PLS Path Modeling Crash Course

hosted by PLS School

University of Twente, Campushotel Drienerburght

Start: Friday, 28 Apr 2017 09:00

End: Friday, 28 Apr 2017 18:00

  • Contents

    This intensive one-day seminar provides a profound introduction to variance-based structural equation modeling and PLS path modeling. Participants will learn how to apply variance-based structural equation modeling by means of the software ADANCO 2.0. The instructor will make use of several examples applications from business research.

    This seminar covers the fundamental issues in variance-based structural equation modeling and PLS path modeling:

    • theoretical underpinning of variance-based structural equation modeling, 
    • different forms of construct measurement (reflective, causal-formative, composite),
    • assumptions and data requirements,
    • creating identified structural equation models,
    • model assessment incl. overall goodness of fit testing,
    • significance tests by means of the bootstrap,
    • modeling of mediating effects,
    • second-order constructs,
    • interpreting and reporting the outcomes of PLS path modeling analyses.

    Moreover, this seminar will discuss recent developments in variance-based structural equation modeling and PLS path modeling, including:

    • consistent PLS (PLSc) to estimate common factor models within the PLS path modeling framework,
    • overall goodness of fit: goodness of fit tests and the SRMR for composites and common factor models, and
    • a new criterion for discriminant validity: The heterotrait-monotrait ratio of correlations (HTMT).

    Participants will receive a 1-year fully functional version of ADANCO 2.0 (Professional Edition).

  • Who should attend?

    This seminar is designed for practicing business professionals and for full-time faculty and students who are engaged in, or interested in, current techniques to perform Structural Equation Modeling (SEM) using PLS Path Modeling. To receive the scientist discount, a participant must currently be a faculty member or a student at a college or university. Knowledge about uni- and bivariate analysis is recommendable. A basic knowledge of SEM techniques is helpful, but not required. 

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