ASA Connect

Washington Statistical Society Statistical Seminar Event
Title: Transparency in Statistical Information for all Federal Statistical Agencies
 
Speaker: Dr. Dan Gillman, Information Scientist, Office of Surveys Methods Research, US Bureau of Labor Statistics (BLS)
Chair: Dr. Michael Jadoo, Office of Productivity and Technology, BLS
Date: Thursday, May 19, 2022
Time: 4:00 – 5:00PM EST

Abstract:

In the United States, the thirteen principal statistical agencies, units, or programs in many other agencies produce various key statistics ranging through many subject areas. Assuring these statistics are widely available, trustworthy, and authoritative is essential for policy makers and program administrators at all levels of government, private sector decision makers, researchers, the media, and for the public. Transparency of official estimates and the ability to inspect the methodologies (designs, algorithms, code, definitions) behind the production of these estimates is necessary for understanding and accepting the trust and authority of them. This needs to be accomplished through the production and dissemination of all relevant documentation.
 
In April 2019, CNSTAT (Committee on National Statistics under the National Academies of Science, Medicine, and Engineering), sponsored by NSF/NCSES, approved a panel to investigate transparency and reproducibility in federal statistics. The panel issued its report (Transparency in Statistical Information for the National Center for Science and Engineering Statistics and All Federal Statistical Agencies) in November 2021. Dan Gillman will describe this report and provide a technical discussion of how documentation (metadata in a more formal sense) supports transparency. Three main criteria for metadata supporting transparency are considered: conformance to a standard, quality of metadata, and usability of a system to find, link, and use metadata. These criteria are defined, and how they relate to transparency will be discussed.
 
About the speaker: Dan Gillman is an information scientist in the Office of Surveys Methods Research at the US Bureau of Labor Statistics (BLS). His research interests include metadata, standards, classification, terminology, and transparency. He recently served on the Panel on Transparency and Reproducibility in Federal Statistics sponsored by the US Committee for National Statistics under the National Academy of Sciences, for which the report was issued in November 2021. In addition, Dan has been an integral member of and has chaired several national and international standards development efforts, especially for statistical metadata. He is also an internal consultant to several metadata systems development projects at BLS and the Department of Labor and has led the effort to build a taxonomy of terms for all BLS time series.
 
For additional information, please contact Yan Li (yli6@umd.edu), WSS Methodology Program Chair.

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