The Relationship Between Root Mean Square Error of Approximation and Model Misspecification in Confirmatory Factor Analysis Models

By Victoria Savalei. 2012

105 citations recorded by the source corpus.

Subfields: Applied Psychology, Experimental and Cognitive Psychology, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/24780ed7c081e5eb113f842559775a5a56589f6c

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In Applied Psychology

In Experimental and Cognitive Psychology

In Management Science and Operations Research

Citations in the corpus

Bootstrapping Confidence Intervals for Fit Indexes in Structural Equation Modeling (2016, background): “…values for approximate fit indexes (e.g., Beauducel & Wittmann, 2005; Hooper, Coughlan, & Mullen, 2008; Hu & Bentler, 1999; Kenny & McCoach, 2003; MacCallum et al., 1996; Savalei, 2012), nearly everyone agrees that fit indexes provide useful supplementary information about the quality of the model.”
Assessing Fit in Ordinal Factor Analysis Models: SRMR vs. RMSEA (2020, methodology): “As a result, any population value (say 0.05) has a different meaning depending on the structure and size of the model (Chen, Curran, Bollen, Kirby, & Paxton, 2008; Savalei, 2012; Shi, Lee, & Maydeu-Olivares, 2019).”
On doing better science: From thrill of discovery to policy implications (2017, unknown): “…models have biased estimates (Bollen, Kirby, Curran, Paxton, & Chen, 2007; Hayduk, 2014; McIntosh, 2007); indexes of fit (CFI, RMSEA) should not be trusted because they depend on idiosyncrasies of the model (Chen, Curran, Bollen, Kirby, & Paxton, 2008; Marsh, Hau, & Wen, 2004; Savalei, 2012).”
Assessing Fit in Structural Equation Models: A Monte-Carlo Evaluation of RMSEA Versus SRMR Confidence Intervals and Tests of Close Fit (2018, methodology): “It is worth noting that the population RMSEA decreased as the interfactor correlation increased, but also as model size increased (see Savalei, 2012, for more discussion).”
Examination of the Weighted Root Mean Square Residual: Evidence for Trustworthiness? (2018, methodology): “Similar findings were observed with WLSMV-based χ2 and the RMSEA index, where sample values inaccurately reported a more positive view of model–data fit (Bandalos, 2008; Brosseau-Liard et al., 2012; Savalei, 2012; Shi et al., 2017).”
Estimating the Maximum Likelihood Root Mean Square Error of Approximation (RMSEA) with Non-normal Data: A Monte-Carlo Study (2020, background): “…and tests of close fit appear to be more precise when fitting models to ordinal data (treating 5 The results of this additional set of simulations is available from the authors upon request. it as continuous, e.g., Rhemtulla, Brosseau-Liard, & Savalei, 2012), than when data are truly continuous.”
Understanding the Model Size Effect on SEM Fit Indices (2018, background): “The findings regarding the effects of p on population RMSEA are consistent with the conclusions drawn by Kenny and McCoach (2003) and Savalei (2012).”