Intrigued by conflicting data about effective predictors of task and job performance, Ackerman and Cianciolo tested 81 adults on cognitive skills such as pattern recognition, paper folding and other spatial abilities; numerical ability and problem solving; memory and scanning tests. The psychologists also derived a general-ability score by combining spatial and numerical abilities. Ackerman and Cianciolo then trained the participants to use complex computer software, the TRACON (Terminal Radar Approach Control) air-traffic control training simulator similar to that used by the U.S. Federal Aviation Administration, Dept. of Defense and other organizations.
Ackerman and Cianciolo manipulated the TRACON software to see how well participants performed under changing conditions. They increased and decreased task complexity, such as the number of arrivals or overflights, and also varied its content, such as shifting from tasks requiring them to solve spatial problems to tasks calling for them to process perceptual input faster.
The researchers analyzed participants' TRACON use to see how well the abilities tests predicted performance in light of changing conditions. In the first major finding, specific cognitive abilities correlated with -- and thus could be used to predict -- task performance. Researchers were able to place participants in rank order with their ability scores and their performance in parallel.
In the second major finding, changing task complexity -- a different aspect of job content -- did not affect the relationships among abilities and performance. When the task was easier to perform, higher-ability participants held their advantage over lower-ability participants. As a result, ability tests were unable to forecast who might do better on more or less complex tasks.
"A focus on content abilities (such as numerical, spatial or verbal), rather than on general intelligence, may be more valuable in predicting performance on particular jobs," says Ackerman. "We can also generalize that particular ability tests may be valuable predictors of performance in jobs with different levels of complexity, such as numerical ability tests predicting success in both accounting and bookkeeping."
The results, although preliminary, suggest that test batteries used to select people for particular jobs or tasks might predict performance better if they focus less on estimating overall, general ability, and more on the content of the job -- such as verbal abilities for writing, and spatial abilities for air-traffic control. Ackerman and Cianciolo conclude that candidate-selection test batteries that focus on content may be more useful than tests assumed to measure an underlying aspect of cognition, such as encoding or categorization, or than broad general-reasoning tests.
Article: "Ability and Task Constraint Determinants of Complex Task Performance;" Phillip L. Ackerman, Ph.D., Georgia Institute of Technology, and Anna T. Cianciolo, Ph.D., Yale University; Journal of Experimental Psychology: Applied, Vol. 8, No. 3.
(Full text of the article is available from the APA Public Affairs Office and at http://www.apa.org/journals/xap/press_releases/september_2002/xap83194.html
Phillip Ackerman can be reached at phillip.ackerman@psych.gatech.edu, or by phone at 404-894-5611 or 404-242-6011.
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