Re:st: Pscores: Interpretation of results

["Ariel Linden, DrPH" <ariel.linden@gmail.com>]

Duru,

I am somewhat confused with the information you have provided as well as the
questions you are raising?

Did you run the propensity score modesl separately for each country or did
you put them altogether in one data-base and then run the logistic
regression?

If you ran the models separately for each country, you will very likely get
different propensity score ranges. After all, the outcome is the probability
of "being reached on a mobile phone". Would you expect this outcome to be
the same in each country? Were the same sampling methods applied in each
country?

You also ask how we'd explain different sample sizes and numbe of
covariates? I honestly don't know what this means. You, as the researcher,
should be telling us where you drew your sample sizes from and which
covariates you used in your modeling process.


So now to a possible solution:

Put data from all countries in one database. Generate the propensity score
for the entire set. One of the matching variables could be "country", so you
would ensure matches were country specific. In doing this aggregate, the
"scale" of the propensity score will be uniform. Whatever covariates you use
for the propensity score (and outcome models) should be encompassing for all
countries.

As for the outcome model, I would suggest you choose a propensity
score-based weighting model rather than matching. You'll have a much larger
sample size to work with (basically the entire population as opposed to
being limited to only the matches)...

I hope this helps

Ariel   
 	 

Date: Thu, 11 Nov 2010 15:18:35 +0100
From: Duru <duru80@gmail.com>
Subject: st: Pscores: Interpretation of results

Hi all,

Sorry for the previous mail.

I have estimated propensity scores for being reached on a mobile phone
in a telephone survey for five countries (using pscore in Stata).
There was good overlap in each country. Yet, pscores for some
countries ranged between .1-.4 while some other countries it was
.1-.8, for both control (landline respondents) and the treatment
(mobile respondents) groups. I used the same number of covariates in
each country, and the sample size of the treatment group was smaller
in those countries with lower propensity scores. Any ideas, how we
explain lower/higher propensity scores across countries? Sample size
and number of covariates?

Thanks,

Duru

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