st: RE: Attrition question...

[Nick Cox <n.j.cox@durham.ac.uk>]

A solution follows by analogy with an FAQ: 

FAQ     . . . . . . . . . . . . . . . . . .  Dropping spells of missing values
        . . . . . . . . . . . . . . . . . . . . . . . N. J. Cox and G. Longton
        3/07    How can I drop spells of missing values at the
                beginning and end of panel data?
                http://www.stata.com/support/faqs/data/dropmiss.html

See its section 5 in particular. 

I assume panel identifier -id- and time variable -time- and that the variable -interviewed- is a string variable. 

gsort id -time 
by id : drop if sum(interviewed  == "y") == 0 

Alternatively, assume that -interviewed- is an indicator variable 1 for interviewed and 0 for not. The second line becomes 

. by id: drop if sum(interviewed) == 0

Although your posting does not explain some key features of your dataset, you should be able to modify solutions like this after study of the FAQ.  

On the other hand, I don't understand where the -reshape wide- idea comes from. Datasets like this are best kept long. Also, I don't know what IPTW weights are. 

Nick 
n.j.cox@durham.ac.uk

David Diego Torres, MA(Sociology) PhD Candidate in Sociology

I have a longitudinal file on which I just completed multiple  
imputation.  It has been reshaped to long format.  I need to cycle  
through each case and drop all instances for which someone was missing  
in a round and was never interviewed again up to the most recent  
survey round.  Is there a quick and easy way to do this?

For a case like this:

id     interviewed?     ...      ...

1           y
1           n
1           y
1           y
1           y
1           n
1           n
1           n
1           n

I would like to drop all information from round six on.

The purpose is to drop the fully attrited cases, then reshape wide,  
then back to long.  Then I can calculate IPTW weights.


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