SmartPLS 4: PLS Model Creation and Interpretation of the Results

SmartPLS4: PLS Model Creation and Interpreting Results
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Welcome to our comprehensive tutorial on performing statistical analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM). In this video, we dive into the world of Smart PLS analysis, where we'll cover the key steps to create models and interpret results.
Key Points:
• We recap the various analyses available in Smart PLS, such as PLS SCM, regression, and CBSCM, setting the stage for our exploration.
• Our workspace centers around the creation of a new project named "PLSEM Training," with a dataset titled "Customer Satisfaction" containing 201 samples in CSV format.
• Watch as we demonstrate how to craft a model by selecting indicators for each observed variable, like "CS1" and "CS2," forming a construct for customer satisfaction.
• Indicators for other constructs like "PQ" (Perceived Quality) and "CE" (Customer Expectation) are effortlessly grouped, resulting in intuitive constructs.
• We delve into path coefficients, crucial in measuring the strength of relationships between latent variables. The positive impacts of "CE" and "PQ" on "CS" are evident but not overwhelmingly strong.
• The discussion extends to outer loadings, a measure of relationships between indicators and latent variables. Notably, indicators like "CS1" and "PQ1" display strong relationships.
• Reliability and validity are explored, assessing the internal consistency of scales. Cronbach alpha values indicate good reliability for constructs, but we emphasize the importance of examining average variance extracted (AVE) as well.
• Discriminant validity is confirmed through Fornell-Larcker criterion, supporting the distinctiveness of latent variables.
• Collinearity statistics and model fit measures like SRMR, RMSEA, GFI, and NFI are scrutinized, revealing the model's fit to the data.
• We touch upon R-squared values and composite reliability, crucial metrics in understanding how much variance in dependent variables is explained by independent variables.
• The video concludes by spotlighting the values in the model, including regression weights and loadings, giving viewers an in-depth understanding of how different elements interplay.
Join us in unraveling the world of PLS-SEM analysis as we guide you through creating models, analyzing relationships, and comprehending fit indices to draw meaningful insights from your data.
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Keywords:
SmartPLS4 Analysis, PLS-SEM Analysis, Creating Models in SmartPLS4, Interpreting Results in SmartPLS4, SmartPLS4 Tutorial, PLS-SEM Tutorial, Structural Equation Modeling Tutorial, Partial Least Squares Tutorial, Customer Satisfaction Analysis, Perceived Quality Analysis, Customer Expectation Analysis, Path Coefficients Analysis, Outer Loadings Analysis, Reliability Analysis, Validity Analysis, Discriminant Validity Analysis, Collinearity Statistics Analysis, Model Fit Measures Analysis, R-squared Values Analysis, Composite Reliability Analysis, SmartPLS4 Software, PLS-SEM Software, Structural Equation Modeling Software, Partial Least Squares Software, Customer Satisfaction
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[@Draggy-And-Addie]

The first video to actually explain the logic behind the statistic. Very good make video, thank you very much 😊🙏🏻

[@MyEasyStatistics]

Thank you 👍

[@swathireddy8636]

Perfect interpretation of the PLS Model Sir. Thank you

[@jamalabdikarim3760]

Well explained 👏
Short & sharp 👌
Thanks 😊
Thanks so much for your devotion of time & effort

[@afreenfatima3989]

Very nicely explained

[@user-it2ws4re7t]

Excellent interpretation.