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QUALITY ASSURANCE REVIEWS: HOW THEY DIFFER FROM PEER REVIEWS

Research papers and reports written by scientists and engineers in the United States Environmental Protection Agency are reviewed by the agency's quality assurance staff. EPA papers and reports are subjected to peer reviews that check for the validity of conclusions and the general agreement wi...

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Bibliographic Details
Published in:Quality assurance (San Diego, Calif.) Calif.), 1999-02, Vol.6 (2), p.75-85
Main Author: Adams, Nancy H
Format: Article
Language:English
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Summary:Research papers and reports written by scientists and engineers in the United States Environmental Protection Agency are reviewed by the agency's quality assurance staff. EPA papers and reports are subjected to peer reviews that check for the validity of conclusions and the general agreement with the body of technical knowledge in the subject area. Quality assurance reviews differ from peer reviews in that the focus of the quality assurance review is on the following criteria : Consistency: Were reasonable and consistent units of measurement and generally acceptable formulas used throughout? Are the appropriate number of significant figures reported? Correctness: Were matrix-compatible methods used? Were measure ments within the working range of the method? Can measurements be traced to a recognized standard or source (e.g., the National Institute of Standards and Technology)? Can calculations be verified, starting from representative raw data and proceeding to the summary data presented in the paper or report? Coherence: Do the stated conclusions follow from the data presented? Are the assumptions clearly stated? Are inconsistencies between data and conclusions discussed? Clarity: Are special terms and acronyms defined? Can a person with a general technical background in the subject understand the paper or report? Conformance: Did the study follow the test/quality assurance plan, with appropriate calibrations and other quality-control checks, audits, and data validations? If not, is there a discussion of problems? Concordance: Were data quality objectives met? Were the data quality indicator goals achieved for precision, accuracy, representativeness, comparability, and completeness? The importance of these quality assurance review criteria are discussed along with examples from current work.
ISSN:1052-9411
1521-0677
DOI:10.1080/105294199277879