1. Generate the “propensity scores” (an estimate of how likely it is that an individual with certain characteristics will end up in treatment A)
a) Select logistic regression (Analyze --> Regression --> Binary Logistic)
b) Select the dependent variable (whether the client received treatment A or not). This has to be a dichotomous variable. If it does not exist in the form you want it, use “Recode (into different variable)” under “Transform” in the SPSS menu before running the logistic regression.
c) Move all the variables you believe important into the box for “Covariates.” (e.g. gender, age etc. Important variables = Those that influence both the outcome of the treatment and whether the person receives treatment A or not).
d) In the menu for logistic regression, first click “Save” and select “Probabilities” under “Predicted Values.” After this click “Continue.” (We need to save the result of the regression since we are later going to compare individuals with similar propensity score values.)
e) Click “OK” and in the unlikely case that no mistake has been made, SPSS will run the regression and add a new column to your dataset which represents the “Propensity score” (often automatically labelled “pre_1”, “pre_2” and so on) You will also get an output with lots of information about the regression result (coefficient values, how many cases it correctly predicts and so on. Ignore this for now.)
2. Compare individuals with similar propensity scores (using subclassification)
a) In the SPSS menu system, select “Transform” --> ”Categorize Variables” and select the variable you just created/saved under in the binary regression (the propensity score, often labelled “pre_1”). Also change the number of groups to 5. The new categorized variable will (automatically) be called “npre_1”, “npre_2” and so on.
b) You can now compare the groups within the same category by – for instance – “Analyze” --> “Descriptive Statistics” --> “Crosstabs” or “OLAP Cubes” under “Analyse” --> “Reports” and choosing “npre_1” as the layer/classification variable. By so doing you will get the mean result for those with similar propensity scores (here defined as less than 0.2 difference) who received treatment A compared those who did not.
