Multicollinearity and Measurement Error in Structural Equation Models: Implications for Theory Testing
By Rajdeep Grewal, Joseph A. Cote, H. Baumgartner. 2004
1,250 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/1f506284573adc5a4f109aa3fa3ac68e86affcd8
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In Computational Theory and Mathematics
- Marko Sarstedt
- Christian M. Ringle
- Olivier Furrer
- Matthias Ehrgott
- Sarah Desirée Schaefer
- Ralf Terlutter
- David Cray
- Irene R. R. Lu
In Statistics and Probability
- Christian M. Ringle
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- Olivier Furrer
- Jeffrey D. Long
Citations in the corpus
- Supply Chain Contagion (2008, background): “Grewal, Cote, and Baumgartner (2004) highlight the problems that the presence of multicollinearity can create when structural equation modeling procedures are used.”
- Mean-Centering Does Not Alleviate Collinearity Problems in Moderated Multiple Regression Models (2007, background): “How do we diagnose collinearity? Grewal et al. (2004) caution researchers to be careful about relying on available diagnostics for what constitutes different levels of collinearity.”
- Buffering and Amplifying Effects of Relationship Commitment in Business-to-Business Relationships (2010, methodology): “7To test whether the slopes are significantly different from a hypothesized value of zero for each level of ethical violation, we first calculated the standard error for each level using the covariance matrix for the estimates.”
- Effects of Missing Data Methods in Structural Equation Modeling With Nonnormal Longitudinal Data (2009, result): “According to the results of Grewal, Cote, and Baumgartner (2004) , when multicollinearity is extreme, Type II error rates can be unacceptably high—greater than 80%—in cases where the correlations are greater than .”
- The Longitudinal Relationships Between Basic Psychological Needs Satisfaction at School and School-Related Subjective Well-Being in Adolescents (2013, background): “Finally, it would be pointed out that the good reliabilities of the three need subscales and a large sample size (N = 576) can effectively protect against the deleterious effects of multicollinearity (Grewal et al. 2004).”