Prof. James Gaskin opened the session by explaining the growing relevance of Structural Equation Modeling (SEM) in academic research. He emphasized the use of Partial Least Squares (PLS-SEM) when dealing with:

• Complex models
• Smaller sample sizes
• Non-normal data
• Predictive research objectives

Participants were introduced to:

• Project setup
• Importing datasets
• Creating models
• Defining constructs and indicators

Prof. Gaskin demonstrated essential features of SmartPLS, including drawing structural models, assigning variable types, and running initial checks.

A major component of the session was dedicated to evaluating the measurement model. Key topics included:

• Indicator reliability
• Internal consistency reliability
• Convergent validity
• Discriminant validity

Prof. Gaskin also addressed common mistakes made during model building and methods to diagnose and correct them.

Participants learned how to:

• Assess path coefficients
• Review effect sizes
• Interpret R² and predictive relevance
• Conduct bootstrapping
• Make data-driven interpretations of SEM results

The practical demonstrations helped participants understand the application of SEM to real research problems.

Both sessions included live demonstrations where attendees:

• Constructed models
• Tested hypotheses
• Explored moderating and mediating effects
• Interpreted SmartPLS output

Participants worked on example datasets to reinforce learning.