More powerful parameter tests? No, rather biased parameter estimates. Some reflections on path analysis with weighted composites

Florian Schuberth, Tamara Schamberger, Jörg Henseler*

*Corresponding author for this work

Research output: Contribution to journalComment/Letter to the editorAcademicpeer-review

1 Citation (Scopus)
14 Downloads (Pure)

Abstract

Recently, a study compared the effect size and statistical power of covariance-based structural equation modeling (CB-SEM) and path analysis using various types of composite scores (Deng, L., & Yuan, K.-H., Behavior Research Methods, 55, 1460–1479, 2023). This comparison uses nine empirical datasets to estimate eleven models. Based on the meta-comparison, that study concludes that path analysis via weighted composites yields “path coefficients with less relative errors, as reflected by greater effect size and statistical power” (ibidem, p. 1475). In our paper, we object to this central conclusion. We demonstrate that the justification these authors provided for comparing CB-SEM and path analysis via weighted composites is not well grounded. Similarly, we explain that their employed study design, i.e., a meta-comparison, is very limited in its ability to compare the effect size and power delivered across these methods. Finally, we replicated Deng and Yuan’s (ibidem) meta-comparison and show that CB-SEM using the normal-distribution-based maximum likelihood estimator does not necessarily deliver smaller effect sizes than path analysis via composites if a different scaling method is employed for CB-SEM.
Original languageEnglish
Number of pages11
JournalBehavior research methods
Early online date7 Nov 2023
DOIs
Publication statusE-pub ahead of print/First online - 7 Nov 2023

Keywords

  • UT-Hybrid-D

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