This chapter focused on the various ways aggregation can be utilized to extract implicit information from the data and make it available for use in constructing derived fields that have the potential to yield deeper analytical insights. Adding aggregated variables back to the original dataset is a simple but powerful technique that supports the creation of fields better tailored for predictive modeling. Examples of one- and two-level aggregation were used to show how new datasets can be created to allow modeling at a different unit of analysis. Leveraging the high-level aggregations to identify key records in the original data was also demonstrated.
Finally, the data structuring capabilities in SPSS Statistics was introduced using a basic cases-to-rows consolidation example that illustrated how this allows calculations that would not otherwise be possible. With these data...