United States Department of Transportation
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Guide to Good Statistical Practice in the Transportation Field
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2003-05-01
[PDF-561.00 KB]
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Abstract:Quality of data has many faces. Primarily, it has to be relevant (i.e., useful) to its users. Relevance is achieved through a series of steps starting with a planning process that links user needs to data requirements. It continues through acquisition of data that is accurate in measuring what it was designed to measure and produced in a timely manner. Finally, the data must be made accessible and easy to interpret for the users. In a more global sense, data systems also need to be complete and comparable (to both other data systems and to earlier versions). The creation of data that address all of the facets of quality is a unified effort of all of the development phases from the initial data system objectives, through system design, collection, processing, and dissemination to the users. These sequential phases are like links in a chain. The sufficiency of each phase must be maintained to achieve relevance. This document is intended to help management and data system owners achieve relevance through that sequential process. This guidebook may be down loaded as a single PDF file (39 p.; 150kb) or viewed as individual chapters in HTML format. Each chapter gives principles of collection, guidelines, and references.
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