SmartPLS Professional 3.3.2 x64 full license (Windows / MacOS)

SmartPLS Professional 3.3.2 free download

SmartPLS

SmartPLS is a software with graphical user interface for variance-based structural equation modeling (SEM) using the partial least squares (PLS) path modeling method. Besides estimating path models with latent variables using the PLS-SEM algorithm, the software computes standard results assessment criteria (e.g., for the reflective and formative measurement models, the structural model, and the goodness of fit) and it supports additional statistical analyses (e.g., confirmatory tetrad analysis, importance-performance map analysis, segmentation, multigroup). Since SmartPLS is programmed in Java, it can be executed and run on different computer operating systems such as Windows and Mac.

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.

SmartPLS is the workhorse for all PLS-SEM analyses

  • 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

Download SmartPLS Professional 3.3.2 full license

SmartPLS Professional 3.3.2 x64, SmartPLS Professional 3.3.0 x86, SmartPLS Pro 3.3.2 macos, SmartPLS Pro 3.3.2 macos crack, SmartPLS Pro 3.3.2 crack

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Download SmartPLS Professional 3.3.2 windows / macos
Download SmartPLS Professional 3.3.2 windows / macos

Release Notes

Version 3.3.2, released 2020-04-21
  • Improved: Upgraded internal libraries.
Version 3.3.1, released 2020-04-21
  • Improved: Upgraded internal libraries.

Version 3.3.0, released 2020-04-11

  • Improved: Updated embedded Java version
  • Improved: Mac OS installer includes Java
Version 3.2.9, released 2020-01-08
  • Feature: Descriptive statistics for latent variable scores, residuals, and prediction errors
  • Feature: Preferences option to hide leading zeros in the results output
  • Feature: Implementation of consistent PLS-MGA and permutation
  • Localization: Translation to Urdu
  • Localization: Update of the existing language versions
  • Improved: Added specific indirect effects to the PLS-MGA and permutation results
  • Improved: PLS-MGA two-tailed p-values
  • Improved: Added intercepts for unstandardized coefficients IPMA results
  • Improved: Software speed and performance
  • Improved: Blindfolding missing data treatment (i.e., it does not support pairwise deletion anymore)
  • Improved: Added construct level results (e.g., R²) in the modeling window of bootstrapping
  • Improved: Descriptive statistics for indicator data (e.g., group-specific outcomes)
  • Improved: Excel export of results
  • Fixed: PLSpredict LV predictions
  • Fixed: Changing the sort order in the result tables (e.g., by variable name)
  • Fixed: Pairwise deletion to treat missing values
  • Fixed: Highlighting of p-values for PLS-MGA results
Version 3.2.8, released 2018-11-22
  • Feature: Implementation of predictive model selection criteria for PLS and PLSc
  • Feature: New unique case identifier (i.e., a fixed number for each observation in the dataset, which is useful, for example, when using the casewise deletion option, multigroup or segmentation analyses)
  • Improved: Inclusion of all specific indirect effects for the mediator analysis
  • Improved: Color highlighting of significant p-values in bootstrapping, permutation, and multigroup analysis (MGA)
  • Improved: Results presentation for bootstrapped fit indices
  • Improved: Software activation procedure
  • Localization: Translation to Korean (100%)
  • Localization: Translation to Malay (100%)
  • Localization: Translation to Polish (>20%)
  • Localization: Translation to Rumanian (>50%)
  • Localization: Correction of incompletions and little issues in the different language translations
  • Fixed: Residual correlation instead of covariance for PLSc
  • Fixed: Bootstrapping sometimes stopped when using “complete bootstrapping” with repeated indicators due to problems in the fit calculation for these models
  • Removed: Lohmöller’s initial weighting scheme (complexity reduction)
  • Removed: Bootstrap sign change options (i.e., individual sign changes, construct level sign changes; complexity reduction)
  • Removed: Double bootstrapping (increased bootstrapping performance; complexity reduction)
  • Removed: d_g1 version of the exact fit measures (complexity reduction)

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