Archive for: December, 2015

Gender Balance in NIH High Risk Research Programs-Overall Award Pool versus Applicant Pool

Dec 10 2015 Published by under Uncategorized

I recently posted analysis regarding the gender distribution between the awardees and the applicant pools for the four NIH High Risk Research (Pioneer, New Innovator, Early Independence, and Transformative R01) programs for 2015. Data regarding the awardees can be gleaned from NIH RePORTER or the NIH Common Fund website. However, data regarding the applicants are not available except through Freedom on Information Act (FOIA) requests. This is how I obtained the data for the 2015 applicants. I subsequently submitted a request for data regarding the applicants pools for all years since each award program was created. While I intended to get data for each year separately, I was not sufficiently clear in my request and I instead received aggregate data over all years. I have clarified my request and am awaiting a response. In the meantime, I want to share the analysis of the aggregate data.

Here are the data:

Program                     Applicants (Male)   Applicants (Female)       Applicants

.                                                                                                         (Gender not given)

Pioneer                              2051 (75.4%)           666 (24.6%)                    53

New Innovator               3498 (67.2%)         1706 (32.8%)                   120

Early Independence        233 (59.9%)            156 (40.1%)                    62

Transformative R01      3245 (78.4%)            894 (21.6%)                  160

 

A glance at these data reveals a couple of points. First, the gender balance of the applicant pool follows a pattern that could be anticipated based on the career stage mix with the closest balance for the Early Independence ("Skip the postdoc") program, followed by the New Innovator Award program, the Pioneer program, and, lastly, the Transformative R01 program. Second, while the number of investigators with genders withheld or unknown is less that 4% for three of the programs, it is 13.7% for the Early Independence program. This introduces some uncertainly in the analysis as will be discussed later.

How does these results compare with the Awardee pools?

Program                        Awardees (Male)        Awardees (Female)

Pioneer                              110 (71.0%)                   45 (29.0%)

New Innovator               263 (66.0%)                 135 (34.0%)

Early Independence         54 (75.0%)                   18 (25.0%)

Transformative R01       164 (83.2%)                   33 (16.8%)

 

Let's compare the applicant pool with the awardee pool program by program. For the Pioneer program, women correspond to 24.6% of the applicant pool and 29.0% of the awardee pool. This difference is not statistically significant with a p value 0f 0.18.

For the New Innovator program, women correspond to 32.8% of the applicant pool and 34.0% of the awardee pool. This small difference is not statistically significant with a p value of 0.62.

For the Early Independence program, women make up 40.1% of the applicant pool but only 25% of the awardee pool. This difference is statistically significant with a p value of 0.0049. Recall that 62 of the Early Independence applicants had genders that were unknown or withheld. If we assume that these applicants were split with the same proportions as the remainder of the pool (60% male, 40% female), the p value is decreased slightly to 0.0040. If we assume that the applicants of unknown gender were 50% male, 50% female, the p value is increased t0 0.0090. Only if we assume that all or nearly all of the applicants of unknown gender were male (and there is not reason to think that this is true), does the p value go above 0.05 (p value = 0.078 if all are assumed male). Thus, it appears very likely that there is a statistically significant decrease in the proportion of women in the Early Independence awardee pool compared with the applicant pool. This observation supports my proposal that NIH should investigate this program carefully to try to understand the source(s) of this disparity.

For the Transformative R01, women make up 21.6% of the applicant pool and 16.8% of the awardee pool. This difference is not statistically significant with a p value of 0.092. However, it is noteworthy that both the applicant pool and the awardee pool are strongly dominated by male principal investigators. This may reflect the relative seniority of applicants to this program or other factors about the program structure or management. This will require more analysis.

These results are summarized in the figure below:

High Risk Programs-Gender

I await the data broken down by year to see if there are meaningful trends over time. In the meanwhile, these data do provide the scientific community and the NIH some food for thought.

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Nelson Diversity Surveys: A Rich Data Source regarding Women and Minorities in Science

Dec 03 2015 Published by under Uncategorized

The diversity (or relative lack thereof) across university campuses is a central issue in academia. Many institutions have ongoing diversity programs include several high-profile institutions (e.g. Yale, NYU, Brown) that have recently announced new initiatives. The diversity of the faculty represents a crucial subset of this issue and the diversity of the science and engineering faculty is an important component of this.

The development and implementation of programs intended to increase diversity depends, in part, on the availability of data to provide baselines and to monitor progress. The university (as opposed to medical center) based community is blessed with the Nelson Diversity Surveys, robust data sets regarding the representation of women and minorities in university science and engineering departments, generated through the efforts of Professor Donna Nelson and her colleagues. These surveys include essentially complete information regarding the number of faculty members in the top (in terms of federal funding) 50-100 departments in  Chemistry, Biological Sciences, Astronomy/Astrophysics, Physics, Mathematics/Statistics, Computer Science, Psychology, Political Science, Sociology, Economics, Chemical Engineering, Electrical Engineering, Civil Engineering, Mechanical Engineering, and Earth Sciences, broken out by rank, gender (Male, Female) and race/ethnicity (White (non-Hispanic), Black, Hispanic, Asian, and Native American). Importantly, these data are not samples, but complete snapshots taken in 2002, 2007, and, more recently 2012) obtained by polling and relentlessly following up with each department. The reports including data for 2002 and 2007 are freely available and the 2012 data should soon be available once the data validation and initial analysis are complete. I first became aware of these data when I was at NIGMS and we subsequently had Professor Nelson speak at one of our Advisory County meetings.

To give an example of the richness of these data, one Table (for the top 50 chemistry departments in 2007) is shown below. Note that the numbers presented as decimals represent women faculty members.Chemistry-Nelson-07Report-RotatedSome overall statistics for this subset of the Nelson Diversity Survey can be derived. Overall, these chemistry faculty were 13.7% women, 1.6% Black, 2.2% Hispanic, 10.0% Asian, and 0.2% Native American. These data can be compared with other population statistics. For example, the percentages of Ph.D.s awarded in Chemistry from 1995-2006 (also provided in the report) were 32.4% for women, 3.5% for Blacks, 3.4% for Hispanics, 12.8% for Asians, and 0.6% for Native Americans. Thus, the data reveal (not at all surprisingly) that the representations of all of these groups within the faculty is lower than they are in the recent Ph.D. population.

The availability of data broken down by department makes it possible to examine these averages in much more detail. The departments ranged in size from 18 to 56 faculty with a median of 33. Below is a histogram of the number of departments versus the percentage of women in each department. For comparison, a Poisson distribution with a mean of lambda = 13 (the distribution that would be expected if recruitment of women faculty into a given department were random with the same overall rate).

Women-Hist-Poisson

The fit to the Poisson distribution is approximate, but with substantial probability density moved out of the center toward departments with lower or higher percentages of women. Being cautious not to over-interpret this small data set (although it is certain possible to examine other fields to see if similar trends are observed), this supports the hypothesis that departments that already have a higher percentage of women tend to recruit more women (keeping in mind the low overall percentages for chemistry departments in general).

Below are histograms depicting the number of departments with a given percentage of faculty in each minority racial/ethnic group.

Chemistry_Histograms

62% or more of the departments did not have a single Black faculty member. Similarly, 50% of the departments did not have a single Hispanic faculty member. The distribution for the percentage of Asian faculty members resembles that for women with an approximately Poisson distribution with some probably density moved out of the center. Only 3 departments identified have even one Native American faculty member. 13 of the 50 departments did not have a single Black, Hispanic, or Native American faculty member.

This quick pass through a small subset of the Nelson Diversity Surveys reveals some of the potential for exploring details and developing and testing hypotheses. Expanding this across different fields and, most interestingly, across time with the release of the 2012 data, should provide insights to help focus discussions of diversity and implementation strategies for improving diversity for the benefit of all.

 

Update:  Dr. Nelson informed me that the 2012 results will be released at the American Chemical Society meeting in March.

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