Necessary Condition Analysis and Structural Equation Modeling in SmartPLS
Time and place: January 16, 2025 (2 hours live online session, at 10-12 am), self-taught video-based online sessions (approx. 18hrs.), February 11-12, 2025 (live session), Aalto University
Description: The course discusses the combined use of necessary condition analysis (NCA) and structural equation modeling (SEM) using SmartPLS. The course is focused on a hands-on approach to applying the methods to diverse research problems and running the relevant analyses using the software.
Credits: 6 ECTS
Grading: 1-5
Instructors: Professor Marko Sarstedt (LMU Munich School of Management), Professor Christian Ringle (Hamburg University of Technology)
Course coordinator and contact information: johanna.frosen@aalto.fi, Aalto School of Business
Goal and overview
This ten-parts online course participants to the state-of-the-art of partial least squares structural equation modeling (e.g., Hair et al., 2022; Ringle et al., 2023; Sarstedt et al., 2021) using the SmartPLS 4 software (Ringle et al., 2024). The first day of the course kicks of with an introduction that explains its key goals, benefits and components including the PLS-SEM Academy video-based sessions and the SmartPLS 4 software. The subsequent video-based online sessions provide a profound introduction to PLS-SEM. Participants will learn the foundations of PLS-SEM and how to apply it by means of the SmartPLS 4 software. The course continues with advanced topics including the importance-performance map analysis (IPMA), mediation, moderation, and higher-order models (e.g., Becker et al., 2023; Hair et al., 2024). At the end of the course, a live session address additional advanced topics of PLS-SEM including predictive model assessment and comparison, the use of the necessary condition analysis in PLS-SEM, and dealing with observed and unobserved heterogeneity. A wrap-up session includes case study exercises, questions and answers, and an outlook on additional advanced topics.
The relevance of PLS-SEM in business research
PLS-SEM is a composite-based approach to SEM, which aims at maximizing the explained variance of dependent constructs in the path model. Researchers and practitioners use PLS-SEM especially when they conduct studies on success factors and the sources of competitive advantage.
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. The goal of PLS-SEM is the explanation of variances (prediction-oriented character of the methodology) rather than explaining covariances (theory testing via covariance-based SEM, CB-SEM; Rigdon et al., 2017). The application of the PLS-SEM method is of high interest if the assumptions of CB-SEM are violated, and the proposed cause-and-effect relationships are not sufficiently explored. An additional advantage of the PLS-SEM method is the unrestricted inclusion of latent variables in small to very complex path models that draw on either/both reflective or formative measurements models. PLS-SEM has received considerable attention in a variety of disciplines (e.g., Ali et al., 2018; Khan et al., 2019; Nitzl & Chin, 2017; Ringle et al., 2020; Sarstedt et al., 2022), which resulted in several highly cited publications (e.g., Web of Science).
Who should attend?
This online course has been designed for full-time faculty and PhD/DBA doctoral students who are interested in learning how to design their research towards more rigorous and publishable outputs that potentially survive the test of time and are more frequently read and cited. A basic knowledge of univariate and multivariate statistics and SEM techniques is helpful, but not required.
Registration:
Participants must register by email to both johanna.frosen@aalto.fi and sarstedt@lmu.de by January 8, 2025.
Instructors
Dr. Dr. h.c. Marko Sarstedt, Professor of Marketing
Ludwig-Maximilians-University Munich, Germany, and Babeș-Bolyai University, Romania
Marko is a chaired Professor of Marketing at the Ludwig-Maximilians-University Munich (Germany) and an Adjunct Professor at Babeș-Bolyai University, Romania. His main research interest is the advancement of research methods to enhance the understanding of consumer behavior. His research has been published in Nature Human Behavior, Journal of Marketing Research, Journal of the Academy of Marketing Science, Multivariate Behavioral Research, Organizational Research Methods, MIS Quarterly, and Psychometrika, among others. His research ranks among the most frequently cited in the social sciences. Professor Sarstedt has won numerous best paper and citation awards, including five Emerald Citations of Excellence Awards and two AMS William R. Darden Awards. Professor Sarstedt has been named member of Clarivate Analytics’ Highly Cited Researchers List, which includes the “world’s most impactful scientific researchers.” In March 2022, he was awarded an honorary doctorate from Babeș-Bolyai-University Cluj-Napoca for his research achievements and contributions to international exchange.
More information on Marko and his list of publications: https://www.marketing.bwl.uni-muenchen.de/personen/professor/prof_-dr_-marko-sarstedt/index.html
Google Scholar https://scholar.google.de/citations?user=KnnmEP4AAAAJ&hl=de
Email: sarstedt@lmu.de
Dr. Christian M. Ringle, Professor of Management and Decision Sciences,
Hamburg University of Technology, Germany, and James Cook University, Australia
Christian is a chaired Professor of Management and Decision Sciences at the Hamburg University of Technology (Germany) and James Cook University (Australia). His research, which has been cited more than 250,000 time (Google Scholar), focuses on management and marketing topics, method development, business analytics, machine learning, and the application of business research methods to decision making. His contributions have been published in journals such as Industrial Marketing Management, International Journal of Research in Marketing, Information Systems Research, Journal of Business Research, Journal of Service Research, Journal of the Academy of Marketing Science, Long Range Planning, and MIS Quarterly. Since 2018, Christian has been included in the Clarivate Analytics’ Highly Researchers list. He regularly teaches doctoral seminars on business analytics and multivariate statistics. Christian is a co-founder and co-developer of the statistical software SmartPLS (https://www.smartpls.com).
More information on Christian M. Ringle and his list of publications: http://www.tuhh.de/mds/team/prof-dr-c-m-ringle.html
Google Scholar: https://scholar.google.de/citations?user=y5F176wAAAAJ&hl=de
Email: c.ringle@tuhh.de
Learning outcomes
This online course is designed to look at the stages of research question development and theorizing together with the subsequent methodological implementation using the multivariate analysis method PLS-SEM in business and management research. The learning objectives are to (1) contribute to theory by establishing a useful PLS path model, (2) develop an in-depth methodological appreciation of the PLS-SEM approach (the nature of theoretical modelling, analytical objectives, and related statistics), (3) acquire knowledge to evaluate measurement results, and (4) understand complementary analytical techniques.
Specifically, following the workshop participants will understand the following topics:
• Model development and fundamentals of PLS-SEM.
• PLS path model estimation.
• Assessment and reporting of measurement and structural model results including Bootstrapping.
• New criteria for model assessment such as HTMT for discriminant validity and goodness of fit (e.g., SRMR).
• Higher-order constructs (e.g., second-order models).
• Mediating effects.
• Moderating effects (interaction effects).
• Nonlinear relationships
• Importance-performance map analysis (IPMA) of PLS-SEM results.
• Prediction-oriented results analysis using BIC, PLSpredict, and CVPAT.
• Predictive model comparison using CVPAT.
• Necessary condition analysis (NCA) and the NCA use in PLS-SEM
• Combined use of the NCA and the IPMA in PLS-SEM (cIPMA)
• Overview on invariance assessment and permutation-based multigroup analysis
• Short introduction of FIMX-PLS and PLS-POS to uncover unobserved heterogeneity
In addition, the participants will be able to use the SmartPLS 4 software for their PLS-SEM analyses.
Teaching and learning methods
• Online lectures/presentations: The sessions will cover theory and its application.
• Computer exercises use the latest SmartPLS 4 version: Specifically, theoretical explanations underlying the software procedures and practical exercises where participants will apply their learning to real-world examples provided by the instructor.
• Download and install the SmartPLS 4 software from http://www.smartpls.com before coming to the workshop (participants will receive detailed instructions shortly before the course starts)
• All participants will get a 60-days license for SmartPLS 4 Professional.
• All participants receive course certificates after successful completion of online quizzes.
Tentative schedule and Content
Total number of hours: 32
• 2 hours live via Zoom (January 16, 2025 at 10am)
• 18 hours video-based via the PLS-SEM Academy
• 12 hours (1,5 day) live in presence (February 11-12, 2025)
Live online and video based sessions:
• A comprehensive introduction: Everything you need to know about the principles of PLS-SEM
https://www.pls-sem-academy.com/p/pls-sem-a-comprehensive-introduction
• Mediator analysis: Comprehend mediator analysis and learn its execution in PLS-SEM
https://www.pls-sem-academy.com/p/mediator-analysis
• Moderator analysis (extensions) and Nonlinear effects: Understand moderator analysis and learn its execution in PLS-SEM
• Higher-order models: Learn how to specify, estimate, and validate higher-order models in PLS-SEM
https://www.pls-sem-academy.com/p/higher-order-models
• Importance-performance map analysis (IPMA): Everything you need to know about the IPMA
https://www.pls-sem-academy.com/p/importance-performance-map-analysis-ipma
Live session
Key topics:
• Recap of the video-based sessions
• Predictive model assessment (BIC, PLSpredict, CVPAT) & SmartPLS case study
• Predictive model comparison using CVPAT & SmartPLS case study
• Foundations of the necessary condition analysis (NCA)
• The NCA use in PLS-SEM & SmartPLS case study
• IPMA recap
• Combined use of the NCA and the IPMA in PLS-SEM (cIPMA) & SmartPLS case study
• Observed and unobserved heterogeneity
• Overview on measurement invariance assessment & SmartPLS case study
• Permutation-based multigroup analysis (MGA) & SmartPLS case study
• Short introduction of FIMX-PLS and PLS-POS to uncover unobserved heterogeneity
• Wrap-up