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Module 6: Working with Secondary Data. Common Measures Training Chelmsford, MA September 28, 2006. Goals of Module. Discuss general quality issues relevant to secondary data Discuss general rules for when and how to use secondary data

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module 6 working with secondary data

Module 6: Working with Secondary Data

Common Measures Training

Chelmsford, MA

September 28, 2006

goals of module
Goals of Module
  • Discuss general quality issues relevant to secondary data
  • Discuss general rules for when and how to use secondary data
  • Identify secondary data sources and quality issues likely to arise in Common Measures project
definition of secondary data reprise
Definition of Secondary Data (Reprise)

Secondary data are…

  • Any data that are already collected
  • Any other data not collected with purposes of the Common Measures project in mind

Functional definition: any data whose quality you cannot influence.

Note: Facility-submitted data can be primary or secondary

examples secondary data sources
Examples: Secondary Data Sources
  • Compliance databases
  • Permitting or notification databases
    • E.g., air source registration
  • Self-certification or inspection results from last year’s ERP
  • ERP data collected this year by a non-participating agency
  • Member survey conducted by trade association
  • Environmental sampling data collected for another purpose
  • Other sources this project might consider?
secondary data and data quality
Secondary Data and Data Quality
  • Unlike primary data, little or no control over quality.
  • How to handle secondary data:
    • Accept or reject it, based on DQOs
      • E.g., does it meet our representativeness requirement?
    • If accepted, use it properly and transparently
dqi issues for secondary data
DQI Issues for Secondary Data
  • Some common quality issues with secondary data…
completeness
Completeness
  • Facility universe could be incomplete, or geographically limited
  • Data based on a different definition of the sector could be incomplete for your purposes
  • Data might only be available for a smaller number of indicators than desired
slide8
Bias
  • Consider the source: From industry, or other interested party?
  • Consider the motive: Were inspectors looking to find fault, or to gather a holistic impression of sector performance?
representativeness
Representativeness
  • In a compliance database, problem facilities might be over-represented—or under-represented.
  • Data could range from non-random sample to census
comparability
Comparability

We look at every DQI through the lens of comparability as well

  • No two data collection efforts are identical
  • But there must be a certain amount of similarity for data sets to be compared
evaluating acceptability
Evaluating Acceptability

Consider the range of today\'s topics, e.g.,

  • Timing (How long ago? What time of year?)
  • How was the universe defined?
  • What indicators were used?
  • How were indicator questions phrased?
  • How were data gathered and processed?
  • Were data quality objectives established? Were they met?
availability of information to assess acceptability
Availability of Information to Assess Acceptability
  • Especially in the case of older secondary sources, you might find a lack of information about the circumstances under which data were collected.
  • However, many regulatory data sets will have quality plans associated with them, which are good source of information.
evaluating data sources
Evaluating Data Sources

Find at least one DQI issue per data source:

  • Compliance databases
  • Permitting or notification databases (e.g., air source registration)
  • Self-certification or inspection results from last year’s ERP
  • ERP data collected this year by a non-participating agency
  • Member survey conducted by trade association
  • Environmental sampling data collected for another purpose
  • Other sources this project might consider?
for more information
For more information…

Contact Michael Crow

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