1. Jibby Medina & Kelly Ward CLEANING THE ELSA LIFE HISTORY INTERVIEW

2. Overview ELSA Life History Interview Similarities & differences with SHARELIFE Objectives of cleaning Problems identified Future tasks

3. Key facts about ELSA People aged 50+ and their partners Multi-disciplinary – health, economic & social Longitudinal – every 2 years (2002 to present) Collaboration between NatCen, UCL and IFS Funding – UK Government Departments & US National Institute on Aging

4. ELSA Life History

5. The Life History Interview collected data on: Children & fertility Partners Accommodation Housing and geographical mobility Living situation when aged 10 Work history Health & health care Relationships with parents as a child Other important life events

6. How we collect Life Histories Life grid method - utilises our understanding of memory processes in order to help people remember past events Respondents selected from ELSA Wave 3 7855 individual interviews

7. ELSA Calendar

9. SHARE Calendar

10. SHARELIFE & ELSA

11. SHARELIFE & ELSA Similarities Programmes are very similar Collecting similar information Use calendar to help respondents answer questions Same age group of respondents

12. SHARELIFE & ELSA Differences Additional questions (e.g. health care) Some questions omitted (e.g. full address) Longer interview Some general program differences Designed to be used in many European countries

13. Cleaning Objectives & Problems Identified

14. General problems 1.Errors in recording gender – complications when checking data on pregnancies and fathering of children 2.Respondents coded as entering a module but no subsequent information collected – i.e. programme looks like interviewer has skipped through questions.

15. Objectives of cleaning A.Checking all variable labels and value labels are clear and make sense B.Checking range values operate within the CAPI limits C.Checking the routing of each question has been followed accordingly D.Checking consistency of dates collected

16. A. Checking variable names and labels 1.It is vital that the variable names and labels correspond with the questionnaire. The ELSA dataset has used the following variable naming system: 1.RA= Accommodation module 2.RW= Employment module Each variable within RW starts with this prefix e.g. RWFTC

17. A. Checking variable names and labels 2.Variable labels produced from CAPI often have unnecessary information included. Need to tidy these up and ensure that information identifies the purpose of the variable e.g RWFTE “Age finished education” should be “Age finished continuous full-time education at school or college”

18. B. Checking range values 1.Respondents saying they had given birth to additional children then list 0 children at a subsequent question – consider amending range check for future to 1+ 2.Date of illness >= the respondent's date of birth 3.Age at first menstrual period = e.g. 42

19. B. Checking range values 4.From multi-code questions to dichotomous data Invalid values for dichotomous data from multi-code data: RHPBC: Which conditions on this card, if any, accounted for ill health or disability (that you had as an adult)?” 1 Back pain 6 Diabetes or high blood sugar 2 Arthritis7 Stroke 3 Osteoporosis8 Asthma 4 Angina or heart attack9 Respiratory problems 5 Other heart disease10 Severe headaches or migraines Variables for each condition should be coded as 0 / 1 (did not have / had condition), N/A or DK.

20. C. Checking routing has been followed 1.Respondents who answer one question but do NOT answer the subsequent question as they should RCCHC: Our records show that when we last interviewed you, you had a child called MARY, whose date of birth was 22 MAY 1979. Are these details correct? 1 Yes - Details correct330 cases 2 No - Some details need changing 20 cases 3 No - Respondent never had this child 5 cases IF child details = correct OR need changing [IF RCCHC = 1, 2] RCALV: Is this child still alive? 350 cases should answer – but 345 did 1 Yes310 cases 2 No 35 cases -1 Not applicable 10 cases

21. C. Checking routing has been followed 2. Respondents with non-valid responses at one point in a routed sequence RPLTR: Did you legally marry PETER at a later time? (incl. legally recognised Civil Partnership). 1 Yes200 2 No100 -1 Not applicable 80 RPREL: Can I check, did you get married with a religious ceremony of some kind, or at a register office or approved premises? 1 Religious ceremony of some kind102 2 Civil marriage in register office or approved premises 50 3 Both religious ceremony and register office/approved premises 50 -1 Not applicable178 RPSTL: Are you still living with PETER? 1 Yes250 2 No 50 -1 Not applicable 80

22. C. Checking routing has been followed 3.Respondents recorded as not entering a module, yet they had provided answers later in the module 4.Respondents recorded as answering ‘don’t know’ RABO: I would like to ask you about the first residence you lived in when you were born. Did you live there for more than six months?” – Some respondents answer DK to this and then went on to be coded as N/A at this next question – RASTB: In what year did you start living in your first residence that you lived in for six months or more ?

23. C. Checking routing has been followed 6.Respondents expected to provide additional string information ROINTRO: So far we have asked you about some specific areas of your life. We understand that there may be other aspects of your life that are important. Is there anything else that has happened in your life that you'd like to tell us about? Yes / No If Yes, enter details [String] Some respondents answer ‘Yes’ but then there is no detail. So recode these to ‘No’

24. D. Checking consistency of dates 1.Date / year of death > DOB RCALV: Is this child still alive? RCDBC: Can I check, was Mary's date of birth 22/05/1979? 2.Respondents’ dates of employment must be consistent RASTP: In what year did you start your first paid job which lasted for 6 months or more? VS RWEDY: In what year did you stop doing that job? Range of values allowed = 1850 to 9997. Year ended job >= year started same job if the person is in the same job. Plus, wide range allows for values e.g. 9996 to be entered in error, potentially referring to 1996!

25. Future Tasks

26. Wave 3 Life History data Wave 3 data We have checked, cleaned and documented the dataset We have also derived a small number of variables to assist users Currently the data is archived as a flat file which includes all the life history details Additional Enhancements: In the future, we may potentially create additional subsets of data at a hierarchical level i.e. an employment history dataset Future waves Uncertain when we will collect life history again at the moment. Benefits of having checked first collection means that we now have list of problems that we can use to enhance future waves

27. Questions

28. Thank you www.natcen.ac.uk