the biases that blind us - harvard university · subsequent similar results: # mathematics job...

THE BIASES THAT BLIND US: HOW GENDER STEREOTYPES CONSTRAIN OPPORTUNITIES

FOR WOMEN IN STEM

Corinne Moss-Racusin, Ph.D. Eva Pietri, Erin Hennes, Jack Dovidio, Victoria Brescoll, Helena Rabasco, Nava Caluori, Jo Handelsman

Harvard Kennedy School Women and Public Policy Forum

September 24, 2015

Outline

!  Brief lit review !  STEM gender disparity !  Role of gender stereotypes

!  Exp. 1: Faculty gender bias !  Exp. 2: Consequences of bias for students’ STEM engagement !  Intervention research to reduce gender bias

!  Exp. 3: General population participants !  Exp. 4: STEM faculty participants

Persistent Lack of Diversity

Last 10 Nobel Laureates in Physics (196 Total) 2 women of 196 total laureates (1%)

Last 10 Nobel Laureates in Chemistry (166 Total) 4 women of 166 total laureates (2%)

Persistent Lack of Diversity

0 10 20 30 40 50 60 70 80 90

100

Men

Women

National Science Foundation, Division of Science Resources Statistics, Survey of Doctoral Recipients, 2011

% o

f to

tal f

acul

ty

S & E Doctorate Holders Employed Full-Time in U.S. Universities, by Gender

The Gender Disparity in STEM: Faculty

The Gender Disparity in STEM: Undergraduate Majors

What Factors Contribute to the STEM Gender Disparity?

!  Intrinsic ability ! Little supporting evidence (e.g., Halpern, 2007; Hyde, 2006; Spelke, 2005)

!  Occupational and lifestyle choices ! Women’s preference for other fields, family caregiving ! Correlational evidence (e,g., Ceci & Williams, 2010; 2011; 2012)

!  Gender bias? ! = male and female science students " = faculty reactions? ! Or biasing role of stereotypes (Nosek et al., 2002)?

?

Gender Stereotypes in STEM

!  Discrepancy between traits ascribed to women vs. traits ascribed to men and scientists (Prentice & Carranza, 2002; Rudman, Moss-Racusin, Phelan & Nauts, 2012) ! Women: communal (emotional, supportive, modest, focused on

family) ! Men: agentic (analytic, risk-taking, self-promoting, career-

oriented) ! Resulting stereotype " women are less likely to excel in STEM

relative to men

!  Large, diverse sample (N = 61,228): strong automatic (d = .72) and self-reported (d = .73) association of men with science (relative to women) (Nosek, Banaji & Greenwald, 2002)

Gender Bias in STEM?

!  Resources inequitably distributed between men and women in STEM (MIT, 1999)

! Access to lab space, assignment to committee work, etc.

!  Experimental evidence of bias in other fields (e.g., Heilman et al., 2004; Moss-Racusin, Phelan & Rudman, 2010; Moss-Racusin & Johnson, 2015)

! Male-stereotypic fields (e.g., consulting, corporate law) ! Female-stereotypic fields (e.g., nursing, elementary education)

!  Female STEM students report experiencing bias (Steele et al., 2002).

!  No experimental tests for bias in STEM

Method

!  Participants: research faculty (N = 127) !  Biologists, Chemists, Physicists !  Demographics representative of National averages

!  Rated student lab manager applicant !  Identical materials attributed to male OR female student

!  Faculty gender, age, race, science field, rank " No effects Dependent variables (Moss-Racusin & Rudman, 2010)

!  Competence !  Hiring !  Salary conferral !  Mentoring

Moss-Racusin et al., PNAS, 2012

“John” ½ Participants

“Jennifer” ½ Participants

1

2

3

4

5

6

7

Competence Hireability Mentoring

Male Student Female Student

Student Target Gender Differences


All ts > 3.77 All ps < .001 All ds > .67

No effect of faculty gender

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

Male Student Female Student

Yearly Salary

t(124) = 3.42**, d = .60

Student Target Gender Differences


Nearly $4,000/year difference

No effect of faculty gender

Summary

!  First experimental evidence of faculty bias in STEM ! Subsequent similar results:

# Mathematics job (Reuben et al., 2014)

# Prospective Doctoral student mentoring (Milkman et al., 2012; 2015)

# Conference abstracts (Knoblock-Westerwick et al., 2013)

! Direct + indirect impact on gender disparity !  Bias was not exhibited by a subgroup of faculty

! Driven by = exposure to pervasive cultural stereotypes !  Clear implications for STEM meritocracy

! Contradicts goal to advance most talented scientists, regardless of background

! Consequences for students’ STEM engagement?

Method !  Gender bias " women’s underrepresentation?

!  Occupational and lifestyle choices " underrepresentation? (Ceci, Ginther, Kahn, & Williams, 2014)

!  Participants: undergraduate students (N = 99) !  70% female !  Age: M = 18.88, range 17 - 25

!  Read news article covering research on gender bias in STEM !  Identical article, evidence of gender bias OR gender equality

Dependent variables !  Awareness of bias !  Positive attitudes toward STEM !  Sense of belonging in STEM !  STEM aspirations

Moss-Racusin, Rabasco, & Caluori, in prep

Results: Awareness of Bias

0

1

2

3

4

5

6

Gender Equality Gender Bias

Women

Men

*

*

Bias Condition: F(1,3) = 60.30, p < .001, d = 1.78 Participant Gender: F(1,3) = 7.37, p < .01, d = .68


Results: Positive Attitudes Toward STEM

0

1

2

3

4

5

6


Women

Men

Bias Condition: F(1,3) = 4.53, p < .05, d = -.48


Results: Sense of Belonging in STEM

0

1

2

3

4

5

6


Women

Men

Bias Condition: F(1,3) = 7.60, p < .01, d = -.53


Results: STEM Aspirations

0

1

2

3

4

5

6


Women

Men

Bias Condition: F(1,3) = 2.97, p = .08, d = -.28


Summary

!  STEM gender bias has real consequences ! Undermines students’ positivity, sense of belonging, aspirations

!  Negative effects for both male and female students ! Gender bias as a broad deterrent ! Restricts access to talent

!  Effective interventions needed to reduce STEM gender bias


Testing a Novel Intervention

!  Goal: develop evidence-based intervention to raise awareness of + reduce bias in STEM !  Few diversity interventions for STEM community (Moss-Racusin et al., 2014)

!  Mixed results for traditional diversity trainings (Dobbin & Kalev, 2013)

!  May imply blame, increase reactance (Legault et al., 2011)

!  Approach: utilize engrossing media ! Reduces intergroup prejudice + conflict (Paluck, 2009)

! Vivid exposure to counter-stereotypic exemplars (Dasgupta & Asgari, 2004; Lai et al., 2014)

(Moss-Racusin et al., Science, 2014; Pietri et al., 2015; Moss-Racusin et al., in prep)

Testing a Novel Intervention

!  Intervention: 12 high-quality, 5 minute films !  Partnered with professional playwright, actors, director ! Communicated results of published empirical research on bias

!  Presented bias research results in one of two formats: ! Narrative condition (6 films): entertaining stories !  Intellectual condition (6 films): straightforward presentation of facts ! Underlying research findings identical across conditions

(Pietri et al., 2015; Moss-Racusin et al., in prep)

Intervention: Narrative Condition


Compelling characters illustrate empirical evidence of bias

Intervention: Intellectual Condition


Interesting interviews communicate empirical evidence of bias

Content Equivalence Across Conditions Target Article Main Finding

(Intellectual Condition) Illustration of Main Finding

(Narrative Condition)

Rudman & Glick, 1999 Backlash (social and economic penalties) against agentic women

During practice conference talks, department gives negative feedback to an agentic female graduate student; praises similar male


Method !  Control condition: Interesting science documentaries = to

intervention ! # female and male scientists ! = entertaining as narrative condition ! = informative as intellectual condition ! No bias-related information !  Random assignment to intellectual, narrative, or control condition

!  Participants: general population (N = 450, 54% female) !  2 data collection time points

!  Immediately post-intervention !  6 months later

!  Dependent Variables ! Awareness of Bias (Pietri et al., 2015)

! Gender Bias: Modern Sexism Scale (Swim et al., 1995) (Moss-Racusin et al., in prep)

Results: Awareness of Bias

2

2.5

3

3.5

4

4.5

Control Narrative Intellectual

Post-Intervention 6 months later

Mor

e A

war

enes

s

(Moss-Racusin et al., in prep)

t(203) = -4.73, p < .01, d = .65 t(203) = -5.47, p < .01, d = .82

No significant decay

Results: Gender Bias

1 1.2 1.4 1.6 1.8

2 2.2 2.4 2.6


Post-Intervention 6 months later

Mor

e G

ende

r Bi

as


t(203) = 2.66, p = .01, d = .41


Preliminary Summary

!  Key results of intervention movies: !  Increased awareness of gender bias !  Reduced gender bias !  Effects persist up to 6 months

!  Unanswered question: Do findings generalize? !  Experiment 2: replicate with academic scientists (N = 172)

!  Biology, Chemistry, Engineering, Physics !  55% Female !  25% ethnic minority !  Average Age = 43 !  60% at Research 1 Universities

!  3 time points (results reflect change from baseline) !  Baseline (1 week pre-intervention) !  Immediately post-intervention !  1 week post-intervention


Results: Awareness of Bias M

ore

Aw

aren

ess

(cha

nge

from

bas

elin

e)

t(128) = 5.89, p < .01, d = .96


t(128) = 2.82, p = .01, d = .46 t(128) = -3.13, p < .01, d = .51

-0.4

-0.2

0

0.2

0.4

0.6


Post-Intervention One Week Later No significant decay

Results: Gender Bias

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2


Post-Intervention One Week Later

Mor

e G

ende

r Bi

as

(cha

nge

from

bas

elin

e)


t(128) = -4.36, p < .01, d = .64 t(128) = -2.09, p = .04, d = .31 t(128) = 2.28, p = .02, d = .34


Summary and Next Steps

!  Where we are: ! Promising intervention to increase awareness of + reduce STEM

gender bias ! Preliminary evidence: intellectual approach may be

particularly effective, especially for STEM populations

!  Where we are going ! Additional explicit outcomes

# Emotions, collective action, blame, sense of belonging, etc.

! Implicit + behavioral outcomes ! Develop, test and implement STEM diversity intervention

Final Thoughts: Why We Should Care About Bias in STEM

!  Pressing shortage of STEM workers !  1,000,000 person deficit over next decade (PCAST, 2012)

!  STEM jobs are good jobs ! Unemployment rates lower than other fields (Carnevale, Cheah, & Strohl,

2012) ! Women in STEM earn at least $6,000/year more than women in

non-STEM fields (Institute for Women’s Policy Research, 2012)

!  Problem isn’t fixing itself ! No age effect in our studies—younger cohorts aren’t less biased

!  Diverse groups often do better work !  …especially when different perspectives are valued (e.g., Ely &

Thomas, 2001)

!  Gender parity: best interest of national competitiveness and advancement of scientific enterprise !  Effective bias interventions " + meritocracy, diversity, excellence

Thank you!

!  Collaborators !  Eva Pietri !  Erin Hennes !  John Dovidio ! Victoria Brescoll !  Jo Handelsman ! Helena Rabasco ! Nava Caluori

!  Funding Sources ! Howard Hughes Medical Institute ! Alfred P. Sloan Foundation !  Smithsonian Institution !  Skidmore College Faculty Development Grant

!  Skidmore Social Cognition and Intergroup Dynamics (SCID) Lab

the biases that blind us - harvard university · subsequent similar results: # mathematics job...

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