Online training: PLS Path Modeling approach, Dec 2015 (in French)

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The PLS Path Modeling approach is an advanced tool that lets you analyse multiple tables using the Partial Least Square approach. It is conceptually similar to the structural equation modeling with latent variables. It is typically used in marketing, psychology and social sciences. 

Subscribe to this 1 day session on the PLS Path Modeling approach.

This is an online session. All you need is a computer with an internet connexion!

This session is over

The PLS Path Modeling approach, 1 day training

This training will let you understand the PLS (Partial Least Square) Path Modeling approach. This method lays on the intersection between structural equation modeling involving latent variables and multiple table analysis methods. Those two angles will be tackled during this training. The training is followed by practical application exercises and tests to be submitted online for correction.

Prerequisites

Basic knowledge in statistics

Objectives

  • Understand the PLS Path Modeling (PLS-PM) approach
  • Learn how to verify validity assumptions and know when to apply the PLS-PM approach
  • Learn how to use software to apply the PLS approach (XLSTAT-PLSPM)
  • Learn how to read and interpret the PLS-PM outputs

Training program

  • Introduction to structural equation models with latent variables and to multi-table analyses
  • Introduction to the PLS approach (history and foundations)
  • The PLS algorithm
  • Model validation methods
  • Introduction to advanced features
    • Moderating effects
    • Multi-group comparisons
    • Segmentation
  • Applications on real data

Price

Company/Private

€290.00
per participant

Dates

Start at:

End at:

Language

French

Location


Trainers' profiles


Emmanuel Jakobowicz

Founder of Stat4decision

With more than 15 years of experience in data-oriented projects, Emmanuel Jakobowicz is passionate about data science and entrepreneurship. He founded Stat4decision to propose a new way of assisting people in exploring their data. He owns a PhD in applied statistics and computer science. He also is an engineer in mathematics specialized in machine learning. His work experience includes research and development at Electricité de France and software development at Addinsoft XLSTAT where he was a partner, CTO, Chief Scientist, consultant and trainer for large companies, research institutes and universities.

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