Quantitative coding is the process of categorising the collected non-numerical information into groups and assigning the numerical codes to these groups. The fact of numeric coding is shared by all statistical software and among others, it facilitates data conversion and measurement comparisons.
The meaning of codes must be documented. Specialized analytic software (SPSS, SAS, STATA, etc.) lets the user assign labels directly to the codes. For the principles of the construction of labels, please, see the sub-section 'Organising variables'. If the software does not allow us to assign code labels directly to data, we have to document the codes in a separate document as part of the metadata.
In the accordion below you find coding recommendations which are inspired by ICPSR (2012).
Coders may vary in the way they assign codes to variable values, i.e. each of them uses the same coding scheme in a slightly different way. This results in so-called “coder variance”. Coder variance is a specific source of non-sampling error (i.e., error additional to the statistical “sampling” error) and may cause systematic deviations of the sample.
Coding of textual information is a complicated cognitive process and the coder may pose a significant influence on the information that appears in the database, as well as become a source of systematic error. That is why the implementation of complicated coding schemes often requires the construction of a theoretically and technically well-founded design and requires specific coder’s competences and training.