Partial Least Squares Structural Equation Modeling (PLS-SEM): Recent Advances in Model Assessment by Prof. Dr. Marko Sarstedt
Marko Sarstedt is chaired professor of marketing at the Otto-von-Guericke-University Magdeburg (Germany). He previously was an assistant professor of quantitative methods in marketing and management at the Ludwig- Maximilians-University Munich (Germany). He regularly teaches doctoral seminars on multivariate statistics, structural equation modeling, and measurement at institutions worldwide such as Alfaisal University Riyadh, Georgia State University, IMT Dubai, Michigan State University, University of Technology Sydney, and many more. His main research is in the application and advancement of structural equation modeling methods to further the understanding of consumer behavior and to improve decision-making. His research has been published in world-leading journals in various fields such as the Journal of Marketing Research, Journal of the Academy of Marketing Science, Organizational Research Methods, MIS Quarterly, International Journal of Research in Marketing, Tourism Management, Long Range Planning, Journal of World Business, and Journal of Business Research. Marko has also 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). His research ranks among the most frequently cited in the social sciences with more than 20,000 citations according to Google Scholar. Marko has won numerous best paper and citation awards, including five Emerald Citations of Excellence awards. His research has been covered by the leading media outlets such as Die Zeit, Huffington Post, and Spiegel, and has been featured in documentaries on consumer behavior on arte and MDR. According to the 2017 F.A.Z. ranking, he is among the five most influential researchers in Germany, Austria, and Switzerland.
Partial least squares structural equation modeling (PLS-SEM) has recently received considerable attention in a variety of disciplines, including marketing, strategic management, and hospitality management. 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.
The increasing popularity of the method goes hand in hand with a range of methodological extensions, which have greatly extended the methodological toolbox of researchers working with PLS-SEM. For example, recent research has brought forward the PLSpredict procedure for generating holdout sample-based point predictions in PLS path models on an item or construct level. Other researchers have started reexamining goodness-of-fit measures proposed in the early days of PLS-SEM or suggesting new ones, thereby broadening the method’s applicability. This webinar will introduce these recent advances in PLS-SEM-based model assessment, focusing on the assessment of a model’s in-sample and out-of-sample predictive power. More specifically, participants will understand the following topics: