For an Anti-racist Approach to Methods Education

• In Event: Understanding and Combating Bias, Controversy, and Fear in PS Curriculum & Classroom

Abstract

How can we apply anti-racist principles when teaching quantitative methods? In this project, I argue that anti-racist pedagogy can and should be integrated into methods education. When presented with statistical applications to social science questions, students may believe that statistical studies are objective (“numbers speak for themselves”), a misconception that can lead to biased interpretations of race-related statistics. Provoking students to reflect on such interpretations should be a component of anti-racist education and may enhance students’ abilities to interpret data more broadly.
The purposes of this project are threefold: (1) argue that anti-racist pedagogy can and should be a part of teaching data analytics; (2) outline a series of class activities implemented in an introductory methods course and intended to incorporate anti-racist principles; and (3) analyze students’ reactions to such class activities.

Initially, students gather in groups to discuss the epistemic properties of quantitative and qualitative studies. Each group is asked to fill a two-by-two table with the pros and cons of quantitative and qualitative approaches to social science. In this activity, students typically reveal a misconception by stating that quantitative studies are inherently unbiased- that quantitative studies are always “objective.” This disclosure of inaccurate prior knowledge supports the next activity, where students discuss approaches to measurement. After learning how researchers use different approaches to measure certain variables, students gather in groups to devise measurement strategies for additional variables. This activity reveals the discretion involved in making measurement choices and the potential for bias that these choices introduce. After students become aware of this potential for bias through their reasoning, they are reminded of their prior understanding of the supposed unbiasedness of quantitative research.

Later in the course, students are asked to read an excerpt from the book “White Logic, White Methods: Racism and Methodology” (Zuberi, Tukufu, and Eduardo Bonilla-Silva, eds. 2008. Rowman & Littlefield). The book excerpt offers a critical discussion of the use of statistical methods to discuss the “effect of race” and the implications of approaching race as a social construct in quantitative studies. In class, students are asked to critically reflect on the meaning of an “effect of race” and the conception of race as a social construct. This activity introduces students to a reflection on race as a variable in political science, which is instrumental to their learning in the subsequent activities.

Next, students are assigned a three-minute excerpt from the film “White Like Me” (written by Tim Wise, Scott Morris, and Jeremy Earp, Media Education Foundation, 2013). The interview and the film excerpt discuss how U.S. government policies in the 20th century promoted unequal access to subsidized mortgage loans and fostered segregation by discriminating against Black people. In class, students are asked to discuss how such policies may affect present-day racial inequalities, including but not limited to homeownership. This activity uses a historical example to invite students to reflect on causal mechanisms and path dependency in a learning process that lays the groundwork for the final activity.

Lastly, students interpret an individual-level regression table using survey data with a sample of American voters collected in 2016. The regression shows that Black respondents are less likely to be homeowners. In class, students are asked to discuss the regression results, considering their knowledge of the material presented in the previous activities. This activity relies on previous discussions to invite students to reflect on the interpretation of race as a variable in quantitative studies and discuss the importance of having qualitative knowledge about cases.

Responses to a pilot execution of this approach suggest that students appreciated the class activities and have improved their learning on using race as a variable in quantitative political science. Some students come into methods courses with the misconception that quantitative research is objective and dispenses with background knowledge. If such misconception goes unaddressed, a regression showing that Black people are less likely to be homeowners may elicit a biased interpretation that blames Black people for this outcome. After thinking critically about race as a variable and discussing historical events, students are more likely to avoid mistakes when conducting and interpreting quantitative research in political science.

Author

• Leonardo Falabella, The London School of Economics and Political Science