Non-g Factors, Ability Tilt, and Non-g Residuals: Buz's Challenge II

Co-Authors: Dale Hancock, Karrie Elpers, and Miguel Gonzalez, University of Texas at San Antonio, and Miranda Richmond, Vanderbilt UniversityBuz challenged us to identify factors unrelated to g, non-g factors, that predict... [ view full abstract ]

Co-Authors: 

Dale Hancock, Karrie Elpers, and Miguel Gonzalez, University of Texas at San Antonio, and Miranda Richmond, Vanderbilt University

Buz challenged us to identify factors unrelated to g, non-g factors, that predict school and work performance. This is a tall order for intelligence researchers because measures of intelligence typically trump other measures in predicting work and school performance. Specific abilities are strong possibilities, and can be conceptualized using non-g residuals of standardized tests and ability tilt (Buz’s apt term for individuals’ relative differences between math and verbal scores on standardized tests). Math (> verbal) and Verbal (> math) tilts predict outcomes in different areas (STEM and the humanities). We checked content validity and predicted outcomes with standardized tests and other data from the 1997 National Longitudinal Survey of Youth, a representative sample of youth in the United States (N ≈ 2000). We used the 12 tests of the ASVAB to measure g, and regressed it from the verbal and math subtests of the SAT, ACT, and PSAT to measure ability tilt and generate ASVAB subtest non-g residuals. The correlations assessed in structural equation models included four specific abilities based on the ASVAB (math, verbal, shop, speed), college majors in STEM and the humanities, and jobs in STEM and verbal fields (e.g., chemist and journalist).Tilt and non-g residuals (from the SAT, ACT, PSAT) were associated with the expected related ASVAB content residuals and predicted math and verbal outcomes in content-related ways. Math tilt and residuals were positively associated with math criteria (ASVAB math ability, STEM majors, STEM jobs) and negatively with verbal criteria (ASVAB verbal ability, humanities majors, verbal jobs). Verbal tilt and residuals showed the opposite patterns. In contrast, math and verbal tilt were not associated with non-academic abilities (shop or speed), providing discriminant validity. All significant effects (betas) were unrelated to g, and ranged in size from small (around .20) to large (.60 or higher). The patterns support investment theories, though not uniquely so. Such theories argue that investment (time and effort) in a specific area (math) comes at the expense of investment in competing areas (verbal). This differential investment leads to opportunity costs and yields negative associations between non-g factors (tilt and residuals) and competing criteria (math tilt and verbal ability).