1. Tool for Assessing Impact of Changing Editing Rules On Cost & Quality Alaa Al-Hamad, Begoña Martín, Gary Brown Processing, Editing & Imputation Branch Business Surveys

2. 1. Overview Data Editing in the ONS Error Detection Rules Problems Surveys Managers Dilemma Proposed Tool Tool illustration & output Conclusion and Further Work

3. A costly component of the data cleaning process, in the ONS, is data editing Data Editing is defined as An activity aimed at detecting and correcting errors in data – ONS Glossary In practice this involves: the detection of error suspect data (using Editing Rules) Ex. Fail if A + B ‘>‘ (estimated parameter) Verification/correction of error suspect data from source 2. Editing in the ONS

4. If rule parameters are too conservative increased response burden (unnecessary recontacts) reduced data quality (over-validation errors and biases) costly in terms of staff & resources If rule parameters are too liberal Allows uncorrected errors through reduced data quality costly in terms of reputation less costly in terms of staff & resources 3. Detection Rules Problems

5. When managers are asked to achieve savings ‘Savings vs Quality Impact’ An easy way to make quick savings is to loosen the rules parameters so that less data will be edited The challenge is: Where to stop. What impact will such action have on the estimates? Remember Quality loss is not defined solely by number of error failure but also by the size of the error 4. Surveys Managers Dilemma

6. 5. Proposed Tool Ideally what is required is a dynamic routine for editing rules parameters that is applicable to all business surveys and: offers a choice of different quality measurement criteria considers all editing rules simultaneously outputs proposed changes to parameters outputs savings and quality loss per changed rule and in total A dynamic routine has not yet been developed so we have pursued a pragmatic solution with the same criteria

7. 6. Suitable Measurements A Measure of Savings: Savings = Number of records no longer require editing A measure of impact: Exact impact on final estimates is difficult to calculate time consuming costly Instead, use relative change = where X = a response before and after parameter change. w = a calibration weight.

8. 7. Routine illustration Existing Rules Fail Pass No error Error B*

9. 7. Routine illustration Existing Rules Loosen Rules Fail Pass No error Error B* Pass A Fail Pass Fail Pass B Savings # (A + B) Errors missed # (B)

10. 8. Example of Rules Changes Rule 1 Rule 2 Rule 3 Alter Gate 1 Alter Gate 2 Alter Gate 3

11. 9. Routine Results RulesRoutine Output Gate1Gate2Gate3Savings Errors Missed Relative Change (%) 6004010111770.56 60040502051711 60040 1921581.28 60040201601261.32 250401002432092.96 300401002432092.96 600401002432092.96 600302002742403.93 600402002742403.93 600502002742403.93

12. 10. Conclusions Often changes to validation rules to achieve saving are made in isolation and without consideration of the impact of these changes on the quality of the survey output In this work we are offering a simple but effective decision support tool –to quantify savings & loss in quality resulting from changing editing rules –help managers identify the editing rules that have the most impact on quality - Identify the parameters that minimise quality loss given set savings, and vice versa

13. 11. Further Work Other elements of further work Make the routine more dynamic Enhancing the impact measure Investigating varying the parameters by domains (eg Standard Industrial Classification (SIC), employment sizeband) Apply the routine to other surveys

14. Over to you! 12. Questions