{"id":2701,"date":"2026-08-10T08:58:23","date_gmt":"2026-08-10T08:58:23","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/udemy-essentials-of-smartpls-4-for-research-and-data-analysis-2025-3\/"},"modified":"2026-08-10T08:58:23","modified_gmt":"2026-08-10T08:58:23","slug":"udemy-essentials-of-smartpls-4-for-research-and-data-analysis-2025-3","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/udemy-essentials-of-smartpls-4-for-research-and-data-analysis-2025-3\/","title":{"rendered":"Udemy \u2013 Essentials of SmartPLS 4: For Research and Data Analysis 2025-3"},"content":{"rendered":"
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Description<\/span><\/h2>\n

Essentials of SmartPLS 4 course: For Research and Data Analysis. The PLS-SEM technique is a composition-based approach to SEM that aims to maximize the explained variance of the dependent constructs in the path model. Researchers and practitioners use PLS-SEM, especially when conducting studies on success factors and sources of competitive advantage. Compared to other SEM techniques, PLS-SEM allows researchers to estimate very complex models with many constructs and indicator variables. In addition, PLS-SEM allows the estimation of reflective and shaping constructs and generally offers great flexibility in terms of data requirements. The purpose of PLS-SEM is to explain variances (the predictive nature of the method) instead of explaining covariances (theory testing through covariance-based SEM, CB-SEM). If the assumptions of CB-SEM are violated and the proposed cause-and-effect relationships are not sufficiently investigated, the application of the PLS-SEM method is highly desirable. This course will include the following:<\/span><\/p>\n

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