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SMART PLS: SEM WORKSHOP

Takeaway from Workshop:

  • Understanding PLS-SEM Basics:

    • 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.

    • It’s a powerful tool for building and testing relationships between latent constructs (unobservable variables) and measured indicators.

  • Model Specification:

    • Learn to define both the measurement model (linking constructs to their indicators) and the structural model (linking constructs to each other).

    • Emphasis on correct specification of formative vs. reflective indicators and the impact of this on model outcomes.

  • Path Modeling and Bootstrapping:

    • Bootstrapping (a resampling technique) is key in PLS-SEM to determine the significance of path coefficients.

    • The workshop usually covers how to interpret path coefficients, weights, and loadings, as well as model fit and reliability.

  • Evaluation of Model Quality:

    • 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).

    • Common model fit metrics in PLS-SEM include SRMR (Standardized Root Mean Residual) and NFI (Normed Fit Index).

  • Predictive Power and Importance-Performance Map Analysis (IPMA):

    • 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.

  • Software Training:

    • 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.

  • Interpreting Results and Reporting:

    • 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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