Tackling Bias Best Practices for Recruiting and Retaining

Tackling Bias Best Practices for Recruiting and Retaining

Tackling Bias Best Practices for Recruiting and Retaining a Diverse Faculty Anne M. Etgen, PhD Kathie L. Olsen, PhD Frederick L. Smyth, PhD ADVANCE-PAID GRANT IWiN (Increasing Women in Neuroscience) is a program funded by an NSF ADVANCEPAID grant awarded to SfN that is designed to enhance recruitment, retention and promotion of women and underrepresented minority faculty. Leaky Pipeline: Women drop out at most transitions in particular the transition to Tenure Track Faculty: 55%

60% 50% 40% 30% 20% 10% 0% G d ra te a u CNDP/SfN 2011

45% 44% 29% u St t n de s P td s o

No s c o re u n e T n- ck a Tr ty

l cu a F re u n e T ck a Tr 27% ty l cu

a F l u F l rs o ss e of r P Growth of women neuroscientists in tenuretrack faculty positions is slow (% total) Year

Graduate Student Postdoc NonTenure Track 1986 Tenure Track 15 23 20

9 27 22 13 24 32 27 19 1991 1998

Assistant Associate Full Professor Professor Professor 2000 47 40 43 21 30 26 14

2003 50 42 43 25 33 28 21 2005

52 41 38 25 32 27 21 2007 52 44

44 26 36 28 21 2009 54 37 44

29 34 31 26 2011 57 49 50 29 34

32 25 *Data from annual ANDP/CNDP surveys Full Time Faculty Member Salaries Ethnicity Findings in the 2011 SfN Survey (%) PhD Student Postdoc Non-Tenure Track

Faculty Assistant Professor Associate Professor Professor AfricanAmerican 4 2 2 2 1

1 Asian 16 27 25 23 11 7 Caucasian 65

56 63 64 78 84 Hispanic 5 6 3 6

5 3 Native American 1 0 (n=3) 0 0 (n=1) 0 0 (n=2)

Pacific Islander 0 (n=4) 0 (n=5) 0 (n=1) 0 0 0 (n=1) Other 3

3 1 2 1 1 No answer 6 5 6

3 4 4 Fred Smyth Department of Psychology University of Virginia Implicit Gender Bias in STEM Reading Test Reading Test The procedure is quite simple. First, you arrange things into different groups. Of course, one pile may be sufficient,

depending on how much there is to do. If you have to go somewhere else due to the lack of facilities, that is the next step; otherwise, you are pretty well set. Make sense? Washing Clothes Washing Clothes The procedure is quite simple. First, you arrange things into different groups. Of course, one pile may be sufficient, depending on how much there is to do. If you have to go somewhere else due to the lack of facilities, that is the next step; otherwise, you are pretty well set. Washing Clothes

Makes sense now? Schemas Stereotypes Stereotypes Explicit and Implicit Conscious, intentional, subject to logic. Unconscious, automatic, logic irrelevant. Stereotypes Explicit and Implicit Conscious,

intentional, subject to logic. Unconscious, automatic, logic irrelevant. Stereotypes Explicit and Implicit Conscious, intentional, subject to logic. Gender and STEM Feelings Condry & Condry, 1976

Feelings Debbie Danny Condry & Condry, 1976 Feelings Debbie Danny Afraid Angry Condry & Condry, 1976 Percent offered explanations

Parents explanations of Museum Science Exhibits Crowley et al., 2001 Percent offered explanations Parents explanations of Museum Science Exhibits Crowley et al., 2001 Percent offered explanations Parents explanations of Museum Science Exhibits Crowley et al., 2001

Percent offered explanations Parents explanations of Museum Science Exhibits Crowley et al., 2001 Percent offered explanations Parents explanations of Museum Science Exhibits Crowley et al., 2001 Percent offered explanations Parents explanations of Museum Science Exhibits

Crowley et al., 2001 Rating STEM Facultys judgments of lab manager applicant Competence Hireability Mentoring Moss-Racusin et al., 2012 STEM Facultys judgments of lab manager applicant Applicant Name

Rating Male Female Competence Hireability Mentoring Moss-Racusin et al., 2012 STEM Facultys judgments of lab manager applicant Applicant Name Rating

Male Female Competence Hireability Mentoring Moss-Racusin et al., 2012 STEM Facultys judgments of lab manager applicant Applicant Name Rating Male

Female Competence Hireability Mentoring Moss-Racusin et al., 2012 STEM Facultys judgments of lab manager applicant Applicant Name Rating Male Female

Competence Hireability Mentoring Moss-Racusin et al., 2012 STEM Facultys judgments of lab manager applicant Applicant Name Rating Male Female Competence

Hireability Mentoring Moss-Racusin et al., 2012 Salary Offered Salary Offered Male Female Applicant Gender Moss-Racusin et al., 2012 Salary Offered Salary Offered

Male Female Applicant Gender Moss-Racusin et al., 2012 Salary Offered Salary Offered But women more likeable! Male Female

Applicant Gender Moss-Racusin et al., 2012 Take Home 1. Implicit gender bias affects STEM outcomes 2. Humility Measuring Implicit Bias (in individuals) Implicit Association Test (IAT) Greenwald, McGhee & Schwarz, 1998 VTech UVa VTech

UVa Left Right Training VTech UVa Left Right Training

Good Bad Left Right Training Good Left Bad Love Training Right

Good Left Bad Love Training Right VTech UVa Good Bad

Left Right Test Phase 1 VTech UVa Good Bad Left Right Test Phase 1

VTech UVa Good Bad Left Right Test Phase 1 VTech UVa

Good Bad Left Hate Test Phase 1 Right VTech UVa Good Bad

Left Hate Test Phase 1 Right VTech UVa Good Bad Left

Right Test Phase 2 UVa VTech Good Bad Left Right Test Phase 2 Which sorting is faster, fewer errors? VTech

Good UVa Bad OR UVa Good VTech Bad implicit.harvard.edu/implicit/ Demonstration Options Demonstration Options

Gender-Science on Project Implicit Male Liberal Arts Female Science Gender-Science on Project Implicit Male Liberal Arts Female Science OR Female Liberal Arts

Male Science Gender-Science on Project Implicit Male Liberal Arts Female Science Easier for 70% Female Liberal Arts Male Science Gender-Science on Project Implicit

Easier for 10% Male Liberal Arts Female Science Easier for 70% Female Liberal Arts Male Science Gender-Science on Project Implicit Easier for 10%

Male Liberal Arts Female Science No Difference for 20% Easier for 70% Female Liberal Arts Male Science Not one-size-fits-all Gender-Science on Project Implicit N u m b er o f R esp o n d en ts

Number of Participants 30000 30000 25000 25000 20000 20000 70% 15000 15000 10000

10000 10% 5000 5000 0 Im plicit Scie nce =Male / Arts =Fe m ale Ste re otyping Science=Female 0 Science=male Stereotype Gender-Science on Project Implicit N u m b er o f R esp o n d en ts Number of Participants 30000

30000 25000 25000 20000 20000 70% 15000 15000 10000 10000 10% 5000

5000 0 Im plicit Scie nce =Male / Arts =Fe m ale Ste re otyping Science=Female 0 Science=male Stereotype Gender-Science on Project Implicit N u m b er o f R esp o n d en ts Number of Participants 30000 30000 25000 25000

20000 20000 70% 15000 15000 10000 10000 10% 5000 5000 0 Im plicit Scie nce =Male / Arts =Fe m ale Ste re otyping Science=Female

0 Science=male Stereotype Same for Men and Women (unless) Male Respondents 8000 16000 7000 14000 70% 5000 4000

3000 2000 11% 10000 8000 6000 4000 1000 2000 0

0 Im plicit Science=Male / Arts=Fem ale Stereotyping 71% 12000 Number of Respondents 6000 Number of Respondents Female Respondents 10% Im plicit Science=Male / Arts=Fem ale Stereotyping Academic Identity Matters

Implicit Science=Male IAT d zero) (SDs from 1.5 Academic Identity Matters 1 0.5 Female-Male Cohens d .68 .19 .69 .26 .48 .02 .15 -.37 -.30 -.81 -.78 0 .63 i i ci ci ts ies ies on on ory ss gy c

r c A it ud ati ati ist e lo S S S yS n i ho lth fo ife h m an St ic uc -H s r n -L -P u c I a fr o um al un Ed ci y B s e er

h y t e g S m H g H t lo a P l e m /P u a L

l M i p io no a c w u C o m B gi a s i o S L V C

En Major Field Smyth, 2013 Implicit Science=Male IAT d zero) (SDs from 1.5 Academic Identity Matters 1 0.5 Scienceness Female-Male

Cohens d .68 .19 .69 .26 .48 .02 .15 -.37 -.30 -.81 -.78 0 .63 i i ci ci ts ies ies on on ory ss gy c r c A it ud ati ati ist e lo S S S yS n i ho lth fo ife h m an St ic uc -H s r

n -L -P u c I a fr o um al un Ed ci y B s e er h y t e g S m H g H

t lo a P l e m /P u a L l M i p io no a c w u C o

m B gi a s i o S L V C En Major Field Smyth, 2013 Implicit Science=Male IAT d zero) (SDs from

1.5 Women (N=124,479) 1 0.5 Female-Male Cohens d .68 .19 .69 .26 .48 .02 .15 -.37 -.30 -.81 -.78 0 .63 i i ci ci ts ies ies on on ory ss gy c r c A it ud ati ati ist e lo S S S yS

n i ho lth fo ife h m an St ic uc -H s r n -L -P u c I a fr o um al un Ed ci y B s e er h y t e g

S m H g H t lo a P l e m /P u a L l M i p io no a

c w u C o m B gi a s i o S L V C En Major Field Smyth, 2013

Implicit Science=Male IAT d zero) (SDs from 1.5 Women (N=124,479) 1 0.5 Men (N=52,456) Female-Male Cohens d .68 .19 .69 .26 .48 .02 .15 -.37 -.30 -.81 -.78 0 .63 i

i ci ci ts ies ies on on ory ss gy c r c A it ud ati ati ist e lo S S S yS n i ho lth fo ife h m an St ic uc -H s r n -L -P u c I a fr o um al un Ed ci

y B s e er h y t e g S m H g H t lo a P l e m /P

u a L l M i p io no a c w u C o m B gi a s i o S

L V C En Major Field Smyth, 2013 Implicit Science=Male IAT d zero) (SDs from 1.5 Women (N=124,479) 1 0.5

Men (N=52,456) Female-Male Cohens d .68 .19 .69 .26 .48 .02 .15 -.37 -.30 -.81 -.78 0 .63 i i ci ci ts ies ies on on ory ss gy c r c n i S S A it ud ati ati ist e

S S o g l n y n o n i o t h e m f c h

c t f a l S ni u i-H us ch al In Li r E P o r m - y- th te B sy rf u ga mu Ed Sc e r e e H t og a es H e m

P l /l P u ol -M m a L o i p a c i in e w C u o m B a s g st S

o S L n Vi C E 1 a V Major Field U Smyth, 2013 Environment Matters International Variation Nosek, Smyth et al., 2009, PNAS

8th-grade TIMSS Gender Gap Nosek, Smyth et al., 2009, PNAS Male Advantage TIMSS Science 8th-grade TIMSS Gender Gap 30 20 10 0 -10 -20 -30 0.15 0.25 Nosek, Smyth et al., 2009, PNAS 0.35 0.45 0.55 0.65

Science Science==Male MaleIAT IATD 0.75 Male Advantage TIMSS Science 8th-grade TIMSS Gender Gap 30 20 10 0 -10 -20 -30 0.15 0.25

Nosek, Smyth et al., 2009, PNAS 0.35 0.45 0.55 0.65 Science Science==Male MaleIAT IATD 0.75 Male Advantage TIMSS Science Greater 8th-grade Boys Advantage correlated with greater country-level implicit bias, r = .60 30 20 10 0 -10

-20 -30 0.15 0.25 Nosek, Smyth et al., 2009, PNAS 0.35 0.45 0.55 0.65 Science = Male IAT 0.75 Math-is-male stereotype in Differential Equations Courses Male Students Female Students Martin, von Oertzen, Smyth et al. (2013)

Math-is-male stereotype in Differential Equations Courses Male Students Female Students Martin, von Oertzen, Smyth et al. (2013) Math-is-male stereotype in Differential Equations Courses Male Students Female Students Martin, von Oertzen, Smyth et al. (2013) Take Home 1. 2.

Implicit gender bias affects STEM outcomes Humility Take Home 1. 2. 3. Implicit gender bias affects STEM outcomes Humility Implicit biases can change Take Home 1. 2. 3. 4.

Implicit gender bias affects STEM outcomes Humility Implicit biases can change Self-concepts and environments matter Take Home 1. 2. 3. 4. Implicit gender bias affects STEM outcomes Humility Implicit biases can change Self-concepts and environments matter To Do 1.

Education, measurement and evaluation. Take Home 1. 2. 3. 4. Implicit gender bias affects STEM outcomes Humility Implicit biases can change Self-concepts and environments matter To Do 1. 2. Education, measurement and evaluation. More longitudinal research

Thank you Brian Nosek, Irina Mitrea, Tai Melcher, Kate Ratliff, Dan Martin, Will Guilford, Ed Berger, Reid Bailey, Dana Elzey, Rich Price Project Implicit National Science Foundation REC-0634041 UVa Learning Assessment Grants Program Strategies for breaking the cycle Increase conscious awareness of bias and how bias leads to overlooking talent Implicit Association Test: https://implicit.harvard.edu/implicit/

Develop more explicit criteria (less ambiguity) Alter departmental policies and practices Implicit biases most influential when Criteria unclear Decisions made rapidly Decisions are complex Information is ambiguous or incomplete Youre stressed, tired. The Ohio State University Dr. Scott Herness, Assoc. Dean, Grad. School Progress to Date: Recruiting a Diverse Faculty Workshop

Mirror workshop; repeated regularly w/ALD Recognizing Implicit Bias Online Video http://www.youtube.com/watch?v=UZHxFU7TYo4 Available for all Search Committees ADVANCE program http://www.ceos.osu.edu/index.php?id=52 University of Minnesota Scott Lanyon -Dept. of Ecology, Evolution, and Behavior IWiN information now presented to all search committees in the College of Biological Sciences 1. I meet with every search committee to talk about Schema and to provide data/references about bias. 2. I provide suggestions about how to increase the number

of women and under-represented groups in the applicant pool. 3. I provide suggestions on how to minimize bias in the evaluation of applications. 4. I provide suggestions to chairs and committee members on how to handle situations if bias is introduced. Implicit Leadership-is-Male stereotype undone by exposure to female leaders Dasgupta & Asgari, 2004 Leaders-are-male IAT (ms) Exposure to female profs Female students Coed college

Co-ed college Womens college Dasgupta & Asgari, 2004 Recruiting Strategies Prime the pump searching begins before position is available Search committee composition Job description open searches

Advertisement and active recruiting Promote awareness of the issues Interviewing tips Active Recruiting and Open Searches Can Help Increase Diversity The difference achieved by one UMich department Evaluation of Candidates: Promote Awareness of Evaluation Bias Awareness of evaluation bias is a critical first step. Spread awareness to the others on the search committee. Evaluation bias can be counteracted. Bauer and Baltes, 2002, Sex Roles 9/10, 465.

Focus on Multiple Specific Criteria during Evaluation Weigh judgments that reflect examination of all materials and direct contact with the candidate. Specify evaluations of scholarly productivity, research funding, teaching ability, ability to be a conscientious departmental/university member, fit with the departments priorities. Avoid global evaluations. IWiN (http:// www.sfn.org/Careers-and-Training/Women-in-Neuroscience/Department-Chair-Training-to-In crease-Diversity/Workshop-Resources ) and ADVANCE (http://www.umich.edu/%7Eadvproj/CandidateEvaluationTool.doc) have evaluation forms that can be modified to fit your situation. Bauer

and Baltes, 2002, Sex Roles 9/10, 465. Thank you STRIDE: University of Michigans ADVANCE Program Project Implicit National Science Foundation Society for Neuroscience SfN members can continue the Conversation on NeurOnLine Top Mistakes in Recruitment

Committee does not have a diverse pool. The committee discussed information about the candidate that is inappropriate. Asking counter-productive questions. Telling a woman or underrepresented minority candidate that "we want you because we need diversity." The candidate does not meet others like themselves during the visit. Committee or faculty make summary judgments about candidates without using specific criteria.

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