SMART PLS: SEM WORKSHOP
Takeaway from Workshop:
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Understanding PLS-SEM Basics:
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PLS-SEM is a variance-based method suited for complex predictive models, especially when data is non-normal or sample sizes are small to medium.
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It’s a powerful tool for building and testing relationships between latent constructs (unobservable variables) and measured indicators.
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Model Specification:
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Learn to define both the measurement model (linking constructs to their indicators) and the structural model (linking constructs to each other).
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Emphasis on correct specification of formative vs. reflective indicators and the impact of this on model outcomes.
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Path Modeling and Bootstrapping:
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Bootstrapping (a resampling technique) is key in PLS-SEM to determine the significance of path coefficients.
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The workshop usually covers how to interpret path coefficients, weights, and loadings, as well as model fit and reliability.
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Evaluation of Model Quality:
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Learn to evaluate reliability (e.g., Cronbach’s alpha and composite reliability) and validity (e.g., AVE for convergent validity, Fornell-Larcker criterion for discriminant validity).
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Common model fit metrics in PLS-SEM include SRMR (Standardized Root Mean Residual) and NFI (Normed Fit Index).
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Predictive Power and Importance-Performance Map Analysis (IPMA):
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Workshops often cover how to use IPMA in PLS-SEM to assess the importance of constructs for prediction and the performance of constructs in the model, aiding in actionable insights.
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Software Training:
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Hands-on training on software like SmartPLS or WarpPLS, where you learn to set up, run, and interpret SEM models within a user-friendly interface.
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Interpreting Results and Reporting:
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Best practices for reporting PLS-SEM results, including guidelines on presenting coefficients, significance levels, and fit indices, as well as common pitfalls to avoid in interpretation.
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