Partial Least Squares SEM Using SmartPLS

This course has been designed to familiarize participants with the potentials of using the multivariate analysis method partial least squares SEM in their research. The objectives of this course are to provide (1) an in-depth methodological introduction into the PLS-SEM approach the nature of causal modeling, analytical objectives, some statistics), (2) the evaluation of measurement results, and (3) complementary analytical techniques. More specifically, participants will understand the following topics:

- Model development and fundamentals of PLS-SEM
- PLS path model estimation including the weighted PLS (WPLS) and consistent PLS (PLSc)
- Assessment and reporting of measurement and structural model results including Bootstrapping
- New criteria for model assessment such as HTMT for discriminant validity and goodness of fit (e.g., SRMR).
- Prediction-oriented results analysis including Blindfolding and PLSpredict
- Importance-performance map analysis (IPMA) of PLS-SEM results
- Higher-order constructs (e.g., second-order models)
- Mediating effects
- Moderating effects (interaction effects)
- Nonlinear relationships (quadratic effects)
- Measurement invariance testing (MICOM)
- Multigroup analysis
- Segmentation and uncovering unobserved heterogeneity by FIMIX-PLS and PLS-POS

This course has been designed for full-time faculty and PhD students who are interested in learning how to use the PLS-SEM method in their own research applications. A basic knowledge of multivariate statistics and SEM techniques is helpful, but not required.

All exercises use the SmartPLS software (www.smartpls.com) - participants get a free license key!