Introduction to Partial Least Squares Structural Equation Modelling (Pls-Sem) Using Stata

About

PLS-SEM, also referred to as partial least squares path modelling, is an alternative approach to SEM which is being increasingly used in social sciences, psychology, business administration and marketing. PLS-SEM can be viewed as a component-based SEM alternative to the covariance-based structural equation modelling, 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 PLS-SEM software packages available, this course introduces participants to the PLS-SEM methodology through the user-written Stata package, plssem, developed by the course instructors.

The course is of particular interest to researchers and professional working in social sciences, psychology, business administration, marketing and management. Due to its introductory nature, it is however, 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. During the course, theoretical concepts are reinforced by applied case study examples, in which the course tutor discusses current research issues, highlighting potential pitfalls and the advantages of individual techniques.

Target group

The course is of particular interest to researchers and professional working in social sciences, psychology, business administration, marketing and management.

Course aim

PLS-SEM, also referred to as partial least squares path modelling, is an alternative approach to SEM which is being increasingly used in social sciences, psychology, business administration and marketing. PLS-SEM can be viewed as a component-based SEM alternative to the covariance-based structural equation modelling, 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 PLS-SEM software packages available, this course introduces participants to the PLS-SEM methodology through the user-written Stata package, plssem, developed by the course instructors.

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