Robust Procedures in Structural Equation Modeling
By Ke-Hai Yuan, Peter M. Bentler. 2007
72 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/310b665ffc1b2739b82b48a6752adc2f837ab882
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Mike W.-L. Cheung
- Morten Moshagen
- Edgar Erdfelder
- Xiaoling Zhong
- Ke-Hai Yuan
- Daniel M. McNeish
- Kristopher J Preacher
- Davood Tofighi
In Management Science and Operations Research
- Mike W.-L. Cheung
- Ke-Hai Yuan
- Edgar Erdfelder
- Xiaoling Zhong
- Morten Moshagen
- Kristopher J Preacher
- Daniel M. McNeish
- Dexin Shi
In Statistics and Probability
- Mike W.-L. Cheung
- Ke-Hai Yuan
- Edgar Erdfelder
- Xiaoling Zhong
- Morten Moshagen
- Peter M. Bentler
- Paul Michel Bernard Verhaeghen
- S. Jak
Citations in the corpus
- 8. Outliers, Leverage Observations, and Influential Cases in Factor Analysis: Using Robust Procedures to Minimize Their Effect (2008, methodology): “These results implicitly imply that T ML and its extensions (e.g., Satorra and Bentler 1994; Yuan and Bentler 1998b) are only robust to good leverage observations, not robust to bad leverage observations or general outlying cases (see also Yuan and Bentler 2001, 2007).”
- A New Strategy for Testing Structural Equation Models (2016, methodology): “When applying the proposed method under such circumstances, it is recommended to employ robust test statistics (see Yuan & Bentler, 2007).”
- Modeling dependent effect sizes with three-level meta-analyses: a structural equation modeling approach. (2014, methodology): “The use of robust statistics to address model misspecification is popular in the SEM literature (e.g., Yuan & Bentler, 2007).”