Premature conclusions about the signal‐to‐noise ratio in structural equation modeling research: A commentary on Yuan and Fang (2023)
DOI10.1111/bmsp.12304zbMath1529.62086OpenAlexW4366236124MaRDI QIDQ6185863
Unnamed Author, Unnamed Author, Jörg Henseler, Unnamed Author, Unnamed Author
Publication date: 30 January 2024
Published in: British Journal of Mathematical and Statistical Psychology (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1111/bmsp.12304
effect sizecomposite modelfactor score regressioncovariance-based structural equation modelingHenseler-Ogasawara specificationpartial least squares structural equation modelingregression analysis with weighted compositessum scores
Factor analysis and principal components; correspondence analysis (62H25) Linear regression; mixed models (62J05) Applications of statistics to psychology (62P15)
Cites Work
- Estimation of the signal-to-noise in the linear regression model
- Consistent and asymptotically normal PLS estimators for linear structural equations
- Statistical Analysis of Financial Data in S-Plus
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
- Which method delivers greater signal‐to‐noise ratio: Structural equation modelling or regression analysis with weighted composites?
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