Finite Sampling Properties of the Point Estimates and Confidence Intervals of the RMSEA

By P. Curran, Kenneth A. Bollen, Feinian Chen, Pamela Paxton, James B. Kirby. 2003

160 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/26bb5191f8ead061b390c101aedfead944e1e6b8

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. Albert Maydeu-Olivares
3. Yves Rosseel
4. P. Curran
5. Kenneth A. Bollen
6. D. McCoach
7. David A. Kenny
8. Burcu Kaniskan

In Statistics and Probability

1. Kenneth A. Bollen
2. Dexin Shi
3. Albert Maydeu-Olivares
4. Yves Rosseel
5. P. Curran
6. Feinian Chen
7. James B. Kirby
8. Chuanji Gao

Citations in the corpus

• An Empirical Evaluation of the Use of Fixed Cutoff Points in RMSEA Test Statistic in Structural Equation Models (2008, methodology): “…sample RMSEA values and CIs appear to be unbiased estimates of the corresponding Chen et al. / Evaluation of Fixed Cutoff Points 467 population values, at least for models with small to moderate misspecification, and when the sample size is reasonably large (Curran et al. 2002; Curran et al. 2003).”
• Assessing Fit in Structural Equation Models: A Monte-Carlo Evaluation of RMSEA Versus SRMR Confidence Intervals and Tests of Close Fit (2018, methodology): “For instance, Curran et al. (2003) examined the accuracy of CIs and p values for tests of close fit for the RMSEA with normally distributed data.”
• Small-Sample Robust Estimators of Noncentrality-Based and Incremental Model Fit (2009, unknown): “In summary, the results for T ML revalidate findings of Curran et al. (2002), Curran et al. (2003), Olsson et al. (2004), and Hu and Bentler (1999): Traditional estimators of noncentrality– based population model fit like RMˆSEA should not be applied when the sample size is small .”
• Estimating the Maximum Likelihood Root Mean Square Error of Approximation (RMSEA) with Non-normal Data: A Monte-Carlo Study (2020, background): “Repeatedly, research has also found inflated sample estimates of RMSEA (BrosseauLiard et al., 2012; Curran et al., 2003; Fan et al., 1999), poor coverage of CIs (Brosseau-Liard et al., 2012; MaydeuOlivares, 2017b; Maydeu-Olivares, Shi, & Rosseel, 2018), and inflated false-positive error rate of…”
• The Performance of RMSEA in Models With Small Degrees of Freedom (2015, methodology): “(Curran et al. 2003, p. 248)”