This document discusses data quality issues and methods for addressing them. It defines two perspectives on quality - conformance to requirements and fitness for use. There are four costs of poor quality: reputation, prevention, detection, and repair. Methods for addressing quality issues include data editing, imputation, and fabrication. Data editing involves techniques like range tests, deterministic tests, and probabilistic tests to identify potential errors. Imputation is used to handle missing or misreported data, but care must be taken not to over-impute. Fabrication poses a threat as intentionally entering false data undermines quality. Overall the document emphasizes improving quality through understanding sources of error rather than over-editing data.

1. Data Quality
Issues and “Fixes”

2. Two Definitions of Quality
• Conformance to Requirements
• (Traditional Producer-Oriented
Definition)
• Fitness for Use
• (Modern Client-Oriented
Definition)

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. 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. Quality and Cost 2 Worlds

6. Repair Methods
• Goal is “Fixing” to Fit Use
• Data Editing
• Data Imputation
• Data Fabrication
• Raking at NSS

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. Data Editing Techniques
• Minimizing Processing Errors
• Definitional (e.g., Range) Tests
• Deterministic Tests
• Probabilistic Tests
– Outlier Tests
– Ratio Tests

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. 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. Not all errors need to be
corrected
Resist your Perfectionist
Tendencies

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. 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. 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. 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)