# Gender Similarities And Differences Hyde Pdf

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- Gender differences in prejudice: a biological and social psychological analysis
- The gender similarities hypothesis
- Evaluating gender similarities and differences using metasynthesis
- Janet Shibley Hyde

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## Gender differences in prejudice: a biological and social psychological analysis

Thank you for visiting nature. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Some scientists and public figures have hypothesized that women and men differ in their pursuit of careers in science, technology, engineering, and mathematics STEM owing to biological differences in mathematics aptitude.

However, little evidence supports such claims. Some studies of children and adults show gender differences in mathematics performance but in those studies it is impossible to disentangle intrinsic, biological differences from sociocultural influences. To investigate the early biology of mathematics and gender, we tested for gender differences in the neural processes of mathematics in young children. We implemented both frequentist and Bayesian analyses that quantify gender similarities and differences in neural processes.

Across all analyses girls and boys showed significant gender similarities in neural functioning, indicating that boys and girls engage the same neural system during mathematics development. Limited evidence for intrinsic, biological gender differences in mathematics ability has fueled debate about the underrepresentation of girls and women in STEM fields science, technology, engineering, and mathematics. Some have suggested that girls and women are underrepresented in careers in STEM owing to biological differences.

Here, we combine frequentist and Bayesian statistical approaches to test for gender similarities and differences in the neural processing of mathematics during early childhood. Although evidence for behavioral gender differences in mathematics is weak in older children, adolescents, and adults, it is important to consider when and how any differences might emerge. One possibility is that despite established gender similarities on behavioral tasks in early childhood, 5 , 10 , 12 , 13 the underlying biological or neural processes could differ between boys and girls.

Alternatively, boys and girls may show significant, widespread biological similarities in the neural processes of mathematics during early childhood. This outcome would be consistent with yet untested claims that boys and girls share a core biology for mathematical cognition. To compare the neural processes underlying mathematics development, we used functional magnetic resonance imaging fMRI to measure neural activity in 3—year-old children while they watched video clips that targeted early childhood mathematics skills e.

Data were combined across natural viewing tasks by normalizing each subject to a within-task adult baseline using intersubject correlations. Intersubject correlations were conducted by comparing each child with every other child and comparing each child with every adult within a comparison group 63 total adults, 25 women, who watched one set of video clips.

Therefore, for each child, their measure of neural maturity is averaged across all adults who watched the same video. First, we conducted frequentist statistical tests of differences two-sided, independent samples t tests and similarities in neural maturity.

Similarities in mean neural maturity were assessed using statistical equivalence statistics. Complementary to this approach, we conducted a Bayes Factor analysis, which also allows for the interpretation of both significant differences and significant similarities. The Bayes Factor analysis weighs the evidence for an alternative hypothesis against the evidence for the null hypothesis by taking the ratio of the posterior probabilities for the two hypotheses the Bayes Factor.

Following previous work, 13 the prior for the alternative hypothesis of gender differences was the default Cauchy distribution centered on the prior for the null hypothesis with a width of 0. The prior for the null hypothesis was 0. Some have claimed that differences in the upper and lower tails of the distributions drive gender differences. In a final analysis, we calculated intersubject correlations across children to obtain measures of within-gender and between-gender neural similarity.

If gender differences in neural activity have a biological categorical origin rooted in childhood, these categorical differences should be evident in the brain. In contrast, if gender differences in neural activity do not originate from categorical differences in early childhood, there should instead be widespread similarities.

In fact, girls and boys showed statistically equivalent levels of neural maturity throughout the brain Fig. This variance cluster was small 15 voxels and girls exhibited more variance than boys at equal neural amplitudes to boys, which did not result in a mean difference between groups in this region.

Whole-brain analyses. Using frequentist analyses t tests , there are no regions showing significant gender differences at the standard threshold.

The plot to the right shows the percent of voxels across the brain that show substantial support for gender similarities and differences. The regions that show evidence of gender similarities are consistent across frequentist and Bayesian approaches. The pattern of large-scale statistical similarities between boys and girls from the frequentist analyses was replicated in the Bayesian analysis. In each voxel of the brain, the weight of evidence for the null and alternative hypotheses were indexed by the Bayes factor B 01 for the null hypothesis of gender similarities, B 10 for the alternative hypothesis of gender differences.

Bayes factors suggesting that the data provide substantial support of the hypotheses are displayed in Fig. Importantly, across all three natural viewing tasks, children engaged numerical processes in the brain. Children showed number-selective neural activation in the intraparietal sulci IPS during the mathematics content in the educational videos Fig.

This is evidence that children engaged mathematical neural processes during the educational videos, and that boys and girls did so equally. Number and math selectivity in the intraparietal sulci IPS. Individual data for boys are shown in blue and for girls are shown in red. Although, samples for each individual study are small, effects are consistent with the larger patterns reported in the following analyses.

Next, we compared the rate of mathematics development in boys and girls. Math ability was statistically equivalent across children and did not show gender differences in mean ability or variance Fig. Nor did the relation between gender and math ability change across age Fig.

These behavioral data were previously included as part of a larger behavioral study that showed no differences and statistical equivalence in math ability in this age group. Gender similarities in math ability. Importantly, no cortical regions showed an interaction between math ability and gender.

We then identified mathematical processing networks by testing for regions that showed higher neural maturity in children with stronger math skills. Math ability, gender, and the interaction between math ability and gender were entered as predictors of neural maturity in a whole-brain regression.

This regression revealed that math ability predicted neural maturity in both gender groups in the IPS, prefrontal cortex, and middle temporal gyrus Fig. In other words, mathematical processing networks develop at the same rate for girls and boys.

In accord with the whole-brain analyses, these regions showed statistical equivalence, not statistical differences, and no differences in variance Fig. This shows that within key number processing regions of the brain, girls and boys show the same degree of development in mathematical processing.

Region-of-interest analyses. Outliers are those data points beyond the whisker ranges. Finally, we examined neural similarity in children of the same versus different genders. Intersubject correlations were calculated between children, resulting in maps of same-gender neural similarity comparing girls with girls and boys with boys and different-gender neural similarity comparing girls with boys and boys with girls. These difference maps were then subjected to a one-sample t test vs 0.

The regions that showed strong neural similarity between children were identical for statistical comparisons of the same gender and different genders. Child-to-child neural saimilarity. The third column shows the overlap of the first two columns in light green.

Importantly, regions are identical across neural similarity calculated to children of the same gender and to children of different genders. We saw no evidence of gender differences in neural responses to mathematics content, neural responses during educational video viewing, or rates of neural development for mathematical processing in early childhood, and in fact we found statistical equivalence between boys and girls throughout the brain.

Tests of statistical equivalence and a Bayes Factor analysis show gender similarities throughout the number processing network. Any test of cognitive ability that shows gender differences faces the difficulty of disentangling biological factors from social ones. For instance, 4- to 7-year-old boys show an advantage over girls in tests of spatial skills, but parents also report more-spatial play with their boys compared with their girls, 14 suggesting a possible sociocultural influence on gender differences in spatial cognition.

Similarly, in math and science, teachers tend to show differential distributions of time spent encouraging students, praising students, and explaining concepts to students, with boys receiving more time than girls. Given the broad similarities between boys and girls, gender differences observed in STEM performance during adolescence or adulthood are unlikely to originate from early childhood differences in the brain or cognition.

Instead, the data show that the neural functions underlying mathematical cognition are similar between genders and represent one heterogeneous population rather than two categorical groups.

In total, typically-developing 3- to year-old children 55 girls and 63 adults 35 women participated in one of three studies. Informed written consent was obtained from adult participants and parents of children, and informed written assent was obtained for children 7 years and older.

This paradigm consisted of a The Clips ranged from In this study, participants listened to pre-recorded audio tracks of someone counting or saying the alphabet. Short clips from child-friendly cartoons were presented on the screen during the sequences. Audio tracks were removed from the cartoons and were replaced with quieter, child-friendly instrumental music.

Cartoon tracks were matched across sequences. Fifteen blocks were presented throughout the experimental paradigm and were separated by 4-s of blackscreen. Participants reported whether they were the same or different by pressing a button only if the two stimuli matched. Number comparisons were made across notation i. Stimuli were presented in a blocked design. Each block consisted of three 2-s trials from the same condition, separated by a 2-s intertrial interval. Prior to the scanning sessions, children participated in a minute training session in a mock scanner to familiarize them with the scanning environment and to practice remaining motionless.

Adults received verbal instructions prior to scanning and did not participate in a training session. The primary paradigms from Studies 1, 2, and 3 were volumes, volumes, and volumes, respectively, and the Number Localizer paradigm was distributed over two to four functional runs of volumes each.

Data from previously published studies were analyzed as originally reported for consistency. For Studies 2 and 3, the first two TRs of each run were discarded. Functional data were registered to high-resolution anatomy images for each participant in native space. Preprocessing consisted of slice scan time corrected cubic spline interpolation , motion correction with respect to the first volume in the run, and linear trend removal in the temporal domain cutoff: two cycles within the run.

The functional data from the Number Localizer were not smoothed. Adult and child echo-planar and anatomical volumes were then normalized into Talairach space 34 using piecewise affine transformation based on manual identification of anatomical landmarks. Analyses were performed on processed data in Talairach space. Average framewise displacement 35 , 36 was regressed across the brain for each child to control for sudden changes in volume-to-volume head motion.

## The gender similarities hypothesis

The Differences Model, which argues that men and women are vastly different psychologically, dominates the popular media and feeds the stereotypes of the lay public. I propose, in contrast, the Gender Similarities Hypothesis, which holds that males and females are similar on most, but not all, psychological variables. I review relevant meta-analyses of research on psychological gender differences, including mathematical performance, verbal ability, and spatial ability, as well as self-esteem and leadership. Gender differences are small or nonexistent in almost all of these areas, consistent with the Gender Similarities Hypothesis. I then explore the implications of this hypothesis for mathematics and science education, including the question of single-sex education, and for the career trajectories of women in science.

She is known for her research on human sexuality , sex differences , gender development , gender and science, and feminist theory , and is considered one of the leading academics in the field of gender studies. Hyde received her BA in mathematics from Oberlin College in She continued her education at the University of California, Berkeley , where she completed her Ph. D in psychology in She received an honorary doctorate degree in social science from Denison University in

Thank you for visiting nature. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Some scientists and public figures have hypothesized that women and men differ in their pursuit of careers in science, technology, engineering, and mathematics STEM owing to biological differences in mathematics aptitude. However, little evidence supports such claims.

## Evaluating gender similarities and differences using metasynthesis

Sex differences in personality are believed to be comparatively small. However, research in this area has suffered from significant methodological limitations. We advance a set of guidelines for overcoming those limitations: a measure personality with a higher resolution than that afforded by the Big Five; b estimate sex differences on latent factors; and c assess global sex differences with multivariate effect sizes. We then apply these guidelines to a large, representative adult sample, and obtain what is presently the best estimate of global sex differences in personality. Multigroup latent variable modeling was used to estimate sex differences on individual personality dimensions, which were then aggregated to yield a multivariate effect size Mahalanobis D.

*In Study 1, two college samples and two samples of pedestrians selected 10 out of a list of 48 wishes. In Study 2, two college samples rated 20 wishes. Although ethnicity data were not gathered, the populations from which the samples were drawn are ethnically diverse.*

### Janet Shibley Hyde

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Но это значит… значит… что мы не можем… - Это значит, что нужен другой план действий. - Фонтейн, как обычно, говорил спокойно и деловито. Глаза Джаббы по-прежнему выражали шок и растерянность, когда сзади раздался душераздирающий крик: - Джабба.

Беккер заметил металлический блеск в тот самый миг, когда убийца поднимал пистолет, и, как спринтер, срывающийся с места при звуке стартового выстрела, рванулся. Насмерть перепуганный священник упал, чаша взлетела вверх, и красное вино разлилось по белому мрамору пола. Монахи и служки у алтаря бросились врассыпную, а Беккер тем временем перемахнул через ограждение. Глушитель кашлянул, Беккер плашмя упал на пол. Пуля ударилась о мрамор совсем рядом, и в следующее мгновение он уже летел вниз по гранитным ступеням к узкому проходу, выходя из которого священнослужители поднимались на алтарь как бы по милости Божьей. У подножия ступенек Беккер споткнулся и, потеряв равновесие, неуправляемо заскользил по отполированному камню. Острая боль пронзила вес его тело, когда он приземлился на бок, но мгновение спустя он уже был на ногах и, скрываемый занавешенным входом, сбежал вниз по деревянным ступенькам.

Previous research has further established that men and women develop and possess different characteristics in personality traits (Hyde ;.

This paper investigates gender differences in personality traits, both at the level of the Big Five and at the sublevel of two aspects within each Big Five domain.

Janet Shibley Hyde.

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