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Dissertation, /ˈdɪsərˌteɪʃən/, /dɪsəˈteɪʃən/.

Other forms: dissertations

A dissertation is a long piece of writing that uses research to bring to light an original idea. Don't go to grad school unless you're prepared to write, say, a 300-page dissertation on some topic.

In everyday speech, we sometimes accuse people of delivering dissertations when they overload us with dull information. If you're annoyed with a long memo from your office manager about keeping the kitchen clean, you could mutter to a coworker, “How’d you like that dissertation Felix posted about rinsing out our mugs?”

• noun a treatise advancing a new point of view resulting from research; usually a requirement for an advanced academic degree synonyms: thesis see more see less type of: tractate , treatise a formal exposition

Vocabulary lists containing dissertation

Find lists of SAT words organized by every letter of the alphabet here: A , B , C , D , E , F , G , H , I , J, K & L , M , N , O , P , Q , R , S , T , U , V , and W, X, Y & Z .

Set in Afghanistan during a time of political and social upheaval, this novel traces the decades-long friendship of two boys from different social classes.

A group of extraordinarily talented African-American women help NASA achieve some of its greatest successes, even as they face discrimination and oppression.

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Thesaurus for Dissertation

Related terms for dissertation - synonyms, antonyms and sentences with dissertation, similar meaning.

• disquisition
• composition

Opposite meaning

• doublespeak
• gibberishes
• word salads
• creative writing
• brief introduction
• be oblivious to
• be speechless
• bite one's tongue

Common usage

• comic verse
• organic poetry
• miscommunication
• table chart
• in this essay
• collaborative discussion
• valid argument
• online database
• previous paragraph
• previous chapter
• academic discourse
• comprehensive

Sentence Examples

Proper usage in context.

• A dissertation on the novels of the Brontë sisters
• A dissertation topic, there are issues such as the following
• Dr. Brennan approved my new dissertation subject
• File containing notes for Michel Longtin's dissertation defense
• For my dissertation I was assigned USS Kelvin

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Dissertation Synonyms

• disquisition

Words Related to Dissertation

Related words are words that are directly connected to each other through their meaning, even if they are not synonyms or antonyms. This connection may be general or specific, or the words may appear frequently together.

• dissertation-project
• project-dissertation
• double-module

Dissertation Sentence Examples

He bequeathed his estates to Cambridge University for the purpose of maintaining two divinity scholars (-C30 a year each) at St John's College, of founding a prize for a dissertation , and of instituting the offices of Christian advocate and of Christian preacher or Hulsean lecturer.

The author gives a romantic description of the meeting with Cleopatra, with an interpolated dissertation on amour courtois as understood by the trouveres.

To this was appended a critical dissertation on the historians who had dealt with the period (Zur Kritik neuerer Geschichtschreiber), which, showing as it did how untrustworthy was much of traditional history, was to be for modern history as epoch-marking as the critical work of Niebuhr had been in ancient history.

Many of the propositions contained in his dissertation are general; but the demonstrations are not supplied for the case of seven squares.

Dr Peckard, vice-chancellor of the university of Cambridge, who entertained strong convictions against the slave trade, proposed in 1785 as subject for a Latin prize dissertation the question, " An liceat invitos in servitutem dare."

Related Articles

Words near Dissertation in the Thesaurus

• dissentience
• dissentient
• dissertation
• dissertations
• disservices

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• Knowledge Base
• Dissertation

What Is a Dissertation? | Guide, Examples, & Template

A dissertation is a long-form piece of academic writing based on original research conducted by you. It is usually submitted as the final step in order to finish a PhD program.

Your dissertation is probably the longest piece of writing you’ve ever completed. It requires solid research, writing, and analysis skills, and it can be intimidating to know where to begin.

Your department likely has guidelines related to how your dissertation should be structured. When in doubt, consult with your supervisor.

You can also download our full dissertation template in the format of your choice below. The template includes a ready-made table of contents with notes on what to include in each chapter, easily adaptable to your department’s requirements.

Download Word template Download Google Docs template

• In the US, a dissertation generally refers to the collection of research you conducted to obtain a PhD.
• In other countries (such as the UK), a dissertation often refers to the research you conduct to obtain your bachelor’s or master’s degree.

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Table of contents

Dissertation committee and prospectus process, how to write and structure a dissertation, acknowledgements or preface, list of figures and tables, list of abbreviations, introduction, literature review, methodology, reference list, proofreading and editing, defending your dissertation, free checklist and lecture slides.

When you’ve finished your coursework, as well as any comprehensive exams or other requirements, you advance to “ABD” (All But Dissertation) status. This means you’ve completed everything except your dissertation.

Prior to starting to write, you must form your committee and write your prospectus or proposal . Your committee comprises your adviser and a few other faculty members. They can be from your own department, or, if your work is more interdisciplinary, from other departments. Your committee will guide you through the dissertation process, and ultimately decide whether you pass your dissertation defense and receive your PhD.

Your prospectus is a formal document presented to your committee, usually orally in a defense, outlining your research aims and objectives and showing why your topic is relevant . After passing your prospectus defense, you’re ready to start your research and writing.

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The structure of your dissertation depends on a variety of factors, such as your discipline, topic, and approach. Dissertations in the humanities are often structured more like a long essay , building an overall argument to support a central thesis , with chapters organized around different themes or case studies.

However, hard science and social science dissertations typically include a review of existing works, a methodology section, an analysis of your original research, and a presentation of your results , presented in different chapters.

Dissertation examples

We’ve compiled a list of dissertation examples to help you get started.

• Example dissertation #1: Heat, Wildfire and Energy Demand: An Examination of Residential Buildings and Community Equity (a dissertation by C. A. Antonopoulos about the impact of extreme heat and wildfire on residential buildings and occupant exposure risks).
• Example dissertation #2: Exploring Income Volatility and Financial Health Among Middle-Income Households (a dissertation by M. Addo about income volatility and declining economic security among middle-income households).
• Example dissertation #3: The Use of Mindfulness Meditation to Increase the Efficacy of Mirror Visual Feedback for Reducing Phantom Limb Pain in Amputees (a dissertation by N. S. Mills about the effect of mindfulness-based interventions on the relationship between mirror visual feedback and the pain level in amputees with phantom limb pain).

The very first page of your document contains your dissertation title, your name, department, institution, degree program, and submission date. Sometimes it also includes your student number, your supervisor’s name, and the university’s logo.

Read more about title pages

The acknowledgements section is usually optional and gives space for you to thank everyone who helped you in writing your dissertation. This might include your supervisors, participants in your research, and friends or family who supported you. In some cases, your acknowledgements are part of a preface.

Read more about acknowledgements Read more about prefaces

The abstract is a short summary of your dissertation, usually about 150 to 300 words long. Though this may seem very short, it’s one of the most important parts of your dissertation, because it introduces your work to your audience.

Your abstract should:

• State your main topic and the aims of your research
• Describe your methods
• Summarize your main results
• State your conclusions

Read more about abstracts

The table of contents lists all of your chapters, along with corresponding subheadings and page numbers. This gives your reader an overview of your structure and helps them easily navigate your document.

Remember to include all main parts of your dissertation in your table of contents, even the appendices. It’s easy to generate a table automatically in Word if you used heading styles. Generally speaking, you only include level 2 and level 3 headings, not every subheading you included in your finished work.

Read more about tables of contents

While not usually mandatory, it’s nice to include a list of figures and tables to help guide your reader if you have used a lot of these in your dissertation. It’s easy to generate one of these in Word using the Insert Caption feature.

Read more about lists of figures and tables

Similarly, if you have used a lot of abbreviations (especially industry-specific ones) in your dissertation, you can include them in an alphabetized list of abbreviations so that the reader can easily look up their meanings.

Read more about lists of abbreviations

In addition to the list of abbreviations, if you find yourself using a lot of highly specialized terms that you worry will not be familiar to your reader, consider including a glossary. Here, alphabetize the terms and include a brief description or definition.

Read more about glossaries

The introduction serves to set up your dissertation’s topic, purpose, and relevance. It tells the reader what to expect in the rest of your dissertation. The introduction should:

• Establish your research topic , giving the background information needed to contextualize your work
• Narrow down the focus and define the scope of your research
• Discuss the state of existing research on the topic, showing your work’s relevance to a broader problem or debate
• Clearly state your research questions and objectives
• Outline the flow of the rest of your work

Everything in the introduction should be clear, engaging, and relevant. By the end, the reader should understand the what, why, and how of your research.

Read more about introductions

A formative part of your research is your literature review . This helps you gain a thorough understanding of the academic work that already exists on your topic.

Literature reviews encompass:

• Finding relevant sources (e.g., books and journal articles)
• Assessing the credibility of your sources
• Critically analyzing and evaluating each source
• Drawing connections between them (e.g., themes, patterns, conflicts, or gaps) to strengthen your overall point

A literature review is not merely a summary of existing sources. Your literature review should have a coherent structure and argument that leads to a clear justification for your own research. It may aim to:

• Address a gap in the literature or build on existing knowledge
• Take a new theoretical or methodological approach to your topic
• Propose a solution to an unresolved problem or advance one side of a theoretical debate

Read more about literature reviews

Theoretical framework

Your literature review can often form the basis for your theoretical framework. Here, you define and analyze the key theories, concepts, and models that frame your research.

Read more about theoretical frameworks

Your methodology chapter describes how you conducted your research, allowing your reader to critically assess its credibility. Your methodology section should accurately report what you did, as well as convince your reader that this was the best way to answer your research question.

A methodology section should generally include:

• The overall research approach ( quantitative vs. qualitative ) and research methods (e.g., a longitudinal study )
• Your data collection methods (e.g., interviews or a controlled experiment )
• Details of where, when, and with whom the research took place
• Any tools and materials you used (e.g., computer programs, lab equipment)
• Your data analysis methods (e.g., statistical analysis , discourse analysis )
• An evaluation or justification of your methods

Read more about methodology sections

Your results section should highlight what your methodology discovered. You can structure this section around sub-questions, hypotheses , or themes, but avoid including any subjective or speculative interpretation here.

Your results section should:

• Concisely state each relevant result together with relevant descriptive statistics (e.g., mean , standard deviation ) and inferential statistics (e.g., test statistics , p values )
• Briefly state how the result relates to the question or whether the hypothesis was supported
• Report all results that are relevant to your research questions , including any that did not meet your expectations.

Additional data (including raw numbers, full questionnaires, or interview transcripts) can be included as an appendix. You can include tables and figures, but only if they help the reader better understand your results. Read more about results sections

Your discussion section is your opportunity to explore the meaning and implications of your results in relation to your research question. Here, interpret your results in detail, discussing whether they met your expectations and how well they fit with the framework that you built in earlier chapters. Refer back to relevant source material to show how your results fit within existing research in your field.

Some guiding questions include:

• What do your results mean?
• Why do your results matter?
• What limitations do the results have?

If any of the results were unexpected, offer explanations for why this might be. It’s a good idea to consider alternative interpretations of your data.

Read more about discussion sections

Your dissertation’s conclusion should concisely answer your main research question, leaving your reader with a clear understanding of your central argument and emphasizing what your research has contributed to the field.

In some disciplines, the conclusion is just a short section preceding the discussion section, but in other contexts, it is the final chapter of your work. Here, you wrap up your dissertation with a final reflection on what you found, with recommendations for future research and concluding remarks.

It’s important to leave the reader with a clear impression of why your research matters. What have you added to what was already known? Why is your research necessary for the future of your field?

Read more about conclusions

It is crucial to include a reference list or list of works cited with the full details of all the sources that you used, in order to avoid plagiarism. Be sure to choose one citation style and follow it consistently throughout your dissertation. Each style has strict and specific formatting requirements.

Common styles include MLA , Chicago , and APA , but which style you use is often set by your department or your field.

Create APA citations Create MLA citations

Your dissertation should contain only essential information that directly contributes to answering your research question. Documents such as interview transcripts or survey questions can be added as appendices, rather than adding them to the main body.

Read more about appendices

Making sure that all of your sections are in the right place is only the first step to a well-written dissertation. Don’t forget to leave plenty of time for editing and proofreading, as grammar mistakes and sloppy spelling errors can really negatively impact your work.

Dissertations can take up to five years to write, so you will definitely want to make sure that everything is perfect before submitting. You may want to consider using a professional dissertation editing service , AI proofreader or grammar checker to make sure your final project is perfect prior to submitting.

After your written dissertation is approved, your committee will schedule a defense. Similarly to defending your prospectus, dissertation defenses are oral presentations of your work. You’ll present your dissertation, and your committee will ask you questions. Many departments allow family members, friends, and other people who are interested to join as well.

After your defense, your committee will meet, and then inform you whether you have passed. Keep in mind that defenses are usually just a formality; most committees will have resolved any serious issues with your work with you far prior to your defense, giving you ample time to fix any problems.

As you write your dissertation, you can use this simple checklist to make sure you’ve included all the essentials.

Checklist: Dissertation

My title page includes all information required by my university.

I have included acknowledgements thanking those who helped me.

My abstract provides a concise summary of the dissertation, giving the reader a clear idea of my key results or arguments.

I have created a table of contents to help the reader navigate my dissertation. It includes all chapter titles, but excludes the title page, acknowledgements, and abstract.

My introduction leads into my topic in an engaging way and shows the relevance of my research.

My introduction clearly defines the focus of my research, stating my research questions and research objectives .

My introduction includes an overview of the dissertation’s structure (reading guide).

I have conducted a literature review in which I (1) critically engage with sources, evaluating the strengths and weaknesses of existing research, (2) discuss patterns, themes, and debates in the literature, and (3) address a gap or show how my research contributes to existing research.

I have clearly outlined the theoretical framework of my research, explaining the theories and models that support my approach.

I have thoroughly described my methodology , explaining how I collected data and analyzed data.

I have concisely and objectively reported all relevant results .

I have (1) evaluated and interpreted the meaning of the results and (2) acknowledged any important limitations of the results in my discussion .

I have clearly stated the answer to my main research question in the conclusion .

I have clearly explained the implications of my conclusion, emphasizing what new insight my research has contributed.

I have provided relevant recommendations for further research or practice.

If relevant, I have included appendices with supplemental information.

I have included an in-text citation every time I use words, ideas, or information from a source.

I have listed every source in a reference list at the end of my dissertation.

I have consistently followed the rules of my chosen citation style .

I have followed all formatting guidelines provided by my university.

Congratulations!

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noun as in belief, assumption to be tested

Strongest matches

• proposition
• supposition

Strong matches

• contestation
• postulation
• presumption
• presupposition

noun as in written dissertation

• argumentation
• composition
• disquisition

Weak matches

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Example sentences.

In “Back Home,” Gil also revisits the nostalgia for the South explored in his Johns Hopkins thesis, “Circle of Stone.”

At least father and son were in alignment on this central thesis: acting “gay”—bad; being thought of as gay—bad.

Her doctoral thesis, says Ramin Takloo at the University of Illinois, was simply outstanding.

Marshall McLuhan long ago argued the now accepted thesis that different mediums have different influences on thinking.

He wrote his Master's thesis on the underrepresentation of young people in Congress.

And indeed for most young men a college thesis is but an exercise for sharpening the wits, rarely dangerous in its later effects.

It will be for the reader to determine whether the main thesis of the book has gained or lost by the new evidence.

But the word thesis, when applied to Systems, does not mean the 'position' of single notes, but of groups of notes.

This conclusion, it need hardly be said, is in entire agreement with the main thesis of the preceding pages.

Sundry outlying Indians, with ammunition to waste, took belly and knee rests and strengthened the thesis to the contrary.

Related Words

Words related to thesis are not direct synonyms, but are associated with the word thesis . Browse related words to learn more about word associations.

noun as in putting regard in as true

• expectation
• understanding

noun as in main part of written work

• dissertation

noun as in written or musical creation

• arrangement
• literary work
• short story

noun as in argument for idea

• advancement
• affirmation
• asseveration
• declaration
• explanation
• maintaining
• predication

Viewing 5 / 44 related words

On this page you'll find 90 synonyms, antonyms, and words related to thesis, such as: contention, hypothesis, opinion, premise, proposition, and supposition.

From Roget's 21st Century Thesaurus, Third Edition Copyright © 2013 by the Philip Lief Group.

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• Knowledge Base
• Dissertation

What Is a Dissertation? | 5 Essential Questions to Get Started

Published on 26 March 2020 by Jack Caulfield . Revised on 5 May 2022.

A dissertation is a large research project undertaken at the end of a degree. It involves in-depth consideration of a problem or question chosen by the student. It is usually the largest (and final) piece of written work produced during a degree.

The length and structure of a dissertation vary widely depending on the level and field of study. However, there are some key questions that can help you understand the requirements and get started on your dissertation project.

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Table of contents

When and why do you have to write a dissertation, who will supervise your dissertation, what type of research will you do, how should your dissertation be structured, what formatting and referencing rules do you have to follow, frequently asked questions about dissertations.

A dissertation, sometimes called a thesis, comes at the end of an undergraduate or postgraduate degree. It is a larger project than the other essays you’ve written, requiring a higher word count and a greater depth of research.

You’ll generally work on your dissertation during the final year of your degree, over a longer period than you would take for a standard essay . For example, the dissertation might be your main focus for the last six months of your degree.

Why is the dissertation important?

The dissertation is a test of your capacity for independent research. You are given a lot of autonomy in writing your dissertation: you come up with your own ideas, conduct your own research, and write and structure the text by yourself.

This means that it is an important preparation for your future, whether you continue in academia or not: it teaches you to manage your own time, generate original ideas, and work independently.

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During the planning and writing of your dissertation, you’ll work with a supervisor from your department. The supervisor’s job is to give you feedback and advice throughout the process.

The dissertation supervisor is often assigned by the department, but you might be allowed to indicate preferences or approach potential supervisors. If so, try to pick someone who is familiar with your chosen topic, whom you get along with on a personal level, and whose feedback you’ve found useful in the past.

How will your supervisor help you?

Your supervisor is there to guide you through the dissertation project, but you’re still working independently. They can give feedback on your ideas, but not come up with ideas for you.

You may need to take the initiative to request an initial meeting with your supervisor. Then you can plan out your future meetings and set reasonable deadlines for things like completion of data collection, a structure outline, a first chapter, a first draft, and so on.

Make sure to prepare in advance for your meetings. Formulate your ideas as fully as you can, and determine where exactly you’re having difficulties so you can ask your supervisor for specific advice.

Your approach to your dissertation will vary depending on your field of study. The first thing to consider is whether you will do empirical research , which involves collecting original data, or non-empirical research , which involves analysing sources.

Empirical dissertations (sciences)

An empirical dissertation focuses on collecting and analysing original data. You’ll usually write this type of dissertation if you are studying a subject in the sciences or social sciences.

• What are airline workers’ attitudes towards the challenges posed for their industry by climate change?
• How effective is cognitive behavioural therapy in treating depression in young adults?
• What are the short-term health effects of switching from smoking cigarettes to e-cigarettes?

There are many different empirical research methods you can use to answer these questions – for example, experiments , observations, surveys , and interviews.

When doing empirical research, you need to consider things like the variables you will investigate, the reliability and validity of your measurements, and your sampling method . The aim is to produce robust, reproducible scientific knowledge.

Non-empirical dissertations (arts and humanities)

A non-empirical dissertation works with existing research or other texts, presenting original analysis, critique and argumentation, but no original data. This approach is typical of arts and humanities subjects.

• What attitudes did commentators in the British press take towards the French Revolution in 1789–1792?
• How do the themes of gender and inheritance intersect in Shakespeare’s Macbeth ?
• How did Plato’s Republic and Thomas More’s Utopia influence nineteenth century utopian socialist thought?

The first steps in this type of dissertation are to decide on your topic and begin collecting your primary and secondary sources .

Primary sources are the direct objects of your research. They give you first-hand evidence about your subject. Examples of primary sources include novels, artworks and historical documents.

Secondary sources provide information that informs your analysis. They describe, interpret, or evaluate information from primary sources. For example, you might consider previous analyses of the novel or author you are working on, or theoretical texts that you plan to apply to your primary sources.

Dissertations are divided into chapters and sections. Empirical dissertations usually follow a standard structure, while non-empirical dissertations are more flexible.

Structure of an empirical dissertation

Empirical dissertations generally include these chapters:

• Introduction : An explanation of your topic and the research question(s) you want to answer.
• Literature review : A survey and evaluation of previous research on your topic.
• Methodology : An explanation of how you collected and analysed your data.
• Results : A brief description of what you found.
• Discussion : Interpretation of what these results reveal.
• Conclusion : Answers to your research question(s) and summary of what your findings contribute to knowledge in your field.

Sometimes the order or naming of chapters might be slightly different, but all of the above information must be included in order to produce thorough, valid scientific research.

Other dissertation structures

If your dissertation doesn’t involve data collection, your structure is more flexible. You can think of it like an extended essay – the text should be logically organised in a way that serves your argument:

• Introduction: An explanation of your topic and the question(s) you want to answer.
• Main body: The development of your analysis, usually divided into 2–4 chapters.
• Conclusion: Answers to your research question(s) and summary of what your analysis contributes to knowledge in your field.

The chapters of the main body can be organised around different themes, time periods, or texts. Below you can see some example structures for dissertations in different subjects.

• Political philosophy

This example, on the topic of the British press’s coverage of the French Revolution, shows how you might structure each chapter around a specific theme.

This example, on the topic of Plato’s and More’s influences on utopian socialist thought, shows a different approach to dividing the chapters by theme.

This example, a master’s dissertation on the topic of how writers respond to persecution, shows how you can also use section headings within each chapter. Each of the three chapters deals with a specific text, while the sections are organised thematically.

Like other academic texts, it’s important that your dissertation follows the formatting guidelines set out by your university. You can lose marks unnecessarily over mistakes, so it’s worth taking the time to get all these elements right.

Formatting guidelines concern things like:

• line spacing
• page numbers
• punctuation
• title pages
• presentation of tables and figures

If you’re unsure about the formatting requirements, check with your supervisor or department. You can lose marks unnecessarily over mistakes, so it’s worth taking the time to get all these elements right.

How will you reference your sources?

Referencing means properly listing the sources you cite and refer to in your dissertation, so that the reader can find them. This avoids plagiarism by acknowledging where you’ve used the work of others.

Keep track of everything you read as you prepare your dissertation. The key information to note down for a reference is:

• The publication date
• Page numbers for the parts you refer to (especially when using direct quotes)

Different referencing styles each have their own specific rules for how to reference. The most commonly used styles in UK universities are listed below.

You can use the free APA Reference Generator to automatically create and store your references.

APA Reference Generator

The words ‘ dissertation ’ and ‘thesis’ both refer to a large written research project undertaken to complete a degree, but they are used differently depending on the country:

• In the UK, you write a dissertation at the end of a bachelor’s or master’s degree, and you write a thesis to complete a PhD.
• In the US, it’s the other way around: you may write a thesis at the end of a bachelor’s or master’s degree, and you write a dissertation to complete a PhD.

The main difference is in terms of scale – a dissertation is usually much longer than the other essays you complete during your degree.

Another key difference is that you are given much more independence when working on a dissertation. You choose your own dissertation topic , and you have to conduct the research and write the dissertation yourself (with some assistance from your supervisor).

Dissertation word counts vary widely across different fields, institutions, and levels of education:

• An undergraduate dissertation is typically 8,000–15,000 words
• A master’s dissertation is typically 12,000–50,000 words
• A PhD thesis is typically book-length: 70,000–100,000 words

However, none of these are strict guidelines – your word count may be lower or higher than the numbers stated here. Always check the guidelines provided by your university to determine how long your own dissertation should be.

At the bachelor’s and master’s levels, the dissertation is usually the main focus of your final year. You might work on it (alongside other classes) for the entirety of the final year, or for the last six months. This includes formulating an idea, doing the research, and writing up.

A PhD thesis takes a longer time, as the thesis is the main focus of the degree. A PhD thesis might be being formulated and worked on for the whole four years of the degree program. The writing process alone can take around 18 months.

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Add ${headword} to one of your lists below, or create a new one. {{message}} Something went wrong. There was a problem sending your report. More from M-W To save this word, you'll need to log in. Log In dissertation Definition of dissertation Examples of dissertation in a sentence. These examples are programmatically compiled from various online sources to illustrate current usage of the word 'dissertation.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples. Word History 1651, in the meaning defined above Dictionary Entries Near dissertation dissertative Cite this Entry “Dissertation.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/dissertation. Accessed 12 Jun. 2024. Kids Definition Kids definition of dissertation, more from merriam-webster on dissertation. Thesaurus: All synonyms and antonyms for dissertation Nglish: Translation of dissertation for Spanish Speakers Britannica English: Translation of dissertation for Arabic Speakers Subscribe to America's largest dictionary and get thousands more definitions and advanced search—ad free! Can you solve 4 words at once? Word of the day, efficacious. See Definitions and Examples » Get Word of the Day daily email! Popular in Grammar & Usage What's the difference between 'fascism' and 'socialism', more commonly misspelled words, commonly misspelled words, how to use em dashes (—), en dashes (–) , and hyphens (-), absent letters that are heard anyway, popular in wordplay, the words of the week - june 7, 8 words for lesser-known musical instruments, 9 superb owl words, 10 words for lesser-known games and sports, your favorite band is in the dictionary, games & quizzes. Daily Crossword Word Puzzle Word Finder Word of the Day Synonym of the Day Word of the Year Language stories All featured Gender and sexuality All pop culture Writing hub Grammar essentials Commonly confused All writing tips Pop culture Writing tips Advertisement dissertation [ dis-er- tey -sh uh n ] a written essay, treatise, or thesis, especially one written by a candidate for the degree of Doctor of Philosophy. any formal discourse in speech or writing. / ˌdɪsəˈteɪʃən / a written thesis, often based on original research, usually required for a higher degree a formal discourse Discover More Derived forms. ˌdisserˈtational , adjective ˌdisserˈtationist , noun Other Words From disser·tation·al adjective disser·tation·ist noun Word History and Origins Origin of dissertation 1 Example Sentences Thirteen years ago, while working on her PHD dissertation in Madagascar’s Masoala Peninsula, Borgerson encountered a problem. At Harvard, he received a PhD in government and wrote his dissertation under Henry Kissinger, who became a lifelong friend. I planned to go back to physics after a couple of years and then return to wrap up my dissertation. My buba’s lived experience helped shape me into the girl who wrote her college dissertation on the gender pay gap, arguing for equal parental leave for dads and moms, almost 20 years before any major employer implemented any such thing. My PhD dissertation was a highly theoretical model representing computer systems that were framed as a mathematical model, and if they were interconnected in such a way that these interconnected computers would communicate like cells in the body. A terrific cultural studies dissertation awaits on how the fortunes of the Cheneys provide a mirror on a changing America. Today, he visits online forums and bombards them with dissertation-length comments. In her dissertation, McFate had asked whether ‘good anthropology’ might lead to ‘better killing.’ Heritage has distanced itself from Richwine and his dissertation. No single dissertation will alter the status quo on its own. I've never had time to write home about it, for I felt that it required a dissertation in itself to do it justice. Dr. Pitcairn, published at Leyden his dissertation on the circulation of the blood through the veins. Start not, reader, I am not going to trouble you with a poetical dissertation; no, no! Dissertation sur les Assassins, Académie des Inscriptions, tom. This dissertation, which is illustrated by several plates, will repay for the time spent in reading it. Related Words A generative AI reset: Rewiring to turn potential into value in 2024 It’s time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI’s enormous potential value is harder than expected . With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI transformations: competitive advantage comes from building organizational and technological capabilities to broadly innovate, deploy, and improve solutions at scale—in effect, rewiring the business  for distributed digital and AI innovation. About QuantumBlack, AI by McKinsey QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe. Companies looking to score early wins with gen AI should move quickly. But those hoping that gen AI offers a shortcut past the tough—and necessary—organizational surgery are likely to meet with disappointing results. Launching pilots is (relatively) easy; getting pilots to scale and create meaningful value is hard because they require a broad set of changes to the way work actually gets done. Let’s briefly look at what this has meant for one Pacific region telecommunications company. The company hired a chief data and AI officer with a mandate to “enable the organization to create value with data and AI.” The chief data and AI officer worked with the business to develop the strategic vision and implement the road map for the use cases. After a scan of domains (that is, customer journeys or functions) and use case opportunities across the enterprise, leadership prioritized the home-servicing/maintenance domain to pilot and then scale as part of a larger sequencing of initiatives. They targeted, in particular, the development of a gen AI tool to help dispatchers and service operators better predict the types of calls and parts needed when servicing homes. Leadership put in place cross-functional product teams with shared objectives and incentives to build the gen AI tool. As part of an effort to upskill the entire enterprise to better work with data and gen AI tools, they also set up a data and AI academy, which the dispatchers and service operators enrolled in as part of their training. To provide the technology and data underpinnings for gen AI, the chief data and AI officer also selected a large language model (LLM) and cloud provider that could meet the needs of the domain as well as serve other parts of the enterprise. The chief data and AI officer also oversaw the implementation of a data architecture so that the clean and reliable data (including service histories and inventory databases) needed to build the gen AI tool could be delivered quickly and responsibly. Creating value beyond the hype Let’s deliver on the promise of technology from strategy to scale. Our book Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI (Wiley, June 2023) provides a detailed manual on the six capabilities needed to deliver the kind of broad change that harnesses digital and AI technology. In this article, we will explore how to extend each of those capabilities to implement a successful gen AI program at scale. While recognizing that these are still early days and that there is much more to learn, our experience has shown that breaking open the gen AI opportunity requires companies to rewire how they work in the following ways. Figure out where gen AI copilots can give you a real competitive advantage The broad excitement around gen AI and its relative ease of use has led to a burst of experimentation across organizations. Most of these initiatives, however, won’t generate a competitive advantage. One bank, for example, bought tens of thousands of GitHub Copilot licenses, but since it didn’t have a clear sense of how to work with the technology, progress was slow. Another unfocused effort we often see is when companies move to incorporate gen AI into their customer service capabilities. Customer service is a commodity capability, not part of the core business, for most companies. While gen AI might help with productivity in such cases, it won’t create a competitive advantage. To create competitive advantage, companies should first understand the difference between being a “taker” (a user of available tools, often via APIs and subscription services), a “shaper” (an integrator of available models with proprietary data), and a “maker” (a builder of LLMs). For now, the maker approach is too expensive for most companies, so the sweet spot for businesses is implementing a taker model for productivity improvements while building shaper applications for competitive advantage. Much of gen AI’s near-term value is closely tied to its ability to help people do their current jobs better. In this way, gen AI tools act as copilots that work side by side with an employee, creating an initial block of code that a developer can adapt, for example, or drafting a requisition order for a new part that a maintenance worker in the field can review and submit (see sidebar “Copilot examples across three generative AI archetypes”). This means companies should be focusing on where copilot technology can have the biggest impact on their priority programs. Copilot examples across three generative AI archetypes “Taker” copilots help real estate customers sift through property options and find the most promising one, write code for a developer, and summarize investor transcripts. “Shaper” copilots provide recommendations to sales reps for upselling customers by connecting generative AI tools to customer relationship management systems, financial systems, and customer behavior histories; create virtual assistants to personalize treatments for patients; and recommend solutions for maintenance workers based on historical data. “Maker” copilots are foundation models that lab scientists at pharmaceutical companies can use to find and test new and better drugs more quickly. Some industrial companies, for example, have identified maintenance as a critical domain for their business. Reviewing maintenance reports and spending time with workers on the front lines can help determine where a gen AI copilot could make a big difference, such as in identifying issues with equipment failures quickly and early on. A gen AI copilot can also help identify root causes of truck breakdowns and recommend resolutions much more quickly than usual, as well as act as an ongoing source for best practices or standard operating procedures. The challenge with copilots is figuring out how to generate revenue from increased productivity. In the case of customer service centers, for example, companies can stop recruiting new agents and use attrition to potentially achieve real financial gains. Defining the plans for how to generate revenue from the increased productivity up front, therefore, is crucial to capturing the value. McKinsey Live Event: Unlocking the full value of gen AI Join our colleagues Jessica Lamb and Gayatri Shenai on April 8, as they discuss how companies can navigate the ever-changing world of gen AI. Upskill the talent you have but be clear about the gen-AI-specific skills you need By now, most companies have a decent understanding of the technical gen AI skills they need, such as model fine-tuning, vector database administration, prompt engineering, and context engineering. In many cases, these are skills that you can train your existing workforce to develop. Those with existing AI and machine learning (ML) capabilities have a strong head start. Data engineers, for example, can learn multimodal processing and vector database management, MLOps (ML operations) engineers can extend their skills to LLMOps (LLM operations), and data scientists can develop prompt engineering, bias detection, and fine-tuning skills. A sample of new generative AI skills needed The following are examples of new skills needed for the successful deployment of generative AI tools: data scientist: prompt engineering in-context learning bias detection pattern identification reinforcement learning from human feedback hyperparameter/large language model fine-tuning; transfer learning data engineer: data wrangling and data warehousing data pipeline construction multimodal processing vector database management The learning process can take two to three months to get to a decent level of competence because of the complexities in learning what various LLMs can and can’t do and how best to use them. The coders need to gain experience building software, testing, and validating answers, for example. It took one financial-services company three months to train its best data scientists to a high level of competence. While courses and documentation are available—many LLM providers have boot camps for developers—we have found that the most effective way to build capabilities at scale is through apprenticeship, training people to then train others, and building communities of practitioners. Rotating experts through teams to train others, scheduling regular sessions for people to share learnings, and hosting biweekly documentation review sessions are practices that have proven successful in building communities of practitioners (see sidebar “A sample of new generative AI skills needed”). It’s important to bear in mind that successful gen AI skills are about more than coding proficiency. Our experience in developing our own gen AI platform, Lilli , showed us that the best gen AI technical talent has design skills to uncover where to focus solutions, contextual understanding to ensure the most relevant and high-quality answers are generated, collaboration skills to work well with knowledge experts (to test and validate answers and develop an appropriate curation approach), strong forensic skills to figure out causes of breakdowns (is the issue the data, the interpretation of the user’s intent, the quality of metadata on embeddings, or something else?), and anticipation skills to conceive of and plan for possible outcomes and to put the right kind of tracking into their code. A pure coder who doesn’t intrinsically have these skills may not be as useful a team member. While current upskilling is largely based on a “learn on the job” approach, we see a rapid market emerging for people who have learned these skills over the past year. That skill growth is moving quickly. GitHub reported that developers were working on gen AI projects “in big numbers,” and that 65,000 public gen AI projects were created on its platform in 2023—a jump of almost 250 percent over the previous year. If your company is just starting its gen AI journey, you could consider hiring two or three senior engineers who have built a gen AI shaper product for their companies. This could greatly accelerate your efforts. Form a centralized team to establish standards that enable responsible scaling To ensure that all parts of the business can scale gen AI capabilities, centralizing competencies is a natural first move. The critical focus for this central team will be to develop and put in place protocols and standards to support scale, ensuring that teams can access models while also minimizing risk and containing costs. The team’s work could include, for example, procuring models and prescribing ways to access them, developing standards for data readiness, setting up approved prompt libraries, and allocating resources. While developing Lilli, our team had its mind on scale when it created an open plug-in architecture and setting standards for how APIs should function and be built.  They developed standardized tooling and infrastructure where teams could securely experiment and access a GPT LLM , a gateway with preapproved APIs that teams could access, and a self-serve developer portal. Our goal is that this approach, over time, can help shift “Lilli as a product” (that a handful of teams use to build specific solutions) to “Lilli as a platform” (that teams across the enterprise can access to build other products). For teams developing gen AI solutions, squad composition will be similar to AI teams but with data engineers and data scientists with gen AI experience and more contributors from risk management, compliance, and legal functions. The general idea of staffing squads with resources that are federated from the different expertise areas will not change, but the skill composition of a gen-AI-intensive squad will. Set up the technology architecture to scale Building a gen AI model is often relatively straightforward, but making it fully operational at scale is a different matter entirely. We’ve seen engineers build a basic chatbot in a week, but releasing a stable, accurate, and compliant version that scales can take four months. That’s why, our experience shows, the actual model costs may be less than 10 to 15 percent of the total costs of the solution. Building for scale doesn’t mean building a new technology architecture. But it does mean focusing on a few core decisions that simplify and speed up processes without breaking the bank. Three such decisions stand out: Focus on reusing your technology. Reusing code can increase the development speed of gen AI use cases by 30 to 50 percent. One good approach is simply creating a source for approved tools, code, and components. A financial-services company, for example, created a library of production-grade tools, which had been approved by both the security and legal teams, and made them available in a library for teams to use. More important is taking the time to identify and build those capabilities that are common across the most priority use cases. The same financial-services company, for example, identified three components that could be reused for more than 100 identified use cases. By building those first, they were able to generate a significant portion of the code base for all the identified use cases—essentially giving every application a big head start. Focus the architecture on enabling efficient connections between gen AI models and internal systems. For gen AI models to work effectively in the shaper archetype, they need access to a business’s data and applications. Advances in integration and orchestration frameworks have significantly reduced the effort required to make those connections. But laying out what those integrations are and how to enable them is critical to ensure these models work efficiently and to avoid the complexity that creates technical debt  (the “tax” a company pays in terms of time and resources needed to redress existing technology issues). Chief information officers and chief technology officers can define reference architectures and integration standards for their organizations. Key elements should include a model hub, which contains trained and approved models that can be provisioned on demand; standard APIs that act as bridges connecting gen AI models to applications or data; and context management and caching, which speed up processing by providing models with relevant information from enterprise data sources. Build up your testing and quality assurance capabilities. Our own experience building Lilli taught us to prioritize testing over development. Our team invested in not only developing testing protocols for each stage of development but also aligning the entire team so that, for example, it was clear who specifically needed to sign off on each stage of the process. This slowed down initial development but sped up the overall delivery pace and quality by cutting back on errors and the time needed to fix mistakes. Ensure data quality and focus on unstructured data to fuel your models The ability of a business to generate and scale value from gen AI models will depend on how well it takes advantage of its own data. As with technology, targeted upgrades to existing data architecture  are needed to maximize the future strategic benefits of gen AI: Be targeted in ramping up your data quality and data augmentation efforts. While data quality has always been an important issue, the scale and scope of data that gen AI models can use—especially unstructured data—has made this issue much more consequential. For this reason, it’s critical to get the data foundations right, from clarifying decision rights to defining clear data processes to establishing taxonomies so models can access the data they need. The companies that do this well tie their data quality and augmentation efforts to the specific AI/gen AI application and use case—you don’t need this data foundation to extend to every corner of the enterprise. This could mean, for example, developing a new data repository for all equipment specifications and reported issues to better support maintenance copilot applications. Understand what value is locked into your unstructured data. Most organizations have traditionally focused their data efforts on structured data (values that can be organized in tables, such as prices and features). But the real value from LLMs comes from their ability to work with unstructured data (for example, PowerPoint slides, videos, and text). Companies can map out which unstructured data sources are most valuable and establish metadata tagging standards so models can process the data and teams can find what they need (tagging is particularly important to help companies remove data from models as well, if necessary). Be creative in thinking about data opportunities. Some companies, for example, are interviewing senior employees as they retire and feeding that captured institutional knowledge into an LLM to help improve their copilot performance. Optimize to lower costs at scale. There is often as much as a tenfold difference between what companies pay for data and what they could be paying if they optimized their data infrastructure and underlying costs. This issue often stems from companies scaling their proofs of concept without optimizing their data approach. Two costs generally stand out. One is storage costs arising from companies uploading terabytes of data into the cloud and wanting that data available 24/7. In practice, companies rarely need more than 10 percent of their data to have that level of availability, and accessing the rest over a 24- or 48-hour period is a much cheaper option. The other costs relate to computation with models that require on-call access to thousands of processors to run. This is especially the case when companies are building their own models (the maker archetype) but also when they are using pretrained models and running them with their own data and use cases (the shaper archetype). Companies could take a close look at how they can optimize computation costs on cloud platforms—for instance, putting some models in a queue to run when processors aren’t being used (such as when Americans go to bed and consumption of computing services like Netflix decreases) is a much cheaper option. Build trust and reusability to drive adoption and scale Because many people have concerns about gen AI, the bar on explaining how these tools work is much higher than for most solutions. People who use the tools want to know how they work, not just what they do. So it’s important to invest extra time and money to build trust by ensuring model accuracy and making it easy to check answers. One insurance company, for example, created a gen AI tool to help manage claims. As part of the tool, it listed all the guardrails that had been put in place, and for each answer provided a link to the sentence or page of the relevant policy documents. The company also used an LLM to generate many variations of the same question to ensure answer consistency. These steps, among others, were critical to helping end users build trust in the tool. Part of the training for maintenance teams using a gen AI tool should be to help them understand the limitations of models and how best to get the right answers. That includes teaching workers strategies to get to the best answer as fast as possible by starting with broad questions then narrowing them down. This provides the model with more context, and it also helps remove any bias of the people who might think they know the answer already. Having model interfaces that look and feel the same as existing tools also helps users feel less pressured to learn something new each time a new application is introduced. Getting to scale means that businesses will need to stop building one-off solutions that are hard to use for other similar use cases. One global energy and materials company, for example, has established ease of reuse as a key requirement for all gen AI models, and has found in early iterations that 50 to 60 percent of its components can be reused. This means setting standards for developing gen AI assets (for example, prompts and context) that can be easily reused for other cases. While many of the risk issues relating to gen AI are evolutions of discussions that were already brewing—for instance, data privacy, security, bias risk, job displacement, and intellectual property protection—gen AI has greatly expanded that risk landscape. Just 21 percent of companies reporting AI adoption say they have established policies governing employees’ use of gen AI technologies. Similarly, a set of tests for AI/gen AI solutions should be established to demonstrate that data privacy, debiasing, and intellectual property protection are respected. Some organizations, in fact, are proposing to release models accompanied with documentation that details their performance characteristics. Documenting your decisions and rationales can be particularly helpful in conversations with regulators. In some ways, this article is premature—so much is changing that we’ll likely have a profoundly different understanding of gen AI and its capabilities in a year’s time. But the core truths of finding value and driving change will still apply. How well companies have learned those lessons may largely determine how successful they’ll be in capturing that value. The authors wish to thank Michael Chui, Juan Couto, Ben Ellencweig, Josh Gartner, Bryce Hall, Holger Harreis, Phil Hudelson, Suzana Iacob, Sid Kamath, Neerav Kingsland, Kitti Lakner, Robert Levin, Matej Macak, Lapo Mori, Alex Peluffo, Aldo Rosales, Erik Roth, Abdul Wahab Shaikh, and Stephen Xu for their contributions to this article. This article was edited by Barr Seitz, an editorial director in the New York office. Explore a career with us Related articles. The economic potential of generative AI: The next productivity frontier Rewired to outcompete Meet Lilli, our generative AI tool that’s a researcher, a time saver, and an inspiration 251 IMAGES How to Write a Dissertation Abstract- Step by Step Guidance Dissertation Examples Sample Dissertation Timeline 12000 Word Dissertation Breakdown for New Students Table of Contents for Dissertation/ Research Paper & Example 9 Tips to Write a Dissertation Acknowledgement with Examples VIDEO Congrats LDonald tries to read the word “dissertation” Writing a 5000 word dissertation draft in 17 hours?! I couldn’t cope😩 ROAUS EP 6 Elevate Your PhD with Expert Research Advisory Solutions THESIS How many references should a 10000 dissertation have? COMMENTS 12 Synonyms & Antonyms for DISSERTATION Find 12 different ways to say DISSERTATION, along with antonyms, related words, and example sentences at Thesaurus.com. DISSERTATION Synonyms: 21 Similar Words Synonyms for DISSERTATION: thesis, essay, treatise, article, commentary, paper, composition, theme, editorial, column Dissertation synonyms Another way to say Dissertation? Synonyms for Dissertation (other words and phrases for Dissertation). Synonyms for Dissertation. 591 other terms for dissertation- words and phrases with similar meaning. Lists. synonyms. antonyms. ... work. n. # novel, ... What is another word for dissertation Synonyms for dissertation include essay, treatise, paper, thesis, discourse, study, exposition, critique, disquisition and composition. Find more similar words at ... DISSERTATIONS Synonyms: 21 Similar Words Synonyms for DISSERTATIONS: essays, commentaries, treatises, articles, theses, papers, themes, columns, compositions, editorials DISSERTATION DISSERTATION - Synonyms, related words and examples | Cambridge English Thesaurus DISSERTATION Synonyms Synonyms for DISSERTATION in English: thesis, essay, discourse, critique, exposition, treatise, disquisition, essay, review, analysis, … Dissertation dissertation: 1 n a treatise advancing a new point of view resulting from research; usually a requirement for an advanced academic degree Synonyms: thesis Type of: treatise a formal exposition DISSERTATION in Thesaurus: 100+ Synonyms & Antonyms for DISSERTATION Most related words/phrases with sentence examples define Dissertation meaning and usage. Thesaurus for Dissertation. Related terms for dissertation- synonyms, antonyms and sentences with dissertation. Lists. synonyms. antonyms. definitions. sentences. thesaurus. Parts of speech. nouns. verbs. Synonyms Similar meaning. View all. Dissertation Synonyms: 14 Synonyms and Antonyms for ... Synonyms for DISSERTATION: thesis, discourse, treatise, exposition, disquisition, essay, critique, debate, discussion, commentary, lecture, monograph, tract, thesis. What Is a Dissertation? A dissertation is a long-form piece of academic writing based on original research conducted by you. It is usually submitted as the final step in order to finish a PhD program. Your dissertation is probably the longest piece of writing you've ever completed. It requires solid research, writing, and analysis skills, and it can be intimidating ... 48 Synonyms & Antonyms for THESIS Find 48 different ways to say THESIS, along with antonyms, related words, and example sentences at Thesaurus.com. What Is a Dissertation? Revised on 5 May 2022. A dissertation is a large research project undertaken at the end of a degree. It involves in-depth consideration of a problem or question chosen by the student. It is usually the largest (and final) piece of written work produced during a degree. The length and structure of a dissertation vary widely depending on the ... dissertation dissertation - WordReference thesaurus: synonyms, discussion and more. All Free. ... 'dissertation' also found in these entries (note: many are not synonyms or translations): body - composition - descant - discourse - discussion - disquisition - essay - excursus - exposition - paper - text - thesis - writing. What is another word for thesis Synonyms for thesis include hypothesis, supposition, theory, belief, assumption, opinion, argument, surmise, notion and postulation. Find more similar words at ... Synonyms of THESIS Synonyms for THESIS: dissertation, essay, monograph, paper, treatise, proposition, contention, hypothesis, idea, opinion, … THESIS Synonyms: 44 Similar and Opposite Words Synonyms for THESIS: argument, contention, assertion, hypothesis, theory, guess, assumption, hunch; Antonyms of THESIS: fact, knowledge, assurance, certainty Synonyms of DISSERTATION Synonyms for DISSERTATION: thesis, critique, discourse, disquisition, essay, exposition, treatise, … THESIS THESIS - Synonyms, related words and examples | Cambridge English Thesaurus Dissertation Definition & Meaning How to use dissertation in a sentence. an extended usually written treatment of a subject; specifically : one submitted for a doctorate… See the full definition DISSERTATION Definition & Meaning Dissertation definition: a written essay, treatise, or thesis, especially one written by a candidate for the degree of Doctor of Philosophy.. See examples of DISSERTATION used in a sentence. The competitive advantage of generative AI It's time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI's enormous potential value is harder than expected.. With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI ... THESIS Synonyms Synonyms for THESIS in English: proposition, theory, hypothesis, idea, view, opinion, proposal, contention, line of argument, dissertation, … assignment essay homework paper report resume review speech thesis