Approaches To Validation

What can ability tests do for psychological science? What do they predict to and how longitudinally robust are they? Huge amounts of data have been compiled over the years on general and specific abilities. That general ability is related to learning, training, and work performance is widely acknowledged (Corno, Cronbach, et al., 2002; Cronbach & Snow, 1977; Gottfredson, 1997, 2003a; Jensen, 1980, 1998; Schmidt & Hunter, 1998) , although, when predicting performance, specific abilities can add incremental validity (Lubinski & Dawis, 1992). This literature does not need to be reviewed here. Rather, this section will be restricted to two points: niche selection and predicting group membership.

It is important to keep in mind that different criteria are needed to answer different psychological questions. Predicting individual differences in learning, training, and work performance is important for validating ability tests, but performance it is not always the optimal criterion variable (cf. Humphreys et al., 1993; Lubinski, Webb, Morelock, & Benbow, 2001; Murray, 1998; Wilk, Desmarais, & Sackett, 1995; Wilk & Sackett, 1996). There are other criteria that matter. For example, students and workers do not select educational tracks and occupational paths randomly. They do so in part based on the level and pattern of their general and specific abilities. General ability level has more to do with educational or occupation level or prestige (e.g., uniform levels of high prestige cut across doctor, lawyer, and professor), whereas specific abilities differentially predispose development toward learning about and working with different media (e.g., working with ideas, working with people, working with things). Because making choices is different than performance after choice, the criteria needed for validating the role that abilities play in making choices are different. For answering these questions, the prediction of group membership is more optimal. Investigations along these lines are more associated with names like Truman Kelley, Phillip Rulon, and Maurice Tatsuoka. These validation designs involve multivariate discriminant function analyses aimed at classification and selection, rather than multiple regression analyses predicting individual differences in learning and work performance (see Humphreys et al., 1993, for a review).

For example, the four panels of Figure 8.4 track a group of intellectually precocious participants at three time points over a 20-year interval. At age 13, participants were in the top 1% of their age mates in general intellectual ability; at this time, they were also assessed on quantitative, spatial, and verbal reasoning measures (Shea et al., 2001). At ages 18, 23, and 33, individual differences in their mathematical, spatial, and verbal abilities assessed in early adolescence were related in distinct ways to subsequent preferences for contrasting disciplines and ultimate educational and occupational group membership. Specifically, panels A and B, respectively, show whether participants' favorite and least favorite high school course was in math/science or the humanities/social sciences. Panels C and D, respectively, reflect college major at age 23 and occupation at age 33.

All four panels represent a three-dimensional view of how mathematical (X), verbal (Y), and spatial (Z) ability factor into educational-vocational preferences and choice. For all four panels, all three abilities are standardized in z-score units (A and B are within sex, C and D are combined across sex). For each labeled group within each panel, the direction of the arrows represents whether spatial ability (Z-axis) was above (right) or below (left) the grand mean for spatial ability. These arrows were scaled in the same units of measurement as the SAT (math and verbal) scores. Thus, one can envision how far apart these groups are in three-dimensional space in standard deviation units as a function of these three abilities. Across these developmentally sequenced panels, exceptional verbal ability, relative to mathematical and spatial ability, is characteristic of group membership in the social sciences and humanities, whereas higher levels of math and spatial abilities, relative to verbal abilities, characterize group membership in engineering and math/computer science. For example, engineering is relatively high space, high math, and relatively low verbal. Other sciences appeared to require appreciable amounts of all three abilities. These findings were highly consistent for other outcome criteria as well (e.g., graduate field of study; Shea et al., 2001). Across all time points, all three abilities achieved incremental validity relative to the other two in predicting group membership. This amount of differentiation could not have been achieved with one dimension, or what these measures have in

A. Favorite High School Course (Age 18)

B. Least Favorite High School Course (Age 18)

Humanities/Social Science Males (90)

Humanities/Social Science Females (65)

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