Search

Individual differences in Cattell-Horn-Carroll (CHC) cognitive abilities are related to individual differences in math problem solving. However, it is less clear whether cognitive abilities are associated with math problem solving directly or indirectly via math component skills and whether these relations differ across grade levels. We used multigroup structural equation models to examine direct and indirect CHC-based cognitive ability relations with math problem solving across six grade-level groups using the Kaufman Assessment Battery for Children, Second Edition and the Kaufman Tests of Educational Achievement, Second Edition co-normed standardization sample data ( N = 2,117). After testing factorial invariance of the cognitive constructs across grade levels, we assessed whether the main findings were similar across higher-order and bifactor models. In the higher-order model, the Crystallized Ability, Visual Processing, and Short-Term Memory constucts had direct and indirect relations with math problem solving, whereas the Learning Efficiency and Retrieval Fluency constructs had only indirect relations with math problem solving via math computation. The integrated cognitive ability and math achievement relations were generally consistent across the CHC models of intelligence. In the higher-order model, the g factor operated indirectly on math computation and math problem solving, whereas in the bifactor model, the first-order G factor had direct relations with math computation and math problem solving. In both models, g/G was the most consistent and largest cognitive predictor of math skills. Last, the relation of math computation with math problem solving increased as grade level increased. Theoretical implications for math development and considerations for school psychologists are discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

The gender similarities hypothesis by J. S. Hyde ( 2005 ), based on large-scale reviews of studies, concludes that boys and girls are more alike than different on most psychological variables, including academic skills such as reading and math (J. S. Hyde, 2005 ). Writing is an academic skill that may be an exception. The authors investigated gender differences in academic achievement using a large, nationally stratified sample of children and adolescents ranging from ages 7-19 years (N = 2,027). Achievement data were from the conormed sample for the Kaufman intelligence and achievement tests. Multiple-indicator, multiple-cause, and multigroup mean and covariance structure models were used to test for mean differences. Girls had higher latent reading ability and higher scores on a test of math computation, but the effect sizes were consistent with the gender similarities hypothesis. Conversely, girls scored higher on spelling and written expression, with effect sizes inconsistent with the gender similarities hypothesis. The findings remained the same after controlling for cognitive ability. Girls outperform boys on tasks of writing.

Reliable and meaningful sex differences exist in specific cognitive abilities despite no reliable or meaningful sex difference in general intelligence. Here we use Cattell-Horn-Carroll (CHC) theory to highlight research findings related to sex differences in intelligence, with a focus on studies of test scores from comprehensive intelligence measures that were obtained from large and representative samples of children and adolescents. Female advantages in latent processing speed and male advantages in latent visual processing are the most meaningful and consistently reported sex differences regarding CHC broad cognitive abilities. Differences have been reported in narrow and specific ability constructs such as mental rotation and object memory location. In academic achievement, the largest and most consistent findings are female advantages in writing, whereas male advantages at higher math ability levels are also found. Empirical descriptions of sex differences should consider the breadth of the construct under study and incorporate analysis beyond simple mean differences. Score analysis methods that utilize multiple-group confirmatory factor models and multiple-indicator multiple cause models are useful to address the former, and analysis methods such as quantile regression and male-female ratio calculations along score distributions are useful to address the latter. An understanding of why specific ability differences exist in combination and in the presence of similarities will improve researchers' understanding of human cognition and educational achievements. • Despite no differences in general intelligence, there are sex differences in specific abilities. • Reliable and meaningful female advantages are found in processing speed and writing. • Reliable and meaningful male advantages are found in visual processing. • Sex differences research should consider construct breadth and moving beyond mean differences only. [ABSTRACT FROM AUTHOR]