I do not have a definitive answer for you, Henrique. I assume that
you restricted your control samples to households which were
eligible to be beneficiaries. If so, I would not run the logistic
regression with the survey weightsthe weights are not apt to
represent the restricted population. More fundamentally,
propensity score methods are designed to control for confounding.
But, confounding is a property of the sample, not of the population.
A more important concern may be the clustering of housholds in
"projects" and "subregions". This clustering will violate the
assumption of independence required by the programs you are using.
Steven
On Apr 25, 2008, at 9:57 PM, Henrique Neder wrote:
Steven
The primary sampling units (PSUs) are the projects and ultimate
sample units
are the households. In the first stage we selected a number of
projects. In
the second stage we selected 3 households in each project. We have
in the
sample 318 beneficiaries and 404 controls.
My concern is that by not considering the logit model with weights
representing the expansion of the sample to the population, making
it more
representative (because the sample is not selfweighted) I will
eventually
estimate the average treatment effects with bias. But when I use
weights in
logit models estimations the matched samples does not balance. It
is very
strange that in the command psmatch2 there is not an option
provided for
weighting. In the command pscore this option exists. But the
estimation
commands coupled with the latter command (atts, attr) does not
have this
option. My question is: is it necessary to use weights in the
estimation of
atts when I used this in the balancing tests?
Henrique
Mensagem original
De: ownerstatalist@hsphsun2.harvard.edu
[mailto:ownerstatalist@hsphsun2.harvard.edu] Em nome de Steven
Samuels
Enviada em: sextafeira, 25 de abril de 2008 08:57
Para: statalist@hsphsun2.harvard.edu
Assunto: Re: st: using pscore and pstest

Henrique, how were the program and beneficiary subsamples selected?
Are the beneficiary and control sampling units projects or
households? Was there control of the number of the beneficiary and
control unitsfor example to achieve approximately equal numbers, or
minimum numbers of each?
Steven
On Apr 24, 2008, at 8:32 PM, Henrique Neder wrote:
Dear
I have a problem with the use of the pscore and pstest commands. My
sample
is a cluster sample in that the sample observations are selected by
the
followings steps:
1  The survey is made in five States and in each State we selected
some
number of projects (the primary sample units  PSUs) in all sub
regions of
this State.
2  In each project we selected a fixed number of households.
The sample weights are of the fweights form, calculated as the
ratio 
number of households in each subregion (universe) / the number of
households (observations) selected in the sample and inside that
same
subregion. Alternatively, we have a pweight, because the projects
were
selected with PPS (with probability proportionate to the number
of the
households in each project in the universe). This sample of
projects were
selected as a subsample of the projects in a previous survey (that
is, the
second survey was a subsample of the projects in the first survey).
We have in the survey two subsamples: a subsample of a program
beneficiaries and a control subsample.
I executed the commands pstest and pscore using a logit model with
y = dummy
of participation program and yi (covariates) = variables that
explain the
participation. In the first execution I executed the commands with
propensities scores obtained through non weighted logit regressions
and some
models are balanced in the pstest and pscore commands. But when I
use the
weighted logits, the balancing tests are not satisfied (the pscore
command
stops and in the pstest the covariates are unbalanced with small p
values
and the conjoint chisquare test with low pvalue, too).
My questions are:
1  It is necessary to execute the commands with propensities scores
obtained through weighed logit regressions to estimate the atts?
2  It is adequate to test balancing with pstest and pscore without
weights
and estimate the atts with propensities scores obtained through
weighted
logit regressions?
3  I read that a sample proposal is to weight the treated
observations with
the weight = 1 / propensity and the untreated with the weight = 1/
(1 
propensity). In my case, is this solution appropriate? Does it
correct the
estimates considering the representation of the program effects
in the
universe?
Best regards
Henrique Dantas Neder
Universidade Federal de Uberlāndia  Minas Gerais  Brazil
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