Hi all
<<
Suppose you have data on every kid in the school, not a survey, and every
school in the district, and you want to test for some form of sex
discrimination in assignment to a program. Well, just see if more girls
than boys (or vice versa) are assigned, and there is your evidence of
discrimination, right?
>>
I think the problem doesn't disappear when working with explanatory
variables: assume that assignment to a programme is determined by low income
and that girls in your whole population are generally richer than boys, then
you have an over-proportion of assigned boys, although not sex but income is
the true explanatory variable. The whole story is even more complicated
assuming relationships with further explanatory variables.
As soon as you do a multiple regression you need significance tests even if
working with the whole population, am I wrong?
Regards
Paolo
_____________________________________________________________________
lic. oec. publ. Paolo Pamini
Assistent Mathematik
Institut f�r Operations Research
Universit�t Z�rich
Moussonstr. 15
CH-8044 Z�rich
www.ior.uzh.ch
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Austin Nichols
Sent: 18 January 2008 21:03
To: [email protected]
Subject: Re: st: when your sample is the entire population
David Greenberg <[email protected]>:
I already made my own view clear at
http://www.stata.com/statalist/archive/2008-01/msg00472.html
but I can't think of a model that I would use where the notion of a
superpopulation is not necessary, much less not "ridiculous" (your
word, not mine). Suppose you have data on every kid in the school, not
a survey, and every school in the district, and you want to test for
some form of sex discrimination in assignment to a program. Well,
just see if more girls than boys (or vice versa) are assigned, and
there is your evidence of discrimination, right?
On Jan 18, 2008 2:49 PM, David Greenberg <[email protected]> wrote:
> There is an old debate going back to the 1970s about the meaningfulness of
statistics when dealing with entire populations. If I recall correctly,
Judith Tanner edited a book of papers on the subject. Proponents of testing
in this circumstance say that we can think of the population of countries as
having been sampled from an imaginary larger population of all possible
countries, but I think this is ridiculous. We know that the existing set of
countries was generated by historical processes (conquest, secessions, and
the like) that wasn't random. With time series data it may make sense to
imagine a hypothetical random generating process from which a certain
stretch of time has been sampled. Ther may also be circumstances where
something like bootstrap standard errors could be informative. Suppose you
are studying all the children in a school. You would not have a simple
random sample, but might still want to know how sensitive your results are
to the possibility that a few
ch
>
> ildren were not there on the day you passed out your survey instrument
because they were sick or truant. David Greenberg, Sociology Department, New
York University
>
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