From Newsgroup: comp.databases.theory

SmartPLS 4: A Powerful Software for PLS-SEM
SmartPLS is a software with graphical user interface for variance-based structural equation modeling (SEM) using the partial least squares (PLS) path modeling method[^6^]. 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 4 is the latest version of the software, which offers many new and useful features, such as:
CTA: Component-based Tree Analysis for identifying relevant segments of observations and explaining heterogeneity in path models.
IPMA: Importance-Performance Map Analysis for assessing the relevance of exogenous latent variables and their indicators.
PLSpredict: A prediction-oriented approach to PLS-SEM that allows testing the predictive accuracy of path models.
FIMIX: Finite Mixture Partial Least Squares for uncovering unobserved heterogeneity in path models.
POS: Prediction-Oriented Segmentation for identifying segments of observations with different prediction quality.
Bootstrapping MGA: A multigroup analysis technique based on bootstrapping for testing the significance of group differences.
Permutation MGA: A multigroup analysis technique based on permutation for testing the significance of group differences.
Path analysis and PROCESS: A tool for estimating and testing mediation, moderation, and conditional process models.
NCA: Network Component Analysis for estimating the impact of transcription factors on gene expression data.
SmartPLS 4 also has an intuitive and user-friendly interface that allows users to draw models, import data, run analyses, and export reports with ease. Users can customize their models by changing colors, borders, fonts, shapes, arrows, etc. Users can also export their charts and reports to Excel or as HTML websites. SmartPLS 4 supports various data formats, such as CSV, Excel, and SPSS. Users can also generate new datasets from their results, which is useful especially for higher-order models.
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SmartPLS 4 is available for download at https://www.smartpls.com/downloads/. Users can get started with a free 30-days trial that does not require a credit card. Users can also choose from different versions of the software, such as Student, Professional, or Enterprise. SmartPLS offers a 50% discount for all employees and students of academic institutions. SmartPLS also provides email support and personal method support via Skype for its users.
SmartPLS 4 is a powerful software for PLS-SEM that combines state of the art methods with an easy to use and intuitive graphical user interface[^4^]. It is a milestone in latent variable modeling that can help researchers and scholars to better understand measurement issues and structural patterns in their data[^4^].
In this article, we will provide a brief overview of some of the main features and benefits of SmartPLS 4. We will also show a simple example of how to use the software to estimate a PLS-SEM model.
CTA: Component-based Tree Analysis
One of the new features of SmartPLS 4 is CTA, which stands for Component-based Tree Analysis. CTA is a technique for identifying relevant segments of observations and explaining heterogeneity in path models. CTA can help researchers to answer questions such as:
Which segments of observations have different structural relationships?
Which segments of observations have different levels of endogenous latent variable scores?
Which exogenous latent variables or indicators are relevant for segmenting the observations?
CTA works by recursively splitting the observations into two groups based on a splitting criterion that maximizes the difference between the groups in terms of a target criterion. The target criterion can be either a path coefficient or an endogenous latent variable score. The splitting criterion can be either an exogenous latent variable score or an indicator value. CTA produces a tree diagram that shows the segments and the splitting criteria. CTA also provides various statistics and tests to evaluate the quality and significance of the segmentation.
IPMA: Importance-Performance Map Analysis
Another new feature of SmartPLS 4 is IPMA, which stands for Importance-Performance Map Analysis. IPMA is a technique for assessing the relevance of exogenous latent variables and their indicators for improving the performance of an endogenous latent variable. IPMA can help researchers to answer questions such as:
Which exogenous latent variables have a high impact on the endogenous latent variable?
Which exogenous latent variables have a low performance in terms of their average scores?
Which indicators have a high impact on their corresponding exogenous latent variable?
Which indicators have a low performance in terms of their average values?
IPMA works by computing two measures for each exogenous latent variable and each indicator: importance and performance. Importance is the product of the total effect and the standard deviation of the exogenous latent variable or indicator. Performance is the mean value of the exogenous latent variable or indicator. IPMA plots the importance and performance measures on a two-dimensional map that shows four quadrants: high importance-high performance, high importance-low performance, low importance-high performance, and low importance-low performance. IPMA helps researchers to identify the areas where improvement is needed and where improvement is not necessary.
PLSpredict: A Prediction-Oriented Approach to PLS-SEM
A third new feature of SmartPLS 4 is PLSpredict, which is a prediction-oriented approach to PLS-SEM that allows testing the predictive accuracy of path models. PLSpredict can help researchers to answer questions such as:
How well does the PLS-SEM model predict new or holdout data?
How does the PLS-SEM model compare to alternative models in terms of predictive accuracy?
Which model specifications (e.g., weighting scheme, algorithm) lead to better predictions?
PLSpredict works by splitting the data into two subsets: estimation and validation. The estimation subset is used to estimate the PLS-SEM model and obtain the path coefficients and factor scores. The validation subset is used to test the predictive accuracy of the model by comparing the predicted and observed values of the endogenous latent variables or indicators. PLSpredict provides various measures and tests to evaluate the predictive accuracy, such as root mean squared error (RMSE), mean absolute error (MAE), standardized root mean squared residual (SRMR), R-squared, Q-squared, etc. PLSpredict also allows comparing different models or model specifications by using cross-validation or holdout validation techniques.
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