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From |
"Ariel Linden, DrPH" <ariel.linden@gmail.com> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
Re: st: Pscores: Interpretation of results |

Date |
Sat, 13 Nov 2010 10:33:58 -0800 |

Duru, Here are some thoughts/comments/questions, and they are a bit out of order relative to your statements below: You state "The sampling method is the same for all countries, RDD (LL+cell numbers), but you end up with different proportions of respondents reached on a mobile and landline because the frequency of mobile use, number of landline/cell only population, call times etc. change by country" Well, this straight out tells you that countries will differ in their use of mobiles. So if I were building a logistic regression model, by country, with the outcome being "the probability of using a mobile" conditional on observed covariates, then given the difference in proportions of users in each country (either due to your sampling methodology or other reason - bias, confounding, or reality), I would somewhat expect to see different propensity score ranges. Also, if the covariates you are using in fact do differentiate between mobile users and landline users, then that is telling you something as well about the what are the observed explanatory variables that help explain this difference. Which brings me to the next point. I am not clear if the use of mobile phones is, in fact, the outcome variable in addition to being used as the propensity score estimation model? You state "I am trying to test whether respondents reached on a mobile differ substantially from those reached on a landline with respect to my survey outcomes -after we control for socio-demographic variables." It seems to me that you are asking the question backwards: "what are the characteristics of those using mobiles vs landlines". If that is the case, it doesn't make sense to use a propensity scoring methodology at all. You're making everyone conditionally look alike (as you would in an RCT), which means that you won't be able to see what makes them different? It could be that I am just not clear on what you are trying to achieve here. It almost seems to be that you should be running some basic stratification tables comparing characteristics of those using mobiles versus landlines. Then you can compare these characteristics by country to see if they are different. Perhaps you should look at discrimminant analyses procedures. I hope this helps Ariel Date: Fri, 12 Nov 2010 21:26:55 +0100 From: Duru <duru80@gmail.com> Subject: Re: st: Pscores: Interpretation of results Thanks very much Ariel. Actually, I ran the PS models separately in each country and used slightly different specifications since some covariates did not differ significantly between mobile and landline samples in some countries.The sampling method is the same for all countries, RDD (LL+cell numbers), but you end up with different proportions of respondents reached on a mobile and landline because the frequency of mobile use, number of landline/cell only population, call times etc. change by country. I am trying to test whether respondents reached on a mobile differ substantially from those reached on a landline with respect to my survey outcomes -after we control for socio-demographic variables. Yes, I was expecting propensity scores to differ by country. However, I am not sure how to explain low pscores for treatment groups in some countries. How do I explain if all cases in a treatment group to have propensity scores below .4? Sample size of the treatment group is lower in those countries, this seemed to be a potential reason since in a small sample I imagine there will be high number of different combination of covariates relative to the sample size. Then, number of covariates might be another factor, I thought! Of course I maybe totally misled. I havent come accross any article discussing these things. They simply look at the overlap and proceed. Any ideas, references are appreciated. PS: Using one data base with all countries is a jolie good idea for comparison. Although I couldnt satisfy the balancing property using the same model in each country. Still, might be different in an combined version. Plus, need to figure out how to exact match on country. Thanks very much again, Duru * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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