Improving the Root Mean Square Error of Approximation for Nonnormal Conditions in Structural Equation Modeling

By J. Nevitt, G. R. Hancock. 2000

156 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/cf686dde823a1923b7fbd66da59fac852cd755e8

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In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Estimating the Maximum Likelihood Root Mean Square Error of Approximation (RMSEA) with Non-normal Data: A Monte-Carlo Study (2020, methodology): “In fact, the RMSEA computed based on the naïve approach have population values change with the degree of nonnormality (Brosseau-Liard et al., 2012; Curran et al., 1996; Nevitt & Hancock, 2000; Savalei, 2018).”
High-performance work systems and occupational safety. (2005, background): “…(RMSEA) of .10 may not be considered a good fit by some (e.g., MacCallum, Browne, & Sugawara, 1996), current research (e.g., Curran, Bollen, Paxton, Kirby, & Chen, 2002; Hayduk & Glaser, 2000; Nevitt & Hancock, 2000) has raised concerns over RMSEA criterion and sample size sensitivity.”
Longitudinal Cross-Gender Factorial Invariance of the Academic Motivation Scale (2006, methodology): “The overall fit indexes used in the study were the SBχ2 statistic, the SBχ2/df ratio (an acceptable cutoff value is 3.00), the robust comparative fit index (CFI; Bentler, 1990), the standardized root mean square residual (SRMR; Bentler, 1995), and the adjusted root mean squared error of approximatio”
Financial Decision Making in Family Firms (2014, unknown): “1990; Fan, Thompson, & Wang, 1999; Nevitt & Hancock, 2000; Tucker & Lewis, 1973).”
An Empirical Evaluation of the Use of Fixed Cutoff Points in RMSEA Test Statistic in Structural Equation Models (2008, methodology): “In addition, as Nevitt and Hancock (2000) acknowledged, it was difficult to evaluate the hypothesis tests for misspecified models under nonnormal conditions because the true lack of fit in the population for the misspecified models is due to both misspecification and nonnormality.”
Assessing Fit in Ordinal Factor Analysis Models: SRMR vs. RMSEA (2020, methodology): “…has examined the performance of point estimates, confidence intervals and tests of close fit using the RMSEA (Curran, Bollen, Chen, Paxton, & Kirby, 2003; Fan, Thompson, & Wang, 1999; Hu & Bentler, 1998; Kenny et al., 2015; Kenny & McCoach, 2003; Nasser & Wisenbaker, 2003; Nevitt & Hancock, 2000).”
Assessing Fit in Structural Equation Models: A Monte-Carlo Evaluation of RMSEA Versus SRMR Confidence Intervals and Tests of Close Fit (2018, unknown): “…performance of point estimates, CIs, and tests of close fit using the RMSEA under ML estimation (Curran, Bollen, Chen, Paxton, & Kirby, 2003; Fan, Thompson, & Wang, 1999; Hu & Bentler, 1998; Kenny, Kaniskan, & McCoach, 2015; Kenny & McCoach, 2003; Nasser & Wisenbaker, 2003; Nevitt & Hancock, 2000).”
Examination of the Weighted Root Mean Square Residual: Evidence for Trustworthiness? (2018, background): “Nevitt and Hancock (2000) found that the Satorra–Bentler scaled RMSEA outperformed the unadjusted index.”
Evaluating Small Sample Approaches for Model Test Statistics in Structural Equation Modeling (2004, background): “While some promising research has been conducted examining the behavior of such modified fit indices (Anderson, 1996; Nevitt & Hancock, 2000), there is clearly the need for further examination of modified fit indices that might be corrected for small samples as well as nonnormal distributional…”
The structure of goal contents across 15 cultures. (2005, methodology): “Finally, the SB scaling corrections allow the calculation of an adjusted RMSEA for nonnormal conditions (see Nevitt & Hancock, 2000).”
Self-Efficacy for Self-Regulated Learning (2008, methodology): “…greater than 5.0 (see Bentler, 2005), hence we opted to base all subsequent decisions on robust chi-square indexes and robust versions of the CFI and RMSEA (readers are referred to Bentler, 2005; Nevitt & Hancock, 2000; Satorra & Bentler, 2001, for further explanation of robust statistics).”
The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation (2021, unknown): “As a natural derivation, the square root of mean square error (RMSE) has been widely adopted ( Nevitt & Hancock, 2000 ; Hancock & Freeman, 2001 ; Applegate et al., 2003 ; Kelley & Lai, 2011 ) to standardize the units of measures of MSE .”
A New Strategy for Testing Structural Equation Models (2016, result): “At small to moderate sample sizes and, by implication, low power, the test of not-close fit often fails to provide supporting evidence for correct models, whereas the test of close fit fails to reject misspecified models (Nevitt & Hancock, 2000).”