building and testing a UX model (using R and the lavaan package or SmartPLS)

   • ​Interpreting SEM output and translating results into design insights

6. ​Common Pitfalls and Best Practices

   • ​How to avoid overfitting, missing data issues, and poor construct design

   • ​Integrating SEM into iterative UX research and stakeholder reporting