Sometimes, a data set may have “holes” in them, i.e., missing values. Some
statistical procedures such as regression analysis will not work as well, or at all on
data set with missing values. The observations with missing values have to be either
deleted or the missing values have to be substituted in order for a statistical procedure
to produce meaningful results. Thus we may want to know  the number of missing values
and the distribution of those missing values so we have a better idea on what to do with
the observations with missing values. Let’s look at  the following data set.

 LANDVAL  IMPROVAL    TOTVAL  SALEPRIC SALTOAPR

   30000     64831     94831    118500   1.25
 30000     50765     80765     93900    .
   46651     18573     65224         .   1.16
   45990     91402         .    184000   1.34
   42394         .     40575    168000   1.43
       .      3351     51102    169000   1.12
   63596      2182     65778         .   1.26
   56658     53806     10464    255000   1.21
   51428     72451         .         .   1.18
   93200         .      4321    422000   1.04
   76125     78172     54297    290000   1.14
       .     61934     16294    237000   1.10
   65376     34458         .    286500   1.43
  42400         .     57446         .    .
   40800     92606     33406    168000   1.26

1. Number of missing values vs. number of  non missing values

The first thing we are going to look at is what  the variables are  that have
a lot of missing values. We just use the command frequencies with option
/format=notable.

FREQUENCIES VARIABLES=landval improval totval salepric saltoapr 
  /FORMAT=NOTABLE
  /ORDER= ANALYSIS .

So we know the number of missing values in each variable. For instance, variable
salepric
has four and saltoapr has two missing values. This will help us to
identify variables that may have a large number of missing values and perhaps we may want
exclude those from analysis.   

2. Number of missing values in each observation and its distribution

We can also look at the distribution of missing values across
observations. For example we use command count to create a  new
variable cmisscounting the number of  missing values across each
observation. Looking at its frequency table we know that there are four observations with
no missing values,  nine observations with one missing values, one observation with
two missing values and one observation with three missing values. If we are willing to
substitute one missing value per observation, we will be able to reclaim nine observations
back to get a valid data set that is 13/15=87% of the size of the original one. 

COUNT
  cmiss = landval improval totval salepric saltoapr  (MISSING).

FREQUENCIES VARIABLES=cmiss    
  /ORDER=  ANALYSIS .

  3. Distribution of missing values

We can also look at the patterns of  missing values.We can recode each variable into a dummy variable such that 1 is missing and 0 is
nonmissing.  Then we use the aggregate command to compute the frequency for each
pattern of missing data. 

RECODE
  landval improval totval salepric saltoapr
  (MISSING=1)  (ELSE=0)  INTO  land1  impr1  totv1  sale1  salt1 .
EXECUTE .
AGGREGATE
  /OUTFILE='AGGR.SAV'
  /BREAK=land1 impr1 totv1 sale1 salt1
  /N_BREAK=N.

   File AGGR.SAV has the following variables and observations.

   LAND1    IMPR1    TOTV1    SALE1    SALT1  N_BREAK

     .00      .00      .00      .00      .00       4
     .00      .00      .00      .00     1.00       1
     .00      .00      .00     1.00      .00       2
     .00      .00     1.00      .00      .00       2
     .00      .00     1.00     1.00      .00       1
     .00     1.00      .00      .00      .00       2
     .00     1.00      .00     1.00     1.00       1
    1.00      .00      .00      .00      .00       2

Now we see that there are four observations with no missing values, one observation
with one missing value in variable saltoapr, two observations with missing value in
variable salepric and one observation with  missing value in both variable
totval
and salepric, etc. If we want to delete some observations from the
original data set, we have a better idea now on which observation to delete, e.g. the
observation corresponding to the 7th row above.