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WHY SMARTPLS?

  • PLS makes fewer demands regarding sample size than other methods
  • PLS does not require normal-distributed input data. Normal distributions are usually desirable, especially when working with CB-SEM. In contrast, PLS-SEM generally makes no assumptions about data distributions
  • PLS can be applied to complex structural equation models with a large number of constructs.
  • PLS is able to handle both reflective and formative constructs.
  • PLS is better suited for theory development than for theory
    testing.
  • PLS is especially useful for prediction.
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