Workshop on Faculty Recruitment for Diversity and Excellence

ADVANCE Office of Faculty DevelopmentStrategies and Tactics for Recruiting to Improve Diversity and Excellence

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Strategies and Tactics for Recruiting to Improve Diversity and Excellence

Jackie Isaacs, Professor of Mechanical and Industrial Engineering, ChairDan Danielsen, Professor of LawJack Dennerlein, Professor of Physical TherapyJudith Hall, University Distinguished Professor of PsychologyMarjorie Platt, Professor of AccountingKathrin Zippel, Professor of Sociology

Jan Rinehart, Executive Director ADVANCE Office of Faculty DevelopmentErinn Taylor de Barroso, Assistant Director ADVANCE Office of Faculty Development

Previous STRIDE members: George Adams, Mechanical & Industrial Engr.; Dana Brooks, Electrical & Computer Engr.; Agnes Chan, Computer and Information Sciences; Barry Chung, Counseling and Applied Educational Psychology;Max Diem, Chemistry and Chemical Biology; Luis Falcon, Sociology and Anthropology; Craig Ferris, Professor Psychology;Miriam Leeser, Professor Electrical and Computer Engineering; Ineke Marshall, Sociology and Anthropology, Criminal Justice; Ramiro Martinez, Professor Criminology and Criminal Justice; Carla Mattos, Professor of Chemistry and Chemical Biology; Sanjeev Mukerjee, Chemistry and Chemical Biology; Rajmohan Rajaraman, Professor Computer and Information Sciences; Mark Williams, Professor of Physics

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Introductions

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STRIDE Workshop Goals

Participants will discuss and learn from each other…

• How to improve the search process • Strategies to avoid implicit bias • Good practices for search committees

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Agenda

• Introduction• Activity: Highest risk for bias• Research on implicit bias • Activity: Effective practices• Neutralizing implicit bias• Activity: Take away• Evaluation

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What did you think?

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“Bias is something that has to be identified, acknowledged and mitigated against.”

Yassmin Abdel-Magied

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Unconscious bias can result from…..

• Automatic patterns of thoughts that organize our social information and assumptions (schemas).– Reduce the amount of info to process– Reduce ambiguity– Allow people to act without effort – Make decisions faster, easier

• Difficult to change even in light of new information.

• Research shows that we all perceive and treat people based on our schemas about their social groups (race/ethnicity, economic and social status, gender, sexual orientation, disability, culture, academic institution, etc.).

} We keep using them

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Five Stages of a Faculty Search

A. Define criteria and qualitiesB. Actively recruit a diverse pool C. Review and identify the long/short listD. Conduct an effective on campus interviewE. Recommend finalist(s) to Chair

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Activity 2: Identify Challenges

We all make decisions based on implicit associations. In some cases, this can result in bias.

• Decide what stage your group believes is most “at risk” for biased interactions or outcomes (5 minutes)

• Debrief with the larger group (10 minutes)

Research on Implicit Bias

Brochure

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Bias in Evaluation of CVs

When evaluating applications for a lab manager……male and female science faculty rated men more competent

and hire-able than identical female applicants and offered higher salaries to the men

A meta analysis of 111 studies showed…Men were rated more favorably than women for male-dominated

jobsNo strong preference for either gender for female-dominated and

integrated jobsGender bias was reduced when information clearly indicated high

competence during evaluation

A MIT study…Applicants with African-American-sounding names received 50%

fewer call-backs than applicants with white-sounding names

A Meta-Analysis of Gender Stereotypes and Bias in ExperimentalSimulations of Employment Decision Making

Amanda J. KochHuman Resources Research Organization,

Minneapolis, Minnesota

Susan D. D’MelloKorn Ferry, Minneapolis, Minnesota

Paul R. SackettUniversity of Minnesota

Gender bias continues to be a concern in many work settings, leading researchers to identify factors thatinfluence workplace decisions. In this study we examine several of these factors, using an organizingframework of sex distribution within jobs (including male- and female-dominated jobs as well assex-balanced, or integrated, jobs). We conducted random effects meta-analyses including 136 indepen-dent effect sizes from experimental studies (N ! 22,348) and examined the effects of decision-makergender, amount and content of information available to the decision maker, type of evaluation, andmotivation to make careful decisions on gender bias in organizational decisions. We also examined studycharacteristics such as type of participant, publication year, and study design. Our findings revealed thatmen were preferred for male-dominated jobs (i.e., gender-role congruity bias), whereas no strongpreference for either gender was found for female-dominated or integrated jobs. Second, male ratersexhibited greater gender-role congruity bias than did female raters for male-dominated jobs. Third,gender-role congruity bias did not consistently decrease when decision makers were provided withadditional information about those they were rating, but gender-role congruity bias was reduced wheninformation clearly indicated high competence of those being evaluated. Fourth, gender-role congruitybias did not differ between decisions that required comparisons among ratees and decisions made aboutindividual ratees. Fifth, decision makers who were motivated to make careful decisions tended to exhibitless gender-role congruity bias for male-dominated jobs. Finally, for male-dominated jobs, experiencedprofessionals showed smaller gender-role congruity bias than did undergraduates or working adults.

Keywords: gender bias, gender stereotypes, employment discrimination, personnel evaluation,employment decisions

Substantial progress toward gender equality in the United Statescontinues to be made. Today, women are more likely than men tocomplete high school, attain bachelor’s degrees, and earn ad-vanced degrees, and this gap between men and women has beensteadily increasing over the past 30 years (Aud et al., 2011).However, educational progress has not always translated intoequality in the workplace for women. Their salaries and organi-zational ranks continue to lag behind those of men (Aud et al.,2011), suggesting the possibility of continued gender discrimina-tion.There is a long history of research on gender bias in workplace

decisions. Exemplifying the classic trade-off between experimen-

tal control and concerns for realism, one research tradition focuseson true experiments, typically in settings that simulate employmentdecisions (e.g., Nieva & Gutek, 1980; Rosen & Jerdee, 1974). Inthis research, decision makers are presented with informationabout job applicants (e.g., résumés, performance evaluations) inwhich applicant gender and other features are manipulated. In con-trast, a second research tradition examines differences in employ-ment evaluations in field settings, such as job performance andpromotability ratings. In meta-analyses of field studies, both Bo-wen, Swim, and Jacobs (2000) and Roth, Purvis, and Bobko(2012) reported small overall effect sizes for male–female differ-ences (overall d values of ".01 and ".11, respectively, withfemales receiving higher scores than males), but gender differ-ences varied across moderators such as gender stereotypicality ofthe rating measure, rater gender, and type of rating. One advantageof these meta-analyses of field studies assessing gender differencesin ratings is the use of actual employee evaluations, allowing forincreased confidence in the generalizability of findings. However,one drawback is the inability to unambiguously attribute genderdifferences in field studies to any particular cause, such as genderbias or true gender differences in performance. As Bowen et al.(2000) noted, “One of the primary limitations of this meta-analysisis that differences in ratings of women and men could have

This article was published Online First May 26, 2014.Amanda J. Koch, Human Resources Research Organization, Minneap-

olis, Minnesota; Susan D. D’Mello, Korn Ferry, Minneapolis, Minnesota;Paul R. Sackett, Department of Psychology, University of Minnesota.We thank Adam Beatty and Phil Walmsley for helpful comments on

drafts of this article.Correspondence concerning this article should be addressed to Amanda

J. Koch, Human Resources Research Organization, 100 Washington Av-enue South, Suite 1660, Minneapolis, MN 55401. E-mail: akoch@humrro.org

ThisdocumentiscopyrightedbytheAmericanPsychologicalAssociationoroneofitsalliedpublishers.

Thisarticleisintendedsolelyforthepersonaluseoftheindividualuserandisnottobedisseminatedbroadly.

Journal of Applied Psychology © 2014 American Psychological Association2015, Vol. 100, No. 1, 128–161 0021-9010/15/$12.00 http://dx.doi.org/10.1037/a0036734

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Recommendation Letters for Faculty Applicants

Letters for women :• More communal descriptors –

affectionate, warm, kind, nurturing• More references to personal life• More comments that raise doubts:

“It’s amazing how much she’s accomplished.”“It appears her health is stable.”“She is close to my wife.”

• May reveal protected status, ie. marital status, children, etc.

“She has overcome so much as a single mother with 2 kids.”

Letters for men:• More agentic descriptors –

ambitious, dominant, self-confident

• More references to… • CV• Publications• Colleagues

Communal characteristics have a negative relationship with hiring decisions in academia

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Impact of Implicit Bias about Mothers

• Equally qualified men and women evaluated…– Mothers rated less competent– Mothers received half as many call backs as men– Fathers advantaged over childless men

• In a 2007 study, the recommended salary for female job applicants was 7.4% lower for mothers vs. childless women

• However, women academics who marry and have families publish as many articles per year as single women

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Ethnicity is a Significant Factor in Grant Evaluations, 2011

• Analysis of 80,000 NIH grant applications (2000-06) found that 16% submitted by black applicants were approved, compared to 29% for white applicants

• When all other factors were held constant, black applicants were significantly less likely to get funding due to their race

• Factors for the significant differences include:• Bias in peer-review process• Black scientists lack professional networks and mentoring

• Results point to subtle and unintentional yet systematic forms of discrimination

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Expectancies Can Undermine Performance

One person’s stereotypes or assumptions—expectancies—about another person can be accidentally conveyed

– To the candidate– To other faculty members or staff

Expectancies can pertain to the candidate’s abilities, motivation, and attitudes

Unconscious communication of expectancies can influence outcomes unintentionally

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Activity 3: Brainstorm Strategies

• Brainstorm effective practices for avoiding implicit bias for a search stage (10 minutes)

• Debrief with the larger group (15 minutes)– What are two things you will do during your next faculty

search?

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Five Stages of a Faculty Search

A. Define criteria and qualities required for positionB. Actively recruit a diverse pool, and develop

strategies for proactive faculty hiringC. Review and identify the long/short listD. Conduct an effective on campus interviewE. Recommend finalist(s) to Chair

What Can We Do?

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Stage A: Define Criteria and Qualities Required for the Position

• Define attributes such as ‘fit’, ‘excellence’, ‘quality’

• Develop a rubric for initial review and final ranking – don’t use your ‘gut’

• Discuss diversity and its meaning to the dept.

• Write the ad using broad research area

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Sample of Evaluation Rubric

Please rate the candidate on each of the following:

exce

llent

good

neut

ral

fair

poor

unab

le to

ju

dge

Fit with department’s prioritiesEvidence of scholarly impactEvidence of research productivityEvidence of research fundingEvidence of collaborationAbility to make positive contribution to department’s climateAbility to attract and supervise graduate studentsAbility to teach and supervise undergraduates

Please comment on the candidate’s research program:

Please comment on the candidate’s expertise:

Other Comments:

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Stage B: Actively Recruit a Diverse Pool…

• Develop a departmental strategy for year-round strategic recruiting– Know the number of women and minorities receiving PhDs – Build relationships with diverse scholars at national conferences– Broaden institutions from which you recruit and collaborate– Search prestigious fellowship holders including minority fellowships– Go beyond your own network – beyond the people you know

• Send the ad to national women and minority organizations, committees, and caucuses in your discipline

• Don’t assume people are not moveable

Data handout

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Stage B. …and Develop Strategies forRecruiting of Senior Faculty

• Look at who held leadership positions in national organizations• Explore databases of funded awards for diverse candidates

– NIH Research Portfolio Reporting Tool-http://RePORT.nih.gov– NSF Awards Search (http://nsf.gov/awardsearch/)– Web of Science database (available on the library website)– National Academies directories – National Endowment for the Humanities awards– ACLS Fellowships– Guggenheim Fellowships

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Stage C: Review and Identify the Short List

• Use objective criteria contained in the evaluation rubric

• Completely review all applications– Consider PhD/postdocs from schools other than the top

• Be aware of implicit biases:– gender (women rated lower than men)– ethnic names receive fewer call backs– letter writer and reader biases

• Tom Forth's online Gender Bias calculator– women with children

Letter writer handout

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Stage D: Planning for an Effective On-Campus Interview

• Value each candidate as an individual, not as a token• Ask if there are individuals/groups the candidate wants to

meet• Create a list of questions to allow comparison of common

factors for each candidate• Aim for diverse and welcoming audiences when

scheduling meetings – for all candidates• Send candidate’s CV to colleagues before interview

Good Practices for Interviews Handout

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Stage D: On-Campus…Do Not Ask Discriminatory Questions

Federal / state laws and regulations prohibit questions about these classes to protect them: § Family status§ Race§ Religion§ Gender§ Age

§ Arrests§ Citizenship or nationality§ Disability§ Sexual Orientation§ Pregnancy

Northeastern Provost’s Guide for Conducting Interviews: http://www.northeastern.edu/provost/resources/faculty/

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Stage D: On-Campus…Questions That Could Lead to Bias

Inappropriate: Reflects poorlyon the University…• Are you married?• Are you planning to start a

family?• What is your spouse's name?• What is your maiden name?• Do you have any children?• Are you pregnant?• What are your childcare

arrangements?

Appropriate• How can we best accommodate

you?• We offer all candidates

information on our childcare center – there is a website….

• We offer all candidates information on benefits – you can reach out to an HR representative for questions confidentially.

Candidates should be assessed on their ability to perform the job

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Stage E: Recommend Finalist(s) to Chair

• Encourage faculty to complete evaluation rubric for each candidate within 24 hours

• Use consistent objective criteria in evaluation of every candidate

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Faculty Search Resources

• STRIDE slides http://www.northeastern.edu/advance/recruitment/stride-faculty-search-committee-workshop/

• VPAA Resources – Faculty Hiring http://www.northeastern.edu/provost/resources/faculty/– University Search Guide– Guidance for Conducting Interviews – including what not to ask– Candidate Visit Information

• ADVANCE Resources - NU and External http://www.northeastern.edu/advance/resources/– Sample Faculty Candidate Review Matrix– Links to find Ph.D degrees granted by discipline– Candidate institutional information– Partner placement information

• Tom Forth’s Gender Bias Letter Calculator https://www.tomforth.co.uk/genderbias/

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Activity 4: Take aways and evaluation

Reflect on one concrete thing you plan to implement in your search committee as a result of this workshop.

Please take 5 minutes and complete the STRIDE Workshop Evaluation in your packet.

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STRIDE

THANK YOU!

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