To What Extent Do Cognitive Abilities, Conscientiousness, and Physical Activity Predict Academic Performance?

To what extent do cognitive abilities, conscientiousness, and physical activity predict academic performance?

The relative value of specific versus general cognitive abilities for the prediction of practical outcomes has been debated since the inception of modern intelligence theorizing and testing. This editorial introduces a special issue dedicated to exploring this ongoing “great debate”. It provides an overview of the debate, explains the motivation for the special issue and two types of submissions solicited, and briefly illustrates how differing conceptualizations of cognitive abilities demand different analytic strategies for predicting criteria, and that these different strategies can yield conflicting findings about the real-world importance of general versus specific abilities.

Periventricular and deep abnormal white matter differ in associations with cognitive performance at midlife.

Introduction

Developing a Framework of Relationships Among Noncognitive Factors in Doctor of Pharmacy Students’ Academic Performance

Adults’ ratings of children’s personality have been found to be more closely associated with academic performance than children’s self-reports. However, less is known about the relevance of the unique perspectives held by specific adult observers such as teachers and parents for explaining variance in academic performance. In this study, we applied bifactor (S–1) models for 1411 elementary school children to investigate the relative merits of teacher and parent ratings of children’s personalities for academic performance above and beyond the children’s self-reports. We examined these associations using standardized achievement test scores in addition to grades. We found that teachers’ unique views on children’s openness and conscientiousness had the strongest associations with academic performance. Parents’ unique views on children’s neuroticism showed incremental associations above teacher ratings or self-reports. For extraversion and agreeableness, however, children’s self-reports were more strongly associated with academic performance than teacher or parent ratings. These results highlight the differential value of using multiple informants when explaining academic performance with personality traits.

Face cognition (FC) is a specific ability that cannot be fully explained by general cognitive functions. Cortical thickness (CT) is a neural correlate of performance and learning. In this registered report, we used data from the Human Connectome Project (HCP) to investigate the relationship between CT in the core brain network of FC and performance on a psychometric task battery, including tasks with facial content. Using structural equation modelling (SEM), we tested the existence of face-specific interindividual differences at behavioural and neural levels. The measurement models include general and face-specific factors of performance and CT. There was no face-specificity in CT in functionally localized areas. In post hoc analyses, we compared the preregistered, small regions of interest (ROIs) to larger, non-individualized ROIs and identified a face-specific CT factor when large ROIs were considered. We show that this was probably due to low reliability of CT in the functional localization (intra-class correlation coefficients (ICC) between 0.72 and 0.85). Furthermore, general cognitive ability, but not face-specific performance, could be predicted by latent factors of CT with a small effect size. In conclusion, for the core brain network of FC, we provide exploratory evidence (in need of cross-validation) that areas of the cortex sharing a functional purpose did also share morphological properties as measured by CT.

Many studies have found that variation in music training is associated with intellectual abilities, but research disagrees over whether music education should primarily correlate with general intelligence (g) or with specific lower-level cognitive abilities (e.g., fluid reasoning, verbal ability, or spatial reasoning). Past research, however, has not modeled the data in ways that can separate general abilities like g from specific abilities. To examine if the associations between music training and intelligence are general, specific, or both, a bifactor modeling approach was applied to data from a sample of 237 young adults who varied substantially in musical expertise. People completed a range of tasks that measured several lower-order abilities: fluid intelligence, crystallized intelligence (vocabulary knowledge), verbal fluency, and auditory discrimination ability. Simple correlations showed that music training correlated with all 4 lower-order abilities. A bifactor model, however, found that music training had both general (a strong association with g: s = .74 [.50, .98]) and specific (a moderate association with auditory ability: s = .37 [.08, .67]) relationships. The findings reconcile past research on the breadth of music training’s relationships and illustrate a fruitful method for identifying its links with cognitive abilities.

Many older adults have inadequate health-related literacy, which is associated with poor health outcomes. Thus, it is important to identify determinants of health literacy. We investigated relationships between health literacy and general cognitive and sensory abilities, as well as education, health, and demographic variables, in a community sample of middle-aged and older adults. Participants were 314 community-dwelling adults (67% female, 48% African American) diagnosed with chronic heart failure recruited for a pharmacist-based intervention study to improve adherence to chronic heart failure medications. We adminstered demographic, health, education, cognitive (e.g., processing speed, working memory), and sensory measures, and the Short Test of Functional Health Literacy in Adults (STOFHLA), as part of the baseline condition of this study. STOFHLA scores were lower for participants who were older, less educated, male, African American, had more comorbidities, or scored lower on all cognitive ability measures. Hierarchical linear regression analyses showed that education and cognitive ability were independently associated with the STOFHLA measure and explained age differences in health literacy. The association of cognitive abilities and literacy has important implications for health literacy models and for interventions to reduce the impact of low health literacy on health outcomes. For example, medication instructions should be designed to reduce comprehension demands on general cognitive abilities as well as literacy skills.

What counts in preschool number knowledge? A Bayes factor analytic approach toward theoretical model development

The bifactor model is a widely applied model to analyze general and specific abilities. Extensions of bifactor models additionally include criterion variables. In such extended bifactor models, the general and specific factors can be correlated with criterion variables. Moreover, the influence of general and specific factors on criterion variables can be scrutinized in latent multiple regression models that are built on bifactor measurement models. This study employs an extended bifactor model to predict mathematics and English grades by three facets of intelligence (number series, verbal analogies, and unfolding). We show that, if the observed variables do not differ in their loadings, extended bifactor models are not identified and not applicable. Moreover, we reveal that standard errors of regression weights in extended bifactor models can be very large and, thus, lead to invalid conclusions. A formal proof of the nonidentification is presented. Subsequently, we suggest alternative approaches for predicting criterion variables by general and specific factors. In particular, we illustrate how (1) composite ability factors can be defined in extended first-order factor models and (2) how bifactor(S-1) models can be applied. The differences between first-order factor models and bifactor(S-1) models for predicting criterion variables are discussed in detail and illustrated with the empirical example.

This study aimed to develop a Structural Equation Model (SEM) that captures the influence of learning style, personality traits, and emotional intelligence on the academic performance of Grade 4 learners in the Division of Butuan City. Employing a descriptive-predictive quantitative research design, data were collected from 341 learners using standardized survey instruments. The study found that most learners exhibited a kinesthetic learning style and displayed high levels of openness and agreeableness, alongside average levels of neuroticism, extraversion, and conscientiousness. Emotional intelligence dimensions such as self-awareness, motivation, and social skills were strongly manifested, while empathy and emotion regulation appeared moderately. In terms of academic outcomes, most learners achieved “outstanding” or “very satisfactory” general weighted averages (GWA), with none performing below satisfactory levels. Statistical analyses revealed significant positive correlations between academic performance and each of the independent variables. A multiple linear regression model showed that 75.9% of the variance in GWA could be explained by the combined effects of learning style, personality traits, and emotional intelligence. To address potential multicollinearity, a Partial Least Squares (PLS)-based SEM was developed, demonstrating strong reliability, convergent validity, and discriminant validity. The model accounted for 66% of the variance in academic performance and confirmed that these psychosocial and cognitive variables are significant predictors of student success. The study concludes that the interplay among learning preferences, personality, and emotional intelligence offers valuable insights into learner achievement and provides a robust framework for future educational planning and intervention.

In the present study, the role of learning potential assessment as part of cognitive assessment for vocational guidance was investigated for a population (N=262) of junior secondary students. Mean scores, distribution of scores, inter-correlation of scores and predictive validity were evaluated. The mean learning potential scores indicated a level of general reasoning and learning potential higher than the academic level of the students at the time of assessment and the distribution of the scores indicated tertiary level potential for some learners. Statistically significant correlations were found between the LPCAT learning potential scores and three sub-tests of the Differential Aptitude Test (Form R) (DAT-R) namely Verbal Reasoning, Comparison and Spatial Perception. Furthermore, all cognitive scores showed statistically significant correlations with the aggregate end-of-year academic performance in English, Life Orientation and Mathematics. Based on the results of this study, verbal reasoning is a better predictor of aggregate academic performance than learning potential based on non-verbal figural reasoning. A total of 35.3 per cent of the variance in academic performance was predicted by combining learning potential and aptitude scores. The unique explanation of variance in academic performance by means of the LPCAT post-test results alone was 12.9 per cent, while for Verbal Reasoning aptitude alone it was 29.2 per cent.

School grades are still used by universities and employers for selection purposes. Thus, identifying determinants of school grades is important. Broadly, two predictor categories can be differentiated from an individual difference perspective: cognitive abilities and personality traits. Over time, evidence accumulated supporting the notion of the g-factor as the best single predictor of school grades. Specific abilities were shown to add little incremental validity. The current paper aims at reviving research on which cognitive abilities predict performance. Based on ideas of criterion contamination and deficiency as well as Spearman’s ability differentiation hypothesis, two mechanisms are suggested which both would lead to curvilinear relations between specific abilities and grades. While the data set provided for this special issue does not allow testing these mechanisms directly, we tested the idea of curvilinear relations. In particular, polynomial regressions were used. Machine learning was applied to identify the best fitting models in each of the subjects math, German, and English. In particular, we fitted polynomial models with varying degrees and evaluated their accuracy with a leave-one-out validation approach. The results show that tests of specific abilities slightly outperform the g-factor when curvilinearity is assumed. Possible theoretical explanations are discussed.

Infant motor and cognitive abilities and subsequent executive function