This full-day tutorial introduces participants to Structural Equation Models (SEMs) and their estimation using Partial Least Squares Path Modeling (PLS Path Modeling). Structural Equation Models are a statistical technique to simultaneously assess the relationships between unobserved factors (i.e. latent variables) and the way these factors are measured based on observable variables. This is useful, for example, for researchers in our community interested in learning about how different factors influence human information behavior and as a means to validate and optimize measurement instruments for subjective concepts important in IIR, such as engagement, satisfaction, trust etc.

The tutorial is introductory in nature. Audience members need only have basic statistical knowledges. Familiarity with GNU R is essential, as participants will be required to run their analyses using this software on their own computers.

## Topics covered

Topics covered in this tutorial include:

- Refresher of OLS-based regression approaches including estimation and interpretation;
- Basic ideas (latent variables and their measured counterparts, structural model and measurement model relationships) of Structural Equation Modeling in general and PLS Path Modeling in particular including the ways of depicting SEMs graphically, measurement model theory and the PLS algorithm;
- Recent methodological advancement leading to consistent PLS Path Modeling;
- Important measures in applying PLS Path Modeling covering measures to assess measurement model fit (e.g. composite reliability, discriminant validity based on the Heterotrait-Monotrait-Ratio of correlations), as well as structural model fit (e.g. based on a bootstrapped analysis of overall model fit);
- Conducting a use case analysis using GNU R. The theme of this use case is Measuring Users' Trust in Search Engine Results.