Guide to Good Statistical Practice in the Transportation Field

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 accessibleand easy to interpret for the users. In a more global
sense, data systems also need to be completeand 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