![]() ![]() The intuitive graphical user interface lets your create pretty path models with SmartPLS in minutes.ĭo you want to stay on top of your files? Our Project Explorer helps you organizing your data and models efficiently in separate projects. PLS Predict: A technique to determine the predictive quality of the PLS path model.Prediction-oriented segmentation (POS): An approach to identify groups of data.Finite mixture (FIMIX) segmentation: A latent class approach which allows identifying and treating unobserved heterogeneity in path models.Confirmatory tetrad analysis (CTA): A statistical technique which allows for empirical testing the measurement model setup.Nonlinear relationships: Estimation of quadratic effects and their bootstrap-based significance testing.Moderation: Estimation of interaction effects and their bootstrap-based significance testing.Mediation: Estimation of indirect effects and their bootstrap-based significance testing.PLS multi-group analysis (MGA): Analyses the difference and significance of group-specific PLS path model estimations.Importance-performance map analysis (IPMA).Bootstrapping and the use of advanced bootstrapping options.Weighted PLS (WPLS), weighted OLS (WOLS) and weighted consistent PLS (WPLSc).Ordinary least squares (OLS) regression based on sumscores.Partial least squares (PLS) path modeling. ![]()
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