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The Information Technology Laboratory (ITL) at the National Institute of Standards and Technology (NIST) promotes the U.S. economy and public welfare by providing technical leadership for the nation's measurement and standards infrastructure. ITL develops tests, test methods, reference data, proof of concept implementations, and technical analysis to advance the development and productive use of information technology. ITL's responsibilities include the development of technical, physical, administrative, and management standards and guidelines for the cost-effective security and privacy of sensitive unclassified information in Federal computer systems. This Special Publication 800-series reports on ITL's research, guidance, and outreach efforts in computer security and its collaborative activities with industry, government, and academic organizations.

Summary.  Several new estimators of the between‐study variability in a heterogeneous random effects meta‐analysis model are derived. One is the unbiased statistic which is locally optimal for small values of the parameter. Others are Bayes procedures within a class of quadratic statistics for a diffuse prior with different choices of the prior mean. These estimators are compared with the DerSimonian–Laird procedure and the Hedges statistic in particular via the quadratic risk of the treatment effect estimator. A Monte Carlo study supports the usage of confidence intervals derived from the new estimators.

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The usefulness of weighted means statistics as a consensus mean estimator in collaborative studies is discussed. A random effects model designed to combine information from several sources is employed to justify their appeal to metrologists. Some methods of estimating the uncertainties and of constructing confidence intervals are reviewed.