COURSE OVERVIEW

PLS-SEM, also referred to as partial least squares path modelling, is a type of SEM, which is being increasing used in social sciences, psychology, business administration and marketing. In a nutshell, PLS-SEM can be viewed as a component-based SEM alternative to the covariancebased structural equation modelling (CB-SEM) which can be described as a factor-based SEM technique. As such, the PLS-SEM approach provides researchers with a multivariate statistical technique that can readily be used to estimate exploratory or/and complex SEM models. Although there are several stand-alone specialized PLSSEM software packages available, this course introduces participants to the PLS-SEM methodology, through the user-written Stata-package, plssem, developed by the course instructors themselves.

In common with TStat’s workshop philosophy, throughout the workshop, theoretical sessions are reinforced by case study examples, in which the course tutor discusses current research issues, highlighting potential pitfalls and the advantages of individual techniques. In this manner, course leaders are able to bridge the “often difficult” gap between abstract theoretical methodologies, and the practical issues one encounters when dealing with real data.

At the end of the course, participants are expected to be able to autonomously implement the theories and methodologies discussed during the workshop.

TARGET AUDIENCE

The PLS-SEM workshop is of particular interest to researchers and professional working in social sciences, psychology, business administration, marketing and management. Due to its introductory nature however, is it also accessible to individuals, regardless of their respective disciplines or fields, who need to acquire the requisite toolset to apply the PLS-SEM methodology to their own data.

When possible, participants should bring their own datasets to the workshop to work with and discuss with the instructors.

COURSE REQUISITES

It is assumed that participants have previously followed a basic course in statistics. Previous exposure to Stata or other statistical software packages would also be an advantage.