phd synopsis sample in history

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Format of synopsis for PhD | Download Sample.

Guidelines for Writing Ph. D Synopsis.

FORMAT OF SYNOPSIS (MS/MPHIL & PHD). Given below is an outline for synopsis writing. It provides guidelines for organization and presentation of research.

INTRODUCTION OF 2-3 PAGES

• Identify a real world problem
• Describe the undesirable symptoms
• Identify the knowledge gap that needs to be filled in order to help solve the problem
• Support your discussion with solid peer-reviewed references

LITERATURE REVIEW

• Create an Outline or “mindmap” of the key theories and concepts.
• Dig deep into the “ Peer-reviewed” literature for each theory and concept and create an annotated bibliography and literature map
• Write literature review
• Map out the research gap
• Identify the “type(s)” of question that need to be answer to fulfill the purpose
• Develop the main research question and sub-questions
• Develop hypotheses as appropriate
• Identify and diagram the key variables in the research question
• Identify and diagram the key relationships between the variables
• Identify and diagram the key context factors
• Describe the framework
• Research Process
• Based on the research questions, the overall approach (Data Collection, Analysis methods, Validity and Reliability test process)

POSSIBLE OUTCOME AND LIMITATIONS OF YOUR STUDY

• Identify the larger application(s) and meaning(s) of the findings.
• Identify the limitations associated with the findings and conclusion.

BIBLIOGRAPHY OR REFERENCES

Most preferable format:

Font: Times New Roman

Title of the thesis: 18

Main Heading: 14 Bold

Sub Heading: 12 Bold

Spacing 1.5

Reference style: APA/IEEE/Harvard

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Preparing a History PhD proposal

The carefully thought-out and detailed research proposal to be submitted with the formal application is the product of a sometimes prolonged negotiation with your potential supervisor. The supervisor may be enthusiastic about your project or might advise you to consider a different subject or change your angle on it; they may query aspects of your plan such as its breadth, the availability of primary sources or the extent to which you are familiar with the secondary literature. You may be asked to demonstrate the originality of your research question or be advised to consider applying to another institution which may have more appropriate expertise. During this process you will likely be asked to submit a specimen of written-up historical research, such as your Masters or BA dissertation. The sooner you start developing the structure that is expected in a research proposal, the more productive your exchanges with your potential supervisor will be.

You may find different advice for writing a research proposal across different OU webpages. Given that a research proposal can vary significantly across different disciplines, when applying to the History Department you should follow the guidance provided here.

The research proposal you submit in January should be approximately 1000 words, plus a bibliography, and should contain the following:

A title, possibly with a subtitle

The title should not take the form of a question and it may run to a dozen words or more. Like the title of a book, it should clearly convey the topic you propose to work on. A subtitle may explain the chronological or geographical focus of your work, or the methodological approach you will take. Choosing a title is a good way for focusing on the topic you want to investigate and the approach you want to take.

These are examples of poor titles and topics to research:

• Captain Cook’s Third Voyage
• Women in eighteenth-century England

These would be poor topics to research because they lack a strong question and it is not clear which approach they take to their already well-researched subjects. They are generic or merely descriptive. 

Examples of good research topics

• Constructing the Eternal City: visual representations of Rome, 1500-1700
• Rearing citizens for the state: manuals for parents in France, 1900-1950

These projects combine a sharp chronological and geographical focus with a clear indication of how the sources will be analysed to respond to a precise question. In the first case, for example, the premise is that visual representations are critical in the making of a city’s eminence. This indicates the type of sources that will be analysed (paintings, engravings and other visual sources). The chronology is particularly well chosen because in these two centuries Rome turned from being the capital of the Catholic world to becoming the much sought-after destination of the Grand Tour; interesting questions of change and continuity come into focus.

Brief summary of your argument

An acceptable PhD thesis must have a central argument, a 'thesis'.  You need to have something to argue for or against, a point to prove or disprove, a question to answer. What goes into this section of the proposal is a statement of your question and the answer you plan to give, even if, for now, it remains a hypothesis.

Why this subject is important

We expect originality in a thesis and so under this rubric we expect you to explain why the knowledge you seek on the subject you propose to work on is important for its period and place, or for historians’ views on its period and place. Finding some early-modern English laundry lists would not suffice  on its own  to justify writing a PhD thesis about them. But those laundry lists could be important evidence for a thesis about the spread of the Great Plague in London, for example.

Framing your research

Your proposal has to show awareness of other scholarly writing on the subject. This section positions your approach to the subject in relation to approaches in some of those works, summarising how far you think it differs. For instance, you could challenge existing interpretations of the end the Cold War, or you might want to support one historian or another; you could open up a neglected aspect of the debate - say by considering the role of an overlooked group or national government - and perhaps kick-start a debate of your own. All this is to show that you have read  into  your subject and familiarised yourself with its contours. We don’t expect you to have done all your research at the start, but it is essential for you to show familiarity with the key texts and main authors in your chosen field.

What sources might you need to consult in libraries and archives?

Here you should describe or at least list the primary materials you are likely to use in researching your thesis. This demonstrates your confidence that enough relevant sources exist to support a sustained scholarly argument. Many archival catalogues are available online and can be searched remotely, including The National Archives, the National Archives of Scotland, the National Archives (Ireland), the Public Record Office of Northern Ireland and Archives Wales. You can search the London-based Historical Manuscripts Commission and the National Register of Archives, both of which provide access to local county record offices. Databases such as ‘Eighteenth Century Collections Online’ and the British Library’s ‘British Newspapers Online 1600-1900’ will help you identify and locate relevant sources.

What skills are required to work on the sources you plan to use?

You need to show that you have the linguistic competence to pursue your research. With few exceptions, original sources must be read in the original languages; if the principal historical literature is not in English, you must be able to read it too. Palaeographic problems aren’t confined to ancient writing. You might have to tackle early modern or other scripts that are hard to decipher. Even with fluent German, an applicant baffled by the Gothic script and typeface would flounder without undertaking ancillary study. Training is available at The Open University, or in some circumstances you can be funded to undertake training elsewhere, and you should demonstrate awareness of the skills that you need to acquire.

Do you have the technical competence to handle any data-analysis your thesis may require?

Databases, statistical evidence and spreadsheets are used increasingly by historians in certain fields. If your research involves, say, demographic or economic data, you will need to consider whether you have the necessary IT and statistical skills and, if not, how you will acquire them.

How will you arrange access to the libraries and archives where you need to work?

Although primary sources are increasingly available in digitised form, you should consider that important sources may be closed or in private hands. To consult them may require some travelling and so you should be realistic as to what you will be able to do, particularly if you are applying to study part-time as not all archives are open out of regular office hours.

A bibliography

This should come at the end and include a list of the primary sources you plan to use and the relevant secondary literature on the subject. While you should show that you are on top of recent work (and of important older studies) on the topic, there is no point in having a long list of works only marginally related to your subject. As always, specificity is the best policy.

Please follow this link to see an  example of a successful research proposal [PDF].

All this may seem daunting, as if the department is asking you to write a thesis before you apply. But that is not our intention; the advice is to help you perform the necessary spadework before entering the formal application process. Working up a proposal under the headings suggested above will, if your application is successful, save you and your supervisor(s) much time if and when the real work begins.

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How to Format a PhD Synopsis (India)

• By Qamar Mayyasah
• August 26, 2020

Introduction

This article will answer common questions about the PhD synopsis, give guidance on how to write one, and provide my thoughts on samples.

A PhD synopsis is a detailed summary of your proposed research project which justifies the need for your work. It is used to convince academic committees that your project should be approved.

If you are wondering how to write a synopsis for a PhD, then there are several things you must make sure your synopsis includes. Firstly, the reader must be able to read your synopsis and understand what contribution it would make to the research area. You should also explain the research objectives, methodology, data analysation and presentation format. Finally, you should conclude with limitations of your study and how you envisage others building on the findings you make.

PhD Synopsis format for a project

Although the format of a PhD synopsis report may differ between universities, there are many universal recommendations I can give. First, the research project synopsis format must include several fundamental sections which allow you to clearly detail your proposed project.

These sections are outlined below:

Research project title

Clearly define the title of your research project.

Include an introduction which summarises the current knowledge in your research area. This section should explain where gaps in knowledge are, and briefly what your project aims to do to address these gaps.

Literature review

A literature review will be a summary of published literature including journals, papers and other academic documentation which relate to your project. You need to critically appraise these documents: What have others done? What did they find? Where could their work be expanded on?

Aims & Objectives

Clearly define what the purpose of the PhD project is. What questions are you trying to answer? How will you measure success?

Research Methodology

Explain how you will achieve your objectives. Be specific and outline your process; the equipment you will use, data collection strategies, questionnaires you will distribute and data analysation techniques you will employ. This is a critical part of the research synopsis as it demonstrates whether your project is achievable or too ambitious.

You must provide references and citations to any sources you use. Reference materials are needed to acknowledge the original source, allow further reading for those who are interested and avoid claims of plagiarism. A number of different referencing systems exist, so it is important that you use the referencing system outlined in your university guidelines.

Provide a conclusion which should briefly summarise what your PhD research project is and why it is needed. You should also comment on the limitations of your work so that the scope of your study is clear.

In addition to the synopsis format for a PhD, we have outlined the styling rules you should follow:

• Approximately 1” margins on top, bottom, and right of page.
• Approximately 1.25” margin on left of page to allow space for binding.
• Sans serif font (for example Times New Roman).
• Black colour font.
• Size 11pt or 12pt font.

It is important to remember this is general advice to assist with PhD synopsis writing. You must check your university guidelines first as they may have particular rules which you should follow.

PhD Synopsis Samples

I would not recommend using a PhD synopsis sample. This is because every research project is different, and the purpose of a synopsis report is to demonstrate the uniqueness of your project. Instead you should use the above format, and ensure you address each of the sections.

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The unit of analysis refers to the main parameter that you’re investigating in your research project or study.

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The scope of the study is defined at the start of the study. It is used by researchers to set the boundaries and limitations within which the research study will be performed.

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Gabrielle’s a 2nd year Immunology PhD student at the University of Michigan. Her research focus on the complications of obesity and type 2 diabetes in the clearance of respiratory bacterial infections.

Dr Williams gained her PhD in Chemical Engineering at the Rensselaer Polytechnic Institute in Troy, New York in 2020. She is now a Presidential Postdoctoral Fellow at Cornell University, researching simplifying vaccine manufacturing in low-income countries.

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How to Write a Research Synopsis: Template, Examples, & More

Last Updated: February 12, 2024 Fact Checked

Research Synopsis Template

• Organizing & Formatting
• Writing Your Synopsis
• Reviewing & Editing

This article was reviewed by Gerald Posner and by wikiHow staff writer, Raven Minyard, BA . Gerald Posner is an Author & Journalist based in Miami, Florida. With over 35 years of experience, he specializes in investigative journalism, nonfiction books, and editorials. He holds a law degree from UC College of the Law, San Francisco, and a BA in Political Science from the University of California-Berkeley. He’s the author of thirteen books, including several New York Times bestsellers, the winner of the Florida Book Award for General Nonfiction, and has been a finalist for the Pulitzer Prize in History. He was also shortlisted for the Best Business Book of 2020 by the Society for Advancing Business Editing and Writing. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 230,579 times.

A research synopsis describes the plan for your research project and is typically submitted to professors or department heads so they can approve your project. Most synopses are between 3,000 and 4,000 words and provide your research objectives and methods. While the specific types of information you need to include in your synopsis may vary depending on your department guidelines, most synopses include the same basic sections. In this article, we’ll walk you step-by-step through everything you need to know to write a synopsis for research.

Things You Should Know

• Begin your research synopsis by introducing the question your research will answer and its importance to your field.
• List 2 or 3 specific objectives you hope to achieve and how they will advance your field.
• Discuss your methodology to demonstrate why the study design you chose is appropriate for your research question.

Organizing Your Research Synopsis

• Find out what citation format you’re supposed to use, as well as whether you’re expected to use parenthetical references or footnotes in the body of your synopsis.
• If you have questions about anything in your guidelines, ask your instructor or advisor to ensure you follow them correctly.

• Title: the title of your study
• Abstract: a summary of your research synopsis
• Introduction: identifies and describes your research question
• Literature Review: a review of existing relevant research
• Objectives: goals you hope to accomplish through your study
• Hypotheses: results you expect to find through your research
• Methodology and methods: explains the methods you’ll use to complete your study
• References: a list of any references used in citations

Tip: Your synopsis might have additional sections, depending on your discipline and the type of research you're conducting. Talk to your instructor or advisor about which sections are required for your department.

• Keep in mind that you might not end up using all the sources you initially found. After you've finished your synopsis, go back and delete the ones you didn't use.

Writing Your Research Synopsis

• Your title should be a brief and specific reflection of the main objectives of your study. In general, it should be under 50 words and should avoid unneeded phrases like “an investigation into.”
• On the other hand, avoid a title that’s too short, as well. For example, a title like “A Study of Urban Heating” is too short and doesn’t provide any insight into the specifics of your research.

• The introduction allows you to explain to your reader exactly why the question you’re trying to answer is vital and how your knowledge and experience make you the best researcher to tackle it.
• Support most of the statements in your introduction with other studies in the area that support the importance of your question. For example, you might cite a previous study that mentions your problem as an area where further research needs to be done.
• The length of your introduction will vary depending on the overall length of your synopsis as well as the ultimate length of your eventual paper after you’ve finished your research. Generally, it will cover the first page or two of your synopsis.

• For example, try finding relevant literature through educational journals or bulletins from organizations like WHO and CDC.
• Typically, a thorough literature review discusses 8 to 10 previous studies related to your research problem.
• As with the introduction, the length of your literature review will vary depending on the overall length of your synopsis. Generally, it will be about the same length as your introduction.
• Try to use the most current research available and avoid sources over 5 years old.

• For example, an objective for research on urban heating could be “to compare urban heat modification caused by vegetation of mixed species considering the 5 most common urban trees in an area.”
• Generally, the overall objective doesn’t relate to solving a specific problem or answering a specific question. Rather, it describes how your particular project will advance your field.
• For specific objectives, think in terms of action verbs like “quantify” or “compare.” Here, you’re hoping to gain a better understanding of associations between particular variables.

• Specify the sources you used and the reasons you have arrived at your hypotheses. Typically, these will come from prior studies that have shown similar relationships.
• For example, suppose a prior study showed that children who were home-schooled were less likely to be in fraternities or sororities in college. You might use that study to back up a hypothesis that home-schooled children are more independent and less likely to need strong friendship support networks.

• Expect your methodology to be at least as long as either your introduction or your literature review, if not longer. Include enough detail that your reader can fully understand how you’re going to carry out your study.
• This section of your synopsis may include information about how you plan to collect and analyze your data, the overall design of your study, and your sampling methods, if necessary. Include information about the study setting, like the facilities and equipment that are available to you to carry out your study.
• For example, your research work may take place in a hospital, and you may use cluster sampling to gather data.

• Use between 100 and 200 words to give your readers a basic understanding of your research project.
• Include a clear statement of the problem, the main goals or objectives of your study, the theories or conceptual framework your research relies upon, and the methods you’ll use to reach your goals or objectives.

Tip: Jot down a few notes as you draft your other sections that you can compile for your abstract to keep your writing more efficient.

Reviewing and Editing Your Research Synopsis

• If you don’t have that kind of time because you’re up against a deadline, at least take a few hours away from your synopsis before you go back to edit it. Do something entirely unrelated to your research, like taking a walk or going to a movie.

• Eliminate sentences that don’t add any new information. Even the longest synopsis is a brief document—make sure every word needs to be there and counts for something.
• Get rid of jargon and terms of art in your field that could be better explained in plain language. Even though your likely readers are people who are well-versed in your field, providing plain language descriptions shows you know what you’re talking about. Using jargon can seem like you’re trying to sound like you know more than you actually do.

Tip: Free apps, such as Grammarly and Hemingway App, can help you identify grammatical errors as well as areas where your writing could be clearer. However, you shouldn't rely solely on apps since they can miss things.

• Reference list formatting is very particular. Read your references out loud, with the punctuation and spacing, to pick up on errors you wouldn’t have noticed if you’d just read over them.
• Compare your format to the one in the stylebook you’re using and make sure all of your entries are correct.

• Read your synopsis backward by starting on the last word and reading each word separately from the last to the first. This helps isolate spelling errors. Reading backward sentence by sentence helps you isolate grammatical errors without being distracted by the content.
• Print your synopsis and circle every punctuation mark with a red pen. Then, go through them and focus on whether they’re correct.
• Read your synopsis out loud, including the punctuation, as though you were dictating the synopsis.

• Have at least one person who isn’t familiar with your area of study look over your synopsis. If they can understand your project, you know your writing is clear. If any parts confuse them, then that’s an area where you can improve the clarity of your writing.

Expert Q&A

• If you make significant changes to your synopsis after your first or second round of editing, you may need to proofread it again to make sure you didn’t introduce any new errors. Thanks Helpful 0 Not Helpful 0

You Might Also Like

• ↑ https://admin.umt.edu.pk/Media/Site/iib1/FileManager/FORMAT%20OF%20SYNOPSIS%2012-10-2018.pdf
• ↑ https://www.scientificstyleandformat.org/Tools/SSF-Citation-Quick-Guide.html
• ↑ https://numspak.edu.pk/upload/media/Guidelines%20for%20Synopsis%20Writing1531455748.pdf
• ↑ https://www.researchgate.net/publication/279917593_Research_synopsis_guidelines
• ↑ https://writingcenter.unc.edu/tips-and-tools/editing-and-proofreading/
• ↑ https://www.cornerstone.edu/blog-post/six-steps-to-really-edit-your-paper/

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How to Write a Synopsis for PhD With Focus on the Right Format

The correct format of synopsis starts with an introduction and ends with a conclusion, but the confusion lies in knowing what’s in between them. This guide will show you how to do the right format of synopsis for PhD. Moreover, a focus on the importance of synopsis format will also be shed light in this blog. Knowing the correct structure and parts of a PhD summary is super important because it's like a map for your whole research. And if you need some extra help, there are services out there, like APA Formatting Services, that can make sure your summary is top-notch. Whether you're just starting or want to make your summary better, this guide will give you simple tips for writing according to the right PhD Synopsis Format.

Importance of Synopsis Format

The right format of synopsis for PhD is really important because it's like a roadmap for your research. It helps you organize your ideas in a clear and easy-to-follow way. When you stick to the format, it shows that you understand the rules of academic writing and take your research seriously. This makes it easier for your readers to understand what you're trying to say. It's like using a recipe to make sure your dish turns out just right. Plus, when you follow the format, it makes your work look more professional. This is important because it gives a good impression to your professors and peers. So, the PhD Synopsis Format isn't just a bunch of rules - it's a tool that helps you present your research in the best possible way.

Correct PhD thesis Format

A PhD synopsis is like a summary of what you want to study for your doctorate. It's like a roadmap that tells people what your research will be about. Let's break it down:

1. Title : - Choose a clear and catchy title for your research.

2. Introduction : - Introduce the topic you want to study. - Explain why it's important and how it fits into existing knowledge.

3. Research Question : - Clearly state the main question you want to answer in your research.

4. Objectives : - List the specific goals you want to achieve with your research.

5. Literature Review : - Summarize what others have already found about your topic. - Highlight any gaps in the current knowledge.

6. Methodology : - Explain how you will conduct your research. - Mention the methods, tools, and techniques you'll use.

7. Data Collection : - Describe where you'll get your information or data from. - Explain how you'll gather it.

8. Analysis : - Say how you'll examine the data to find answers to your research question.

9. Expected Results : - Predict what you think you'll discover from your research.

10. Significance : - Explain why your research is important for the field and how it can be useful.

11. Timeline : - Give a rough idea of how long each phase of your research will take.

12. References : - List the books, articles, and sources you've used for your synopsis.

Remember, keep it clear and simple. Your synopsis should give a good overview of your research without getting too detailed. Good luck with your PhD journey!

Understand the Guidelines: How Understanding Guidelines Helps in Writing a Synopsis for PhD With Focus on the Right Format.

1. ensures proper organization: - guides you in arranging sections logically and coherently. - helps in presenting important information in a structured way., 2. facilitates clarity and readability: - enables you to use headings and subheadings effectively for clear presentation. - ensures that the reader can easily follow and understand your research., 3. enhances professionalism: - reflects your proficiency in adhering to academic standards. - presents your work in a manner consistent with the expectations of the academic community., 4. minimizes revisions and corrections: - reduces the likelihood of having to make significant changes later on. - saves time and effort in the long run by getting it right from the start., outline your research clearly.

When you outline your research, you're basically giving a clear picture of what your study is all about. This helps the reader know right away what to expect and why your work is important. It's like telling a story – you start with the main idea and then explain the details. This also helps you stay on track. When you know what you want to say, it's easier to put it down on paper. You won't forget important points or get lost in your own thoughts. It's like having a plan before building a house – you know where each brick goes. When you outline your research clearly, it makes writing the synopsis much easier. You already know what to say and in what order. It's like having a blueprint before constructing a building – you know where everything goes. This clarity also benefits the reader. They can easily grasp your research and understand its significance. It's like reading a well-organized story – everything makes sense and keeps the reader engaged. This way, your synopsis becomes a valuable piece of academic work, contributing meaningfully to your field.

Adhere to the Prescribed Format

Importance of adhering to the prescribed format:.

Following a specific format ensures that your synopsis is organized and easy to follow. Each section has a designated place, making it clear for the reader to navigate through your research.

Universities have specific guidelines for a reason. Adhering to the prescribed format shows that you respect and understand these academic standards. It's like speaking the language of academia.

Proper formatting demonstrates a high level of professionalism. It shows that you take your research seriously and are committed to presenting it in a way that meets the expectations of the academic community.

When everyone uses the same format, it avoids confusion. Readers, including your professors and peers, can quickly find the information they need. It's like using a universal key that fits the lock.

Final Thoughts

To sum it up, understanding the right way to set up your PhD summary, as explained in the format of synopsis for PhD, is really important. This format is like a map that helps you present your research in a clear and organized way. It's like following a recipe to make sure your dish turns out just right. Sticking to this format also shows that you take your research seriously and respect the rules of the academic world. If you need some extra help in understanding the importance of synopsis format, services like APA Formatting Services can make sure your summary meets the highest standards. By following these guidelines, you not only make your research easy to read and understand but also show that you're dedicated to producing top-notch work. Remember, a well-organized summary and the right PhD Synopsis Format isn't just informative, it's a big step towards earning your PhD.

Thesis India provides comprehensive support to researchers in India who aspire to make significant contributions to their fields of study. They offer expert assistance in crafting an exceptional doctoral research proposal, which is more than just a document; it is your gateway to an enriching academic voyage. Their team of experienced researchers and writers is dedicated to helping you craft a proposal that showcases the depth of your knowledge and the potential impact of your research. They specialize in providing comprehensive support to researchers like you, ensuring a successful start to your PhD journey with a compelling research proposal. They also offer expert synopsis editing services to ensure your synopsis meets the highest standards. The format of synopsis for PhD may vary from one institution to another, but Thesis India can guide you on how to do the correct PhD Synopsis Format.

1. What should be included in a PhD synopsis?

A PhD synopsis should include the research topic, objectives, methodology, literature review, expected outcomes, and references.

2. How long is a PhD synopsis?

A PhD synopsis is typically about 10-15 pages long.

3. How many pages is a PhD synopsis?

A PhD synopsis is usually between 20-30 pages.

4. How do you start a PhD synopsis presentation?

Start a PhD synopsis presentation by introducing yourself, stating the research topic, and providing an overview of the research objectives.

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National Center for Science and Engineering Statistics

• All previous cycle years

The NSCG is a biennial survey that provides data on the characteristics of the nation's college graduates, with a focus on those in the science and engineering workforce.

Survey Info

• tag for use when URL is provided --> Methodology
• tag for use when URL is provided --> Data
• tag for use when URL is provided --> Analysis

The NSCG is a unique source for examining the relationship of degree field and occupation in addition to other characteristics of college-educated individuals, including work activities, salary, and demographic information.

Areas of Interest

• Science and Engineering Workforce
• STEM Education

Survey Administration

This survey was conducted by the Census Bureau in partnership with the National Center for Science and Engineering Statistics within the National Science Foundation.

Survey Details

• Survey Description (PDF 123 KB)
• Data Tables (PDF 2.1 MB)

Featured Survey Analysis

Effects of the COVID-19 Pandemic on Employment, Earnings, and Professional Engagement: New Insights from the 2021 National Survey of College Graduates

NSCG Overview

Data highlights, the share of u.s. college graduates employed full time trended downward between 2015 and 2021..

Unemployment increased across all levels of education between 2019 and 2021.

Methodology

Survey description, survey overview (2021 survey cycle).

The National Survey of College Graduates (NSCG)—sponsored by the National Center for Science and Engineering Statistics (NCSES) within the National Science Foundation (NSF)—provides data on the characteristics of the nation’s college graduates, with a focus on those in the science and engineering workforce. It samples individuals who are living in the United States during the survey reference week, have at least a bachelor’s degree, and are younger than 76. By surveying college graduates in all academic disciplines, the NSCG provides data useful in understanding the relationship between college education and career opportunities, as well as the relationship between degree field and occupation.

Data collection authority

The information collected in the NSCG is solicited under the authority of the NSF Act of 1950, as amended, and the America COMPETES Reauthorization Act of 2010. The Census Bureau collects the NSCG data under the authority of Title 13, Section 8 of the United States Code. The Office of Management and Budget control number is 3145-0141.

Major changes to recent survey cycle

The 2021 NSCG data collection instrument included new questions to gauge the effects of the coronavirus pandemic on employment, specifically on labor force status, number of hours worked per week, salary, benefits, telecommuting options, and total earned income.

Key Survey Information

Initial survey year, reference period.

The week of 1 February 2021.

Response unit

Individuals with at least a bachelor’s degree.

Sample or census

Population size.

Approximately 68.6 million individuals.

Sample size

Approximately 164,000 individuals.

Key variables

Key variables of interest are listed below.

• Demographics (e.g., age, race, sex, ethnicity, and citizenship)
• Educational history
• Employment status
• Field of degree

Survey Design

Target population.

The NSCG target population includes individuals who meet the following criteria:

• Earned a bachelor’s degree or higher prior to 1 January 2020,
• Are not institutionalized and reside in the United States or Puerto Rico as of 1 February 2021, and
• Are younger than 76 years as of 1 February 2021.

Sampling frame

The 2021 NSCG retains the four-panel rotating panel design that began with the 2010 NSCG. As part of this design, every new panel receives a baseline survey interview and three biennial follow-up interviews before rotating out of the survey.

The 2021 NSCG includes approximately 164,000 sample cases drawn from the following:

• Returning sample from the 2019 NSCG who were originally selected from the 2013 American Community Survey (ACS)
• Returning sample from the 2019 NSCG who were originally selected from the 2015 ACS
• Returning sample from the 2019 NSCG who were originally selected from the 2017 ACS
• New sample selected from the 2019 ACS

Approximately 90,000 cases were selected from the returning sample members for one of the three biennial follow-up interviews that are part of the rotating panel design. For the baseline survey interview, about 74,000 new sample cases were selected from the 2019 ACS.

Sample design

The NSCG uses a stratified sampling design to select its sample from the eligible sampling frame. Within the sampling strata, the NSCG uses probability proportional to size or systematic random sampling techniques to select the NSCG sample. The sampling strata were defined by the cross-classification of the following four variables:

• Young graduate oversample group eligibility indicator (2 levels)
• Demographic group (9 levels)
• Highest degree type (3 levels)
• Detailed occupation group (25 levels)

As has been the case since the 2013 NSCG, the 2021 NSCG includes an oversample of young graduates to improve the precision of estimates for this important population.

Data Collection and Processing

Data collection.

The NSCG uses a trimodal data collection approach: Web survey, mail survey, and computer-assisted telephone interview (CATI). The 2021 NSCG data collection effort lasted approximately 7 months.

Data processing

The data collected in the NSCG are subject to both editing and imputation procedures. The NSCG uses both logical imputation and statistical (hot deck) imputation as part of the data processing effort.

Estimation techniques

Because the NSCG is based on a complex sampling design and subject to nonresponse bias, sampling weights were created for each respondent to support unbiased population estimates. The final analysis weights account for several factors, including the following:

• Adjustments to account for undercoverage of recent immigrants and undercoverage of recent degree-earners
• Adjustment for incorrect names or incomplete address information on the sampling frame
• Differential sampling rates
• Adjustments to account for non-locatability and unit nonresponse
• Adjustments to align the sample distribution with population controls
• Trimming of extreme weights
• Overlap procedures to convert weights that reflect the population of each individual frame (2013 ACS, 2015 ACS, 2017 ACS, and 2019 ACS) into a final sample weight that reflects the 2021 NSCG target population.

The final sample weights enable data users to derive survey-based estimates of the NSCG target population.

Survey Quality Measures

Sampling error.

Estimates of sampling errors associated with this survey were calculated using the successive difference replication method. Please contact the NSCG Survey Manager to obtain the replicate weights.

Coverage error

Any missed housing units or missed individuals within sample households in the ACS would create undercoverage in the NSCG. Additional undercoverage errors may exist because of self-reporting errors in the NSCG sampling frame that led to incorrect classification of individuals as not having a bachelor’s degree or higher when in fact they held such a degree.

Nonresponse error

The weighted response rate for the 2021 NSCG was 65%. Analyses of NSCG nonresponse trends were used to develop nonresponse weighting adjustments to minimize the potential for nonresponse bias in the NSCG estimates. A hot deck imputation method was used to compensate for item nonresponse.

Measurement error

The NSCG is subject to reporting errors from differences in interpretation of questions and by modality (Web, mail, or CATI). To reduce measurement errors, the NSCG questionnaire items were pretested in focus groups and cognitive interviews.

Data Availability and Comparability

Data availability.

Data from 1993 to the present are available at the NSCG Web page .

Data comparability

Year-to-year comparisons can be made among the 1993 to 2021 NSCG survey cycles because many of the core questions remained the same. Small but notable differences exist across some survey years, such as the collection of occupation and education data based on more recent taxonomies. Also, because of the use of different reference months in some survey cycles, seasonal differences may occur when making comparisons across years.

There is overlap in the cases included in the 2010 NSCG through the 2017 NSCG, in the 2013 NSCG through the 2019 NSCG, and in the 2015 NSCG through the 2021 NSCG. This sample overlap consists of cases that originated in the 2013 ACS, 2015 ACS, 2017 ACS, or 2019 ACS. The overlap among cases allows for the ability to conduct longitudinal analysis of this subset of the NSCG sample. To reduce the risk of disclosure, longitudinal analyses can be conducted only within a restricted environment. See the NCSES Restricted-Use Data Licensing and Procedures page to learn more.

Data Products

Publications.

Data from the NSCG are published in NCSES InfoBriefs and data tables, available at https://www.nsf.gov/statistics/srvygrads/ .

Information from this survey is also included in Science and Engineering Indicators and Women, Minorities, and Persons with Disabilities in Science and Engineering .

Electronic access

The NSCG public use data through 2021 are available in the SESTAT data tool and in downloadable files through the NCSES data page . Data from 1993 to 2019 (2021 forthcoming) are also available in the new NCSES interactive data tool . The NSCG restricted use data are available through the Census Bureau’s Federal Statistical Research Data Centers .

Technical Notes

Survey overview, data collection and processing methods, data comparability and changes, definitions.

Purpose. The National Survey of College Graduates (NSCG) provides data on the characteristics of the nation’s college graduates, with a focus on those in the science and engineering (S&E) workforce. It samples individuals who are living in the United States during the survey reference week, have earned at least a bachelor’s degree, and are younger than 76. By surveying college graduates in all academic disciplines, the NSCG provides data useful in understanding the relationship between college education and career opportunities, as well as the relationship between degree field and occupation.

The NSCG is designed to provide demographic, education, and career history information about college graduates and to complement another survey conducted by the National Center for Science and Engineering Statistics (NCSES): the Survey of Doctorate Recipients (SDR, https://www.nsf.gov/statistics/srvydoctoratework/ ). These two surveys share a common reference date, and they use similar questionnaires and data processing guidelines.

These technical notes provide an overview of the 2021 NSCG. Complete details are provided in the 2021 NSCG Methodology Report, available upon request from the NSCG Survey Manager.

Data collection authority. The information collected in the NSCG is solicited under the authority of the National Science Foundation Act of 1950, as amended, and the America COMPETES Reauthorization Act of 2010. The Census Bureau collects the NSCG data, on behalf of NCSES, under the authority of Title 13, Section 8 of the United States Code. The Office of Management and Budget control number is 3145-0141.

Survey contractor. Census Bureau.

Survey sponsor. NCSES.

Frequency. Biennial.

Initial survey year. 1993.

Reference period. The week of 1 February 2021.

Response unit. Individual.

Sample or census. Sample.

Population size. Approximately 68.6 million individuals.

Sample size. Approximately 164,000 individuals.

Target population. The NSCG target population includes individuals who meet the following criteria:

• Earned a bachelor’s degree ​ Bachelor’s degrees include equivalent undergraduate academic degrees awarded by colleges and universities in countries that may name their degrees differently. Bachelor’s degrees include equivalent undergraduate academic degrees awarded by colleges and universities in countries that may name their degrees differently. Bachelor’s degrees include equivalent undergraduate academic degrees awarded by colleges and universities in countries that may name their degrees differently. or higher prior to 1 January 2020
• Are not institutionalized and reside in the United States or Puerto Rico as of 1 February 2021
• Are younger than 76 years as of 1 February 2021

Sampling frame . Using a rotating panel design, the 2021 NSCG includes new sample cases from the 2019 American Community Survey (ACS) and returning sample cases from the 2019 NSCG.

The NSCG sampling frame for new sample cases included the following eligibility requirements:

• Were residing in the United States or Puerto Rico as of the ACS interview date
• Were noninstitutionalized as of the ACS interview date
• Had earned at least a bachelor’s degree as of the ACS interview date
• Would be under the age of 76 as of 1 February 2021
• Did not have an inaccurate name or incomplete address on the ACS data file

Returning sample cases from the 2019 NSCG originated from three different frames (the 2013 ACS, 2015 ACS, and 2017 ACS) and had the following eligibility requirements:

• Were a complete interview or temporarily ineligible during their initial NSCG survey cycle
• During the 2019 NSCG survey cycle, did not refuse to participate and request to be excluded from future NSCG cycles

Sample design . The NSCG sample design is cross-sectional with a rotating panel element. As a cross-sectional study, the NSCG provides estimates of the size and characteristics of the college graduate population for a point in time. As part of the rotating panel design, every new panel receives a baseline survey interview and three biennial follow-up interviews before rotating out of the survey.

The NSCG uses a stratified sampling design to select its sample from the eligible sampling frame. In the new sample, cases were selected using systematic probability proportional to size (PPS) sampling. ​ With PPS sampling, the probability of selection was proportional to the ACS final person-level weight, adjusted to account for imputed educational attainment, incomplete addresses, or invalid names. With PPS sampling, the probability of selection was proportional to the ACS final person-level weight, adjusted to account for imputed educational attainment, incomplete addresses, or invalid names. With PPS sampling, the probability of selection was proportional to the ACS final person-level weight, adjusted to account for imputed educational attainment, incomplete addresses, or invalid names. Among the returning sample, all eligible cases were selected. The sampling strata were defined by the cross-classification of the following four variables:

As has been the case since the 2013 NSCG, the 2021 NSCG includes an oversample of young graduates to improve the precision of estimates for this important population. The 2021 NSCG includes approximately 164,000 sample cases drawn from the following:

• Returning sample from the 2019 NSCG who were originally selected from the 2013 ACS

Data collection . The data collection period lasted approximately 7 months (8 April 2021 to 1 November 2021). The NSCG used a trimodal data collection approach: self-administered online survey (Web), self-administered paper questionnaire (via mail), and computer-assisted telephone interview (CATI). Individuals in the sample generally were started in the Web mode, depending on their available contact information and past preference. After an initial survey invitation, the data collection protocol included sequential contacts by postal mail, e-mail, and telephone that ran throughout the data collection period. At any time during data collection, sample members could choose to complete the survey using any of the three modes. Nonrespondents to the initial survey invitation received follow-up contacts via alternate modes.

Quality assurance procedures were in place at each data collection step (e.g., address updating, printing, package assembly and mailing, questionnaire receipt, data entry, CATI, coding, and post-data collection processing).

Mode . About 89% of the participants completed the survey by Web, 7% by mail, and 4% by CATI.

Response r ates . Response rates were calculated on complete responses, that is, from instruments with responses to all critical items. Critical items are those containing information needed to report labor force participation (including employment status, job title, and job description), college education (including degree type, degree date, and field of study), and location of residency on the reference date. The overall unweighted response rate was 67%; the weighted response rate was 65%. Of the roughly 164,000 persons in the 2021 NSCG sample, 106,279 completed the survey.

Data e diting. Response data had initial editing rules applied relative to the specific mode of capture to check internal consistency and valid range of response. The Web survey captured most of the survey responses and had internal editing controls where appropriate. A computer-assisted data entry (CADE) system was used to process the mailed paper forms. Responses from the three separate modes were merged for subsequent coding, editing, and cleaning necessary to create an analytical database.

Following established NCSES guidelines for coding NSCG survey data, including verbatim responses, staff were trained in conducting a standardized review and coding of occupation and education information, certifications, “other/specify” verbatim responses, state and country geographical information, and postsecondary institution information. For standardized coding of occupation (including auto-coding), the respondent's reported job title, duties and responsibilities, and other work-related information from the questionnaire were reviewed by specially trained coders who corrected respondents’ self-reporting errors to obtain the best occupation codes. For standardized coding of field of study associated with any reported degree (including auto-coding), the respondent’s reported department, degree level, and field of study information from the questionnaire were reviewed by specially trained coders who corrected respondents’ self-reporting errors to obtain the best field of study codes.

Imputation. Logical imputation was primarily accomplished as part of editing. In the editing phase, the answer to a question with missing data was sometimes determined by the answer to another question. In some circumstances, editing procedures found inconsistent data that were blanked out and therefore subject to statistical imputation.

The item nonresponse rates reflect data missing after logical imputation or editing but before statistical imputation. For key employment items—such as employment status, sector of employment, and primary work activity—the item nonresponse rates ranged from 0.0% to 1.1%. Nonresponse to questions deemed sensitive was higher: nonresponse to salary and earned income was 5.4% and 7.8%, respectively, for the new sample members and 4.7% and 6.8%, respectively, for the returning members. Personal demographic data of the new sample members had variable item nonresponse rates, with sex at 0.00%, birth year at 0.04%, marital status at 0.6%, citizenship at 0.4%, ethnicity at 1.4%, and race at 3.1%. The nonresponse rates for returning sample members were 0.8% for marital status and 0.7% for citizenship.

Item nonresponse was typically addressed using statistical imputation methods. Most NSCG variables were subjected to hot-deck imputation, with each variable having its own class and sort variables chosen by regression modeling to identify nearest neighbors for imputed information. For some variables, there was no set of class and sort variables that was reliably related to or suitable for predicting the missing value, such as day of birth. In these instances, random imputation was used, so that the distribution of imputed values was similar to the distribution of reported values without using class or sort variables.

Imputation was not performed on critical items or on verbatim-based variables. In addition, for some missing demographic information, the NSCG imported the corresponding data from the ACS, which had performed its own imputation.

Weighting. Because the NSCG is based on a complex sampling design and subject to nonresponse bias, sampling weights were created for each respondent to support unbiased population estimates. The final analysis weights account for several factors, including the following:

• Overlap procedures to convert weights that reflect the population of each individual frame (2013 ACS, 2015 ACS, 2017 ACS, and 2019 ACS) into a final sample weight that reflects the 2021 NSCG target population

The final sample weights enable data users to derive survey-based estimates of the NSCG target population. The variable name on the NSCG public use data files for the NSCG final sample weight is WTSURVY.

Variance estimation. The successive difference replication method (SDRM) was used to develop replicate weights for variance estimation. The theoretical basis for the SDRM is described in Wolter (1984) and in Fay and Train (1995). As with any replication method, successive difference replication involves constructing numerous subsamples (replicates) from the full sample and computing the statistic of interest for each replicate. The mean square error of the replicate estimates around their corresponding full sample estimate provides an estimate of the sampling variance of the statistic of interest. The 2021 NSCG produced 320 sets of replicate weights.

Disclosure protection. To protect against the disclosure of confidential information provided by NSCG respondents, the estimates presented in NSCG data tables are rounded to the nearest 1,000.

Data table cell values based on counts of respondents that fall below a predetermined threshold are deemed to be sensitive to potential disclosure, and the letter “D” indicates this type of suppression in a table cell.

Sampling error. NSCG estimates are subject to sampling errors. Estimates of sampling errors associated with this survey were calculated using replicate weights. Data table estimates with coefficients of variation (that is, the estimate divided by the standard error) that exceed a predetermined threshold are deemed unreliable and are suppressed. The letter “S” indicates this type of suppression in a table cell.

Coverage error. Coverage error occurs in sample estimates when the sampling frame does not accurately represent the target population and is a type of nonsampling error. Any missed housing units or missed individuals within sample households in the ACS would create undercoverage in the NSCG. Additional undercoverage errors may exist because of self-reporting errors in the NSCG sampling frame that led to incorrect classification of individuals as not having a bachelor's degree or higher when in fact they held such a degree.

Nonresponse error. The weighted response rate for the 2021 NSCG was 65%; the unweighted response rate was 67%. Analyses of NSCG nonresponse trends were used to develop nonresponse weighting adjustments to minimize the potential for nonresponse bias in the NSCG estimates. A hot deck imputation method was used to compensate for item nonresponse.

Measurement error. The NSCG is subject to reporting errors from differences in interpretation of questions and by modality (Web, mail, CATI). To reduce measurement errors, the NSCG questionnaire items were pretested in focus groups and cognitive interviews.

Data comparability. Year-to-year comparisons of the nation’s college-educated population can be made among the 1993, 2003, 2010, 2013, 2015, 2017, 2019, and 2021 survey cycles because many of the core questions remained the same. Since the 1995, 1997, 1999, 2006, and 2008 surveys do not provide full coverage of the nation’s college-educated population, any comparison between these cycles and other cycles should be limited to those individuals educated or employed in S&E fields.

Small but notable differences exist across some survey cycles, however, such as the collection of occupation and education data based on more recent taxonomies. Also, because of the use of different reference months in some survey cycles, seasonal differences may occur when making comparisons across years. Thus, use caution when interpreting cross-cycle comparisons.

There is overlap in the cases included in the 2010 NSCG through the 2017 NSCG, in the 2013 NSCG through the 2019 NSCG, and in the 2015 NSCG through the 2021 NSCG (see figure 1 ). The overlap among cases allows for longitudinal analysis of a subset of the NSCG sample using restricted use data files within NCSES’ Secure Data Access Facility (SDAF). Cases can be linked across survey years using a unique identification variable and single-frame weights are available for each survey year, allowing for the evaluation of estimates from each frame independently. If you are interested in applying for a license to access restricted use NSCG data via the SDAF, please visit NCSES Restricted-Use Data Procedures Guide . Moreover, the Census Bureau offers NSCG restricted use data files that include a few additional data elements. These files can be accessed via the Federal Statistical Research Data Centers .

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Rotating panel design and sample sizes for the National Survey of College Graduates: 2010–21

ACS = American Community Survey; NSCG = National Survey of College Graduates; NSRCG = National Survey of Recent College Graduates.

During a panel’s second survey cycle (in which it is part of the returning sample for the first time), its members include individuals who responded or who were temporarily ineligible during the first cycle. During a panel’s third and fourth cycles, its members include all respondents, nonrespondents, and temporarily ineligible cases from the preceding cycle. Beginning in 2013, the NSCG transitioned to a design that includes an oversample of young graduates to improve the precision of estimates for this important population.

National Center for Science and Engineering Statistics, National Science Foundation, National Survey of College Graduates.

Changes in survey coverage and population . None.

Changes in q uestio n naire

• 2021. To gauge the effects of the coronavirus pandemic on employment, the content of the NSCG questionnaire was modified for 2021 in two ways:
• The response options of long-standing items were revised to identify pandemic-related consequences: for example, reasons for not working, reasons for working part time, reasons for changing employment, and available job benefits.
• New items were added to understand the effects of the pandemic on salaries and earnings and to measure the prevalence of telework.
• 2019. The content of the 2019 NSCG questionnaire remained unchanged from the 2017 NSCG version.
• 2017. The 2017 NSCG questionnaire added two new questions about U.S. military veteran status that are asked on the ACS.
• 2015. The 2015 NSCG questionnaire added a section on professional certifications and licenses.
• 2013. The 2013 NSCG questionnaire added questions about attendance at community colleges, amounts borrowed to finance undergraduate and graduate degrees, and sources of financial support for undergraduate and graduate degrees. The 2013 questionnaire also differed from the 2010 questionnaire by splitting the first response category for the indicator of sample member location on the survey reference date into two categories. “United States, Puerto Rico, or another U.S. territory” became “United States or Puerto Rico” and “Another U.S. territory.”
• 2010. The 2010 NSCG questionnaire added items on components of job satisfaction, importance of job benefits, year of retirement, whether employer is a new business, and degree of difficulty concentrating, remembering, or making decisions.

Changes in reporting procedures or classification

• In past years, NSCG data were combined with data from the SDR and the NSRCG to form the Scientists and Engineers Statistical Data System (SESTAT). The last series of tables produced from SESTAT used 2013 NSCG data. Since then, NSCG data have been used in numerous tables for NCSES’s two congressionally mandated reports ( Science and Engineering Indicators and Women, Minorities, and Persons with Disabilities in Science and Engineering ).

Field of degree. NSCG respondents are asked to report each degree they have earned at the bachelor’s level or higher, along with the major field of study for each degree. The 2021 NSCG used a taxonomy of 142 “detailed” fields of study from which respondents could select the field that best represented their major. These 142 “detailed” fields of study were aggregated into 31 “minor” fields, 7 “major” fields, and 3 “broad” fields (S&E, S&E-related, and non-S&E). (See technical table A-1 for a list and classification of fields of study reported in the NSCG.)

Full-time and part-time employment. Full-time (working 35 hours or more per week) and part-time (working less than 35 hours per week) employment status is for the principal job only and not for all jobs held in the labor force. For example, an individual who works part time in his or her principal job but full time in the labor force would be tabulated as part time.

Highest degree level. NSCG respondents report the degrees they have earned at the bachelor’s level (e.g., BS, BA, AB), master’s level (e.g., MS, MA, MBA), and doctorate level (e.g., PhD, DSc, EdD), as well as other professional degrees (e.g., JD, LLB, MD, DDS, DVM). Because the NSCG is focused on the S&E workforce, the sampling strategy does not include a special effort to collect professional degrees. As such, there is not always sufficient data for the professional degrees to be displayed separately in the tables.

Occupation data. The occupational classification of the respondent was based on his or her principal job (including job title) held during the reference week—or on his or her last job held, if not employed in the reference week (survey questions A5 and A6 as well as A16 and A17). Also used in the occupational classification was a respondent-selected job code (survey questions A7 and A18). (See technical table A-2 for a list and classification of occupations reported in the NSCG.)

Race and ethnicity. Ethnicity is defined as Hispanic or Latino or not Hispanic or Latino. Values for those selecting a single race include American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or Other Pacific Islander, and White. Those persons who report more than one race and who are not of Hispanic or Latino ethnicity also have a separate value.

Salary. Median annual salaries are reported for the principal job, rounded to the nearest $1,000, and computed for individuals employed full time. For individuals employed by educational institutions, no accommodation was made to convert academic year salaries to calendar year salaries.

Sector of employment. Employment sector is a derived variable based on responses to questionnaire items A13, A14, and A15. In the data tables, the category 4-year educational institution includes 4-year colleges or universities, medical schools (including university-affiliated hospitals or medical centers), and university-affiliated research institutes. Two-year and pre-college institutions include community colleges, technical institutes, and other educational institutions (which respondents reported verbatim in the survey questionnaire). For-profit business or industry includes respondents who were self-employed in an incorporated business. Self-employed includes respondents who were self-employed or were a business owner in a non-incorporated business.

Fay RE, Train GF. 1995. Aspects of Survey and Model-Based Postcensal Estimation of Income and Poverty Characteristics for States and Counties. American Statistical Association Pro cee dings of the S ec tion on Go ve rnm e nt Statisti c s , 154–59.

Wolter K. 1984. An Investigation of Some Estimators of Variance for Systematic Sampling. J ournal of the Am e ri c an Statisti c al Asso c iation 79(388):781–90.

Technical Tables

Questionnaires, view archived questionnaires, key data tables.

Recommended data tables

Fields of study of college graduates

Occupations of college graduates, college graduates over time, data tables, work activities and job satisfaction of employed college graduates, median salaries of full-time employed college graduates, demographic characteristics of college graduates, general notes.

The National Survey of College Graduates, conducted by the National Center for Science and Engineering Statistics within the National Science Foundation, is a repeated cross-sectional biennial survey that collects information on the nation’s college-educated workforce. This survey is a unique source for examining the relationship between degree field and occupation, as well as for examining other characteristics of college-educated individuals, including work activities, salary, and demographic information.

Acknowledgments and Suggested Citation

Acknowledgments, suggested citation.

Lynn Milan of the National Center for Science and Engineering Statistics (NCSES) developed and coordinated this report under the leadership of Emilda B. Rivers, NCSES Director; Vipin Arora, NCSES Deputy Director; and John Finamore, NCSES Chief Statistician. Jock Black (NCSES) reviewed the report.

The Census Bureau, under National Science Foundation interagency agreement number NCSE-2040211, collected and tabulated the data for the NSCG. The statistical data tables were compiled by Greg Orlofsky (Census) and verified by Nguyen Tu Tran (DMI). Data and publication processing support was provided by Devi Mishra, Christine Hamel, Tanya Gore, Joe Newman, and Rajinder Raut (NCSES).

NCSES thanks the college graduates who participated in the NSCG for their time and effort in generously contributing to the information included in this report.

National Center for Science and Engineering Statistics (NCSES). 2022. National Survey of College Graduates: 20 21 . NSF 23-306. Alexandria, VA: National Science Foundation. Available at https://ncses.nsf.gov/pubs/nsf23306/ .

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