Point Estimation, Hypothesis Testing, and Interval Estimation Using the RMSEA: Some Comments and a Reply to Hayduk and Glaser

Abstract Hayduk and Glaser (2000) asserted that the most commonly used point estimate of the Root Mean Square Error of Approximation index of fit (Steiger and (b) the truncated point estimate Rt = max(R, 0) effectively throws away a substantial part of the sampling distribution of the test statistic with "proper models," rendering it useless a substantial portion of the time. In this article, I demonstrate that both issues discussed by Hayduk and Glaser are actually not problems at all. The first "problem" derives from a false premise by Hayduk and Glaser that Steiger (1995) specifically warned about in an earlier publication. The second so-called problem results from the point estimate satisfying a fundamental property of a good estimator and can be shown to have virtually no negative implications for statistical practice.