Missing or invalid data are generally too common to ignore. Survey respondents may refuse to answer certain questions, may not know the answer, or answer in a format not expected. If you don't take steps to filter or identify this data, your analysis may not provide accurate results. For numeric data, blank data fields or those containing invalid entries are handled by converting those fields to system missing, which is identifiable by a single period. The reason a value is missing may be important to your analysis. For example, you may find it useful to distinguish between those who refused to answer a question, and those who didn't answer a question because it was not applicable to them.