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Teaching undergraduates with quantitative data in the social sciences at University of California Santa Barbara


Curty, Renata Gonçalves; Greer, Rebecca; White, Torin (2022), Teaching undergraduates with quantitative data in the social sciences at University of California Santa Barbara, Dryad, Dataset,


The interview data was gathered for a project that investigated the practices of instructors who use quantitative data to teach undergraduate courses within the Social Sciences. The study was undertaken by employees of the University of California, Santa Barbara (UCSB) Library, who participated in this research project with 19 other colleges and universities across the U.S. under the direction of Ithaka S+R. Ithaka S+R is a New York-based research organization, which, among other goals, seeks to develop strategies, services, and products to meet evolving academic trends to support faculty and students.

The field of Social Sciences has been notoriously known for valuing the contextual component of data and increasingly entertaining more quantitative and computational approaches to research in response to the prevalence of data literacy skills needed to navigate both personal and professional contexts. Thus, this study becomes particularly timely to identify current instructors’ practices and strategies to teach with data, as well as challenges and opportunities to help them advance their instructional efforts. The fundamental goal of this study is fourfold: 1) Explore the ways in which instructors teach undergraduates with data, 2) Understand instructors’ support needs going forward, 3) Develop actionable recommendations for stakeholders, and 4) Build relationships within UCSB and across higher education institutions. The findings of this study will help to inform new services, policies, and practices not only at the University of California, Santa Barbara Library (UCSB Library), and the broader campus community, but also at other institutions seeking to advance their data instruction in the Social Sciences. 


The project followed a qualitative and exploratory approach to understand current practices of faculty teaching with data. The study was IRB approved and was exempt by the UCSB’s Office of Research in July 2020 (Protocol 1-20-0491). 

The identification and recruitment of potential participants took into account the selection criteria pre-established by Ithaka S+R: a) instructors of courses within the Social Sciences, considering the field as broadly defined, and making the best judgment in cases the discipline intersects with other fields; b) instructors who teach undergraduate courses or courses where most of the students are at the undergraduate level; c) instructors of any rank, including adjuncts and graduate students; as long as they were listed as instructors of record of the selected courses; d) instructors who teach courses were students engage with quantitative/computational data. 

The sampling process followed a combination of strategies to more easily identify instructors on campus that would meet all of the abovementioned requirements. First, we reached out to peers and some stakeholders from various divisions known for supporting educational efforts at the undergraduate level within the Social Science, such as Subject Librarians at UCSB Library, the Interdisciplinary Research Collaboratory (IRC) at UCSB Library, the Center for Innovative Teaching, Research and Learning (CITRAL), and The Institute for Social, Behavioral and Economic Research (ISBER). These conversations elicited a few names for an initial pool of potential interviewees which was complemented by a few names identified through the course list screening with a combination of snowball sampling, in cases where instructors we reached out about the study declined participation, but referred other people we could contact. Beyond the 12 departments listed under the Social Sciences’ division, we also included Psychology and Brain Sciences and Geography in our pool as Ithaka S+R listed Geography and Psychology as social sciences disciplines. Finally, because there is a fine line between the exclusion or inclusion of some disciplines given the high level of interdisciplinarity in the academic sphere, our calls for participation explicitly highlighted the purpose of the study and the targeted group, meaning that only instructors who identify themselves as social scientists participated in the study. 

A total of 22 instructors were invited to participate in this study. Invitations were sent by email followed by two rounds of reminders. Our recruitment efforts resulted in 10 instructors who consented to be interviewed for the study. Interviews were conducted between September 2020 and January 2021, and followed a semi-structured interview guide provided by Ithaka S+R, which were previously discussed and tested during training sessions with all participating institutions.

Due to COVID and the campus shutdown, all interviews were conducted remotely over Zoom and were audio recorded for transcription purposes. Whenever interviewees were comfortable with and their connection allowed video, this option was preferred, taking into account the role of visual cues in the interviewing process. Regardless of whether or not video was captured, transcriptions were generated for speech alone which produced approximately 12 hours of audio recording.  

For some of the interviews, we enabled the auto-generated function on Zoom to facilitate the transcription process. Audio files were transcribed using a combination of techniques. Some used, others used Zoom’s automated closed caption feature, and some were manually transcribed. Regardless of the strategy adopted, all transcripts were revised and compared to original audio to clarify and resolve inaudible passages and inconsistencies. Considering the small pool of interviewees and the fact that usually only a few instructors teach targeted courses within each department in the Social Sciences, we performed the de-identification of the interviews’ transcripts to remove any information that could potentially disclose interviewees’ identity, including departmental affiliation, course names, mentions to the department and other names (e.g. partners, students, and other UCSB affiliates). Each transcript was assigned a code by order of interviewing (UCSB1...UCSB10). 

For our local analysis we performed coding on MAXQDA 2020, one of the leading platforms designed for qualitative data analysis and to facilitate data organization and annotation. This platform was selected as it was licensed and readily available for researchers at UCSB and it allows for better cross-platform data exchange. A mixed-method approach to coding was adopted through the combination of both deductive (top-down) and inductive (bottom-up) strategies. We started with an initial code tree that echoed the main topics present in interview prompts. This initial coding scheme evolved and was refined as we engaged more closely with the data.

Usage Notes

The data folder contains 10 pdf files with de-identified transcriptions of the interviews and the pdf files with the recruitment email and the interview guide.