Evaluation of Structural Equation Models using the Partial Least Squares (PLS) Approach

Götz Oliver, Liehr-Gobbers Kerstin, Krafft Manfred

This paper gives a basic comprehension of the partial least squares approach. In this context, the aim of this paper is to develop a guide for the evaluation of structural equation models, using the current statistical methods methodological knowledge by specifically considering the Partial-Least-Squares (PLS) approach's requirements. As an advantage, the PLS method demands significantly fewer requirements compared to that of covariance structure analyses, but nevertheless delivers consistent estimation results. This makes PLS a valuable tool for testing theories. Another asset of the PLS approach is its ability to deal with formative as well as reflective indicators, even within one structural equation model. This indicates that the PLS approach is appropriate for explorative analysis of structural equation models, too, thus offering a significant contribution to theory development. However, little knowledge is available regarding the evaluating of PLS structural equation models. To overcome this research gap a broad and detailed guideline for the assessment of reflective and formative measurement models as well as of the structural model had been developed. Moreover, to illustrate the guideline, a detailed application of the evaluation criteria had been conducted to an empirical model explaining repeat purchasing behavior.

reflective measurement model; formative measurement model; content validity; indicator reliability; construct reliability; composite reliability; convergent validity; factor reliability; discriminant validity; average variance extracted (AVE); vanishing tetrad test; expert validity; variance inflation factor (VIF); external validity; MIMIC (Multiple effect indicators for multiple causes); phantom variable; nomological validity; Stone-Geisser test

Publication type
Forschungsartikel (Buchbeitrag)

Peer reviewed

Publication status


Book title
Handbook of Partial Least Squares - Concepts, Methods and Applications

Esposito Vinzi Vincenzo, Chin Wynne W., Henseler Jörg, Wang Huiwen

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Heidelberg et al.