Modeling Reflective Higher-Order Constructs using Three Approaches with PLS Path Modeling: A Monte Carlo Comparison

Abstract

Many studies in the social sciences are increasingly modeling higher-order constructs. PLS can be used to investigate models at a higher level of abstraction (Lohmöller, 1989). It is often chosen due to its’ ability to estimate complex models (Chin, 1998). The primary goal of this paper is to demonstrate the relative robustness of various item and construct characteristics on the reproduction of parameter true scores when utilising the two-stage, hierarchical components indicators (repeated indicators) and a newer hybrid technique (where indicators are not repeated). Our Monte Carlo study mirrors a simple substantive branding example. We vary pertinent dimensions such as: sample size, differing item reliabilities and inner weighting schemes. Our contribution is twofold. Firstly, we provide an overview of the approaches to model reflective second-order constructs with PLS. Secondly, based on our simulation, we provide suggestions when to use each approach.

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