TY - JOUR
T1 - Confirmatory composite analysis
AU - Schuberth, Florian
AU - Henseler, Jörg
AU - Dijkstra, Theo K.
N1 - Schuberth, F., Henseler, J., & Dijkstra, T. K. (2018). Confirmatory composite analysis. Frontiers in Psychology, 9(DEC), [02541]. DOI: 10.3389/fpsyg.2018.02541
PY - 2018/12/13
Y1 - 2018/12/13
N2 - 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.
AB - 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.
KW - Artifacts
KW - Composite modeling
KW - Design research
KW - Monte Carlo simulation study
KW - Structural equation modeling
KW - Theory testing
UR - http://www.scopus.com/inward/record.url?scp=85058418956&partnerID=8YFLogxK
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000453336000001
U2 - 10.3389/fpsyg.2018.02541
DO - 10.3389/fpsyg.2018.02541
M3 - Article
AN - SCOPUS:85058418956
VL - 9
JO - Frontiers in Psychology
JF - Frontiers in Psychology
SN - 1664-1078
IS - DEC
M1 - 02541
ER -