Thanks! So as far as I understood 'feend' gives an age when a respondent is supposed to leave further education while still in it. Is it correct?
If it is so, how can I find out the age at which all respondents completed their full-time education. Any help would be appreciated!
#1 Updated by Gundi Knies about 3 years ago
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FEEND on XWAVEDAT reports the age at which the respondent ended further education. There is also a variable SCEND which reports the age left school. I am not sure there is a variable that asks the age the respondent left full-time education; you'd probably want to create this from FEEND and SCEND.
Hope this helps,
#3 Updated by Gundi Knies about 3 years ago
- Category changed from Data documentation to Data analysis
The questions that were asked are listed in the wave-specific questionnaires. SCEND is school leaving age; FEEND is further education leaving age. Interpretation beyond this can only be made by you. Those who never entered further education will not have a value on FEEND. Also, if you look at some of the values of FEEND you will see that some people are well into their 50s or older when they finished further education. That may or may not work for your specific analysis.
#4 Updated by Julia Borodina about 3 years ago
Thanks again! I'm sorry for so many questions but I really find some data confusing. Could you please clarify who can be those people who neither are still at school, nor in further education (scnow is no and fenow is inapplicable) and have feend inapplicable? All of those people have fetype 'none of the above' as well. At the same time, according to 'iqfachi' some of them even have a first or higher degree. Thanks in advance!
#6 Updated by Gundi Knies about 3 years ago
- Target version set to BHPS
no problem, we are here to help. To understand better why some of the people with "degrees" have no information about when they ended further education, you need to examine the questionnaire routing of all the variables concerned (and, if the variable is derived, of all its input variables). Some information is, e.g., not included in the proxy questionnaire (highest education is; feend is not). It may also be that people did not participate in the wave in which a particular question may have applied to them. Perhaps, to understand the general principle of the variables, start by looking at just the first wave and then see how it gets updated as data from later waves are added.