{"id":9634,"date":"2022-11-03T17:07:16","date_gmt":"2022-11-03T17:07:16","guid":{"rendered":"https:\/\/researchwithfawad.com\/?page_id=9634"},"modified":"2022-12-07T14:19:24","modified_gmt":"2022-12-07T14:19:24","slug":"reflecrtive-formarive-model-using-smartpls4","status":"publish","type":"page","link":"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/smartpls4-tutorial-series\/reflecrtive-formarive-model-using-smartpls4\/","title":{"rendered":"Reflective-Formative Higher-Order Model using SmartPLS4"},"content":{"rendered":"\t\t
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Reflective-Formative Models in SmartPLS4 <\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Reflective-Formative Model in SmartPLS4<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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A number of research papers and thesis require the models to have an increased level of complexity. Hence, scholars often try to develop models with multiple higher-order constructs in a single model. Those models may have reflective-reflective, reflective-formative, and lower-order constructs in a single model.<\/p>

Building such models can be complex. In this and the next series of tutorials, I guide research scholars on how to build such complex hierarchical component models in SmartPLS4.<\/p>

However, before scholars watch this video, it is recommended that they have knowledge of higher-order constructs and how they are analyzed. To learn more, please visit the following pages<\/p>

Concept of Higher-Order Constructs<\/a><\/p>

Reflective vs Formative Indicators<\/a><\/p>

Validating Formative Indicators<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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How to Analyze Reflective-Formative Model in SmartPLS4<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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A Reflective-Formative model is one where the first order has reflective indicators while in the second order the dimensions are formative. Here is how it looks<\/p>

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Two Stage Approach<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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Researchers have proposed the two stage approach as an alternative to the repeated indicators approach. In fact, research has proposed two versions of the two-stage approach,<\/p>

  1. The embedded two-stage approach and<\/li>
  2. The disjoint two-stage approach, which slightly differ in their model specification in both stages.<\/li><\/ol>

    For example, while the embedded approach models the entire higher-order construct in its first stage, the disjoint approach initially only draws on the lower-order components (hence the denominations \u201cembedded\u201d and \u201cdis- joint\u201d).<\/p>

    As both versions of the two-stage approach lead to similar results, there is no compelling reason for preferring one over the other. In the following, we introduce first introduce the dis-joint two-stage approach for reflective-formative type higher-order constructs.<\/p>

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    Disjoint Two Stage Approach<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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    The disjoint two-stage approach differs from the embedded two-stage approach in the specification of both stages.<\/p>

    Rather than using the repeated indicators approach in stage one, the disjoint two-stage approach considers only the lower-order components of the higher-order construct (i.e., without the higher-order component) in the path model.<\/p>

    These are directly linked to all other constructs that the higher-order construct is theoretically related to.<\/p>

    To execute the disjoint two-stage approach, researchers then need to save the construct scores, but only those of the lower order components.<\/p>

    In stage two, these scores are then used to measure the higher-order construct. However, different from the embedded two-stage approach, all other constructs in the path model are estimated using their standard multi-item measures as in stage one (e.g., Y 5 in Fig).<\/p>

    \"Disjoint<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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    Validating Higher Order Constructs<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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    When evaluating higher-order models, the same model evaluation criteria generally apply as for any PLS-SEM analysis (Chin, 2010b). However, higher-order constructs need to consider two additional measurement models for which the evaluation criteria apply:<\/p>

    1. The measurement models of the lower-order components, and<\/li>
    2. The measurement model of the higher-order construct as a whole, represented by the relationships between the higher-order component and its lower-order components.<\/li><\/ol>

      In stage one, the estimation and measurement model assessment for the lower-order components is based on the standard model, which draws direct relationships between the constructs. The higher-order component is not included in the PLS path model.<\/p>

      Then, in stage two, the latent variable scores from the stage one results allow creating and estimating the model.<\/p>

      The assessment of the stage two results begins with<\/p>

      First,<\/em><\/strong> HOCs formative measurement model. The result supports the convergent validity of the higher-order construct, when the path coefficient does not significantly differ from the 0.7 threshold (Hair et al., 2017a).<\/p>

      Second,<\/em><\/strong> find that the HOC measurement model is not negatively affected by collinearity and assess the VIF of the LOCs for the HOC.<\/p>

      Third,<\/em><\/strong> Assess the Outer Loading, Weights and its Significance<\/p>

      Finally,<\/em><\/strong> Confirm all structural model evaluation results (e.g., significance and relevance for path coefficients, Q\u00b2, PLS predict).<\/p>

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      Assess the Collinearity Statistics (VIF)<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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      To validate the higher order constructs, we need to first assess the collinearity<\/p>

      \"How<\/p>

      Source: Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) Book (Hair et al., 2017)<\/p><\/div><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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      Assess the Outer Weights and Loadings<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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      Next, outer weights and loading is assessed to establish validity.<\/p>

      \"\"<\/p>

      \u00a0<\/strong><\/p>

      Reference<\/strong><\/p>

      Sarstedt, M., Hair Jr, J. F., Cheah, J. H., Becker, J. M., & Ringle, C. M. (2019). How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australasian Marketing Journal (AMJ)<\/i>, 27<\/i>(3), 197-211.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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      Video Tutorials<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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      How to Analyze Refective-Formative Higher Order Construct?<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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      How to Report Refective-Formative Higher Order Construct?<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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      Theory and Practice of Higher Order Reflective Formative Model?<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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      Additional SmartPLS4 Tutorials<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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