1. Elements of Data Documentation

2.  What are the most important elements to document?  Who will be using the documentation?  How should these elements be documented?

3. Data Level Documentation  Elements to document  Variables: name, labels, question text, length and type in data set  Values: List of valid values, coding  Derived data: algorithm used to create  Missing data: how was it handled?  Question routing (skip patterns)  Error checking/validation

4. Study-Level Documentation  Study Level  Context of project  Details of data collection  Information about data files  File name, date, version, number of cases  Summary of measures  Scaling/Scoring  Validation/modification  Longitudinal information  Naming conventions  Version information

5. Users of Data Documentation  Know your potential audiences  Data managers  Statisticians  Researchers  Outside users

6. Types of Data Documentation  Tabular codebook (Excel)  Good for organizing a large amount of information concisely  Sortable/filterable  Annotated measure  Contains basic variable and value information in context  Data dictionary/Data narrative  Good for measure/study-level information