Removed: Lohmöller’s initial weighting scheme (complexity reduction).Fixed: Bootstrapping sometimes stopped when using “complete bootstrapping” with repeated indicators due to problems in the fit calculation for these models.Fixed: Residual correlation instead of covariance for PLSc.Localization: Correction of incompletions and little issues in the different language translations.Localization: Translation to Rumanian (>50%).Localization: Translation to Polish (>20%).
Localization: Translation to Malay (100%).Localization: Translation to Korean (100%).Improved: Software activation procedure.Improved: Results presentation for bootstrapped fit indices.Improved: Color highlighting of significant p-values in bootstrapping, permutation, and multigroup analysis (MGA).
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).Feature: Implementation of predictive model selection criteria for PLS and PLSc.The software computes standard results assessment criteria (e.g., for the reflective and formative measurement models and the structural model, including the HTMT criterion, bootstrap based significance testing, PLSpredict, and goodness of fit) and it supports additional statistical analyses (e.g., confirmatory tetrad analysis, higher-order models, importance-performance map analysis, latent class segmentation, mediation, moderation, measurement invariance assessment, multigroup analysis). Since SmartPLS is programmed in Java, it can be executed and run on different computer operating systems such as Windows and Mac. Users can estimate models with their data by using basic PLS-SEM, weighted PLS-SEM (WPLS), consistent PLS-SEM (PLSc-SEM), and sumscores regression algorithms.
SmartPLS is a software with graphical user interface for variance-based structural equation modeling (SEM) using the partial least squares (PLS) path modeling method.
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