The main force of PLS-SEM analysis is SmartPLS, SmartPLS software is widely used in fields such as management, marketing, organizational behavior, and information systems. Its principle is to use Partial Least Square (PLS) for statistical analysis.

In SmartPLS, advanced analysis. This software can perform modeling in a convenient environment in less than a minute. In addition, by being able to manage projects, you can effectively manage them. You can also customize the built-in model using existing colors, borders, and fonts. This software changes your project.

SmartPLS is a milestone in latent variable modeling. It combines good methods such as PLS-POS, IPMA, and complex bootstrap routines with an easy-to-use and intuitive graphical user interface.

Here is our (constantly growing) list of all available calculation methods. Related innovative algorithms will also be provided in SmartPLS in the short term. We promise.

1. Partial Least Squares (PLS) Path Modeling
2. Ordinary Least Squares (OLS) Regression Based on Sum Score
3. Consistency PLS (PLSc)
4. Weighted PLS (WPLS), Weighted OLS (WOLS), and Weighted Consistent PLS (WPLSc)
5. Guide and use advanced boot options
6. Blindfold
7. Importance Performance Graph Analysis (IPMA)
8. PLS Multi Group Analysis (MGA): Analyzing the Differences and Significance of PLS Path Model Estimates Specific to Groups
9. Higher-order model
10. Intermediary: Estimation of Indirect Effects and Its Significance Test Based on Bootstrap
11. Moderation: Estimation of Interaction Effects and Its Significance Test Based on Bootstrap
12. Nonlinear relationship: estimation of quadratic effects and its significance test based on bootstrap
13. Confirmatory Quaternion Analysis (CTA): A statistical technique that allows for empirical testing of measurement model settings
14. FIMIX segmentation: a potential class approach that allows for the identification and handling of unobserved heterogeneity in path models
15. Prediction oriented segmentation (POS): a method for identifying data sets
16. PLS prediction: a technique for determining the prediction quality of PLS path models
17. Model selection for prediction

Easily delve into your data!

1. A powerful modeling environment allows you to create path models in minutes.
2. The project manager can help you track all analyses and documents.
3. Customize your model with colors, borders, and fonts to individually emphasize your ideas!
4. The built-in explanations and meaningful default values of the algorithm allow you to easily enter the PLS-SEM world.
5. A well-organized report provides comprehensive insights into the results.
6. Save the results permanently as an HTML report or Excel file.
7. Create data groups to easily run multi group analysis.
8. Create interactive terms and run moderator analysis without any issues.