Modeling critical performance elements can assist the human performance technology practitioner in better understanding the often complex, messy, and fuzzy real world of human performance. As illustrated in the embedded case study, generically developed models that, in turn, can be adapted to context-specific applications often have the greatest practical value. The key in developing any good model is to first identify those key variables—the critical few—that truly account for the greatest variance in performance.
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