|Subject:||Re: Propensity score matching in PSPP|
|Date:||Tue, 01 Oct 2013 16:57:37 -0500|
|User-agent:||Mozilla/5.0 (Windows NT 6.1; WOW64; rv:17.0) Gecko/20130801 Thunderbird/17.0.8|
Wikipedia describes these steps in propensity score matching:|
1.Run logistic regression:
Dependent variable: Y = 1, if participate; Y = 0, otherwise.
Choose appropriate conditioning (instrumental) variables.
Obtain propensity score: predicted probability (p) or log[p/(1 − p)].
2.Match each participant to one or more nonparticipants on propensity score:
Nearest neighbor matching
Mahalanobis metric matching in conjunction with PSM
Difference-in-differences matching (kernel and local linear weights)
3.Multivariate analysis based on new sample
Use analyses appropriate for non-independent matched samples
So, yes PSPP could potentially be used to implement these steps, but you would have to understand these steps and be able to complete each one. OTOH, no, PSPP doesn't have a routine that will automatically perform all these steps.
On 10/1/2013 4:37 PM, Suniya Farooqui wrote:
-- Alan D. Mead, Ph.D. Assistant Professor Industrial and Organizational Psychology Program Department of Psychology Lewis College of Human Sciences Illinois Institute of Technology 3101 South Dearborn, 2nd floor Chicago IL 60616 +312.567.5933 (Campus) +815.588.3846 (Home Office) +267.334.4143 (Mobile) +312.567.3493 (Fax) http://www.iit.edu/~mead http://www.alanmead.org Announcing the Ideas in Testing Research Seminar, October 11, 2013. Free registration. Complete details: http://mypages.iit.edu/~mead/ideas2013/ Announcing the Journal of Computerized Adaptive Testing (JCAT), a peer-reviewed electronic journal designed to advance the science and practice of computerized adaptive testing: http://www.iacat.org/jcat
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