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Exploratory Data Analysis Checklist

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Before formal modeling begins, analysts should take time to conduct an initial exploratory review of the data set. The goal is not yet to answer the research question, but to understand how the data behave.

Review the structure of the data set

  • How many observations and variables are present?
  • Do variable names match the documentation or data dictionary?
  • Are variables stored in the expected data types?

Examine missing data

  • What percentage of values are missing for each variable?
  • Are there patterns in missingness across sites, time periods, or visit types?
  • Are key variables complete enough for the planned analysis?

Check value ranges

  • Do numeric variables fall within plausible ranges?
  • Are there impossible or highly unlikely values?
  • Are measurement units consistent?

Inspect categorical variables

  • Are categories coded consistently?
  • Do unexpected values appear?
  • Are there very small categories that may affect analysis?

Look for duplicated or inconsistent records

  • Are participant identifiers unique?
  • Do multiple records appear where only one should exist?
  • Are dates or timestamps consistent across related variables?

Explore distributions and relationships

  • What do histograms or box plots reveal?
  • Do scatterplots or cross-tabulations suggest relationships?

Compare the data with the study documentation

  • Does the data set match the protocol or data dictionary?
  • Are there variables that appear unexpectedly or are missing entirely?

Document observations

  • Record unusual patterns, inconsistencies, or quality concerns.
  • Note questions that should be discussed with investigators or data managers.