1. The Edit Anders Norberg, Statistics Sweden (SCB) Work Session on Statistical Data Editing Ljubljana, Slovenia, 9-11 May 2011

2. The environment of SELEKT Input, throughput, output, use

3. Suspicion

4. SELEKT 1.1 Survey specific cold adapter (SAS code) Data preparation SAS data set PRE-SELEKT Parameter specifications, Analysis of cold data AUTOSELEKT Score calculation & record flagging Records to FOLLOW-UP Process data and reports Input (hot) survey data Records to IMPUTATION Raw+edited past (cold) survey data Survey specific hot adapter (SAS code) Data preparation SAS data set Table of Parameters Table of Estimates Accepted records CLAN estimation software SNOWDON -X analysis of edits Edits

5. Glossary of Terms on Statistical Data Editing (1) “EDIT RULE SPECIFICATION CHECK RULE SPECIFICATION A set of check rules that should be applied in the given editing task.”

6. Glossary of Terms on Statistical Data Editing (2) “CHECKING RULE A logical condition or a restriction to the value of a data item or a data group which must be met if the data is to be considered correct. In various connections other terms are used, e.g. edit rule.”

7. Recommended Practices for Editing and Imputation in Cross- sectional Business Surveys “EDIT A logical condition or a restriction to the value of a data item or a data group which must be met if the data is to be considered correct. Also known as edit rule or checking rule.”

8. Example 1 if Occupation = ‘Doctor’ and not (29000 < Salary < 71000) then Errcode_A01 = ‘Flag’

9. Example 1 The test variable if Occupation = ‘Doctor’ and not (29000 < Salary < 71000) then Errcode_A01 = ‘Flag’

10. Example 1 The edit group if Occupation = ‘Doctor’ and not (29000 < Salary < 71000) then Errcode_A01 = ‘Flag’

11. Example 1 The acceptance region if Occupation = ‘Doctor’ and not (29000 < Salary < 71000) then Errcode_A01 = ‘Flag’

12. Example 2 The test variable i f Occupation = ‘Doctor’ and not (29000 < Salary < 71000) or Occupation = ‘Nurse’ and not (23300 < Salary < 43800) then Errcode_A02 = ‘Flag’

13. Example 2 The edit groups i f Occupation = ‘Doctor’ and not (29000 < Salary < 71000) or Occupation = ‘Nurse’ and not (23300 < Salary < 43800) then Errcode_A02 = ‘Flag’

14. Example 2 The acceptance regions i f Occupation = ‘Doctor’ and not (29000 < Salary < 71000) or Occupation = ‘Nurse’ and not (23300 < Salary < 43800) then Errcode_A02 = ‘Flag’

15. Edits EDIT GROUP AND ACCEPTANCE REGION Edit identification Edit group Acceptance region EDIT Edit identification Type of edit Active Section Internal error message External error message Instruction for data review Un-edited test variable Error flag

16. Edits EDIT GROUP AND ACCEPTANCE REGION Edit identification Edit group Acceptance region EDIT Edit identification Type of edit Active Section Internal error message External error message Instruction for data review Un-edited test variable Error flag LINK Edit identification Survey variable IMPACT ON STATISTICS Survey variable Potent. impact on statistics 5 4 3 FLAGGING EDITS, VARIABLES AND UNITS EDIT PRACTICAL SUPPORT Edit identification Standard edit rule Edited test variable Suspicion probability value produced by the SELEKT system 2 1

17. My questions (1) Can most edits be described as consisting of the components –test variable –edit group –acceptance region ? What types of edits can not?

18. My questions (2) If the edits can be described this way, what arguments are there for saying that –one edit has only one edit group and one acceptance region –one edit can be composed of many edit groups with one acceptance region each?

19. My questions (3) Can you give me examples of similar modeling of edits metadata storage for edits edit script generator using a standard metadata storage for edits