Sex differences in ability tilt: Support for investment theory

Exploring the Relationship between Cognitive Ability Tilt and Job Performance

Most of the work examining the relationship between intelligence and job performance has conceptualized intelligence as g. Recent findings, however, have supported the claim that more specific factors of intelligence contribute to the prediction of job performance. The present study builds upon prior work on specific cognitive abilities by investigating the relationship between ability tilt, a measure representing differential strength between two specific abilities, and job performance. It was hypothesized that ability tilt would differentially relate to job performance based on whether or not the tilt matched the ability requirements of the job, and that ability tilt would provide incremental validity over g and specific abilities for predicting performance when the tilt matched job requirements. Hypotheses were tested using a large sample from the General Aptitude Test Battery (GATB) database. Ability tilt related with job performance in the expected direction for 27 of the 36 tilt-job combinations examined, with a mean effect size of .04 when the tilt matched job requirements. The mean incremental validities for ability tilt were .007 over g and .003 over g and specific abilities, and, on average, tilt explained 7.1% of the total variance in job performance. The results provide limited evidence that ability tilt may be a useful predictor in addition to ability level, and contribute to our understanding of the role of specific abilities in the workplace.

Men, women and STEM: Why the differences and what should be done?

It is a well-known and widely lamented fact that men outnumber women in a number of fields in STEM (science, technology, engineering and maths). The most commonly discussed explanations for the gender gaps are discrimination and socialization, and the most common policy prescriptions target those ostensible causes. However, a great deal of evidence in the behavioural sciences suggests that discrimination and socialization are only part of the story. The purpose of this paper is to highlight other aspects of the story: aspects that are commonly overlooked or downplayed. More precisely, the paper has two main aims. The first is to examine the evidence that factors other than workplace discrimination contribute to the gender gaps in STEM. These include relatively large average sex differences in career and lifestyle preferences, and relatively small average differences in cognitive aptitudes – some favouring males, others favouring females – which are associated with progressively larger differences the further above the average one looks. The second aim is to examine the evidence suggesting that these sex differences are not purely a product of social factors but also have a substantial biological (i.e. inherited) component. A more complete picture of the causes of the unequal sex ratios in STEM may productively inform policy discussions.

Talent Development in Achievement Domains: A Psychological Framework for Within- and Cross-Domain Research

Achievement in different domains, such as academics, music, or visual arts, plays a central role in all modern societies. Different psychological models aim to describe and explain achievement and its development in different domains. However, there remains a need for a framework that guides empirical research within and across different domains. With the talent-development-in-achievement-domains (TAD) framework, we provide a general talent-development framework applicable to a wide range of achievement domains. The overarching aim of this framework is to support empirical research by focusing on measurable psychological constructs and their meaning at different levels of talent development. Furthermore, the TAD framework can be used for constructing domain-specific talent-development models. With examples for the application of the TAD framework to the domains of mathematics, music, and visual arts, the review provided supports the suitability of the TAD framework for domain-specific model construction and indicates numerous research gaps and open questions that should be addressed in future research.

Gender differences in individual variation in academic grades fail to fit expected patterns for STEM

Fewer women than men pursue careers in science, technology, engineering and mathematics (STEM), despite girls outperforming boys at school in the relevant subjects. According to the 'variability hypothesis', this over-representation of males is driven by gender differences in variance; greater male variability leads to greater numbers of men who exceed the performance threshold. Here, we use recent meta-analytic advances to compare gender differences in academic grades from over 1.6 million students. In line with previous studies we find strong evidence for lower variation among girls than boys, and of higher average grades for girls. However, the gender differences in both mean and variance of grades are smaller in STEM than non-STEM subjects, suggesting that greater variability is insufficient to explain male over-representation in STEM. Simulations of these differences suggest the top 10% of a class contains equal numbers of girls and boys in STEM, but more girls in non-STEM subjects.

Non-g Factors Predict Educational and Occupational Criteria: More than g

In a prior issue of the Journal of Intelligence, I argued that the most important scientific issue in intelligence research was to identify specific abilities with validity beyond g (i.e., variance common to mental tests) (Coyle, T.R. Predictive validity of non-g residuals of tests: More than g. Journal of Intelligence 2014, 2, 21–25.). In this Special Issue, I review my research on specific abilities related to non-g factors. The non-g factors include specific math and verbal abilities based on standardized tests (SAT, ACT, PSAT, Armed Services Vocational Aptitude Battery). I focus on two non-g factors: (a) non-g residuals, obtained after removing g from tests, and (b) ability tilt, defined as within-subject differences between math and verbal scores, yielding math tilt (math > verbal) and verbal tilt (verbal > math). In general, math residuals and tilt positively predict STEM criteria (college majors, jobs, GPAs) and negatively predict humanities criteria, whereas verbal residuals and tilt show the opposite pattern. The paper concludes with suggestions for future research, with a focus on theories of non-g factors (e.g., investment theories, Spearman’s Law of Diminishing Returns, Cognitive Differentiation-Integration Effort Model) and a magnification model of non-g factors.

The Great Debate: General Ability and Specific Abilities in the Prediction of Important Outcomes

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.

Cognitive Capitalism: Economic Freedom Moderates the Effects of Intellectual and Average Classes on Economic Productivity

Cognitive ability stimulates economic productivity. However, the effects of cognitive ability may be stronger in free and open economies, where competition rewards merit and achievement. To test this hypothesis, ability levels of intellectual classes (top 5%) and average classes (country averages) were estimated using international student assessments (Programme for International Student Assessment; Trends in International Mathematics and Science Study; and Progress in International Reading Literacy Study) (N = 99 countries). The ability levels were correlated with indicators of economic freedom (Fraser Institute), scientific achievement (patent rates), innovation (Global Innovation Index), competitiveness (Global Competitiveness Index), and wealth (gross domestic product). Ability levels of intellectual and average classes strongly predicted all economic criteria. In addition, economic freedom moderated the effects of cognitive ability (for both classes), with stronger effects at higher levels of freedom. Effects were particularly robust for scientific achievements when the full range of freedom was analyzed. The results support cognitive capitalism theory: cognitive ability stimulates economic productivity, and its effects are enhanced by economic freedom.