To properly document variables. Proper documentation ensures that co-researchers can use the data and that the study can be properly archived at a later stage.
Each variable needs to be checked on the presence of variable labels, value labels and missing value codes.
Proper documentation of the variables consisting of:
- Variable label or definition
- Value label or definition
- Missing value code(s)
- To check whether each variable is documented including variable labels, value labels and missing value codes.
- To advice the executing researcher about this topic.
Research assistant: N.a.
Variable definitions should be described in a data documentation file (also known as a codebook). Make sure your data file contains variable names that are intelligible. Variable and value labels can also be assigned to the dataset in SPSS and R. Pay attention to assigning missing values.
Often, data imported from (database) programs like Castor, Survalyzer or Blaise already contain missing value definitions and variable and value labels. This can be set relatively simply in the design of the input screen.
Problems can arise when transforming from databases where label information is not available after exporting data to SPSS, for example MS-Access and SQL server databases. SPSS commands such as VARIABLE LABELS, VALUE LABELS and MISSING VALUES can be used to address this issue.
Also see here for more information and examples: https://libguides.vu.nl/rdm/data-documentation