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Partial Least Squares Structural Equation Modeling (PLS-SEM)

20 March 2019
RW I, S 58

Time: 9:00am - 4:00pm

Trainer: Prof. Dr. Marko Sarstedt
Language: English
Registration: until 06 March 2019
for doctoral candidates via BayDOC: https://baydoc.uni-bayreuth.de
for Postdocs and Habilitierende via https://baydoc.uni-bayreuth.de/ubt/de/intern/postdoc/

Partial least squares structural equation modeling (PLS-SEM) has recently received considerable attention in a variety of disciplines, including marketing, strategic management, management information systems, and many more.

PLS 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 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 half-day workshop introduces participants to the state-of-the-art of PLS-SEM using the SmartPLS 3 software. After a brief introduction to the basic principles of structural equation modeling, participants will learn the foundations of PLS-SEM and how to apply the method by means of the SmartPLS software. The workshop will cover various aspects related to the evaluation of measurement and structural model results. For this purpose, the instructor will make use of several examples and exercises.

Qualification objectives

This workshop is designed to familiarize with the potentials of using PLS-SEM in business research. The objectives of this course are to provide a methodological introduction into the PLS-SEM approach (the nature of causal modeling, analytical objectives, some statistics) and the evaluation of measurement and structural model results. More specifically, participants will understand the following topics:

  • Fundamentals of PLS-SEM
  • Current debates about PLS-SEM
  • Assessment and reporting of measurement model results, including the new criterion for discriminant validity testing: The heterotrait-monotrait ratio of correlations (HTMT)
  • Assessment and reporting of structural model results
  • Outlook on advanced topics such as mediation, moderation, higher-order models, and treatment of (un)observed heterogeneity

This course has been designed for 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.

Trainer                                                                                                                                                                                         Marko Sarstedt is a Chaired Professor of Marketing at the Otto-von-Guericke-University Magdeburg (Germany) and Adjunct Professor at the Monash University Malaysia (Malaysia) His main research interests are in the advancement of research methods to further the understanding of consumer behavior. His research has been published in, for example, Journal of Marketing Research, Journal of the Academy of Marketing Science, International Journal of Research in Marketing, Organizational Research Methods, Multivariate Behavioral Research, Decision Sciences, MIS Quarterly, Journal of Business Research, Journal of World Business, Marketing Letters, and Long Range Planning.

Marko has co-edited several special issues of leading journals and co-authored four widely adopted textbooks, including “A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)” (together with Joe F. Hair, G. Tomas M. Hult, and Christian M. Ringle). Marko’s works have been awarded with several citation and best paper awards. According to the 2018 F.A.Z. ranking, he is among the three most influential economists in the category research. He has recently been included in the Clarivate Analytics’ Highly Cited Researchers list. Additional information: http://www.marketing.ovgu.de

Verantwortlich für die Redaktion: Eva Querengässer

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