```Date: Fri, 23 Sep 2005 11:43:48 +0100 Reply-To: Alice Sullivan Sender: "SPSSX(r) Discussion" From: Alice Sullivan Subject: Propensity score matching Content-Type: text/plain; charset="us-ascii" Hi, I am doing an analysis of academic outcomes for children in private and state schools, and am trying to use the propensity score approach. I want to match on the exact propensity score, dropping unmatched cases from the sample. I did try the binning approach, but since my dataset is large (more than 10,000 cases), it was impossible to balance the bins. I have calculated the propensity score using 'save predicted values - probabilities' in binary logistic regression, with the 'treatment' (state/private school) as the dependent variable, and a set of predictors (social class, etc), as follows: LOGISTIC REGRESSION private /METHOD = ENTER region3s faclas7m educatio famtrad kidno mobooks moint Zabilit11 teacha_1 teachmiss abilmiss /CONTRAST (region3s)=Indicator /CONTRAST (faclas7m)=Indicator /CONTRAST (educatio)=Indicator /CONTRAST (famtrad)=Indicator /CONTRAST (kidno)=Indicator /CONTRAST (mobooks)=Indicator /CONTRAST (moint)=Indicator /CONTRAST (abilmiss)=Indicator /CONTRAST (teachmiss)=Indicator /SAVE = PRED /CRITERIA = PIN(.05) POUT(.10) ITERATE(20) CUT(.5) . My problem is that the number of values I get from this is huge - it exceeds 1000, so I can't even run a crosstabs. I can run a table of frequencies, but it's too huge to print out. My questions are: 1. Am I doing something wrong? 2. Is it acceptable to group the propensity scores together - e.g. into percentiles or deciles, before dropping unmatched cases, or would this defeat the object? 3. Has anyone written syntax to identify/drop unmatched cases? (Doing it by hand is a daunting task with so many values!). Many Thanks, Alice Dr. Alice Sullivan Centre for Longitudinal Studies Institute of Education 20 Bedford Way LONDON WC1H OAL ```

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