| Abstract: | Background data measures have proven to be effective predictors of a variety of criteria. Little attention, however, has been given to the substantive princi- ples underlying their application. In this article, we present a model for un- derstanding the structure of differential life history. This model was used subsequently to generate a substantial framework for applying background data measures. These principles were used then to address various issues bear- ing on construct definition, item generation, and performance prediction. It was concluded that systematic application of construct validation principles may do much to enhance the utility of background data scales. |
