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RE: st: RE: Multiple responses on plot level data


From   "Nick Cox" <n.j.cox@durham.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: RE: Multiple responses on plot level data
Date   Thu, 21 Apr 2005 19:14:25 +0100

Your reference to dummies threw me. 

As I see it, your 8 categories are (say) 

----- binary -----   decimal     
fert  man  fanyahu 
0     0    0             0
0     0    1             1
0     1    0             2
0     1    1             3 
1     0    0             4 
1     0    1             5 
1     1    0             6  
1     1    1             7 

which you can thus construct as (e.g.) 

4 * fert + 2 * man + fanyahu 

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

Ronnie Babigumira
 
> Thanks Nick, sorry for the lack of clarity, measure choice is 
> my response (dependent is the term Im more farmiliar with) 
> variable (s). I do have a set of predictor (independent) 
> variables which I did not include in my email.
> 
> Yes I did consider logit models for individual responses. I 
> also considered constructing a dependent variable indicating 
> whether or not any one of the choices was made.
> 
> However, I have been thinking about the mlogit. So you say 8 
> possible choices, would that mean 1, 2, 3 for fert, man, 
> fanyaju rspv and then 5/8 for the diffrent combinations of 
> each. If so, doesnt isnt this a problem because the classes 
> are not exactly independent in the sense that (say 5 if 
> choices 2 and 3 were made on the plot)
 
n.j.cox@durham.ac.uk

> I am not clear whether you regard
> measure choice as a predictor or a response.
> And in any case I can imagine
> situations in which both views are reasonable.
> Soil erosion loss could depend on
> conservation measures, which choice of conservation
> measures might depend on many things.
> 
> If it were the latter, I think it is up to you how you define
> a composite response. Nor is it clear to me that
> you need do that. If this were my problem
> I would look at
> 
> (a) logit models for the individual responses
> 
> and
> 
> (b) a multinomial logit for the 8 possible choices of
> 	fert[iliser?]
> 	man[ure?]
> and	fanyaju[???].
> 
> If conservation measures are predictors, you would
> need to look not just at dummies but also at interactions.
 
Ronnie Babigumira
 
> > Something has been slowly eating away at me and at this point I have
> > decided to seek help from the list. I have plot level data on use of
> > soil conservation measures and would like to construct a "single"
> > dependent variable for each household for use in a 
> multinomial logit.
> > The data look something like this
> >
> > hhd_id	plnum	fert	man	fanyaju
> > 1001	1	0	1	0
> > 1001	2	0	1	1
> > 1001	3	1	0	1
> > 1002	1	0	0	0
> > 1002	2	1	1	1
> > 1003	1	1	0	0
> >
> > Where
> > hhd_id: Household id
> > plnum: Plot number (a household may have more than one plot)
> > And fert, man and fanyaju are 3 possible soil conservation 
> measures a
> > household may undertake (it is possible that more than one 
> measure can
> > be applied to a plot)
> >
> > My question is how do I go about with constructing a single 
> dependent
> > variable for use in a multinomial logit in this case (and
> > would this be
> > correct). I have considered the simplest case where I
> > construct a simple
> > dummy for each plot indicating whether or not a household
> > used at least
> > one of the measures however, I feel that it would be more
> > interesting if
> > I could say something on the determinants for the use of 
> the different
> > measures

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