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The Gender Similarities Hypothesis
Transcript of The Gender Similarities Hypothesis
Danny Hochster, and Brendan Vorobiev The Gender Similarities
Hypothesis Results Evidence Evidence Continued Data Steps of Gender Meta Analyses Meta-Analysis Statistical Method of Meta Analysis Hypothesis What is this? Continued Reason for Popularity Gender Differences Model The gender differences hypothesis
essentially states that men and
women are psychologically very
different. The gender differences hypothesis was popularized partly through books such as "Men Are From Mars, Women Are From Venus," written by John Gray in 1992. Essentially, the book argues that the psychological differences between men and women are enormous. It sold millions of copies around the world in over 40 languages. Similarly, Deborah Tannen in 1991 also published a book titled "You Just Don't Understand: Women and Men in Conversation," which argued that the differences between a man and a woman's pattern of speech is so great that they could belong to completely different linguistic cultures. The gender similarities hypothesis states that women and men are, for the most part, the same for many but not all psychological variables. It holds that women and men are more similar than they are different. Meta analysis relating to this field began with Maccoby and Jacklin's book "The Psychology of Sex Differences," which analyzed over 2000 studies about gender differences in abilities, personality, social behavior and etc. In analyzing the studies, they came to the conclusion that the gender differences hypothesis was bunk and that there was much more evidence for the gender similarities hypothesis. However, later reports from others on Maccoby and Jacklin's studies focused mainly on the gender differences they found. 1. The researcher first looks for all studies related to the topic matter.
2. The researcher then takes the statistics in the study and finds the effect size for each study.
3. The researcher then takes a weighted average of the effect sizes (weighted by sample size) to get an overall look at the magnitude of the differences.
4. Lastly, the researcher needs to perform homogeneity analyses to determine if the group of effect sizes are homogenous. The researcher, Janet Shibley Hyde, obtained a vast quantity of meta-analyses that have been conducted on psychological gender differences. The table on the previous slide only showed the first category, which is cognitive variables. The remaining five assessed verbal or nonverbal communication, social or personality variables, measures of psychological well-being, motor behaviors, and miscellaneous items. The result of the meta-analysis showed that 30% of effect sizes are in the close-to-zero range, and an additional 48% are in the small range. That is, 78% of gender differences are small or close-to-zero. This is a particularly striking values because many of the gender differences analyzed were differences commonly contested to be more favorable for one gender (like mathematical aptitude and aggressive behavior). Table 1
Major Meta-Analyses of Research on Psychological Gender Differences
Study and variable Age No. of reports d
Hyde, Fennema, & Lamon (1990)
Mathematics computation All 45 --0.14
Mathematics concepts All 41 -- 0.03
Mathematics problem solving All 48 +0.08
Hedges & Nowell (1995)
Reading comprehension Adolescents 5* --0.09
Vocabulary Adolescents 4* +0.06
Mathematics Adolescents 6* +0.16
Perceptual speed Adolescents 4* --0.28
Science Adolescents 4* +0.32
Spatial ability Adolescents 2* +0.19
Hyde, Fennema, Ryan, et al. (1990)
Mathematics self-confidence All 56 +0.16
Mathematics anxiety All 53 --0.15
DAT spelling Adolescents 5* --0.45
DAT language Adolescents 5* --0.40
DAT verbal reasoning Adolescents 5* --0.02
DAT abstract reasoning Adolescents 5* --0.04
DAT numerical ability Adolescents 5* --0.10
DAT perceptual speed Adolescents 5* --0.34
DAT mechanical reasoning Adolescents 5* +0.76
DAT space relations Adolescents 5* +0.15
Hyde & Linn (1988)
Vocabulary All 40 --0.02
Reading comprehension All 18 --0.03
Speech production All 12 --0.33
Linn & Petersen (1985)
Spatial perception All 62 +0.44
Mental rotation All 29 +0.73
Spatial visualization All 81 +0.13 Let's begin our discussion of the gender similarities hypothesis with a look at the gender differences model, which has pervaded popular culture for many decades. Before we can fully describe the method used to analyze the differences between males and females, we must discuss the statistical method used as well as the measure. Meta-analysis is a statistical method for combining and synthesizing a bunch of related data in order to analyze one specific question. In the case of gender differences, meta-analysis is particularly useful. Continued The statistics of meta-analysis relies on the concept of effect size, which measures the magnitude of an effect - which in this case refers to the magnitude of the gender differences.
In gender meta-analysis, the measure of effect sizes is denoted by d in the equation
d = Mm - Mf
Sw where Mm refers to the mean score for males, Mf the mean score for females, and Sw the average within-sex standard deviation. d, the effect size, measures how different males and females actually are from one another. A positive d-value indicates that males performed better on a specific task, whereas a negative d-value indicates that females performed better on that task.
The gender similarities hypothesis states that most gender differences are close to zero (d < 0.10) and very few are very large
(d > 1.00). While the data, which will be shown in the next slide, is only the first page, it is already immediately apparent that the differences aren't very large at all. Exceptions However, just because the data supports the gender similarities hypothesis doesn't necessarily mean that men and women are the same in all aspects. In fact, men tend to be better than women in throwing distance and velocity (physical variables) which can be attributed to a greater muscle mass and a wider bone size for males. Other Considerations Context is extremely important in the creating, erasing, or even reversing of psychological gender differences. Context can exert influence at many different levels. For example, Lightdale and Prentice, in a 1994 experiment, demonstrated the importance of gender roles in the supposedly great gender difference in aggression. By removing the gender roles, they showed that the difference in aggression is insignificant. Concern of Over-Inflated Claims Inflated claims about psychological gender differences can hurt both boys as girls. For example, the notion that women speak with a moral voice whereas men speak with a voice of justice has permeated our culture, even though meta-analysis has disapproved this claim. As a result, this only serves to reconfirm the stereotype that women are more caring and nurturing than men. This stereotype can hurt men in that it prevents them from acknowledging that they can be just as nurturing if not even more so. It can also hurt women in that employers look for these characteristics in their female employees, and if these traits are not evident, there could be serious consequences. Conclusion Meta-analysis research on gender differences supports the gender similarities hypothesis. Without external influences, men and women are more psychologically similar than different. 0.2-=no measurable difference
0.8+=large, measurable difference Note: d was greatest (greater than 1.00, reaching up to 2.00) for physical characteristics. In areas where gender differences were most commonly thought to occur (verbal/communicative abilities, aggression, etc), d was closest or very close to 0 Discussion What impact do these findings have on the way we view gender differences? How do the gender differences that are evident happen? Are we conditioned from birth to act certain ways based on our physical appearance? Or do we have naturally occurring differences? How can this research change the way people raise their children? Is this child... psychologically different than this child? Effect Sizes