Laura Crespo SHARE Meeting on Data Cleaning The Analysis of Interviewers’ Remarks Laura Crespo Spanish Team CEMFI Frankfurt December 6, 2007

Laura Crespo SHARE Meeting on Data Cleaning This is based on the remarks and feedback from PL, NL, BE-fr, DK, GR and ES! Comments and suggestions from other countries’ experiences are very welcome!

Laura Crespo SHARE Meeting on Data Cleaning When a “remark” should be recorded?: When a response (or non-response) needs to be commented. When Blaise does not accept the answer provided by the respondent. When a response is difficult to code. When a response needs to be clarified.

Laura Crespo SHARE Meeting on Data Cleaning Therefore, Good news: They may contain very useful info for data cleaning (and also useful for SHARE-users, working-groups, country teams and even the survey agency). They are an important source of info to detect errors, missing info, clarifications, problems with questions. One of the first things to look into. Bad news: Very iwer-specific (large heterogeneity across iwers, questions, and even countries). They need a case-by-case analysis. Very time consuming. At some point, they will need translation to english.

Laura Crespo SHARE Meeting on Data Cleaning Dealing with iwer remarks: Steps 1.Download them (CV and Main) from your country’s data site on 2.Have a look at them and try to define specific categories based on their content and potential use. Very often we will need to check the corresponding question to understand perfectly the remark. Categories: 1.Remarks that should be investigated for data cleaning. 2.Useful remarks for researchers, working groups or country teams. 3.Both (useful for data cleaning and SHARE users). 4.Other remarks that should be investigated.

Laura Crespo SHARE Meeting on Data Cleaning Dealing with iwer remarks: Steps 3. Focus on those that may be useful for data cleaning and identify which correction should be made. 4. Write programs to correct data or flag cases following instructions (examples do files from wave 1?)

Laura Crespo SHARE Meeting on Data Cleaning What I have done so far: Step 2) Categories with different colours 1.Remarks that should be investigated for data cleaning: Specific amounts, years, time periods, frequencies. Amounts in “pesetas” (Pre-Euro currency). Gross terms instead of net terms or viceversa.

Laura Crespo SHARE Meeting on Data Cleaning Answer category: Information that may be recorded or imputed to one of the categories already defined (instead of “Other” option) or should be back-checked with the reported answer: –(EP) employment status, pensions, eligibility for pensions, occupation (ISCO), economic activity (NACE). –(HO) housing status. –(HC) health care payments. –(GS, WS, PT) positions during the tests. –(CH) age of children, education. –(DN) marital status. –(PH) illness and disorders, medication, surgery… –(IV) location and type of house

Laura Crespo SHARE Meeting on Data Cleaning Mistakes by iwers when coding the answers or the proxy status The system does not accept a particular answer (i.e, years, dates, amounts). Corrected information that is included by the iwer when the respondent realizes that he/she made a mistake or reported wrong info previously.

Laura Crespo SHARE Meeting on Data Cleaning 2.Remarks useful for researchers, working groups or country teams: Tests not performed or interrupted due to illness, disabilities, fears, concerns, not safe. Problems encountered during the cognitive or physical tests (due to distraction, nerves, specific physical impairments or conditions,..). Presence of another person during the test. Does not remember/Does not Know. Does not know to read or write.

Laura Crespo SHARE Meeting on Data Cleaning Difficulties with Spanish (language problems). IWERS' opinions about the reliability of the answers: contradictions, attitudes, random answers, reluctance… Further clarifications or explanations of reported answers. Problems or circumstances with the drop-offs (help provided by the iwer, by a relative,…).

Laura Crespo SHARE Meeting on Data Cleaning More specific motives for non-response (private information, does not understand the question). Complaints relating to the length of questionnaire.

Laura Crespo SHARE Meeting on Data Cleaning 3. Both (useful for data cleaning and SHARE users): Use of proxies (need to be back-checked with SMS data and also useful for researcher). 4.Other remarks that should be investigated: Unclear meaning. Phone numbers and addresses (may be important for contacts in next waves). Some examples.

Laura Crespo SHARE Meeting on Data Cleaning Step 3) Focus on remarks for data cleaning and identify the correction needed. Examples: NL, GR’s files. Step 4) Corrections (do files): Instructions on this? Even if a correction or imputation can not be made, the remark could still be useful for SHARE users, working groups/country teams and CentERdata (revision of questionnaire for Wave3).