How to remove blank string values from my analysis outputs in SPSS? I'm fairly new to the world of statistical analysis so please excuse me for asking a probable rookie question.
I created a google forms survey and after I got the desired sample size I used the xls file to load my data to SPSS and experimented with different types of analysis. In my survey there are various node questions. If someone answered "Yes" he would continue with the survey, but if someone answered "No" he would be redirected to the end page (demographics). So you understand that there are many Variables (questions) with missing values (blank answers).
Now when I try to do frequencies analysis for example, the outputs for some variables like the one below account correctly for the missing values and exclude them from the stats.

But for most variables it does not recognize the blank values as missing and includes them in the valid answers thus affecting the stats as shown below.

As you can see the first row of the table above has no value name (blank) and a frequency of 35. Those same 35 cases where excluded in the first table. 
Last but not least I noticed that the type of the variables that are analyzed correctly is Numeric whereas the type of the other variables is String so I guess it has something to do with that.
Can you please help me fix this issue and exclude all missing values from my analysis?
 A: According to SPSS rules, blank values in string (text) variables are valid values. But, if a value is up to 8 characters long you can force it to be a user-missing value, if you want. Use Variable View to define missing values manually or MISSING VALUES command to do it through syntax [for example, MISSING VALUES mystringvar ('wweqwe' 'hsdkj'). In your case, blank values: MISSING VALUES mystringvar (' ').].
Some citations from SPSS Help:
All string values, including null or blank values, are considered to be valid unless you explicitly define them as missing.
Missing values for string variables cannot exceed eight bytes. (There is no limit on the defined width of the string variable, but defined missing values cannot exceed eight bytes.)
To define null or blank values as missing for a string variable, enter a single space in one of the fields under the Discrete missing values selection.

But generally string varibles are not as flexible in analyses as numeric variables. It is always convenient and recommended to recode string variables into numeric ones before statistical analysis. You may use Autorecode option in Transform menu to do it efficiently. Numeric variables can have user-missing as well as system-missing values.
