Data Quality Assessment
Purpose and Goals
DQA is built on a fundamental premise: data quality, as a concept, is meaningful only when it
relates to the intended use of the data. Data quality does not exist in a vacuum; one must know in what context a data set is to be used
in order to establish a relevant yard stick for judging whether or not the data set is adequate. By using the DQA Process, one can answer two
fundamental questions:
- Can the decision (or estimate) be made with the desired confidence, given the quality of the data
set?
- How well can the sampling design be expected to perform over a wide range of possible outcomes?
The first question addresses the data user's immediate needs. For example, if the data provide evidence
strongly in favor of one course of action over another, then the decision maker can proceed knowing that the decision will be supported by unambiguous
data. If, however, the data do not show sufficiently strong evidence to favor one alternative, then the data analysis alerts the decision maker
to this uncertainty. The decision maker now is in a position to make an informed choice about how to proceed (such as collect more or different
data before making the decision, or proceed with the decision despite the relatively high, but acceptable, probability of drawing an erroneous
conclusion).
The second question addresses the data user's potential future needs. For example, if investigators
decide to use a certain sampling design at a different location from where the design was first used, they should determine how well the design
can be expected to perform given that the outcomes and environmental conditions of this sampling event will be different from those of the original
event. Because environmental conditions will vary from one location or time to another, the adequacy of the sampling design approach should be
evaluated over a broad range of possible outcomes and conditions.
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