%A Schuberth,Florian
%A Henseler,Jörg
%A Dijkstra,Theo K.
%D 2018
%J Frontiers in Psychology
%C
%F
%G English
%K Composite modeling,Monte Carlo simulation study,structural equation modeling (SEM),Theory testing,artifacts,Design research (science)
%Q
%R 10.3389/fpsyg.2018.02541
%W
%L
%N 2541
%M
%P
%7
%8 2018-December-13
%9 Methods
%#
%! CCA
%*
%<
%T Confirmatory Composite Analysis
%U https://www.frontiersin.org/article/10.3389/fpsyg.2018.02541
%V 9
%0 JOURNAL ARTICLE
%@ 1664-1078
%X This article introduces confirmatory composite analysis (CCA) as a structural equation modeling technique that aims at testing composite models. In doing so, it overcomes a current weakness of structural equation modeling, i.e., 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.