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Data Quality Assessment (DQA)

 

Data Quality Assessment (DQA) is a rigorous scientific and statistical evaluation to determine if environmental data are of the right type, quality, and quantity to support their intended use. The process involves review of data quality objectives (DQO), sampling purpose, sampling design, sampling methods, documentation, analytical procedures, validation procedures, data reduction procedures, review of database procedures, and review of statistical methods used for decision making. The process is not limited to analytical data, but includes all data types used by the decision makers. Although data validation is often employed during DQA, it is not always necessary. The question that needs to be answered by the data quality assessment is "Are the data appropriate for the intended use?       

DQA is built on a fundamental premise: data quality is meaningful only when it relates to the intended use of the data.  Data Quality does not exist in a vacuum, a reviewer needs to know in what context a data set is to be used in order to establish a relevant yardstick for judging whether or not the data is acceptable.  By using DQA, a reviewer can answer four (4) important questions:

1-Can a decision (or estimate) be made with the desired level of certainty, given the quality of the data?

2- How well did the sampling design perform?

3- If the same sampling design strategy is used again for a similar study, would the data be expected to support the same intended use with the desired level of certainty?

4- Is it likely that sufficient samples were taken to enable the reviewer to see an effect if it was really present?

It is important to note that the DQA process covers all components of decision making including planning, implementation, data review, and decision making. It is not a process that is limited to review of analytical data (see data validation). While data validation and verification are important processes, they are activities that are often applied only to analytical data. High quality analytical data can be useless if the samples were collected improperly, if there are errors in the electronic database, if improper statistical test were selected, or if the sampling plan did not generate sufficient samples.

The five steps in the DQA process are:

1- Review the projectís objectives and Sampling Design

2- Conduct a Preliminary Data Review

3- Select the Statistical Test

4- Verify the Assumptions of the Statistical Test

5- Draw Conclusions from the Data

We look forward to the privilege of serving your project needs.

 

   
 
 

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Data Validation Services  - Data Quality Assessment (DQA) Data Quality Objectives (DQO)  -  Environmental Impact Assessment (EIA)

Laboratory Procurement/Audits  -  Field Audits Quality Assurance Project Plan(QAPjP)

 Data Management   -   Project Chemist  -  Litigation Support   -  Training/Seminars

 

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