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An Empirical Evaluation of the Use of Fixed Cutoff Points in RMSEA Test Statistic in Structural Equation Models

Author

Listed:
  • Feinian Chen

    (North Carolina State University, Raleigh, NC)

  • Patrick J. Curran

    (University of North Carolina at Chapel Hill)

  • Kenneth A. Bollen

    (University of North Carolina at Chapel Hill)

  • James Kirby

    (Agency for Healthcare Research & Quality, Rockville, MD)

  • Pamela Paxton

    (Ohio State University, Columbus)

Abstract

This article is an empirical evaluation of the choice of fixed cutoff points in assessing the root mean square error of approximation (RMSEA) test statistic as a measure of goodness-of-fit in Structural Equation Models. Using simulation data, the authors first examine whether there is any empirical evidence for the use of a universal cutoff, and then compare the practice of using the point estimate of the RMSEA alone versus that of using it jointly with its related confidence interval. The results of the study demonstrate that there is little empirical support for the use of .05 or any other value as universal cutoff values to determine adequate model fit, regardless of whether the point estimate is used alone or jointly with the confidence interval. The authors' analyses suggest that to achieve a certain level of power or Type I error rate, the choice of cutoff values depends on model specifications, degrees of freedom, and sample size.

Suggested Citation

  • Feinian Chen & Patrick J. Curran & Kenneth A. Bollen & James Kirby & Pamela Paxton, 2008. "An Empirical Evaluation of the Use of Fixed Cutoff Points in RMSEA Test Statistic in Structural Equation Models," Sociological Methods & Research, , vol. 36(4), pages 462-494, May.
    • Handle: RePEc:sae:somere:v:36:y:2008:i:4:p:462-494
    DOI: 10.1177/0049124108314720
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    References listed on IDEAS

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    1. Yuan, Ke-Hai & Hayashi, Kentaro & Bentler, Peter M., 2007. "Normal theory likelihood ratio statistic for mean and covariance structure analysis under alternative hypotheses," Journal of Multivariate Analysis, Elsevier, vol. 98(6), pages 1262-1282, July.
    2. James Steiger & Alexander Shapiro & Michael Browne, 1985. "On the multivariate asymptotic distribution of sequential Chi-square statistics," Psychometrika, Springer;The Psychometric Society, vol. 50(3), pages 253-263, September.
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