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Premature conclusions about the signal‐to‐noise ratio in structural equation modeling research
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Orientador(es)
Resumo(s)
In a recent article published in this journal, Yuan and Fang (British Journal of Mathematical and Statistical Psychology, 2023) suggest comparing structural equation modeling (SEM), also known as covariance-based SEM (CB-SEM), estimated by normal-distribution-based maximum likelihood (NML), to regression analysis with (weighted) composites estimated by least squares (LS) in terms of their signal-to-noise ratio (SNR). They summarize their findings in the statement that “[c]ontrary to the common belief that CB-SEM is the preferred method for the analysis of observational data, this article shows that regression analysis via weighted composites yields parameter estimates with much smaller standard errors, and thus corresponds to greater values of the [SNR].” In our commentary, we show that Yuan and Fang have made several incorrect assumptions and claims. Consequently, we recommend that empirical researchers not base their methodological choice regarding CB-SEM and regression analysis with composites on the findings of Yuan and Fang as these findings are premature and require further research.
Descrição
Schuberth, F., Schamberger, T., Rönkkö, M., Liu, Y., & Henseler, J. (2023). Premature conclusions about the signal‐to‐noise ratio in structural equation modeling research: A commentary on Yuan and Fang (2023). British Journal of Mathematical and Statistical Psychology, 76(3), 682-694. https://doi.org/10.1111/bmsp.12304 --- Funding Information:
Jörg Henseler served as a reviewer for Yuan and Fang's ( 2023 ) manuscript. He gratefully acknowledges financial support from FCT Fundação para a Ciência e a Tecnologia (Portugal), national funding through a research grant from the Information Management Research Center – MagIC/NOVA IMS (UIDB/04152/2020). We thank Hao Wu, Associate Editor of the British Journal of Mathematical and Statistical Psychology, for giving us the opportunity to write this commentary. Moreover, we thank Alexandra Elbakyan for her efforts in making science accessible. Finally, we thank Yves Rosseel for his support in replicating Yuan and Fang's results in lavaan. British Journal of Mathematical and Statistical Psychology
Palavras-chave
composite model covariance-based structural equation modeling effect size factor score regression Henseler–Ogasawara specification partial least squares structural equation modeling regression analysis with weighted composites sum scores Statistics and Probability Arts and Humanities (miscellaneous) General Psychology