Population differences in interest and population differences in variability of abilities
may help explain
why there are fewer women in the applicant pool, but the women who choose to enter the pool are
just as capable
as the larger number of men in the pool. This conclusion does not deny that various forms of bias, harassment, and discouragement exist and may contribute to outcome disparities,
nor does it imply that the differences in interest are biologically fixed and cannot be changed in future generations.
You are taking a very nuanced piece and summarize it in a way which does away with all the nuance, plus you highlight statetements which were not questioned by the memo itself. Why? There is a point why sometimes, a bit more reading is required to get the full picture. Not everything can be made to fit into the length of a few tweets.
More information, not less, is what's needed in regards to understanding the dynamics and complexities that are impacting gender disparities in tech. The Google memo itself as well as the outcry against it clearly showed that.
I absolutely agree. Not only does it do away with much of the detail; this summary specifically highlights only those details which support the discrimination hypothesis, after the authors of the article went to great lengths to assemble a maximally complete portrait. This urge to not only simplify, but to simplify along partisan lines is damaging to everyone.
The article is pretty in-depth and nuanced. Here's the conclusion for convenience:
> Our Conclusions about the Greater Male Variability Hypothesis:
> On average, male variability is greater than female variability on a variety of measures of cognitive ability, personality traits, and interests. This means men are more likely to be found at both the low and high end of these distributions (see Halpern et al., 2007; Machin & Pekkarinen, 2008 and, especially, the supplementary materials; for an ungated summary click here). This finding is consistent across decades.
> The gender difference in variability has reduced substantially over time within the United States and is variable across cultures. It is clearly responsive to social and cultural factors (see Hyde & Mertz, 2009; Wai et al., 2010); Educational programs can be effective. It is also clear that there are cultural/societal influences, as the male:female variability ratios can vary considerably across cultures (e.g., Machin & Pekkarinen, 2008).
> While the gender difference in the male:female ratio for the upper tail of the distribution of math test scores (SAT, ACT) narrowed considerably in the United States in the 1980s, it appears to have remained steady since the early 1990s. This can be seen visually in Figure 1 from Wai et al. (2010)
> Therefore at the top end of any distribution of test scores where men have higher variability, we’d expect men to make up more than 50% of the upper end of the tail. Thus, any company drawing from the top 5% is likely to find a pool that contains more males. As one goes further out into the tail (i.e. becomes even more selective) the gender tilt becomes larger.
Further compounding the gender tilt: the women in this elite group generally have much better verbal skills than the men in that elite group (see Reilly, 2012). This means that these women may be better employees than men who match them on quantitative skills, but because they have such superior verbal skills they have more choices available to them when selecting a profession.
> Our Revised Conclusions About the Damore Memo
We maintain that the research findings are complicated. This is evident in both this post and our original one. There are many abstracts containing both red and green text, and some of the top researchers in psychology are represented on both sides of the debate. Furthermore, many of the experts have concluded that:
>> … early experience, biological factors, educational policy, and cultural context affect the number of women and men who pursue advanced study in science and math and that these effects add and interact in complex ways. There are no single or simple answers to the complex questions about sex differences in science and mathematics (Halpern et al., 2009).
> In light of of the research on the Greater Male Variability Hypothesis however, we have revised our original conclusions:
Gender differences in math/science ability, achievement, and performance are small or nil. (See especially the studies by Hyde; see also this review paper by Spelke, 2005). There are two exceptions to this statement:
> Men (on average) score higher than women on most tests of spatial abilities, but the size of this advantage depends on the task and varies from small to large (e.g., Lindberg et al., 2010). There is at least one spatial task that favors females (spatial location memory; see e.g., Galea & Kimura, 1993; Kimura, 1996; Vandenberg & Kuse, 1978). Men also (on average) score higher on mechanical reasoning and tests of mathematical ability, although this latter advantage is small. Women get better grades at all levels of schooling and score higher on a few abilities that are relevant to success in any job (e.g., reading comprehension, writing, social skills). Thus, we assume that this one area of male superiority is not likely to outweigh areas of male inferiority to become a major source of differential outcomes.
9 comments
[ 3.0 ms ] story [ 31.9 ms ] threadThere's 72,000 people who work at Google, and I think it's safe to generalize and say that that lowlife does NOT speak for the majority of us.
Likelihood of the discussion being interesting, even if the article itself is actually (against all the odds) interesting: zero.
Flagged. And you could flag it, too!
may help explain
why there are fewer women in the applicant pool, but the women who choose to enter the pool are
just as capable
as the larger number of men in the pool. This conclusion does not deny that various forms of bias, harassment, and discouragement exist and may contribute to outcome disparities,
nor does it imply that the differences in interest are biologically fixed and cannot be changed in future generations.
More information, not less, is what's needed in regards to understanding the dynamics and complexities that are impacting gender disparities in tech. The Google memo itself as well as the outcry against it clearly showed that.
> Our Conclusions about the Greater Male Variability Hypothesis:
> On average, male variability is greater than female variability on a variety of measures of cognitive ability, personality traits, and interests. This means men are more likely to be found at both the low and high end of these distributions (see Halpern et al., 2007; Machin & Pekkarinen, 2008 and, especially, the supplementary materials; for an ungated summary click here). This finding is consistent across decades.
> The gender difference in variability has reduced substantially over time within the United States and is variable across cultures. It is clearly responsive to social and cultural factors (see Hyde & Mertz, 2009; Wai et al., 2010); Educational programs can be effective. It is also clear that there are cultural/societal influences, as the male:female variability ratios can vary considerably across cultures (e.g., Machin & Pekkarinen, 2008).
> While the gender difference in the male:female ratio for the upper tail of the distribution of math test scores (SAT, ACT) narrowed considerably in the United States in the 1980s, it appears to have remained steady since the early 1990s. This can be seen visually in Figure 1 from Wai et al. (2010)
> Therefore at the top end of any distribution of test scores where men have higher variability, we’d expect men to make up more than 50% of the upper end of the tail. Thus, any company drawing from the top 5% is likely to find a pool that contains more males. As one goes further out into the tail (i.e. becomes even more selective) the gender tilt becomes larger. Further compounding the gender tilt: the women in this elite group generally have much better verbal skills than the men in that elite group (see Reilly, 2012). This means that these women may be better employees than men who match them on quantitative skills, but because they have such superior verbal skills they have more choices available to them when selecting a profession.
> Our Revised Conclusions About the Damore Memo We maintain that the research findings are complicated. This is evident in both this post and our original one. There are many abstracts containing both red and green text, and some of the top researchers in psychology are represented on both sides of the debate. Furthermore, many of the experts have concluded that:
>> … early experience, biological factors, educational policy, and cultural context affect the number of women and men who pursue advanced study in science and math and that these effects add and interact in complex ways. There are no single or simple answers to the complex questions about sex differences in science and mathematics (Halpern et al., 2009).
> In light of of the research on the Greater Male Variability Hypothesis however, we have revised our original conclusions: Gender differences in math/science ability, achievement, and performance are small or nil. (See especially the studies by Hyde; see also this review paper by Spelke, 2005). There are two exceptions to this statement:
> Men (on average) score higher than women on most tests of spatial abilities, but the size of this advantage depends on the task and varies from small to large (e.g., Lindberg et al., 2010). There is at least one spatial task that favors females (spatial location memory; see e.g., Galea & Kimura, 1993; Kimura, 1996; Vandenberg & Kuse, 1978). Men also (on average) score higher on mechanical reasoning and tests of mathematical ability, although this latter advantage is small. Women get better grades at all levels of schooling and score higher on a few abilities that are relevant to success in any job (e.g., reading comprehension, writing, social skills). Thus, we assume that this one area of male superiority is not likely to outweigh areas of male inferiority to become a major source of differential outcomes.
> There is good evidence that men are mor...