Hi Marc,

>In SPPS, I know there is a weight feature. Does it work with logistic

regression ?

Yes, it works well together.

>Is it really a "technique" to (artificially) have a better fit ?

Yes, you get a "better" fit, but in a sense it is rather self-deception.

In reality, the fit is still bad and you cannot rely on the results.

>What modelling techniques are better suited for sparse datasets, in

your opinion ?

SPSS has its exact tests, they are devised for sparse data. Of course,

they cannot create significant results where there is nothing

significant.

Moreover, I do not understand your phrase "1700 on 60000" (sorry for my

bad English). If it means that you have 60,000 respondents and that 1700

of them has 1 in the dependent variable and the rest has 0 here, then

the case is not about sparsity. 1700 is enough for most practical

purposes and you can use logistic regression without desperation. If its

result is not significant, then it simply means that your "dependent"

variable does not depend on the selected predictors.

Hope this helps

Jan

-----Original Message-----

From: SPSSX(r) Discussion [mailto:

[hidden email]] On Behalf Of

Marc

Sent: Wednesday, July 26, 2006 8:39 AM

To:

[hidden email]
Subject: Logistic regression, few "respondents", and weighting in SPSS

Dear all,

I'm coming with a question concerning logistic regression and SPSS.

We're in front of a situation here where we have very few "respondents"

(1 in the field to predict) in a logistic regression. Only 1700 on

60000. I think it's a situation called "sparsity", isn't it ?

When doing a logistic regression, we have a low fit. As I see it, it's

because of this sparse dataset. I was told that a way to solve that kind

of problem in LR, is to weight the responding cases, to "artificially"

raise their representativity in the dataset.

I've looked that up in the classical "bibles" of logistic regression

(Menard, Lemeshow, Jaccard), but haven't found any discussion of

sparsity, or situations with few respondents.

In SPPS, I know there is a weight feature. Does it work with logistic

regression ? Is it really a "technique" to (artificially) have a better

fit ?

What modelling techniques are better suited for sparse datasets, in your

opinion ?

Thank you so much for helping out !

Marc.