It sounds like you may not have been informed as to whether the data are a) for those students who were selected to receive an offer of participation, some of whom subsequently elected to participate, or b) for all students, some of whom received a participation offer. Apparently you have a variable recording which students participated in the intervention. You could run model for intervention participation. If a) applies, I'd expect that the resulting comparison group would be (much) more comparable to intervention group than if b) applies. Comparable, meaning that the two groups differ on few or none of the available covariates.
Gene Maguin
-----Original Message-----
From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of dcaudio
Sent: Wednesday, April 15, 2015 9:36 PM
To: [hidden email]
Subject: Ideal variables to estimate propensity scores
Hi!
I want to know whether it makes sense to use PSM to estimate an intervention
*given* the variables I have available to model the probability of receiving the treatment.
The intervention is a program intended to increase college-going success, similar to an Advanced Placement course.
I have data for gender, race, SES, special ed and limited english proficiency designations, and math and reading achievement scores before the treatment. I also have data for school, school district, and urbanicity.
Data for outcome variables of interest include enrollment in college and college GPA.
One goal of PSM is to find a matched, control group that can be compared to the treatment group. I have read that the best way to do this is to have variables that are associated with the selection criteria for the treatment and the outcomes of interest. In my case, while prior achievement scores are likely to predict the outcome, I do not know the selection criteria and therefore I don't know the relationship between the available data and the selection criteria. The reason some students find themselves in the treatment is unknown, and might or might not be related to the variables I have available.
I believe that matching on the available demographics and achievement scores is better than not matching. But I don't know whether it makes sense to match based on variables whose relationship to the selection criteria are unknown.
Put another way, imagine I had data on an ice cream consumption and that this consumption is not associated with receiving the treatment aimed to improve college success. Also imagine, albeit a bit far-fetched, that consuming ice cream is associated with the outcome variables (maybe sugar increases college success?). Would it still make sense to match on ice cream consumption even though it is not related to the selection process for receiving the treatment?
Any and all ideas would be appreciated!
--
View this message in context: http://spssx-discussion.1045642.n5.nabble.com/Ideal-variables-to-estimate-propensity-scores-tp5729234.html
Sent from the SPSSX Discussion mailing list archive at Nabble.com.
=====================
To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD
=====================
To manage your subscription to SPSSX-L, send a message to
[hidden email] (not to SPSSX-L), with no body text except the
command. To leave the list, send the command
SIGNOFF SPSSX-L
For a list of commands to manage subscriptions, send the command
INFO REFCARD
If you reply to this email, your message will be added to the discussion below:http://spssx-discussion.1045642.n5.nabble.com/Ideal-variables-to-estimate-propensity-scores-tp5729234p5729235.html
| Free forum by Nabble | Edit this page |