Understanding Society User Support

I have the variable(s) ivfio from xwaveid merged into my master dataset (indresp) in both long and wide formats.

In terms of when I change the xwaveid data into the long format and merge it to the master (so that the variables b_ivfio, c_ivfio, d_ivfio become a single variable) what I just get when I tabulate wave and ivfio is just full, proxy and telephone interviews.
e.g.

tabulate wave ivfio

gives us:

wave of BHPSUS full inte proxyint Total
W1 2010/11 1,964 54 2,018
W2 2011/12 1,805 62 1,867
W3 2012/13 1,700 38 1,738

On the other hand when I simply merge in the xwaveid data (in its wide format) to the master and i tabulate wave by each of the ivfio's e.g. tabulate wave b_ivfio , I get for the current wave (_b) the full and proxy interviews as well as subsequent waves with other reasons e.g

tabulate wave b_ivfio (wave b is wave one)

gives us:

wave of BHPSUS full inte proxy int refusal other non moved ill/away youtint youth non refusal/n Total
W1 2010/11 1,964 54 0 0 0 0 0 0 0 2,018
W2 2011/12 1,765 16 6 24 3 5 31 4 9 1,868
W3 2012/13 1,605 14 5 25 1 3 67 7 5 1,736

I dont really understand what the ones in the wide format mean - in wave 2, the wave b variable has 1,765 full interviews, 6 refusals etc.

Getting to the point.... I want to identify those who drop out and those who rejoin.
How do I use this ivfio variable to do that? I think I use the long formatted merge? Then to get the drop outs what do I do?

Many thanks,
Gretta

Gretta Mohan wrote:

It feels like a basic question but I cannot identify respondents of the BHPS and Understanding Society who drop out year on year and those who re-join. I do not think there is a variable to identify these people.
I am using stata and the data is in long format.

I want to do an attrition analysis to see if attrition is health related - this drop out, re-joining attrition health analysis has been done by Jones, Koolman and Rice (2005).

Many thanks

Hi Gretta,
have you tried appending the datafiles in long format, xtset the dataset, and then use the lag (L.) and lead (F.) functions in Stata to generate the variables of interest? You could also use the _merge variables that are created when you merge data files on pidp in wide format to generate an indicator for whether or not a person was present before. you'll probably need to play around with this until you have the exact indicators you are looking for but should be doable.
hope this helps,
Gundi