Partial Least Squares SEM Using SmartPLS: Foundations and Advanced Topics

hosted by Prof. Dr. Christian Ringle and the Northern Institute of Technology (NIT)

Northern Institute of Technology (NIT)

Start: Wednesday, 20 Nov 2019 09:00

End: Saturday, 23 Nov 2019 17:00

  • Course Objectives

    Course Language: English!

    This four-day course introduces participants to the state-of-the-art of partial least squares structural equation modeling (PLS-SEM) using the SmartPLS 3 software. The first two days of the course 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 3 software. The instructors will make use of several examples and exercises. Starting on the third day and continuing on the fourth day, the course covers extensions and new developments to PLS-SEM.

    PLS-SEM 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-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.

    PLS-SEM has recently received considerable attention in a variety of disciplines, including

    • marketing (Hair et al 2011, according to Google scholar the most-cited article ever published in Journal of Marketing Theory and Practice; Hair et al. 2012a, according to Google scholar the most-cited Journal of the Academy of Marketing Science article since 2012),
    • strategic management (Hair et al. 2012b, according to Google scholar the most-cited Long Range Planning article since 2012),
    • management information systems (Ringle et al. 2012, according to Google scholar the fifth-most cited MIS Quarterly article since 2012), and
    • business administration in general (Sarstedt et al. 2016, according to Google scholar the second-most cited article published in the Journal of Business Research since 2016).

    Also, several review studies across different disciplines substantiate that PLS-SEM has become a standard method in the multivariate analysis toolbox: e.g., Human Resource Management (Ringle et al., 2018), Hospitality Research (Ali et al., 2018), Information Systems Research (Hair et al., 2017), Management Accounting (Nitzl, 2016), International Business (Richter et al., 2016), Tourism (do Valle and Assaker, 2016), Psychology (Willaby et al., 2015), Supply Chain Management (Kaufmann and Gaeckler, 2015), Family Business (Sarstedt et al., 2014), Operations Management (Peng and Lai, 2012), Strategic Management (Hair et al., 2012), Marketing (Hair et al., 2012), Management Information Systems (Ringle et al., 2012), Accounting (Lee et al., 2011), and International Marketing (Henseler et al., 2009).

    Take a look at recent PLS-SEM publications!

  • Learning Outcomes

    This course has been designed to familiarize participants with the potentials 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 into the PLS-SEM approach the nature of causal modeling, analytical objectives, some statistics), (2) the evaluation of measurement results, and (3) complementary analytical techniques. More specifically, participants will understand the following topics:

    • Model development and fundamentals of PLS-SEM and latent variable models
    • PLS path model estimation including the weighted PLS (WPLS) and consistent PLS (PLSc)
    • Assessment and reporting of measurement and structural model results including Bootstrapping
    • New criteria for model assessment such as HTMT for discriminant validity, goodness of fit (e.g., SRMR), and model comparison & model selection
    • Prediction-oriented results analysis including Blindfolding and PLSpredict
    • Endogeneity in PLS-SEM
    • Importance-performance map analysis (IPMA) of PLS-SEM results
    • Higher-order constructs (e.g., second-order models)
    • Mediating effects
    • Moderating effects (interaction effects)
    • Nonlinear relationships (quadratic effects)
    • Measurement invariance testing (MICOM)
    • Multigroup analysis (MGA)
    • Segmentation and uncovering unobserved heterogeneity by FIMIX-PLS and PLS-POS

    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. A basic knowledge of multivariate statistics and SEM techniques is helpful, but not required.

    Participants will receive a certificate of attendance. Universities usually acknowledge this course with a workload of 6 ECTS.

  • Teaching and learning methods

    • The course is based on the PLS-SEM textbooks:
                               Hair, J. F., Hult, G. T. M., Ringle, C. M., and Sarstedt, M. 2017. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). 2nd edition. Thousand Oaks, CA: Sage.
                      Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. 2018. Advanced Issues in Partial Least Squares Structural Equation Modeling (PLS-SEM). Thousand Oaks, CA: Sage.
    • Presentations: The session will cover theory and its application.
    • Computer exercises using the latest SmartPLS 3 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.
           Course participants will receive a 60-days fully
    functional version of the software SmartPLS 3.

Short event description

This four-day course introduces participants to the state-of-the-art of partial least squares structural equation modeling (PLS-SEM) using the world-leading PLS-SEM software SmartPLS.


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