Creating and using codebooks
A codebook or data dictionary helps people understand your data, by explaining what the variable names and values in your data files (i.e., the metadata) mean. As such, a codebook is important for making your research more reproducible. Obviously, a codebook can be very beneficial for direct collaborations and your future self, but you might also consider using one if you plan to (openly) share datasets.
A Primer on creating Codebooks
Before you start creating a codebook, consider reading this primer on creating data dictionaries and shareable datasets:
Creating a Qualtrics Data Dictionary
If you are using Qualtrics to collect questionnare data, you can use the Data Dictionary Creator to create a codebook for your dataset.
Creating a Markdown Codebook from your R dataframe
If you use R to analyze your data, you can use the codebook package to create a codebook based on the dataframe you are working with.
Creating a Castor Data Dictionary
If you are using the Castor Electronic Data Capture system to capture, process and integrate your data, you are required to build your study into the system. Before you start building your study, it is recommended to make a data dictionary, which the building process much easier. The added bonus is that you also have a data dictionary to use for your own research and collaborations. You can find out more about creating a Castor data dictionary here.
If you did not make a Data Dictionary before building the study, or if you want to easily check some changes you have made later, you can also export a data dictionary for your study.