freq var = fname lname age.

Statistics

		FNAME	LNAME	AGE
N	Valid	7	8	8
	Missing	2	1	1

FNAME

		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Al	1	11.1	14.3	14.3
	Andy	1	11.1	14.3	28.6
	Ann	1	11.1	14.3	42.9
	Beth	1	11.1	14.3	57.1
	Bob	1	11.1	14.3	71.4
	Pam	1	11.1	14.3	85.7
	Pete	1	11.1	14.3	100.0
	Total	7	77.8	100.0
Missing		2	22.2
Total		9	100.0

LNAME

		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Andersen	1	11.1	12.5	12.5
	Brown	1	11.1	12.5	25.0
	Glenn	1	11.1	12.5	37.5
	Jensen	1	11.1	12.5	50.0
	Jones	1	11.1	12.5	62.5
	Peterson	1	11.1	12.5	75.0
	Smith	1	11.1	12.5	87.5
	Wright	1	11.1	12.5	100.0
	Total	8	88.9	100.0
Missing		1	11.1
Total		9	100.0

AGE

		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	21.00	2	22.2	25.0	25.0
	22.00	1	11.1	12.5	37.5
	23.00	1	11.1	12.5	50.0
	25.00	1	11.1	12.5	62.5
	26.00	1	11.1	12.5	75.0
	29.00	2	22.2	25.0	100.0
	Total	8	88.9	100.0
Missing	System	1	11.1
Total		9	100.0

Now the frequencies are as we would expect them to be.

You can also use the display dictionary command to see
that the missing values have been properly assigned.

display dictionary.

Note that we did not assign any missing values to
id or age; therefore, none are shown under those variables.

You can also use the missing values command to
delete previously declared missing values. To do this, do not type anything in the parentheses after the variable
listed on the missing values command. If you use the SPSS keyword
all instead of a variable or a list of
variables, you will delete all user-defined missing values for all variables, both string and numeric.

The second way to determine the number of missing
values for a string variable is to create a new variable that has a value of one if the cell in the
original variable is not empty (i.e., there is a character of some sort in there,)
and one if it is empty. Next, the describe command is used to sum up the number of ones (i.e., the number of missing
values). For our example, we will create a new variable for each string variable in our data set. 
The variable
missf will indicate the missing values for fname, and missl will indicate the
missing values for lname. We will use two functions with the compute command to create our new variables. The
rtrim function trims the blank spaces from the right of the variable. If there is nothing but spaces, it trims the
length to zero. The length function determines length of the value. Next, the expression is evaluated for each case. If the
length is zero, the expression is true and a one is placed in the new variable. If the expression is false, then
a zero is placed in the new variable.

compute missf = (length(rtrim(fname)) = 0).
compute missl = (length(rtrim(lname)) = 0).
execute.

desc var = missf missl
 /statistics = sum.

Descriptive Statistics

	N	Sum
MISSF	9	2.00
MISSL	9	1.00
Valid N (listwise)	9

list.
      ID FNAME LNAME         AGE    MISSF    MISSL
    1.00 Beth  Jones         .        .00      .00
    2.00 Bob   Jensen      23.00      .00      .00
    3.00       Andersen    25.00     1.00      .00
    4.00 Andy  Smith       26.00      .00      .00
    5.00 Al    Peterson    21.00      .00      .00
    6.00 Ann   Glenn       22.00      .00      .00
    7.00 Pete              29.00      .00     1.00
    8.00 Pam   Wright      21.00      .00      .00
    9.00       Brown       29.00     1.00      .00
Number of cases read:  9    Number of cases listed:  9