1. Gender Diversity in STEM
Education and Careers
Janet Shibley Hyde
University of Wisconsin
jshyde@wisc.edu

2. Collaborators
 Elizabeth Fennema, Sara Lindberg,
Marcia Linn, Amy Ellis, Janet Mertz,
Nicole Else-Quest
 Special thanks to NSF for funding, REC
0635444

3. The Differences Model

4. Innate, Biological Causes

5. The Deficit Model
 Harvard President
Lawrence
Summers, who
claimed, in a
controversial
speech, that
women do not have
the math ability to
succeed in science
and engineering
(January, 2005)

6. Meta-Analysis: A Method for
Assessing Psychological
Gender Differences
 A quantitative literature review
 A method for quantitatively combining
the results of numerous studies on a
given question

7. Effect Size: The Size of the
Gender Difference
d = MM – MF
sw
(Cohen)

8. Cohen’s Guidelines for
Interpreting Effect Sizes
d = .20 small
d = .50 medium
d = .80 large
(Hyde: d ≤ .10 trivial)

9. Stereotypes of Gender
Differences in Abilities
 Verbal
 Mathematical
 Spatial
 All of these, and many others, are
needed for success in STEM careers.

10. Quiz Question
 In the U. S. today, what percentage of
bachelor’s degrees in mathematics go
to women?

11. Answer:
48%
Source: NSF

12. New Meta-Analysis
State Assessments, 2008
 Annual assessments by states of all
children’s mathematics performance
(and other areas) mandated by No
Child Left Behind (NCLB)
 Contacted departments of education in
all 50 states asking for data needed to
compute d
 Responses from 10 states
 Testing of more than 7 million children
Hyde, Lindberg, Linn, Ellis, & Williams, Science, 2008

13. Grade d
Grade 2 0.06
Grade 3 0.04
Grade 4 -0.01
Grade 5 -0.01 Overall
Grade 6 -0.01 d = .0065
Grade 7 -0.02
Grade 8 -0.02
Grade 9 -0.01
Grade 10 0.04
Grade 11 0.06

14. Conclusion
 Girls have reached parity with boys in
math performance at all grade levels:
Gender similarities
 We cannot afford to lose women from
the STEM talent pool because people
think they can’t do math – when they
can.

15. Cross-national Trends in
Math Performance
20
18
16
14
Math score
12
10 Boys
8 Girls
6
4
2
0
U.S. Taiwan Japan
5th graders, word problems
Lummis & Stevenson, 1990

16. But, says Larry Summers…
 Two separate issues
– Gender differences/similarities in the
general population – average differences
– Gender differences in the upper tail of the
distribution, the highly talented
 How can there be differences in the
tail with no gender difference in
average scores?
– Gender differences in variance

17. The Greater Male Variability
Hypothesis
 Originally proposed more than 100
years ago
 Variance ratio
VR = VarM / VarF
VR > 1.0 means greater male variability

18. Grade VR
Grade 2 1.11
Grade 3 1.11
Grade 4 1.11
Grade 5 1.14
Grade 6 1.14
Grade 7 1.16
Grade 8 1.21
Grade 9 1.14
Grade 10 1.18
Grade 11 1.17
Hyde et al. (2008)

19. Theoretical Distributions
(Hedges & Friedman, 1993)
If d = 0.05 and VR = 1.12, persons
above 95%ile
Males: Females = 1.34
99.9%ile, exceptional talent
Males: Females = 2.15
But: only 18% of engineering PhDs go to women
Male: female = 4.5

20. Variance Ratios in Other
Nations, PISA 2003 (Hyde & Mertz,
2009)
US 1.19
Denmark 0.99
Netherlands 1.00
Indonesia 0.95
Greater male variability is not universal!

21. The Role of Culture in
Identifying and Nurturing
Mathematically Talented
Women
Percentage of U.S. PhD’s in Mathematics
Awarded to Women
Green & LaDuke, 2009

22. Costs to Overinflated Claims
of Gender Differences
 Education
– Single-sex
classrooms and
schools, in the
absence of
empirical
support

23. If it’s not gender differences
in math ability, what is it?
 Gender differences in 3-dimensional
spatial skills, d = 0.50
– Absent from the school curriculum
– Let’s add it!
 Gender differences in interest
 Utility value

24. Gender and Interest (Su et al.,
Psych Bull, 2009)
 Men prefer working with things
 Women prefer working with people
d = 0.93

25. How does engineering
portray itself?
 Is it about things?
– Calculations, electrical circuits, designing
bridges
 Or is it about people?
– Helping people
– Biomedical engineering
Thanks to Dr. Sheryl Sorby

26. Utility Value or Usefulness
(Hulleman & Harackiewicz, Science, 2009)
 Utility value: how useful is an activity
(e.g., high school science course) to
the student
– In everyday life
– In the future (e.g., a planned career)
– Personal relevance
– Motivation

27. Utility Value or Usefulness
(Hulleman & Harackiewicz, Science, 2009)
262 9th graders, across science classes taught by 7 teachers
INTERVENTION CONTROL
Write about usefulness
of material Summarize material
4 times across semester 4 times across semester
END-OF-SEMESTER END-OF-SEMESTER
MEASURES MEASURES
Interest Interest
Grade Grade

28. Implications for
low SES students

29. Thank you!
Janet Hyde
jshyde@wisc.edu

30. The Gender Similarities
Hypothesis
 Men and women are very similar on
most (not all) psychological variables.
– Over 46 meta-analyses and 124 effect
sizes for gender differences,
 30% of d values near 0: 0 – 0.10
 48% of d values near .20: 0.11 – 0.35
Hyde, American Psychologist, 2005

32. Gender Differences in
Spatial Ability
(Linn & Petersen, 1985)
d
Spatial Perception +.44
Spatial Visualization +.13
3-dimensional Mental Rotation +.73

33. 3-Dimensional Mental
Rotation

34. Can Women Be Found among
the Mathematically Elite?
 Previous analyses have examined high
scorers: the top 5% or 1% of the entire
distribution
 Doesn’t get at those who are profoundly
gifted in mathematics
 Another data set: the Putnam Mathematical
Competition
– Taken by 3,500 undergrad math students in U.S.
and Canada
– Majority can’t solve any of the 12 problems; the
top 25 scorers solve 5 or more problems

35. Women Among Top 25 in Putnam, 1992-2007
Name Year Birth IMO Medals
Country
Olena 2004 Russia 1 gold, 1 silver
Bormashenko
Ana Caralana 2003, 2004 Romania 1 gold, 2 silver
Ioana Dumitriu 1995, 1996 Romania
Julie Kerr 1992 USA
Suehyun Kwon 2003 South Korea 1 gold
Alison Miller 2004-2007 USA 1 gold
Greta Panova 2001 Bulgaria 1 gold, 2 silver
Dana Pascovici 1992 Romania
Melanie Wood 2001, 2002 USA 2 silver
Wai-Ling Yee 1999 Canada
Inna Zakharevich 2004 Russia
IMO = International
Andreescu, Gallian, Kane, & Mertz, Notices of the AMS (2008) Math Olympiad

36. How to Interpret the
Putnam Data?
 Women exist among those who are
profoundly gifted in mathematics
 Is the glass half full or half empty?
Focus on women who made it, or
preponderance of males?
 Clear role of culture in discovering and
nurturing mathematical talent among
girls and women

37. The Greater Male Variability
Hypothesis
Assuming d = 0, VR = 1.2 Green = female
Orange = male
Hyde & Mertz, PNAS, 2009 Brown = overlap

38. Item Complexity
 Coded items from state assessments
using 4-level Depth of Knowledge
(Webb)
–1 = Recall
–2 = Skill/Concept
–3 = Strategic Thinking
–4 = Extended Thinking

39. National Assessment of
Educational Progress
(NAEP)
 NAEP categorizes items as easy,
medium, or hard.
 Took hard items and coded for item
complexity
 Analyzed hard items at Levels 3 and 4
for gender differences
 Result, grade 12
d = 0.07

40. Results: Item Complexity
in State Assessments
 For most states and grade levels, no
items were at Levels 3 or 4.
 Problems
– We don’t have good data on gender
differences in complex problem solving
with state assessment data
– Teachers teach to the test
 Policy implication: revise tests to
include complex problem solving

41. The Role of Culture
r = .44
r = .44