Confirmatory Composite Analysis

Florian Schuberth (Corresponding Author), Jörg Henseler, T. K. Dijkstra

Research output: Contribution to journalArticleAcademicpeer-review

7 Citations (Scopus)
18 Downloads (Pure)

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.
Original languageEnglish
Article number02541
Number of pages14
JournalFrontiers in psychology
Volume9
Issue numberDEC
DOIs
Publication statusPublished - 13 Dec 2018

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Artifacts
Statistical Factor Analysis
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Keywords

  • Artifacts
  • Composite modeling
  • Design research
  • Monte Carlo simulation study
  • Structural equation modeling
  • Theory testing

Cite this

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Confirmatory Composite Analysis. / Schuberth, Florian (Corresponding Author); Henseler, Jörg ; Dijkstra, T. K.

In: Frontiers in psychology, Vol. 9, No. DEC, 02541, 13.12.2018.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - Schuberth, Florian

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AU - Dijkstra, T. K.

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