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’ practi..., 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).Â

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.Â

The sampling process followed a combination of strategies to more easily identify instructo..., The data folder contains 10Â pdf files with de-identified transcriptions of the interviews and the pdf files with the recruitment email and the interview guide.Â

The sample comprises of undergraduate social science students who were studying on introductory statistics courses at twelve UK universities (n=677). The data were collected anonymously between 2015 and 2017. The main mode of data collection was via a survey questionnaire that was completed in class during teaching. Where it was not possible to administer a physical questionnaire in a class setting there was an online version of the survey questionnaire available. Survey results were linked with final course grade via the student number. The research received ethical approval from the University of Edinburgh. The survey data collection tool was based on a previous survey by Payne, G., Hodgkinson, L., Williams, M. (2009). SN 6137. This survey tool was adapted to include a validated measure of statistics anxiety, the Statistics Anxiety Rating Scale, along with a number of other variables of interest.This project was part of the National Centre for Research Methods 2014-19 funding phase. It was led by Professor John MacInnes, University of Edinburgh. The project collected data from social science undergraduate students studying on general introductory courses in social statistics from a dozen UK universities. This was linked to information on course performance. The survey data collection tool was based on a previous survey by Payne, G., Hodgkinson, L., Williams, M. (2009). SN 6137. This was adapted to include a validated measure of statistics anxiety, the Statistics Anxiety Rating Scale.

The sample comprises of undergraduate social science students who were studying on introductory statistics courses at twelve UK universities (n=677). The data were collected anonymously between 2015 and 2017. The main mode of data collection was via a survey questionnaire that was completed in class during teaching. Where it was not possible to administer a physical questionnaire in a class setting there was an online version of the survey questionnaire available. Survey results were linked with final course grade via the student number. The research received ethical approval from the University of Edinburgh. The survey data collection tool was based on a previous survey by Payne, G., Hodgkinson, L., Williams, M. (2009). SN 6137. This survey tool was adapted to include a validated measure of statistics anxiety, the Statistics Anxiety Rating Scale, along with a number of other variables of interest.

This project was part of the National Centre for Research Methods 2014-19 funding phase. It was led by Professor John MacInnes, University of Edinburgh. The project collected data from social science undergraduate students studying on general introductory courses in social statistics from a dozen UK universities. This was linked to information on course performance. The survey data collection tool was based on a previous survey by Payne, G., Hodgkinson, L., Williams, M. (2009). SN 6137. This was adapted to include a validated measure of statistics anxiety, the Statistics Anxiety Rating Scale.

Training Survey_ Power BI Hands-On Dataset (1-112)

Teaching undergraduate political methodology courses is a challenging task, yet has garnered little pedagogical discussion within the discipline. With the growing use of technology in the classroom, as well as the growing demand for data science and data literacy in our society, better understanding how we use statistical software in these courses is warranted. In this short paper, we shed light on current practices in teaching political methodology courses, with a particular emphasis on the use of statistical software. Combining an analysis of 93 course syllabi with a quantitative survey of research method instructors, we provide key information on the structure of these courses and how they incorporate statistical software. Our results reflect the growing importance of data literacy within the discipline, and suggest that more intentional discussions of research method pedagogy are needed in the future.

This article introduces how to teach an interactive one-semester-long statistics and programming class. The setting can also be applied to shorter and longer classes as well as for beginner and advanced courses. I propose a project-based seminar that also inherits elements of an inverted classroom. Thanks to this character, the seminar supports the students' learning progress and can also create engaging virtual classes. To showcase how to apply a project-based seminar setting to teaching statistics and programming classes, I use an introductory class to data wrangling and management with the statistical software R. Students are guided through a typical data science workflow that requires data management, data wrangling, and ends with visualizing and presenting first research results during a mini-conference.

The data is summary statistics of three iterations of online courses for would-be social innovators and activists, including participation and interaction data.

A quick refresher course for those who have had statistical training in the past or a fast-paced introduction to basic statistics for beginners. Statistical measures such as percentages, averages, frequency and standard error are used widely. But how are they calculated, and exactly what do they tell us? This one day workshop will help participants develop an appreciation of the potential of statistics and a critical eye of when and how they should or shouldn't be used.

This course introduces methods for harnessing data to answer questions of cultural, social, economic, and policy interest. We will start with essential notions of probability and statistics. We will proceed to cover techniques in modern data analysis: regression and econometrics, design of experiments, randomized control trials (and A/B testing), machine learning, and data visualization. We will illustrate these concepts with applications drawn from real-world examples and frontier research. Finally, we will provide instruction on the use of the statistical package R, and opportunities for students to perform self-directed empirical analyses.

Statistics instructors have a unique opportunity to engage students in work around Diversity, Equity, and Inclusion (DEI) since there is an abundance of data that can be incorporated into courses with DEI contexts. An online survey was conducted to explore how faculty teaching introductory college statistics integrate DEI into their courses. About 300 participants reflected on their institution’s priorities related to DEI and what has aided and constrained them from incorporating DEI practices when teaching statistics. We found that 77% of participants indicated that they do include DEI practices in their teaching. Results show that participants at research intensive institutions are least likely to incorporate DEI, and those that have more years of teaching experience are less likely to incorporate DEI into their courses. Constraints that prevent instructors from incorporating DEI-related activities include lack of resources and time and concern about student discomfort. Additionally, those who felt most prepared to incorporate DEI were typically individuals who had engaged in professional development focused on DEI and teaching. Since there has not been a survey of this nature, these results will be useful as a metric for the inclusion of DEI into introductory statistics classes.

Background: Our instructional team at the The University of North Carolina at Chapel Hill led an innovative project that used IDEO.org's design thinking process to create a brand-new interdisciplinary graduate course, housed in the school of public health, titled Design Thinking for the Public Good. We offer our course design process as a case study of the use of design thinking for course design.Methods: We collected data and generated insights through a variety of inspiration, ideation, and implementation design thinking methods alongside members of our three stakeholder groups: (2) faculty who teach or have taught courses related to design thinking at our higher education institution; (2) design thinking experts at ours and other institutions and outside of higher education; and (3) graduate students at our institution.Results: We learned that interdisciplinary design thinking courses should include growth-oriented reflection, explicit group work skills, and content with a real-world application.Conclusions: Our course design process and findings can be replicated to design courses regardless of area of study, level, or format.

Case-based learning (CBL) is a highly effective approach in health professions education that remains underutilized in the field of statistics. To promote broader adoption, we have developed realistic cases designed to help learners practice critical thinking and devise appropriate solutions for complex scenarios. These cases, used in our graduate-level statistical collaboration course, incorporate social factors and real-world implications. The first case explores a newly graduated biostatistician navigating the challenges of meeting a client’s expectations. The second focuses on a junior biostatistician developing a statistical analysis plan for a client with limited statistical knowledge. The third case, inspired by the development of the American Statistical Association’s ethical guidelines, deliberates their need. Both training materials and fully developed cases are provided. We also discuss strategies for educators to create and facilitate similar cases. By engaging students in thoughtful consideration of these scenarios within a safe, structured environment, we encourage them to examine their assumptions and conclusions. This preparation equips them for real-world roles as consultants and team scientists. Additionally, student feedback from course evaluations and surveys indicates strong support for CBL, with the majority recommending its use in similar educational contexts.

These data and files are to replicate the results in "Course-based research and mentorship: Results from a multi-term research academy at a minority-serving institution" which is accepted for publication in PS: Political Science and Politics.

Statistic anxiety is the feeling of worrying and tension that students experience when taking statistics courses, especially in social sciences programs. Studying statistic anxiety and the related variables is crucial because this anxiety negatively and significantly affects students’ achievement and learning.

Data used in the analyses for Esparza & Smith, 2023 published in Ecosphere in the Eco-Education track.The file entitled "node list" contains information on each of the nodes (people) in the field course social system across all timepoints (early, middle, late). This dataset is anonymized, with each node assigned a unique ID in the first column. Additionally, this dataset includes information on the role of each node (e.g., student, family, instructor) and their community type (e.g., personal, professional).The files entitled "edge list" (early, middle, late) each include information on who sent ties to whom and how frequently the interaction(s) occurred within the respective time point.These data were collected using name generators, which typically include two types of questions that probe the names of those with whom an individual interacted (i.e., name generators) and information on those an individual interacted with (i.e., name interpreters). In this study, students completed a name generator survey at weeks six, eleven, and fifteen of the 15-week semester (i.e., timepoint) in a field course with an embedded course-based field research project. See Esparza and Smith (In review) Materials and Methods for more information.The files entitled "node list metadata" and "edge list metadata" include detailed information on the data included in the "node list" and "edge list" CSV. files. These files include the ranges/categories and a description of each variable included in the datasets.All data are anonymized so as to protect the identity and privacy of all research participants and their listed contacts. The research has been approved by the Cornell University Institutional Review Board under the exempt protocol #2001009364.

This dataset contains data on language learning at the Goethe-Institut in countries all over the world and contains numbers on student registrations (1990 - 2014), sold course units (1972 - 1989 and 1998 - 2014) and exam participation (1986 - 2014). If you use this data, please cite the related publication.

Replication Data for: Grading for Equity? The Limits of Course-Level Interventions in Closing Achievement Gaps in Political Science Education. Published in Journal of Political Science Education. 2025. https://doi.org/10.1080/15512169.2025.2538574

The importance of data science in society today is undeniable, and now is the time to prepare data science talent (National Academies 2018). Data science demands collaboration, but collaboration within political science departments has been weak in teaching data science. Bridging substantive and methods courses can critically aid in teaching data science because it facilitates this collaboration. Our innovation is to integrate data science into both substantive and methods courses through a dedicated data science course and modules on data science topics taught in substantive courses. This approach not only allows more teaching and practices of data science methods, but also helps students understand how social, economic and political biases, and incentives can affect their data.

Singapore Enrolment In Higher Degree Courses: Humanities & Social Sciences data was reported at 2,825.000 Number in 2017. This records an increase from the previous number of 2,148.000 Number for 2016. Singapore Enrolment In Higher Degree Courses: Humanities & Social Sciences data is updated yearly, averaging 1,662.000 Number from Dec 1993 (Median) to 2017, with 25 observations. The data reached an all-time high of 2,825.000 Number in 2017 and a record low of 346.000 Number in 1993. Singapore Enrolment In Higher Degree Courses: Humanities & Social Sciences data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Singapore – Table SG.G070: Education Statistics: Enrolment.

Hypothesis: The measurement of course attainment is often highly involved, so most of the time the concerned faculty members falter to take corrective action in time. Finding: This dataset provides an easy insight to indicate the course attainment as a various assessment tool is used while delivering the course. It allows the faculty members to quickly adapt to newer teaching and learning strategy meeting the course outcome. In the absence of this, course attainment is seen more as post-processing when one has completed the course. How data was collected: The input required to indicate the course attainment is the target value, university norms for the allocation of grades, list of the assessment tool and count of students. It provides the gross course attainment independent of course outcome level. How to interpret and use it: Higher the count of student for a given performance level, indicate that course attainment value assigned to that level.