Estimated as linear combinations of their indicators (i.e., they are determinate)
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Used for predictive purposes
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Can be used as input for subsequent analyses
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Not affected by data limitations and inadequacies |
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Parameter estimates
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Structural model relationships are generally underestimated, and measurement model relationships are generally overestimated compared to solutions obtained using data from common factor models
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Unbiased and consistent when estimating data from composite models
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High levels of statistical power compared to alternative methods, such as CB-SEM and multiple regression with sum scores
The concept of fit – as defined in CB-SEM – does not apply to PLS-SEM. Efforts to introduce model fit measures have generally proven unsuccessful
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Evaluation of the measurement models
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Reflective measurement models are assessed on the grounds of indicator reliability, internal consistency reliability, convergent validity, and discriminant validity
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Formative measurement models are assessed on the grounds of convergent validity, indicator collinearity, and the significance and relevance of indicator weights
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Evaluation of the structural model
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Collinearity among sets of predictor constructs
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Significance and relevance of path coefficients
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Criteria available to assess the model’s in-sample (i.e., explanatory) power and out-of-sample predictive power (PLSpredict)
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Additional analyses
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Methodological research has substantially extended the original PLS-SEM method by introducing advanced modeling, assessment, and analysis procedures. Some examples include: