Assessing measurement model quality in PLS-SEM using confirmatory composite analysis

• Published in: Journal of business research 2020-03, Vol.109, p.101-110
• Main Authors: Hair, Joe F., Howard, Matt C., Nitzl, Christian
• Format: Article
• Language: English
• Subjects: CCA; CFA; Confirmatory composite analysis; Confirmatory factor analysis; Measurement model confirmation; PLS-SEM
• ISSN: 0148-2963; 1873-7978
• DOI: 10.1016/j.jbusres.2019.11.069

•Confirmatory composite analysis (CCA) can confirm measurement models using PLS-SEM.•CCA has benefits relative to confirmatory factor analysis (CFA).•CCA can confirm both reflective and formative measurement models.•Guidelines for the proper application of CCA are provided.•PLSpredict procedure for out-of-sample prediction with CCA is explained.•CCA also does not require fit to confirm measurement models. Confirmatory factor analysis (CFA) has historically been used to develop and improve reflectively measured constructs based on the domain sampling model. Compared to CFA, confirmatory composite analysis (CCA) is a recently proposed alternative approach applied to confirm measurement models when using partial least squares structural equation modeling (PLS-SEM). CCA is a series of steps executed with PLS-SEM to confirm both reflective and formative measurement models of established measures that are being updated or adapted to a different context. CCA is also useful for developing new measures. Finally, CCA offers several advantages over other approaches for confirming measurement models consisting of linear composites.