This two-day course advances participants knowledge of partial least squares structural equation modeling (PLS-SEM) using the world-leading PLS-SEM software SmartPLS.
The first day of the course starts with a brief recap of the more basic and advanced model evaluation criteria and some useful additional modelling tools like IPMA, higher-order constructs and mediation. The second day will cover the assessment of data heterogeneity in different forms, i.e., observed (e.g., multi-group, moderation, non-linear effects) and unobserved (e.g., segmentation using FIMIX-PLS and PLS-POS).
PLS-SEM is a composite-based approach to SEM, which aims at maximizing the explained variance of dependent constructs in the path model. Compared to other SEM techniques, PLS-SEM allows researchers to estimate very complex models with many constructs and indicator variables. Furthermore, PLS-SEM allows to estimate reflective and formative constructs and generally offers much flexibility in terms of data requirements.
This course has been designed to familiarize participants with the potentials of using the multivariate analysis method PLS-SEM in their research. The objectives of this course are to (1) provide advanced knowledge on the PLS-SEM approach, (2) gain insights on additional analysis methods in the PLS-SEM modelling framework that increase publication success, and (3) improve PLS-SEM modeling and analytical skills. More specifically, participants will understand the following topics:
Prof. Dr. Christian Ringle
Prof. Marko Sarstedt
Dr. Jan-Michael Becker
This course continues where we left off with the Foundations of PLS-SEM using Smart-PLS course. It has been designed to advance the PLS-SEM knowledge of full-time faculty and PhD students who want to master the PLS-SEM method for their own research applications. Basic knowledge of PLS-SEM method and the nature of latent variable modeling is required.
Participants will receive a certificate of attendance. Universities and academic institutions usually acknowledge this course with a workload of 6 ECTS.