![]() This occurs regularly in social surveys, because respondents refuse, of simply forget, to answer questions. The two most common occasions of missing values are the following: First, even though there should be a value, there is none. ![]() Missing values are values of a variable that for some reasons should not be counted as 'real' data values. Many chapters on data transformations have special sections devoted to the treatment of missing values. This chapter explains why they arise and how to define them. Missing values are a topic that deserves special attention. System missing values occur when no value can obtained for a variable during data transformations. User defined missing values indicate data values that either are indeed missing or that for some other purpose should not be used in most analyses (like 'does not apply').
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