Course overview

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

Format, dates and times: Online (live via Zoom, no recordings) / November 27-28, 2023, 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 course on advanced PLS-SEM topics.

Course objectives

This two-day online course introduces participants to the state-of-the-art 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 offers 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 (Hair et al., 2017 | 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 provides a profound introduction to latent variable models and PLS-SEM. Participants will learn the foundations of PLS-SEM and how to apply it by means of the SmartPLS 4 software. The second day will cover the evaluation of the measurement and structural models, including recent developments in model evaluation. The instructors will make use of several examples and hands-on exercises using the SmartPLS 4 software. 

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 provide (1) an in-depth methodological introduction to the PLS-SEM approach (the nature of causal modeling, analytical objectives, and some statistics), (2) the evaluation of measurement results, and (3) the evaluation of the structural model results. More specifically, participants will understand the following topics:

• Fundamentals of latent variable modeling and model development
• An introduction to PLS-SEM and its characteristics
• When (not) to use PLS-SEM
• PLS path model estimation using the standard, and weighted PLS (WPLS) algorithms
• Assessment and reporting of measurement and structural model results including bootstrapping
• Prediction-oriented results analysis including PLS_predict
• New criteria for model assessment such as HTMT for discriminant validity, goodness-of-fit indices (e.g., SRMR), and metrics for model comparisons (e.g., BIC)
• Mediation analysis
• Outlook on advanced techniques such as the necessary condition analysis (NCA)

This course has been designed for full-time faculty and PhD students who are interested in learning how to use the PLS-SEM method in their own research applications. Basic knowledge of multivariate statistics and SEM techniques is helpful, but not 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 PLS-SEM textbook by 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.

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)

November 27, 2023

09:00-10:30 Foundations of structural equation modeling

11:00-12:30 The nature of latent variables and specifying the measurement model (reflective/formative)

13:30-15:00 Introduction to PLS-SEM and the SmartPLS software

15:30-17:00 Model estimation: the PLS-SEM algorithm and the weighted PLS-SEM algorithm (WPLS) & SmartPLS exercises

November 28, 2023

09:00-10:30 Assessing measurement model results (part I) & SmartPLS exercises

11:00-12:30 Assessing measurement model results (part II) and bootstrapping & SmartPLS exercises

13:30-15:00 Assessing structural model results (part I) and prediction-oriented assessment of PLS-SEM results (PLSpredict) & SmartPLS exercises

15:30-17:00 Assessing structural model results (part II), goodness of fit criteria in PLS-SEM and mediation & SmartPLS exercises

December 11, 2023

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., Hult, G.T.M., Ringle, C.M., & Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling. 3rd Ed. Thousand Oaks: Sage.
• 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., & Moisescu, O. I. (2023). Quantifying Uncertainty in PLS-SEM-based Mediation Analyses. Journal of Marketing Analytics, Advance online publication.
• Sharma, P. M., 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.
• 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 M. Ringle is a Professor of Management and the Director of the Institute for Human Resource Management and Organizations at the Hamburg University of Technology (TUHH). He holds a Ph.D. from the University of Hamburg, Faculty of Business and Economics. His research has been published in well-known journals such as Information Systems Research (ISR), International Journal of Research in Marketing (IJRM), Journal of Business Research (JBR), Journal of Service Research (JSR), Journal of the Academy of Marketing Science (JAMS), Long Range Planning (LRP), MIS Quarterly (MISQ), and Organizational Research Methods (ORM). Dr. Ringle has been included in the Clarivate Analytics' Highly Researchers list. He co-authored the textbook on PLS-SEM and is a co-founder of SmartPLS, a software tool with a graphical user interface for the application of the PLS-SEM method. More information...

Selected publications:

• Guenther, P., Guenther, M., Ringle, C. M., Zaefarian, G., & Cartwright, S. (2023). Improving PLS-SEM Use for Business Marketing Research. Industrial Marketing Management, 111, 127-142.
• 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., 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.
• 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.
• 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.
• Ringle, C.M., Sarstedt, M., Mitchell, R., & Gudergan, S.P. (2020). Partial Least Squares Structural Equation Modeling in HRM Research. The International Journal of Human Resource Management, 31(12), 1617-1643.
• 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, 56(6), 1662-1677.
• Shmueli, G., Sarstedt, M., Hair, J.F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C.M. (2019). Predictive Model Assessment in PLS-SEM: Guidelines for Using PLSpredict, European Journal of Marketing, 53(11), 2322-2347.

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(1), 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) (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) (2^nd edition). Thousand Oaks, CA: Sage.

 

Articles:

• Becker, J.-M. and Ismail, I. R. (2016), Accounting for Sampling Weights in PLS Path Modeling: Simulations and Empirical Examples, European Management Journal, 34(6), 606-617.
• 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. 
• Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A New Criterion for Assessing Discriminant Validity in Variance-based Structural Equation Modeling. Journal of the Academy of Marketing Science, 43(1), 115-135.
• Ringle, C. M., Sarstedt, M., Sinkovics, N., & Sinkovics, R. R. (2023). A Perspective on Using Partial LEast Squares Structural Equation Modeling in Data Articles. Data in Brief, 48(June), 109074.
• Sarstedt, M., Hair, J. F., Nitzl, C., Ringle, C. M., & Howard, M. C. (2020). Beyond a Tandem Analysis of SEM and PROCESS: Use PLS-SEM for Mediation Analyses! International Journal of Market Research, 62(3), 288–299.
• 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 Research in the Last Decade. Psychology & Marketing, 39(5), 1035-1064. 
• Sarstedt, M., Hair, J. F., & Ringle, C. M. (2022). "PLS-SEM: Indeed a Silver Bullet" – Retrospective Observations and Recent Advances. Journal of Marketing Theory & Practice, forthcoming. 
• Sarstedt, M., Hair, J. F., Ringle, C. M., Thiele, K. O., & Gudergan, S. P. (2016). Estimation Issues with PLS and CBSEM: Where the Bias Lies! Journal of Business Research, 69(10), 3998-4010.
• Sarstedt, M., Ringle, C. M., & Hair, J. F. (2022). Partial Least Squares Structural Equation Modeling. In Homburg, C., Klarmann, M., & Vomberg, A. (Eds.), Handbook of Market Research. Cham: Springer, 587–632.
• 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, 56(6), 1662-1677. 
• Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322-2347.

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).