Recognizing Structural Barriers

Especially in STEM-related fields, recruitment tends to favor white men (Moss- Racusin et al. Reference Moss-Racusin, Dovidio, Brescoll, Graham and Handelsman2012). Thus, we intentionally recruit underrepresented students, rather than relying on student self-selection (Barnes Reference Barnes2018; Chaudhary and Berhe Reference Chaudhary and Berhe2020).Footnote ¹ A self-selected Data Lab participant pool would likely mirror existing patterns in political methodology and the undergraduate population more broadly.

Yet because a targeted recruitment strategy is complicated by federal legal precedent (see Texas A&M University 2021), we emphasize our mission in the application and recruit through faculty (many of whom are from underrepresented groups themselves), historically black universities (HBCUs), and surrounding HSIs. Given that URM students tend to seek out support from descriptively representative faculty (Ensher and Murphy Reference Ensher and Murphy1997), these individuals are often best suited for sharing information about the lab opportunity and encouraging applicants.Footnote ²

To reduce barriers facing URM students (see Reinhardt & King, this issue), our application does not require letters of recommendation, strict GPA requirements, or strong research skills: interest in a graduate program or STEM career is the only requirement. We ask applicants for a resume, an unofficial transcript, and responses to a set of questions on their background and interest in social science research. For example, prospective participants are asked about their desire to learn and apply data science skills in their future careers. In many ways, completing and submitting an application are considered a “litmus test” for a student’s genuine interest in learning STEM skills. By taking the time to apply to the Data Lab, students are demonstrating their commitment to learning about quantitative social science. Our hope is to cast a wide net and so recruit students who have not been asked to participate in or have considered participating in this type of program before. We also consider whether someone recommended the student for the program, which can signal support from trusted colleagues or past participants.

Although most participants in the Data Lab are political science majors, some major in other fields: majoring in political science is not a requirement. Perhaps counterintuitively, including non-major participants contributes to the Data Lab’s goal of ultimately diversifying political methodology. First, the Data Lab introduces students to the idea of advanced study in political science through program features like research presentations and meetings with faculty and graduate students. These features introduce non-majors to the idea of graduate study within political science, which they may not have previously considered or known about. Second, building a community of URM students interested in STEM, regardless of major, creates an inclusive learning environment with many positive externalities.

Final admission decisions are made by the departmental Climate and Inclusion Committee, which comprises a diverse group of faculty members and one graduate student. The committee views applicants holistically, gauging their desire to attend, diversity of experiences, and challenges that make the lab an appropriate fit. Thus far, all attending students have been engaged, and many have used the lab as a stepping-stone to future opportunities. One student wrote, “The Lab helped me clear up some doubts I had about grad school and gave me more confidence to pursue academia as a career.”

Representing Scholars from Intersectionally Marginalized Groups in Institutional and Organizational Leadership

Due to limited funding in 2020, we recruited at geographically proximate institutions but expanded to the University of Puerto Rico (UPR) through the MIGAP program (Tormos- Aponte and Velez-Serrano Reference Tormos-Aponte and Velez-Serrano2020). In 2020, the Data Lab hosted 13 students from Texas A&M, 7 students from UPR, and 5 from nearby universities. In total, 40% of the 25 participants were Hispanic/Latino, and 20% identified as Black. Seventy-six percent of the participants were women, of whom 68% were nonwhite. This contrasts to APSA membership as of February 2020, which was 5.92% Hispanic/Latino, 4.88% Black, and 37.39% women; of those women, 23.84% identified as nonwhite.

In 2021, the Data Lab was held online because of the pandemic, which allowed us to expand the scope of our recruitment efforts to include advertising through the American Political Science Association and Twitter. Networking through existing intersectional opportunity structures (e.g., Women Also Know Stuff, People of Color Also Know Stuff) allowed us to connect to more students and yielded participants from across the United States. In 2021, 74% of the 31 participants were nonwhite, with 6 participating remotely from the UPR and 9 others participating from outside Texas A&M University. Compared to 2020, a smaller percentage of participants were Black (6%) or women (58%). However, of the women in attendance, 67% identified as nonwhite. Figure 1 displays the demographic breakdown of the 2020 and 2021 Data Lab participants compared to the APSA membership (at large).

Figure 1

Percent URM Scholars in APSA Membership vs. Data Lab Participants

Notes: Percent URM Scholars in APSA Membership vs. Data Lab Participants

In addition to providing opportunity, the learning environment must take the unique needs of URM students into account. This involves faculty and staff building a culture for minority student success (Museus Reference Museus2011). Undergraduate students (including URMs) who participate in research experiences are more likely to pursue graduate education. Hathaway, Nagda, and Gregerman (Reference Hathaway, Nagda and Gregerman2002, 14) suggest that this relationship occurs because research work is usually done in small social groups where faculty members humanize the educational experience and assume responsibility for student outcomes. In addition, descriptively representative faculty and staff enhance student performance and retention (Fairlie, Hoffmann, and Oreopoulos Reference Fairlie, Hoffmann and Oreopoulos2014; Hoffmann and Oreopoulos Reference Hoffmann and Oreopoulos2009).

Selection of instructors and TAs is crucial to the success of the Data Lab. Bilingual instruction that involves active student engagement can promote “STEM meaning-making” (National Academies of Sciences, Engineering, and Medicine 2018). Given the large proportion of Spanish-speaking participants, we sought out a Latinx instructor from Stata who speaks with students in both Spanish and English. TAs are members of underrepresented groups and are selected based on their ability to connect with students. In a follow-up survey from 2020, one Hispanic participant commented that the best thing about the Data Lab was “that the mentors were always by our side” and that the instructor “knew how to answer questions in both Spanish and English.” Participants also mentioned that they liked the opportunity to interact with current graduate students and hear about faculty research; another participant noted that the best part of the Data Lab was the interaction with graduate students who were “super helpful and friendly,” making the student feel “very welcomed.”

Typically, undergraduate students of color receive very little exposure to political science research (Dickinson, Jackson, and Williams Reference Dickinson, Jackson and Williams2020). Our Data Lab students participate in two research talks during the workshop. The faculty giving the talks are selected carefully: we choose individuals who “act as behavioral models, as representations of the possible, and [as] inspirations” (Morgenroth, Ryan, and Peters Reference Morgenroth, Ryan and Peters2015, 477). In 2020 and 2021, all the speakers were junior faculty (one woman and one man in each year). One Data Lab participant stated that it made a difference that the invited speakers were “honest, humble, young and inspiring.”

Above all, everyone on the institutional side of the Data Lab was committed to building a culture dedicated to URM student success (as highlighted by Museus Reference Museus2011). The Data Lab creates an inclusive learning environment by ensuring that students see themselves among their peers and their instructors (Narayanan et al. Reference Narayanan, Cunningham, Arteaga, Welch, Maxwell and Bude2018). The targeted recruitment strategy of the Data Lab ensured that students participated in a learning environment alongside a group of their peers (figure 1). This is in line with Hagedorn and coauthors (Reference Hagedorn, Chi, Cepeda and McLain2007) who find that Latino student success increases when Latino faculty are present and there is a “critical mass” of Latino students.

Prioritizing Issues Facing Intersectionally Marginalized Groups

In a pre-lab survey, we ask students whether “a career using quantitative methods is attainable for people like me.” Given that students self-select into the program, we expected a bias toward agreement. Surprisingly, only 18% of participants strongly agreed with this statement before participating in the workshop. Afterward, however, 33% of survey respondents strongly agreed and another 48% agreed with this statement; in addition, 58% of respondents stated that they were “a lot more likely” or “somewhat more likely” to consider a PhD in political science. There are many possible reasons for this increase, including descriptive representation and the building of new skills among lab participants. Yet, we know that providing institutional support, including tangible resources and external benefits (Tsui Reference Tsui2007), also matters.

External institutional partnerships—in our case, with APSA’s Minority Graduate Placement Program (MIGAP)—are crucial for developing diversity pipelines (Mealy Reference Mealy2015). MIGAP, created by Tormos-Aponte and Velez-Serrano (Reference Tormos-Aponte and Velez-Serrano2020), responds to the hurdles facing Latinx students as they pursue graduate studies. The program works with students at the University of Puerto Rico to support and fund the graduate-school application process. MIGAP’s vision is to create a nationwide network of political science graduate studies placement and preparation programs.

We support the institutional goals of MIGAP by acknowledging the resource inequalities that exist, particularly between the University of Puerto Rico and more abundantly sourced institutions. Cutting or eliminating costs (including hidden costs such as time) to underresourced students is critical to encouraging their participation in STEM opportunities (Tormos-Aponte and Velez-Serrano Reference Tormos-Aponte and Velez-Serrano2020).With internal grant support, we are able to pay our TAs and provide financial assistance to participants to cover the costs of their travel and meals. In addition to direct financial support, we rely on Stata Corp for donated time and access to software.Footnote ³ During our virtual session in 2021, we mailed to students a locally sourced “care package” that included coffee, tea, and university gear. We also offer certificates of completion and letters of recommendation on request.

Despite the short length of the Data Lab, 68% of participants said their knowledge of Stata improved “a great deal” or “a lot.” No student reported zero improvement. In addition to developing coding skills, students benefited from interactions with faculty and graduate students. After a one-on-one faculty meeting, one participant noted that this was “one of the most meaningful conversations [they’ve] had related to graduate interests.”