Understanding Robust Corrections in Structural Equation Modeling

By Victoria Savalei. 2014

156 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/981bee864cbdca2b88e12243d7a682e27c7dea35

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Science and Operations Research

1. Dexin Shi
2. Carl F. Falk
3. Ning Jiang
4. Jin Liu
5. Kristopher J Preacher
6. Yanyun Yang
7. Yan Xia
8. Edgar C. Merkle

In Statistics and Probability

1. Carl F. Falk
2. Dexin Shi
3. Kristopher J Preacher
4. Ning Jiang
5. Jin Liu
6. Yan Xia
7. Edgar C. Merkle
8. C. Distefano

Citations in the corpus

• Testing non-nested structural equation models (2014, methodology): “ to study the power of the Vuong tests in specic situations. Future work could compare the Vuong statistics to both the classical LRT and to other robust statistics (see Satorra & Bentler, 1994; Savalei, 2014) when the candidate models are both incorrect. This is trickier than it sounds, because”
• Why Ordinal Variables Can (Almost) Always Be Treated as Continuous Variables: Clarifying Assumptions of Robust Continuous and Ordinal Factor Analysis Estimation Methods (2020, unknown): “Moreover, the socalled robust ML estimator (MLR; Savalei, 2014; Yuan and Bentler, 2007) provides valid statistical inference in amisspecified model (Satorra, 1992), and improvements have been proposed (Lai, 2019).”
• RMSEA, CFI, and TLI in structural equation modeling with ordered categorical data: The story they tell depends on the estimation methods (2018, methodology): “Second, although the scaled indices are commonly reported for sample-level analysis in SEM applications, they are calculated without theoretical justification, as has been evidenced by Brosseau-Liard and Savalei (2014) and Brosseau-Liard et al. (2012).”
• Are Robust Standard Errors the Best Approach for Interval Estimation With Nonnormal Data in Structural Equation Modeling? (2018, result): “Such adjustments often allow SEs to better estimate the amount of sampling variability in the parameter estimates (for a tutorial, see Savalei, 2014), and this result is supported by simulation research (Chou, Bentler & Satorra, 1991; Curran, 1994; Maydeu-Olivares, 2017).”
• Examination of the Weighted Root Mean Square Residual: Evidence for Trustworthiness? (2018, methodology): “These fit indexes could be considered as “corrected” by the WLSMV estimator (i.e., robust indexes), given that the scaling factors have been applied in the computation of the χ2 (Brosseau-Liard et al., 2012; Savalei, 2014).”