Thinking in Data Domains
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A practical way to understand large data sets is to organize variables into logical domains.
A domain is a group of variables that belong together because they describe the same type of information or originate from the same part of the data collection process.
Common domain patterns
Survey waves
- baseline assessments
- follow-up surveys
- annual visits
- sub-study questionnaires
Clinical domains
- imaging or radiology
- genetics
- laboratory measurements
- pulmonary or cardiac assessments
- medication and treatment history
Participant characteristics
- age
- sex
- mutation type
- enrollment site
- disease severity
Variable types
- date variables
- continuous measurements
- categorical variables
- text fields
Why this helps
Thinking in domains helps analysts:
- reconstruct study design
- plan exploratory analysis
- organize cleaning work
- communicate more clearly with collaborators
- turn a huge variable list into a meaningful system