In the academic year of 2020/2021 in the United States, 85,581 doctoral degrees were earned in health professions and related programs - the most out of any field of study. A further 35,976 doctoral degrees were earned in legal professions and studies.

During the period between 2013 and 2019, student enrollment in doctoral programs in Chile increased from 4.65 thousand students in 2013 to 5.93 thousand students in 2019. In that year, the number of female students enrolled in university amounted to 407 thousand.

In the academic year of 2020/21, about 85,370 male and 108,690 female students earned a doctoral degree in the United States. By the academic year of 2031/32, these figures are expected to increase to about 88,110 and 141,500 respectively.

Persistence and graduation of doctorate degree students, within the field of study grouping (Variant of the Classification of Instructional Programs (CIP) Canada 2021 Version 1.0 for Science, technology, engineering and mathematics (STEM) and Business, humanities, health, arts, social science and education (BHASE) groupings) and province or territory of first enrolment, by demographic characteristics. The STEM grouping includes fields of study in science, technology, engineering, and mathematics and computer sciences. The BHASE grouping includes fields of study in business, humanities, health, arts, social science, education, legal studies, trades, services, natural resources and conservation.

Proportion of students who started in a doctorate degree program and pursued or graduated with a different educational qualification, within Canada, by demographic characteristics.

A great advantage of our rigorous doctoral training is that as PhD economists we speak a common language that allows for efficient vetting and quick dissemination of ideas and insights. But what good is sophisticated grammar and a powerful vocabulary if the contents of our narratives are lacking? Our top three criteria for admissions to PhD programs are prior coursework in math, the quantitative GRE score, and prior coursework in economics. To attract top talent and prevent becoming a stagnant discipline that loses the influence we have in society and academia, students' creativity, originality, and drive should receive more weight.

In the academic year of 2020/21, about 690 doctoral degrees were earned by American Indian or Alaskan Native students in the United States. In that year, a further 23,479 non-resident aliens earned doctoral degrees in the U.S.

Summary of statistical analysis regarding undergraduate GPA and time to degree.

Graph and download economic data for Consumer Unit Characteristics: Percent Never Attended School and Other by Highest Education: College Graduate: Master's, Professional, Doctoral Degree (CXU980320LB1409M) from 2012 to 2022 about doctoral degree, schools, consumer unit, professional, tertiary schooling, education, percent, and USA.

Summary of statistical analysis regarding GRE percentiles and time to degree.

Graph and download economic data for Consumer Unit Characteristics: Percent High School (9-12) by Highest Education: College Graduate: Master's, Professional, Doctoral Degree (CXU980300LB1409M) from 2012 to 2023 about doctoral degree, consumer unit, professional, secondary schooling, secondary, tertiary schooling, education, percent, and USA.

In the 2020/21 academic year, public doctoral higher education institutions in the United States spent an average of 20,100 U.S. dollars per full-time equivalent (FTE) student on education and related expenses. This compares to 18,660 U.S. dollars for the 2010/11 academic year, when adjusted to 2020 dollars.

Abstract (en): We study the research productivity of new graduates from North American PhD programs in economics from 1986 to 2000. We find that research productivity drops off very quickly with class rank at all departments, and that the rank of the graduate departments themselves provides a surprisingly poor prediction of future research success. For example, at the top ten departments as a group, the median graduate has fewer than 0.03 American Economic Review (AER)-equivalent publications at year six after graduation, an untenurable record almost anywhere. We also find that PhD graduates of equal percentile rank from certain lower-ranked departments have stronger publication records than their counterparts at higher-ranked departments. In our data, for example, Carnegie Mellon's graduates at the 85th percentile of year-six research productivity outperform 85th percentile graduates of the University of Chicago, the University of Pennsylvania, Stanford, and Berkeley. These results suggest that even the top departments are not doing a very good job of training the great majority of their students to be successful research economists. Hiring committees may find these results helpful when trying to balance class rank and place of graduate in evaluating job candidates, and current graduate students may wish to re-evaluate their academic strategies in light of these findings.

In 2018, the overall satisfaction for the programs completed by doctoral graduates in 2014 equaled to 6.8 points. The aspect which received the lowest rate was the training's quantity, while the autonomy and degree of responsibility during the PhD programs obtained the highest rate.

This dataset contains summary data for each of the 49 analyzed texts in a research accepted for publication in the Scandinavian Journal of Educational Research. The article investigates patterns of systematic metaphors used to characterize various aspects of the doctoral education period, based on analysis of dissertation acknowledgements (DAs) from doctoral dissertations across academic disciplines and written by researchers from four PhD programs offered by a Norwegian university. A discourse dynamics approach was applied to the data, allowing for the identification of metaphors employed about these topics followed by the categorization of the identified metaphors into broader categories. The resulting overview of the systematic metaphorical patterns in DAs provides empirical evidence concerning how doctoral researchers view their experiences, useful in mentoring situations as a starting point for addressing attitudes, beliefs and values about the various challenges and rewards involved in doctoral trajectories.

Summary of statistical analysis regarding GRE percentiles and candidacy exam passage or failure.

Anonymised raw data, cleaned data analysis and the questionnaire for Nature's inaugural survey of post-doctoral researchers, which attracted more than 7,600 respondents from across the world.

Graph and download economic data for Income Before Taxes: Public Assistance, Supplemental Security Income, SNAP by Highest Education: College Graduate: Master's, Professional, Doctoral Degree (CXUWELFARELB1409M) from 2012 to 2023 about doctoral degree, supplements, assistance, social assistance, public, professional, SNAP, food stamps, tertiary schooling, tax, education, food, income, and USA.

Graph and download economic data for Consumer Unit Characteristics: Percent High School (9-12) by Education: Master's, Professional, Doctorate (CXU980300LB1309M) from 1996 to 2009 about doctoral degree, consumer unit, professional, secondary schooling, secondary, tertiary schooling, education, percent, and USA.

OVERVIEW

This data file, compiled from multiple online sources, presents 2013–2017 publication counts—articles, articles in high-impact journals, books, and books from high-impact publishers—for 2,132 professors and associate professors in 426 U.S. departments of sociology. It also includes information on institutional characteristics (e.g., institution type, highest sociology degree offered, department size) and individual characteristics (e.g., academic rank, gender, PhD year, PhD institution).

The data may be useful for investigations of scholarly productivity, the correlates of scholarly productivity, and the contributions of particular individuals and institutions. Complete population data are presented for the top 26 doctoral programs, doctoral institutions other than R1 universities, the top liberal arts colleges, and other bachelor's institutions. Sample data are presented for Carnegie R1 universities (other than the top 26) and master's institutions.

USER NOTES

Please see our paper in Scholarly Assessment Reports, freely available at https://doi.org/10.29024/sar.36 , for full information about the data set and the methods used in its compilation. The section numbers used here refer to the Appendix of that paper. See the References, below, for other papers that have made use of these data.

The data file is a single Excel file with five worksheets: Sampling, Articles, Books, Individuals, and Departments. Each worksheet has a simple rectangular format, and the cells include just text and values—no formulas or links. A few general notes apply to all five worksheets.

• The yellow column headings represent institutional (departmental) data. The blue column headings represent data for individual faculty.

• iType is institution type, as described in section A.2—TopR (top research universities), R1 (other R1 universities), OD (other doctoral universities), M (master's institutions), TopLA (top liberal arts colleges), or B (other bachelor's institutions). nType provides the same information, but as a single-digit code that is more useful for sorting the rows; 1=TopR, 2=R1, 3=OD, 4=M, 5=TopLA, and 6=B.

• Inst is a four-digit institution code. The first digit corresponds to nType, and the last three digits allow for alphabetical sorting by institution name. Indiv is a one- or two-digit code that can be used to sort the individuals by name within each department. The Inst, nType, and Indiv codes are consistent across the five worksheets.

• For binary variables such as Full professor and Female, 1 indicates yes (full professor or female) and 0 indicates no (associate professor or male).

The five worksheets represent five distinct stages in the data compilation process. First, the Sampling worksheet lists the 1,530 base-population institutions (see section A.3) and presents the characteristics of the faculty included in the data file. Each row with an entry in the Individual column represents a faculty member at one of the 426 institutions included in the data set. Each row without an entry in the Individual column represents an institution that either (a) did not meet the criteria for inclusion (section A.1) or (b) was not needed to attain the desired sample size for the R1 or M groups (section A.3).

The Articles worksheet includes the data compiled from SocINDEX, as described in section A.6. Each row with an entry in the Journal column represents an article written by one of the 2,132 faculty included in the data. Each row without an entry in the Journal column represents a faculty member without any article listings in SocINDEX for the 2013–2017 period. (Note that SocINDEX items other than peer-reviewed articles—editorials, letters, etc.—may be listed in the Journal column but assigned a value of 1 in the Excluded column and a value of 0 in the Article credit and HI article credit columns. We assigned no credit for items such as editorial and letters, but other researchers may wish to include them.) The N and i columns represent, for each article, the number of authors (N) and the faculty member's place in the byline (i), as described in section A.8. The CiteScore and Highest percentile columns were used to identify high-impact journals, as indicated in the HI journal column. The Article credit and HI article credit columns are article counts, adjusted for co-authorship.

The Books worksheet includes data compiled from Amazon and other sources, as described in section A.7. Each row with an entry in the Book column represents a book written by one of the 2,132 faculty. Each row without an entry in the Book column represents a faculty member without any book listings in Amazon during the 2013–2017 period. The publication counts in the Books worksheet—Book credit and HI book credit—follow the same format as those in the Articles worksheet.

The Individuals worksheet consolidates information from the Articles and Books worksheets so that each of the 2,132 individuals is represented by a single row. The worksheet also includes several categorical variables calculated or otherwise derived from the raw data—Years since PhD, for instance, and the three corresponding binary variables. We suspect that many data users will be most interested in the Individuals worksheet.

The Departments worksheet collapses the individual data so that each of the 426 institutions (departments) is represented by a single row. Individual characteristics such as Female and Years since PhD are presented as percentages or averages—% Female and Avg years since PhD, for instance. Each of the four productivity measures is represented by a departmental total, an average (the total divided by the number of full and associate professors), a departmental standard deviation, and a departmental median.