Advanced Topics of PLS-SEM Using SmartPLS 4

Course overview

Title and language: Advanced Topics of PLS-SEM using SmartPLS 4 / course language is English

Format, dates and times: Online (live via Zoom, no recordings) / December 5-6, 2024, from 9 am to 5 pm (German time)

Organizer and certificate: Northern Institute of Technology Management (NIT); the NIT grants a course certificate

Instructors: Prof. Dr. Dr. h.c. Marko Sarstedt, Prof. Dr. Christian M. Ringle, and Prof. Dr. Jan-Michael Becker

Registration: Scroll to the end of this page - also, consider joining our upcoming course on the foundations of PLS-SEM.

Course objectives

This two-day online course introduces participants to advanced topics of partial least squares structural equation modeling (PLS-SEM) using the SmartPLS 4 software. PLS is a composite-based approach to SEM. Compared to other SEM techniques, PLS-SEM allows researchers to estimate very complex models with many constructs and indicators. Furthermore, the method allows estimating reflectively and formatively specified constructs and generally offer much flexibility in terms of data requirements.

Several review studies substantiate that PLS-SEM has become a standard method in various research fields, including higher education (Ghasemy et al. 2020 | Higher Education), human resource management (Ringle et al., 2018 | International Journal of Human Resource Management), hospitality management (Ali et al., 2018 | International Journal of Contemporary Hospitality Management), information systems research (Sabol et al., 2023 | Industrial Management & Data Systems), Management Accounting (Nitzl, 2016 | Journal of Accounting Literature), international business (Richter et al., 2016 | International Marketing Review), tourism (do Valle and Assaker, 2016 | Journal of Travel Research), psychology (Willaby et al., 2015 | Personality and Individual Differences), supply chain management (Kaufmann and Gaeckler, 2015 | Journal of Purchasing and Supply Management), family business (Sarstedt et al., 2014 | Journal of Family Business Strategy), operations management (Peng and Lai, 2012 | Journal of Operations Management), strategic management (Hair et al., 2012 | Long Range Planning), marketing (Sarstedt et al., 2022 | Psychology & Marketing), management information systems (Ringle et al., 2012 | MIS Quarterly), accounting (Lee et al., 2011 | International Journal of Accounting Information Systems), software engineering (Russo & Stol, 2021 | ACM Computing Surveys), and international marketing (Henseler et al., 2009 | Advances in International Marketing).

The first day of the course starts with a brief recap of the more basic and advanced model evaluation criteria and some useful additional modeling tools like necessary condition analysis, higher-order constructs, and endogeneity treatment. The second day will cover the assessment of data heterogeneity in different forms, i.e., observed (e.g., moderation analysis, non-linear effects, multigroup analysis) and unobserved (e.g., segmentation using FIMIX-PLS and PLS-POS).

Learning outcomes

This course has been designed to familiarize participants with the potential of using the multivariate analysis method PLS-SEM in their research. The objectives of this course are to (1) provide advanced knowledge on the PLS-SEM approach, (2) gain insights on additional analysis methods in the PLS path modeling framework that increase publication success, and (3) improve analytical skills when using PLS-SEM. More specifically, participants will understand the following topics:

• Short recap on the fundamentals of PLS-SEM-based model evaluation
• Necessary condition analysis
• Endogeneity and Gaussian copulas
• Higher-order constructs (e.g., second-order constructs)
• Moderation analysis (analysis of interaction effects)
• Nonlinear effects (quadratic effects)
• Measurement invariance testing (MICOM)
• Multigroup analysis
• Uncovering unobserved heterogeneity using latent class analysis (FIMIX-PLS and PLS-POS)

This course has been designed for researchers and practitioners who have already been exposed to PLS-SEM (e.g., in the Foundations of PLS-SEM using SmartPLS 4 course). Hence, a basic knowledge of the PLS-SEM method and latent variable modeling in general is required.

Participants will receive a certificate of attendance, issued by the Northern Institute of Technology Management (NIT). Universities and academic institutions usually acknowledge this course with a workload of 3 ECTS.

Teaching and learning methods

The course is based on the following two PLS-SEM textbooks:

• Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3^rd edition). Thousand Oaks, CA: Sage.
• Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2024). Advanced Issues in Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd edition) Thousand Oaks, CA: Sage.

Presentations: The session will cover theory and its application.

Course language: English.

Case studies: Participants will apply their learning to real-world examples provided by the instructors.

Software: Computer exercises use the latest SmartPLS 4 version. Course participants will receive a 90-days fully functional version of the software SmartPLS 4.

Preliminary course schedule (Central European Time)

December 5, 2024

09:00-10:30 Short recap on the fundamentals of PLS-SEM-based model evaluation & SmartPLS exercises

11:00-12:30 Necessary condition analysis & SmartPLS exercises

13:30-15:00 Higher-order constructs & SmartPLS exercises

15:30-17:00 Endogeneity and Gaussian copulas & SmartPLS exercises

December 6, 2024

09:00-10:30 Measurement model invariance assessment (MICOM), multigroup analysis & SmartPLS exercises

11:00-12:30 Moderation analysis (analysis of interaction effects), nonlinear effects & SmartPLS exercises

13:30-15:00 Uncovering unobserved heterogeneity using latent class analysis (FIMIX-PLS) & SmartPLS exercises

15:30-17:00 Uncovering unobserved heterogeneity using latent class analysis (PLS-POS) & SmartPLS exercises

December 9, 2024

09:00-10:00 PLS-SEM Clinic - Our experts will be available to answer PLS-SEM-related questions regarding your own research models

Instructors

This course will be taught by three leading academics in the PLS-SEM field: Marko Sarstedt, Christian M. Ringle, and Jan-Michael Becker.

Prof. Dr. Dr. h.c. Marko Sarstedt

Marko Sarstedt is a chaired professor of marketing at the Ludwig-Maximilians-University Munich (Germany) and an adjunct research professor at Babeș-Bolyai-University Cluj-Napoca (Romania). His main research interest is the advancement of research methods to further 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. Marko has been repeatedly 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...

Selected publications:

• Cho, G., Sarstedt, M., & Hwang, H. (2021). A Comparative Evaluation of Factor- and Component-based Structural Equation Modelling Approaches Under (In)correct Construct Representations. British Journal of Mathematical and Statistical Psychology, 43(1), 115-135.
• Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2024). Advanced Issues in Partial Least Squares Structural Equation Modeling (PLS-SEM). 2nd Ed. Thousand Oaks, CA: Sage.
• Hair, J.F., Sarstedt, M., Ringle, C.M., Sharma, P.N., & Liengaard, B.D. (2024). Going Beyond the Untold Facts in PLS-SEM and Moving Forward. European Journal of Marketing, 58(13), 81-106.
• Hair, J.F., Sarstedt, M., Ringle, C.M., Sharma, P.N., & Liengaard, B.D. (2024). The Shortcomings of Equal Weights Estimation and the Composite Equivalence Index in PLS-SEM. European Journal of Marketing, 58 (13), 30-55.
• Rigdon, E. E., Becker, J.-M., & Sarstedt, M. (2019). Parceling Cannot Reduce Factor Indeterminacy in Factor Analysis: A Research Note. Psychometrika, 84(3), 772-780.
• Rigdon, E. E., Becker, J.-M., & Sarstedt, M. (2019). Factor Indeterminacy as Metrological Uncertainty: Implications for Advancing Psychological Measurement. Multivariate Behavioral Research, 54(3), 429-443.
• Rigdon, E. E., Sarstedt, M., & Becker, J.-M. (2020). Quantify Uncertainty in Behavioral Research. Nature Human Behaviour, 4, 329-331.
• Sarstedt, M., Hair, J.F., Cheah, J.-H., Becker, J.-M., & Ringle, C.M. (2019). How to Specify, Estimate, and Validate Higher-order Constructs in PLS-SEM. Australasian Marketing Journal, 27(3), 197-211.
• Sarstedt, M., Richter, N.F., Hauff, S., & Ringle, C.M. (2024). Combined Importance–performance Map Analysis (cIPMA) in Partial Least Squares Structural Equation Modeling (PLS–SEM): A SmartPLS 4 Tutorial. Journal of Marketing Analytics, advance online publication.
• Sharma, P. N., Sarstedt, M., Shmueli, G. Kim, K. H. & Thiele, K. O. (2019). PLS-Based Model Selection: The Role of Alternative Explanations in Information Systems Research.Journal of the Association for Information Systems, 20(4), 346-397.
• Sharma, P. N., Shmueli, G., Sarstedt, M., Danks, N. & Ray, S. (2021). Prediction-oriented Model Selection in Partial Least Squares Path Modeling. Decision Sciences, 52(3), 567-607.

Prof. Dr. Christian M. Ringle

Christian is a Chaired Professor and the Director of the Institute of Management and Decision Sciences at the Hamburg University of Technology, Germany. His research, which has been cited more than 250,000 times (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, Ringle 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. More information…

Selected publications:

• Hair, J.F., Hult, G.T.M., Ringle, C.M., & Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling. 3rd Ed. Thousand Oaks: Sage.
• Hair, J.F., Ringle, C.M., Gudergan, S.P., Fischer, A., Nitzl, C., & Menictas, C. (2019). Partial Least Squares Structural Equation Modeling-based Discrete Choice Modeling: An Illustration in Modeling Retailer Choice. Business Research, 12, 115-140.
• Hair, J.F., Risher, J.J., Sarstedt, M., & Ringle, C.M. (2019). When to Use and How to Report the Results of PLS-SEM. European Business Review, 31(1), 2-24.
• Hair, J.F., Sarstedt, M., Ringle, C.M., Sharma, P.N., & Liengaard, B.D. (2024). Going Beyond the Untold Facts in PLS-SEM and Moving Forward. European Journal of Marketing, 58(13), 81-106.
• Hair, J.F., Sarstedt, M., Ringle, C.M., Sharma, P.N., & Liengaard, B.D. (2024). The Shortcomings of Equal Weights Estimation and the Composite Equivalence Index in PLS-SEM. European Journal of Marketing, 58 (13), 30-55.
• Hauff, S., Richter, N.F., Sarstedt, M., & Ringle, C.M. (2024). Importance and Performance in PLS-SEM and NCA: Introducing the Combined Importance-Performance Map Analysis (cIPMA). Journal of Retailing and Consumer Services, 78, 103723.
• Hult, G.T.M., Hair, J.F., Proksch, D., Sarstedt, M., Pinkwart, A., & Ringle, C.M. (2018). Addressing Endogeneity in International Marketing Applications of Partial Least Squares Structural Equation Modeling. Journal of International Marketing, 26(3), 1-21.
• Liengaard, B., Sharma, P.N., Hult, G.T.M., Jensen, M.B., Sarstedt, M., Hair, J.F., & Ringle, C.M. (2020). Prediction: Coveted, Yet Forsaken? Introducing a Cross-validated Predictive Ability Test in Partial Least Squares Path Modeling. Decision Sciences, 52(2), 362-392.
• Richter, N.F., Schubring, S., Hauff, S., Ringle, C.M., & Sarstedt, M. (2020). When Predictors of Outcomes are Necessary: Guidelines for the Combined Use of PLS-SEM and NCA. Industrial Management & Data Systems, 120(12), 2243-2267.
• Ringle, C.M., & Sarstedt, M. (2016). Gain More Insight from Your PLS-SEM Results: The Importance-Performance Map Analysis. Industrial Management & Data Systems, 116(9), 1865-1886.
• Ringle, C.M., Sarstedt, M., Sinkovics, N., & Sinkovics, R.R. (2023). A Perspective on Using Partial Least Squares Structural Equation Modelling in Data Articles. Data in Brief, 48, 109074.
• Sharma, P.N., Liengaard, B.D., Hair, J.F., Sarstedt, M., & Ringle, C.M. (2023). Predictive Model Assessment and Selection in Composite-based Modeling Using PLS-SEM: Extensions and Guidelines for Using CVPAT, European Journal of Marketing, 57(6), 1662-1677.

Prof. Dr. Jan-Michael Becker

Jan-Michael Becker is an Associate Professor in the Department of Marketing at the BI Norwegian Business School. He received his doctorate degree from the University of Cologne in Germany, where he also worked as a postdoctoral researcher and lecturer in Marketing. He has been a visiting scholar at leading international business schools like Georgia State University, Atlanta, USA, and the University of Waikato, Hamilton, New Zealand. His research interests focus on data analytics, structural equation modeling (SEM), PLS path modeling, unobserved heterogeneity, and measurement theory, as well as bridging marketing and IS problems. His research has been published in several premier academic journals, including Information Systems Research, MIS Quarterly, Journal of the Academy of Marketing Science, International Journal of Research in Marketing, Psychometrika, Multivariate Behavioral Research, and Nature Human Behaviour. He is a co-founder of the SmartPLS software application. More information...

Selected publications:

• Becker, J.-M., Cheah, J.-H., Gholamzade, R., Ringle, C.M., & Sarstedt, M. (2023). PLS-SEM’s Most Wanted Guidance. International Journal of Contemporary Hospitality Management, 35(1), 321-346.
• Becker, J.-M., & Ismail, I.R. (2016). Accounting for Sampling Weights in PLS Path Modeling: Simulations and Empirical Examples. European Management Journal, 34(6), 606-617.
• Becker, J.-M., Klein, K. & Wetzels, M. (2012). Hierarchical Latent Variable Models in PLS-SEM: Guidelines for Using Reflective-formative Type Models. Long Range Planning, 45(5-6), 359-394.
• Becker, J.-M., Proksch, D., & Ringle, C.M. (2022). Revisiting Gaussian Copulas to Handle Endogenous Regressors. Journal of the Academy of Marketing Science, 50, 46-66.
• Becker, J.-M., Rai, A., Ringle, C.M. & Völckner, F. (2013). Discovering Unobserved Heterogeneity in Structural Equation Models to Avert Validity Threats. MIS Quarterly, 37(3), 665-694.
• Rigdon, E.E., Becker, J.-M., Rai, A., Ringle, C.M., Diamantopoulos, A., Karahanna, E., Straub, D. & Dijkstra, T.K. (2014). Conflating antecedents and formative indicators: A comment on Aguirre-Urreta and Marakas. Information Systems Research, 25(4), 780-784.
• Rigdon, E.E., Becker, J- M., & Sarstedt, M. (2019). Parceling Cannot Reduce Factor Indeterminacy in Factor Analysis: A Research Note. Psychometrika, 84(3), 772-780.
• Rigdon, E.E., Becker, J.-M., & Sarstedt, M. (2019). Factor Indeterminacy as Metrological Uncertainty: Implications for Advancing Psychological Measurement. Multivariate Behavioral Research, 54(3), 429-443.
• Rigdon, E.E., Sarstedt, M., & Becker, J.-M. (2020). Quantify Uncertainty in Behavioral Research. Nature Human Behaviour, 4, 329-331.
• Sarstedt, M., Hair, J.F., Cheah, J.-H., Becker, J-M., Ringle, C.M. (2019). How to Specify, Estimate, and Validate Higher-Order Constructs in PLS-SEM Australasian Marketing Journal, 27(3), 197-211.

Teaching Resources

Textbooks on PLS-SEM:

• Hair, J.F., Hult, G.T.M., Ringle, C. M., & Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3rd edition). Thousand Oaks, CA: Sage.
• Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2024). Advanced Issues in Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd edition) Thousand Oaks, CA: Sage.

 

Articles:

• Basco, R., Hair, J.F., Ringle, C.M., & Sarstedt, M. (2023): Advancing Family Business Research Through Modeling Nonlinear Relationships: Comparing PLS-SEM and Multiple Regression. Journal of Family Business Strategy, 13(3), 100457.
• Becker, J.-M., Cheah, J.H., Gholamzade, R., Ringle, C. M., & Sarstedt, M. (2023). PLS-SEM's Most Wanted Guidance. International Journal of Contemporary Hospitality Management, 35(1), 321-346.
• Becker, J.-M., Klein, K., & Wetzels, M. (2012). Formative Hierarchical Latent Variable Models in PLS-SEM: Recommendations and Guidelines. Long Range Planning, 45(5/6), 359-394.
• Becker, J.-M., Proksch, D., & Ringle, C. M. (2022). Revisiting Gaussian Copulas to Handle Endogenous Regressors. Journal of the Academy of Marketing Science, 50, 46-66.
• Becker, J.-M., Rai, A., Ringle, C. M., and Völckner, F. (2013). Discovering Unobserved Heterogeneity in Structural Equation Models to Avert Validity Threats. MIS Quarterly, 37(3), 665-694.
• Becker, J-M., Ringle, C. M. & Sarstedt, M. (2018). Estimating Moderating Effects in PLS-SEM and PLSc-SEM: Interaction Term Generation*Data Treatment. Journal of Applied Structural Equation Modeling, 2(2), 1-21.
• Hair, J.F., Sarstedt, M., Ringle, C.M., Sharma, P.N., & Liengaard, B.D. (2024). Going Beyond the Untold Facts in PLS-SEM and Moving Forward. European Journal of Marketing, 58(13), 81-106.
• Henseler, J., Ringle, C. M., & Sarstedt, M. (2016). Testing Measurement Invariance of Composites Using Partial Least Squares. International Marketing Review, 33(3), 405-431.
• Hult, G. T. M., Hair, J. F., Proksch, D., Sarstedt, M., Pinkwart, A. & Ringle, C. M. (2018). Addressing Endogeneity in International Marketing Applications of Partial Least Squares Structural Equation Modeling, Journal of International Marketing, 26(3), 1-21.
• Richter, N. F., Schubring, S., Hauff, S., Ringle, C. M., & Sarstedt, M. (2020). When Predictors of Outcomes are Necessary: Guidelines for the Combined use of PLS-SEM and NCA. Industrial Management & Data Systems, 120(12), 2243-2267.
• Sarstedt, M., Hair, J. F., Cheah, J.-H., Becker, J-M., & Ringle, C. M. (2019). How to Specify, Estimate, and Validate Higher-Order Constructs in PLS-SEM. Australasian Marketing Journal, 27(3), 197-211.
• Sarstedt, M., Hair, J. F., Pick, M., Liengaard B. D., Radomir, L., & Ringle, C. M. (2022). Progress in Partial Least Squares Structural Equation Modeling Use in Marketing in the Last Decade. Psychology & Marketing, 39(5), 1035-1064.
• Sarstedt, M., Radomir, L., Moisescu, O. I., & Ringle, C. M. (2022). Latent Class Analysis in PLS-SEM: A Review and Recommendations for Future Applications. Journal of Business Research, 138, 398-407. 
• Sarstedt, M., Ringle, C. M., Cheah, J.-H., Ting, H., Moisescu, O. I., & Radomir, L. (2020). Structural Model Robustness Checks in PLS-SEM. Tourism Economics, 26 (4), 531-554.
• Sarstedt, M., Ringle, C. M., & Hair, J. F. (2018). Treating Unobserved Heterogeneity in PLS-SEM: A Multi-method Approach. In H. Latan & R. Noonan (Eds.), Partial Least Squares Structural Equation Modeling: Basic Concepts, Methodological Issues and Applications. New York: Springer. 

Please also take a look at our collection of PLS-SEM-related literature!

Software

The course uses the software SmartPLS 4 Professional for all PLS-SEM applications and exercises.

All participants get a fully functional 90 days SmartPLS 4 Professional license key. Please visit https://www.smartpls.com/ to download and install the latest version of the software.

The SmartPLS webpage also provides literature and much useful information on PLS-SEM and hosts a discussion forum for users.

This event will be completely online via Zoom with the registered participants. To participate online, you need a Zoom installation and a sufficiently fast Internet connection. Via Zoom, we enable all online participants to actively participate in the course. For example, you can ask questions, perform the exercises and case studies and participate in the virtual coffee breaks.

Contact

We will hold the event completely online via Zoom with the registered participants.

All registered participants (who paid the course fee), will receive the Zoom link and the course resources (slides, SmartPLS course key, literature, etc.) about 4 days before the course starts.

In case of any questions, please contact Prof. Dr. Dr. h.c. Marko Sarstedt, Prof. Dr. Christian M. Ringle, and Prof. Dr. Jan-Michael Becker via email:

nit-courses@smartpls.com

Registration terms and payment methods

• This event will be completely online via Zoom with the registered participants. The course fee covers the participation in the course, including the PLS-SEM clinic, a 90-day professional license of the SmartPLS 4 software, course material (e.g., slides, datasets, project files), and a certificate of participation issued by the Northern Institute of Technology Management (NIT).
• We limit the number of participants in order to offer you the best possible learning experience and to enable discussions as well as individual feedback. Unfortunately, we cannot make reservations. So please order your ticket as soon as possible for the seats still available.
• We offer payment by credit card and PayPal at registration (preferred option). Using either of these payment methods, you are immediately admitted to the course and have secured your place.
• You will receive a receipt after your payment (which you can use, for example, to submit to your organization).
• We also offer payment by invoice up to 21 days before the course starts. The invoice must be paid within 14 days (for transfers outside the EUR area, the OUR option must be selected so that we receive the full invoice amount). Participants whose invoices have not been settled before the start of the course cannot be admitted.
• Cancellation of paid registrations is possible up to 14 days before the course starts. We will refund the course fee minus transaction costs (usually 5% of the course fee).