For over a century, the structure of intelligence has been dominated by factor analytic methods that presume tests are indicators of latent entities (e.g., general intelligence or g). Recently, psychometric network methods and theories (e.g., process overlap theory; dynamic mutualism) have provided alternatives to g-centric factor models. However, few studies have investigated contemporary cognitive measures using network methods. We apply a Gaussian graphical network model to the age 9–19 standardization sample of the Woodcock–Johnson Tests of Cognitive Ability—Fourth Edition. Results support the primary broad abilities from the Cattell–Horn–Carroll (CHC) theory and suggest that the working memory–attentional control complex may be central to understanding a CHC network model of intelligence. Supplementary multidimensional scaling analyses indicate the existence of possible higher-order dimensions (PPIK; triadic theory; System I-II cognitive processing) as well as separate learning and retrieval aspects of long-term memory. Overall, the network approach offers a viable alternative to factor models with a g-centric bias (i.e., bifactor models) that have led to erroneous conclusions regarding the utility of broad CHC scores in test interpretation beyond the full-scale IQ, g.

Carroll’s treatise on the structure of human cognitive abilities is a milestone in psychometric intelligence research. Thirty years later, Carroll’s work continues to influence research on intelligence theories and the development and interpretation of intelligence tests. A historical review of the relations between the 3S and CHC theories necessitates the recommendation that the theories of Cattell, Horn, and Carroll be reframed as a family of obliquely correlated CHC theories—not a single CHC theory. Next, a previously unpublished Carroll exploratory factor analysis of 46 cognitive and achievement tests is presented. A complimentary bifactor analysis is presented that reinforces Carroll’s conclusion that his 3S model more accurately represents the structure of human intelligence than two prominent alternative models. Finally, a Carroll-recommended higher-stratum psychometric network analysis (PNA) of CHC cognitive, reading, and math variables is presented. The PNA results demonstrate how PNA can complement factor analysis and serve as a framework for identifying and empirically evaluating cognitive–achievement causal relations and mechanisms (e.g., developmental cascade and investment theories), with an eye toward improved cognitive–achievement intervention research. It is believed that Carroll, given his long-standing interest in school learning, would welcome the integration of theory-driven factor and PNA research.

Consideration of the influence of English language skills during testing is an understandable requirement for fair and valid cognitive test interpretation. Several professional standards and expert recommendations exist to guide psychologists as they attempt to engage in best practices when assessing English learners (ELs). Nonetheless, relatively few evidence-based recommendations for practice have been specified for psychologists. To address this issue, we used a mixed-effects modeling approach to examine the influences of test characteristics (i.e., test directions) and examinee characteristics (i.e., expressive and receptive language abilities) on cognitive test performance. Our results suggest that language abilities appear to have a significant influence on cognitive test performance, whereas test characteristics do not influence performance, after accounting for language abilities. Implications for practice include the assessment of expressive and receptive language abilities of EL students prior to administering, scoring, and interpreting cognitive test scores.

The Cognitive-Affective-Motivation Model of Learning (CAMML) is a proposed framework for integrating contemporary motivation, affective (Big 5 personality) and cognitive (CHC theory) constructs in the practice of school psychologists (SPs). The central tenet of this article is that SPs need to integrate motivation alongside affective and cognitive constructs vis-à-vis an updated trilogy-of-the-mind (cognitive, conative, affective) model of intellectual functioning. CAMML builds on Richard Snow’s seminal research on academic aptitudes—which are not synonymous with cognitive abilities. Learning aptitude complexes are academic domain-specific cognitive abilities and personal investment mechanisms (motivation and self-regulation) that collectively produce a student’s readiness to learn in a specific domain. CAMML incorporates the “crossing the Rubicon” commitment pathway model of motivated self-regulated learning. It is recommended SPs take a fresh look at motivation theory, constructs, and research, embedded in the CAMML aptitude framework, by going back-to-the-future guided by the wisdom of giants from the field of cognition, intelligence, and educational psychology.