Missing or invalid data are generally too common to ignore. Survey respondents may refuse to answer certain questions, may not know the answer, or may answer in an unexpected format.
If you don’t filter or identify these data, your analysis may not provide accurate results.
For numeric data, empty data fields or fields containing invalid entries are converted to system-missing, which is identifiable by a single period (shown in fig. below).
The reason a value is missing may be important to your analysis. For example, you may find it useful to distinguish between those respondents who refused to answer a question and those respondents who didn’t answer a question because it was not applicable.
Missing Values for a Numeric Variable
►Click the Variable View tab at the bottom of the Data Editor window.
►Click the Missing cell in the age row, and then click the button on the right side of the cell to open the Missing Values dialog box (shown in fig. below):
In this dialog box, you can specify up to three distinct missing values, or you can specify a range of values plus one additional discrete value.
►Select Discrete missing values.
►Type 999 in the first text box and leave the other two text boxes empty.
►Click OK to save your changes and return to the Data Editor.
Now that the missing data value has been added, a label can be applied to that value. Click the Values cell in the age row, and then click the button on the right side of the cell to open the Value Labels dialog box.
►Type 999 in the Value field.
►Type No Response in the Label field.
►Click Add to add this label to your data file.
► Click OK to save your changes and return to the Data Editor.
Disclaimer: This post uses the ‘official’ explanation of the IBM SPSS Statistics. We thought it could be useful to new/novice researchers if we extract it.