2Day Workshop
2Day Workshop on PLS Path Modeling (June 15th16th, 2017)
After a return to the origins of structural equation modeling (SEM), the PLSPM algorithm will be presented. A methodology to interpret results will then be suggested on the basis of real life cases. The training session will be illustrated by applications using XLSTAT.
DAY 1: Algorithm, Estimation and Practice

Introduction to Structural Equation Modeling
 Covariancebased and Componentbased apporaches
 PLS Path Modeling Algorithm

PLS Path Modeling algorithm for model estimation:
 Measurement (Outer) Model Specification and Estimation Modes (Reflective vs. Formative  Mode A and Mode B)
 Structural (Inner) Model Specification and Estimation Schemes (Centroid  Factorial  Path Weighting Schemes)
 PLS algorithm for computing Latent Variable Scores

PLS Algorithm for the case of one and two blocks:
 Principal Component Analysis, Tucker°Øs Interbattery Analysis, Canonical Correlation Analysis, PLS Regression, Redundancy Analysis
 Hierarchical PLSPM and the superblock option

PLS Path Modeling algorithm for model estimation:
 Introductory Tutorial on XLSTATPLSPM with Case Studies
 Model Specification: exploring graphical interface features
 Model Estimation: measurement and structural options
 Scaling Latent Variable Scores: standardized vs. normalized
 Output Retrieval (graphical and tabular) and Interpretation:
 Outer weights, normalized weights, standardized loadings
 Path coefficients (direct, indirect and total effects), R^{2}, standardized path coefficients, contribution to R^{2}, simple and partial correlations
 Latent variables scores (casewise values, summary statistics)
DAY 2: Model Assessment, Improvement and Advances in PLSPM
 Model Assessment and Improvement: Diagnostics and Solutions
 Convergent validity: composite reliability, eigenvalues, condition number, critical value, weights and loadings, average variance extracted (AVE), communality
 Discriminant validity: crossloadings vs. loadings, latent variables correlations vs. AVE
 Predictive relevance: Redundancy, R^{2}, Absolute and Relative Goodness of Fit (GoF), Effect Size f^{2}
 Statistical significance:? Bootstrapping, Jackknifing, ttest, Ftest, critical ratios
 Crossvalidation: Blindfolding, CVCommunality, CVRedundancy
 Handling Missing Data:? Lohm?ller°Øs option, Impact on Latent Variable Scores
 Continuous Moderating Effects
 Why & How to Investigate Moderating Effects?
 Discrete (categorical) vs. continuous moderator variable
 Methods for Assessing Interaction Effects: ProductIndicator, TwoStage, Hybrid, Orthogonalizing
 Interaction with Formative Indicators
 Centering or Standardizing the Indicators
 Choosing the appropriate method
 Additional Methods for Non Linear Relations: Measurement and Structural Level

Discrete Moderating Effects: MultiGroup Comparison
 Bootstrap parametric approaches: ttest, empirical confidence intervals
 Permutationbased comparisons

Mediating Effects
 Mediator vs. Confounder
 Causal Steps for Testing Mediation
 Methods for Assessing Mediating Effects: Sobel, resampling
 Mediator versus Moderator
 Moderated Mediation

Handling Multidimensionality
 Detection of Block Multidimensionality
 Mode PLS for the Measurement Model: a continuum from Mode A to Mode B
 PLS Regression to cope with multicollinearity in the Structural Model
 Uncovering Segments
 Definition of Unobserved Heterogeneity
 REBUSPLSPM
 This is the only Workshop that provides training on the most comprehensive PLS software package  XLSTATPLSPM (http://www.xlstat.com/en/products/xlstatplspm/). The detailed agenda and registration procedure are available at:https://www.xlstat.com/en/training/a2dayworkshoponplspathmodelingviaxlstatsoftwarechina