“SmartPLS 3 is a milestone in latent variable modeling. It combines state of the art methods (e.g., PLS-POS, IPMA, complex bootstrapping routines) with an easy to use and intuitive graphical user interface.”

Running your SmartPLS analyses is fun and hassle free.

Get deep insights into your data easily!

The powerful modeling environment lets you create a path model in minutes.

The project manager helps you to keep track of all your analyzes and files.

Customize your model with colors, borders and fonts to underline your ideas individually!

In-built explanations of the algorithms and meaningful defaults give you an easy start into the PLS-SEM world.

Well-organized reports provide full insights into your results.

Save your results permanently as HTML report or Excel file.

Create data groups to run multi group analyses effortlessly.

Create interaction terms and run moderator analyses without any problems.

SmartPLS is the workhorse for all PLS-SEM analyses – for beginners as well as experts

Here is our (constantly growing) list of all available calculation methods. Relevant innovative algorithms will also be made available in SmartPLS within a short time. We promise.

  • Partial least squares (PLS) path modeling
  • Ordinary least squares (OLS) regression based on sumscores
  • Consistent PLS (PLSc)
  • Weighted PLS (WPLS), weighted OLS (WOLS) and weighted consistent PLS (WPLSc)
  • Bootstrapping and the use of advanced bootstrapping options
  • Blindfolding
  • Importance-performance map analysis (IPMA)
  • PLS multi-group analysis (MGA): Analyses the difference and significance of group-specific PLS path model estimations
  • Higher-order Models
  • Mediation: Estimation of indirect effects and their bootstrap-based significance testing
  • Moderation: Estimation of interaction effects and their bootstrap-based significance testing
  • Nonlinear relationships: Estimation of quadratic effects and their bootstrap-based significance testing
  • Confirmatory tetrad analysis (CTA): A statistical technique which allows for empirical testing the measurement model setup
  • Finite mixture (FIMIX) segmentation: A latent class approach which allows identifying and treating unobserved heterogeneity in path models
  • Prediction-oriented segmentation (POS): An approach to identify groups of data
  • PLS Predict: A technique to determine the predictive quality of the PLS path model
  • Prediction-oriented model selection



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