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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:
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:
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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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
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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.
We’ve compiled a list of dissertation examples to help you get started.
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:
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:
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:
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:
Read more about literature reviews
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:
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:
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:
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.
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.
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Home » Dissertation – Format, Example and Template
Table of Contents
Definition:
Dissertation is a lengthy and detailed academic document that presents the results of original research on a specific topic or question. It is usually required as a final project for a doctoral degree or a master’s degree.
In Research , a dissertation refers to a substantial research project that students undertake in order to obtain an advanced degree such as a Ph.D. or a Master’s degree.
Dissertation typically involves the exploration of a particular research question or topic in-depth, and it requires students to conduct original research, analyze data, and present their findings in a scholarly manner. It is often the culmination of years of study and represents a significant contribution to the academic field.
Types of Dissertation are as follows:
An empirical dissertation is a research study that uses primary data collected through surveys, experiments, or observations. It typically follows a quantitative research approach and uses statistical methods to analyze the data.
A non-empirical dissertation is based on secondary sources, such as books, articles, and online resources. It typically follows a qualitative research approach and uses methods such as content analysis or discourse analysis.
A narrative dissertation is a personal account of the researcher’s experience or journey. It typically follows a qualitative research approach and uses methods such as interviews, focus groups, or ethnography.
A systematic literature review is a comprehensive analysis of existing research on a specific topic. It typically follows a qualitative research approach and uses methods such as meta-analysis or thematic analysis.
A case study dissertation is an in-depth analysis of a specific individual, group, or organization. It typically follows a qualitative research approach and uses methods such as interviews, observations, or document analysis.
A mixed-methods dissertation combines both quantitative and qualitative research approaches to gather and analyze data. It typically uses methods such as surveys, interviews, and focus groups, as well as statistical analysis.
Here are some general steps to help guide you through the process of writing a dissertation:
The format of a dissertation may vary depending on the institution and field of study, but generally, it follows a similar structure:
Dissertation Outline is as follows:
Title Page:
Table of Contents:
I. Introduction
II. Literature Review
III. Methodology
IV. Results
V. Discussion
VI. Conclusion
VII. References
VIII. Appendices
Here is an example Dissertation for students:
Title : Exploring the Effects of Mindfulness Meditation on Academic Achievement and Well-being among College Students
This dissertation aims to investigate the impact of mindfulness meditation on the academic achievement and well-being of college students. Mindfulness meditation has gained popularity as a technique for reducing stress and enhancing mental health, but its effects on academic performance have not been extensively studied. Using a randomized controlled trial design, the study will compare the academic performance and well-being of college students who practice mindfulness meditation with those who do not. The study will also examine the moderating role of personality traits and demographic factors on the effects of mindfulness meditation.
Chapter Outline:
Chapter 1: Introduction
Chapter 2: Literature Review
Chapter 3: Methodology
Chapter 4: Results
Chapter 5: Discussion
Chapter 6: Conclusion
References :
List of all the sources cited in the dissertation
Appendices :
Additional materials such as the survey questionnaire, interview guide, and consent forms.
Note : This is just an example and the structure of a dissertation may vary depending on the specific requirements and guidelines provided by the institution or the supervisor.
The length of a dissertation can vary depending on the field of study, the level of degree being pursued, and the specific requirements of the institution. Generally, a dissertation for a doctoral degree can range from 80,000 to 100,000 words, while a dissertation for a master’s degree may be shorter, typically ranging from 20,000 to 50,000 words. However, it is important to note that these are general guidelines and the actual length of a dissertation can vary widely depending on the specific requirements of the program and the research topic being studied. It is always best to consult with your academic advisor or the guidelines provided by your institution for more specific information on dissertation length.
Here are some applications of a dissertation:
Here are some situations where writing a dissertation may be necessary:
some common purposes of a dissertation include:
Some advantages of writing a dissertation include:
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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.
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.
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.
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.
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.
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.
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.
The following are examples of new skills needed for the successful deployment of generative AI tools:
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.
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.
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:
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:
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.
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What is dissertation meaning in Punjabi? The word or phrase dissertation refers to a treatise advancing a new point of view resulting from research; usually a requirement for an advanced academic degree. See dissertation meaning in Punjabi, dissertation definition, translation and meaning of dissertation in Punjabi. Find dissertation similar ...
dissertation meaning in Punjabi. What is dissertation in Punjabi? Pronunciation, translation, synonyms, examples, rhymes, definitions of dissertation ਡਿਸਰ੍ਟੇਸ਼ਨ / ਡਿਸਰ੍ਟੈਸ਼ਨ in Punjabi
Teaching and Assessment at University of the Punjab" is accepted hereby at the. Department of. Educational Research. and. Evaluation, Institute of. Education. and. Research, University of the Punjab, Lahore in partial fulfillment of the requirements for. the award of the degree of Master of Educational Research and Assessment (MERA).
dissertation meaning in punjabi: ਖੋਜ-ਪ੍ਰਣਾਲੀ | Learn detailed meaning of dissertation in punjabi dictionary with audio prononciations, definitions and usage. This page also provides synonyms and grammar usage of dissertation in punjabi.
Meaning of DISSERTATION in Punjabi. DISSERTATION ਦਾ ਪੰਜਾਬੀ ਵਿੱਚ ਅਰਥ
A Minimalist Comparison of Punjabi and English 3 to meaning and interpretation) through their respective interfaces as a ... research has pivoted around 'Strong Minimalist Thesis' (SMT): Language is an optimal solution to the interface conditions. It implies that language must satisfy the conditions imposed by the S-M and C-I systems at the ...
Dissertation Meaning in Punjabi - Free download as PDF File (.pdf), Text File (.txt) or read online for free. The document discusses the challenges of writing a dissertation and how HelpWriting.net can provide assistance. It notes that writing a dissertation requires meticulous planning, extensive research, and strong writing skills.
thesis meaning in punjabi: ਥੀਸਿਸ | Learn detailed meaning of thesis in punjabi dictionary with audio prononciations, definitions and usage. This page also provides synonyms and grammar usage of thesis in punjabi
Meaning of Dissertation in Punjabi - Free download as PDF File (.pdf), Text File (.txt) or read online for free. Scribd is the world's largest social reading and publishing site.
A dissertation is designed to be your own. Meaning that what you write about should be a new idea, a new topic, or question that is still unanswered in your field. Something that you will need to collect new data on, potentially interview people for and explore what information is already available. Generally, an idea will need to be approved ...
Punjabi, sometimes spelled Panjabi, [j] is an Indo-Aryan language natively spoken by the Punjabi people. Punjabi is the most popular first language in Pakistan, with 80.5 million native speakers as per the 2017 census, and the 11th most popular in India, with 31.1 million native speakers, as per the 2011 census .
A dissertation (or thesis) is a process. Okay, so now that you understand that a dissertation is a research project (which is testing your ability to undertake quality research), let's go a little deeper into what that means in practical terms. The best way to understand a dissertation is to view it as a process - more specifically a ...
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 ...
English Punjabi Dictionary and Translation. This site provides an English to Punjabi Dictionary and a Punjabi to English Dictionary. Started in 2003, this site is now used by millions of people in over a hundred countries around the world. GET IT ON.
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 ...
Dissertation. Definition: Dissertation is a lengthy and detailed academic document that presents the results of original research on a specific topic or question. It is usually required as a final project for a doctoral degree or a master's degree. Dissertation Meaning in Research.
Punjabi meaning: 1. a person from the Punjab area of Pakistan and Northwest India 2. the language spoken in the…. Learn more.
Master's thesis, capstone project, or internship. Types of master's degrees. Master's degrees fall under an array of categories, the most common being Master of Arts (MA) and Master of Science (MS) degrees. MA degrees typically focus on humanities subjects, while MS degrees tend to prepare you for technical fields.
When we can extract meaning from data, it empowers us to make better decisions. And we're living in a time when we have more data than ever at our fingertips. Companies are wisening up to the benefits of leveraging data. Data analysis can help a bank to personalize customer interactions, a health care system to predict future health needs, or ...
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 ...
diffusion, dispersal, dispersion, dispersion. Examples. "the diffusion of knowledge". "the dispersion of the troops". the property of being diffused or dispersed. Synonyms. diffusion. the opening of a subject to widespread discussion and debate. Synonyms.
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