Posted by
Dennis Deck on
Sep 01, 2006; 6:55am
URL: http://spssx-discussion.165.s1.nabble.com/variables-fro-factor-scores-tp1070665p1070668.html
Sometimes stratification, covariate, or weighting make more sense than
matching.
* Here we save the propensity score (CDp), modeling assignment to the
College Dreams program (CD=1 for tx group and 0 for comparison).
LOGISTIC
Vars= CD with Male LowInc Minority NonTrad Transfer Absences HighRisk
GPA5 AvgAch
/Save=PRED(CDp) /Classplot /External .
* Here we calculate quintiles (CDq) on the propensity score for use in
stratification and evaluating overlap of the group distributions .
RANK CDp /NTiles(5) INTO CDq .
* Here we compute the inverse probability of treatment weights (IPTW)
based
on the estimated propensity score .
DO IF (CD=1) .
+ COMPUTE iCDp = 1 / CDp .
ELSE .
+ COMPUTE iCDp = 1 / (1-CDp) .
END IF .
* Here we apply the weights in an outcome analysis predicting the odds
of HS
graduation for treated vs untreated students .
WEIGHT BY iCDp .
LOGISTIC
Vars= Graduation with CD Male GPA5 HighRisk
/Save= Pred(GradP) /Classplot /External .
WEIGHT OFF .
There is more to good propensity score analysis but the syntax is
straightforward - at least when using the PS to define strata,
covariate, or weights. Matching is more complicated.
Dennis Deck, PhD
RMC Research Corporation
[hidden email]
-----Original Message-----
From: Todd McDonald [mailto:
[hidden email]]
Sent: Thursday, August 31, 2006 10:13 AM
Subject: propensity scoring
Does SPSS have the ability to create matched groups using propensity
scores?
Todd
---------------------------------
How low will we go? Check out Yahoo! Messenger's low PC-to-Phone call
rates.