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ILCS Raking


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ILCS Raking

  1. 1. CR RC Data Quality Issues and “Fixes” Dr. Fritz Scheuren July 3, 2009 (for academic purposes only)
  2. 2. Two Definitions of Quality • Conformance to Requirements • (Traditional Producer-Oriented Definition) • Fitness for Use • (Modern Client-Oriented Definition)
  3. 3. Definition of Process Quality • Process Improvements Focus • (Do It Right the First Time) • Can be Reduced to Slogans • Can also lead to Continuous Improvements • Kaisen
  4. 4. Be Real Four Quality Costs • Costs of Reputation and Loss of Business from Inaction • Cost of Prevention to Avoid Errors • Cost of Detection to Find Errors • Cost of Repairing Errors Found
  5. 5. Quality and Cost 2 Worlds
  6. 6. Repair Methods • Goal is “Fixing” to Fit Use • Data Editing • Data Imputation • Data Fabrication • Raking at NSS
  7. 7. Data Editing • Honest Differences of Opinion or Real Errors? • Need for Redundancy in System for Can’t Fail Items • Achieving Measurability to Frame Expectations and Improvements
  8. 8. Data Editing Techniques • Minimizing Processing Errors • Definitional (e.g., Range) Tests • Deterministic Tests • Probabilistic Tests – Outlier Tests – Ratio Tests
  9. 9. Types of Edits Illustrated • Range Test Age Negative • Deterministic Tests If Age =14, then code as Child • Probabilistic Tests If Income $1,000,000, take a look
  10. 10. Practical Editing Tips • Edit for Diagnosis, not just Correction • Don’t Edit Outside Your Confidence Interval • Preserve the Original Dataset as Backup to Avoid Irreversible Changes • Keep Tallies of all Errors Found
  11. 11. Not all errors need to be corrected Resist your Perfectionist Tendencies
  12. 12. More Practical Edit Tips • Use your skilled staff to improve system rather than just edit data • Never just depend on Intuition but still use it too! • Employ Redundancy, Frugally!
  13. 13. Capture Recapture Methods (Double Keying Example) • Two-by-Two Table with Cells A B C D • Comparing Data Keyed the Same each time (A) with Errors Detected, (B and C) • How to Estimate D? • One Model D = BC/A?
  14. 14. Bottom Line Take-Away • Use Data Checking to Understand Data’s Fitness for Use • Edit but Don’t Over-Edit • Use Edit Checks to Prevent Future Errors
  15. 15. Data Editing and Data Imputation • Joint Role of Imputation and Editing No Clear Line? • Editing “fixes” Often are Model-Based Hunches • Data Quality (editing) • Information Quality (imputation)
  16. 16. Imputation Versus Editing • What is Imputation? • Handles Missing and Misreported Data • Imputation Goal is roughly right! Information Quality • Editing Goal often “correction” Exactly right? Data Quality
  17. 17. Data Imputation Techniques • Imputation Needs More Justification when Data Quality is the Goal • Must be no more than Cosmetic in Nature, if done at all • Can only be Aggressively applied for Information Quality Goal
  18. 18. Fellegi-Holt Example • Identify Errors with Automated Edit Detection Software • Hot Deck acceptable values from Records that Pass Edits • Can be worth doing if errors are minor or cosmetic (e.g., Rounding)
  19. 19. More on Imputation • Treat Influential Errors Individually not just Automatically • That Said, Software Fixes can lead to Better Documentation (Paradata Matters) • Need to Measure Variance Impacts • Provide a natural break to Overediting but seldom used for this.
  20. 20. Edit/Imputation Summary • Most Editing Mainly Eliminates the Bad • Replacing it with a (Good?)Guess of some Sort • Imputation emphasizes Guessing even more
  21. 21. More Editing/Imputation • Best Imputation Practice tries to quantify Guessing impact on Information Quality • Editing has not improved as much as Imputation • Editing/Imputation needs more Joint Theory, especially to Measure and Use Mean Square Error Impacts
  22. 22. First Illustrative Example • Fabrication/Falsification • Illustrate the General Points about Editing and Imputation • Emphasize Importance of Fabrication threat to Quality
  23. 23. Fabrication/Falsification • Respondent/Interviewer Make up Data • How Common? • How to Reduce? • How to Detect?
  24. 24. Right Structure Right Resources • Examine Practice Elsewhere? • Website • Key is right incentives • Good staff/training • But Eternal Vigilance
  25. 25. Second Illustration • Raking Application at NSS • To link up to Next Talk • To illustrate Information Quality that is fit for use despite Data Quality
  26. 26. Raking Quality “Fix” • What is Raking? • How does it improve quality? Not Data Quality But Information Quality • Sometimes both -- Better Point Estimates More Stable (smaller variances)
  27. 27. Quality Summary • Editing Data Quality • Imputation Information Quality • Raking Information Quality • Fabrication Can Harm Both • Must be guarded against always
  28. 28. Almost Done Now • Tried to Stay Practical, with a Frank Discussion of Key Weaknesses in Current Practice • Deeper Understanding of Data Quality • But at an Applied Level
  29. 29. ÞÝáñѳϳÉáõ ÃÛáõ Ý Fritz Scheuren