Creating codebooks for research data

🔗 GitHub Repository | 📄 Codebook generator Web-App

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Codebooks / data dictionaries

Also known as data dictionaries, codebooks are essential to describe the contents, structure, and layout of a dataset. This ensures proper documentation, and further understanding and reuse by other researchers as a reference for data analysis and interpretation.

Key Components of a Codebook

As a document (table level) artifact, a codebook defines the variables of a dataset as clearly as possible. Please consider the following attributes:

• Variable Name: A unique identifier for the variable name on the data table (e.g., EMPLOY1 or VAR001).

• Variable Label: A brief disciplinary description of the variable (e.g., “Employment Status”).

• Variable type: Indicates the type of variable (e.g numeric, integer, character, boolean).

• Ranges or labels: Contains the range or variable levels depending on the type (e.g “0-100”, “Levels = A1, A2, A3”.).

• Missing values: Indicates the number (if any) of missing variables for each column.

• Units: Measurement units for the variable (e.g., “centimeters”, “squared meters”).

Tip

Depending on the discpline, more attributes could be described to make the dataset understandable. Crystal Lewis offers codebook examples.

How to create a codebook

Creating codebooks is a good research practice that should be implemented during the research process. Keep the format as simple as possible. The web-based codebook generator allows the user to download a .CSV codebook derived from a given data table.

Example of a codebook

Variable	Label	Type	Range-Levels	Missing values
Stage	Experimental stage	Factor	1, 2, 3, 4	NA
Intervention	Intervention Group	Factor	G1, G2, G3	NA
Age	Participant age	Numeric	18-26	1
Sex	Biological sex	Factor	Men, Women	NA
Score	Cognitive score	Numeric	1-20	NA

Commitment to reproducible science

Codebooks are crucial for research transparency, reproducibility, and long-term data preservation.

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