The concept of practical intelligence reflects the idea that there might be some ability besides general mental abilities g), some street smarts or common sense that predicts how successfully individuals handle situations in their actual lives in the form of appropriate responses, given facts and circumstances as they are discovered, and considering a person’s short- and long-range goals.
This definition of practical intelligence is in some ways different from the usual conception and measurement g. First, unlike tasks assessing g, tasks for practical intelligence aim at the individual’s own long-and short-range goals and are usually of the individual’s own intrinsic interest, rather than being formulated by others. Second, the task is encountered during a situation connected to the individual’s ordinary experience; and, because it is quite uncommon in classic assessments of g but rather ordinary in real life, those facts of the situation as they are discovered may not suffice to make well-informed decisions and may change during exposition to the problem at hand. Finally, although the situation is oftentimes not well-defined, there is more than one possible correct answer and more than one method of correct solution.
Approaches to Practical Intelligence
Given the rather wide and situation-specific definition of practical intelligence, the construct has been addressed via different approaches, namely practical know-how, practical mathematics, practical planning, practical presupposition, social judgment, and prototypes of practical intelligence.
Practical know-how refers to solving tasks such as repairing machines or navigating the ocean without appropriate information such as formal education, technical manuals, or specialized tools. The most prominent form of practical know-how is tacit knowledge, practical know-how that usually is not openly expressed or stated and must be acquired in the absence of direct instruction. Frequently, tacit knowledge is further classified by its focus (i.e., how to handle oneself, others, and one’s task), but measures addressing these different foci usually load onto a common factor.
Practical mathematics refers to street mathematics, that is, mathematical calculations undertaken in everyday life that differ from the abstract mathematics formally taught in schools and that are oftentimes conducted in forms of mental shortcuts, such as when searching for the best buys in supermarkets or filling orders of different quantities with minimal waste.
Practical planning refers to how people organize their everyday activities and reorganize when something goes wrong. Thus although everyone will have a routine of getting up in the mornings and getting ready to work, the effectiveness of different strategies used to react to problems, such as a failed alarm clock, may differ.
Practical presupposition refers to concept learning in everyday situations that allows individuals to discover regularities in their environment, such as general ideas about the likely preferences, decisions, and actions of individuals from different groups.
Social judgment can also be treated as an aspect of practical intelligence. Given the social nature of our lives, practical intelligence may be reflected in the attainment of transactional goals and in the individuals’ adaptation to their social environments, that is, their success at meeting the requirements of diverse social roles.
Prototypes of practical intelligence refers to a conceptualization introduced by Ulric Neisser, who argued that it was not possible to define intelligence as any one thing. Instead, he suggested defining practical intelligence as the extent to which an individual resembles a prototypical person who would be an ideal exemplar of the target concept.
Practical Intelligence Research and Measurement
Given the somewhat idiosyncratic nature of practical intelligence, much research has been done in the form of case studies showing how practically intelligent individuals improvise to complete their task by adapting whatever resources are at hand (practical know-how); handle problems arising in their daily routines (practical planning); or solve mathematical problems easily when undertaken in a context with which they are familiar (demanding the right amount of money when selling a certain number of coconuts, each of which costs a certain amount) but not, however, when presented with the same problem in an abstract form, such as “How much is 4 times 35?”
Some of these approaches have also made use of John Flanagan’s critical incident technique (CIT), which allows the identification of the strategies that individuals actually use when performing specific tasks and the specific, situationally relevant aspects of this behavior. A frequent measurement approach, however, uses simulations (sometimes based on CITs). These simulations can exhibit high fidelity, that is, they try to replicate the represented situation as realistically as possible and require individuals to respond as if they were in the actual situation, such as assessment centers, group discussions, and to a certain degree in-basket tests. Yet most prominent, particularly for the assessment of tacit knowledge, are low-fidelity simulations that present a situation to individuals orally or in writing. Individuals have to either describe how they would react in the situation, as in situational interviews, or rate the quality of diverse possible reactions, including situational judgment tests. A special kind of situational judgment test frequently used to assess tacit knowledge is the tacit knowledge inventory; these tests have been developed for management, sales, military leadership, college studies, and academic psychology. These inventories usually use longer and more elaborate scenario descriptions than most situational judgment tests. They are scored by giving points for answers that were more common among experts than novices, by judging the degree to which participants’ responses conform to professional rules of thumb, or by computing the (oftentimes squared) difference between participants’ responses and an expert prototype. Finally, practical intelligence, particularly involving practical presuppositions, has been tested in the laboratory, such as by giving individuals descriptions of a person (e.g., a father of four versus a student) and a target (e.g., a car with specific features) that was congruent, irrelevant, or incongruent to the person. Participants should indicate how much the person would like the target. In another study children performed considerably worse at predicting the movement of geometric forms on a computer screen with the help of a cursor than when the same algorithm was used in a computer game in which the geometric forms were birds, bees, and butterflies and the cursor a net.
Concerns and Directions for Future Research
The concept of practical intelligence has not gone unchallenged. Although some proponents of practical intelligence argue that practical intelligence is different from and superior to g, some authors, such as L. S. Gottfredson and colleagues in 2003, conceptually and empirically discredit this argument on the basis that practical intelligence and g correlate, and it appears that practical intelligence demonstrates incremental validity above g only for tasks that are both simple and well learned—conditions under which the influence of g is reduced, anyway.
Consequently, other authors have argued that practical intelligence is nothing else but job knowledge. Finally, research by M. A. McDaniel and colleagues on situational judgment tests suggests that what is measured in practical intelligence may be a function of g, job knowledge, and different personality factors such as emotional stability, agreeableness, and conscientiousness.
Besides further analysis of the nomological network of practical intelligence, the use of practical intelligence in personnel selection merits further research. Although practical intelligence tests may have little incremental validity over and above cognitive ability tests, their obvious task-relatedness may increase their face validity to applicants; hence their acceptance.
- Gottfredson, L. S. (2003). Dissecting practical intelligence theory: Its claims and evidence. Intelligence, 31(4), 343-397.
- McDaniel, M. A., Morgeson, F. P., Finnegan, E. B., Campion, M. A., & Braverman, E. P. (2001). Predicting job performance using situational judgment tests: A clarification of the literature. Journal of Applied Psychology, 80(4), 730-740.
- Neisser, U. (1976). General, academic, and artificial intelligence. In L. B. Resnick (Ed.), The nature of intelligence. Hillsdale, NJ: Lawrence Erlbaum.
- Schmidt, F. L., & Hunter, J. E. (1993). Tacit knowledge, practical intelligence, general mental ability, and job knowledge. Current Directions in Psychological Science, 2, 8-9.
- Sternberg, R. J., Forsythe, G. B., Hedlund, J., Horvath, J. A., Wagner, R. K., Williams, W. M., et al. (2000). Practical intelligence in everyday life. New York: Cambridge University Press.
- Wagner, R. K. (2000). Practical intelligence. In R. J. Sternberg (Ed.), Handbook of intelligence (pp. 380-395). New York: Cambridge University Press.