Confirmatory composite analysis in human development research

Tamara Schamberger*, Florian Schuberth, Jörg Henseler

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)
49 Downloads (Pure)

Abstract

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
Pages (from-to)89-100
Number of pages12
JournalInternational journal of behavioral development
Volume47
Issue number1
Early online date30 Aug 2022
DOIs
Publication statusPublished - Jan 2023

Keywords

  • UT-Hybrid-D

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