Obtain specified measurement data.
The data necessary for analysis are obtained and checked for completeness and integrity.
Example Work Products
- Base and derived measurement data sets
- Results of data integrity tests
1. Obtain data for base measures.
Data are collected as necessary for previously used and newly specified base measures. Existing data are gathered from project records or elsewhere in the organization.
2. Generate data for derived measures.
Values are newly calculated for all derived measures.
3. Perform data integrity checks as close to the source of data as possible.
All measurements are subject to error in specifying or recording data. It is always better to identify these errors and sources of missing data early in the measurement and analysis cycle.
Checks can include scans for missing data, out-of-bounds data values, and unusual patterns and correlation across measures. It is particularly important to do the following:
- Test and correct for inconsistency of classifications made by human judgment (i.e., to determine how frequently people make differing classification decisions based on the same information, otherwise known as “inter-coder reliability”).
- Empirically examine the relationships among measures that are used to calculate additional derived measures. Doing so can ensure that important distinctions are not overlooked and that derived measures convey their intended meanings (otherwise known as “criterion validity”).