In spite of being the best possible lower bound to reliability, the greatest lower bound (g.l.b.) has rarely been used in practice, no doubt due to its serious upward sampling bias. A pioneering attempt to estimate this bias was made by Shapiro and ten Berge (2000), but their bias formula has both theoretical and empirical limitations. We propose a new Wherry-like adjusted g.l.b. estimator that has less sampling bias than the classical estimator. Resampling methods are further used to correct the bias of this new estimator in both direct and indirect ways. Numerical simulations confirm the effectiveness of the bootstrap bias-correction to the adjusted g.l.b.
* This study was supported, in part, by grants DA01070 and DA00017 from the US National Institute on Drug Abuse and by grant 5R44CA137841 from the US National Cancer Institute. This study was presented at the 82th Symposium of the Behaviormetric Society of Japan on Recent Developments in Latent Variables Modeling, Tokyo University, Tokyo, Japan, August, 2004. Requests for reprints should be sent to Peter M. Bentler, Department of Psychology, UCLA, Box 951563, Los Angeles, CA 90095-1563. E-mail: email@example.com.
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