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