{"id":3309,"date":"2021-08-26T10:38:35","date_gmt":"2021-08-26T10:38:35","guid":{"rendered":"https:\/\/researchwithfawad.com\/?page_id=3309"},"modified":"2021-09-11T10:45:16","modified_gmt":"2021-09-11T10:45:16","slug":"concept-of-higher-order-constructs-in-pls-sem","status":"publish","type":"page","link":"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/basic-and-advance-data-analysis-using-smart-pls\/concept-of-higher-order-constructs-in-pls-sem\/","title":{"rendered":"Concept of Higher-Order Constructs in PLS-SEM"},"content":{"rendered":"\t\t
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Concept of Higher Order Constructs<\/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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Concept of Higher Order Constructs\n<\/h3>\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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Higher-order constructs (also known as hierarchical component models in the context of PLS-SEM) provide a framework for researchers to model a construct on a more abstract dimension (referred to as higher-order component) and its more concrete subdimensions (referred to as lower-order components).<\/p>

HCM refers to a more general construct that is measured at a higher level of abstraction, while simultaneously assessing several sub-components (dimensions). Hence, by specifying lower-order components, HCM cover concrete traits of a more general conceptual variable of interest (Hair et al. 2018).<\/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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Advantages\n<\/h3>\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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Higher-order constructs help to reduce the number of path model relationships, thereby achieving model parsimony. Instead of specifying relationships between multiple independent and dependent constructs in a path model.\u00a0<\/p>

Researchers can summarize the independent constructs in a higher-order construct, making the relationships from the (then) lower-order components to the dependent constructs in the model obsolete.<\/p>

Higher-order constructs also help to overcome the bandwidth-fidelity dilemma. According to which there is a tradeoff \u201cbetween variety of information (bandwidth) and thoroughness of testing to obtain more certain information (fidelity).\u201d<\/p>

Typically, bandwidth is used as a synonymous of complexity and variability. The opposite of bandwidth is narrowness. On the other hand, fidelity is a synonymous of accuracy and specificity. The opposite of fidelity is impreciseness.<\/p>

Finally, higher-order constructs provide a means for reducing collinearity among formative indicators by offering a vehicle to re-arrange the indicators and\/or constructs across different concrete sub-dimensions of the more abstract construct.<\/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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Types of Higher Order Constructs<\/h3>\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 reap the benefits of higher order constructs, researchers must address at least three concerns.<\/p>

First<\/i><\/b>, the higher-order construct\u2019s conceptualization and specification needs to be grounded in well-developed measurement theory. In fact, this step can be as challenging and tedious as developing a new measurement scale.<\/p>

Specifically, when implementing a higher-order construct, researchers have to decide on<\/p>

  1. The measurement model specification of the lower-order components, and<\/li>
  2. The relationship between the higher-order component and its lower-order components, both of which can be reflective or formative in nature.<\/li><\/ol>

    As a result, research has proposed four types of higher-order constructs (see Figure): reflective-reflective, reflective-formative, formative-reflective, and formative-formative.<\/p>

    Prior studies on higher-order constructs in PLS-SEM have shown that reflective-reflective and reflective-formative higher- order types feature prominently in different fields.<\/p>

    \"Types<\/p>

    Second<\/i><\/b>, researchers can choose among different approaches to identify the higher-order construct. Prominent approaches are the repeated indicators approach or the two-stage approach (Hair et al., 2018).<\/p>

    Third<\/i><\/b>, evaluating the measurement quality of higher-order constructs is highly challenging. For example,<\/p>

    1. Some authors do not assess the reliability and validity of the lower-order components and higher-order components,<\/li>
    2. Others erroneously interpret the relationships between higher- and lower-order components as structural model relationships\u2014instead of assessing the lower-order components as elements of the higher-order construct\u2019s measurement model.<\/li>
    3. Researchers frequently analyze the discriminant validity of the lower-order components, they neglect the discriminant validity assessment of the higher-order construct as a whole.<\/li><\/ol>

      References<\/strong><\/p>

      Salgado, J. F. (2017). Bandwidth-fidelity dilemma. Encyclopedia of Personality and Individual Differences, eds Zeigler-Hill V., Shackelford TK, editors.(Berlin: Springer International Publishing<\/i>, 1-4.<\/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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      Other SmartPLS Tutorials<\/h5>