I wanted to thank everyone for their responses. I am new to using R and MatchIt and this listserve has been very helpful!
I was able to replicate the summary matched data statistics in SPSS by weighting the matched sample by the "weights" variable.
Hope that helps!
Thanks again!
Kerry
Kerry Richmond
Department of Criminology and Criminal Justice
University of Maryland, College Park
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Hi,
I have a question about how to recreate matched groups once I transfer the data from R into another statistical program (such as SPSS). I don't have a problem doing so if I use 1:1 matching, however, when I match with replacement or use 2:1 matching (where there are not the same number of treated and control cases), the means of the covariates for the treated and control groups do not come out to be the same as they were in the summary data in R. I understand that this has something to do with the "weights" variable, but I'm not sure how to incorporate this so that I can recreate the same matched samples.
Any help you can provide would be greatly appreciated!
Thanks!
Kerry
Kerry Richmond
Department of Criminology and Criminal Justice
University of Maryland, College Park
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Hi everyone,
When you try subclassification, is there a way to extract the matched
control-treatment subjects of each subclass (the default is 6
subclasses) seperately? How could you know which students have been assigned
in the first, second, third, forth, fifth and sixth subclass respectively?
thank you very much,
Katerina Passa
Katerina Passa
Research Associate
Human Services & Systems
ICF International
10530 Rosehaven Street
Suite 400
Fairfax, VA 22030
Fax: (703) 385-3206
apassa(a)icfi.com
Hello,
I would like to verify my understanding of the kind of question for which
MatchIt is a useful tool.
My understanding is that one would use MatchIt if the focus of the research was
the causal effect of the *treatment* variable. All other variables are control
variables, and would be treated as such during the parametric analysis. In other
words, one would not use MatchIt and then test for the explanatory power of the
"control" variables. If MatchIt creates a dataset with perfect matches then the
"control" variables should have no explanatory power.
Perhaps this is obvious... I just wanted to check that I am not missing
something.
Thank you!
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Orsolya Lazar
Department of Political Science
University of Arizona
Tucson, AZ 85721-0027
olazar(a)u.arizona.edu
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Hi all,
I am new to MatchIt and would like to learn whether it matters if the treatment cannot be
predicted by any covariates.
The treatment in my study is "experiencing a policy change." My treatment and control groups are
limited English proficient (LEP) students from two cohorts, before and after the policy change (I
also compare the performance of non-LEP students from the same two cohorts for
difference-in-differences). To reduce model dependence, I plan to match the LEP students from the
two cohorts (and the non-LEP students from the two cohorts) on gender, ethnicity, etc. Does it
matter that the treatment is not really a function of any of these covariates?
Many thanks,
Qian
******************************************************
Qian Guo
Doctoral student, Harvard Graduate School of Education
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