Sex differences on g and non-g intellectual performance reveal potential sources of STEM discrepancies

Lemos, Gina C.; Abad, Francisco; Almeida, Leandro S.; Colom, Roberto

doi:10.1016/j.intell.2012.10.009

Cited by 28 publications

(22 citation statements)

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“…Several studies corroborate these results, identifying general cognitive abilities as a relevant predictor of mathematics achievement (e.g. [128][129][130][131][132]). In stages of transition in schooling, such as the transition from elementary to middle school, general cognitive abilities play a more relevant role for mathematics learning, helping students to acquire the new skills and abilities required to learn new academic subjects and topics [132].…”

Section: Plos Onementioning

confidence: 67%

The role of cognitive and non-cognitive factors in mathematics achievement: The importance of the quality of the student-teacher relationship in middle school

et al. 2020

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There is increasing evidence that several factors, including both cognitive and non-cognitive ones, play an important role in mathematics achievement. Relatively little is known about how socio-emotional features and the quality of the student-teacher relationship correlate with mathematics achievement among adolescents in transition to middle school. The aim of the present study is to examine the role of cognitive factors (general cognitive abilities), non-cognitive factors (math anxiety and self-esteem), and the quality of the student-teacher relationship on mathematics achievement. A large sample of Italian sixth graders was evaluated upon entering middle school. The results showed that general cognitive ability was the best predictor of mathematics achievement. As regards non-cognitive factors, the level of math anxiety was effective in predicting mathematics achievement, after controlling for other measures including self-esteem and the quality of the student-teacher relationship. In particular, we found that the quality of the student-teacher relationship had an indirect influence on mathematics achievement through the mediation of math anxiety. Our findings seem to indicate that the quality of the student-teacher relationship may be related to mathematics achievement, through its effects on math anxiety. This may have important implications for practitioners and educators, as we can suggest that interventions devoted to improving the quality of the student-teacher relationship may play a positive role in both preventing math anxiety and promoting mathematics learning.

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Section: Plos Onementioning

confidence: 67%

The role of cognitive and non-cognitive factors in mathematics achievement: The importance of the quality of the student-teacher relationship in middle school

et al. 2020

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“…The results prove that the creative performance of the students is largely conditioned by the figural or verbal content of the tasks given (TTCT and PIC, respectively). This seems to be very relevant because also in the area of intelligence, some factorial methods point towards a cognitive ability structure taking into account the task verbal, numerical or spacial-figural contents - (Beauducel, Brocke, & Liepmann, 2001;Lemos, Abad, Almeida, & Colom, 2013).…”

Section: Discussionmentioning

confidence: 99%

Runing ahead: Pensamiento divergente y sus dimensiones: ¿De qué hablamos y qué evaluamos?

et al. 2016

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Título: Pensamiento divergente y sus dimensiones: ¿de qué hablamos y qué evaluamos? Resumen: Este artículo analiza el papel de los procesos cognitivos subyacentes en el pensamiento divergente y los contenidos sobre los que se aplican dichos procesos (verbal y figurativo). La muestra utilizada es de 260 alumnos con edades comprendidas entre los 8 y los 15 años. Para la evaluación del pensamiento divergente, se utilizó el Test Figurativo de Pensamiento Creativo de Torrance (TTCT), y la Prueba de Imaginación Creativa (PIC) de contenido verbal. Los resultados obtenidos sugieren que, aunque ambas pruebas evalúen el pensamiento divergente y tengan el referencial psicométrico de Guilford, sus puntuaciones no están significativamente correlacionadas. Los resultados de los análisis factoriales confirmatorios muestran que el modelo que mejor explica las puntuaciones en estas pruebas refleja la existencia de dos factores autónomos que se corresponden con cada una de las pruebas, por tanto, identificándose más con los contenidos, dominios y demandas de la tarea que con los procesos cognitivos que tradicionalmente se consideran en la definición y medida de la creatividad. Palabras clave: creatividad; evaluación de la creatividad; pensamiento divergente; tests de creatividad.Abstract: This paper examines the role of latent cognitive process and the contents of task (verbal and figural) in divergent thinking. The sample was composed of 260 students, attending different public and semi-public schools in the Murcia Region (Spain), with ages ranging from 8 to 15 years old. Creativity was assessed with the Torrance Test of Creativity Thinking (TTCT) and the Test of Creative Imagination (PIC). Results suggest that, even though both tests are based on the psychometric approach and Guilford´s theory of creativity, their scores are not significantly correlated. Results from confirmatory factor analysis suggest two independent factors (one for each test), more related to tasks' demands and contents than with the cognitive processes traditionally considered in the definition and measurement of creativity.

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“…Mechanical Reasoning. A second, less widely known cognitive sex difference is that, on average, males as a group score higher than females as a group on tests of mechanical reasoningthat is, tests of the ability to solve problems involving mechanical principles and physical laws (Flores-Mendoza et al, 2013;Lemos et al, 2013). Unlike most cognitive sex differences, this one is rather large even at the mean (d = .8-1), and thus larger still at the right-hand tail of the distribution (Hedges & Friedman, 1993;Hedges & Nowell, 1995).…”

Section: The Relevant Differencesmentioning

confidence: 99%

Men, Women, and STEM: Why the Differences and What Should Be Done?

Stewart‐Williams¹,

Halsey²

2018

Preprint

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It is a well-known and widely lamented fact that men outnumber women in a number of fields in STEM, including physics, mathematics, and computer science. The most commonly discussed explanations for the gender gaps are discrimination and socialization, and the most common policy prescriptions are targeted at those ostensible causes. However, a great deal of evidence in the behavioral 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 make a significant contribution to the gender gaps in STEM. These factors include relatively large average sex differences in career and lifestyle preferences, and relatively small average differences in cognitive aptitudessome favoring males, others favoring femaleswhich are associated with progressively larger differences the further above the average one looks. The second aim is to evaluate the evidence suggesting that these sex differences are not purely a product of social factors but also have a substantial biological (or "innate") component. A more complete picture of the many causes of the unequal sex ratios in STEM may productively inform policy debates, and is likely to improve women's situation across the STEM fields. Men, Women, and ScienceWhy the Differences and What Should Be Done?Never has the issue of gender disparities been as widely discussed, or as bitterly contested, as it has been in recent years. From the Oscars to the political podium, from TV shows to the workplace, disparities are identified and debate inevitably ensues. In the occupational realm, one of the primary focuses of this debate has been the differential representation of men and women in STEM (science, technology, engineering, and math). 1 This was epitomized by the infamous "Google memo," in which then-Google employee James Damore (2017) questioned the extent to which observed gender disparities in STEM are a product of workplace discrimination. The memo, and Damore's subsequent dismissal from Google, provoked a great deal of discussion and debate about the causes of STEM disparities and the origins of human sex differences. Unfortunately, much of this debate was decidedly inaccurate in its presentation of the research on the topic. A great deal was said about bias and discrimination, but relatively little about other factors contributing to STEM gender gaps (e.g., Chachra, 2017). Furthermore, to the extent that other factors were mentionedfactors such as average sex differences in academic interests these were typically attributed to socialization, rather than to biology or to a 1 Strictly speaking, the issue is not STEM per se, but rather STEM fields that focus on the non-living world, or that have a strong spatial or mathematical component. According to Ceci et al. (2014), the STEM fields should be divided into GE...

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Sex differences on g and non-g intellectual performance reveal potential sources of STEM discrepancies

Cited by 28 publications

References 53 publications

The role of cognitive and non-cognitive factors in mathematics achievement: The importance of the quality of the student-teacher relationship in middle school

The role of cognitive and non-cognitive factors in mathematics achievement: The importance of the quality of the student-teacher relationship in middle school

Runing ahead: Pensamiento divergente y sus dimensiones: ¿De qué hablamos y qué evaluamos?

Men, Women, and STEM: Why the Differences and What Should Be Done?

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