Data Quality Objectives (DQO) process

The DQO Process is a seven-step planning approach to develop sampling designs for data collection activities that support decision making. This process uses systematic planning and statistical hypothesis testing to differentiate between two or more clearly defined alternatives. The DQO Process is iterative and allows the planning team to incorporate new information and modify outputs from previous steps as inputs for a subsequent step. Although the principles of systematic planning and the DQO Process are applicable to all scientific studies, the DQO Process is particularly designed to address problems that require making a decision between two clear alternatives. The final outcome of the DQO Process is a design for collecting data (e.g., the number of samples to collect, and when, where, and how to collect the samples), together with limits on the probabilities of making decision errors.

The DQO process has seven main steps which are described in the following sections.

STEP 1: STATE THE PROBLEM

STEP 2: IDENTIFY THE DECISION

STEP 3: IDENTIFY THE INPUTS

STEP 4: DEFINE THE BOUNDARIES

STEP 5: DEVELOP A DECISION RULE

STEP 6: LIMITS ON THE DECISION ERROR

STEP 7: OPTIMISE THE DESIGN

VSP Assumes You Got Steps 1-5 Right!

References:

EPA. 2006a. Guidance on Systematic Planning Using the Data Quality Objectives Process. EPA QA/G-4, EPA/240/B-06/001, U.S. Environmental Protection Agency, Office of Environmental Information, Washington DC.