2003/10/29 by Patrick J. Curran, Kenneth A. Bollen, Feinian Chen +2 · 166 citations
Decision Sciences · Mathematics · #Advanced Causal Inference Techniques #Algorithm #Computation #Computer science #Confidence interval #Econometrics #Mathematics #Psychometric Methodologies and Testing #Sample (material) #Sample size determination #Sampling (signal processing) #Sampling distribution #Statistical Methods and Bayesian Inference #Statistics #Structural equation modeling
paper · doi:10.1177/0049124103256130
published in Sociological Methods & Research 32(2), 208-252 (SAGE Publishing)
openalex publication_date 2003/10/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/26
A key advantage of the root mean square error of approximation (RMSEA) is that under certain assumptions, the sample estimate has a known sampling distribution that allows for the computation of confidence intervals. However, little is known about the finite sampling behaviors of this measure under violations of these ideal asymptotic conditions. This information is critical for developing optimal criteria for using the RMSEA to evaluate model fit in practice. Using data generated from a computer simulation study, the authors empirically tested a set of theoretically generated research hypotheses about the sampling characteristics of the RMSEA under conditions commonly encountered in applied social science research. The results suggest that both the sample estimates and confidence intervals are accurate for sample sizes of n = 200 and higher, but caution is warranted in the use of these measures at smaller sample sizes, at least for the types of models considered here.