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What Are The Key Factors Influencing Data Quality?

Data Quality Indicators

The Quality of Data Collection & Analysis

• Precision • Sensitivity • Representativeness • Comparability • Completeness • Bias

• Verification • Validation • Integrity

1.1.2 Data Quality Indicators and Quality of Data Collection and Analysis4

Data Quality indicators used in this project are presented below.

  • Precision is the measure of agreement among repeated measurements of the same property under identical or substantially similar conditions.

  • Sensitivity is the capability of a method or instrument to discriminate between measurement responses representing different levels of the variable of interest.

  • Representativeness is the measure of the degree to which data suitably represent a characteristic of a population, parameter variations at a sampling point, a process condition, or an environmental condition.

  • Comparability is a qualitative expression of the measure of confidence that two or more data sets may contribute to a common analysis.

  • Completeness is a measure of the amount of valid data obtained from a measurement system, expressed as a percentage of the number of valid measurements that should have been collected.

  • Bias is systematic or persistent distortion of a measurement process that causes error in one direction.

  • Data validation is an analyte and sample matrix-specific process to determine the analytical quality of a specific data set.

4 Source: EPA Introduction to Data Quality Indicators http://epa.gov/quality/trcourse.html

The States Common Measures Project Final Report

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