Write a codebook for a student research spreadsheet

Define the unit of each row

Start by writing what one row represents: a permitted observation, sample, response or other unit. Explain whether repeated rows can relate to the same participant or object and how that relationship is represented under the project's rules. This prevents another reader from treating repeated measurements as unrelated cases.

Identify the approved record key and keep sensitive linking information in its required separate location. Do not include participant names in an ordinary explanatory file merely to make the spreadsheet easier to recognise. A codebook should support interpretation while respecting the same access and privacy rules as the research workflow.

Describe each variable plainly

For every column, record its exact name, meaning, expected type and units where relevant. Explain coded categories in full. A label such as status needs a definition of each allowed value and the circumstances in which it is assigned. Avoid using another unexplained abbreviation as the entire description.

Include the source or method used to obtain the value when it matters to interpretation. Distinguish directly collected data from derived calculations. If a variable changes meaning between phases of the project, document the change and version rather than quietly reusing the same column heading for a different concept.

Preserve identifiers and missing meanings

Mark identifiers as identifiers, including any required leading zeros or fixed length. Microsoft's guidance explains why text handling can be needed for such fields. Test the import or export process with representative values and record the expected format. A codebook can reveal a changed identifier, but it cannot always reconstruct a lost original.

Define missing values explicitly. Separate not collected, not applicable, unavailable and genuine zero where the research design distinguishes them. Use the project's agreed codes and explain them in the documentation. Do not invent a convenient missing-value convention midway through analysis without checking how it affects existing records and planned calculations.

Document derived fields

For a calculated column, record the rule, relevant inputs and any inclusion or exclusion condition. Include the software or script reference where appropriate. A column called score is incomplete without an explanation of how it was obtained. Keep enough detail for another authorised person to reproduce the calculation under the same assumptions.

If you correct an error in the derivation, update the documentation and affected outputs together. Preserve the appropriate version record. Avoid changing a formula in one workbook while leaving the submitted or archived codebook describing the old method. The data and its explanation should tell the same story at each meaningful project stage.

Test the codebook against real records

Ask an authorised reader to interpret a small permitted sample using the codebook. Check whether they can identify units, category meanings and missing values without asking you to fill gaps from memory. Use their questions to improve the document. Do not share restricted records outside the approved team for a convenience test.

Keep the codebook with the authorised dataset and update it when the structure changes. Verify that the handover or submission includes the matching version. Hardware is rarely the main obstacle here; a simple accurate document can make an existing spreadsheet much more useful than a larger storage device containing unexplained columns.

Your next steps

• Define what one row represents.
• Document every variable and unit.
• Explain identifiers and missing-value codes.
• Keep derived-field rules and dataset versions aligned.

Keep planning

• Name research project files so you can explain them later
• Make spreadsheet coursework easy to check and reproduce
• Choose a student laptop from your course requirements
• Keep coursework backed up and practise getting it back