0
Gender Diversity in STEM
Education and Careers
  Janet Shibley Hyde
  University of Wisconsin
  jshyde@wisc.edu
Collaborators

   Elizabeth Fennema, Sara Lindberg,
    Marcia Linn, Amy Ellis, Janet Mertz,
    Nicole Else-Quest

   S...
The Differences Model
Innate, Biological Causes
The Deficit Model

   Harvard President
    Lawrence
    Summers, who
    claimed, in a
    controversial
    speech, tha...
Meta-Analysis: A Method for
Assessing Psychological
Gender Differences
   A quantitative literature review
   A method f...
Effect Size: The Size of the
Gender Difference


   d   =   MM – MF
              sw
             (Cohen)
Cohen’s Guidelines for
Interpreting Effect Sizes

       d = .20      small

       d = .50      medium

       d = .80   ...
Stereotypes of Gender
Differences in Abilities
   Verbal
   Mathematical
   Spatial
   All of these, and many others, ...
Quiz Question

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



              48%



Source: NSF
New Meta-Analysis
        State Assessments, 2008
         Annual assessments by states of all
          children’s mathe...
Grade          d
Grade   2     0.06
Grade   3     0.04
Grade   4    -0.01
Grade   5    -0.01   Overall
Grade   6    -0.01 ...
Conclusion

   Girls have reached parity with boys in
    math performance at all grade levels:
    Gender similarities

...
Cross-national Trends in
       Math Performance
                     20
                     18
                     16
 ...
But, says Larry Summers…
     Two separate issues
      – Gender differences/similarities in the
        general populati...
The Greater Male Variability
Hypothesis
   Originally proposed more than 100
    years ago

   Variance ratio
       VR ...
Grade         VR
                Grade   2    1.11
                Grade   3    1.11
                Grade   4    1.11
   ...
Theoretical Distributions
  (Hedges & Friedman, 1993)



  If d = 0.05 and VR = 1.12, persons
    above 95%ile
    Males: ...
Variance Ratios in Other
Nations, PISA 2003 (Hyde & Mertz,
2009)



    US                           1.19
    Denmark     ...
The Role of Culture in
     Identifying and Nurturing
     Mathematically Talented
     Women




                       P...
Costs to Overinflated Claims
of Gender Differences
   Education
    – Single-sex
      classrooms and
      schools, in t...
If it’s not gender differences
in math ability, what is it?
   Gender differences in 3-dimensional
    spatial skills, d ...
Gender and Interest (Su et al.,
Psych Bull, 2009)

   Men prefer working with things
   Women prefer working with people...
How does engineering
portray itself?
   Is it about things?
    – Calculations, electrical circuits, designing
      brid...
Utility Value or Usefulness
(Hulleman & Harackiewicz, Science, 2009)


   Utility value: how useful is an activity
    (e...
Utility Value or Usefulness
 (Hulleman & Harackiewicz, Science, 2009)


262 9th graders, across science classes taught by ...
Implications for
low SES students
Thank you!


Janet Hyde
jshyde@wisc.edu
The Gender Similarities
 Hypothesis

   Men and women are very similar on
    most (not all) psychological variables.

  ...
Gender Differences in
Spatial Ability
(Linn & Petersen, 1985)
                                   d

 Spatial Perception   ...
3-Dimensional Mental
Rotation
Can Women Be Found among
      the Mathematically Elite?
   Previous analyses have examined high
    scorers: the top 5% ...
Women Among Top 25 in Putnam, 1992-2007
       Name                  Year            Birth             IMO Medals
        ...
How to Interpret the
Putnam Data?
   Women exist among those who are
    profoundly gifted in mathematics
   Is the glas...
The Greater Male Variability
     Hypothesis




Assuming d = 0, VR = 1.2   Green = female
                           Oran...
Item Complexity
   Coded items from state assessments
    using 4-level Depth of Knowledge
    (Webb)
    –1   =   Recall...
National Assessment of
Educational Progress
(NAEP)
   NAEP categorizes items as easy,
    medium, or hard.
   Took hard ...
Results: Item Complexity
in State Assessments
   For most states and grade levels, no
    items were at Levels 3 or 4.
 ...
The Role of Culture



                           r = .44
                 r = .44
Gender Diversity in STEM Education and Careers - Janet Hyde
Upcoming SlideShare
Loading in...5
×

Gender Diversity in STEM Education and Careers - Janet Hyde

2,655

Published on

Published in: Education, Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
2,655
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
15
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Transcript of "Gender Diversity in STEM Education and Careers - Janet Hyde"

  1. 1. Gender Diversity in STEM Education and Careers Janet Shibley Hyde University of Wisconsin jshyde@wisc.edu
  2. 2. Collaborators  Elizabeth Fennema, Sara Lindberg, Marcia Linn, Amy Ellis, Janet Mertz, Nicole Else-Quest  Special thanks to NSF for funding, REC 0635444
  3. 3. The Differences Model
  4. 4. Innate, Biological Causes
  5. 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. 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. 7. Effect Size: The Size of the Gender Difference d = MM – MF sw (Cohen)
  8. 8. Cohen’s Guidelines for Interpreting Effect Sizes d = .20 small d = .50 medium d = .80 large (Hyde: d ≤ .10 trivial)
  9. 9. Stereotypes of Gender Differences in Abilities  Verbal  Mathematical  Spatial  All of these, and many others, are needed for success in STEM careers.
  10. 10. Quiz Question  In the U. S. today, what percentage of bachelor’s degrees in mathematics go to women?
  11. 11. Answer: 48% Source: NSF
  12. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 22. Costs to Overinflated Claims of Gender Differences  Education – Single-sex classrooms and schools, in the absence of empirical support
  23. 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. 24. Gender and Interest (Su et al., Psych Bull, 2009)  Men prefer working with things  Women prefer working with people d = 0.93
  25. 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. 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. 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. 28. Implications for low SES students
  29. 29. Thank you! Janet Hyde jshyde@wisc.edu
  30. 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
  31. 31. Gender Differences in Spatial Ability (Linn & Petersen, 1985) d Spatial Perception +.44 Spatial Visualization +.13 3-dimensional Mental Rotation +.73
  32. 32. 3-Dimensional Mental Rotation
  33. 33. 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
  34. 34. 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
  35. 35. 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
  36. 36. The Greater Male Variability Hypothesis Assuming d = 0, VR = 1.2 Green = female Orange = male Hyde & Mertz, PNAS, 2009 Brown = overlap
  37. 37. 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
  38. 38. 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
  39. 39. 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
  40. 40. The Role of Culture r = .44 r = .44
  1. A particular slide catching your eye?

    Clipping is a handy way to collect important slides you want to go back to later.

×