Confirmatory composite analysis in human development research

Tamara Svenja Schamberger*, Florian Schuberth, Jörg Henseler

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Research in human development often relies on composites, that is, composed variables such as indices. Their composite nature renders these variables inaccessible to conventional factor-centric psychometric validation techniques such as confirmatory factor analysis (CFA). In the context of human development research, there is currently no appropriate technique available for assessing composites with the same degree of rigor comparable to that known from CFA. As a remedy, this article presents confirmatory composite analysis (CCA), a statistical approach suitable to assess composites. CCA is a special type of structural equation modeling that consists of model specification, model identification, model estimation, and model assessment. This article explains CCA and its steps. In addition, it illustrates CCA’s use by means of an illustrative example.
Original languageEnglish
JournalInternational journal of behavioral development
Early online date30 Aug 2022
Publication statusE-pub ahead of print/First online - 30 Aug 2022


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