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1. This was archived from https://www.ncbi.nlm.nih.gov/pubmed/21300892 on 4 March 2018 

 
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Proc Natl Acad Sci U S A. 2011 Feb 22;108(8):3157-62. doi: 10.1073/pnas.1014871108. Epub 2011 Feb 7.

 

Understanding current causes of women's underrepresentation in science.

 

Abstract

 

Explanations for women's underrepresentation in math-intensive fields of science often focus on sex discrimination in grant and manuscript reviewing, interviewing, and hiring. Claims that women scientists suffer discrimination in these arenas rest on a set of studies undergirding policies and programs aimed at remediation. More recent and robust empiricism, however, fails to support assertions of discrimination in these domains. To better understand women's underrepresentation in math-intensive fields and its causes, we reprise claims of discrimination and their evidentiary bases. Based on a review of the past 20 y of data, we suggest that some of these claims are no longer valid and, if uncritically accepted as current causes of women's lack of progress, can delay or prevent understanding of contemporary determinants of women's underrepresentation. We conclude that differential gendered outcomes in the real world result from differences in resources attributable to choices, whether free or constrained, and that such choices could be influenced and better informed through education if resources were so directed. Thus, the ongoing focus on sex discrimination in reviewing, interviewing, and hiring represents costly, misplaced effort: Society is engaged in the present in solving problems of the past, rather than in addressing meaningful limitations deterring women's participation in science, technology, engineering, and mathematics careers today. Addressing today's causes of underrepresentation requires focusing on education and policy changes that will make institutions responsive to differing biological realities of the sexes. Finally, we suggest potential avenues of intervention to increase gender fairness that accord with current, as opposed to historical, findings.

Comment in

PMID: 21300892
 
PMCID: PMC3044353
 
DOI: 10.1073/pnas.1014871108
[Indexed for MEDLINE] 
Free PMC Article
2 comments
  • Stephen Ceci2016 Oct 12 11:14 a.m.

    Below Hilda Bastian criticizes our 2011 article in the Proceedings of the National Academy of Sciences. The criticisms reflect a simplistic rendering of the rich data landscape on women in academic science. Our conclusion was valid in 2011 and since then new scholarship has continued to support it. Below is an abbreviated response to Bastian’s claims, but a somewhat longer account can be found at: http://www.human.cornell.edu/hd/ciws/publications.cfm Claim 1: Our work failed to represent all research on the topic. This criticism does not take into account the quality of the research and the need to use judgment on study inclusion. Rather than calculate mean effect sizes based on all published studies, it is important to down-weight ones that have been refuted or supplanted. We did this in our narrative review in 2011. Nothing we wrote has changed and the intervening research has reinforced our conclusion of gender-neutrality in journal reviews, grant reviews, and tenure-track hiring. For example, Marcia McNutt, editor of Science, wrote "there was some good news from a panel representing major journals…such as the American Chemical Society (ACS) and the American Geophysical Union (AGU)…female authors are published either at a rate proportional to that at which they submit to those journals, or at proportionally higher rates, as compared with their male colleagues." McNutt, 2016, p. 1035) This may surprise those who read claims that women were selected as reviewers less often than their fraction of the submission pool, but it is true: women’s acceptance rates were, if anything, in excess of men’s. This is not cherry-picking, nor can it be erased by aberrations. These are large-scale analyses of acceptance rates of major journals, and it shows the landscape is either gender-fair or women actually have an advantage—in contrast to what Dr. Bastian alleges. The same is true of funding. To illustrate why it is important to move beyond factoring all the studies into a mean effect size, we offer three examples athttp://www.human.cornell.edu/hd/ciws/publications.cfm For example Bornmann et al.’s finding of gender bias in funding using a large sample of grant applications. However, Marsh et al. reanalyzed these findings using a multilevel measurement model and arrived at a different conclusion. Bornmann himself was a coauthor on the Marsh et al. publication and agreed that the new finding of gender-neutrality supplanted his earlier one of gender bias. Marsh et al. found that the mean of the weighted effect sizes based on the 353,725 applicants was actually +.02--in favor of women! (see p. 1301): "The most important result of our study is that for grant applications that include disciplines across the higher education community, there is no evidence for any gender effects in favor of men, and even some evidence in favor of women…This lack of gender difference for grant proposals is very robust, as indicated by the lack of study-to-study variation in the results (nonsignificant tests of heterogeneity) and the lack of interaction effects. This non effect of gender generalized across discipline, the different countries (and funding agencies) considered here, and the publication year.” (p. 1311) Marsh, Bornmann, et al. (2009) (DOI: 10.3102/0034654309334143)

    The rest of our paper concerned hiring and journal publishing. We stand by our conclusion in these two domains as well, as the scientific literature since then has supported us. We do not have time or space here to describe in detail the evidence for this assertion, but the interested reader can find much of it in our over 200 analyses (http://psi.sagepub.com/content/15/3/75.abstract?patientinform-links=yes&legid=sppsi;15/3/75 DOI:10.1177/1529100614541236)Unsurprisingly, the PNAS reviewers were knowledgeable about these domains and agreed with our conclusion. It is incumbent on anyone arguing otherwise to subject their evidence to peer review and show how it overturns our conclusion. Does our claim that gender bias in hiring and publishing lacks support mean there are no gender barriers? Of course not; we have written frequently about them: we have discussed an article that Bastian appears to believe we are unaware of—showing differences in letters of recommendation written for women and men. And we have written about other barriers facing women scientists, such as their teaching ratings downgraded and their lower tenure rates in biology and psychology. However, we stand by our claim that the domains of hiring, funding, and publications are largely gender-neutral. Unless peer reviewers who are experts in this area agree there is compelling counter-evidence, we believe our conclusion reflects the best scientific evidence. Claim 2: We failed to specify what we meant by “women”. Bastian points out differences between women of color, class, etc. We agree these are potentially important moderating factors and we applaud researchers who report their data broken down this way. But the literature on peer review, funding, and hiring rarely reports differences by ethnicity, class, or sexual orientation. Most of the few studies to do so emerged after our study was published. Claim 3: Bastian criticized us for not taking into consideration the size and trajectory of fields, suggesting those with large numbers of scholars may overwhelm smaller ones, or the temporal trajectory of some fields is ahead of others. Field-specific gender differences are a valid consideration but in funding they have been small or non-existent according to several large-scale analyses. Jayasinghe et al.’s (2004) comprehensive analysis of gender effects in reviews of grant proposals (10,023 reviews by 6,233 external assessors of 2,331 proposals from 9 different disciplines), found no gender unfairness in any discipline nor any disciplinary x gender. If anyone has compelling evidence of disciplinary bias against women authors and PIs, they should submit it and allow the peer review process judge how compelling it is. As far as differences among fields in their trajectories, we have done extensive analyses on this, which can be found at the same site above. In these analyses we examined temporal changes in 8 disciplines in salary, tenure, promotion, satisfaction, productivity, impact, etc. With some exceptions we alluded to above, the picture was mainly gender-fair. Finally, Bastian raises analytic issues. We agree these are central. This is why we minimized small-scale, poorly-analyzed reports. We gave more attention to large journals and grant agencies that allowed multilevel models, instead of or in addition to Fixed and Random effects analyses that sometimes violated fundamental statistical assumptions. Both Fixed effect and Random-effects models have limitations. (The latter assumes features of the studies themselves contribute to variability in effect sizes independent of random sampling error, whereas multilevel models permit multiple outcomes included without violating statistical assumptions such as the independence of effect sizes from the same study due to using the same funding agency or multiple disciplines within the same funding agency.) Mean effect sizes are not the analytic endpoint when there is systematic variation among studies beyond that accounted for by sampling variability, which is omnipresent in these studies; it is important to determine which study characteristics account for study-to-study variation. In the past, some have cherry-pick aberrations to support claims of bias, and our 2011 report went beyond doing this to situate claims amidst large-scale, well-analyzed studies, minimizing problematic studies. Although women scientists continue to face challenges that we have written about elsewhere, these challenges are not in the three domains of tenure-track hiring, funding, and publishing.

    Steve Ceci and Wendy M. Williams

  • Hilda Bastian 2016 Sep 06 08:45 a.m.edited 1 of 1 people found this helpful

    The conclusions of this review are not supported by the findings of the studies included in it, and much of the evidence cited contradicts the authors’ conclusions. The review suffers from extensive methodological weaknesses, particularly study selection bias and selective reporting. Out of hundreds of studies that were likely to be eligible in the 3 main areas they address (Dehdarirad, 2015), they include only 35. It is not a review of 20 years of data: it is a review based on selected data from the last 20 years. The basis for that selection is not reported.

    Their description of the results of these studies includes, in my opinion, severe levels of 2 key types of review spin (Yavchitz A, 2016): misleading reporting and misleading interpretation. The review contains numerous errors in key issues such as reporting numbers and the methodology of studies. Conclusions about the quality of some evidence are drawn by the authors, but the basis for these judgments is unclear and no methodical process for assessing quality is reported or evident.

    The 3 main areas covered by the review – journal publications, grant applications, and hiring – are also at high risk of publication bias, which is not addressed by the review. Discrimination against women is the subject of legislation in most, if not all, the countries in which these studies were done. Journals, funding agencies, and academic institutions may not be enthusiastic about broadcasting evidence of gender bias.

    For example, of the many thousands of science journals published in 2011, only 6 studies are cited, conducted in 8 to 13 journals in 2 areas of science. In one of those, the author approached 24 journals: only 5 agreed to participate (Tregenza, 2002).

    Ceci and Williams conclude that only 4 of the 35 unique studies they cited suggest the possibility of some gender bias. However, in my opinion an additional 7 studies clearly concluded gender bias remained a problem needing consideration, and others found signs suggesting bias may have been present. Altogether, in 19 studies (54%), there is either selective reporting and descriptions that spin study results in the direction of this review’s conclusions, or inaccurate reporting that could affect the weight placed on the evidence by a knowledgeable reader.

    I identified no instance of spin that did not favor the authors’ conclusions. Some of the studies referenced did not address the questions for which they were cited. Several are short reports in letters, 1 relies on a press release, and another is a news report of a talk.

    Variations in disciplines are not adequately addressed. The authors concentrate on time periods as critical, but the evidence shows that not all disciplines have reached the same level of development in relation to gender participation. Issues related to international differences, and different experiences for groups of women who may experience additional discrimination are not addressed. Although the conclusions are universally framed, they do not address women in science outside academia.

    The authors address only 3 possible explanations for women’s underrepresentation in science: discrimination, women’s choices and preferences (especially relating to motherhood), and gender differences in mathematics ability. They argue that only women’s choices, particularly in relation to family, are a big enough factor to explain women’s underrepresentation. What is arguably the dominant hypothesis in the field is not addressed: that men are overrepresented in science because of cumulative advantage. Advantages do not have to be large individually, to contribute to the end result of underrepresentation in elite institutions and positions. (I have also commented on another paper in which they advance their hypothesis about motherhood and women scientists (Williams WM, 2012) - link to comment.)

    In addition, they do not address the full range of issues within the 3 areas they consider. For example, in grants and hiring, they do not address analyses of potential bias in letters of recommendation (e.g. Van Den Brink, 2006Schmader T, 2007).

    In my opinion, this review is irredeemably flawed and should be retracted.

    My methodological critique and individual notes on studies are included at my blog.

    Disclosures: I work at the National Institutes of Health (NIH), but not in the granting or women in science policy spheres. The views I express are personal, and do not necessarily reflect those of the NIH. I am an academic editor at PLOS Medicine and on the human ethics advisory group for PLOS One. I am undertaking research in various aspects of publication ethics.

 

2. This was archived from https://www.ncbi.nlm.nih.gov/pubmed/24596430/ on 4 March 2018

 

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Am Sci. 2012 Mar 1;100(2):138-145.

 

When Scientists Choose Motherhood: A single factor goes a long way in explaining the dearth of women in math-intensive fields. How can we address it?

 
PMID: 24596430
 
PMCID: PMC3939045
 
DOI: 10.1511/2012.95.138

PubMed Commons

1 comment
  • Hilda Bastian2016 Sep 06 08:43 a.m.edited 1 of 1 people found this helpful

    The authors of this paper state: “Our own findings as well as research by others show that the effect of children on women’s academic careers is so remarkable that it eclipses other factors in contributing to women’s underrepresentation in academic science”.

    This paper fails to support this contention in 5 ways:

    1. Addressing only a subset of the range of factors that potentially contribute to women’s underrepresentation.

    2. Relying on a selected set of literature that fails to discount alternative explanations, in particular that there is no one single factor that accounts for the phenomenon of women’s underrepresentation in science. Multiple contributing factors, even small ones, can contribute to cumulative advantage for men in science (National Academy of Sciences (US), National Academy of Engineering (US), and Institute of Medicine (US) Committee on Maximizing the Potential of Women in Academic Science and Engineering, 2007).

    3. No method to quantify and comparatively weigh contributing factors that could underpin the single remarkable factor hypothesis.

    4. Not satisfactorily demonstrating that motherhood consistently results in high levels of underrepresentation across disciplines of academic science, and not in all other academic careers.

    5. It generalizes to all of academic science, based exclusively on American data of family responsibilities and science careers.

    The authors rely heavily on their previous work: Ceci SJ, 2011. I have addressed that in a PubMed Commons comment (link to comment). That paper also does not contain adequate evidence to sustain the contention of the claim about the motherhood hypothesis presented here.

    The only data sets presented in support of this hypothesis are (in order of appearance):

    • A study including 586 graduate students in 1992 in the US, surveyed again in 2003 and 2004 (Lubinski D, 2006).

    • A figure of the number of ovarian follicles women have by age from birth to 51, overlaid with key scientists’ career stages.

    • A national faculty survey on career and family in 1998 (with over 10,116 respondents across scientific and non-scientific disciplines) (Jacobs, 2004).

    • 2 selected examples of studies from their previous review chosen to illustrate their argument that there is a level playing field for women in the science workforce, along with a blanket claim that I do not believe the evidence in their review supports (Ceci SJ, 2011).

    • A study that included 2 major components (Goulden, 2009):

      (a) Modeling of data from the Survey of Doctorate Recipients (SDR), which had limited data on potential contributing factors to women’s careers (see for example (Bentley 2004). Women with young children had a 4-13% lower odds of achieving tenure than women without, which is not a considerably higher contribution to gender differences than has been in other studies. (Note that age of children is one of the areas with relatively high missing data in the SDR (Hoffer 2002.)

      (b) A survey of 45 female doctoral and postdoctoral at the University of California, including 16 “new mothers”.

    • A survey with 2,503 respondents from 2008/2009 which found that women were more likely than men to wish they had more children (Ecklund EH, 2011) (although it is not included in the article’s list of references, the study was readily identifiable). Williams and Ceci report “Often this regret is associated with leaving the academy”. However, Ecklund and Lincoln report that there was no gender difference in the desire to leave academic science among these respondents. Further, they conclude, “the effect on life satisfaction of having fewer children than desired is more pronounced for male than female faculty, with life satisfaction strongly related to career satisfaction”.

    • A study of people early in their careers, graduating with MBAs from a single US business school between 1990 and 2006. It had a low response rate (31%) and including 629 women (Bertrand, 2010).

    This data basis is inadequate to support the paper’s conclusions and presents highly selected data. The article included a separate extended bibliography, but the basis for the identification and selection of the studies in the bibliography and in the article is not given. In relation to the major review on which they rely (Ceci SJ, 2011), an unsystematic approach and lack of methods to minimize bias has resulted in a very misleading sample of data, and biased reporting and interpretation of that data (see my comment in PubMed Commons).

    Finally, central to the argument presented here is the hypothesis that as societal and policy changes have reduced the impact of blatant and conscious discrimination, the salience of motherhood as a relative barrier to the progression of women’s scientific careers has assumed greater significance.

    However, those same societal changes have also been affecting how people manage and accommodate family responsibilities and careers. For example, later childbirth and fewer children is an ongoing trend in the US (Matthews TJ, 2009Matthews TJ, 2014), which partially results from, and contributes to, changing attitudes to motherhood and parenting over time. Similarly, increasing workforce participation by women has been changing, and continues to rapidly change, men’s roles in parenting Cabrera NJ, 2000. The authors acknowledge that there has been some accommodation by academic institutions, but their analysis remains largely one-sided.

    For example, this statement is made with neither current nor longitudinal data cited in support: “Men more often have stay-at-home spouses or spouses in flexible careers who bear and raise children while the men are free to focus on academic work”. Indeed, a study they cite in another context found that both men and women scientists with children worked fewer hours than those without children, but similar hours to each other (Ecklund EH, 2011).

    I agree with the authors that much remains to be done to accommodate family responsibilities of all types, not just motherhood. But that will not be a single magic bullet that counteracts the cumulative impact of biases and barriers affecting women related to gender, race, and more as well as family responsibilities. These authors have not made their case for the claim that, “It is when academic scientists choose to be mothers that their real problems start”.

    In addition to comments here on PubMed Commons on the previous review by these authors that supports this paper, I have discussed it on my blog

    Disclosures: I work at the National Institutes of Health (NIH), but not in the granting or women in science policy spheres. The views I express are personal, and do not necessarily reflect those of the NIH. I am an academic editor at PLOS Medicine and on the human ethics advisory group for PLOS One. I am undertaking research in various aspects of publication ethics.

  • This article was mentioned in a comment by Hilda Bastian2016 Sep 06 08:45 a.m.

    See:Understanding current causes of women's underrepresentation in science. [Proc Natl Acad Sci U S A. 2011.]

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