Building Quantitative Skills in Postsecondary Social Sciences Courses

by Dylan Ruediger and Ruby MacDougall, Ithaka S+R

Data analysis skills and quantitative reasoning are central to college student’s career prospects and to contemporary civic life. Colleges and universities in the United States have invested heavily in support of programs designed to ensure that graduates learn these difficult competencies, a task that pandemic-era declines in math skills among high school graduates will exacerbate in the coming years. Social science courses, both within the general education curricula and in upper division courses for majors, that include data skills as a core component are growing quickly.

Yet, consensus on best practices for data-driven pedagogy remains elusive, even as research methods in many social science disciplines have become increasingly grounded in quantitative methodologies. To assist libraries, centers for teaching and learning, and other units that support student learning, Ithaka S+R, a not-for-profit research organization, collaborated with librarians from twenty colleges and universities to conduct 219 semi-structured interviews with social science instructors from a wide range of disciplines. Our recent report from the project, “Fostering Data Literacy: Teaching with Quantitative Data in the Social Sciences,” provides an unusually detailed exploration of two key challenges to teaching with data: helping students overcome anxieties about math and synchronizing the interconnected methodological, software, and analytic competencies students will need as they tackle increasingly complex encounters with data across what one faculty member called the “full lifecycle from finding data to presenting results.”

Key Findings

• Career skills are emphasized across the curriculum and are important factors in the software and methods that many instructors teach.

• Instructors focus on the critical interrogation of quantitative information in introductory classes, while teaching students to conduct their own research and analysis in upper division courses.

• Teaching students to use analytical software is a hands-on process requiring a significant amount of valuable instructional time, sometimes at the cost of teaching discipline-specific perspectives.

• Instructors generally avoid asking students to locate data on their own because most students struggle to find appropriate datasets. Even instructors find it difficult to locate datasets of the right size and complexity for use in middle and upper division courses.

• Faculty rely heavily on teaching assistants and liaison librarians for support when teaching with data. Teaching assistants play a critical role in teaching students to clean data and use software, and librarians help students with data discovery as well as information and data literacy.

• Both faculty and staff rely more heavily on web tutorials and other informal instructional resources than on workshops and other services offered by campus units to learn new information.

Student’s Fear of Math

In both highly selective and access-oriented institutions, instructors often described their students as terrified of math and statistics. An associate professor at an elite institution noted that they “hear so often . . . I’m not a stats person. I’m not a math person. I’m really bad at stats.” Likewise, a communications professor frequently encounters students who express some variation on the idea that as a communications major, “I don’t do math.” In fact, some faculty – including one who taught a statistics course aimed at social science majors, characterized their course as a “class for students who have an absolutely terrible fear of math and a fear of quantitative data.” To be clear, social science  instructors are not the only faculty who encounter students who dread statistics. However, they may see a disproportionate number of these students. Many reported that their students often enroll in social science courses because they believe them to be qualitatively oriented. 

Faculty report many specific methods for helping students gain confidence, the most common of which are rooted in empathy or contextualization. Faculty often share their own fear of statistics with students as a way of encouraging them to come to see statistics as a tool rather than an “enemy.” Instructors also make sustained efforts to contextualize quantitative challenges within disciplinary contexts and social, political, or cultural issues about which students are likely to be concerned. Many instructors who described these efforts believe it helps lessen students’ anxieties.

Synchronizing Data Literacy Skills

Even if students become more comfortable with the idea of quantitative analysis, they still need to learn to do it. This is another area of significant challenge because quantitative analysis is not a single competency but a constellation of skills. Students need to understand basic computing concepts such as file directories, gain a degree of fluency with analytic software, become familiar with best practices for cleaning data, and be able to contextualize findings within disciplinary traditions and methodologies. Moreover, growth in these skills is interdependent, and students who fall behind in one area are likely to struggle in others.

Our interviews suggest that software is a particularly common choke point. Many instructors describe investing large amounts of instructional time helping students learn software. A linguist spoke of the necessity of spending weeks “just getting [students] comfortable with R,” while a sociologist estimated that roughly a quarter of their instructional time was spent “going over aspects of SPSS.” Instructors expressed frustrations about the amount of time they spent teaching software, which came at the expense of teaching content and domain knowledge. However, faculty recognized that higher-level data literacy and disciplinary methodologies are now dependent on software skills and understand that taking shortcuts with software skills will ultimately hurt their students.

Collaboration in Support of Student Learning

The complexity of data literacy ensures that no single course or instructor can be successful without collaboration with others. The departments and institutions that are most successful have created a web of resources and collaborators to support faculty pedagogy and student learning. Though faculty autonomy often makes it difficult, one of the most promising approaches is to systematically coordinate student learning across the course of a major. This can involve making decisions to standardize the software used in all the department’s courses and/or developing sequences of courses calibrated to progressively guide students to increasingly complex data analysis.

Many other university units play significant roles as providers of supplementary resources for students. Libraries are the most important of these units and are especially active in helping students locate datasets for use in assignments. Faculty repeatedly indicated that they directed students to library collections or Libguides and encouraged students to consult with librarians. Many invited subject librarians to guest lecture about library collections and information literacy. Centers for Teaching and Learning, tutoring and writing centers, and – increasingly – student-run data analysis organizations and competitions were also mentioned as important resources. However, faculty expressed mixed opinions about how often students accessed the resources that were available to them.

Overall, our project interviews demonstrate that social science instructors have developed a range of methods for teaching quantitative analysis, which are now an important component of the curricula across the social sciences. Instructors recognize the importance of these skills for twenty-first century civic life and for student’s future careers. But teaching with data is difficult, and students enter social science classrooms with anxieties about math and limited knowledge of how to proceed. Although individual faculty invest considerable time helping students acquire the cluster of skills they need to understand, interpret, and manipulate data, significant barriers remain. The transition from the simplified and carefully orchestrated encounters with data common in intro courses to the more open-ended and active task of creating their own knowledge is often very difficult on students. Finding ways to enhance collaboration within departments and across the university will be necessary to further the goals of developing data analysis and quantitative reasoning competency in the social science student body.  

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