The SCORE Procedure

(View the complete code for this example.)

This example shows how to use PROC SCORE with factor scoring coefficients. First, the FACTOR procedure produces an output data set containing scoring coefficients in observations identified by _TYPE_=’SCORE’. These data, together with the original data set Fitness, are supplied to PROC SCORE, resulting in a data set containing scores Factor1 and Factor2. The following statements produce Output 102.1.1 through Output 102.1.3:

   /* This data set contains only the first 12 observations   */
   /* from the full data set used in the chapter on PROC REG. */
data Fitness;
   input Age Weight Oxygen RunTime RestPulse RunPulse @@;
   datalines;
44 89.47  44.609 11.37 62 178     40 75.07  45.313 10.07 62 185
44 85.84  54.297  8.65 45 156     42 68.15  59.571  8.17 40 166
38 89.02  49.874  9.22 55 178     47 77.45  44.811 11.63 58 176
40 75.98  45.681 11.95 70 176     43 81.19  49.091 10.85 64 162
44 81.42  39.442 13.08 63 174     38 81.87  60.055  8.63 48 170
44 73.03  50.541 10.13 45 168     45 87.66  37.388 14.03 56 186
;

proc factor data=Fitness outstat=FactOut
            method=prin rotate=varimax score;
   var Age Weight RunTime RunPulse RestPulse;
   title 'Factor Scoring Example';
run;

proc print data=FactOut;
   title2 'Data Set from PROC FACTOR';
run;

proc score data=Fitness score=FactOut out=FScore;
   var Age Weight RunTime RunPulse RestPulse;
run;

proc print data=FScore;
   title2 'Data Set from PROC SCORE';
run;

Output 102.1.1 shows the PROC FACTOR output. The scoring coefficients for the two factors are shown at the end of the PROC FACTOR output.

Output 102.1.2 lists the OUTSTAT= data set from PROC FACTOR. Note that observations 18 and 19 have _TYPE_=’SCORE’. Observations 1 and 2 have _TYPE_=’MEAN’ and _TYPE_=’STD’, respectively. These four observations are used by PROC SCORE.

Since the PROC SCORE statement does not contain the NOSTD option, the data in the Fitness data set are standardized before scoring. For each variable specified in the VAR statement, the mean and standard deviation are obtained from the FactOut data set. For each observation in the Fitness data set, the variables are then standardized. For example, for observation 1 in the Fitness data set, the variable Age is standardized to .

After the data in the Fitness data set are standardized, the standardized values of the variables in the VAR statement are multiplied by the matching coefficients in the FactOut data set, and the resulting products are summed. This sum is output as a value of the new score variable.

Output 102.1.3 displays the FScore data set produced by PROC SCORE. This data set contains the variables Age, Weight, Oxygen, RunTime, RestPulse, and RunPulse from the Fitness data set. It also contains Factor1 and Factor2, the two new score variables.