Confirmatory Composite Analysis.

Florian Schuberth,Theo K Dijkstra,Jörg Henseler

doi:10.3389/fpsyg.2018.02541

Abstract

This article introduces confirmatory composite analysis (CCA) as a structural equation modeling technique that aims at testing composite models. It facilitates the operationalization and assessment of design concepts, so-called artifacts. CCA entails the same steps as confirmatory factor analysis: model specification, model identification, model estimation, and model assessment. Composite models are specified such that they consist of a set of interrelated composites, all of which emerge as linear combinations of observable variables. Researchers must ensure theoretical identification of their specified model. For the estimation of the model, several estimators are available; in particular Kettenring's extensions of canonical correlation analysis provide consistent estimates. Model assessment mainly relies on the Bollen-Stine bootstrap to assess the discrepancy between the empirical and the estimated model-implied indicator covariance matrix. A Monte Carlo simulation examines the efficacy of CCA, and demonstrates that CCA is able to detect various forms of model misspecification.

Highlights

Structural equation modeling with latent variables (SEM) comprises confirmatory factor analysis (CFA) and path analysis, combining methodological developments from different disciplines such as psychology, sociology, and economics, while covering a broad variety of traditional multivariate statistical procedures (Bollen, 1989; Muthén, 2002)
It follows the same steps usually applied in SEM and enables researchers to analyze a variety of situations, in particular, beyond the realm of social and behavioral sciences
confirmatory composite analysis (CCA) allows for dealing with research questions that could not be appropriately dealt with yet in the framework of CFA or more generally in SEM

Summary

Introduction

Structural equation modeling with latent variables (SEM) comprises confirmatory factor analysis (CFA) and path analysis, combining methodological developments from different disciplines such as psychology, sociology, and economics, while covering a broad variety of traditional multivariate statistical procedures (Bollen, 1989; Muthén, 2002). It is capable of expressing theoretical concepts by means of multiple observable indicators to connect them via the structural model as well as to account for measurement error. SEM is able to deal with categorical (Muthén, 1984) as well as longitudinal data (Little, 2013) and can be used to model non-linear relationships between the constructs (Klein and Moosbrugger, 2000).

Methods

Results

Conclusion