There are times when you would like to compare two data sets to see if they
are exactly the same.  For example, if two people enter the same data
(double data entry), you would want to know if any discrepancies exist
between the two datasets (the rationale of double data entry), and if so, where
those discrepancies are. We start by reading in the two datasets, one entered by
person1 and the second by person2.  The two data sets are identical, except
that we created a missing value in the ninth row, second variable, in the first
data set, and we changed the very last entry from 51 to 52 in the second data
set.

After entering each data set, we need to sort the data set.  In our
example, we will sort the data set on all variables, starting with the first
variable in the data set.  We use the SPSS keyword all to do this.
We use this method because it is very general and will work in many situations.
(However, if you want to compare the files on only a few variables in the data
set, you will need to list the variables in the same order in both sorts and on
the by subcommand of the update command.)  After sorting the data
set, we save it.  We do this for both data sets.

data list list
 /id female race ses * schtype (A3) prog read write math science socst.
begin data.
 147 1 1 3 pub 1 47  62  53  53  61
 108 0 1 2 pub 2 34  33  41  36  36
  18 0 3 2 pub 3 50  33  49  44  36
 153 0 1 2 pub 3 39  31  40  39  51
  50 0 2 2 pub 2 50  59  42  53  61
  51 1 2 1 pub 2 42  36  42  31  39
 102 0 1 1 pub 1 52  41  51  53  56
  57 1 1 2 pub 1 71  65  72  66  56
 160 . 1 2 pub 1 55  65  55  50  61
 136 0 1 2 pub 1 65  59  70  63  51
end data.
sort cases by all.
save outfile "D:person1.sav".

data list list
 /id female race ses * schtype (A3) prog read write math science socst.
begin data.
 147 1 1 3 pub 1 47  62  53  53  61
 108 0 1 2 pub 2 34  33  41  36  36
  18 0 3 2 pub 3 50  33  49  44  36
 153 0 1 2 pub 3 39  31  40  39  51
  50 0 2 2 pub 2 50  59  42  53  61
  51 1 2 1 pub 2 42  36  42  31  39
 102 0 1 1 pub 1 52  41  51  53  56
  57 1 1 2 pub 1 71  65  72  66  56
 160 1 1 2 pub 1 55  65  55  50  61
 136 0 1 2 pub 1 65  59  70  63  52
end data.
sort cases by all.
save outfile "D:person2.sav".

Now we can use the update command to compare the two data files.
We need to use the SPSS keyword all on the by subcommand, because
that is how we sorted the data sets.  Also, we use the in subcommand
to create a flag variable, which we called flag1, to indicate which rows
match and which rows do not match.  We use the label values command
to add value labels to flag1, and finally we run a frequency on flag1.
As we can see, there are two mismatches.

update file = "D:person1.sav"
/in = flag1
/file = "D:person2.sav"
/by all.
exe.

save outfile "D:combo.sav".

value labels flag1 0 'mismatch' 1 'match'.
freq var = flag1.

Finally, if we look at our new data set, combo, we see that we now have 12
rows of data instead of the original 10.  A new row is added to the data
set for each mismatched row, so that you can see where the mismatch is.  If
there are two mismatches in a row, the row is listed only once, so you will need
to compare the values for each variable to find all of the mismatches.

                                    scht                                     fla
      id   female     race      ses ype      prog     read    write    socst g1

   18.00      .00     3.00     2.00 pub      3.00    50.00    33.00    36.00  1
   50.00      .00     2.00     2.00 pub      2.00    50.00    59.00    61.00  1
   51.00     1.00     2.00     1.00 pub      2.00    42.00    36.00    39.00  1
   57.00     1.00     1.00     2.00 pub      1.00    71.00    65.00    56.00  1
  102.00      .00     1.00     1.00 pub      1.00    52.00    41.00    56.00  1
  108.00      .00     1.00     2.00 pub      2.00    34.00    33.00    36.00  1
  136.00      .00     1.00     2.00 pub      1.00    65.00    59.00    51.00  1
  136.00      .00     1.00     2.00 pub      1.00    65.00    59.00    52.00  0
  147.00     1.00     1.00     3.00 pub      1.00    47.00    62.00    61.00  1
  153.00      .00     1.00     2.00 pub      3.00    39.00    31.00    51.00  1
  160.00      .       1.00     2.00 pub      1.00    55.00    65.00    61.00  1
  160.00     1.00     1.00     2.00 pub      1.00    55.00    65.00    61.00  0

Number of cases read:  12    Number of cases listed:  12