In the academic year of 2022, it is expected that 551,460 female and 331,530 male students will earn a Master’s degree in the United States. These figures are a significant increase from the academic year of 1950, when 16,980 female students and 41,220 male students earned a Master’s degree.

What is a Master’s degree?

A Master’s degree is an academic degree granted by universities after finishing a Bachelor’s degree. Master’s degrees focus in on a specific field and are more specialized than a Bachelor’s. A typical Master’s program is about two years long, with the final semester focusing on the thesis. Master’s degree programs are usually harder to get into than Bachelor’s degree programs, due to the rigor of the program. Because these programs are so competitive, those with a Master’s degree are typically paid more than those with a Bachelor’s degree.

Master’s degrees in the United States

The number of master’s degrees granted in the United States has steadily increased since the 1970s and is expected to continue to increase. In 2021, the Master’s degree program with the worst job prospects in the United States by mid-career median pay was counseling, while the program with the best job prospects was a physician's assistant.

College Completion Dataset

Graduation Rates, Race, Efficiency Measures and More

By Jonathan Ortiz [source]

About this dataset

This College Completion dataset provides an invaluable insight into the success and progress of college students in the United States. It contains graduation rates, race and other data to offer a comprehensive view of college completion in America. The data is sourced from two primary sources – the National Center for Education Statistics (NCES)’ Integrated Postsecondary Education System (IPEDS) and Voluntary System of Accountability’s Student Success and Progress rate.

At four-year institutions, the graduation figures come from IPEDS for first-time, full-time degree seeking students at the undergraduate level, who entered college six years earlier at four-year institutions or three years earlier at two-year institutions. Furthermore, colleges report how many students completed their program within 100 percent and 150 percent of normal time which corresponds with graduation within four years or six year respectively. Students reported as being of two or more races are included in totals but not shown separately

When analyzing race and ethnicity data NCES have classified student demographics since 2009 into seven categories; White non-Hispanic; Black non Hispanic; American Indian/ Alaskan native ; Asian/ Pacific Islander ; Unknown race or ethnicity ; Non resident with two new categorize Native Hawaiian or Other Pacific Islander combined with Asian plus students belonging to several races. Also worth noting is that different classifications for graduate data stemming from 2008 could be due to variations in time frame examined & groupings used by particular colleges – those who can’t be identified from National Student Clearinghouse records won’t be subjected to penalty by these locations .

When it comes down to efficiency measures parameters like “Awards per 100 Full Time Undergraduate Students which includes all undergraduate completions reported by a particular institution including associate degrees & certificates less than 4 year programme will assist us here while we also take into consideration measures like expenditure categories , Pell grant percentage , endowment values , average student aid amounts & full time faculty members contributing outstandingly towards instructional research / public service initiatives .

When trying to quantify outcomes back up Median Estimated SAT score metric helps us when it is derived either on 25th percentile basis / 75th percentile basis with all these factors further qualified by identifying required criteria meeting 90% threshold when incoming students are considered for relevance . Last but not least , Average Student Aid equalizes amount granted by institution dividing same over total sum received against what was allotted that particular year .

All this analysis gives an opportunity get a holistic overview about performance , potential deficits &

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How to use the dataset

This dataset contains data on student success, graduation rates, race and gender demographics, an efficiency measure to compare colleges across states and more. It is a great source of information to help you better understand college completion and student success in the United States.

In this guide we’ll explain how to use the data so that you can find out the best colleges for students with certain characteristics or focus on your target completion rate. We’ll also provide some useful tips for getting the most out of this dataset when seeking guidance on which institutions offer the highest graduation rates or have a good reputation for success in terms of completing programs within normal timeframes.

Before getting into specifics about interpreting this dataset, it is important that you understand that each row represents information about a particular institution – such as its state affiliation, level (two-year vs four-year), control (public vs private), name and website. Each column contains various demographic information such as rate of awarding degrees compared to other institutions in its sector; race/ethnicity Makeup; full-time faculty percentage; median SAT score among first-time students; awards/grants comparison versus national average/state average - all applicable depending on institution location — and more!

When using this dataset, our suggestion is that you begin by forming a hypothesis or research question concerning student completion at a given school based upon observable characteristics like financ...

Introduction This STEM advising outreach program was developed for undergraduate students who are contemplating future applications to PhD programs in the life sciences. The audience of ~20 students ranged in academic stage, and was composed mostly of life sciences undergraduates enrolled at Bowdoin College.

We have previously described two similar outreach events (ref. 1,2); this 90-minute combination of seminar and discussion built on that pilot program. This session at Bowdoin College was intended to complement the advising that students receive from their primary research mentors on campus. Although undergraduates at many excellent institutions have access to extensive pre-professional advising for careers in medicine, law and some other directions, the structure of advising for scientific research and the many career options that rely on PhD training is less consistent. Independent study or thesis research mentors are often a student’s primary source of advice. Career advisors have confirmed that reiteration and reinforcement of advising principles by professionals external to the school environment is helpful. Therefore, this outreach program’s content was developed with a goal of demystifying PhD programs and the benefits that they provide. The topics covered included (a) determining the key differences between programs, (b) understanding how PhD admissions works, (c) preparing an effective application, (d) proactive planning to strengthen one’s professional portfolio (internships, independent research, cultivating mentors), (e) key transferable skills that most students learn in graduate school, (f) what career streams are open to life science PhDs, and, (g) some national and institutional data on student career aspirations and outcomes (ref. 3). Methods The approach of bringing a faculty member and an administrative staff member who both have life science PhD training backgrounds was intentional. This allowed the program to portray different perspectives and experience to guide student career development, while offering credible witnesses to the types of experiences, skills and knowledge gained through PhD training. Central to the method of this outreach program is the willingness of graduate educators to meet the students on their own ground. The speakers guided students through a process of identifying national graduate programs that might best serve their individual interests and preferences. In addition to recruiting prospective applicants to Harvard Medical School (HMS) summer internships and PhD programs, the speakers made an explicit appeal to students to hone their professional portfolio proactively by discussing important skills that undergraduates need to be competitive in admissions and the career workplace including acquiring training in statistics and programming, soliciting diverse mentorship, acquiring authentic research experiences/internships, conducting thesis research, and obtaining fellowships). By reinforcing much of the anecdotal and formal advising content that is made available by faculty mentors and career counselors, our host saw the value of external experts to validate guidance.

This event built off our most recent event (ref. 2); we delivered a presentation covering the relevant topics and transitioned into an open discussion featuring a third visitor in our team. In contrast to the aforementioned previous event, the time constraint at lunch time prevented us from doing a formal panel. Our third speaker was a HMS Curriculum Fellow (ref. 4) whose career goals included teaching at a comparable institution (primarily undergraduate institution, PUI).

Students were encouraged to have lunch during the session, as the program was held at midday to avoid conflicts with other academic or extracurricular events. ResultsAs the principal goal of the session was to encourage and engage students, not to evaluate them, and the students ranged widely in stage and long-term career objectives, there were no assessment surveys of learning gains. Informally, student engagement was excellent as judged by the frequency and thoughtful nature of questions asked during the discussion phase of the session. Ad hoc student feedback directly following the event was extremely positive, as was our host’s follow up by email after the event. The success of the program was also evident by an invitation for a repeat of the program or other forms of collaboration in the future, including the possibility of reciprocal visits to HMS.DiscussionThis advising session was a continued refinement of our prototype, and thus served to prepare us for a series of similar events across a larger network of colleges. Our decision to incorporate a HMS Curriculum Fellow served three purposes: (1) to engage speaker who pursued doctoral training at three different institutions (UCLA, Tufts University, Harvard University), (2) to broaden the range of career trajectories presented as outcomes from doctoral programs, and (3) to provide networking and career development opportunities for the Curriculum Fellow.

Data on the top universities for Computer Science in 2025.

This statistic shows the leading universities worldwide with the highest scores on the Global University Employability Ranking index for 2018. In 2018, the university from which graduates were the most employable was Harvard University, with an index score of 2,243.

The Colleges and Universities feature class/shapefile is composed of all Post Secondary Education facilities as defined by the Integrated Post Secondary Education System (IPEDS, http://nces.ed.gov/ipeds/), National Center for Education Statistics (NCES, https://nces.ed.gov/), US Department of Education for the 2018-2019 school year. Included are Doctoral/Research Universities, Masters Colleges and Universities, Baccalaureate Colleges, Associates Colleges, Theological seminaries, Medical Schools and other health care professions, Schools of engineering and technology, business and management, art, music, design, Law schools, Teachers colleges, Tribal colleges, and other specialized institutions. Overall, this data layer covers all 50 states, as well as Puerto Rico and other assorted U.S. territories. This feature class contains all MEDS/MEDS+ as approved by the National Geospatial-Intelligence Agency (NGA) Homeland Security Infrastructure Program (HSIP) Team. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the "Place Keyword" section of the metadata. This feature class does not have a relationship class but is related to Supplemental Colleges. Colleges and Universities that are not included in the NCES IPEDS data are added to the Supplemental Colleges feature class when found. This release includes the addition of 175 new records, the removal of 468 no longer reported by NCES, and modifications to the spatial location and/or attribution of 6682 records.

The recent movement underscoring the importance of career taxonomies has helped usher in a new era of transparency in PhD career outcomes. The convergence of discipline-specific organizational movements, interdisciplinary collaborations, and federal initiatives has helped to increase PhD career outcomes tracking and reporting. Transparent and publicly available PhD career outcomes are being used by institutions to attract top applicants, as prospective graduate students are factoring in these outcomes when deciding on the program and institution in which to enroll for their PhD studies. Given the increasing trend to track PhD career outcomes, the number of institutional efforts and supporting offices for these studies have increased, as has the variety of methods being used to classify and report/visualize outcomes. This report comprehensively synthesizes existing PhD career taxonomy tools, resources, and visualization options to help catalyze and empower institutions to develop and publish their own PhD career outcomes. Similar fields between taxonomies were mapped to create a new crosswalk tool, thereby serving as an empirical review of the career outcome tracking systems available. Moreover, this work spotlights organizations, consortia, and funding agencies that are steering policy changes toward greater transparency in PhD career outcomes reporting. Such transparency not only attracts top talent to universities, but also propels research progress and technological innovation forward. Therefore, university administrators must be well-versed in government policies that may impact their PhD students. Engaging with government relations offices and establishing dialogues with policymakers are crucial steps toward staying informed about relevant legislation and advocating for more resources. For instance, much of the recent science legislation in the U.S. Congress, including the Creating Helpful Incentives to Produce Semiconductors (CHIPS) and Science Act, significantly impacts federal agency programs influencing universities. To ensure sustained development, it is imperative to support initiatives that enhance transparency, both in terms of legislation and resources. Increased funding for programs supporting transparency will aid legislatures and institutions in staying informed and responsive. Many efforts presented in this publication have received support from federal and state governments or philantrophic sources, underscoring the need for multifaceted support to initiate and perpetuate this level of systemic change.

Graph and download economic data for Income Before Taxes: Public Assistance, Supplemental Security Income, SNAP by Highest Education: Less Than College Graduate: Less Than High School Graduate (CXUWELFARELB1403M) from 2012 to 2023 about supplements, no college, assistance, social assistance, public, secondary schooling, secondary, SNAP, food stamps, tax, education, food, income, and USA.

We collected data on every tenure-track (TT) faculty member in the 122 PhD-granting Political Science departments in the United States to identify which graduate programs place faculty in our discipline’s research universities. The top 20% of departments produced 75% of all faculty while the bottom 50% accounted for less than 5% of all TT faculty at a research university. Forty-nine programs did not have a single graduate placed in a TT-position at a PhD-granting department in the last 10 years, and 18 programs do not have a single graduate in a TT-position at a PhD-granting department at all. The overwhelming majority of TT faculty are at a lower or equally ranked department. The results have important implications for prospective graduate students and the future of our discipline.

In 2024, the average salary of a Washington University EMBA graduate three years after graduation was over 627,000 U.S. dollars. Ceibs graduates could expect the second highest salary, reaching 536,000 U.S. dollars the same year. EMBAThe Executive MBA (EMBA) program is a graduate level business degree program and primarily designed for executives, managers, and similar business leaders. These programs are usually targeted towards working professionals, often in the middle stages of their career, and are often flexible to allow for part-time attendance while the student still works. An EMBA program allows a student to increase career options and update or add to their existing skill set. The best universities worldwide In the United States, Princeton University was considered one of the best colleges, with graduates earning a median of 177,000 U.S. dollars after 10 years. According to the Times Higher Education ranking of the best universities worldwide, the University of Oxford in the UK was considered the best.

This paper investigates the dynamics of discrimination in Political Science PhD programs with a survey of current political science graduate students in the top 50 departments. We focus on mentorship, funding, sexual harassment, racism, homophobia, and labor exploitation: 20% of respondents report labor exploitation, 19% experienced racial discrimination, 9% report sexual harassment and 6% experienced homophobia. Discrimination is uneven across individuals: Some groups of graduate students experience widespread discrimination, especially racial discrimination, while other groups are largely unaware of these issues. We ran a survey experiment to gauge the impact of misconduct on formal reporting mechanisms and find that hearing about racial discrimination has a chilling effect on reporting. Importantly, we find that experiencing discrimination harms how satisfied students are in their programs. We find that factors linked to student vulnerability, like international status and funding, are significantly associated with harassment, and that reporting discrimination predicts more discrimination.

Data on the top universities for Law in 2025.

Data on the top universities for Engineering in 2025, including disciplines such as Chemical Engineering, Civil Engineering, and Mechanical and Aerospace Engineering.

Introduction This STEM advising outreach program was developed for undergraduate students who are contemplating future applications to PhD programs in the life sciences. The audience of ~15 students ranged in academic stage, and was composed of life sciences undergraduates enrolled at Colby College.

We have previously described similar outreach events (ref. 1-3); this 90-minute combination of seminar and discussion built on those pilot programs. This session at Colby College was intended to complement the advising that students receive from their primary research mentors on campus. Although undergraduates at many excellent institutions have access to extensive pre-professional advising for careers in medicine, law and some other directions, the structure of advising for scientific research and the many career options that rely on PhD training is less consistent, and often relies on individual mentors whose training backgrounds and career trajectories are quite diverse. Independent study or thesis research mentors are often a student’s primary source of advice. Career advisors have confirmed that reiteration and reinforcement of advising principles by professionals external to the school environment is helpful. Therefore, this outreach program’s content was developed with a goal of demystifying PhD programs and the benefits that they provide. The topics covered included (a) determining the key differences between programs, (b) understanding how PhD admissions works, (c) preparing an effective application, (d) proactive planning to strengthen one’s professional portfolio (including internships, independent research, and cultivating mentors), (e) key transferable skills that most students learn in graduate school, (f) what career streams are open to life science PhDs, and, (g) some national and institutional data on student career aspirations and outcomes (ref. 4). MethodsThe approach of bringing a faculty member and an administrative staff member who both have life science PhD training backgrounds and with program administrative experience was intentional. This allowed the speakers to portray different perspectives and experience to guide student career development, while offering credible reflections on the types of experiences, skills and knowledge gained through PhD training. Central to the method of this outreach program is the willingness of graduate educators to meet the students on their own ground. The speakers guided students through a process of identifying national graduate programs that might best serve their individual interests and preferences. In addition to recruiting prospective applicants to Harvard Medical School (HMS) summer internships and PhD programs, the speakers made an explicit appeal to students to hone their professional portfolio proactively. Students were encouraged to seek out opportunities to develop skills that undergraduates need to be competitive in admissions to graduate programs, that trainees need during graduate school, and that doctoral alumni apply in the careers and workplaces that come after. To that end, students were encouraged to pursue training in statistics and programming, develop a mentoring network, acquire authentic research experiences and pursue internships, conduct thesis research, and apply for fellowships. By reinforcing much of the anecdotal and formal advising content that is made available by faculty mentors and career counselors, our host saw the value of external experts to validate prior guidance offered on campus.

This event built off our most recent event (ref. 3); we delivered a presentation covering the relevant topics and transitioned into an open discussion featuring a third visitor on our team. In contrast to the previous events (ref. 2), we did not use a panel format after the presentation. Our third speaker was a HMS Curriculum Fellow (ref. 5) whose career goals included teaching at a comparable institution (primarily undergraduate institution, PUI).

This event was held at the end of the day, and prior to dinner, to avoid conflicts with other academic or extracurricular events.ResultsAs the principal goal of the session was to encourage and engage students, not to evaluate them, and the students ranged widely in stage and long-term career objectives, there were no assessment surveys of learning gains. Informally, student engagement was excellent as judged by the frequency and thoughtful nature of questions asked during the discussion phase of the session. Ad hoc student feedback directly following the event was extremely positive. Our host’s participation and feedback was also encouraging; in particular, we learned that the portion of our presentation devoted to transferable skills gained from a PhD was well-received. The success of the program was also evident by an invitation for a repeat of the program or other forms of collaboration in the future, including the possibility of reciprocal visits to HMS.DiscussionThis advising session was a continued refinement of our prototype, which we continued to develop for an expanding network of colleges. Our decision to incorporate a HMS Curriculum Fellow served three purposes: (1) to engage another speaker so our team represented professionals who pursued doctoral training at three different institutions (UCLA, Tufts University, Harvard University), (2) to broaden the range of career trajectories presented as outcomes from doctoral programs, and (3) to provide networking and career development opportunities for the Curriculum Fellow.

The purpose of this project is to improve the accuracy of statistical software by providing reference datasets with certified computational results that enable the objective evaluation of statistical software. Currently datasets and certified values are provided for assessing the accuracy of software for univariate statistics, linear regression, nonlinear regression, and analysis of variance. The collection includes both generated and 'real-world' data of varying levels of difficulty. Generated datasets are designed to challenge specific computations. These include the classic Wampler datasets for testing linear regression algorithms and the Simon & Lesage datasets for testing analysis of variance algorithms. Real-world data include challenging datasets such as the Longley data for linear regression, and more benign datasets such as the Daniel & Wood data for nonlinear regression. Certified values are 'best-available' solutions. The certification procedure is described in the web pages for each statistical method. Datasets are ordered by level of difficulty (lower, average, and higher). Strictly speaking the level of difficulty of a dataset depends on the algorithm. These levels are merely provided as rough guidance for the user. Producing correct results on all datasets of higher difficulty does not imply that your software will pass all datasets of average or even lower difficulty. Similarly, producing correct results for all datasets in this collection does not imply that your software will do the same for your particular dataset. It will, however, provide some degree of assurance, in the sense that your package provides correct results for datasets known to yield incorrect results for some software. The Statistical Reference Datasets is also supported by the Standard Reference Data Program.

Estimate of educational attainment for population 18 years old and over whose highest degree was a high school diploma or its equivalent, people who attended college but did not receive a degree, and people who received an associate's, bachelor's, master's, or professional or doctorate degree (ACS variable S1501_C02_014E from table S1501). People who reported completing the 12th grade but not receiving a diploma are not included.

For more information about the subject definitions, see the ACS technical documentation (https://www.census.gov/programs-surveys/acs/technical-documentation/code-lists.html).

Data for 2020 are based on the experimental estimates from the 1-year American Community Survey released by the Census Bureau instead of the traditional 1-year estimates. For more information, visit the 2020 ACS 1-Year Experimental Data Release page (https://www.census.gov/programs-surveys/acs/data/experimental-data.html).

Single-year estimates from the American Community Survey (ACS) are "period" estimates derived from a data sample collected over a period of time, as opposed to "point-in-time" estimates such as those from past decennial censuses. ACS single-year estimates include data collected over a 12-month period; explicitly the calendar year (e.g., the 2015 ACS covers the period from January 2015 through December 2015).

Please see the ACS handbook (https://www.census.gov/content/dam/Census/library/publications/2018/acs/acs_general_handbook_2018.pdf) (Section 3, "Understanding and Using ACS Single-Year and Multiyear Estimates" p. 13) for a comprehensive set of details and clarifications.

Overall educational attainment measures the highest level of education attained by a given individual: for example, an individual counted in the percentage of the measured population with a master’s or professional degree can be assumed to also have a bachelor’s degree and a high school diploma, but they are not counted in the population percentages for those two categories. Overall educational attainment is the broadest education indicator available, providing information about the measured county population as a whole.

Only members of the population aged 25 and older are included in these educational attainment estimates, sourced from the U.S. Census Bureau American Community Survey (ACS).

Champaign County has high educational attainment: over 48 percent of the county's population aged 25 or older has a bachelor's degree or graduate or professional degree as their highest level of education. In comparison, the percentage of the population aged 25 or older in the United States and Illinois with a bachelor's degree in 2023 was 21.8% (+/-0.1) and 22.8% (+/-0.2), respectively. The population aged 25 or older in the U.S. and Illinois with a graduate or professional degree in 2022, respectively, was 14.3% (+/-0.1) and 15.5% (+/-0.2).

Educational attainment data was sourced from the U.S. Census Bureau’s American Community Survey 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Educational Attainment for the Population 25 Years and Over.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (16 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (29 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (6 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (4 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (4 June 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (13 September 2018). U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).

The proportion of male and female postsecondary graduates, by Classification of Instructional Programs, Primary groupings (CIP_PG), International Standard Classification of Education (ISCED) and age group.

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 best master's degree for getting a job was considered to be Physicians Assistant with a mid-career median salary of 97,133 U.S. dollars in 2021. Salaries for nurse practitioner and computer science master's were also high.