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

• 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

• 大約 200MB 的可用硬碟空間
• 至少2GB的RAM
• Windows 或 MacOSX 的作業系統

• Fixed: Resolved PLS Predict issues with binary constructs and standardized PLS-SEM results for accurate predictions.
• Fixed: Pairwise regression with intercept problems affecting unstandardized PLS-SEM and PLS Predict.
• Fixed: MIMIC model validation issues in CB-SEM.
• Improved: Eliminated indicator duplication from "create data file" function, streamlining data management.
• Improved: Goodness-of-fit evaluation in CB-SEM.
• Improved: Added simple validations to identify model identification problems in CB-SEM models.
• Improved: Enhanced validation in data import dialog for covariance data files, ensuring smoother data handling.
• Improved: Additional sample projects.
• Improved: Translations

Version 4.0.9.5, released 2022-06-23

• Fixed: Minor CB-SEM and CBSEM-Bootstrapping calculation issues.
• Fixed: Minor translation issues.
• Improved: Updated internal libraries.
• Improved: Added option to exclude memory consumptive per-sample results from Bootstrapping and Permutation reports.
• Improved: Validation and calculation of second order constructs in CB-SEM models.
• Improved: Changed defaults for creating data files from reports to include other columns.
• Improved: Additional and extended sample projects.

Version 4.0.7.8, released 2022-08-22

• Improved: Documentation.
• Improved: Algorithm dialog.
• Improved: Speed of all algorithms.
• Improved: Fundamentally renewed and improved results reports.
• Improved: Charts and figures from model results.
• Improved: Additional results for descriptive statistics and many algorithms.
• Improved: Performance of Excel report generation.
• Improved: Generation of data groups.
• Improved: Bootstrapping with fixed seed option.
• Improved: Higher-order models using the two-stage approach, since the first-stage construct scores can now be stored in a data set within SmartPLS.
• Fixed: License issues.

Version 3.3.9, released 2022-03-28

• Fixed: Issues with trial.

Version 3.3.8, released 2022-03-27

• Fixed: Minor stability issues.
• Improved: Upgraded internal libraries.

Version 3.3.7, released 2022-01-23

• Improved: Updated translation files.
• Improved: Upgraded internal libraries.

Version 3.3.6, released 2022-01-19

• Improved: Improved performance of several algorithms.
• Improved: Upgraded internal libraries.

Version 3.3.5, released 2021-12-20

• Fixed: Deadlocks occurred on some computers during save operations.

Version 3.3.4, released 2021-12-16

• Fixed: Log4J Vulnerability fix. Log4J updated to a patched version (2.16.0)
• Improved: Upgraded internal libraries.

Version 3.3.3, released 2021-01-11

• Improved: Upgraded internal libraries.
• Improved: GUI adjustments, for a better display under Windows 10 and MacOSX Big Sur.
• Improved: Indicators that contain non-numeric values are marked with an exclamation mark + tooltip in the datafile editor.
• Fixed: Display errors and refresh problems under MacOSX Big Sure.
• Fixed: Performance issue with PLSC algorithm.
• Fixed: Installation problem in the Persian language area.

SmartPLS 是所有 PLS-SEM 分析的主要解決方案 - 適合初學者和專家

• 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