Module 6

Data Management Plans  Definitions ◦ Quality assurance ◦ Quality control ◦ Data contamination ◦ Error Types ◦ Error Handling  QA/QC best practices ◦ Before data collection ◦ During data collection/entry ◦ After data collection/entry

Data Management Plans  After completing this lesson, the participant will be able to: ◦ Define data quality control ◦ Define data quality assurance ◦ Perform quality control and assurance on their data at all stages of the research cycle (before data collection, during data collection/entry, and afterward)

Data Management Plans CollectAssureDescribeDepositPreserveDiscoverIntegrateAnalyze Managing the quality of data during data collection and entry is important, but data quality is monitored and managed throughout the data life cycle

Data Management Plans  Process or phenomenon, other than the one of interest, that affects the variable value  Erroneous values

Data Management Plans  Errors of Commission ◦ Incorrect or inaccurate data ◦ Causes: malfunctioning instrument, mistyped data  Errors of Omission ◦ Data or metadata not recorded ◦ Incomplete data record ◦ Causes: inadequate documentation, human error, anomalies in the field  Logic Errors ◦ Improbable values or value-combinations ◦ Usually detected during data review by a human who is a subject matter expert

Data Management Plans Preventing bad data from contaminating a data set  Quality assurance ◦ Activities that ensure quality of data before collection ◦ Verifying the quality of data obtained from others before use  Quality control ◦ Monitoring and maintaining the quality of data during the research life cycle

Data Management Plans  Create a suitable structure to store the data ◦ Database or well-designed spreadsheet  Define & enforce standards ◦ Metadata ◦ Formats ◦ Codes ◦ Measurement units  Assign responsibility for data quality ◦ Be sure assigned person is educated in QA/QC

Data Management Plans  Double-entry ◦ Data keyed in by two independent people and then checked for agreement with computer verification  Use text-to-speech program to read data back ◦ Serves as a ‘second person’ to help when one is not available  Use a properly designed database ◦ Atomize data: each value is stored (changed) in only one place ◦ Minimize errors using column, row, and relationship validation ◦ Use consistent terminology  Document all changes to data ◦ Avoids duplicate error checking ◦ Allows undo if necessary

Data Management Plans Data Review and Certification for Use It is important to review the data for quality and to certify it for use. Certification allows others to use the data knowing it meets a predetermined level of quality and completeness. Errors found need to be corrected, with documentation of the correction activity annotated on original data sheets. A person familiar with the kind of data being reviewed is essential, because some errors are cryptic and require recognition of a logical inconsistency (ex: incorrect equipment indicated for a particular type of parameter measured)

Data Management Plans Table 1 from Edwards (2000). An illegal-data filter, written in SAS (the data set "All" exists prior to this DATA step, containing the data to be filtered, variable names Y1, Y2, etc., and an observation identifier variable ID). Data Checkum; Set All; message=repeat(" ",39); If Y1 1 then do; message="Y1 is not on the interval [0,1]"; output; end; If Floor(Y2) NE Y2 then do; message="Y2 is not an integer"; output; end; If Y3>Y4 then do; message="Y3 is larger than Y4"; output; end; : (add as many such statements as desired...) : If message NE repeat(" ",39); keep ID message; Proc Print Data=Checkum; Example of Illegal Data Filter

Data Management Plans Look for outliers  Outliers: extreme values for a variable given the statistical model being used  Goal is not to eliminate outliers but to identify potential data contamination, and verify true values

Data Management Plans  Methods to look for outliers ◦ Graphical  Normal probability plots  Regression  Scatter plots  Maps ◦ Statistical  Be sure to transform data when looking for outliers graphically on a graph

Data Management Plans  Document your QA/QC activities to certify data for use ◦ Don’t waste anyones time by forcing them to re-check data that were already QA/QC’d  Record all changes made to the data ◦ All changes from the original record need to be documented to be defensible

Data Management Plans Data contamination results from a process or phenomenon that adversely affects data integrity or allows erroneous values to enter a dataset Quality Assurance and quality control are strategies to: -prevent errors from entering a data set -ensure quality of data -monitor and maintain the quality of data It is important to define and enforce quality assurance and quality control standards before, during, and after the collection and entry of data

Data Management Plans  Edwards, D, Data Quality Assurance. In Ecological Data: Design, Management and Processing. WK Michener and JW Brunt, Eds. Blackwell Science. p  Cook, RB, RJ Olson, P Kanciruk, and LA Hook, Best practices for preparing ecological data sets to share and archive. Bulletin of the Ecological Society of America 82(2):  Chapman, AD, Principles of Data Quality. Report for the Global Biodiversity Information Facility, 2004, Copenhagen. resources/download-publications/bookelets/ resources/download-publications/bookelets/  Grubbs’ Test for outliers. Wikipedia entry, accessed November  Vanderbilt, K. Quality Assurance & Quality Control. Presentation.

Data Management Plans We want to hear from you! CLICK the arrow to take our short survey.