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How to write a masters dissertation or thesis: top tips.

It is completely normal to find the idea of writing a masters thesis or dissertation slightly daunting, even for students who have written one before at undergraduate level. Though, don’t feel put off by the idea. You’ll have plenty of time to complete it, and plenty of support from your supervisor and peers.

One of the main challenges that students face is putting their ideas and findings into words. Writing is a skill in itself, but with the right advice, you’ll find it much easier to get into the flow of writing your masters thesis or dissertation.

We’ve put together a step-by-step guide on how to write a dissertation or thesis for your masters degree, with top tips to consider at each stage in the process.

1. Understand your dissertation or thesis topic

There are slight differences between theses and dissertations , although both require a high standard of writing skill and knowledge in your topic. They are also formatted very similarly.

At first, writing a masters thesis can feel like running a 100m race – the course feels very quick and like there is not as much time for thinking! However, you’ll usually have a summer semester dedicated to completing your dissertation – giving plenty of time and space to write a strong academic piece.

By comparison, writing a PhD thesis can feel like running a marathon, working on the same topic for 3-4 years can be laborious. But in many ways, the approach to both of these tasks is quite similar.

Before writing your masters dissertation, get to know your research topic inside out. Not only will understanding your topic help you conduct better research, it will also help you write better dissertation content.

Also consider the main purpose of your dissertation. You are writing to put forward a theory or unique research angle – so make your purpose clear in your writing.

Top writing tip: when researching your topic, look out for specific terms and writing patterns used by other academics. It is likely that there will be a lot of jargon and important themes across research papers in your chosen dissertation topic. 

2. Structure your dissertation or thesis

Writing a thesis is a unique experience and there is no general consensus on what the best way to structure it is. 

As a postgraduate student , you’ll probably decide what kind of structure suits your research project best after consultation with your supervisor. You’ll also have a chance to look at previous masters students’ theses in your university library.

To some extent, all postgraduate dissertations are unique. Though they almost always consist of chapters. The number of chapters you cover will vary depending on the research. 

A masters dissertation or thesis organised into chapters would typically look like this: 

Write down your structure and use these as headings that you’ll write for later on.

Top writing tip : ease each chapter together with a paragraph that links the end of a chapter to the start of a new chapter. For example, you could say something along the lines of “in the next section, these findings are evaluated in more detail”. This makes it easier for the reader to understand each chapter and helps your writing flow better.

3. Write up your literature review

One of the best places to start when writing your masters dissertation is with the literature review. This involves researching and evaluating existing academic literature in order to identify any gaps for your own research.

Many students prefer to write the literature review chapter first, as this is where several of the underpinning theories and concepts exist. This section helps set the stage for the rest of your dissertation, and will help inform the writing of your other dissertation chapters.

What to include in your literature review

The literature review chapter is more than just a summary of existing research, it is an evaluation of how this research has informed your own unique research.

Demonstrate how the different pieces of research fit together. Are there overlapping theories? Are there disagreements between researchers?

Highlight the gap in the research. This is key, as a dissertation is mostly about developing your own unique research. Is there an unexplored avenue of research? Has existing research failed to disprove a particular theory?

Back up your methodology. Demonstrate why your methodology is appropriate by discussing where it has been used successfully in other research.

4. Write up your research

For instance, a more theoretical-based research topic might encompass more writing from a philosophical perspective. Qualitative data might require a lot more evaluation and discussion than quantitative research. 

Methodology chapter

The methodology chapter is all about how you carried out your research and which specific techniques you used to gather data. You should write about broader methodological approaches (e.g. qualitative, quantitative and mixed methods), and then go into more detail about your chosen data collection strategy. 

Data collection strategies include things like interviews, questionnaires, surveys, content analyses, discourse analyses and many more.

Data analysis and findings chapters

The data analysis or findings chapter should cover what you actually discovered during your research project. It should be detailed, specific and objective (don’t worry, you’ll have time for evaluation later on in your dissertation)

Write up your findings in a way that is easy to understand. For example, if you have a lot of numerical data, this could be easier to digest in tables.

This will make it easier for you to dive into some deeper analysis in later chapters. Remember, the reader will refer back to your data analysis section to cross-reference your later evaluations against your actual findings – so presenting your data in a simple manner is beneficial.

Think about how you can segment your data into categories. For instance, it can be useful to segment interview transcripts by interviewee. 

Top writing tip : write up notes on how you might phrase a certain part of the research. This will help bring the best out of your writing. There is nothing worse than when you think of the perfect way to phrase something and then you completely forget it.

5. Discuss and evaluate

Once you’ve presented your findings, it’s time to evaluate and discuss them.

It might feel difficult to differentiate between your findings and discussion sections, because you are essentially talking about the same data. The easiest way to remember the difference is that your findings simply present the data, whereas your discussion tells the story of this data.

Your evaluation breaks the story down, explaining the key findings, what went well and what didn’t go so well.

In your discussion chapter, you’ll have chance to expand on the results from your findings section. For example, explain what certain numbers mean and draw relationships between different pieces of data.

Top writing tip: don’t be afraid to point out the shortcomings of your research. You will receive higher marks for writing objectively. For example, if you didn’t receive as many interview responses as expected, evaluate how this has impacted your research and findings. Don’t let your ego get in the way!

6. Write your introduction

Your introduction sets the scene for the rest of your masters dissertation. You might be wondering why writing an introduction isn't at the start of our step-by-step list, and that’s because many students write this chapter last.

Here’s what your introduction chapter should cover:

Problem statement

Research question

Significance of your research

This tells the reader what you’ll be researching as well as its importance. You’ll have a good idea of what to include here from your original dissertation proposal , though it’s fairly common for research to change once it gets started.

Writing or at least revisiting this section last can be really helpful, since you’ll have a more well-rounded view of what your research actually covers once it has been completed and written up.

Masters dissertation writing tips

When to start writing your thesis or dissertation.

When you should start writing your masters thesis or dissertation depends on the scope of the research project and the duration of your course. In some cases, your research project may be relatively short and you may not be able to write much of your thesis before completing the project. 

But regardless of the nature of your research project and of the scope of your course, you should start writing your thesis or at least some of its sections as early as possible, and there are a number of good reasons for this:

Academic writing is about practice, not talent. The first steps of writing your dissertation will help you get into the swing of your project. Write early to help you prepare in good time.

Write things as you do them. This is a good way to keep your dissertation full of fresh ideas and ensure that you don’t forget valuable information.

The first draft is never perfect. Give yourself time to edit and improve your dissertation. It’s likely that you’ll need to make at least one or two more drafts before your final submission.

Writing early on will help you stay motivated when writing all subsequent drafts.

Thinking and writing are very connected. As you write, new ideas and concepts will come to mind. So writing early on is a great way to generate new ideas.

How to improve your writing skills

The best way of improving your dissertation or thesis writing skills is to:

 Finish the first draft of your masters thesis as early as possible and send it to your supervisor for revision. Your supervisor will correct your draft and point out any writing errors. This process will be repeated a few times which will help you recognise and correct writing mistakes yourself as time progresses.

If you are not a native English speaker, it may be useful to ask your English friends to read a part of your thesis and warn you about any recurring writing mistakes. Read our section on English language support for more advice. 

Most universities have writing centres that offer writing courses and other kinds of support for postgraduate students. Attending these courses may help you improve your writing and meet other postgraduate students with whom you will be able to discuss what constitutes a well-written thesis.

Read academic articles and search for writing resources on the internet. This will help you adopt an academic writing style, which will eventually become effortless with practice.

Keep track of your bibliography 

The easiest way to keep the track of all the articles you have read for your research is to create a database where you can summarise each article/chapter into a few most important bullet points to help you remember their content. 

Another useful tool for doing this effectively is to learn how to use specific reference management software (RMS) such as EndNote. RMS is relatively simple to use and saves a lot of time when it comes to organising your bibliography. This may come in very handy, especially if your reference section is suspiciously missing two hours before you need to submit your dissertation! 

Avoid accidental plagiarism

Plagiarism may cost you your postgraduate degree and it is important that you consciously avoid it when writing your thesis or dissertation. 

Occasionally, postgraduate students commit plagiarism unintentionally. This can happen when sections are copy and pasted from journal articles they are citing instead of simply rephrasing them. Whenever you are presenting information from another academic source, make sure you reference the source and avoid writing the statement exactly as it is written in the original paper.

What kind of format should your thesis have?

Read your university’s guidelines before you actually start writing your thesis so you don’t have to waste time changing the format further down the line. However in general, most universities will require you to use 1.5-2 line spacing, font size 12 for text, and to print your thesis on A4 paper. These formatting guidelines may not necessarily result in the most aesthetically appealing thesis, however beauty is not always practical, and a nice looking thesis can be a more tiring reading experience for your postgrad examiner .

When should I submit my thesis?

The length of time it takes to complete your MSc or MA thesis will vary from student to student. This is because people work at different speeds, projects vary in difficulty, and some projects encounter more problems than others. 

Obviously, you should submit your MSc thesis or MA thesis when it is finished! Every university will say in its regulations that it is the student who must decide when it is ready to submit. 

However, your supervisor will advise you whether your work is ready and you should take their advice on this. If your supervisor says that your work is not ready, then it is probably unwise to submit it. Usually your supervisor will read your final thesis or dissertation draft and will let you know what’s required before submitting your final draft.

Set yourself a target for completion. This will help you stay on track and avoid falling behind. You may also only have funding for the year, so it is important to ensure you submit your dissertation before the deadline – and also ensure you don’t miss out on your graduation ceremony ! 

To set your target date, work backwards from the final completion and submission date, and aim to have your final draft completed at least three months before that final date.

Don’t leave your submission until the last minute – submit your work in good time before the final deadline. Consider what else you’ll have going on around that time. Are you moving back home? Do you have a holiday? Do you have other plans?

If you need to have finished by the end of June to be able to go to a graduation ceremony in July, then you should leave a suitable amount of time for this. You can build this into your dissertation project planning at the start of your research.

It is important to remember that handing in your thesis or dissertation is not the end of your masters program . There will be a period of time of one to three months between the time you submit and your final day. Some courses may even require a viva to discuss your research project, though this is more common at PhD level . 

If you have passed, you will need to make arrangements for the thesis to be properly bound and resubmitted, which will take a week or two. You may also have minor corrections to make to the work, which could take up to a month or so. This means that you need to allow a period of at least three months between submitting your thesis and the time when your program will be completely finished. Of course, it is also possible you may be asked after the viva to do more work on your thesis and resubmit it before the examiners will agree to award the degree – so there may be an even longer time period before you have finished.

How do I submit the MA or MSc dissertation?

Most universities will have a clear procedure for submitting a masters dissertation. Some universities require your ‘intention to submit’. This notifies them that you are ready to submit and allows the university to appoint an external examiner.

This normally has to be completed at least three months before the date on which you think you will be ready to submit.

When your MA or MSc dissertation is ready, you will have to print several copies and have them bound. The number of copies varies between universities, but the university usually requires three – one for each of the examiners and one for your supervisor.

However, you will need one more copy – for yourself! These copies must be softbound, not hardbound. The theses you see on the library shelves will be bound in an impressive hardback cover, but you can only get your work bound like this once you have passed. 

You should submit your dissertation or thesis for examination in soft paper or card covers, and your university will give you detailed guidance on how it should be bound. They will also recommend places where you can get the work done.

The next stage is to hand in your work, in the way and to the place that is indicated in your university’s regulations. All you can do then is sit and wait for the examination – but submitting your thesis is often a time of great relief and celebration!

Some universities only require a digital submission, where you upload your dissertation as a file through their online submission system.

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/images/cornell/logo35pt_cornell_white.svg" alt="masters in research paper"> Cornell University --> Graduate School

Guide to writing your thesis/dissertation, definition of dissertation and thesis.

The dissertation or thesis is a scholarly treatise that substantiates a specific point of view as a result of original research that is conducted by students during their graduate study. At Cornell, the thesis is a requirement for the receipt of the M.A. and M.S. degrees and some professional master’s degrees. The dissertation is a requirement of the Ph.D. degree.

Formatting Requirement and Standards

The Graduate School sets the minimum format for your thesis or dissertation, while you, your special committee, and your advisor/chair decide upon the content and length. Grammar, punctuation, spelling, and other mechanical issues are your sole responsibility. Generally, the thesis and dissertation should conform to the standards of leading academic journals in your field. The Graduate School does not monitor the thesis or dissertation for mechanics, content, or style.

“Papers Option” Dissertation or Thesis

A “papers option” is available only to students in certain fields, which are listed on the Fields Permitting the Use of Papers Option page , or by approved petition. If you choose the papers option, your dissertation or thesis is organized as a series of relatively independent chapters or papers that you have submitted or will be submitting to journals in the field. You must be the only author or the first author of the papers to be used in the dissertation. The papers-option dissertation or thesis must meet all format and submission requirements, and a singular referencing convention must be used throughout.

ProQuest Electronic Submissions

The dissertation and thesis become permanent records of your original research, and in the case of doctoral research, the Graduate School requires publication of the dissertation and abstract in its original form. All Cornell master’s theses and doctoral dissertations require an electronic submission through ProQuest, which fills orders for paper or digital copies of the thesis and dissertation and makes a digital version available online via their subscription database, ProQuest Dissertations & Theses . For master’s theses, only the abstract is available. ProQuest provides worldwide distribution of your work from the master copy. You retain control over your dissertation and are free to grant publishing rights as you see fit. The formatting requirements contained in this guide meet all ProQuest specifications.

Copies of Dissertation and Thesis

Copies of Ph.D. dissertations and master’s theses are also uploaded in PDF format to the Cornell Library Repository, eCommons . A print copy of each master’s thesis and doctoral dissertation is submitted to Cornell University Library by ProQuest.

Harvard University Theses, Dissertations, and Prize Papers

The Harvard University Archives ’ collection of theses, dissertations, and prize papers document the wide range of academic research undertaken by Harvard students over the course of the University’s history.

Beyond their value as pieces of original research, these collections document the history of American higher education, chronicling both the growth of Harvard as a major research institution as well as the development of numerous academic fields. They are also an important source of biographical information, offering insight into the academic careers of the authors.

Spanning from the ‘theses and quaestiones’ of the 17th and 18th centuries to the current yearly output of student research, they include both the first Harvard Ph.D. dissertation (by William Byerly, Ph.D . 1873) and the dissertation of the first woman to earn a doctorate from Harvard ( Lorna Myrtle Hodgkinson , Ed.D. 1922).

Other highlights include:

• The collection of Mathematical theses, 1782-1839
• The 1895 Ph.D. dissertation of W.E.B. Du Bois, The suppression of the African slave trade in the United States, 1638-1871
• Ph.D. dissertations of astronomer Cecilia Payne-Gaposchkin (Ph.D. 1925) and physicist John Hasbrouck Van Vleck (Ph.D. 1922)
• Undergraduate honors theses of novelist John Updike (A.B. 1954), filmmaker Terrence Malick (A.B. 1966),  and U.S. poet laureate Tracy Smith (A.B. 1994)
• Undergraduate prize papers and dissertations of philosophers Ralph Waldo Emerson (A.B. 1821), George Santayana (Ph.D. 1889), and W.V. Quine (Ph.D. 1932)
• Undergraduate honors theses of U.S. President John F. Kennedy (A.B. 1940) and Chief Justice John Roberts (A.B. 1976)

What does a prize-winning thesis look like?

If you're a Harvard undergraduate writing your own thesis, it can be helpful to review recent prize-winning theses. The Harvard University Archives has made available for digital lending all of the Thomas Hoopes Prize winners from the 2019-2021 academic years.

Accessing These Materials

How to access materials at the Harvard University Archives

How to find and request dissertations, in person or virtually

How to find and request undergraduate honors theses

How to find and request Thomas Temple Hoopes Prize papers

How to find and request Bowdoin Prize papers

• email: Email
• Phone number 617-495-2461

Related Collections

Harvard faculty personal and professional archives, harvard student life collections: arts, sports, politics and social life, access materials at the harvard university archives.

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How to Write Excellent Graduate-Level Papers

“How to Write Excellent Graduate-Level Papers” brought to you by the Student Academic Success Center (SASC) at UNE.

Becoming a better writer – the process

Breaking a writing project down into phases helps with motivation as well as managing your time and workload effectively. The phases of the process – prewriting, drafting, revision, and editing – are described below. Each step allows you to focus your energy in a particular way, with it all adding up to a more thoughtful, clear piece of writing.

The phases don’t have to be done in a set, linear order, if that’s not effective for you. If you like to write some rough draft paragraphs first, then go back and do a post-draft outline or revise those paragraphs before continuing, that’s fine. The key is to make sure each part of the process is done thoroughly before you consider your paper finished.

Let’s start with using prewriting to get the process rolling:

Using various prewriting strategies can help you avoid procrastinating and start a draft on the right track. You aren’t under pressure to develop a paper yet – this is about unlocking the flow of ideas. Play around with some of these strategies to find ones that work best for you:

• Tap into your curiosity

When you’re faced with an assignment, spend some time simply wondering about the topic. What intrigues you? Why should you and others in your profession care about it? Come up with a couple of relevant questions that you want to explore. Then consider which questions are most meaningful to you personally and professionally—and why? This can be done on paper, in conversation with someone else, or internally.

• Relate the assignment to your profession

Think about why the assignment is important to your field of study and work as a health professional, a social worker, an educator, etc. Making your assignment as personally and professionally relevant as possible helps with generating the motivation to start writing and keeping the momentum through the process. View this as an opportunity to learn useful information.

• Use the assignment itself as an outline

Copy the assignment and paste it into a new document. Break it apart visually by adding line spaces and/or tabs. This will help you more easily identify key concepts which need to be explained and verbs that indicate critical thinking is required (e.g., analyze, compare, evaluate). Create a rough outline using parts of the assignment as headings for different sections of the paper.

Similarly, you could annotate the assignment by marking up the key words and concepts and making little notes in the margins about what to add or how sections or ideas might tie together.

• Leverage what you already know, and then research with a purpose

Another very helpful strategy is to identify key concepts in the assignment description, then brainstorm what you already know about them based on the class readings or videos. Next, make a list of questions you still have about the concepts and overall topic. These will help drive the additional research needed to fill in your gaps of knowledge and locate credible evidence to support your explanations.

Having those questions makes researching more efficient because you have a purpose for reading: you’re looking for pieces of information rather than simply reading articles.

Read more: Faculty Spotlight: Lori Rand, Writing Specialist at SASC

The drafting phase involves determining your focus and starting to develop paragraph ideas within a structure. Keep a copy of the assignment on your draft as you write. Clarify the point of your paper – what is the main question that the assignment asking you to answer?

Think of a draft as packaging ideas into paragraphs that all relate to the paper’s main focus, as summed up in the thesis statement. For clarity, try to keep each paragraph focused on one idea at a time. However, because this phase is about getting thoughts down, and thoughts often jump around, drafting tends to be messy. That’s okay! The next step, revision, is where you really improve the writing.

In this phase, you can work on improving how you are guiding your reader through your thinking. Your reader will understand your ideas more easily if they are clearly focused, well-developed with specific evidence (correctly cited), and nicely organized.

Two strategies to guide you through revision include SASC’s Revision Checklist and Post-draft Outline, found here under Writing Resources. A writing appointment is also a great way to learn about and practice revision skills.

Editing is the final, polishing phase; it involves correcting sentence-level issues and technical aspects, such as word choice and grammar. Readers pick up these issues quickly because they can be the most obvious. Carelessness with grammar or word choice can lead to misunderstandings and make your writing seem unprofessional.

Trust the process

As mentioned earlier, the writing process is not necessarily a linear, step-by-step approach; it’s recursive, so it’s highly likely you’ll move back and forth between phases as you figure out your focus and organization of ideas.

Using this process gets easier with practice, and it works well in any writing situations, not just for graduate school assignments and scholarly papers.

Once you develop the most efficient method for your learning style, not only will you get faster, you will produce better academic papers.

Book an appointment

The SASC can help with all phases of the writing process via an Online Writing Support Appointment.  Visit the Online Student page for more details about writing support and resources.

For more online education insider tips and guides, subscribe today!

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• Communication
• Graduate Level Writing Tips

Graduate-Level Writing Tips: Definitions, Do’s and Don’ts

Debra Davenport, PhD

In your communication master’s program, you will be expected to demonstrate well-honed writing skills in your essays. Your courses will require proficiency in real-world business communications, as well as scholarly writing and the use of APA formatting.

Real-world written business communications may include:

• Executive summaries
• News releases
• Media advisories
• Company fact sheets
• Business reports

Academic papers are those you will write in your courses that:

• Review and discuss the scholarly literature
• Synthesize theories, models and course readings
• Present critical analysis, research and scholarly insight in an objective manner
• Are formatted according to APA standards
• Are written in the scholarly voice

What Is the Scholarly Voice?

Essentially, the scholarly voice is unbiased, high-level and evidence-based writing that reflects the epitome of good grammar, syntax and tone. Follow the do’s and don’ts below to excel at this format in your graduate school essays.

Scholarly Resources:

• https://owl.english.purdue.edu/owl/resource/683/1/
• http://blog.apastyle.org/
• http://academicguides.waldenu.edu/writingcenter/scholarlyvoice
• http://academicguides.waldenu.edu/writingcenter/scholarlyvoice/tone

The “Do’s” of Scholarly Writing

1. Use proper syntax. Syntax is defined by the Oxford Dictionary as “the arrangement of words and phrases to create well-formed sentences in a language.” Syntax is an important aspect of writing that helps to ensure clarity. Incorrect syntax often results in sentences and paragraphs that do not make sense, and this can pose serious perceptual issues for professional communicators. See this article for a number of examples.

2. Follow the rules of punctuation. Common errors include incorrect placement of quotation marks and erroneous use of the semicolon. As an example, note that quotation marks follow periods and commas, (“The sky is blue.”)

3. Include references, citations and /or footnotes, no matter what kind of document you’re writing. Taking the time to locate sources that substantiate your statements demonstrate your proficiency as a scholar-practitioner and your commitment to excellence. Citations are required in your academic papers, but clients also appreciate this attention to detail. When pitching a project or campaign, the inclusion of reputable sources will support your recommendations and boost your own credibility.

4. Proofread and edit your work. Many errors are missed during the first proofread; be prepared to review your work multiple times.

The “Don’ts” in Scholarly Writing

1. Don’t write in the second person narrative. The second person voice is typically used in articles like this one, where the writer is intending to inform and instruct. According to WritingCommons.org , “writing from the second person point of view can weaken the effectiveness of the writing in research and argument papers. Using second person can make the work sound as if the writer is giving directions or offering advice to his or her readers, rather than informing [them].”

Here is a comparison of second and third person perspectives from WritingCommons.org:

• Weak: You should read the statistics about the number of suicides that happen to your average victim of bullying! (2nd person)
• Stronger: The statistics from a variety of research reports indicate that the suicide rate is high among victims of bullying; they are under so much psychological pressure that they may resort to taking their own lives. (3rd person)

2. Don’t rely on software to correct your writing. Certainly, tools such as spell check, grammar check and grammarly have some benefit, but they cannot replace firsthand knowledge and mastery of proper writing. I recall one particular paper I received several years ago that was, quite literally, gibberish. When I inquired about the content of the student’s paper, she replied, “Well, I used grammar check!”

Don’t hesitate to seek writing coaching if you have questions or concerns about any aspect of good writing. As graduate students in a masters-level communication program, writing excellence should be a top priority.

By taking an informed and proactive approach to your writing, you will strengthen your academic performance, hone your professional and communication skills and enhance your career.

Dr. Debra Davenport is an online faculty member for Purdue’s online Master of Science in Communication degree program. The program can be completed in just 20 months and covers numerous topics critical for advancement in the communication industry, including crisis communication, social media engagement, focus group planning and implementation, survey design and survey analysis, public relations theory, professional writing, and communication ethics.

Find out more about what you can do with a MS in Communication from Purdue University. Call us today at 877-497-5851 to speak to an admissions advisor, or request more information .

*The views and opinions expressed are of the author and do not represent the Brian Lamb School of Communication.

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Masters research paper guidelines

See the PDF version of the masters research paper guidelines .

The master’s research paper is worth 2 units of credit towards the MA or MES degree. The student will normally prepare a master’s research paper over three terms, in two stages

• the research paper proposal, and
• the completed research paper.

At University of Waterloo this paper is considered a “milestone” and at Wilfrid Laurier University (WLU) this paper is recorded as "GG 698".

I. The proposal

Each research paper MA/MES student will have a supervisor and a reader. The student will develop a research paper proposal for approval by her/his supervisor prior to the end of the first term.

Detailed guidelines for the preparation of the research paper proposal are attached.

II. The completed paper

Each research paper MA/MES student will have a supervisor and a reader. The student will develop a research paper proposal for approval by her/his supervisor prior to the end of the first term. A copy of the approved research paper proposal will be kept in the student’s file.

The research paper will normally be completed in the Spring (third) term. The paper should be approximately 8,000 - 12,000 words and be organized into clearly defined sections on problem statement, status of research, research procedure, findings, and conclusions. Student and supervisor together must agree on the organization of the paper into discrete chapters and on the necessity or suitability of maps, statistics or appendices.

Research papers can take a variety of forms such as a journal article format or a standard research paper. In principle, the research paper shall be of such quality that it is publishable in a refereed review journal relevant to the discipline in question.

The research paper must be evaluated by the student’s supervisor and one reader, who will review the paper independently, and then agree upon a final numerical grade.

Guidelines for the preparation of the research paper proposal

The title should be as short as possible with key words given prominent place.

Proposal format

Divide your proposal into 5 sections: the problem statement (1-2 pages), status of research (10 pages), research procedure including a time frame for each task (2-3 pages), references cited (1-2 pages), and a chapter outline for the research paper (1-2 pages). The text should be presented as a series of well integrated paragraphs. Some ideas on what to include in each section are provided below.

Section 1 - Problem statement (1-2 pages)

• Ease your reader into the proposal. Identify current activity in your research area and indicate reasons for your interest in the area.
• Clearly and succinctly state what you intend to do. In one sentence, identify your problem statement, either as a question, statement, or hypothesis.
• Briefly indicate the scholarly and practical/social relevance of your project. Here you should state the contribution that your work will make, i.e. why bother?

Section 2 - Status of research (about 10 pages)

Place your research into context with previous work. The literature review should be presented in a way that justifies both your topic and your methodological approach. It is normal to go from the general to the specific. For example:

The first paragraph might describe the general area of human or physical geography that is involved and identify landmark works, key authors, and the main research emphasis. At this general level, much has been written and you will need to be selective in what you reference. The idea is to give a brief historical overview of the field.

The next paragraph(s) might focus on research that is similar to your own. Try to provide a brief overview of the different questions that have been asked and the most common methodologies that have been used. Include references to works that exemplify or illustrate these various questions and approaches. The purpose is to establish what already is known about the general problem, so it is clear how your study will contribute to further understanding.

Finally, you will want to provide more detailed comments on research studies that are very similar to your own, noting what questions have been answered, what questions are left unanswered, and what evidence and methodologies appear appropriate for research of this type. You may find only a few studies that fit into this category (or possibly none). Studies that fit into this last category can sometimes provide a blueprint for your own research.

Section 3 - Research procedure (about 2 or 3 pages)

This is where you state how you plan to operationalize the research problem, i.e. how will you accomplish your research goal? Consider the following:

What general approach or framework will you use? synthesis and critical evaluation of qualitative materials? survey work? statistical analysis of quantitative data? comparison of different cases/places? numerical modelling? reasoned logical argument? development/application of a technique for a specific type of problem? etc. The general approach largely determines both the information and techniques needed to answer your question and can usually be explained in one sentence.

What information/data is needed to answer your question? How much information will you need? What should it look like? Where and how will you get this information - from direct field measurements? questionnaires? secondary data (e.g. census or other government data)? air photos, maps, or archives? participant observation? published literature? etc. Check out as far and as early as possible the availability, reliability, comprehensiveness, costs, and format of data. Also be careful about logistics, such as the need for specialized computer support or training, language or distance barriers, and the need to have all research involving human subjects reviewed by the Office of Research Ethics.

- What techniques will you employ in the examination of your data? Be as specific as you can. Decide before you collect the data whether you want to make statements of inference as this will affect how the data must be collected. Decide how you would like to present the evidence (as statistics, graphs, tables, verbal argument). Determine what skills will be needed for data collection and data analysis, e.g. field techniques, survey design methods, library skills, techniques like content analysis, cost-benefit analysis, parametric and non-parametric statistics, GIS. Decide how you will develop your skills in these areas and make concrete plans to do so. Remember - the research paper is an opportunity to learn.

-Prepare a time frame that indicates when you will undertake the various tasks that are necessary for the completion of the project. Present this as a chart in the proposal.

Section 4 - References (about 1 or 2 pages, 20-40 references)

A reference list is not the same as a bibliography; a reference list includes only those materials that have been cited in the proposal. As a general rule, references are needed when the information is not general knowledge or when specific points are being made. An acceptable method must be used consistently. The author-date system is strongly recommended as it is the most widely used method in the social sciences. Remember that the page number is included in the reference only when you are using direct quotes or when you are reproducing tables or figures. Of course, page numbers for articles are given in the reference list.

Section 5 - Outline for the completed research paper

Most research papers are 40-60 pages long and contain 4-6 chapters. Usually you will have an introductory chapter, followed by a literature review or research context chapter, followed by a methodology chapter, followed by one or more results chapters, followed by a concluding chapter. Give your chapters appropriate titles and decide on the approximate length of each chapter. Then decide what is likely to be included in each chapter and organize these themes into chapter subsections. Give these subsections titles and once again indicate the approximate length of each.

Writing style

Model your writing style after a refereed academic journal. Expect to rewrite and rewrite and rewrite. Reorganizing paragraphs, polishing sentences and searching for the best word are all part of the revision process. Identify your weaknesses (spelling, grammar, adjective use, useless phrases, etc.) and work on them. Don't treat what you have written as sacred. If necessary, scrap part of your text entirely and start with a fresh piece of paper or a blank computer screen.

Referencing guidelines

For detailed guidelines on the appropriate formatting of references consult a reference relevant to the discipline in question, such as:

Northey, M. & Knight, D. (1992). Making Sense in Geography and Environmental Studies: a student’s guide to research writing and style. Toronto: Oxford University Press.

Graphical, tabular and photographic illustrations

Graphs, maps and tables all provide information and so they can be used in any report, including a proposal. Never include filler, however, such as graphs that are not referred to in the text or tables that contain too much detail. Always think about how information can be best communicated to the reader. Be careful so as not to over describe a graph or table; just make the points which are central to your argument.

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The Research Paper

There will come a time in most students' careers when they are assigned a research paper. Such an assignment often creates a great deal of unneeded anxiety in the student, which may result in procrastination and a feeling of confusion and inadequacy. This anxiety frequently stems from the fact that many students are unfamiliar and inexperienced with this genre of writing. Never fear—inexperience and unfamiliarity are situations you can change through practice! Writing a research paper is an essential aspect of academics and should not be avoided on account of one's anxiety. In fact, the process of writing a research paper can be one of the more rewarding experiences one may encounter in academics. What is more, many students will continue to do research throughout their careers, which is one of the reasons this topic is so important.

Becoming an experienced researcher and writer in any field or discipline takes a great deal of practice. There are few individuals for whom this process comes naturally. Remember, even the most seasoned academic veterans have had to learn how to write a research paper at some point in their career. Therefore, with diligence, organization, practice, a willingness to learn (and to make mistakes!), and, perhaps most important of all, patience, students will find that they can achieve great things through their research and writing.

The pages in this section cover the following topic areas related to the process of writing a research paper:

• Genre - This section will provide an overview for understanding the difference between an analytical and argumentative research paper.
• Choosing a Topic - This section will guide the student through the process of choosing topics, whether the topic be one that is assigned or one that the student chooses themselves.
• Identifying an Audience - This section will help the student understand the often times confusing topic of audience by offering some basic guidelines for the process.
• Where Do I Begin - This section concludes the handout by offering several links to resources at Purdue, and also provides an overview of the final stages of writing a research paper.

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Biostatistics Graduate Program

Julia thome is first author of public health reports paper.

Posted by duthip1 on Wednesday, May 29, 2024 in News .

Congratulations to PhD candidate Julia Thome on the publication of Reporting of Child Maltreatment During the COVID-19 Pandemic in a Southern State in the United States in  Public Health Reports last week, online ahead of print. The paper was co-authored by associate professor Rameela Raman and colleagues at the Vanderbilt Center of Excellence for Children in State Custody, which is within the Vanderbilt Department of Psychiatry & Behavioral Sciences. Thome, Raman, and the other members of this team studied how COVID-19 stay-at-home orders may have affected trends in child maltreatment allegations across different socioeconomic groups.

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Staff Working Papers Working Paper 23-03: The Credit Supply Channel of Monetary Policy Tightening and its Distributional Impacts

Author: Joshua Bosshardt (FHFA); Marco Di Maggio (Harvard University); Ali Kakhbod (University of California, Berkeley); ​Amir Kermani (University of California, Berkeley)​​

*Revised November 2023

​​Abstract:

This paper studies how tightening monetary policy transmits to the economy through the mortgage market and sheds new light on the distributional consequences at both individual and regional levels. We find that credit supply factors, specifically restrictions on the debt-to-income (DTI) ratio, account for most of the decline in mortgages. These effects are even more pronounced for minority and middle-income borrowers, who find themselves excluded from the credit market. Additionally, regions with historically high DTI ratios exhibit greater reductions in mortgage originations, house prices, and consumption.​

A revised version of this paper has been accepted for publication and is forthcoming in the  Journal of Financial Economics ​​.

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• Published: 17 October 2023

The impact of founder personalities on startup success

• Paul X. McCarthy 1 , 2 ,
• Xian Gong 3 ,
• Fabian Braesemann 4 , 5 ,
• Fabian Stephany 4 , 5 ,
• Marian-Andrei Rizoiu 3 &
• Margaret L. Kern 6  

Scientific Reports volume  13 , Article number:  17200 ( 2023 ) Cite this article

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An Author Correction to this article was published on 07 May 2024

This article has been updated

Startup companies solve many of today’s most challenging problems, such as the decarbonisation of the economy or the development of novel life-saving vaccines. Startups are a vital source of innovation, yet the most innovative are also the least likely to survive. The probability of success of startups has been shown to relate to several firm-level factors such as industry, location and the economy of the day. Still, attention has increasingly considered internal factors relating to the firm’s founding team, including their previous experiences and failures, their centrality in a global network of other founders and investors, as well as the team’s size. The effects of founders’ personalities on the success of new ventures are, however, mainly unknown. Here, we show that founder personality traits are a significant feature of a firm’s ultimate success. We draw upon detailed data about the success of a large-scale global sample of startups (n = 21,187). We find that the Big Five personality traits of startup founders across 30 dimensions significantly differ from that of the population at large. Key personality facets that distinguish successful entrepreneurs include a preference for variety, novelty and starting new things (openness to adventure), like being the centre of attention (lower levels of modesty) and being exuberant (higher activity levels). We do not find one ’Founder-type’ personality; instead, six different personality types appear. Our results also demonstrate the benefits of larger, personality-diverse teams in startups, which show an increased likelihood of success. The findings emphasise the role of the diversity of personality types as a novel dimension of team diversity that influences performance and success.

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Introduction.

The success of startups is vital to economic growth and renewal, with a small number of young, high-growth firms creating a disproportionately large share of all new jobs 1 , 2 . Startups create jobs and drive economic growth, and they are also an essential vehicle for solving some of society’s most pressing challenges.

As a poignant example, six centuries ago, the German city of Mainz was abuzz as the birthplace of the world’s first moveable-type press created by Johannes Gutenberg. However, in the early part of this century, it faced several economic challenges, including rising unemployment and a significant and growing municipal debt. Then in 2008, two Turkish immigrants formed the company BioNTech in Mainz with another university research colleague. Together they pioneered new mRNA-based technologies. In 2020, BioNTech partnered with US pharmaceutical giant Pfizer to create one of only a handful of vaccines worldwide for Covid-19, saving an estimated six million lives 3 . The economic benefit to Europe and, in particular, the German city where the vaccine was developed has been significant, with windfall tax receipts to the government clearing Mainz’s €1.3bn debt and enabling tax rates to be reduced, attracting other businesses to the region as well as inspiring a whole new generation of startups 4 .

While stories such as the success of BioNTech are often retold and remembered, their success is the exception rather than the rule. The overwhelming majority of startups ultimately fail. One study of 775 startups in Canada that successfully attracted external investment found only 35% were still operating seven years later 5 .

But what determines the success of these ‘lucky few’? When assessing the success factors of startups, especially in the early-stage unproven phase, venture capitalists and other investors offer valuable insights. Three different schools of thought characterise their perspectives: first, supply-side or product investors : those who prioritise investing in firms they consider to have novel and superior products and services, investing in companies with intellectual property such as patents and trademarks. Secondly, demand-side or market-based investors : those who prioritise investing in areas of highest market interest, such as in hot areas of technology like quantum computing or recurrent or emerging large-scale social and economic challenges such as the decarbonisation of the economy. Thirdly, talent investors : those who prioritise the foundation team above the startup’s initial products or what industry or problem it is looking to address.

Investors who adopt the third perspective and prioritise talent often recognise that a good team can overcome many challenges in the lead-up to product-market fit. And while the initial products of a startup may or may not work a successful and well-functioning team has the potential to pivot to new markets and new products, even if the initial ones prove untenable. Not surprisingly, an industry ‘autopsy’ into 101 tech startup failures found 23% were due to not having the right team—the number three cause of failure ahead of running out of cash or not having a product that meets the market need 6 .

Accordingly, early entrepreneurship research was focused on the personality of founders, but the focus shifted away in the mid-1980s onwards towards more environmental factors such as venture capital financing 7 , 8 , 9 , networks 10 , location 11 and due to a range of issues and challenges identified with the early entrepreneurship personality research 12 , 13 . At the turn of the 21st century, some scholars began exploring ways to combine context and personality and reconcile entrepreneurs’ individual traits with features of their environment. In her influential work ’The Sociology of Entrepreneurship’, Patricia H. Thornton 14 discusses two perspectives on entrepreneurship: the supply-side perspective (personality theory) and the demand-side perspective (environmental approach). The supply-side perspective focuses on the individual traits of entrepreneurs. In contrast, the demand-side perspective focuses on the context in which entrepreneurship occurs, with factors such as finance, industry and geography each playing their part. In the past two decades, there has been a revival of interest and research that explores how entrepreneurs’ personality relates to the success of their ventures. This new and growing body of research includes several reviews and meta-studies, which show that personality traits play an important role in both career success and entrepreneurship 15 , 16 , 17 , 18 , 19 , that there is heterogeneity in definitions and samples used in research on entrepreneurship 16 , 18 , and that founder personality plays an important role in overall startup outcomes 17 , 19 .

Motivated by the pivotal role of the personality of founders on startup success outlined in these recent contributions, we investigate two main research questions:

Which personality features characterise founders?

Do their personalities, particularly the diversity of personality types in founder teams, play a role in startup success?

We aim to understand whether certain founder personalities and their combinations relate to startup success, defined as whether their company has been acquired, acquired another company or listed on a public stock exchange. For the quantitative analysis, we draw on a previously published methodology 20 , which matches people to their ‘ideal’ jobs based on social media-inferred personality traits.

We find that personality traits matter for startup success. In addition to firm-level factors of location, industry and company age, we show that founders’ specific Big Five personality traits, such as adventurousness and openness, are significantly more widespread among successful startups. As we find that companies with multi-founder teams are more likely to succeed, we cluster founders in six different and distinct personality groups to underline the relevance of the complementarity in personality traits among founder teams. Startups with diverse and specific combinations of founder types (e. g., an adventurous ‘Leader’, a conscientious ‘Accomplisher’, and an extroverted ‘Developer’) have significantly higher odds of success.

We organise the rest of this paper as follows. In the Section " Results ", we introduce the data used and the methods applied to relate founders’ psychological traits with their startups’ success. We introduce the natural language processing method to derive individual and team personality characteristics and the clustering technique to identify personality groups. Then, we present the result for multi-variate regression analysis that allows us to relate firm success with external and personality features. Subsequently, the Section " Discussion " mentions limitations and opportunities for future research in this domain. In the Section " Methods ", we describe the data, the variables in use, and the clustering in greater detail. Robustness checks and additional analyses can be found in the Supplementary Information.

Our analysis relies on two datasets. We infer individual personality facets via a previously published methodology 20 from Twitter user profiles. Here, we restrict our analysis to founders with a Crunchbase profile. Crunchbase is the world’s largest directory on startups. It provides information about more than one million companies, primarily focused on funding and investors. A company’s public Crunchbase profile can be considered a digital business card of an early-stage venture. As such, the founding teams tend to provide information about themselves, including their educational background or a link to their Twitter account.

We infer the personality profiles of the founding teams of early-stage ventures from their publicly available Twitter profiles, using the methodology described by Kern et al. 20 . Then, we correlate this information to data from Crunchbase to determine whether particular combinations of personality traits correspond to the success of early-stage ventures. The final dataset used in the success prediction model contains n = 21,187 startup companies (for more details on the data see the Methods section and SI section  A.5 ).

Revisions of Crunchbase as a data source for investigations on a firm and industry level confirm the platform to be a useful and valuable source of data for startups research, as comparisons with other sources at micro-level, e.g., VentureXpert or PwC, also suggest that the platform’s coverage is very comprehensive, especially for start-ups located in the United States 21 . Moreover, aggregate statistics on funding rounds by country and year are quite similar to those produced with other established sources, going to validate the use of Crunchbase as a reliable source in terms of coverage of funded ventures. For instance, Crunchbase covers about the same number of investment rounds in the analogous sectors as collected by the National Venture Capital Association 22 . However, we acknowledge that the data source might suffer from registration latency (a certain delay between the foundation of the company and its actual registration on Crunchbase) and success bias in company status (the likeliness that failed companies decide to delete their profile from the database).

The definition of startup success

The success of startups is uncertain, dependent on many factors and can be measured in various ways. Due to the likelihood of failure in startups, some large-scale studies have looked at which features predict startup survival rates 23 , and others focus on fundraising from external investors at various stages 24 . Success for startups can be measured in multiple ways, such as the amount of external investment attracted, the number of new products shipped or the annual growth in revenue. But sometimes external investments are misguided, revenue growth can be short-lived, and new products may fail to find traction.

Success in a startup is typically staged and can appear in different forms and times. For example, a startup may be seen to be successful when it finds a clear solution to a widely recognised problem, such as developing a successful vaccine. On the other hand, it could be achieving some measure of commercial success, such as rapidly accelerating sales or becoming profitable or at least cash positive. Or it could be reaching an exit for foundation investors via a trade sale, acquisition or listing of its shares for sale on a public stock exchange via an Initial Public Offering (IPO).

For our study, we focused on the startup’s extrinsic success rather than the founders’ intrinsic success per se, as its more visible, objective and measurable. A frequently considered measure of success is the attraction of external investment by venture capitalists 25 . However, this is not in and of itself a good measure of clear, incontrovertible success, particularly for early-stage ventures. This is because it reflects investors’ expectations of a startup’s success potential rather than actual business success. Similarly, we considered other measures like revenue growth 26 , liquidity events 27 , 28 , 29 , profitability 30 and social impact 31 , all of which have benefits as they capture incremental success, but each also comes with operational measurement challenges.

Therefore, we apply the success definition initially introduced by Bonaventura et al. 32 , namely that a startup is acquired, acquires another company or has an initial public offering (IPO). We consider any of these major capital liquidation events as a clear threshold signal that the company has matured from an early-stage venture to becoming or is on its way to becoming a mature company with clear and often significant business growth prospects. Together these three major liquidity events capture the primary forms of exit for external investors (an acquisition or trade sale and an IPO). For companies with a longer autonomous growth runway, acquiring another company marks a similar milestone of scale, maturity and capability.

Using multifactor analysis and a binary classification prediction model of startup success, we looked at many variables together and their relative influence on the probability of the success of startups. We looked at seven categories of factors through three lenses of firm-level factors: (1) location, (2) industry, (3) age of the startup; founder-level factors: (4) number of founders, (5) gender of founders, (6) personality characteristics of founders and; lastly team-level factors: (7) founder-team personality combinations. The model performance and relative impacts on the probability of startup success of each of these categories of founders are illustrated in more detail in section  A.6 of the Supplementary Information (in particular Extended Data Fig.  19 and Extended Data Fig.  20 ). In total, we considered over three hundred variables (n = 323) and their relative significant associations with success.

The personality of founders

Besides product-market, industry, and firm-level factors (see SI section  A.1 ), research suggests that the personalities of founders play a crucial role in startup success 19 . Therefore, we examine the personality characteristics of individual startup founders and teams of founders in relationship to their firm’s success by applying the success definition used by Bonaventura et al. 32 .

Employing established methods 33 , 34 , 35 , we inferred the personality traits across 30 dimensions (Big Five facets) of a large global sample of startup founders. The startup founders cohort was created from a subset of founders from the global startup industry directory Crunchbase, who are also active on the social media platform Twitter.

To measure the personality of the founders, we used the Big Five, a popular model of personality which includes five core traits: Openness to Experience, Conscientiousness, Extraversion, Agreeableness, and Emotional stability. Each of these traits can be further broken down into thirty distinct facets. Studies have found that the Big Five predict meaningful life outcomes, such as physical and mental health, longevity, social relationships, health-related behaviours, antisocial behaviour, and social contribution, at levels on par with intelligence and socioeconomic status 36 Using machine learning to infer personality traits by analysing the use of language and activity on social media has been shown to be more accurate than predictions of coworkers, friends and family and similar in accuracy to the judgement of spouses 37 . Further, as other research has shown, we assume that personality traits remain stable in adulthood even through significant life events 38 , 39 , 40 . Personality traits have been shown to emerge continuously from those already evident in adolescence 41 and are not significantly influenced by external life events such as becoming divorced or unemployed 42 . This suggests that the direction of any measurable effect goes from founder personalities to startup success and not vice versa.

As a first investigation to what extent personality traits might relate to entrepreneurship, we use the personality characteristics of individuals to predict whether they were an entrepreneur or an employee. We trained and tested a machine-learning random forest classifier to distinguish and classify entrepreneurs from employees and vice-versa using inferred personality vectors alone. As a result, we found we could correctly predict entrepreneurs with 77% accuracy and employees with 88% accuracy (Fig.  1 A). Thus, based on personality information alone, we correctly predict all unseen new samples with 82.5% accuracy (See SI section  A.2 for more details on this analysis, the classification modelling and prediction accuracy).

We explored in greater detail which personality features are most prominent among entrepreneurs. We found that the subdomain or facet of Adventurousness within the Big Five Domain of Openness was significant and had the largest effect size. The facet of Modesty within the Big Five Domain of Agreeableness and Activity Level within the Big Five Domain of Extraversion was the subsequent most considerable effect (Fig.  1 B). Adventurousness in the Big Five framework is defined as the preference for variety, novelty and starting new things—which are consistent with the role of a startup founder whose role, especially in the early life of the company, is to explore things that do not scale easily 43 and is about developing and testing new products, services and business models with the market.

Once we derived and tested the Big Five personality features for each entrepreneur in our data set, we examined whether there is evidence indicating that startup founders naturally cluster according to their personality features using a Hopkins test (see Extended Data Figure  6 ). We discovered clear clustering tendencies in the data compared with other renowned reference data sets known to have clusters. Then, once we established the founder data clusters, we used agglomerative hierarchical clustering. This ‘bottom-up’ clustering technique initially treats each observation as an individual cluster. Then it merges them to create a hierarchy of possible cluster schemes with differing numbers of groups (See Extended Data Fig.  7 ). And lastly, we identified the optimum number of clusters based on the outcome of four different clustering performance measurements: Davies-Bouldin Index, Silhouette coefficients, Calinski-Harabas Index and Dunn Index (see Extended Data Figure  8 ). We find that the optimum number of clusters of startup founders based on their personality features is six (labelled #0 through to #5), as shown in Fig.  1 C.

To better understand the context of different founder types, we positioned each of the six types of founders within an occupation-personality matrix established from previous research 44 . This research showed that ‘each job has its own personality’ using a substantial sample of employees across various jobs. Utilising the methodology employed in this study, we assigned labels to the cluster names #0 to #5, which correspond to the identified occupation tribes that best describe the personality facets represented by the clusters (see Extended Data Fig.  9 for an overview of these tribes, as identified by McCarthy et al. 44 ).

Utilising this approach, we identify three ’purebred’ clusters: #0, #2 and #5, whose members are dominated by a single tribe (larger than 60% of all individuals in each cluster are characterised by one tribe). Thus, these clusters represent and share personality attributes of these previously identified occupation-personality tribes 44 , which have the following known distinctive personality attributes (see also Table  1 ):

Accomplishers (#0) —Organised & outgoing. confident, down-to-earth, content, accommodating, mild-tempered & self-assured.

Leaders (#2) —Adventurous, persistent, dispassionate, assertive, self-controlled, calm under pressure, philosophical, excitement-seeking & confident.

Fighters (#5) —Spontaneous and impulsive, tough, sceptical, and uncompromising.

We labelled these clusters with the tribe names, acknowledging that labels are somewhat arbitrary, based on our best interpretation of the data (See SI section  A.3 for more details).

For the remaining three clusters #1, #3 and #4, we can see they are ‘hybrids’, meaning that the founders within them come from a mix of different tribes, with no one tribe representing more than 50% of the members of that cluster. However, the tribes with the largest share were noted as #1 Experts/Engineers, #3 Fighters, and #4 Operators.

To label these three hybrid clusters, we examined the closest occupations to the median personality features of each cluster. We selected a name that reflected the common themes of these occupations, namely:

Experts/Engineers (#1) as the closest roles included Materials Engineers and Chemical Engineers. This is consistent with this cluster’s personality footprint, which is highest in openness in the facets of imagination and intellect.

Developers (#3) as the closest roles include Application Developers and related technology roles such as Business Systems Analysts and Product Managers.

Operators (#4) as the closest roles include service, maintenance and operations functions, including Bicycle Mechanic, Mechanic and Service Manager. This is also consistent with one of the key personality traits of high conscientiousness in the facet of orderliness and high agreeableness in the facet of humility for founders in this cluster.

Founder-Level Factors of Startup Success. ( A ), Successful entrepreneurs differ from successful employees. They can be accurately distinguished using a classifier with personality information alone. ( B ), Successful entrepreneurs have different Big Five facet distributions, especially on adventurousness, modesty and activity level. ( C ), Founders come in six different types: Fighters, Operators, Accomplishers, Leaders, Engineers and Developers (FOALED) ( D ), Each founder Personality-Type has its distinct facet.

Together, these six different types of startup founders (Fig.  1 C) represent a framework we call the FOALED model of founder types—an acronym of Fighters, Operators, Accomplishers, Leaders, Engineers and D evelopers.

Each founder’s personality type has its distinct facet footprint (for more details, see Extended Data Figure  10 in SI section  A.3 ). Also, we observe a central core of correlated features that are high for all types of entrepreneurs, including intellect, adventurousness and activity level (Fig.  1 D).To test the robustness of the clustering of the personality facets, we compare the mean scores of the individual facets per cluster with a 20-fold resampling of the data and find that the clusters are, overall, largely robust against resampling (see Extended Data Figure  11 in SI section  A.3 for more details).

We also find that the clusters accord with the distribution of founders’ roles in their startups. For example, Accomplishers are often Chief Executive Officers, Chief Financial Officers, or Chief Operating Officers, while Fighters tend to be Chief Technical Officers, Chief Product Officers, or Chief Commercial Officers (see Extended Data Fig.  12 in SI section  A.4 for more details).

The ensemble theory of success

While founders’ individual personality traits, such as Adventurousness or Openness, show to be related to their firms’ success, we also hypothesise that the combination, or ensemble, of personality characteristics of a founding team impacts the chances of success. The logic behind this reasoning is complementarity, which is proposed by contemporary research on the functional roles of founder teams. Examples of these clear functional roles have evolved in established industries such as film and television, construction, and advertising 45 . When we subsequently explored the combinations of personality types among founders and their relationship to the probability of startup success, adjusted for a range of other factors in a multi-factorial analysis, we found significantly increased chances of success for mixed foundation teams:

Initially, we find that firms with multiple founders are more likely to succeed, as illustrated in Fig.  2 A, which shows firms with three or more founders are more than twice as likely to succeed than solo-founded startups. This finding is consistent with investors’ advice to founders and previous studies 46 . We also noted that some personality types of founders increase the probability of success more than others, as shown in SI section  A.6 (Extended Data Figures  16 and 17 ). Also, we note that gender differences play out in the distribution of personality facets: successful female founders and successful male founders show facet scores that are more similar to each other than are non-successful female founders to non-successful male founders (see Extended Data Figure  18 ).

The Ensemble Theory of Team-Level Factors of Startup Success. ( A ) Having a larger founder team elevates the chances of success. This can be due to multiple reasons, e.g., a more extensive network or knowledge base but also personality diversity. ( B ) We show that joint personality combinations of founders are significantly related to higher chances of success. This is because it takes more than one founder to cover all beneficial personality traits that ‘breed’ success. ( C ) In our multifactor model, we show that firms with diverse and specific combinations of types of founders have significantly higher odds of success.

Access to more extensive networks and capital could explain the benefits of having more founders. Still, as we find here, it also offers a greater diversity of combined personalities, naturally providing a broader range of maximum traits. So, for example, one founder may be more open and adventurous, and another could be highly agreeable and trustworthy, thus, potentially complementing each other’s particular strengths associated with startup success.

The benefits of larger and more personality-diverse foundation teams can be seen in the apparent differences between successful and unsuccessful firms based on their combined Big Five personality team footprints, as illustrated in Fig.  2 B. Here, maximum values for each Big Five trait of a startup’s co-founders are mapped; stratified by successful and non-successful companies. Founder teams of successful startups tend to score higher on Openness, Conscientiousness, Extraversion, and Agreeableness.

When examining the combinations of founders with different personality types, we find that some ensembles of personalities were significantly correlated with greater chances of startup success—while controlling for other variables in the model—as shown in Fig.  2 C (for more details on the modelling, the predictive performance and the coefficient estimates of the final model, see Extended Data Figures  19 , 20 , and 21 in SI section  A.6 ).

Three combinations of trio-founder companies were more than twice as likely to succeed than other combinations, namely teams with (1) a Leader and two Developers , (2) an Operator and two Developers , and (3) an Expert/Engineer , Leader and Developer . To illustrate the potential mechanisms on how personality traits might influence the success of startups, we provide some examples of well-known, successful startup founders and their characteristic personality traits in Extended Data Figure  22 .

Startups are one of the key mechanisms for brilliant ideas to become solutions to some of the world’s most challenging economic and social problems. Examples include the Google search algorithm, disability technology startup Fingerwork’s touchscreen technology that became the basis of the Apple iPhone, or the Biontech mRNA technology that powered Pfizer’s COVID-19 vaccine.

We have shown that founders’ personalities and the combination of personalities in the founding team of a startup have a material and significant impact on its likelihood of success. We have also shown that successful startup founders’ personality traits are significantly different from those of successful employees—so much so that a simple predictor can be trained to distinguish between employees and entrepreneurs with more than 80% accuracy using personality trait data alone.

Just as occupation-personality maps derived from data can provide career guidance tools, so too can data on successful entrepreneurs’ personality traits help people decide whether becoming a founder may be a good choice for them.

We have learnt through this research that there is not one type of ideal ’entrepreneurial’ personality but six different types. Many successful startups have multiple co-founders with a combination of these different personality types.

To a large extent, founding a startup is a team sport; therefore, diversity and complementarity of personalities matter in the foundation team. It has an outsized impact on the company’s likelihood of success. While all startups are high risk, the risk becomes lower with more founders, particularly if they have distinct personality traits.

Our work demonstrates the benefits of personality diversity among the founding team of startups. Greater awareness of this novel form of diversity may help create more resilient startups capable of more significant innovation and impact.

The data-driven research approach presented here comes with certain methodological limitations. The principal data sources of this study—Crunchbase and Twitter—are extensive and comprehensive, but there are characterised by some known and likely sample biases.

Crunchbase is the principal public chronicle of venture capital funding. So, there is some likely sample bias toward: (1) Startup companies that are funded externally: self-funded or bootstrapped companies are less likely to be represented in Crunchbase; (2) technology companies, as that is Crunchbase’s roots; (3) multi-founder companies; (4) male founders: while the representation of female founders is now double that of the mid-2000s, women still represent less than 25% of the sample; (5) companies that succeed: companies that fail, especially those that fail early, are likely to be less represented in the data.

Samples were also limited to those founders who are active on Twitter, which adds additional selection biases. For example, Twitter users typically are younger, more educated and have a higher median income 47 . Another limitation of our approach is the potentially biased presentation of a person’s digital identity on social media, which is the basis for identifying personality traits. For example, recent research suggests that the language and emotional tone used by entrepreneurs in social media can be affected by events such as business failure 48 , which might complicate the personality trait inference.

In addition to sampling biases within the data, there are also significant historical biases in startup culture. For many aspects of the entrepreneurship ecosystem, women, for example, are at a disadvantage 49 . Male-founded companies have historically dominated most startup ecosystems worldwide, representing the majority of founders and the overwhelming majority of venture capital investors. As a result, startups with women have historically attracted significantly fewer funds 50 , in part due to the male bias among venture investors, although this is now changing, albeit slowly 51 .

The research presented here provides quantitative evidence for the relevance of personality types and the diversity of personalities in startups. At the same time, it brings up other questions on how personality traits are related to other factors associated with success, such as:

Will the recent growing focus on promoting and investing in female founders change the nature, composition and dynamics of startups and their personalities leading to a more diverse personality landscape in startups?

Will the growth of startups outside of the United States change what success looks like to investors and hence the role of different personality traits and their association to diverse success metrics?

Many of today’s most renowned entrepreneurs are either Baby Boomers (such as Gates, Branson, Bloomberg) or Generation Xers (such as Benioff, Cannon-Brookes, Musk). However, as we can see, personality is both a predictor and driver of success in entrepreneurship. Will generation-wide differences in personality and outlook affect startups and their success?

Moreover, the findings shown here have natural extensions and applications beyond startups, such as for new projects within large established companies. While not technically startups, many large enterprises and industries such as construction, engineering and the film industry rely on forming new project-based, cross-functional teams that are often new ventures and share many characteristics of startups.

There is also potential for extending this research in other settings in government, NGOs, and within the research community. In scientific research, for example, team diversity in terms of age, ethnicity and gender has been shown to be predictive of impact, and personality diversity may be another critical dimension 52 .

Another extension of the study could investigate the development of the language used by startup founders on social media over time. Such an extension could investigate whether the language (and inferred psychological characteristics) change as the entrepreneurs’ ventures go through major business events such as foundation, funding, or exit.

Overall, this study demonstrates, first, that startup founders have significantly different personalities than employees. Secondly, besides firm-level factors, which are known to influence firm success, we show that a range of founder-level factors, notably the character traits of its founders, significantly impact a startup’s likelihood of success. Lastly, we looked at team-level factors. We discovered in a multifactor analysis that personality-diverse teams have the most considerable impact on the probability of a startup’s success, underlining the importance of personality diversity as a relevant factor of team performance and success.

Data sources

Entrepreneurs dataset.

Data about the founders of startups were collected from Crunchbase (Table  2 ), an open reference platform for business information about private and public companies, primarily early-stage startups. It is one of the largest and most comprehensive data sets of its kind and has been used in over 100 peer-reviewed research articles about economic and managerial research.

Crunchbase contains data on over two million companies - mainly startup companies and the companies who partner with them, acquire them and invest in them, as well as profiles on well over one million individuals active in the entrepreneurial ecosystem worldwide from over 200 countries and spans. Crunchbase started in the technology startup space, and it now covers all sectors, specifically focusing on entrepreneurship, investment and high-growth companies.

While Crunchbase contains data on over one million individuals in the entrepreneurial ecosystem, some are not entrepreneurs or startup founders but play other roles, such as investors, lawyers or executives at companies that acquire startups. To create a subset of only entrepreneurs, we selected a subset of 32,732 who self-identify as founders and co-founders (by job title) and who are also publicly active on the social media platform Twitter. We also removed those who also are venture capitalists to distinguish between investors and founders.

We selected founders active on Twitter to be able to use natural language processing to infer their Big Five personality features using an open-vocabulary approach shown to be accurate in the previous research by analysing users’ unstructured text, such as Twitter posts in our case. For this project, as with previous research 20 , we employed a commercial service, IBM Watson Personality Insight, to infer personality facets. This service provides raw scores and percentile scores of Big Five Domains (Openness, Conscientiousness, Extraversion, Agreeableness and Emotional Stability) and the corresponding 30 subdomains or facets. In addition, the public content of Twitter posts was collected, and there are 32,732 profiles that each had enough Twitter posts (more than 150 words) to get relatively accurate personality scores (less than 12.7% Average Mean Absolute Error).

The entrepreneurs’ dataset is analysed in combination with other data about the companies they founded to explore questions about the nature and patterns of personality traits of entrepreneurs and the relationships between these patterns and company success.

For the multifactor analysis, we further filtered the data in several preparatory steps for the success prediction modelling (for more details, see SI section  A.5 ). In particular, we removed data points with missing values (Extended Data Fig.  13 ) and kept only companies in the data that were founded from 1990 onward to ensure consistency with previous research 32 (see Extended Data Fig.  14 ). After cleaning, filtering and pre-processing the data, we ended up with data from 25,214 founders who founded 21,187 startup companies to be used in the multifactor analysis. Of those, 3442 startups in the data were successful, 2362 in the first seven years after they were founded (see Extended Data Figure  15 for more details).

Entrepreneurs and employees dataset

To investigate whether startup founders show personality traits that are similar or different from the population at large (i. e. the entrepreneurs vs employees sub-analysis shown in Fig.  1 A and B), we filtered the entrepreneurs’ data further: we reduced the sample to those founders of companies, which attracted more than US$100k in investment to create a reference set of successful entrepreneurs (n \(=\) 4400).

To create a control group of employees who are not also entrepreneurs or very unlikely to be of have been entrepreneurs, we leveraged the fact that while some occupational titles like CEO, CTO and Public Speaker are commonly shared by founders and co-founders, some others such as Cashier , Zoologist and Detective very rarely co-occur seem to be founders or co-founders. To illustrate, many company founders also adopt regular occupation titles such as CEO or CTO. Many founders will be Founder and CEO or Co-founder and CTO. While founders are often CEOs or CTOs, the reverse is not necessarily true, as many CEOs are professional executives that were not involved in the establishment or ownership of the firm.

Using data from LinkedIn, we created an Entrepreneurial Occupation Index (EOI) based on the ratio of entrepreneurs for each of the 624 occupations used in a previous study of occupation-personality fit 44 . It was calculated based on the percentage of all people working in the occupation from LinkedIn compared to those who shared the title Founder or Co-founder (See SI section  A.2 for more details). A reference set of employees (n=6685) was then selected across the 112 different occupations with the lowest propensity for entrepreneurship (less than 0.5% EOI) from a large corpus of Twitter users with known occupations, which is also drawn from the previous occupational-personality fit study 44 .

These two data sets were used to test whether it may be possible to distinguish successful entrepreneurs from successful employees based on the different patterns of personality traits alone.

Hierarchical clustering

We applied several clustering techniques and tests to the personality vectors of the entrepreneurs’ data set to determine if there are natural clusters and, if so, how many are the optimum number.

Firstly, to determine if there is a natural typology to founder personalities, we applied the Hopkins statistic—a statistical test we used to answer whether the entrepreneurs’ dataset contains inherent clusters. It measures the clustering tendency based on the ratio of the sum of distances of real points within a sample of the entrepreneurs’ dataset to their nearest neighbours and the sum of distances of randomly selected artificial points from a simulated uniform distribution to their nearest neighbours in the real entrepreneurs’ dataset. The ratio measures the difference between the entrepreneurs’ data distribution and the simulated uniform distribution, which tests the randomness of the data. The range of Hopkins statistics is from 0 to 1. The scores are close to 0, 0.5 and 1, respectively, indicating whether the dataset is uniformly distributed, randomly distributed or highly clustered.

To cluster the founders by personality facets, we used Agglomerative Hierarchical Clustering (AHC)—a bottom-up approach that treats an individual data point as a singleton cluster and then iteratively merges pairs of clusters until all data points are included in the single big collection. Ward’s linkage method is used to choose the pair of groups for minimising the increase in the within-cluster variance after combining. AHC was widely applied to clustering analysis since a tree hierarchy output is more informative and interpretable than K-means. Dendrograms were used to visualise the hierarchy to provide the perspective of the optimal number of clusters. The heights of the dendrogram represent the distance between groups, with lower heights representing more similar groups of observations. A horizontal line through the dendrogram was drawn to distinguish the number of significantly different clusters with higher heights. However, as it is not possible to determine the optimum number of clusters from the dendrogram, we applied other clustering performance metrics to analyse the optimal number of groups.

A range of Clustering performance metrics were used to help determine the optimal number of clusters in the dataset after an apparent clustering tendency was confirmed. The following metrics were implemented to evaluate the differences between within-cluster and between-cluster distances comprehensively: Dunn Index, Calinski-Harabasz Index, Davies-Bouldin Index and Silhouette Index. The Dunn Index measures the ratio of the minimum inter-cluster separation and the maximum intra-cluster diameter. At the same time, the Calinski-Harabasz Index improves the measurement of the Dunn Index by calculating the ratio of the average sum of squared dispersion of inter-cluster and intra-cluster. The Davies-Bouldin Index simplifies the process by treating each cluster individually. It compares the sum of the average distance among intra-cluster data points to the cluster centre of two separate groups with the distance between their centre points. Finally, the Silhouette Index is the overall average of the silhouette coefficients for each sample. The coefficient measures the similarity of the data point to its cluster compared with the other groups. Higher scores of the Dunn, Calinski-Harabasz and Silhouette Index and a lower score of the Davies-Bouldin Index indicate better clustering configuration.

Classification modelling

Classification algorithms.

To obtain a comprehensive and robust conclusion in the analysis predicting whether a given set of personality traits corresponds to an entrepreneur or an employee, we explored the following classifiers: Naïve Bayes, Elastic Net regularisation, Support Vector Machine, Random Forest, Gradient Boosting and Stacked Ensemble. The Naïve Bayes classifier is a probabilistic algorithm based on Bayes’ theorem with assumptions of independent features and equiprobable classes. Compared with other more complex classifiers, it saves computing time for large datasets and performs better if the assumptions hold. However, in the real world, those assumptions are generally violated. Elastic Net regularisation combines the penalties of Lasso and Ridge to regularise the Logistic classifier. It eliminates the limitation of multicollinearity in the Lasso method and improves the limitation of feature selection in the Ridge method. Even though Elastic Net is as simple as the Naïve Bayes classifier, it is more time-consuming. The Support Vector Machine (SVM) aims to find the ideal line or hyperplane to separate successful entrepreneurs and employees in this study. The dividing line can be non-linear based on a non-linear kernel, such as the Radial Basis Function Kernel. Therefore, it performs well on high-dimensional data while the ’right’ kernel selection needs to be tuned. Random Forest (RF) and Gradient Boosting Trees (GBT) are ensembles of decision trees. All trees are trained independently and simultaneously in RF, while a new tree is trained each time and corrected by previously trained trees in GBT. RF is a more robust and straightforward model since it does not have many hyperparameters to tune. GBT optimises the objective function and learns a more accurate model since there is a successive learning and correction process. Stacked Ensemble combines all existing classifiers through a Logistic Regression. Better than bagging with only variance reduction and boosting with only bias reduction, the ensemble leverages the benefit of model diversity with both lower variance and bias. All the above classification algorithms distinguish successful entrepreneurs and employees based on the personality matrix.

Evaluation metrics

A range of evaluation metrics comprehensively explains the performance of a classification prediction. The most straightforward metric is accuracy, which measures the overall portion of correct predictions. It will mislead the performance of an imbalanced dataset. The F1 score is better than accuracy by combining precision and recall and considering the False Negatives and False Positives. Specificity measures the proportion of detecting the true negative rate that correctly identifies employees, while Positive Predictive Value (PPV) calculates the probability of accurately predicting successful entrepreneurs. Area Under the Receiver Operating Characteristic Curve (AUROC) determines the capability of the algorithm to distinguish between successful entrepreneurs and employees. A higher value means the classifier performs better on separating the classes.

Feature importance

To further understand and interpret the classifier, it is critical to identify variables with significant predictive power on the target. Feature importance of tree-based models measures Gini importance scores for all predictors, which evaluate the overall impact of the model after cutting off the specific feature. The measurements consider all interactions among features. However, it does not provide insights into the directions of impacts since the importance only indicates the ability to distinguish different classes.

Statistical analysis

T-test, Cohen’s D and two-sample Kolmogorov-Smirnov test are introduced to explore how the mean values and distributions of personality facets between entrepreneurs and employees differ. The T-test is applied to determine whether the mean of personality facets of two group samples are significantly different from one another or not. The facets with significant differences detected by the hypothesis testing are critical to separate the two groups. Cohen’s d is to measure the effect size of the results of the previous t-test, which is the ratio of the mean difference to the pooled standard deviation. A larger Cohen’s d score indicates that the mean difference is greater than the variability of the whole sample. Moreover, it is interesting to check whether the two groups’ personality facets’ probability distributions are from the same distribution through the two-sample Kolmogorov-Smirnov test. There is no assumption about the distributions, but the test is sensitive to deviations near the centre rather than the tail.

Privacy and ethics

The focus of this research is to provide high-level insights about groups of startups, founders and types of founder teams rather than on specific individuals or companies. While we used unit record data from the publicly available data of company profiles from Crunchbase , we removed all identifiers from the underlying data on individual companies and founders and generated aggregate results, which formed the basis for our analysis and conclusions.

Data availability

A dataset which includes only aggregated statistics about the success of startups and the factors that influence is released as part of this research. Underlying data for all figures and the code to reproduce them are available on GitHub: https://github.com/Braesemann/FounderPersonalities . Please contact Fabian Braesemann ( [email protected] ) in case you have any further questions.

Change history

07 may 2024.

A Correction to this paper has been published: https://doi.org/10.1038/s41598-024-61082-7

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Acknowledgements

We thank Gary Brewer from BuiltWith ; Leni Mayo from Influx , Rachel Slattery from TeamSlatts and Daniel Petre from AirTree Ventures for their ongoing generosity and insights about startups, founders and venture investments. We also thank Tim Li from Crunchbase for advice and liaison regarding data on startups and Richard Slatter for advice and referrals in Twitter .

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Contributions

All authors designed research; All authors analysed data and undertook investigation; F.B. and F.S. led multi-factor analysis; P.M., X.G. and M.A.R. led the founder/employee prediction; M.L.K. led personality insights; X.G. collected and tabulated the data; X.G., F.B., and F.S. created figures; X.G. created final art, and all authors wrote the paper.

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Correspondence to Fabian Braesemann .

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The original online version of this Article was revised: The Data Availability section in the original version of this Article was incomplete, the link to the GitHub repository was omitted. Full information regarding the corrections made can be found in the correction for this Article.

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McCarthy, P.X., Gong, X., Braesemann, F. et al. The impact of founder personalities on startup success. Sci Rep 13 , 17200 (2023). https://doi.org/10.1038/s41598-023-41980-y

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DOI : https://doi.org/10.1038/s41598-023-41980-y

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