Task Analysis and Performance Prediction
To date, Nonlinear Causal Resource Analysis (NCRA) has been our most productive analytic tool and is most promising with regard to the potential for near-term impact on a widespread basis. It is a relatively straightforward, and perhaps elegant, methodology based on GSPT concepts that allows one to: (1) conduct task analyses by inference (i.e., build models that relate the demand imposed on "more basic" performance resource to a given level of performance that can be achieved in a higher level task, and (2) use these models to predict level of performance in a high level task using only selected measures of an individual's lower level performance capacities. Moreover, NCRA can determine which lower level performance capacity is that individual's "limiting resource". The latter has obvious implications for performance enhancement. No other objective method is known to exist that can provide this information.
NCRA is based on the notion that one can estimate by inference the minimum demand for a given performance resource to achieve a given level of performance in a selected higher level task (HLT). This is achieved by:
This method is being investigated as a cause-and-effect substitute for multivariate regression to obtain predictions of performance in various HLTs. Unlike regression analyses, NCRA has inherent attributes that permit task analysis in addition to performance prediction. Such analyses result in a profile of "performance resource demands" for a specified level of performance in a given high level task.