Category CRITICAL TRANSITIONS IN THE. CAREERS OF SCIENCE, ENGINEERING

SUMMARY OF FINDINGS

The analyses in this chapter reveal a number of important findings about the application, recruitment, interview, and hiring process.

[55] Shirley Tilghman, 2004, “Ensuring the Future Participation of Women in Science, Mathematics, and Engineering,” in National Research Council, The Markey Scholars Conference: Proceedings, Washington, DC: National Academies Press, pp. 7-12.

[56] Because we performed a large number of t-tests on our faculty survey data, we will only report as significant those results with p < .05 in order to protect ourselves from false positives. Results near p < .05 will be reported as approaching significance. For the regression analyses on our survey data, reported in the final outcomes section of this chapter, we will report any results with p < .05 as significant. The reader will want to note that there are some instances in which the differences are statistically significant, but the absolute differences are quite small.

[57] We also performed a large number of t-tests on the NSOPF:04 data, so we followed the rule for reporting significance in these data that is described in the previous footnote.

[58] Data was created using the Department of Education’s Data Analysis System (DAS) available online at http://www. nces. ed. gov/dasol/. Gender was used as the row variable. The column variable was average total hours per week worked. Filters were only Research I institutions; full-time employed; with faculty status; assistant, associate, or full professors; with instructional duties for credit; and with principal fields of teaching as engineering, biological sciences, physical sciences, mathematics, and computer sciences.

[59] See previous footnote on how the DAS analysis was conducted.

[60] The committee acknowledges that the p-values for all the data presented for its faculty and depart­mental surveys are unadjusted and the fact that many of the data presented are interconnected.

[61] Kramer, C. Y., 1956, Extension of multiple range tests to group means with unequal numbers of replication, Biometrics, 12, 307-310.

[62] The comparisons between men and women overall, and by discipline, in terms of the number of thesis committees a faculty member served on are not reliable, due both to small sample sizes and to the long­tailed distribution of this response; a few large values in response can strongly affect the comparison.

[63] Note that the definition the NSOPF uses is different from the definition used in the faculty survey.

[64] The 2002 Cornell Higher Education Research Institute (CHERI) Survey on Start-up Costs and Laboratory Allocation Rules: Summary of the Findings is available at http://www. ilr. cornell. edu/cheri/ surveys/2002surveyResults. html, accessed October 7, 2008. See also the presentation by Ronald G. Ehrenberg, Michael J. Rizzo, and George H. Jakubson, “Who Bears the Growing Cost of Science at Universities?” presented at the 2003 Conference. See also Ronald G. Ehrenberg, Michael J. Rizzo, and Scott S. Condie, “Start-up Costs in American Research Universities,” CHERI working paper, WP-33, March 2003, Cornell University.

[65] Sara Rimer, “For Women in Science, Slow Progress in Academia,” New York Times, April 15, 2005.

[66] See, for example, a thorough assessment conducted by New Mexico State University in 2003, “Space Allocation Survey,” available at http://www. advance. nmsu. edu/Documents/PDF/ann-rpt-03.pdf.

[67] Available at http://news-service. stanford. edu/news/2003/may21/womenfaculty-521.html.

[68] Note that the University of Pennsylvania’s research used an unusual metric of research space per grant dollar.

[69] University of Pennsylvania Gender Equity Committee, “The Gender Equality Report, Executive Summary, Almanac, Vol. 48, No. 14, December 4, 2001, available at http://www. upenn. edu/almanac/ v48/n14/GenderEquity. html. See the full report at: http://www. upenn. edu/almanac/v48pdf/011204/ GenderEquity. pdf.

[70] CWRU Equity Study Committee, “Resource Equity at Case Western Reserve University: Results of Faculty Focus Groups,” March 3, 2003, pp. 46-47. Available at http://www. case. edu/president/ aaction/resourcequity2003 .doc.

[71] Purdue conducted a survey in 2001, which asked female and male faculty whether they were satisfied with the amount of lab space. Women were less satisfied. (This is different from how much lab

space each gender has.) Available at http://www. cyto. purdue. edu/facsurvey/faculty/survey/http://www. cyto. purdue. edu/facsurvey/faculty/survey/results/intro. htm.

[72] The medians for men and women faculty in civil engineering were quite similar, while the means were significantly different.

[73] Mathematics was dropped from this analysis, as only 11 respondents in mathematics reported having lab space.

[74] Specifically, the observation for any respondent reporting any non-zero number, in practice from 0.5 to 19 postdocs, was changed to 1.

[75] See for example, Center for Research on Learning and Teaching (CRLT), The University of Michigan, “Resources on Faculty Mentoring.” Available at http://www. crlt. umich. edu/publinks/ facment. html.

[76] Other possible measures include original discoveries and patents. On gender differences in patent­ing, see Ding et al. (2006).

[77] In addition, the following changes to the data were made: There were about a dozen observations in which respondents reported numbers of less than $1,000. It was assumed that these numbers, such as $60 or $100 actually meant $60,000 or $100,000. It was also assumed that a single entry of $1.3 was in fact $1.3 million.

[78] Inspection of the data revealed that the survey results were highly influenced by a single senior female faculty in civil engineering who reported having no grant funding, and she was removed from

the survey results.

[79] Out of the 1,179 respondents, 4 responses were considered to be outliers and were removed.

[80] Note that one reason to get an outside offer is to put pressure on a faculty member’s current department to match a better offer. The faculty member might not actually want to leave his or her current department.

[81] The report, focusing on gender, is available at http://www. gseacademic. harvard. edu/~coache/ downloads7SNS_report_gender. pdf.

[82] Gender Equity Committee on Academic Climate, 2003. An Assessment of the Academic Climate for Faculty at UCLA, Los Angeles, CA: University of California at Los Angeles.

[83] In future studies, these two events should be separated, because male faculty tend to be older and are more likely to retire, while female faculty tend to be younger and are less likely to leave due to retirement.

[84] However, planning to leave or receiving outside offers are less than ideal proxies for job satisfac­tion. For example, faculty may plan to leave a position to retire.

[85] Some faculty remain associate professors and never come up for full professor status.

[86] For a discussion of issues and strategies related to bringing women into executive positions in academia, see NRC (2006).

[87] The committee acknowledges that the p-values for all the data presented for the study’s surveys of faculty and departments are unadjusted and that many of the data presented are interconnected.

[88] It may be that the only time the decision-making process becomes publicly visible is during litiga­tion brought by faculty denied tenure or promotion.

[89] This general finding is commonly stated, even though individual institutions might have tenure or promotion rates that are comparable for men and women. As Nancy Hopkins (2006:18) notes in the case of the Massachusetts Institute of Technology (MIT), “Overall the tenure rates for men and women are almost identical in both the Schools of Science and Engineering.” Looking at a broader segment of academia is thus necessary to see if MIT, to continue the example, is representative of many institutions or is an outlier.

[90] This is done by subtracting the year an individual received a Ph. D. from the survey year.

See for instance Persell (1983) and McElrath (1992).

[92] In nine cases involving men who were up for tenure, the outcome was unknown.

[93] Anonymous, March 1, 1999, Women and Tenure at the Institute, MIT News Office, available at http://web. mit. edu/newsoffice/1999/trwomen. html. See also Hopkins (2006).

[94] See Appendix 5-2 and 5-3 for detailed tables.

[95] Alvarez, R. M. and J. Brehm, 1995, American ambivalence towards abortion policy: develop­ment of a heteroskedatic probit model of competing values, American Journal of Political Science, 39, 1055-1089.

[96] In the NSOPF data, there are many more men in the sample than women and the standard errors for women are much larger.

[97] Cox, D. R. and Oaks, D., 1984, Analysis of Survival Data, London: Chapman & Hall.

[98] Note that URLs may have changed between the preparation and release of this report.

[99] The sample was sent to the contractor. Once it was confirmed to have reached the contractor, the original file was deleted. Neither the committee nor the National Academies would know the names of potential respondents to the faculty survey.

[100] Fortunately, almost all e-mails were correct. “Bounce backs,” or non-working e-mails, were cor­rected. It is possible, though, that the wrong e-mail was collected and used, but that the contractor was not aware that this was an incorrect e-mail, and the respondent was never contacted.

C3. How were you made aware of your institution’s policy on tenure?

Yes No

I was given a written policy.

The chair or administration told me about the policy.

[102] learned about the policy from other faculty Other,…

[103] The results of analyses are not strictly comparable, as the earlier report used a different definition of S&E, among other differences.

[104] From the Survey of Earned Doctorates (SED) field list, this is equivalent to any field coded from 005 to 599.

[105] The one recent exception appears to be the medical or health sciences, where the proportion of women among Ph. D.s seemed to have leveled off.

[106] Recall that Long’s definition of S&E includes the social and behavioral sciences and is thus broader than the definition employed here.

[107] The committee’s charge did not include a focus on exploring the reasons for gender differences in labor force outcomes outside of academia. Readers should refer to Long (2001) and Xie and Shauman (2003) for a discussion of such factors.

[108] These data are for just the natural sciences and engineering.

Ellipses omitted.

[110] Other includes industry, government, and the nonprofit sector. Education in this table includes K-12 positions.

[111] See Alexander C. McCormick, “The 2000 Carnegie Classification: Background and Description (excerpt),” available at http://www. carnegiefoundation. org/dynamic/downloads/file_1_341.pdf [ac­cessed on November 4, 2008]. The Carnegie Foundation updated its classification system in 2005 and is available at http://www. carnegiefoundation. org/classifications/.

[112] Reports for 80 of the 88 Research I institutions were collected and posted to the National Acad­emies’ Committee on Women in Science and Engineering (CWSEM) homepage, located at http:// www7.nationalacademies. org/cwse/1gender_faculty_links. html.

[113] This is part of the reason why most of the statistical analyses carried out use regression. A few scholars have used event history or hazard models. See for example Weiss and Lillard (1982), Kahn (1993), and Ginther (2001). See Allison (1984) for an introductory description of the methodology.

[114] Conducted on odd numbered years until 2003, thereafter on even numbered years, beginning in 2006.

[115] The National Center for Education Statistics also conducted a survey of department chairs during the 1988 NSOPF, but the chairs survey was only done this one time.

[116] “Survey Methodology: Survey of Doctorate Recipients,” NSF Web site at http://www. nsf. gov/sbe/ srs/ssdr/sdrmeth. htm [accessed on March 17, 2004].

[117] Interestingly, research is adding care of older family members—for similar reasons as care of children (e. g., Sax et al., 2002).

[118] A review by the Women in Science & Engineering Leadership Institute (WISELI) at the University of Wisconsin-Madison titled, “Reviewing Applicants: Research on Bias and Assumptions” identified several studies suggesting that female candidates may have a tougher time. Available at http://wiseli. engr. wisc. edu/doc/BiasBrochure_2ndEd. pdf [accessed on October 7, 2008].

[119] Data for 1979 are from NRC (2001a) and were calculated by taking total number of male and female faculty at Research I institutions and subtracting male and female faculty at Research I institu­tions who were in social and behavior sciences. Data for 2003 are also from the Survey of Doctorate Recipients (SDR) as calculated by staff, using the same definition of S&E.

[120] This measure is deficient in two ways. First, the potential applicant pool includes postdocs, in­dividuals with Ph. D.s from foreign institutions, individuals from outside academia, and individuals with current academic positions who are interested in switching to a new position (Ehrenberg, 1992). For example, in a study of physics hires in 2000, Kirby et al. (2001) found that 34 percent of new hires in doctorate-granting institutions had earned Ph. D.s outside of the United States. Likewise, in computer science (Zweben, 2005:10), for 2003-2004: “Thus, more than 75% of the faculty hires made this past year by Ph. D.-granting CS/CE [computer science/computer engineering] departments appear to have been new Ph. D.s, with the rest consisting of a combination of faculty who changed academic positions, persons joining academia from government and industry, new Ph. D.s from outside of North America and from disciplines outside of CS/CE, and non-PhD. holders (e. g., taking a teaching faculty appointment).” Second, it fails to account for the preferences of doctorates.

[121] Ellipses omitted.

[122] See also Bain and Cummings (2000).

[123] This is not a new problem. Stake et al. (1981) found letters of recommendation were more favor­able when the letter writers and the job seekers were of the same gender.

[124] Fractional courses were rounded up to the nearest integral number of courses. Missing data was removed from the data prior to analysis. Finally, the data were from the committee’s survey of faculty.

Years Between Starting Employment and Achieving Full. Professor Status, by Gender

Percentage breakdown of the number of years between full professor rank achieved and first faculty or instructional staff by gender, for full-time faculty at Research I institutions with instructional duties for credit, teaching biology, physi­cal sciences, engineering, mathematics or computer science, fall 2003.

Years Between Achieved Full Professor and First Started Employment at Postsecondary Institution

0 years

1-5

6-10

11-15

16 or more

Estimates

Total

7.8 (1.13)

6.2 (1.21)

35.4 (2.54)

39.3 (2.33)

11.3 (1.29)

Men

8.4 (1.22)

6.7 (1.31)

36.3 (2.68)

39.4 (2.47)

9.3 (1.16)

Women

#

#

26.4 (7.54)

38.6 (6.26)

31.3 (7.61)

NOTE: Numbers in parentheses represent standard errors of each mean.

# — Too few cases to provide a reliable estimate

SOURCE: National Center for Education Statistics (NCES), National Study of Postsecondary Faculty (NSOPF):2004 National Study of Postsecondary Faculty, March 30, 2006

Patterns of Nonresponse for Tenure Decisions

Field

Departments Reporting Tenure Cases

Departments Reporting No Cases

Responding

Departments

Departments

Surveyed

Biology

59

17

76

87

Chemistry

58

18

76

87

Civil engineering

46

9

55

69

Electrical engineering

44

15

59

77

Mathematics

57

17

74

86

Physics

60

17

77

86

Total

324

93

417

492

SOURCE: Departmental survey conducted by the Committee on Gender Differences in Careers of Science, Engineering, and Mathematics Faculty.

Patterns of Nonresponse for Promotion Decisions

Field

Departments Reporting Promotion Cases

Departments Reporting No Cases

Responding

Departments

Departments

Surveyed

Biology

42

31

73

87

Chemistry

68

6

74

87

Civil engineering

41

14

55

69

Electrical engineering

43

16

59

77

Mathematics

46

27

73

86

Physics

49

28

77

86

Total

289

122

411

492

SOURCE: Departmental survey conducted by the Committee on Gender Differences in Careers of Science, Engineering, and Mathematics Faculty.

[1] Cathleen Synge Morawetz, Professor Emerita, the Courant Institute of Mathematical Sciences, New York University and Yu Xie, Frederick G. L. Huetwell Professor of Sociology, University of Michigan resigned their committee appointments in 2004.

iV

[2] See Massachusetts Institute of Technology (1999).

[3] National Science Foundation (2006); Figure A2-1 and Table A2-1 in Appendix 2-1.

[4] National Science Foundation, Survey of Doctorate Recipients, 1995-2003; Figure A2-3 in Ap­pendix 2-1.

[5] See Tables 2-1 and 2-2.

[6] See also the four reasons suggested by NAS, NAE, and IOM (2007): global competitiveness, law, economics, and ethics.

[7] For a list of gender equity studies conducted by Research I institutions, see the CWSEM Web site at http://www. nas. edu/cwsem.

[8] The average annual support for a doctoral student is $50,000 according to a new study (NAS, NAE, and IOM, 2007). The average doctoral student takes 7 years to complete a Ph. D., suggesting support for a single student could be $350,000.

[9] Arden L. Bement, Jr., “Remarks, Setting the Agenda for 21st Century Science,” at the meeting of the Council of Scientific Society Presidents, December 5, 2005. Available at http://www. nsf. gov/ news/speeches/bement/05/alb051205_societypres. jsp.

[10] See Statement of Senator Ron Wyden, Hearing on Title IX and Science, U. S. Senate Committee on Commerce, Science and Transportation, October 3, 2002.

[11] In addition to this activity, the Government Accountability Office was asked to complete a study on Title IX (GAO, 2004), and the RAND Corporation conducted a study on gender differences in federal funding (Hosek et al., 2005).

[12] The term “sciences and engineering” is often defined as the academic disciplines of physical sciences (including astronomy, chemistry, and physics); earth, atmospheric, and ocean sciences; mathematical and computer sciences; biological and agricultural sciences; and engineering (in all its forms). Additionally, psychology and the social sciences (including economics, political science, and sociology) may also be treated as science fields. Non-S&E fields are defined to include the various arts and humanities. The natural sciences and engineering are defined in this study as agricultural sciences, biological sciences, health sciences, engineering, computer and information sciences, math­ematics, and physical sciences. Further gradations can be seen in the Survey of Earned Doctorates list of fields of study. Our definition includes Ph. D. fields coded as between 005 and 599, inclusive. Refer to the questionnaire, an example of which is found at http://www. nsf. gov/statistics/nsf06308/ pdf/nsf06308.pdf.

[13] Research I institutions are defined as institutions which offer, beyond baccalaureate programs, doctoral programs which award 50 or more doctoral degrees annually. In addition these institutions receive a substantial amount ($40 million or more) of federal support. Note that this definition is based on the 1994 classification devised by The Carnegie Foundation for the Advancement of Teaching. The classification scheme was redone in 2000 and 2005. See “Carnegie Classifications” at http://www. carnegiefoundation. org/classifications/ for further details.

[14] The National Science Foundation (2002:2-3) notes: “Research universities enroll only 19 percent of the students in higher education, but they play the largest role in S&E degree production. They produce most of the engineering degrees and a large proportion of natural and social science degrees at both the graduate and undergraduate levels. In 1998, the nation’s 127 research universities awarded more than 42 percent of all S&E bachelor’s degrees and 52 percent of all S&E master’s degrees.” For example, of the 8,350 Ph. D.s granted in the life sciences in 2002, 2,608 Ph. D.s (31 percent) were granted by just 20 Research I institutions (Hoffer et al., 2003). These institutions “are also the most conducive organizational contexts for a prestigious research career” (NRC, 2001a:124). On federal academic S&E support, see Richard J. Bennof, Federal Science and Engineering Obligations to Academic and Nonprofit Institutions Reached Record Highs in FY 2002, NSF InfoBrief, June 2004, (NSF 04-324).

[15] The four science fields were chosen, partly because they represent the “standard” or well-known science fields. In addition, professional associations in the areas of chemistry, mathematics, and phys­ics collect data on their fields. Readers should note that “biological sciences” is a broad term, and may include agricultural or health sciences. Likewise, mathematics data sometimes include data for statistics or computer science. Finally, physics data may include astronomy.

Civil engineering was chosen as a middle ground among the various engineering fields. According to Gibbons (2004), during the 2002-2003 academic year, more than 8,000 students received civil engineering baccalaureate degrees—the fourth largest amount—and women received 23.4 percent of those degrees. This lies between a high for environmental engineering (42.1 percent of degrees went to women) and a low of 11.7 percent for engineering technology. About 3,600 students received master’s degrees—the fifth largest amount—and women received 25.2 percent of them, between 42.2 percent for environmental engineering and 9.0 percent for petroleum. The third largest amount— 631 doctoral degrees were awarded and women received 18.4 percent of them, between 33.3 percent for engineering management and zero percent in mining and in architectural engineering. Finally, for faculty, civil engineering had the third highest number of faculty members: 3,320, and 10.9 percent of tenured/ tenure-track teaching faculty were women. Fields with the lowest percentage of women were aero­space, petroleum, and mining (all at 5.0 percent); while the highest were biomedical (16.6 percent), industrial/manufacturing (15.4 percent), and environmental (14.7).

[16] National Research Council, 2001, From Scarcity to Visibility: Gender Differences in the Careers of Doctoral Scientists and Engineers. Washington, DC: National Academy Press.

[17] National Research Council, 2005, To Recruit and Advance: Women Students and Faculty in U. S. Science and Engineering, Washington, DC: National Academies Press.

[18] National Academies, 2007, Beyond Bias and Barriers: Fulfilling the Potential of Women in Aca­demic Science and Engineering. Washington, DC: National Academies Press.

[19] Ibid, p. 2.

[20] Ibid, p. 3.

[21] Marschke et al. (2007), write, however, that progress for female faculty has been “glacial” and “excruciatingly slow.”

[22] Additional information on the surveys can be found at SRS Survey of Doctoral Recipients at http://www. nsf. gov/statistics/showsrvy. cfm? srvy_CatID=3&srvy_Seri=5, accessed on June 13, 2006; and National Study of Postsecondary Faculty—Overview at http://www. nces. ed. gov/surveys/nsopf/, accessed on June 13, 2006.

[23] See for example NSF (2004b).

[24] For further details on the AAAS surveys, see Chander and Mervis (2001) and Holden (2004).

[25] For further details see Byrum (2001), Ivie et al. (2003), Kirkman et al. (2006), Long (2000, 2002), Marasco (2003), and Vardi et al. (2003).

[26] The percentage of women participating in science and engineering education, however, is lower than the corresponding percentage of women in the U. S. population of 18- to 30-year-olds. See Kristen Olson, Despite Increases, Women and Minorities Still Underrepresented in Undergraduate and Graduate S&E Education, NSF Data Brief, January 15, 1999 (NSF 99-320).

[27] Note here S&E is defined as engineering, natural sciences, and the social and behavioral sciences.

[28] Data tabulated by staff, derived from National Science Foundation WebCASPAR database.

[29] Data tabulated by staff, derived from National Science Foundation WebCASPAR database.

[30] Other studies come to similar conclusions. For example, women comprised only 14 percent of all faculty in astronomy in 2003 (Ivie, 2004) and 13 percent of all faculty in physics in 2006 (Dresselhaus, 2007). In mathematics in 2005, only 11 percent of full-time, tenure-track or tenured faculty in doctoral departments were women, while 24 percent of non-tenure-track, full-time faculty were women (Kirkman et al., 2006). In engineering, only 11.3 percent of tenured or tenure-track faculty members were women in 2006 (Gibbons, 2007). It should be noted, though, that over time, these percentages are slowly rising.

[31] In 2006, all of the top 50 chemistry departments had at least one woman on faculty (Marasco, 2006). Continuing the examination of chemistry, for 30 Research I institutions that hired at least five faculty during 1988 and 1997, the percentage of women among hires ranged from 50 percent in one case to zero percent in 8 cases. Some departments hired a greater proportion of women than might be expected in comparison to the proportion of women in the doctoral pool, though in most cases, the proportion of women hired was lower (NAS, NAE, and IOM, 2007).

[32] Doctorate-granting institutions are defined as Groups I, II, III, IV, and V. See Kirkman et al. (2006) for complete definitions.

[33] Note these are small gains over 2001 data (compare with NSF, 2003b). The figures here do not agree with those in Table 1-1 due to differences in year of reference, sampling and nonsampling errors, and definitional differences.

[34] The exception was computer science: 10.8 percent of assistant professors, 14.4 percent of associ­ate professors, and 8.3 percent of full professors were women.

[35] Data for chemistry are from 2003; data for physics and civil engineering are from 2002. Newer

data are available in chemistry. See Marasco (2006) for percentage of female faculty at the nation’s top 50 chemistry departments from 2000 to 2006. See NAS, NAE, and IOM (2007) for numbers of male and female faculty in chemistry from 1966-1999.

[36] This is a general trend. According to data collected by the AAUP, about 40 percent of men were full professors, compared to about 20 percent of women. In addition, a greater percentage of women were instructors, lecturers, or had no rank (Curtis, 2004).

[37] Recent data have cast doubt on this position, suggesting significant differences might not occur (Ginther and Kahn, 2006).

[38] Perna’s (2002) analysis suggested that female faculty were less likely to receive supplemental earnings, such as from institutional sources or private consulting.

[39] Data were created using the Department of Education’s Data Analysis System (DAS), available online at http://www. nces. ed. gov/dasol/. Gender was used as the row variable. The column variables were mean percent time spent on research activities, mean percent time spent on instruction, and mean percent time spent on other unspecified activities. Filters were only Research I institutions, full-time employed, with faculty status, with instructional duties for credit, and with principal fields of teach­ing as agriculture and home economics, engineering, first-professional health sciences, nursing, other health sciences, biological sciences, physical sciences, mathematics, and computer sciences.

[40] Administrative and other activities are defined as those that occur at the respondent’s institution such as administration, professional growth, service, and other activities not related to teaching or research.

[41] As Nettles et al. (2000:8) noted: “Some researchers have argued that most faculty reward systems are based on research performance” (Hansen 1988), and existing research supports this assertion (e. g., Fairweather 1995, 1996; Gomez-Mejia and Balkin 1992; Ferber and Green 1982; Lewis and Becker 1979; Tuckman and Hageman 1976). See also Fairweather (2002).

[42] Although at least one study of 210 departments of computer science conducted in 2002 for the period 1995-2000 found that female faculty had lower turnover than men (Cohoon et al., 2003).

[43] See also Amey (1996).

[44] However, some institutions do release their analyses of hiring. An excellent example is the 2003 gender equity report undertaken at the University of Pennsylvania, which presents important data for consideration and evaluation while maintaining anonymity. See http://www. upenn. edu/almanac/v50/ n16/gender_equity. html. See also the report, University of California: Some Campuses and Academic Departments Need to Take Additional Steps to Resolve Gender Disparities among Professors, Report by the California State Auditor, 2001, available at http://www. bsa. ca. gov/pdfs/reports/2000-131.pdf. See also the report by the Commission on the Status of Women at Columbia University, Advance­ment of Women Through the Academic Ranks of the Columbia University Graduate School of Arts and Sciences: Where Are the Leaks in the Pipeline?, available at http://www. columbia. edu/cu/senate/ annual_reports/01-02/Pipeline2a_as_dist. doc. pdf.

[45] The committee acknowledges that the p-values for all the data presented are unadjusted and that many of the data presented are interconnected.

[46] A limitation of the survey was that it did not ask for the gender of every candidate offered a particular position.

[47] Note that this analysis implies nothing about the quality of applicants. Some people apply for jobs for which they are not a very good fit. The committee did not assess whether male and female applicants would behave any differently in this regard.

[48] Recall that the committee’s survey was stratified in order to collect similar numbers of respon­dents in each of the six disciplinary areas, and therefore respondents from different disciplines have different survey weights.

[49] These estimates would be useful as national estimates only in situations in which the disciplines are relatively homogeneous with respect to a given characteristic and the nonresponse which occurred was such that nonrespondents did not differ in their characteristics from respondents.

[50] These figures are medians. The median was used because the data are skewed; there are a few positions that had hundreds of applicants. The mean number of applications for tenure-track jobs was 85 applications from men and 17 from women. The mean number of applications for tenured jobs was 78 from men and 17 from women.

[51] For a discussion of how to define the “pool of qualified candidates,” see NAS, NAE, and IOM (2007).

[52] The vast majority of both tenure-track (94 percent) and tenured (83.5 percent) positions had at least one female applicant.

[53] Note, however, that we do not know if the person first offered and the person hired are the same person, where the genders are the same. Nor do we know how many offers were made before some­one was eventually hired. Since men outnumber women in the offers made, one would expect that the proportion of times women turn down an offer, resulting in a man being ultimately hired, should be higher than the proportion of times that men turn down an offer, resulting in a woman ultimately being hired.

[54] However, analysis presented in this chapter does not find an effect of the number of family – friendly policies on the percentage of female applicants. The impact of such policies on applications may bear further study.

NOTE: Many of the 417 departments provided multiple answers to the open-ended survey question, and 71 departments that reported that they have taken steps other than those listed in the table. SOURCE: Survey of departments carried out by the Committee on Gender Differences in Careers of Science, Engineering, and Mathematics Faculty.

advertising was the most frequently cited action, followed by general advertising. These were followed by recruiting at conferences, contacting women directly, and using personal contacts and assistance from on-campus diversity offices.

In addition, for most departments the total number of steps taken was not large. As shown in Table 3-10, 23 percent reported taking no specific action, and 43 percent reported taking just one. Only slightly more than 10 percent reported taking three or more steps.

Years Between Starting Employment and Achieving. Associate Professor Status, by Gender

Percentage breakdown of the number of years between associate professor rank achieved and first faculty or instructional staff by gender, for full-time faculty at Research I institutions with instructional duties for credit, teaching biology, physical sciences, engineering, mathematics or computer science, fall 2003.

Years Between Achieved Associate Professor and First Started Employment at Postsecondary Institution

0

1-5

6-10

11-15

16 or more

Estimates

Total

3.9 (1.43)

18.9 (2.72)

60.1 (3.11)

9.8 (2.08)

7.4 (1.60)

Men

4.1 (1.61)

20.0 (2.77)

60.3 (3.45)

8.4 (2.02)

7.1 (1.77)

Women

#

13.1 (9.38)

58.7 (9.8)

16.8 (6.92)

8.7 (4.23)

NOTE: Numbers in parentheses represent standard errors of each mean.

# — Too few cases to provide a reliable estimate

SOURCE: National Center for Education Statistics (NCES), National Study of Postsecondary Faculty (NSOPF):2004 National Study of Postsecondary Faculty, March 30, 2006.

Appendix 5-5

Knowledge of Tenure Procedures by Gender, Rank, and. Presence of a Mentor

Presence of a Mentor by Gender and Rank

Rank

Gender

Men

Women

Professor

19 (279)

28(233)

Associate professor

55 (194)

93 (255)

Assistant professor

108(208)

142 (235)

NOTES: Sample sizes are in parentheses. For example, of 279 respondents, 19 male full professors stated that they had a mentor at some point in their careers.

SOURCE: Survey of faculty conducted by the Committee on Gender Differences in Careers of Sci­ence, Engineering, and Mathematics Faculty.

Knowledge of Institutional Tenure Policies by Gender and Presence of a Mentor

Response

Men

Mentor

No Mentor

Women

Mentor

No Mentor

No institutional tenure policy present

3

2

2

4

Tenure policy present but not known

30

39

27

42

Knows institution’s tenure policies

136

387

221

357

NOTES: A total of 84 men (13 with mentors) and 70 women (13 with mentors) chose not to respond to this question.

SOURCE: Survey of faculty conducted by the Committee on Gender Differences in Careers of Sci­ence, Engineering, and Mathematics Faculty.

Knowledge of Institutional Promotion Policies by Gender and Rank

Men

Women

Response

Professor

Assoc.

Professor

Asst.

Professor

Professor

Assoc.

Professor

Asst.

Professor

No institutional promotion policy present

1

1

3

3

4

3

Promotion policy present but not known

16

29

71

12

68

90

Knows

institution’s

promotion

policies

221

141

115

164

158

130

NOTES: A total of 83 men (41 professors, 23 associate professors, and 19 assistant professors) and 71 women (34 professors, 25 associate professors, and 12 assistant professors) chose not to respond to this.

SOURCE: Survey of faculty conducted by the Committee on Gender Differences in Careers of Sci­ence, Engineering, and Mathematics Faculty.

Detailed Tenure Information from Departmental Survey

Men Women

Tenured

Not tenured

Total

Tenured

Not tenured

Total

Biology

89

16

105

29

5

34

Chemistry

79

22

101

11

0

11

Civil engineering

74

15

89

11

2

13

Electrical engineering

91

10

101

9

0

9

Mathematics

106

16

122

14

1

15

Physics

106

7

113

5

0

5

High-prestige institution

79

22

101

11

1

13

Medium-prestige institution

74

12

86

15

0

15

Low-prestige institution

392

52

444

60

7

67

Total

545

86

631

95

Public institution

425

54

479

62

5

67

Private institution

130

32

162

17

3

20

Total

555

86

641

81

Stop-the-tenure-clock policy

113

22

135

16

1

17

No stop-the-tenure-clock policy

417

60

477

60

6

66

Total

530

82

612

83

NOTES: There were 755 tenure decisions reported by 319 departments that reported having at least 1 tenure case during the 2 years of the study. In 631 of those tenure decisions, the candidate was a man. In 124 decisions, the candidate was a woman. We deleted 37 cases in which the candidate was a woman but the department reported having no female tenure-track faculty at the assistant or associate professor levels. Thus there are only 87 tenure decisions involving women. The column labeled Tenured shows the number of decisions that were positive, while the column labeled Not tenured shows the number of negative decisions. There were five decisions for which information about the stop-the-tenure-clock policy was missing that involved women and 19 decisions that involved men.

SOURCE: Departmental surveys conducted by the Committee on Gender Differences in Careers of Science, Engineering, and Mathematics Faculty.

Time Spent in Both Assistant and Associate Professorships

Number Tenured

Number of Cases

Percent women among tenure-track faculty

0 – 10

3

3

10.1 – 25

32

32

25.1 – 50

30

35

50.1 – 75

10

13

75.1 – 100

3

3

Percent women among all faculty

0 – 10

14

14

10.1 – 25

51

55

25.1 – 50

13

17

NOTES: The percentage of women in the tenure pool was computed as the total number of women on tenure-track (both assistant and associate) divided by the total number of tenure-track faculty (both assistant and associate). The percentage of women among all faculty was computed as the total num­ber of women of all ranks, tenured or tenure-track, divided by the total number of faculty of all ranks, tenured or tenure-track. Again, we did not consider the 37 tenure decisions involving a woman where the number of tenure-track women was reported to be zero.

SOURCE: Departmental survey conducted by the Committee on Gender Differences in Careers of Science, Engineering, and Mathematics Faculty.

Appendix 5-4

Distribution of Undergraduate Course Load for Faculty. by Gender and Discipline

Two statistical tests were carried out. First, a chi-square test of independence of rows was applied to determine whether the pattern of the number of under­graduate courses taught[124] by men and women differed. (These tests were either on three or four degrees of freedom.) The tests were not significant at the.05 level except for electrical engineering. It is important to mention that one could have different patterns without having women teach more of fewer courses. For instance, men might teach 1 or 2 courses more often than women do, who in turn might teach 0 or 3 courses more often, but where the mean number of courses remained close.

Therefore, we added a simple two-sample t-test of the average number of courses for men and women. The means are displayed below for each of the dis­ciplinary areas. The t-tests were all not significant at the.05 for the null hypothesis of no difference, again except for electrical engineering. It is clear from the table that men teach more undergraduate courses than do women.

BIOLOGY

Courses

Taught 0

1

2

3

4

Total

Men 31

55

12

2

0

100

Women 31

58

11

2

2

104

Total 62

113

23

4

2

204

Chi-squared test of independence: 2.05 (4 degrees of freedom), p-value 0.73. Means: Men.85 vs. Women.90, t-test is equal to -0.51 p-value 0.61.

CHEMISTRY

Courses Taught

0

1

2

3

Total

Men

43

49

8

1

101

Women

43

48

4

2

97

Total

86

97

12

3

198

Chi-squared test of independence: 1.60 (3 degrees of freedom), p-value 0.66. Means: Men.67 vs. Women.64, t-test is equal to 0.36 p-value 0.72.

MATHEMATICS

Courses Taught

0

1

2

3

Total

Men

21

30

15

2

68

Women

22

38

24

0

84

Total

43

68

39

2

152

Chi-squared test of independence: 3.39 (3 degrees of freedom), p-value 0.33.

Means: Men.97 vs. Women 1.02, t-test is equal to -0.42 p-value 0.68.

ELECTRICAL ENGINEERING

Courses Taught

0

1

2

3

Total

Men

33

46

14

1

94

Women

44

41

4

2

91

Total

77

87

18

3

185

Chi-squared test of independence: 7.70 (3 degrees of freedom), p-value 0.05.

Means: Men.82 vs. Women.60, t-test is equal to 2.09 p-value 0.04.

PHYSICS

Courses Taught

0

1

2

3

Total

Men

33

53

9

0

95

Women

31

66

14

1

112

Total

64

119

23

1

207

Chi-squared test of independence: 2.19 (3 degrees of freedom), p-value 0.53.

Means: Men.75 vs. Women.87, t-test is equal to -1.34 p-value 0.18

CIVIL ENGINEERING

Courses Taught 0

1

2

3

4

Total

Men 22

44

13

4

0

83

Women 36

67

13

3

1

120

Total 58

111

26

7

1

203

Chi-squared test of independence: 2.63 (4 degrees of freedom), p-value 0.62. Means: Men.99 vs. Women.88, t-test is equal to 0.94p-value 0.35.

Percentage of Faculty Members Who Do No Graduate Teaching

Discipline

Men

Women

Chemistry

42.0 (100)

37.5 (96)

Mathematics

63.4 (82)

56.0 (116)

Electrical engineering

55.3 (94)

48.9 (90)

Physics

55.9 (68)

44.6 (83)

Civil engineering

35.1 (97)

22.3 (112)

NOTE: Numbers in parentheses are the total number of respondents in each category.

SOURCE: Survey of faculty conducted by the Committee on Gender Differences in Careers of Sci­ence, Engineering, and Mathematics Faculty.

Percentage of Faculty Members Receiving a Reduced Teaching Load When Hired

Discipline

Men

Women

Chemistry

76.9 (52)

80.8 (52)

Mathematics

75.6 (41)

87.0 (69)

Electrical engineering

82.7 (52)

85.5 (55)

Physics

64.5 (31)

71.7 (46)

Civil engineering

70.0 (50)

75.3 (69)

NOTE: Numbers in parentheses are the total number of respondents in each category.

SOURCE: Survey of faculty conducted by the Committee on Gender Differences in Careers of Sci­ence, Engineering, and Mathematics Faculty.

Percentage of Faculty Members Who Served on an Undergraduate Thesis or Honors Committee

Discipline

Men

Women

Biology

36.6 (93)

45.3 (96)

Chemistry

26.0 (77)

30.4 (79)

Mathematics

15.4 (65)

13.0 (92)

Electrical engineering

36.6 (93)

45.3 (86)

Physics

26.0 (77)

30.4 (79)

Civil engineering

15.4 (65)

13.0 (92)

NOTE: Numbers in parentheses are the total number of respondents in each category.

SOURCE: Survey of faculty conducted by the Committee on Gender Differences in Careers of Sci­ence, Engineering, and Mathematics Faculty.

Percentage of Faculty Members Who Served on and Chaired an Undergraduate Thesis or Honors Committee

Discipline

Men

Women

Served

Chair

Served

Chair

Biology

62.30 (38)

37.30 (23)

59.52 (50)

40.78 (34)

Chemistry

57.14 (20)

42.86 (15)

46.15 (24)

53.85 (28)

Mathematics

85.71 (6)

14.29 (1)

30.00 (3)

70.00 (7)

Electrical engineering

43.59 (17)

56.41 (22)

50.00 (17)

50.00 (17)

Physics

62.50 (20)

37.50 (12)

54.54 (18)

45.46 (15)

Civil engineering

62.50 (10)

37.50 (6)

42.86 (12)

57.14 (16)

NOTE: Numbers in parentheses are the total number of respondents in each category.

SOURCE: Survey of faculty conducted by the Committee on Gender Differences in Careers of Sci­ence, Engineering, and Mathematics Faculty.

Distribution of Number of Graduate Thesis or Honors Committees for Research I Tenure and Tenure-Track Faculty: Men/Women

Discipline

0

1-3

4-5

6-10

11-30

Total

Biology

9.3

34.3

24.1

22.2

10.2

108

5.1

41.5

12.7

26.3

14.4

118

Chemistry

6.5

32.7

19.6

23.4

17.8

107

6.0

39.0

15.0

25.0

15.0

100

Mathematics

43.7

47.9

5.6

2.8

0

71

35.6

49.4

10.3

3.4

1.2

87

Electrical engineering

11.0

54.0

19.0

12.0

4.0

99

19.0

37.0

25.0

16.0

3.0

100

Physics

15.4

61.5

16.4

5.8

1.0

104

29.9

50.4

17.1

2.6

0

117

Civil engineering

4.8

54.8

23.8

10.7

5.9

84

11.8

18.4

22.2

19.6

14.3

113

NOTE: These are percentages of men and women who fall into each category.

SOURCE: Survey of faculty conducted by the Committee on Gender Differences in Careers of Sci­ence, Engineering, and Mathematics Faculty.

Percentage of Time Spent in Administration or Committee Work on Campus and Service to the Profession Outside the University for Tenured and Tenure-Track Faculty at Research I Institutions: Men/Women

Discipline

Mean Hours (standard deviation, sample size)

Biology

13.1 (10.7,

110)

15.6 (11.7,

117)

Chemistry

14.6 (12.5,

108)

14.8 (10.7,

96)

Mathematics

12.7 (14.3,

81)

13.6 (11.0,

82)

Electrical engineering

12.9 (11.3,

101)

17.6 (16.3,

102)

Physics

13.8 (11.5,

108)

13.9 (12.6,

119)

Civil engineering

19.3 (17.9,

85)

17.1 (13.5,

116)

SOURCE: Survey of faculty conducted by the Committee on Gender Differences in Careers of Sci­ence, Engineering, and Mathematics Faculty.

Distribution of Number of Service Committees for Research I Tenure and Tenure-Track Faculty: Men/Women

Discipline

0

1

2

3

4

5

>5

Total

Biology

34.1

21.4

15.9

13.5

7.9

5.6

1.6

126

25.6

18.8

16.5

18.8

14.3

1.5

4.5

133

Chemistry

30.3

16.4

23.8

18.0

4.9

4.9

1.6

122

25.0

24.1

17.0

18.7

8.0

5.4

1.8

112

Mathematics

49.5

20.2

11.1

9.1

7.1

1.0

2.0

99

41.7

17.5

15.5

13.6

6.8

4.9

0.0

103

Electrical engineering

36.2

28.4

17.2

6.9

6.0

4.3

0.9

116

34.5

21.6

17.2

15.5

6.0

5.2

0.0

116

Physics

24.0

21.5

22.3

18.2

5.0

3.3

5.8

121

30.9

30.1

22.8

00

00

2.9

1.5

2.9

136

Civil engineering

28.9

18.6

22.7

9.3

15.5

2.1

3.1

97

21.9

10.6

25.2

18.7

5.7

9.8

8.1

123

SOURCE: Departmental survey conducted by the Committee on Gender Differences in Careers of Science, Engineering, and Mathematics Faculty.

Mean Salary by Gender and Professorial Rank for Tenure and Tenure-Track Faculty in Research I Institutions

Discipline

Rank

Mean (1000s) Men

Mean (1000s) Women

Biology

1

101.9 (34)

93.5 (34)

Chemistry

1

112.9 (43)

101.7 (28)

Mathematics

1

106.5 (40)

101.1 (26)

Electrical engineering

1

107.9 (27)

110.2 (33)

Physics

1

110.0 (48)

93.7 (33)

Civil engineering

1

115.0 (24)

102.5 (26)

Biology

2

72.8 (31)

68.2 (48)

Chemistry

2

72.9 (28)

72.7 (36)

Mathematics

2

68.1 (17)

69.0 (29)

Electrical engineering

2

83.8 (25)

93.5 (34)

Physics

2

73.2 (31)

74.8 (34)

Civil engineering

2

81.8 (30)

81.3 (42)

Biology

3

62.2 (35)

59.5 (26)

Chemistry

3

59.6 (33)

62.9 (30)

Mathematics

3

61.1 (22)

58.4 (32)

Electrical engineering

3

76.6 (43)

76.2 (30)

Physics

3

65.1 (26)

65.0 (44)

Civil engineering

3

71.1 (25)

68.9 (42)

NOTES: Rank is denoted as full (1), associate (2), or assistant (3) professor. Salaries are expressed as number of thousands of dollars with number of respondents in parentheses. For example, 34 men at the full professor rank in biology responded that they earn an average of $101,900 per year. Of 1,404 full-time faculty members who responded, only 1,179 included salary data. The salaries expressed are 9-month salaries. Some clearly high outliers were removed. Twenty percent of the respondents replied back with salaries below $100 for 9 months. Since it was likely that these values were actually in the thousands, these numbers were multiplied by 1,000 for the final value rather than omitting the information.

SOURCE: Survey of faculty conducted by the Committee on Gender Differences in Careers of Sci­ence, Engineering, and Mathematics Faculty.

Discipline

Men

Women

Biology

66.1 (62)

63.3 (49)

Chemistry

71.2 (59)

81.8 (55)

Mathematics

42.9 (35)

29.1 (55)

Electrical engineering

85.2 (61)

77.4 (62)

Physics

71.4 (49)

63.5 (74)

Civil engineering

80.0 (45)

85.5 (69)

NOTES: Only one-half of those surveyed responded to this question. Numbers in parentheses are the total number of respondents in each category. For example, 66.1 percent of men in biology out of 62 respondents received summer salary support.

SOURCE: Survey of faculty conducted by the Committee on Gender Differences in Careers of Sci­ence, Engineering, and Mathematics Faculty.

Discipline

Men

Women

Biology

45.2 (62)

44.9 (49)

Chemistry

50.8 (59)

32.7 (55)

Mathematics

62.9 (35)

80.0 (55)

Electrical engineering

62.3 (61)

53.2 (62)

Physics

59.2 (49)

64.9 (74)

Civil engineering

64.4 (45)

71.0 (69)

NOTES: Only one-half of those surveyed responded to this question. The numbers are expressed in percentages of the total respondents (parentheses). For example, 45.2 percent of male faculty in biol­ogy out of a total of 62 respondents said they had received travel support.

SOURCE: Survey of faculty conducted by the Committee on Gender Differences in Careers of Sci­ence, Engineering, and Mathematics Faculty.

Median Square Footage of Lab Space of Faculty Who Report Doing Experimental Work

Discipline

Men

Women

Biology

1200 (97)

1050 (106)

Chemistry

1500 (94)

1500 (88)

Mathematics

a

a

Electrical engineering

550 (50)

450(53)

Physics

1079 (55)

800 (59)

Civil engineering

738 (50)

800 (64)

"Mathematics was excluded from this analysis because the small sample size was inadequate for analysis and ran the risk of potentially violating confidentiality.

NOTE: The median square footage of lab space given to faculty members that identify at least some of their research as “experimental.” The number of respondents is in parentheses.

SOURCE: Survey of faculty conducted by the Committee on Gender Differences in Careers of Sci­ence, Engineering, and Mathematics Faculty.

Faculty Who Have Received More Lab Space Since Hire (Values Are Percentages)

Discipline

Men

Women

%

(n/total)

%

(n/total)

Biology

0.25

14/56

0.28

13/47

Chemistry

0.43

23/54

0.49

23/47

Mathematics

a

a

Electrical engineering

0.24

8/34

0.26

10/38

Physics

0.24

7/29

0.29

12/42

Civil engineering

0.16

5/31

0.14

5/35

Overall

0.28

57/204

0.30

63/209

"Mathematics was excluded from this analysis because the small sample size was inadequate for analysis and ran the risk of potentially violating confidentiality.

NOTE: Sample sizes are indicated in parentheses.

SOURCE: Survey of faculty conducted by the Committee on Gender Differences in Careers of Sci­ence, Engineering, and Mathematics Faculty.

Discipline

Men

Women

Biology

99.2 (107)

94.2 (120)

Chemistry

92.5 (107)

88.8 (98)

Mathematics

100.0 (68)

97.0 (66)

Electrical engineering

90.4 (104)

88.8 (98)

Physics

99.0 (103)

97.2 (109)

Civil engineering

87.5 (80)

88.6 (114)

NOTES: The numbers are expressed in percent of total respondents (in parentheses). For example, 99.2 percent of men in biology, out of a total of 107 respondents, stated that they had sufficient equipment.

SOURCE: Survey of faculty conducted by the Committee on Gender Differences in Careers of Sci­ence, Engineering, and Mathematics Faculty.

Number of Postdoctorate Students for Tenured and Tenure-Track Faculty in Research I Institutions (presented by Men and Women)

Discipline

0

1

2

3

4

>4

Weighted

Average

Total

Biology

37.7

25.5

11.3

2.8

7.5

9.4

1.33

106

51.3

25.2

6.7

2.5

5.0

5.9

0.96

119

Chemistry

46.4

15.2

15.2

8.0

4.5

7.1

1.23

112

39.6

30.7

12.9

6.9

3.0

3.0

1.04

101

Civil engineering

76.6

11.7

11.7

16.9

0.0

0.0

0.86

77

76.9

15.4

6.6

12.1

1.1

0.0

0.69

91

Electrical engineering

79.8

19.2

9.6

0.0

0.0

0.0

0.38

104

72.5

16.7

00

00

0.0

0.0

0.0

0.34

102

Mathematics

42.1

30.8

19.6

0.0

1.9

0.9

0.82

107

37.8

40.3

16.0

1.7

0.8

0.8

0.85

119

Physics

74.4

20.9

3.5

5.8

0.0

0.0

0.45

86

74.6

18.6

5.1

4.2

0.8

0.0

0.45

118

NOTES: Numbers are expressed as percentage of total and provide the distribution of the number of postdoctorate students and the number of postdoctorate students for each discipline. The final column “>4” deicts when the number of doctoral students was 5 or greater.

SOURCE: Survey of faculty conducted by the Committee on Gender Differences in Careers of Sci­ence, Engineering, and Mathematics Faculty.

Discipline

Men

Women

Biology

50.0 (92)

41.6 (89)

Chemistry

54.0 (100)

30.7 (88)

Civil engineering

72.2 (72)

46.9 (81)

Electrical engineering

56.2 (89)

47.9 (94)

Mathematics

54.9 (102)

44.6 (112)

Physics

35.3 (68)

28.6 (98)

NOTES: Numbers are expressed as percentage of total respondents (in parentheses). For example, 50 percent of men in biology out of 92 respondents believed that they received sufficient clerical support.

SOURCE: Survey of faculty conducted by the Committee on Gender Differences in Careers of Sci­ence, Engineering, and Mathematics Faculty.

Discipline

Men

Women

Biology

53.8 (52)

53.7 (67)

Chemistry

54.5 (55)

60.3 (63)

Mathematics

54.0 (50)

50.0 (88)

Electrical engineering

48.4 (64)

72.9 (59)

Physics

28.2 (39)

51.8 (56)

Civil engineering

49.2 (63)

58.5 (82)

NOTES: Faculty in this table includes tenure-track and tenured faculty. Numbers in parentheses are the total number of respondents in each category.

SOURCE: Survey of faculty conducted by the Committee on Gender Differences in Careers of Sci­ence, Engineering, and Mathematics Faculty.

Distribution of the Number of Graduate Students for Tenured and Tenure-Track Faculty in Research I Institutions (presented by Men and Women)

Number of Students

Sample

Discipline

0

1

2

3

4

5

>5

Size

Biology

6.6

9.4

21.7

24.5

13.2

6.6

17.9

106

6.7

14.3

14.3

16.0

13.4

5.9

29.4

119

Chemistry

4.5

5.4

8.9

18.8

8.9

7.1

46.4

112

1.0

5.9

8.9

8.9

16.8

11.9

45.5

101

Mathematics

36.4

30.0

11.7

9.1

9.1

0.0

3.9

77

38.5

23.1

20.9

4.4

6.6

2.2

4.4

91

Electrical engineering

6.7

9.6

11.5

8.7

9.6

7.7

46.2

104

4.9

5.9

00

00

10.8

15.7

00

00

45.1

102

Physics

6.5

16.8

18.7

24.3

13.1

10.3

10.3

107

9.2

20.2

17.6

21.8

11.8

7.6

11.8

119

Civil engineering

5.8

4.6

11.6

16.2

17.4

11.6

32.6

86

2.5

7.6

6.8

12.7

11.0

11.9

47.5

118

NOTE: Final column of “>5” depicts 6 or greater graduate students.

SOURCE: Survey of faculty conducted by the Committee on Gender Differences in Careers of Sci­ence, Engineering, and Mathematics Faculty.

Mean Number of Articles Published in Refereed Journals (sole and co-authored) Over the Past 3 Years for Tenured and Tenure-Track Faculty in Research I Institutions

Discipline

Men

Women

Biology

6.7(81)

6.2 (81)

Chemistry

15.8 (89)

9.4 (79)

Civil engineering

5.3 (69)

4.5 (79)

Electrical engineering

5.8 (102)

7.5 (98)

Mathematics

12.4 (94)

10.4 (106)

Physics

7.6 (85)

6.3 (109)

NOTES: Numbers in parentheses are the total number of respondents in each category.

SOURCE: Survey of faculty conducted by the Committee on Gender Differences in Careers of Sci­ence, Engineering, and Mathematics Faculty.

Discipline

Gender

Mentor

Probability of Grant

SD

n

Biology

Male

No

0.91

0.06

12

Biology

Male

Yes

0.87

0.10

15

Biology

Female

No

0.64

0.14

5

Biology

Female

Yes

0.88

0.10

5

Chemistry

Male

No

0.89

0.11

12

Chemistry

Male

Yes

0.96

0.04

9

Chemistry

Female

No

0.77

0.08

6

Chemistry

Female

Yes

0.95

0.04

12

Mathematics

Male

No

0.72

0.19

10

Mathematics

Male

Yes

0.83

0.05

6

Mathematics

Female

No

0.59

0.17

7

Mathematics

Female

Yes

0.91

0.08

12

Electrical engineering

Male

No

0.84

0.16

9

Electrical engineering

Male

Yes

0.87

0.11

21

Electrical engineering

Female

No

0.37

0.09

12

Electrical engineering

Female

Yes

0.86

0.06

12

Physics

Male

No

0.9

0.05

13

Physics

Male

Yes

0.92

0.04

10

Physics

Female

No

0.71

0.18

12

Physics

Female

Yes

0.95

0.03

19

Civil engineering

Male

No

0.87

0.07

10

Civil engineering

Male

Yes

0.53

0.04

12

Civil engineering

Female

No

1

0.00

11

Civil engineering

Female

Yes

1

0.00

12

SOURCE: Survey of faculty conducted by the Committee on Gender Differences in Careers of Sci­ence, Engineering, and Mathematics Faculty.

Discipline

Gender

Mentor

Probability of Grant

SD

n

Biology

Male

No

0.91

0.09

12

Biology

Male

Yes

0.93

0.12

5

Biology

Female

No

0.83

0.12

23

Biology

Female

Yes

0.97

0.02

10

Chemistry

Male

No

0.94

0.07

13

Chemistry

Male

Yes

0.95

0.07

8

Chemistry

Female

No

0.89

0.09

12

Chemistry

Female

Yes

0.98

0.01

7

Mathematics

Male

No

0.54

0.23

9

Mathematics

Male

Yes

0.74

0.07

2

Mathematics

Female

No

0.85

0.1

12

Mathematics

Female

Yes

0.98

0.03

3

Electrical engineering

Male

No

0.88

0.05

9

Electrical engineering

Male

Yes

0.87

0.09

3

Electrical engineering

Female

No

0.7

0.14

10

Electrical engineering

Female

Yes

0.95

0.02

10

Physics

Male

No

0.93

0.05

11

Physics

Male

Yes

0.96

0.05

10

Physics

Female

No

0.88

0.08

13

Physics

Female

Yes

0.98

0.02

17

Civil engineering

Male

No

0.87

0.09

12

Civil engineering

Male

Yes

0.95

0.02

8

Civil engineering

Female

No

1

0

16

Civil engineering

Female

Yes

1

0

11

SOURCE: Survey of faculty conducted by the Committee on Gender Differences in Careers of Sci­ence, Engineering, and Mathematics Faculty.

Percentage of Faculty Missing Salary Data by Gender and Discipline

Discipline

Men

Women

Biology

20.6 (126)

17.3 (133)

Chemistry

13.9 (122)

15.2 (112)

Civil engineering

20.2 (99)

15.5 (103)

Electrical engineering

18.1 (116)

16.4 (116)

Mathematics

12.4 (121)

15.4 (136)

Physics

17.5 (97)

10.6 (123)

NIOTES: Number in parentheses are the total number of respondents in each category. For example, 20.6 percent of men in biology out of 126 total respondents were missing salary data.

SOURCE: Survey of faculty conducted by the Committee on Gender Differences in Careers of Sci­ence, Engineering, and Mathematics Faculty.

Percentage of Tenured and Tenure-Track Faculty at Research I Institutions That Were Nominated for at Least One Award

Discipline

Men

Women

Biology

28.4 (109)

15.1 (119)

Chemistry

39.0 (113)

38.6 (101)

Mathematics

31.7 (82)

18.3 (93)

Electrical engineering

19.0 (105)

25.2 (103)

Physics

32.4 (108)

35.2 (122)

Civil engineering

15.3 (85)

23.9 (117)

NOTES: Number in parentheses are the total number of respondents in each category. For example, 28.4 percent of men in biology out of 109 total respondents have been nominated for an award. SOURCE: Survey of faculty conducted by the Committee on Gender Differences in Careers of Sci­ence, Engineering, and Mathematics Faculty.

Percentage of Tenured and Tenure-Track Research I Faculty with Offers to Leave

Discipline

Men

Women

Biology

24.1 (79)

22.3 (94)

Chemistry

38.8 (85)

25.7 (74)

Mathematics

18.1 (72)

33.3 (66)

Electrical engineering

29.3 (58)

46.6 (73)

Physics

36.5 (85)

28.6 (84)

Civil engineering

47.8 (67)

43.8 (73)

NOTES: Number in parentheses are the total number of respondents in each category. For example, 24.1 percent of men in biology out of 79 respondents stated that they had received offers from other universities.

SOURCE: Survey of faculty conducted by the Committee on Gender Differences in Careers of Sci­ence, Engineering, and Mathematics Faculty.

Percentage of Tenured and Tenure-Track Faculty at Research I Institutions Planning to Leave or Retire

Discipline

Men

Women

Biology

43.1

45.8

Chemistry

31.3

41.0

Mathematics

45.6

41.2

Electrical engineering

29.0

26.5

Physics

31.5

28.3

Civil engineering

36.5

42.5

SOURCE: Survey of faculty conducted by the Committee on Gender Differences in Careers of Sci­ence, Engineering, and Mathematics Faculty.

Appendix 5-1

VARIANCE OF A NONLINEAR FUNCTION OF PARAMETERS

Suppose that we fit a model to a response variable that has been transformed using some function g as above, and obtain an estimate of a mean L в ■ Pro­grams including SAS will also output an estimate of the variance of L в ■ We can compute the estimate of the mean in the original scale by applying the inverse transformation g-1 to Lв as described above. In order to obtain an estimate of the variance of g-1 (L в), however, we need to make use of, for example, the Delta method, which we now explain.

Подпись: Var(H (в)) = Подпись: dH (в) дв VARIANCE OF A NONLINEAR FUNCTION OF PARAMETERS

Given any non-linear function H of some scalar-valued random variable в, И(в) and given s2, the variance of в, we can obtain an expression for the variance of И(в) as follows:

For example, suppose that we used a log transformation on a response variable and obtained an LSM in the transformed scale that we denote L в, with estimated variance <OL■ The estimate of the mean in the original scale is obtained by apply­ing the inverse transformation to the LSM:

m = LSM.. = exp (L в)

original

Подпись: O2 = Подпись: д exp (L в) dL 'в Подпись: oL, в =[exp(Lв)] О,в■

The variance of m is given by:

Suppose now that the response variable was binary and that we used a logit transformation so that

Given an MLE в and an estimate of L в the least squares mean in the trans­formed scale, we compute m and &m as follows:

Подпись: m =

VARIANCE OF A NONLINEAR FUNCTION OF PARAMETERS

exp ( l в)

1 + exp (L в)

Г 2 = exP (L ‘P) Г

m |^1 + exp( L’P) ів

Given a point estimate of the least squares mean in the original scale and an approximation to its variance, we can compute an approximate 100(1-a)% con­fidence interval for the true mean in the original scale in the usual manner:

100(1- a)% for m = m ± tdfa,2Г,

where df is the appropriate degrees of freedom. In our case, and due to relatively large sample sizes everywhere, the t critical value can be replaced by the corre­sponding upper al2 tail of the standard normal distribution.

Main Considerations for Taking a Position by Number of Respondents Saying

“Yes”

Consideration

Gender of Respondent Male

Female

Pay

90

88

Benefits

65

62

Promotion opportunities

101

91

Start-up package

131

117

Funding opportunities

96

100

Family-related reasons

120

168

Job location

156

176

Collegiality

170

209

Reputation of department or university

184

224

Quality of research facilities

152

155

Access to research facilities

130

134

Opportunities for research collaboration

179

216

Desire to build or lead a new program or area of research

165

152

This was the only offer I received

52

48

NOTE: There were a total of 612 males and 666 females that responded in each category.

LEAST-SQUARES MEANS

Least-squares means of the response, also known as adjusted means or mar­ginal means can be computed for each classification or qualitative effect in the model. Examples of qualitative effects in our models include type of institution (two levels: public or private) discipline (with six categories in our study), gen­der of chair of search committee, and others. Least-squares means are predicted population margins or within-effect level means adjusted for the other effects in the model. If the design is balanced, the least-squares means (LSM) equal the observed marginal means. Our study design is highly unbalanced and thus the LSM of the response variable for any effect level will not coincide with the simple within-effect level mean response.

Подпись: LSM(Ai) = L' £ LEAST-SQUARES MEANS

Each least-squares mean is computed as L ‘£ for a given vector L. For exam­ple, in a model with two factors A and B, where A has three levels and B has two levels, the least squares mean response for the first level of factor A is given by:

where the first coefficient 1 in L corresponds to the intercept, the next three coef­ficients correspond to the three levels of factor A and the last two coefficients correspond to the two levels of factor B. If the model also includes an interaction between A and B, then L and /3 has an additional 3 * 2 elements. The correspond­ing values of the additional six elements in L would be L for the two interaction levels involving the first level of factor A (A1B1, A1B2) and 0 for the four interaction levels that do not involve the first level of factor A (A2B1, A B2, A3B1, A3B2). The coefficient vector L is constructed in a similar way to compute the LSM of y (or a transformation of y) for the remaining two levels of A, two levels of B, and even for the six levels of the interaction between A and B if it is present in the model.

When the response variable has been transformed prior to fitting the model, the LSM is computed in the transformed scale and must be then transformed back into the original scale. If we have MLEs of the regression coefficients, we can easily compute the LSMs in the original scale simply by applying the inverse
transformation to L в ■ For example, if g(u) = log(u) = xfi and L в is the least squares mean in the transformed scale, we can compute the LSM in the original scale as

LSM^ = Г’ (LSM rammed ) = Г’ (L B) = exp(L ‘B)

If the transformation was the logit transformation, the LSM in the original scale is computed as

Подпись: exp(L' B) 1 + exp (L'B) LSM^al = g" (LSM ranfrmed ) = g" (L B)

TRANSFORMATIONS

We let y denote a response variable such as the proportion of women in the applicant pool or annual salary or number of manuscripts published in a year, and use x to denote a vector of covariates that might include type of institution, disci­pline, proportion of women on the search committee, etc. If y can be assumed to be normally distributed with some mean m and some variance s2 then we typically fit a linear regression model to y that establishes that m = xP, where P is a vector of unknown regression coefficients.

When the response y is not normally distributed (for example, because y can only take on values 0 and 1) then we can define h = XP and then choose a trans­formation g of m such that

g(M) = H = xP.

For example, if the response variable is a proportion, the logit transformation

g(X> = log [~T~

11-H.

is appropriate. Wheny is a count variable (as in the number of manuscripts pub­lished in a year) the usual transformation is the log transformation.

One approach to obtaining estimates of P is the method of maximum likeli­hood. Let p denote the maximum likelihood estimate (MLE) of p. A nice prop­erty of MLEs is invariance; in general, the MLE of a function h(P) is equal to the function of the MLE of P, thus

h(P) = h(P).

In particular, if n = x£, then

£ = g_1(n).

The difficulty arises when we wish to also estimate the variance of £ for example to then obtain a confidence interval around the point estimate £ . To do so, we typically need to resort to linearization techniques that allow us to com­pute an approximation to the variance of a non-linear function of the parameters. A method that can be used for this purpose is called the Delta method and is described below.

Marginal Mean and Variance of Transformed Response Variables

Data collected in the departmental and faculty surveys were used to answer various research questions in this report. Statistical analyses consisted essentially of fitting various types of regression models, including multiple linear regression, logistic regression, and Poisson regression models depending on the distributional assumptions that were appropriate for each response variable of interest. In some cases, the response variable was transformed so that the assumption of normality for the response in the transformed scale was plausible. Marginal or least-squares means were calculated (sometimes in the transformed scale) for effects of interest in the models.