Mark Negative Values as Missing#

Some datasets use negative codes, such as -1 for “Don’t know” and -2 for “Not applicable”, to represent missing responses. Colectica can mark these categories as missing throughout a dataset.

Navigate to the dataset.
In the Advanced area of the Dataset ribbon tab, click Mark negative values as missing.
Wait for the command to finish, then review the affected variables and their code lists.
Use Save on the quick access toolbar to save your edits.

How Codes Are Processed#

The command examines the code lists used by the dataset’s variables, including codes within a hierarchy. When a code can be read as an integer less than zero, its category is marked as missing. For example, -1 is treated as a missing code, but -1.5 and NA are not processed as negative integers.

Existing missing-category flags are preserved. The command changes the metadata describing the codes; it does not replace values in the data file.

Note

Categories may be shared by several variables or code lists. Marking a shared category as missing affects every item that uses that category.

When no integer-valued code has a category that remains non-missing, Colectica changes the variable to a numeric representation and uses its code representation as a managed missing-values representation. This also applies when a code list has no integer-valued codes. Review these variables to ensure their resulting representations describe your data correctly.