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Codebook

As explained in Effective Data Analysis

A codebook is documentation that describes a dataset's variables, coding schemes, methodology, and usage information.

A codebook documents a dataset’s variables, coding schemes, methodology, and usage information. It helps analysts understand how a resource was developed and use it responsibly. section 10.3.3

A codebook commonly records:

  • Dataset characteristics, including missing-value codes, sample size, and sampling strategies. section 10.3.3
  • Variable names, detailed descriptions, survey questions, and supported data types such as choices, integers, and Boolean values. section 10.3.3
  • Caveats and limitations that may affect result quality, including missing values. section 10.3.3
  • Summary statistics for questions, which can support part of exploratory analysis. section 10.3.3

Matching survey topics to dataset columns

The University of Michigan’s Monitoring the Future survey provides a codebook with extensive methodology and data descriptions. Searching it helps match topics of interest to dataset column names before analysis. section 10.3.3

How Effective Data Analysis covers it

How deep it goes
Describes methodology, variables, coding, data types, limitations, and summary statistics, with no further analytical workflow specified.
Examples
Uses the Monitoring the Future survey codebook to match research topics with dataset columns and inspect question-level statistics.
What you can do after
Evaluate/understand a dataset’s structure, provenance, coding, limitations, and suitability before analyzing it.
How it connects
Connects datasets with variables, survey questions, missing values, sampling strategies, exploratory analysis, and data quality.

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