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A data analysis dissertation is a complex and challenging project requiring significant time, effort, and expertise. Fortunately, it is possible to successfully complete a data analysis dissertation with careful planning and execution.

As a student, you must know how important it is to have a strong and well-written dissertation, especially regarding data analysis. Proper data analysis is crucial to the success of your research and can often make or break your dissertation.

To get a better understanding, you may review the data analysis dissertation examples listed below;

• Impact of Leadership Style on the Job Satisfaction of Nurses
• Effect of Brand Love on Consumer Buying Behaviour in Dietary Supplement Sector
• An Insight Into Alternative Dispute Resolution
• An Investigation of Cyberbullying and its Impact on Adolescent Mental Health in UK

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Types of data analysis for dissertation.

The various types of data Analysis in a Dissertation are as follows;

1.   Qualitative Data Analysis

Qualitative data analysis is a type of data analysis that involves analyzing data that cannot be measured numerically. This data type includes interviews, focus groups, and open-ended surveys. Qualitative data analysis can be used to identify patterns and themes in the data.

2.   Quantitative Data Analysis

Quantitative data analysis is a type of data analysis that involves analyzing data that can be measured numerically. This data type includes test scores, income levels, and crime rates. Quantitative data analysis can be used to test hypotheses and to look for relationships between variables.

3.   Descriptive Data Analysis

Descriptive data analysis is a type of data analysis that involves describing the characteristics of a dataset. This type of data analysis summarizes the main features of a dataset.

4.   Inferential Data Analysis

Inferential data analysis is a type of data analysis that involves making predictions based on a dataset. This type of data analysis can be used to test hypotheses and make predictions about future events.

5.   Exploratory Data Analysis

Exploratory data analysis is a type of data analysis that involves exploring a data set to understand it better. This type of data analysis can identify patterns and relationships in the data.

Time Period to Plan and Complete a Data Analysis Dissertation?

When planning dissertation data analysis, it is important to consider the dissertation methodology structure and time series analysis as they will give you an understanding of how long each stage will take. For example, using a qualitative research method, your data analysis will involve coding and categorizing your data.

This can be time-consuming, so allowing enough time in your schedule is important. Once you have coded and categorized your data, you will need to write up your findings. Again, this can take some time, so factor this into your schedule.

Finally, you will need to proofread and edit your dissertation before submitting it. All told, a data analysis dissertation can take anywhere from several weeks to several months to complete, depending on the project’s complexity. Therefore, starting planning early and allowing enough time in your schedule to complete the task is important.

Essential Strategies for Data Analysis Dissertation

A.   Planning

The first step in any dissertation is planning. You must decide what you want to write about and how you want to structure your argument. This planning will involve deciding what data you want to analyze and what methods you will use for a data analysis dissertation.

B.   Prototyping

Once you have a plan for your dissertation, it’s time to start writing. However, creating a prototype is important before diving head-first into writing your dissertation. A prototype is a rough draft of your argument that allows you to get feedback from your advisor and committee members. This feedback will help you fine-tune your argument before you start writing the final version of your dissertation.

C.   Executing

After you have created a plan and prototype for your data analysis dissertation, it’s time to start writing the final version. This process will involve collecting and analyzing data and writing up your results. You will also need to create a conclusion section that ties everything together.

D.   Presenting

The final step in acing your data analysis dissertation is presenting it to your committee. This presentation should be well-organized and professionally presented. During the presentation, you’ll also need to be ready to respond to questions concerning your dissertation.

Data Analysis Tools

Numerous suggestive tools are employed to assess the data and deduce pertinent findings for the discussion section. The tools used to analyze data and get a scientific conclusion are as follows:

a.     Excel

Excel is a spreadsheet program part of the Microsoft Office productivity software suite. Excel is a powerful tool that can be used for various data analysis tasks, such as creating charts and graphs, performing mathematical calculations, and sorting and filtering data.

b.     Google Sheets

Google Sheets is a free online spreadsheet application that is part of the Google Drive suite of productivity software. Google Sheets is similar to Excel in terms of functionality, but it also has some unique features, such as the ability to collaborate with other users in real-time.

c.     SPSS

SPSS is a statistical analysis software program commonly used in the social sciences. SPSS can be used for various data analysis tasks, such as hypothesis testing, factor analysis, and regression analysis.

d.     STATA

STATA is a statistical analysis software program commonly used in the sciences and economics. STATA can be used for data management, statistical modelling, descriptive statistics analysis, and data visualization tasks.

SAS is a commercial statistical analysis software program used by businesses and organizations worldwide. SAS can be used for predictive modelling, market research, and fraud detection.

R is a free, open-source statistical programming language popular among statisticians and data scientists. R can be used for tasks such as data wrangling, machine learning, and creating complex visualizations.

g.     Python

A variety of applications may be used using the distinctive programming language Python, including web development, scientific computing, and artificial intelligence. Python also has a number of modules and libraries that can be used for data analysis tasks, such as numerical computing, statistical modelling, and data visualization.

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Tips to Compose a Successful Data Analysis Dissertation

a.   Choose a Topic You’re Passionate About

The first step to writing a successful data analysis dissertation is to choose a topic you’re passionate about. Not only will this make the research and writing process more enjoyable, but it will also ensure that you produce a high-quality paper.

Choose a topic that is particular enough to be covered in your paper’s scope but not so specific that it will be challenging to obtain enough evidence to substantiate your arguments.

b.   Do Your Research

data analysis in research is an important part of academic writing. Once you’ve selected a topic, it’s time to begin your research. Be sure to consult with your advisor or supervisor frequently during this stage to ensure that you are on the right track. In addition to secondary sources such as books, journal articles, and reports, you should also consider conducting primary research through surveys or interviews. This will give you first-hand insights into your topic that can be invaluable when writing your paper.

c.   Develop a Strong Thesis Statement

After you’ve done your research, it’s time to start developing your thesis statement. It is arguably the most crucial part of your entire paper, so take care to craft a clear and concise statement that encapsulates the main argument of your paper.

Remember that your thesis statement should be arguable—that is, it should be capable of being disputed by someone who disagrees with your point of view. If your thesis statement is not arguable, it will be difficult to write a convincing paper.

d.   Write a Detailed Outline

Once you have developed a strong thesis statement, the next step is to write a detailed outline of your paper. This will offer you a direction to write in and guarantee that your paper makes sense from beginning to end.

Your outline should include an introduction, in which you state your thesis statement; several body paragraphs, each devoted to a different aspect of your argument; and a conclusion, in which you restate your thesis and summarize the main points of your paper.

e.   Write Your First Draft

With your outline in hand, it’s finally time to start writing your first draft. At this stage, don’t worry about perfecting your grammar or making sure every sentence is exactly right—focus on getting all of your ideas down on paper (or onto the screen). Once you have completed your first draft, you can revise it for style and clarity.

And there you have it! Following these simple tips can increase your chances of success when writing your data analysis dissertation. Just remember to start early, give yourself plenty of time to research and revise, and consult with your supervisor frequently throughout the process.

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Studying the above examples gives you valuable insight into the structure and content that should be included in your own data analysis dissertation. You can also learn how to effectively analyze and present your data and make a lasting impact on your readers.

In addition to being a useful resource for completing your dissertation, these examples can also serve as a valuable reference for future academic writing projects. By following these examples and understanding their principles, you can improve your data analysis skills and increase your chances of success in your academic career.

You may also contact Premier Dissertations to develop your data analysis dissertation.

For further assistance, some other resources in the dissertation writing section are shared below;

How Do You Select the Right Data Analysis

How to Write Data Analysis For A Dissertation?

How to Develop a Conceptual Framework in Dissertation?

What is a Hypothesis in a Dissertation?

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How to collect data for your thesis

Collecting theoretical data

Search for theses on your topic, use content-sharing platforms, collecting empirical data, qualitative vs. quantitative data, frequently asked questions about gathering data for your thesis, related articles.

After choosing a topic for your thesis , you’ll need to start gathering data. In this article, we focus on how to effectively collect theoretical and empirical data.

Empirical data : unique research that may be quantitative, qualitative, or mixed.

Theoretical data : secondary, scholarly sources like books and journal articles that provide theoretical context for your research.

Thesis : the culminating, multi-chapter project for a bachelor’s, master’s, or doctoral degree.

Qualitative data : info that cannot be measured, like observations and interviews .

Quantitative data : info that can be measured and written with numbers.

At this point in your academic life, you are already acquainted with the ways of finding potential references. Some obvious sources of theoretical material are:

• edited volumes
• conference proceedings
• online databases like Google Scholar , ERIC , or Scopus

You can also take a look at the top list of academic search engines .

Looking at other theses on your topic can help you see what approaches have been taken and what aspects other writers have focused on. Pay close attention to the list of references and follow the bread-crumbs back to the original theories and specialized authors.

Another method for gathering theoretical data is to read through content-sharing platforms. Many people share their papers and writings on these sites. You can either hunt sources, get some inspiration for your own work or even learn new angles of your topic. 

Some popular content sharing sites are:

With these sites, you have to check the credibility of the sources. You can usually rely on the content, but we recommend double-checking just to be sure. Take a look at our guide on what are credible sources?

The more you know, the better. The guide, " How to undertake a literature search and review for dissertations and final year projects ," will give you all the tools needed for finding literature .

In order to successfully collect empirical data, you have to choose first what type of data you want as an outcome. There are essentially two options, qualitative or quantitative data. Many people mistake one term with the other, so it’s important to understand the differences between qualitative and quantitative research .

Boiled down, qualitative data means words and quantitative means numbers. Both types are considered primary sources . Whichever one adapts best to your research will define the type of methodology to carry out, so choose wisely.

In the end, having in mind what type of outcome you intend and how much time you count on will lead you to choose the best type of empirical data for your research. For a detailed description of each methodology type mentioned above, read more about collecting data .

Once you gather enough theoretical and empirical data, you will need to start writing. But before the actual writing part, you have to structure your thesis to avoid getting lost in the sea of information. Take a look at our guide on how to structure your thesis for some tips and tricks.

The key to knowing what type of data you should collect for your thesis is knowing in advance the type of outcome you intend to have, and the amount of time you count with.

Some obvious sources of theoretical material are journals, libraries and online databases like Google Scholar , ERIC or Scopus , or take a look at the top list of academic search engines . You can also search for theses on your topic or read content sharing platforms, like Medium , Issuu , or Slideshare .

To gather empirical data, you have to choose first what type of data you want. There are two options, qualitative or quantitative data. You can gather data through observations, interviews, focus groups, or with surveys, tests, and existing databases.

Qualitative data means words, information that cannot be measured. It may involve multimedia material or non-textual data. This type of data claims to be detailed, nuanced and contextual.

Quantitative data means numbers, information that can be measured and written with numbers. This type of data claims to be credible, scientific and exact.

11 Tips For Writing a Dissertation Data Analysis

Since the evolution of the fourth industrial revolution – the Digital World; lots of data have surrounded us. There are terabytes of data around us or in data centers that need to be processed and used. The data needs to be appropriately analyzed to process it, and Dissertation data analysis forms its basis. If data analysis is valid and free from errors, the research outcomes will be reliable and lead to a successful dissertation. 

So, in today’s topic, we will cover the need to analyze data, dissertation data analysis, and mainly the tips for writing an outstanding data analysis dissertation. If you are a doctoral student and plan to perform dissertation data analysis on your data, make sure that you give this article a thorough read for the best tips!

What is Data Analysis in Dissertation?

Even f you have the data collected and compiled in the form of facts and figures, it is not enough for proving your research outcomes. There is still a need to apply dissertation data analysis on your data; to use it in the dissertation. It provides scientific support to the thesis and conclusion of the research.

Data Analysis Tools

There are plenty of indicative tests used to analyze data and infer relevant results for the discussion part. Following are some tests  used to perform analysis of data leading to a scientific conclusion:

11 Most Useful Tips for Dissertation Data Analysis

Doctoral students need to perform dissertation data analysis and then dissertation to receive their degree. Many Ph.D. students find it hard to do dissertation data analysis because they are not trained in it.

1. Dissertation Data Analysis Services

The first tip applies to those students who can afford to look for help with their dissertation data analysis work. It’s a viable option, and it can help with time management and with building the other elements of the dissertation with much detail.

Dissertation Analysis services are professional services that help doctoral students with all the basics of their dissertation work, from planning, research and clarification, methodology, dissertation data analysis and review, literature review, and final powerpoint presentation.

One great reference for dissertation data analysis professional services is Statistics Solutions , they’ve been around for over 22 years helping students succeed in their dissertation work. You can find the link to their website here .

Following are some helpful tips for writing a splendid dissertation data analysis:

2. Relevance of Collected Data

It involves  data collection  of your related topic for research. Carefully analyze the data that tends to be suitable for your analysis. Do not just go with irrelevant data leading to complications in the results. Your data must be relevant and fit with your objectives. You must be aware of how the data is going to help in analysis. 

3. Data Analysis

For analysis, it is crucial to use such methods that fit best with the types of data collected and the research objectives. Elaborate on these methods and the ones that justify your data collection methods thoroughly. Make sure to make the reader believe that you did not choose your method randomly. Instead, you arrived at it after critical analysis and prolonged research.

Data analysis involves two approaches –  Qualitative Data Analysis and Quantitative Data Analysis.   Qualitative data analysis  comprises research through experiments, focus groups, and interviews. This approach helps to achieve the objectives by identifying and analyzing common patterns obtained from responses. 

The overall objective of data analysis is to detect patterns and inclinations in data and then present the outcomes implicitly.  It helps in providing a solid foundation for critical conclusions and assisting the researcher to complete the dissertation proposal. 

4. Qualitative Data Analysis

Qualitative data refers to data that does not involve numbers. You are required to carry out an analysis of the data collected through experiments, focus groups, and interviews. This can be a time-taking process because it requires iterative examination and sometimes demanding the application of hermeneutics. Note that using qualitative technique doesn’t only mean generating good outcomes but to unveil more profound knowledge that can be transferrable.

Presenting qualitative data analysis in a dissertation  can also be a challenging task. It contains longer and more detailed responses. Placing such comprehensive data coherently in one chapter of the dissertation can be difficult due to two reasons. Firstly, we cannot figure out clearly which data to include and which one to exclude. Secondly, unlike quantitative data, it becomes problematic to present data in figures and tables. Making information condensed into a visual representation is not possible. As a writer, it is of essence to address both of these challenges.

This method involves analyzing qualitative data based on an argument that a researcher already defines. It’s a comparatively easy approach to analyze data. It is suitable for the researcher with a fair idea about the responses they are likely to receive from the questionnaires.

In this method, the researcher analyzes the data not based on any predefined rules. It is a time-taking process used by students who have very little knowledge of the research phenomenon.

5. Quantitative Data Analysis

The Presentation of quantitative data  depends on the domain to which it is being presented. It is beneficial to consider your audience while writing your findings. Quantitative data for  hard sciences  might require numeric inputs and statistics. As for  natural sciences , such comprehensive analysis is not required.

Following are some of the methods used to perform quantitative data analysis. 

6. Data Presentation Tools

Since large volumes of data need to be represented, it becomes a difficult task to present such an amount of data in coherent ways. To resolve this issue, consider all the available choices you have, such as tables, charts, diagrams, and graphs. 

7. Include Appendix or Addendum

After presenting a large amount of data, your dissertation analysis part might get messy and look disorganized. Also, you would not be cutting down or excluding the data you spent days and months collecting. To avoid this, you should include an appendix part. 

The data you find hard to arrange within the text, include that in the  appendix part of a dissertation . And place questionnaires, copies of focus groups and interviews, and data sheets in the appendix. On the other hand, one must put the statistical analysis and sayings quoted by interviewees within the dissertation. 

8. Thoroughness of Data

Thoroughly demonstrate the ideas and critically analyze each perspective taking care of the points where errors can occur. Always make sure to discuss the anomalies and strengths of your data to add credibility to your research.

9. Discussing Data

Discussion of data involves elaborating the dimensions to classify patterns, themes, and trends in presented data. In addition, to balancing, also take theoretical interpretations into account. Discuss the reliability of your data by assessing their effect and significance. Do not hide the anomalies. While using interviews to discuss the data, make sure you use relevant quotes to develop a strong rationale. 

10. Findings and Results

Findings refer to the facts derived after the analysis of collected data. These outcomes should be stated; clearly, their statements should tightly support your objective and provide logical reasoning and scientific backing to your point. This part comprises of majority part of the dissertation. 

11. Connection with Literature Review

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Wrapping up.

Writing data analysis in the dissertation involves dedication, and its implementations demand sound knowledge and proper planning. Choosing your topic, gathering relevant data, analyzing it, presenting your data and findings correctly, discussing the results, connecting with the literature and conclusions are milestones in it. Among these checkpoints, the Data analysis stage is most important and requires a lot of keenness.

As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.

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A Complete Guide to Dissertation Data Analysis

The analysis chapter is one of the most important parts of a dissertation where you demonstrate the unique research abilities. That is why it often accounts for up to 40% of the total mark. Given the significance of this chapter, it is essential to build your skills in dissertation data analysis .

Typically, the analysis section provides an output of calculations, interpretation of attained results and discussion of these results in light of theories and previous empirical evidence. Oftentimes, the chapter provides qualitative data analysis that do not require any calculations. Since there are different types of research design, let’s look at each type individually.

1. Types of Research

The dissertation topic you have selected, to a considerable degree, informs the way you are going to collect and analyse data. Some topics imply the collection of primary data, while others can be explored using secondary data. Selecting an appropriate data type is vital not only for your ability to achieve the main aim and objectives of your dissertation but also an important part of the dissertation writing process since it is what your whole project will rest on.

Selecting the most appropriate data type for your dissertation may not be as straightforward as it may seem. As you keep diving into your research, you will be discovering more and more details and nuances associated with this or that type of data. At some point, it is important to decide whether you will pursue the qualitative research design or the quantitative research design.

1.1. Qualitative vs Quantitative Research

1.1.1. quantitative research.

Quantitative data is any numerical data which can be used for statistical analysis and mathematical manipulations. This type of data can be used to answer research questions such as ‘How often?’, ‘How much?’, and ‘How many?’. Studies that use this type of data also ask the ‘What’ questions (e.g. What are the determinants of economic growth? To what extent does marketing affect sales? etc.).

An advantage of quantitative data is that it can be verified and conveniently evaluated by researchers. This allows for replicating the research outcomes. In addition, even qualitative data can be quantified and converted to numbers. For example, the use of the Likert scale allows researchers not only to properly assess respondents’ perceptions of and attitudes towards certain phenomena but also to assign a code to each individual response and make it suitable for graphical and statistical analysis. It is also possible to convert the yes/no responses to dummy variables to present them in the form of numbers. Quantitative data is typically analysed using dissertation data analysis software such as Eviews, Matlab, Stata, R, and SPSS.

On the other hand, a significant limitation of purely quantitative methods is that social phenomena explored in economic and behavioural sciences are often complex, so the use of quantitative data does not allow for thoroughly analysing these phenomena. That is, quantitative data can be limited in terms of breadth and depth as compared to qualitative data, which may allow for richer elaboration on the context of the study.

1.1.2. Qualitative Data

Studies that use this type of data usually ask the ‘Why’ and ‘How’ questions (e.g. Why does social media marketing is more effective than traditional marketing? How do consumers make their purchase decisions?). This is non-numerical primary data represented mostly by opinions of relevant persons.

Qualitative data also includes any textual or visual data (infographics) that have been gathered from reports, websites and other secondary sources that do not involve interactions between the researcher and human participants. Examples of the use of secondary qualitative data are texts, images and diagrams you can use in SWOT analysis, PEST analysis, 4Ps analysis, Porter’s Five Forces analysis, most types of Strategic Analysis, etc. Academic articles, journals, books, and conference papers are also examples of secondary qualitative data you can use in your study.

The analysis of qualitative data usually provides deep insights into the phenomenon or issue being under study because respondents are not limited in their ability to give detailed answers. Unlike quantitative research, collecting and analysing qualitative data is more open-ended in eliciting the anecdotes, stories, and lengthy descriptions and evaluations people make of products, services, lifestyle attributes, or any other phenomenon. This is best used in social studies including management and marketing.

It is not always possible to summarise qualitative data as opinions expressed by individuals are multi-faceted. This to some extent limits the dissertation data analysis  as it is not always possible to establish cause-and-effect links between factors represented in a qualitative manner. This is why the results of qualitative analysis can hardly be generalised, and case studies that explore very narrow contexts are often conducted.

For qualitative data analysis, you can use tools such as nVivo and Tableau.

1.2. Primary vs Secondary Research

1.2.1. primary data.

Primary data is data that had not existed prior to your research and you collect it by means of a survey or interviews for the dissertation data analysis chapter. Interviews provide you with the opportunity to collect detailed insights from industry participants about their company, customers, or competitors. Questionnaire surveys allow for obtaining a large amount of data from a sizeable population in a cost-efficient way. Primary data is usually cross-sectional data (i.e., the data collected at one point of time from different respondents). Time-series are found very rarely or almost never in primary data. Nonetheless, depending on the research aims and objectives, certain designs of data collection instruments allow researchers to conduct a longitudinal study.

1.2.2. Secondary data

This data already exist before the research as they have already been generated, refined, summarized and published in official sources for purposes other than those of your study study. Secondary data often carries more legitimacy as compared to primary data and can help the researcher verify primary data. This is the data collected from databases or websites; it does not involve human participants. This can be both cross-sectional data (e.g. an indicator for different countries/companies at one point of time) and time-series (e.g. an indicator for one company/country for several years). A combination of cross-sectional data and time-series data is panel data. Therefore, all a researcher needs to do is to find the data that would be most appropriate for attaining the research objectives.

Examples of secondary quantitative data are share prices; accounting information such as earnings, total asset, revenue, etc.; macroeconomic variables such as GDP, inflation, unemployment, interest rates, etc.; microeconomic variables such as market share, concentration ratio, etc. Accordingly, dissertation topics that will most likely use secondary quantitative data are FDI dissertations, Mergers and Acquisitions dissertations, Event Studies, Economic Growth dissertations, International Trade dissertations, Corporate Governance dissertations.

Two main limitations of secondary data are the following. First, the freely available secondary data may not perfectly suit the purposes of your study so that you will have to additionally collect primary data or change the research objectives. Second, not all high-quality secondary data is freely available. Good sources of financial data such as WRDS, Thomson Bank Banker, Compustat and Bloomberg all stipulate pre-paid access which may not be affordable for a single researcher.

1.3. Quantitative or Qualitative Research… or Both?

Once you have formulated your research aim and objectives and reviewed the most relevant literature in your field, you should decide whether you need qualitative or quantitative data.

If you are willing to test the relationship between variables or examine hypotheses and theories in practice, you should rather focus on collecting quantitative data. Methodologies based on this data provide cut-and-dry results and are highly effective when you need to obtain a large amount of data in a cost-effective manner. Alternatively, qualitative research will help you better understand meanings, experience, beliefs, values and other non-numerical relationships.

While it is totally okay to use either a qualitative or quantitative methodology, using them together will allow you to back up one type of data with another type of data and research your topic in more depth. However, note that using qualitative and quantitative methodologies in combination can take much more time and effort than you originally planned.

2. Types of Analysis

2.1. basic statistical analysis.

The type of statistical analysis that you choose for the results and findings chapter depends on the extent to which you wish to analyse the data and summarise your findings. If you do not major in quantitative subjects but write a dissertation in social sciences, basic statistical analysis will be sufficient. Such an analysis would be based on descriptive statistics such as the mean, the median, standard deviation, and variance. Then, you can enhance the statistical analysis with visual information by showing the distribution of variables in the form of graphs and charts. However, if you major in a quantitative subject such as accounting, economics or finance, you may need to use more advanced statistical analysis.

2.2. Advanced Statistical Analysis

In order to run an advanced analysis, you will most likely need access to statistical software such as Matlab, R or Stata. Whichever program you choose to proceed with, make sure that it is properly documented in your research. Further, using an advanced statistical technique ensures that you are analysing all possible aspects of your data. For example, a difference between basic regression analysis and analysis at an advanced level is that you will need to consider additional tests and deeper explorations of statistical problems with your model. Also, you need to keep the focus on your research question and objectives as getting deeper into statistical details may distract you from the main aim. Ultimately, the aim of your dissertation is to find answers to the research questions that you defined.

Another important aspect to consider here is that the results and findings section is not all about numbers. Apart from tables and graphs, it is also important to ensure that the interpretation of your statistical findings is accurate as well as engaging for the users. Such a combination of advanced statistical software along with a convincing textual discussion goes a long way in ensuring that your dissertation is well received. Although the use of such advanced statistical software may provide you with a variety of outputs, you need to make sure to present the analysis output properly so that the readers understand your conclusions.

3. Examples of Methods of Analysis

3.1. event study.

If you are studying the effects of particular events on prices of financial assets, for example, it is worth to consider the Event Study Methodology. Events such as mergers and acquisitions, new product launches, expansion into new markets, earnings announcements and public offerings can have a major impact on stock prices and valuation of a firm. Event studies are methods used to measure the impact of a particular event or a series of events on the market value. The concept behind this is to try to understand whether sudden and abnormal stock returns can be attributed to market information pertaining to an event.

Event studies are based on the efficient market hypothesis. According to the theory, in an efficient capital market, all the new and relevant information is immediately reflected in the respective asset prices. Although this theory is not universally applicable, there are many instances in which it holds true. An event study implies a step-by-step analysis of the impact that a particular announcement has on a company’s valuation. In normal conditions, without the influence of the analysed event, it is assumed that expected returns on a stock would be determined by the risk-free rate, systematic risk of the stock and risk premium required by investors. These conditions are measured by the capital asset pricing model (CAPM).

There can primarily be three types of announcements which can constitute event studies. These include corporate announcements, macroeconomic announcements, as well as regulatory events. As the name suggests, corporate announcements could include bankruptcies, asset sales, M&As, credit rating downgrades, earnings announcements and announcements of dividends. These events usually have a major impact on stock prices simply because they are directly interlinked with the company. Macroeconomic announcements can include central bank announcements of changes in interest rates, an announcement of inflation projections and economic growth projections. Finally, regulatory announcements such as policy changes and new laws announcement can also impact the stock prices of companies, and therefore can be measured using the method of event studies.

A critical issue in event studies is choosing the right event window during which the analysed announcements are assumed to produce the strongest effect on share prices. According to the efficient market hypothesis, no statistically significant abnormal returns connected with any events would be expected. However, in reality, there could be rumours before official announcements and some investors may act on such rumours. Moreover, investors may react at different times due to differences in speed of information processing and reaction. In order to account for all these factors, event windows usually capture a short period before the announcement to account for rumours and an asymmetrical period after the announcement.

In order to make event studies stronger and statistically meaningful, a large number of similar or related cases are analysed. Then, abnormal returns are cumulated, and their statistical significance is assessed. The t-statistic is often used to evaluate whether the average abnormal returns are different from zero. So, researchers who use event studies are concerned not only with the positive or negative effects of specific events but also with the generalisation of the results and measuring the statistical significance of abnormal returns.

3.2. Regression Analysis

Regression analysis is a mathematical method applied to determine how explored variables are interconnected. In particular, the following questions can be answered. Which factors are the most influential ones? Which of them can be ignored? How do the factors interact with one another? And the main question, how significant are the findings?

The type most often applied in the dissertation studies is the ordinary least squares (OLS) regression analysis that assesses parameters of linear relationships between explored variables. Typically, three forms of OLS analysis are used.

Longitudinal analysis is applied when a single object with several characteristics is explored over a long period of time. In this case, observations represent the changes of the same characteristics over time. Examples of longitudinal samples are macroeconomic parameters in a particular country, preferences and changes in health characteristics of particular persons during their lives etc. Cross-sectional studies on the contrary, explore characteristics of many similar objects such as respondents, companies, countries, students over cities in a certain moment of time. The main similarity between longitudinal and cross-sectional studies is that the data over one dimension, namely across periods of time (days, weeks, years) or across objects, respectively.

However, it is often the case that we need to explore data that change over two dimensions, both across objects and periods of time. In this case, we need to use a panel regression analysis. Its main distinction from the two mentioned above is that specifics of each object (person, company, country) are accounted for.

The common steps of the regression analysis are the following:

• Start with descriptive statistics of the data. This is done to indicate the scope of the data observations included in the sample and identify potential outliers. A common practice is to get rid of the outliers to avoid the distortion of the analysis results.
• Estimate potential multicollinearity. This phenomenon is connected with strong correlation between explanatory variables. Multicollinearity is an undesirable feature of the sample as regression results, in particular the significance of certain variables, may be distorted. Once multicollinearity is detected, the easiest way to eliminate it is to omit one of the correlated variables.
• Run Regressions. First, the overall significance of the model is estimated using the F-statistic. After that, the significance of particular variable coefficient is assessed using t-statistics.
• Don’t forget about diagnostic tests. They are conducted to detect potential imperfections of the sample that could affect the regression outcomes.

Some nuances should be mentioned. When a time series OLS regression analysis is conducted, it is feasible to conduct a full battery of diagnostic tests including the test of linearity (the relationship between the independent and dependent variables should be linear); homoscedasticity (regression residuals should have the same variance); independence of observations; normality of variables; serial correlation (there should no patterns in a particular time series). These tests for longitudinal regression models are available in most software tools such as Eviews and Stata.

3.3. Vector Autoregression

A vector autoregression model (VAR) is a model often used in statistical analysis, which explores interrelationships between several variables that are all treated as endogenous. So, a specific trait of this model is that it includes lagged values of the employed variables as regressors. This allows for estimating not only the instantaneous effects but also dynamic effects in the relationships up to n lags.

In fact, a VAR model consists of k OLS regression equations where k is the number of employed variables. Each equation has its own dependent variable while the explanatory variables are the lagged values of this variable and other variables.

• Selection of the optimal lag length

Information criteria (IC) are employed to determine the optimal lag length. The most commonly used ones are the Akaike, Hannah-Quinn and Schwarz criteria.

• Test for stationarity

A widely used method for estimating stationarity is the Augmented Dickey-Fuller test and the Phillips-Perron test.  If a variable is non-stationary, the first difference should be taken and tested for stationarity in the same way.

• Cointegration test

The variables may be non-stationary but integrated of the same order. In this case, they can be analysed with a Vector Error Correction Model (VECM) instead of VAR. The Johansen cointegration test is conducted to check whether the variables integrated of the same order share a common integrating vector(s). If the variables are cointegrated, VECM is applied in the following analysis instead of a VAR model. VECM is applied to non-transformed non-stationary series whereas VAR is run with transformed or stationary inputs.

• Model Estimation

A VAR model is run with the chosen number of lags and coefficients with standard errors and respective t-statistics are calculated to assess the statistical significance.

• Diagnostic tests

Next, the model is tested for serial correlation using the Breusch-Godfrey test, for heteroscedasticity using the Breusch-Pagan test and for stability.

• Impulse Response Functions (IRFs)

The IRFs are used to graphically represent the results of a VAR model and project the effects of variables on one another.

• Granger causality test

The variables may be related but there may exist no causal relationships between them, or the effect may be bilateral. The Granger test indicates the causal associations between the variables and shows the direction of causality based on interaction of current and past values of a pair of variables in the VAR system.

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Mastering Dissertation Data Analysis: A Comprehensive Guide

By Laura Brown on 29th December 2023

To craft an effective dissertation data analysis chapter, you need to follow some simple steps:

• Start by planning the structure and objectives of the chapter.
• Clearly set the stage by providing a concise overview of your research design and methodology.
• Proceed to thorough data preparation, ensuring accuracy and organisation.
• Justify your methods and present the results using visual aids for clarity.
• Discuss the findings within the context of your research questions.
• Finally, review and edit your chapter to ensure coherence.

This approach will ensure a well-crafted and impactful analysis section.

Before delving into details on how you can come up with an engaging data analysis show in your dissertation, we first need to understand what it is and why it is required.

What Is Data Analysis In A Dissertation?

The data analysis chapter is a crucial section of a research dissertation that involves the examination, interpretation, and synthesis of collected data. In this chapter, researchers employ statistical techniques, qualitative methods, or a combination of both to make sense of the data gathered during the research process.

Why Is The Data Analysis Chapter So Important?

The primary objectives of the data analysis chapter are to identify patterns, trends, relationships, and insights within the data set. Researchers use various tools and software to conduct a thorough analysis, ensuring that the results are both accurate and relevant to the research questions or hypotheses. Ultimately, the findings derived from this chapter contribute to the overall conclusions of the dissertation, providing a basis for drawing meaningful and well-supported insights.

Steps Required To Craft Data Analysis Chapter To Perfection

Now that we have an idea of what a dissertation analysis chapter is and why it is necessary to put it in the dissertation, let’s move towards how we can create one that has a significant impact. Our guide will move around the bulleted points that have been discussed initially in the beginning. So, it’s time to begin.

Step 1: Planning Your Data Analysis Chapter

Planning your data analysis chapter is a critical precursor to its successful execution.

• Begin by outlining the chapter structure to provide a roadmap for your analysis.
• Start with an introduction that succinctly introduces the purpose and significance of the data analysis in the context of your research.
• Following this, delineate the chapter into sections such as Data Preparation, where you detail the steps taken to organise and clean your data.
• Plan on to clearly define the Data Analysis Techniques employed, justifying their relevance to your research objectives.
• As you progress, plan for the Results Presentation, incorporating visual aids for clarity. Lastly, earmark a section for the Discussion of Findings, where you will interpret results within the broader context of your research questions.

This structured approach ensures a comprehensive and cohesive data analysis chapter, setting the stage for a compelling narrative that contributes significantly to your dissertation. You can always seek our dissertation data analysis help to plan your chapter.

Step 2: Setting The Stage – Introduction to Data Analysis

Your primary objective is to establish a solid foundation for the analytical journey. You need to skillfully link your data analysis to your research questions, elucidating the direct relevance and purpose of the upcoming analysis.

Simultaneously, define key concepts to provide clarity and ensure a shared understanding of the terms integral to your study. Following this, offer a concise overview of your data set characteristics, outlining its source, nature, and any noteworthy features.

This meticulous groundwork alongside our help with dissertation data analysis lays the base for a coherent and purposeful chapter, guiding readers seamlessly into the subsequent stages of your dissertation.

Step 3: Data Preparation

Now this is another pivotal phase in the data analysis process, ensuring the integrity and reliability of your findings. You should start with an insightful overview of the data cleaning and preprocessing procedures, highlighting the steps taken to refine and organise your dataset. Then, discuss any challenges encountered during the process and the strategies employed to address them.

Moving forward, delve into the specifics of data transformation procedures, elucidating any alterations made to the raw data for analysis. Clearly describe the methods employed for normalisation, scaling, or any other transformations deemed necessary. It will not only enhance the quality of your analysis but also foster transparency in your research methodology, reinforcing the robustness of your data-driven insights.

Step 4: Data Analysis Techniques

The data analysis section of a dissertation is akin to choosing the right tools for an artistic masterpiece. Carefully weigh the quantitative and qualitative approaches, ensuring a tailored fit for the nature of your data.

Quantitative Analysis

• Descriptive Statistics: Paint a vivid picture of your data through measures like mean, median, and mode. It’s like capturing the essence of your data’s personality.
• Inferential Statistics:Take a leap into the unknown, making educated guesses and inferences about your larger population based on a sample. It’s statistical magic in action.

Qualitative Analysis

• Thematic Analysis: Imagine your data as a novel, and thematic analysis as the tool to uncover its hidden chapters. Dissect the narrative, revealing recurring themes and patterns.
• Content Analysis: Scrutinise your data’s content like detectives, identifying key elements and meanings. It’s a deep dive into the substance of your qualitative data.

Providing Rationale for Chosen Methods

You should also articulate the why behind the chosen methods. It’s not just about numbers or themes; it’s about the story you want your data to tell. Through transparent rationale, you should ensure that your chosen techniques align seamlessly with your research goals, adding depth and credibility to the analysis.

Step 5: Presentation Of Your Results

You can simply break this process into two parts.

a.    Creating Clear and Concise Visualisations

Effectively communicate your findings through meticulously crafted visualisations. Use tables that offer a structured presentation, summarising key data points for quick comprehension. Graphs, on the other hand, visually depict trends and patterns, enhancing overall clarity. Thoughtfully design these visual aids to align with the nature of your data, ensuring they serve as impactful tools for conveying information.

b.    Interpreting and Explaining Results

Go beyond mere presentation by providing insightful interpretation by taking data analysis services for dissertation. Show the significance of your findings within the broader research context. Moreover, articulates the implications of observed patterns or relationships. By weaving a narrative around your results, you guide readers through the relevance and impact of your data analysis, enriching the overall understanding of your dissertation’s key contributions.

Step 6: Discussion of Findings

While discussing your findings and dissertation discussion chapter , it’s like putting together puzzle pieces to understand what your data is saying. You can always take dissertation data analysis help to explain what it all means, connecting back to why you started in the first place.

Be honest about any limitations or possible biases in your study; it’s like showing your cards to make your research more trustworthy. Comparing your results to what other smart people have found before you adds to the conversation, showing where your work fits in.

Looking ahead, you suggest ideas for what future researchers could explore, keeping the conversation going. So, it’s not just about what you found, but also about what comes next and how it all fits into the big picture of what we know.

Step 7: Writing Style and Tone

In order to perfectly come up with this chapter, follow the below points in your writing and adjust the tone accordingly,

• Use clear and concise language to ensure your audience easily understands complex concepts.
• Avoid unnecessary jargon in data analysis for thesis, and if specialised terms are necessary, provide brief explanations.
• Keep your writing style formal and objective, maintaining an academic tone throughout.
• Avoid overly casual language or slang, as the data analysis chapter is a serious academic document.
• Clearly define terms and concepts, providing specific details about your data preparation and analysis procedures.
• Use precise language to convey your ideas, minimising ambiguity.
• Follow a consistent formatting style for headings, subheadings, and citations to enhance readability.
• Ensure that tables, graphs, and visual aids are labelled and formatted uniformly for a polished presentation.
• Connect your analysis to the broader context of your research by explaining the relevance of your chosen methods and the importance of your findings.
• Offer a balance between detail and context, helping readers understand the significance of your data analysis within the larger study.
• Present enough detail to support your findings but avoid overwhelming readers with excessive information.
• Use a balance of text and visual aids to convey information efficiently.
• Maintain reader engagement by incorporating transitions between sections and effectively linking concepts.
• Use a mix of sentence structures to add variety and keep the writing engaging.
• Eliminate grammatical errors, typos, and inconsistencies through thorough proofreading.
• Consider seeking feedback from peers or mentors to ensure the clarity and coherence of your writing.

You can seek a data analysis dissertation example or sample from CrowdWriter to better understand how we write it while following the above-mentioned points.

Step 8: Reviewing and Editing

Reviewing and editing your data analysis chapter is crucial for ensuring its effectiveness and impact. By revising your work, you refine the clarity and coherence of your analysis, enhancing its overall quality.

Seeking feedback from peers, advisors or dissertation data analysis services provides valuable perspectives, helping identify blind spots and areas for improvement. Addressing common writing pitfalls, such as grammatical errors or unclear expressions, ensures your chapter is polished and professional.

Taking the time to review and edit not only strengthens the academic integrity of your work but also contributes to a final product that is clear, compelling, and ready for scholarly scrutiny.

Concluding On This Data Analysis Help

Be it master thesis data analysis, an undergraduate one or for PhD scholars, the steps remain almost the same as we have discussed in this guide. The primary focus is to be connected with your research questions and objectives while writing your data analysis chapter.

Do not lose your focus and choose the right analysis methods and design. Make sure to present your data through various visuals to better explain your data and engage the reader as well. At last, give it a detailed read and seek assistance from experts and your supervisor for further improvement.

Laura Brown, a senior content writer who writes actionable blogs at Crowd Writer.

Raw Data to Excellence: Master Dissertation Analysis

Discover the secrets of successful dissertation data analysis. Get practical advice and useful insights from experienced experts now!

Have you ever found yourself knee-deep in a dissertation , desperately seeking answers from the data you’ve collected? Or have you ever felt clueless with all the data that you’ve collected but don’t know where to start? Fear not, in this article we are going to discuss a method that helps you come out of this situation and that is Dissertation Data Analysis.

Dissertation data analysis is like uncovering hidden treasures within your research findings. It’s where you roll up your sleeves and explore the data you’ve collected, searching for patterns, connections, and those “a-ha!” moments. Whether you’re crunching numbers, dissecting narratives, or diving into qualitative interviews, data analysis is the key that unlocks the potential of your research.

Dissertation Data Analysis

Dissertation data analysis plays a crucial role in conducting rigorous research and drawing meaningful conclusions. It involves the systematic examination, interpretation, and organization of data collected during the research process. The aim is to identify patterns, trends, and relationships that can provide valuable insights into the research topic.

The first step in dissertation data analysis is to carefully prepare and clean the collected data. This may involve removing any irrelevant or incomplete information, addressing missing data, and ensuring data integrity. Once the data is ready, various statistical and analytical techniques can be applied to extract meaningful information.

Descriptive statistics are commonly used to summarize and describe the main characteristics of the data, such as measures of central tendency (e.g., mean, median) and measures of dispersion (e.g., standard deviation, range). These statistics help researchers gain an initial understanding of the data and identify any outliers or anomalies.

Furthermore, qualitative data analysis techniques can be employed when dealing with non-numerical data, such as textual data or interviews. This involves systematically organizing, coding, and categorizing qualitative data to identify themes and patterns.

Types of Research

When considering research types in the context of dissertation data analysis, several approaches can be employed:

1. Quantitative Research

This type of research involves the collection and analysis of numerical data. It focuses on generating statistical information and making objective interpretations. Quantitative research often utilizes surveys, experiments, or structured observations to gather data that can be quantified and analyzed using statistical techniques.

2. Qualitative Research

In contrast to quantitative research, qualitative research focuses on exploring and understanding complex phenomena in depth. It involves collecting non-numerical data such as interviews, observations, or textual materials. Qualitative data analysis involves identifying themes, patterns, and interpretations, often using techniques like content analysis or thematic analysis.

3. Mixed-Methods Research

This approach combines both quantitative and qualitative research methods. Researchers employing mixed-methods research collect and analyze both numerical and non-numerical data to gain a comprehensive understanding of the research topic. The integration of quantitative and qualitative data can provide a more nuanced and comprehensive analysis, allowing for triangulation and validation of findings.

Primary vs. Secondary Research

Primary research.

Primary research involves the collection of original data specifically for the purpose of the dissertation. This data is directly obtained from the source, often through surveys, interviews, experiments, or observations. Researchers design and implement their data collection methods to gather information that is relevant to their research questions and objectives. Data analysis in primary research typically involves processing and analyzing the raw data collected.

Secondary Research

Secondary research involves the analysis of existing data that has been previously collected by other researchers or organizations. This data can be obtained from various sources such as academic journals, books, reports, government databases, or online repositories. Secondary data can be either quantitative or qualitative, depending on the nature of the source material. Data analysis in secondary research involves reviewing, organizing, and synthesizing the available data.

If you wanna deepen into Methodology in Research , also read: What is Methodology in Research and How Can We Write it?

Types of Analysis 

Various types of analysis techniques can be employed to examine and interpret the collected data. Of all those types, the ones that are most important and used are:

• Descriptive Analysis: Descriptive analysis focuses on summarizing and describing the main characteristics of the data. It involves calculating measures of central tendency (e.g., mean, median) and measures of dispersion (e.g., standard deviation, range). Descriptive analysis provides an overview of the data, allowing researchers to understand its distribution, variability, and general patterns.
• Inferential Analysis: Inferential analysis aims to draw conclusions or make inferences about a larger population based on the collected sample data. This type of analysis involves applying statistical techniques, such as hypothesis testing, confidence intervals, and regression analysis, to analyze the data and assess the significance of the findings. Inferential analysis helps researchers make generalizations and draw meaningful conclusions beyond the specific sample under investigation.
• Qualitative Analysis: Qualitative analysis is used to interpret non-numerical data, such as interviews, focus groups, or textual materials. It involves coding, categorizing, and analyzing the data to identify themes, patterns, and relationships. Techniques like content analysis, thematic analysis, or discourse analysis are commonly employed to derive meaningful insights from qualitative data.
• Correlation Analysis: Correlation analysis is used to examine the relationship between two or more variables . It determines the strength and direction of the association between variables. Common correlation techniques include Pearson’s correlation coefficient, Spearman’s rank correlation, or point-biserial correlation, depending on the nature of the variables being analyzed.

Basic Statistical Analysis

When conducting dissertation data analysis, researchers often utilize basic statistical analysis techniques to gain insights and draw conclusions from their data. These techniques involve the application of statistical measures to summarize and examine the data. Here are some common types of basic statistical analysis used in dissertation research:

• Descriptive Statistics
• Frequency Analysis
• Cross-tabulation
• Chi-Square Test
• Correlation Analysis

Advanced Statistical Analysis

In dissertation data analysis, researchers may employ advanced statistical analysis techniques to gain deeper insights and address complex research questions. These techniques go beyond basic statistical measures and involve more sophisticated methods. Here are some examples of advanced statistical analysis commonly used in dissertation research:

Regression Analysis

• Analysis of Variance (ANOVA)
• Factor Analysis
• Cluster Analysis
• Structural Equation Modeling (SEM)
• Time Series Analysis

Examples of Methods of Analysis

Regression analysis is a powerful tool for examining relationships between variables and making predictions. It allows researchers to assess the impact of one or more independent variables on a dependent variable. Different types of regression analysis, such as linear regression, logistic regression, or multiple regression, can be used based on the nature of the variables and research objectives.

Event Study

An event study is a statistical technique that aims to assess the impact of a specific event or intervention on a particular variable of interest. This method is commonly employed in finance, economics, or management to analyze the effects of events such as policy changes, corporate announcements, or market shocks.

Vector Autoregression

Vector Autoregression is a statistical modeling technique used to analyze the dynamic relationships and interactions among multiple time series variables. It is commonly employed in fields such as economics, finance, and social sciences to understand the interdependencies between variables over time.

Preparing Data for Analysis

1. become acquainted with the data.

It is crucial to become acquainted with the data to gain a comprehensive understanding of its characteristics, limitations , and potential insights. This step involves thoroughly exploring and familiarizing oneself with the dataset before conducting any formal analysis by reviewing the dataset to understand its structure and content. Identify the variables included, their definitions, and the overall organization of the data. Gain an understanding of the data collection methods, sampling techniques, and any potential biases or limitations associated with the dataset.

2. Review Research Objectives

This step involves assessing the alignment between the research objectives and the data at hand to ensure that the analysis can effectively address the research questions. Evaluate how well the research objectives and questions align with the variables and data collected. Determine if the available data provides the necessary information to answer the research questions adequately. Identify any gaps or limitations in the data that may hinder the achievement of the research objectives.

3. Creating a Data Structure

This step involves organizing the data into a well-defined structure that aligns with the research objectives and analysis techniques. Organize the data in a tabular format where each row represents an individual case or observation, and each column represents a variable. Ensure that each case has complete and accurate data for all relevant variables. Use consistent units of measurement across variables to facilitate meaningful comparisons.

4. Discover Patterns and Connections

In preparing data for dissertation data analysis, one of the key objectives is to discover patterns and connections within the data. This step involves exploring the dataset to identify relationships, trends, and associations that can provide valuable insights. Visual representations can often reveal patterns that are not immediately apparent in tabular data. 

Qualitative Data Analysis

Qualitative data analysis methods are employed to analyze and interpret non-numerical or textual data. These methods are particularly useful in fields such as social sciences, humanities, and qualitative research studies where the focus is on understanding meaning, context, and subjective experiences. Here are some common qualitative data analysis methods:

Thematic Analysis

The thematic analysis involves identifying and analyzing recurring themes, patterns, or concepts within the qualitative data. Researchers immerse themselves in the data, categorize information into meaningful themes, and explore the relationships between them. This method helps in capturing the underlying meanings and interpretations within the data.

Content Analysis

Content analysis involves systematically coding and categorizing qualitative data based on predefined categories or emerging themes. Researchers examine the content of the data, identify relevant codes, and analyze their frequency or distribution. This method allows for a quantitative summary of qualitative data and helps in identifying patterns or trends across different sources.

Grounded Theory

Grounded theory is an inductive approach to qualitative data analysis that aims to generate theories or concepts from the data itself. Researchers iteratively analyze the data, identify concepts, and develop theoretical explanations based on emerging patterns or relationships. This method focuses on building theory from the ground up and is particularly useful when exploring new or understudied phenomena.

Discourse Analysis

Discourse analysis examines how language and communication shape social interactions, power dynamics, and meaning construction. Researchers analyze the structure, content, and context of language in qualitative data to uncover underlying ideologies, social representations, or discursive practices. This method helps in understanding how individuals or groups make sense of the world through language.

Narrative Analysis

Narrative analysis focuses on the study of stories, personal narratives, or accounts shared by individuals. Researchers analyze the structure, content, and themes within the narratives to identify recurring patterns, plot arcs, or narrative devices. This method provides insights into individuals’ live experiences, identity construction, or sense-making processes.

Applying Data Analysis to Your Dissertation

Applying data analysis to your dissertation is a critical step in deriving meaningful insights and drawing valid conclusions from your research. It involves employing appropriate data analysis techniques to explore, interpret, and present your findings. Here are some key considerations when applying data analysis to your dissertation:

Selecting Analysis Techniques

Choose analysis techniques that align with your research questions, objectives, and the nature of your data. Whether quantitative or qualitative, identify the most suitable statistical tests, modeling approaches, or qualitative analysis methods that can effectively address your research goals. Consider factors such as data type, sample size, measurement scales, and the assumptions associated with the chosen techniques.

Data Preparation

Ensure that your data is properly prepared for analysis. Cleanse and validate your dataset, addressing any missing values, outliers, or data inconsistencies. Code variables, transform data if necessary, and format it appropriately to facilitate accurate and efficient analysis. Pay attention to ethical considerations, data privacy, and confidentiality throughout the data preparation process.

Execution of Analysis

Execute the selected analysis techniques systematically and accurately. Utilize statistical software, programming languages, or qualitative analysis tools to carry out the required computations, calculations, or interpretations. Adhere to established guidelines, protocols, or best practices specific to your chosen analysis techniques to ensure reliability and validity.

Interpretation of Results

Thoroughly interpret the results derived from your analysis. Examine statistical outputs, visual representations, or qualitative findings to understand the implications and significance of the results. Relate the outcomes back to your research questions, objectives, and existing literature. Identify key patterns, relationships, or trends that support or challenge your hypotheses.

Drawing Conclusions

Based on your analysis and interpretation, draw well-supported conclusions that directly address your research objectives. Present the key findings in a clear, concise, and logical manner, emphasizing their relevance and contributions to the research field. Discuss any limitations, potential biases, or alternative explanations that may impact the validity of your conclusions.

Validation and Reliability

Evaluate the validity and reliability of your data analysis by considering the rigor of your methods, the consistency of results, and the triangulation of multiple data sources or perspectives if applicable. Engage in critical self-reflection and seek feedback from peers, mentors, or experts to ensure the robustness of your data analysis and conclusions.

In conclusion, dissertation data analysis is an essential component of the research process, allowing researchers to extract meaningful insights and draw valid conclusions from their data. By employing a range of analysis techniques, researchers can explore relationships, identify patterns, and uncover valuable information to address their research objectives.

Turn Your Data Into Easy-To-Understand And Dynamic Stories

Decoding data is daunting and you might end up in confusion. Here’s where infographics come into the picture. With visuals, you can turn your data into easy-to-understand and dynamic stories that your audience can relate to. Mind the Graph is one such platform that helps scientists to explore a library of visuals and use them to amplify their research work. Sign up now to make your presentation simpler. 

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About Sowjanya Pedada

Sowjanya is a passionate writer and an avid reader. She holds MBA in Agribusiness Management and now is working as a content writer. She loves to play with words and hopes to make a difference in the world through her writings. Apart from writing, she is interested in reading fiction novels and doing craftwork. She also loves to travel and explore different cuisines and spend time with her family and friends.

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Quantitative Dissertations

The Quantitative Dissertations part of Lærd Dissertation helps guide you through the process of doing a quantitative dissertation. When we use the word quantitative to describe quantitative dissertations , we do not simply mean that the dissertation will draw on quantitative research methods or statistical analysis techniques . Quantitative research takes a particular approach to theory, answering research questions and/or hypotheses , setting up a research strategy , making conclusions from results , and so forth. It is also a type of dissertation that is commonly used by undergraduates, master's and doctoral students across degrees, whether traditional science-based subjects, or in the social sciences, psychology, education and business studies, amongst others.

This introduction to the Quantitative Dissertations part of Lærd Dissertation has two goals: (a) to provide you with a sense of the broad characteristics of quantitative research, if you do not know about these characteristics already; and (b) to introduce you to the three main types (routes) of quantitative dissertation that we help you with in Lærd Dissertation: replication-based dissertations ; data-driven dissertations; and theory-driven dissertations . When you have chosen which route you want to follow, we send you off to the relevant parts of Lærd Dissertation where you can find out more.

Characteristics of quantitative dissertations

• Types of quantitative dissertation: Replication, Data and Theory

If you have already read our article that briefly compares qualitative , quantitative and mixed methods dissertations [ here ], you may want to skip this section now . If not, we can say that quantitative dissertations have a number of core characteristics:

They typically attempt to build on and/or test theories , whether adopting an original approach or an approach based on some kind of replication or extension .

They answer quantitative research questions and/or research (or null ) hypotheses .

They are mainly underpinned by positivist or post-positivist research paradigms .

They draw on one of four broad quantitative research designs (i.e., descriptive , experimental , quasi-experimental or relationship-based research designs).

They try to use probability sampling techniques , with the goal of making generalisations from the sample being studied to a wider population , although often end up applying non-probability sampling techniques .

They use research methods that generate quantitative data (e.g., data sets , laboratory-based methods , questionnaires/surveys , structured interviews , structured observation , etc.).

They draw heavily on statistical analysis techniques to examine the data collected, whether descriptive or inferential in nature.

They assess the quality of their findings in terms of their reliability , internal and external validity , and construct validity .

They report their findings using statements , data , tables and graphs that address each research question and/or hypothesis.

They make conclusions in line with the findings , research questions and/or hypotheses , and theories discussed in order to test and/or expand on existing theories, or providing insight for future theories.

If you choose to take on a quantitative dissertation , you will learn more about these characteristics, not only in the Fundamentals section of Lærd Dissertation, but throughout the articles we have written to help guide you through the choices you need to make when doing a quantitative dissertation. For now, we recommend that you read the next section, Types of quantitative dissertation , which will help you choose the type of dissertation you may want to follow.

Types of quantitative dissertation

Replication, data or theory.

When taking on a quantitative dissertation, there are many different routes that you can follow. We focus on three major routes that cover a good proportion of the types of quantitative dissertation that are carried out. We call them Route #1: Replication-based dissertations , Route #2: Data-driven dissertations and Route #3: Theory-driven dissertations . Each of these three routes reflects a very different type of quantitative dissertation that you can take on. In the sections that follow, we describe the main characteristics of these three routes. Rather than being exhaustive, the main goal is to highlight what these types of quantitative research are and what they involve. Whilst you read through each section, try and think about your own dissertation, and whether you think that one of these types of dissertation might be right for you.

Route #1: Replication-based dissertations

Route #2: data-driven dissertations, route #3: theory-driven dissertations.

Most quantitative dissertations at the undergraduate, master's or doctoral level involve some form of replication , whether they are duplicating existing research, making generalisations from it, or extending the research in some way.

In most cases, replication is associated with duplication . In other words, you take a piece of published research and repeat it, typically in an identical way to see if the results that you obtain are the same as the original authors. In some cases, you don't even redo the previous study, but simply request the original data that was collected, and reanalyse it to check that the original authors were accurate in their analysis techniques. However, duplication is a very narrow view of replication, and is partly what has led some journal editors to shy away from accepting replication studies into their journals. The reality is that most research, whether completed by academics or dissertation students at the undergraduate, master's or doctoral level involves either generalisation or extension . This may simply be replicating a piece of research to determine whether the findings are generalizable within a different population or setting/context , or across treatment conditions ; terms we explain in more detail later in our main article on replication-based dissertations [ here ]. Alternately, replication can involve extending existing research to take into account new research designs , methods and measurement procedures , and analysis techniques . As a result, we call these different types of replication study: Route A: Duplication , Route B: Generalisation and Route C: Extension .

In reality, it doesn't matter what you call them. We simply give them these names because (a) they reflect three different routes that you can follow when doing a replication-based dissertation (i.e., Route A: Duplication , Route B: Generalisation and Route C: Extension ), and (b) the things you need to think about when doing your dissertation differ somewhat depending on which of these routes you choose to follow.

At this point, the Lærd Dissertation site focuses on helping guide you through Route #1: Replication-based dissertations . When taking on a Route #1: Replication-based dissertation , we guide you through these three possible routes: Route A: Duplication ; Route B: Generalisation ; and Route C: Extension . Each of these routes has different goals, requires different steps to be taken, and will be written up in its own way. To learn whether a Route #1: Replication-based dissertation is right for you, and if so, which of these routes you want to follow, start with our introductory guide: Route #1: Getting started .

Sometimes the goal of quantitative research is not to build on or test theory, but to uncover the antecedents (i.e., the drivers or causes ) of what are known as stylized facts (also known referred to as empirical regularities or empirical patterns ). Whilst you may not have heard the term before, a stylized fact is simply a fact that is surprising , undocumented , forms a pattern rather than being one-off, and has an important outcome variable , amongst other characteristics. A classic stylized fact was the discovery of the many maladies (i.e., diseases or aliments) that resulted from smoking (e.g., cancers, cardiovascular diseases, etc.). Such a discovery, made during the 1930s, was surprising when you consider that smoking was being promoted by some doctors as having positive health benefits, as well as the fact that smoking was viewed as being stylish at the time (Hambrick, 2007). The challenge of discovering a potential stylized fact, as well as collecting suitable data to test that such a stylized fact exists, makes data-driven dissertations a worthy type of quantitative dissertation to pursue.

Sometimes, the focus of data-driven dissertations is entirely on discovering whether the stylized fact exists (e.g., Do domestic firms receive smaller fines for wrongdoings compared with foreign firms?), and if so, uncovering the antecedents of the stylized fact (e.g., if it was found that domestic firms did receive smaller fines compared with foreign firms for wrongdoings, what was the relationship between the fines received and other factors you measured; e.g., factors such as industry type, firm size, financial performance, etc.?). These data-driven dissertations tend to be empirically-focused , and are often in fields where there is little theory to help ground or justify the research, but also where uncovering the stylized fact and its antecedents makes a significant contribution all by itself. On other occasions, the focus starts with discovering the stylized fact, as well as uncovering its antecedents (e.g., the reasons why the most popular brand of a soft drink is consistently ranked the worst in terms of flavour in a blind taste test). However, the goal is to go one step further and theoretically justify your findings. This can often be achieved when the field you are interested in is more theoretically developed (e.g., theories of decision-making, consumer behaviour, brand exposure, and so on, which may help to explain why the most popular brand of a soft drink is consistently ranked the worst in terms of flavour in a blind taste test). We call these different types of data-driven dissertation: Route A: Empirically-focused and Route B: Theoretically-justified .

In the part of Lærd Dissertation that deals exclusively with Route #2: Data-driven dissertations , which we will be launching shortly, we introduce you to these two routes (i.e., Route A: Empirically-focused and Route B: Theoretically-justified ), before helping you choose between them. Once you have selected the route you plan to follow, we use extensive, step-by-step guides to help you carry out, and subsequently write up your chosen route. If you would like to be notified when this part of Lærd Dissertation becomes available, please leave feedback .

We have all come across theories during our studies. Well-known theories include social capital theory (Social Sciences), motivation theory (Psychology), agency theory (Business Studies), evolutionary theory (Biology), quantum theory (Physics), adaptation theory (Sports Science), and so forth. Irrespective of what we call these theories, and from which subjects they come, all dissertations involves theory to some extent. However, what makes theory-driven dissertations different from other types of quantitative dissertation (i.e., Route #1: Replication-based dissertations and Route #2: Data-driven dissertations ) is that they place most importance on the theoretical contribution that you make.

By theoretical contribution , we mean that theory-driven dissertations aim to add to the literature through their originality and focus on testing , combining or building theory. We emphasize the words testing , combining and building because these reflect three routes that you can adopt when carrying out a theory-driven dissertation: Route A: Testing , Route B: Combining or Route C: Building . In reality, it doesn't matter what we call these three different routes. They are just there to help guide you through the dissertation process. The important point is that we can do different things with theory, which is reflected in the different routes that you can follow.

Sometimes we test theories (i.e., Route A: Testing ). For example, a researcher may have proposed a new theory in a journal article, but not yet tested it in the field by collecting and analysing data to see if the theory makes sense. Sometimes we want to combine two or more well-established theories (i.e., Route B: Combining ). This can provide a new insight into a problem or issue that we think it is important, but remains unexplained by existing theory. In such cases, the use of well-established theories helps when testing these theoretical combinations. On other occasions, we want to go a step further and build new theory from the ground up (i.e., Route C: Building ). Whilst there are many similarities between Route B: Combining and Route C: Building , the building of new theory goes further because even if the theories you are building on are well-established, you are likely to have to create new constructs and measurement procedures in order to test these theories.

In the part of Lærd Dissertation that deals exclusively with Route #3: Theory-driven dissertations , which we will be launching shortly, we introduce you to these three routes (i.e., Route A: Testing , Route B: Combining and Route C: Building ), before helping you choose between them. Once you have selected the route you plan to follow, we use extensive, step-by-step guides to help you carry out, and subsequently write up your chosen route. If you would like to be notified when this part of Lærd Dissertation becomes available, please leave feedback .

Choosing between routes

Which route should i choose.

A majority of students at the undergraduate, master's, and even doctoral level will take on a Route #1: Replication-based dissertation . At this point, it is also the only route that we cover in depth [ NOTE: We will be launching Route #2: Data-driven dissertations and Route #3: Theory-driven dissertations at a later date]. To learn whether a Route #1: Replication-based dissertation is right for you, and if so, how to proceed, start with our introductory guide: Route #1: Getting started . If there is anything you find unclear about what you have just read, please leave feedback .

Hambrick, D. C. (2007). The field of management's devotion to theory: Too much of a good thing? Academy of Management Journal , 50 (6), 1346-1352.

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The Top 3 Types of Dissertation Research Explained

Preparing for your doctoral dissertation takes serious perseverance. You’ve endured years of studies and professional development to get to this point. After sleepless nights and labor-intensive research, you’re ready to present the culmination of all of your hard work. Even with a strong base knowledge, it can be difficult — even daunting — to decide how you will begin writing.

By taking a wide-lens view of the dissertation research process , you can best assess the work you have ahead of you and any gaps in your current research strategy. Subsequently, you’ll begin to develop a timeline so you can work efficiently and cross that finish line with your degree in hand.

What Is a Dissertation?

A dissertation is a published piece of research on a novel topic in your chosen field. Students complete a dissertation as part of a doctoral or PhD program. For most students, a dissertation is the first substantive piece of academic research they will write. 

Because a dissertation becomes a published piece of academic literature that other academics may cite, students must defend it in front of a board of experts consisting of peers in their field, including professors, their advisor, and other industry experts. 

For many students, a dissertation is the first piece of research in a long career full of research. As such, it’s important to choose a topic that’s interesting and engaging.

Types of Dissertation Research

Dissertations can take on many forms, based on research and methods of presentation in front of a committee board of academics and experts in the field. Here, we’ll focus on the three main types of dissertation research to get you one step closer to earning your doctoral degree.

1. Qualitative

The first type of dissertation is known as a qualitative dissertation . A qualitative dissertation mirrors the qualitative research that a doctoral candidate would conduct throughout their studies. This type of research relies on non-numbers-based data collected through things like interviews, focus groups and participant observation. 

The decision to model your dissertation research according to the qualitative method will depend largely on the data itself that you are collecting. For example, dissertation research in the field of education or psychology may lend itself to a qualitative approach, depending on the essence of research. Within a qualitative dissertation research model, a candidate may pursue one or more of the following:

• Case study research
• Autoethnographies
• Narrative research 
• Grounded theory 

Although individual approaches may vary, qualitative dissertations usually include certain foundational characteristics. For example, the type of research conducted to develop a qualitative dissertation often follows an emergent design, meaning that the content and research strategy changes over time. Candidates also rely on research paradigms to further strategize how best to collect and relay their findings. These include critical theory, constructivism and interpretivism, to name a few. 

Because qualitative researchers integrate non-numerical data, their methods of collection often include unstructured interview, focus groups and participant observations. Of course, researchers still need rubrics from which to assess the quality of their findings, even though they won’t be numbers-based. To do so, they subject the data collected to the following criteria: dependability, transferability and validity. 

When it comes time to present their findings, doctoral candidates who produce qualitative dissertation research have several options. Some choose to include case studies, personal findings, narratives, observations and abstracts. Their presentation focuses on theoretical insights based on relevant data points. 

2. Quantitative

Quantitative dissertation research, on the other hand, focuses on the numbers. Candidates employ quantitative research methods to aggregate data that can be easily categorized and analyzed. In addition to traditional statistical analysis, quantitative research also hones specific research strategy based on the type of research questions. Quantitative candidates may also employ theory-driven research, replication-based studies and data-driven dissertations. 

When conducting research, some candidates who rely on quantitative measures focus their work on testing existing theories, while others create an original approach. To refine their approach, quantitative researchers focus on positivist or post-positivist research paradigms. Quantitative research designs focus on descriptive, experimental or relationship-based designs, to name a few. 

To collect the data itself, researchers focus on questionnaires and surveys, structured interviews and observations, data sets and laboratory-based methods. Then, once it’s time to assess the quality of the data, quantitative researchers measure their results against a set of criteria, including: reliability, internal/external validity and construct validity. Quantitative researchers have options when presenting their findings. Candidates convey their results using graphs, data, tables and analytical statements.

3. Mixed-Method

Many PhD candidates also use a hybrid model in which they employ both qualitative and quantitative methods of research. Mixed dissertation research models are fairly new and gaining traction. For a variety of reasons, a mixed-method approach offers candidates both versatility and credibility. It’s a more comprehensive strategy that allows for a wider capture of data with a wide range of presentation optimization. 

In the most common cases, candidates will first use quantitative methods to collect and categorize their data. Then, they’ll rely on qualitative methods to analyze that data and draw meaningful conclusions to relay to their committee panel. 

With a mixed-method approach, although you’re able to collect and analyze a more broad range of data, you run the risk of widening the scope of your dissertation research so much that you’re not able to reach succinct, sustainable conclusions. This is where it becomes critical to outline your research goals and strategy early on in the dissertation process so that the techniques you use to capture data have been thoroughly examined. 

How to Choose a Type of Dissertation Research That’s Right for You

After this overview of application and function, you may still be wondering how to go about choosing a dissertation type that’s right for you and your research proposition. In doing so, you’ll have a couple of things to consider: 

• What are your personal motivations? 
• What are your academic goals? 

It’s important to discern exactly what you hope to get out of your doctoral program . Of course, the presentation of your dissertation is, formally speaking, the pinnacle of your research. However, doctoral candidates must also consider:

• Which contributions they will make to the field
• Who they hope to collaborate with throughout their studies
• What they hope to take away from the experience personally, professionally and academically

Personal Considerations

To discern which type of dissertation research to choose, you have to take a closer look at your learning style, work ethic and even your personality. 

Quantitative research tends to be sequential and patterned-oriented. Steps move in a logical order, so it becomes clear what the next step should be at all times. For most candidates, this makes it easier to devise a timeline and stay on track. It also keeps you from getting overwhelmed by the magnitude of research involved. You’ll be able to assess your progress and make simple adjustments to stay on target. 

On the other hand, maybe you know that your research will involve many interviews and focus groups. You anticipate that you’ll have to coordinate participants’ schedules, and this will require some flexibility. Instead of creating a rigid schedule from the get-go, allowing your research to flow in a non-linear fashion may actually help you accomplish tasks more efficiently, albeit out of order. This also allows you the personal versatility of rerouting research strategy as you collect new data that leads you down other paths. 

After examining the research you need to conduct, consider more broadly: What type of student and researcher are you? In other words, What motivates you to do your best work? 

You’ll need to make sure that your methodology is conducive to the data you’re collecting, and you also need to make sure that it aligns with your work ethic so you set yourself up for success. If jumping from one task to another will cause you extra stress, but planning ahead puts you at ease, a quantitative research method may be best, assuming the type of research allows for this. 

Professional Considerations

The skills you master while working on your dissertation will serve you well beyond the day you earn your degree. Take into account the skills you’d like to develop for your academic and professional future. In addition to the hard skills you will develop in your area of expertise, you’ll also develop soft skills that are transferable to nearly any professional or academic setting. Perhaps you want to hone your ability to strategize a timeline, gather data efficiently or draw clear conclusions about the significance of your data collection. 

If you have considerable experience with quantitative analysis, but lack an extensive qualitative research portfolio, now may be your opportunity to explore — as long as you’re willing to put in the legwork to refine your skills or work closely with your mentor to develop a strategy together. 

Academic Considerations

For many doctoral candidates who hope to pursue a professional career in the world of academia, writing your dissertation is a practice in developing general research strategies that can be applied to any academic project. 

Candidates who are unsure which dissertation type best suits their research should consider whether they will take a philosophical or theoretical approach or come up with a thesis that addresses a specific problem or idea. Narrowing down this approach can sometimes happen even before the research begins. Other times, candidates begin to refine their methods once the data begins to tell a more concrete story.

Next Step: Structuring Your Dissertation Research Schedule

Once you’ve chosen which type of dissertation research you’ll pursue, you’ve already crossed the first hurdle. The next hurdle becomes when and where to fit dedicated research time and visits with your mentor into your schedule. The busyness of day-to-day life shouldn’t prevent you from making your academic dream a reality. In fact, search for programs that assist, not impede, your path to higher levels of academic success. 

Find out more about SNU’s online and on-campus education opportunities so that no matter where you are in life, you can choose the path that’s right for you.

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Home » Dissertation – Format, Example and Template

Dissertation – Format, Example and Template

Table of Contents

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

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

Types of Dissertation are as follows:

Empirical Dissertation

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.

Non-Empirical Dissertation

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.

Narrative Dissertation

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.

Systematic Literature Review

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.

Case Study Dissertation

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.

Mixed-Methods Dissertation

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.

How to Write a Dissertation

Here are some general steps to help guide you through the process of writing a dissertation:

• Choose a topic : Select a topic that you are passionate about and that is relevant to your field of study. It should be specific enough to allow for in-depth research but broad enough to be interesting and engaging.
• Conduct research : Conduct thorough research on your chosen topic, utilizing a variety of sources, including books, academic journals, and online databases. Take detailed notes and organize your information in a way that makes sense to you.
• Create an outline : Develop an outline that will serve as a roadmap for your dissertation. The outline should include the introduction, literature review, methodology, results, discussion, and conclusion.
• Write the introduction: The introduction should provide a brief overview of your topic, the research questions, and the significance of the study. It should also include a clear thesis statement that states your main argument.
• Write the literature review: The literature review should provide a comprehensive analysis of existing research on your topic. It should identify gaps in the research and explain how your study will fill those gaps.
• Write the methodology: The methodology section should explain the research methods you used to collect and analyze data. It should also include a discussion of any limitations or weaknesses in your approach.
• Write the results: The results section should present the findings of your research in a clear and organized manner. Use charts, graphs, and tables to help illustrate your data.
• Write the discussion: The discussion section should interpret your results and explain their significance. It should also address any limitations of the study and suggest areas for future research.
• Write the conclusion: The conclusion should summarize your main findings and restate your thesis statement. It should also provide recommendations for future research.
• Edit and revise: Once you have completed a draft of your dissertation, review it carefully to ensure that it is well-organized, clear, and free of errors. Make any necessary revisions and edits before submitting it to your advisor for review.

Dissertation Format

The format of a dissertation may vary depending on the institution and field of study, but generally, it follows a similar structure:

• Title Page: This includes the title of the dissertation, the author’s name, and the date of submission.
• Abstract : A brief summary of the dissertation’s purpose, methods, and findings.
• Table of Contents: A list of the main sections and subsections of the dissertation, along with their page numbers.
• Introduction : A statement of the problem or research question, a brief overview of the literature, and an explanation of the significance of the study.
• Literature Review : A comprehensive review of the literature relevant to the research question or problem.
• Methodology : A description of the methods used to conduct the research, including data collection and analysis procedures.
• Results : A presentation of the findings of the research, including tables, charts, and graphs.
• Discussion : A discussion of the implications of the findings, their significance in the context of the literature, and limitations of the study.
• Conclusion : A summary of the main points of the study and their implications for future research.
• References : A list of all sources cited in the dissertation.
• Appendices : Additional materials that support the research, such as data tables, charts, or transcripts.

Dissertation Outline

Dissertation Outline is as follows:

Title Page:

• Title of dissertation
• Author name
• Institutional affiliation
• Date of submission
• Brief summary of the dissertation’s research problem, objectives, methods, findings, and implications
• Usually around 250-300 words

Table of Contents:

• List of chapters and sections in the dissertation, with page numbers for each

I. Introduction

• Background and context of the research
• Research problem and objectives
• Significance of the research

II. Literature Review

• Overview of existing literature on the research topic
• Identification of gaps in the literature
• Theoretical framework and concepts

III. Methodology

• Research design and methods used
• Data collection and analysis techniques
• Ethical considerations

IV. Results

• Presentation and analysis of data collected
• Findings and outcomes of the research
• Interpretation of the results

V. Discussion

• Discussion of the results in relation to the research problem and objectives
• Evaluation of the research outcomes and implications
• Suggestions for future research

VI. Conclusion

• Summary of the research findings and outcomes
• Implications for the research topic and field
• Limitations and recommendations for future research

VII. References

• List of sources cited in the dissertation

VIII. Appendices

• Additional materials that support the research, such as tables, figures, or questionnaires.

Example of Dissertation

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

• Background and rationale for the study
• Research questions and objectives
• Significance of the study
• Overview of the dissertation structure

Chapter 2: Literature Review

• Definition and conceptualization of mindfulness meditation
• Theoretical framework of mindfulness meditation
• Empirical research on mindfulness meditation and academic achievement
• Empirical research on mindfulness meditation and well-being
• The role of personality and demographic factors in the effects of mindfulness meditation

Chapter 3: Methodology

• Research design and hypothesis
• Participants and sampling method
• Intervention and procedure
• Measures and instruments
• Data analysis method

Chapter 4: Results

• Descriptive statistics and data screening
• Analysis of main effects
• Analysis of moderating effects
• Post-hoc analyses and sensitivity tests

Chapter 5: Discussion

• Summary of findings
• Implications for theory and practice
• Limitations and directions for future research
• Conclusion and contribution to the literature

Chapter 6: Conclusion

• Recap of the research questions and objectives
• Summary of the key findings
• Contribution to the literature and practice
• Implications for policy and practice
• Final thoughts and recommendations.

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.

How Long is a Dissertation

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.

Applications of Dissertation

Here are some applications of a dissertation:

• Advancing the Field: Dissertations often include new research or a new perspective on existing research, which can help to advance the field. The results of a dissertation can be used by other researchers to build upon or challenge existing knowledge, leading to further advancements in the field.
• Career Advancement: Completing a dissertation demonstrates a high level of expertise in a particular field, which can lead to career advancement opportunities. For example, having a PhD can open doors to higher-paying jobs in academia, research institutions, or the private sector.
• Publishing Opportunities: Dissertations can be published as books or journal articles, which can help to increase the visibility and credibility of the author’s research.
• Personal Growth: The process of writing a dissertation involves a significant amount of research, analysis, and critical thinking. This can help students to develop important skills, such as time management, problem-solving, and communication, which can be valuable in both their personal and professional lives.
• Policy Implications: The findings of a dissertation can have policy implications, particularly in fields such as public health, education, and social sciences. Policymakers can use the research to inform decision-making and improve outcomes for the population.

When to Write a Dissertation

Here are some situations where writing a dissertation may be necessary:

• Pursuing a Doctoral Degree: Writing a dissertation is usually a requirement for earning a doctoral degree, so if you are interested in pursuing a doctorate, you will likely need to write a dissertation.
• Conducting Original Research : Dissertations require students to conduct original research on a specific topic. If you are interested in conducting original research on a topic, writing a dissertation may be the best way to do so.
• Advancing Your Career: Some professions, such as academia and research, may require individuals to have a doctoral degree. Writing a dissertation can help you advance your career by demonstrating your expertise in a particular area.
• Contributing to Knowledge: Dissertations are often based on original research that can contribute to the knowledge base of a field. If you are passionate about advancing knowledge in a particular area, writing a dissertation can help you achieve that goal.
• Meeting Academic Requirements : If you are a graduate student, writing a dissertation may be a requirement for completing your program. Be sure to check with your academic advisor to determine if this is the case for you.

Purpose of Dissertation

some common purposes of a dissertation include:

• To contribute to the knowledge in a particular field : A dissertation is often the culmination of years of research and study, and it should make a significant contribution to the existing body of knowledge in a particular field.
• To demonstrate mastery of a subject: A dissertation requires extensive research, analysis, and writing, and completing one demonstrates a student’s mastery of their subject area.
• To develop critical thinking and research skills : A dissertation requires students to think critically about their research question, analyze data, and draw conclusions based on evidence. These skills are valuable not only in academia but also in many professional fields.
• To demonstrate academic integrity: A dissertation must be conducted and written in accordance with rigorous academic standards, including ethical considerations such as obtaining informed consent, protecting the privacy of participants, and avoiding plagiarism.
• To prepare for an academic career: Completing a dissertation is often a requirement for obtaining a PhD and pursuing a career in academia. It can demonstrate to potential employers that the student has the necessary skills and experience to conduct original research and make meaningful contributions to their field.
• To develop writing and communication skills: A dissertation requires a significant amount of writing and communication skills to convey complex ideas and research findings in a clear and concise manner. This skill set can be valuable in various professional fields.
• To demonstrate independence and initiative: A dissertation requires students to work independently and take initiative in developing their research question, designing their study, collecting and analyzing data, and drawing conclusions. This demonstrates to potential employers or academic institutions that the student is capable of independent research and taking initiative in their work.
• To contribute to policy or practice: Some dissertations may have a practical application, such as informing policy decisions or improving practices in a particular field. These dissertations can have a significant impact on society, and their findings may be used to improve the lives of individuals or communities.
• To pursue personal interests: Some students may choose to pursue a dissertation topic that aligns with their personal interests or passions, providing them with the opportunity to delve deeper into a topic that they find personally meaningful.

Advantage of Dissertation

Some advantages of writing a dissertation include:

• Developing research and analytical skills: The process of writing a dissertation involves conducting extensive research, analyzing data, and presenting findings in a clear and coherent manner. This process can help students develop important research and analytical skills that can be useful in their future careers.
• Demonstrating expertise in a subject: Writing a dissertation allows students to demonstrate their expertise in a particular subject area. It can help establish their credibility as a knowledgeable and competent professional in their field.
• Contributing to the academic community: A well-written dissertation can contribute new knowledge to the academic community and potentially inform future research in the field.
• Improving writing and communication skills : Writing a dissertation requires students to write and present their research in a clear and concise manner. This can help improve their writing and communication skills, which are essential for success in many professions.
• Increasing job opportunities: Completing a dissertation can increase job opportunities in certain fields, particularly in academia and research-based positions.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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The 7 Types of Dissertations Explained: Which One is Right for You?

Your dissertation is a pretty big deal and likely represents years of hard slog studying your subject of expertise.

But did you know there are 7 different types of dissertation ?

The purpose of this article is to demystify the various types of dissertations you might encounter or choose to undertake during your advanced studies. Armed with this knowledge, you can select the most appropriate methodology and framework for your research interests and academic requirements.

Each type of dissertation serves a different academic purpose and requires a unique approach and structure. In this article, we’ll take a look at these differences in detail, providing clear explanations and examples from a range of academic disciplines. By the end of this article, you should have a thorough understanding of the options available for your dissertation and be better prepared to select a path that aligns with your research goals and academic ambitions.

Remember, regardless of what type of dissertation you ultimately decide to pursue, best dissertation proofreading can make all the difference between a pass and a fail.

7 Types of Dissertation

Empirical Dissertations

So, let’s dive right in with empirical dissertations—arguably the most hands-on type of dissertation out there. If you’re studying a field like psychology, biology, or social sciences, you’re probably going to become very familiar with this approach.

What exactly is an empirical dissertation?

It’s all about gathering data. You’ll be conducting your own experiments, surveys, or observations, making this type extremely engaging (and a bit daunting). Essentially, you’re collecting new data from the world or from people, rather than relying on existing data from other studies.

The structure of an empirical dissertation is pretty straightforward but involves rigorous methodology. Typically, it will include an introduction to set up your SMART research question , a literature review to justify why this question needs answering, a methodology section that details how you’ve gone about your data collection, a results chapter presenting your findings, and a discussion that ties everything back to your research question and explores the implications.

This type of dissertation not only tests your ability to conduct research and analyze data but also challenges you to apply theoretical knowledge in practical scenarios. By the end of an empirical dissertation, you should not only have answers to your original questions but also a solid chunk of real-world experience under your belt.

Best suited to: Students who are ready to roll up their sleeves and get their hands dirty.

Theoretical Dissertations

Now, shifting gears, let’s talk about theoretical dissertations. If the empirical dissertation is the hands-on, muddy boots kind of research, the theoretical dissertation is its more contemplative, indoor cousin. Perfect for those of you in fields like philosophy, literature, or certain branches of sociology and psychology, where concrete data might not be the main focus.

What’s the deal with theoretical dissertations? They revolve around developing, exploring, or expanding on existing theories. Instead of collecting new data, you invest your time and effort studying existing research and theoretical frameworks to build an argument or propose a new theory or perspective on an old one.

The structure of a theoretical dissertation generally includes a comprehensive introduction where you lay out your thesis or theory, followed by a detailed literature review that supports and provides the foundation for your thesis. After this, you’ll move into a discussion or analysis section, where you critically analyze your thesis in the light of existing theories and literature. The aim here is to offer a fresh or refined perspective that contributes to the existing body of knowledge.

This type of dissertation is a test of your analytical skills and your ability to synthesize complex ideas into a coherent argument. It’s less about creating new paths and more about mapping the ones already laid out in new ways.

Best suited to: Students who love theory and thrive on crafting arguments.

Case Study Dissertations

Next up, it’s case study dissertations. This type of dissertation is especially attractive if you’re someone who loves storytelling with a purpose or is drawn to in-depth analysis of specific events, individuals, or organizations. It’s a favorite in disciplines like business, education, psychology, and social sciences, where a single case can lead to the development of new theories and concepts.

A case study dissertation involves an intensive investigation of a particular individual, group, organization, or event. You’ll get to the nitty gritty of the specifics, examining various aspects of the case to understand its implications and applications. This method allows you to apply theoretical concepts in a real-world context, providing rich insights that aren’t always accessible through broader surveys or experiments.

The structure of a case study dissertation usually starts with an introduction to the chosen case, followed by a literature review that sets the theoretical framework. You then proceed to a detailed methodology section explaining how you collected and analyzed your data. The core of your dissertation will likely be the case analysis chapter, where you dissect the case in relation to your research question. Finally, you’ll conclude with a discussion of how the case impacts the wider field and what new understandings it brings to the fore.

Best suited to: Those who are meticulous and have a keen eye for detail, a case study dissertation allows you to explore the intricacies of a specific example while contributing to broader academic debates.

Comparative Dissertations

Next up is the comparative dissertation. This type is tailor-made for the analytically minded who love drawing connections and distinctions between different elements. It’s particularly prevalent in fields like law, education, political science, and international relations, where understanding differences and similarities across cases, laws, or educational methods can provide critical insights.

Essentially, a comparative dissertation involves systematically comparing and contrasting two or more entities. These could be policies, theories, populations, or even historical periods, depending on your study area. The goal is to identify patterns or discrepancies that reveal underlying principles or suggest new interpretations of data.

The structure of a dissertation of this nature typically includes a dissertation abstract followed by an introduction that defines the entities being compared and poses your research question. This is followed by a literature review that frames the theoretical bases for comparison. The methodology section should clearly outline the criteria and methods for comparison, ensuring transparency and replicability. The subsequent chapters will then detail the comparative analysis, discussing each entity individually before bringing them together for a comprehensive comparison. Finally, the conclusion synthesizes the findings, highlighting the significance of the differences and similarities discovered.

Best suited to: Those who can juggle multiple themes and variables without losing sight of the overarching question, a comparative dissertation challenges you to remain objective and balanced in your analysis.

Project-Based Dissertations

Project-based dissertations are more practical in nature. This type of dissertation is particularly appealing if you’re inclined towards applying your theoretical knowledge to create something tangible or solve a real-world problem. It’s a common choice in fields like engineering, computer science, and applied arts, where the end product can be a piece of software, an engineering prototype, or a design project.

What makes a project-based dissertation stand out? It centers around a project that you will plan, execute, and manage through the duration of your dissertation process. This could involve designing a new gadget, developing a software program, or creating a marketing plan for a startup. The focus is on applying the skills and knowledge you’ve acquired through your studies to produce a project that has practical and theoretical implications.

The structure of a project-based dissertation generally includes an introduction to the project, its objectives, and its relevance to your field. Following this, you’ll provide a literature review that supports the theories and methodologies you intend to use. The methodology section should detail your project plan, resources, and the processes you will follow. The main body of the dissertation will describe the project development and implementation phases in detail. Finally, the conclusion will evaluate the project’s success, its impact, and potential future developments or applications.

Best suited to: The innovative and the practical, a project-based dissertation allows you to showcase your ability to deliver a concrete outcome that demonstrates your professional capabilities.

Narrative Dissertations

If you’re drawn to writing and storytelling, a narrative dissertation might be right up your alley. This type is particularly popular in fields such as creative arts, literature, and education, where personal narratives or creative elements can be used to explore and communicate complex ideas.

What does a narrative dissertation involve? It’s about crafting a dissertation that primarily uses narrative techniques to convey research findings or explore scholarly questions. This could mean writing in a first-person perspective, incorporating fictional elements, or structuring the dissertation like a series of interconnected stories or essays.

The structure of a narrative dissertation often deviates from the traditional format. It begins with an introduction that sets the stage for the narrative journey. The literature review might be woven into the narrative itself, providing contextual background as the story unfolds. The methodology section explains how narrative methods will be used to explore the research question. The main body is where the narrative takes center stage, presenting research through personal reflections, storytelling, or hypothetical scenarios. The conclusion then ties all narrative threads together, reflecting on the insights gained and their broader implications.

Best suited to: those who think and express themselves best through stories, a narrative dissertation allows you to engage with your topic in a deeply personal and creative way.

Systematic Review Dissertations

Systematic review dissertations are perfect if you’re keen on synthesizing existing research to draw comprehensive conclusions about a specific topic. This type is particularly valuable in fields like healthcare, psychology, and social sciences, where summarizing and evaluating existing studies can provide powerful insights and inform practice and policy.

A systematic review dissertation involves a rigorous and structured approach to reviewing literature. You’ll gather all relevant data from previously published studies to answer a specific research question. The focus is on transparency and reproducibility, employing predefined methods to minimize bias and provide reliable results.

The structure of a systematic review dissertation typically begins with an introduction that outlines the research question and its significance. This is followed by a methodologically detailed section that explains the criteria for selecting studies, the search strategy used, and the methods for data extraction and synthesis. The results section then presents a detailed analysis of the studies included in the review, often using quantitative methods like meta-analysis. The discussion interprets these findings, considering their implications for the field and any limitations. The conclusion suggests areas for further research and summarizes the contributions made by the review.

Best suited to: Those with a keen eye for detail and a systematic approach to research, a systematic review dissertation can significantly impact by clarifying and summarizing existing knowledge.

Methodological Considerations

Choosing the right type of dissertation is an important decision in your academic journey, and several factors should guide your selection. This section aims to help you navigate these choices, ensuring that the methodology and framework you choose align perfectly with your research goals, available resources, and time constraints.

First, consider your discipline’s requirements and norms. Different fields favor different types of dissertations, so understanding what is expected and respected in your area of study is crucial. Next, think about your own strengths and interests. Choose a dissertation type that not only meets academic criteria but also excites you and plays to your strengths, whether they lie in empirical research, theoretical exploration, practical application, or creative expression.

Resource availability is another critical factor. Some types of dissertations, like empirical and project-based, may require access to specific equipment, software, or locations, which can be a deciding factor. Time constraints are also essential to consider; some dissertations, particularly those involving extensive data collection and analysis, may require more time than others.

Finally, discuss your ideas with your advisor or mentor. They can provide valuable insights and feedback that can help you refine your choice and ensure that you are prepared to tackle the challenges ahead. With the right preparation and understanding of what each type of dissertation entails, you can make an informed decision that sets the stage for a successful and rewarding research endeavor.

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• 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

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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!

The end is in sight—your dissertation is nearly ready to submit! Make sure it's perfectly polished with the help of a Scribbr editor.

If you’re an educator, feel free to download and adapt these slides to teach your students about structuring a dissertation.

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How To Write A Dissertation Or Thesis

8 straightforward steps to craft an a-grade dissertation.

By: Derek Jansen (MBA) Expert Reviewed By: Dr Eunice Rautenbach | June 2020

Writing a dissertation or thesis is not a simple task. It takes time, energy and a lot of will power to get you across the finish line. It’s not easy – but it doesn’t necessarily need to be a painful process. If you understand the big-picture process of how to write a dissertation or thesis, your research journey will be a lot smoother.  

In this post, I’m going to outline the big-picture process of how to write a high-quality dissertation or thesis, without losing your mind along the way. If you’re just starting your research, this post is perfect for you. Alternatively, if you’ve already submitted your proposal, this article which covers how to structure a dissertation might be more helpful.

How To Write A Dissertation: 8 Steps

• Clearly understand what a dissertation (or thesis) is
• Find a unique and valuable research topic
• Craft a convincing research proposal
• Write up a strong introduction chapter
• Review the existing literature and compile a literature review
• Design a rigorous research strategy and undertake your own research
• Present the findings of your research
• Draw a conclusion and discuss the implications

Step 1: Understand exactly what a dissertation is

This probably sounds like a no-brainer, but all too often, students come to us for help with their research and the underlying issue is that they don’t fully understand what a dissertation (or thesis) actually is.

So, what is a dissertation?

At its simplest, a dissertation or thesis is a formal piece of research , reflecting the standard research process . But what is the standard research process, you ask? The research process involves 4 key steps:

• Ask a very specific, well-articulated question (s) (your research topic)
• See what other researchers have said about it (if they’ve already answered it)
• If they haven’t answered it adequately, undertake your own data collection and analysis in a scientifically rigorous fashion
• Answer your original question(s), based on your analysis findings

In short, the research process is simply about asking and answering questions in a systematic fashion . This probably sounds pretty obvious, but people often think they’ve done “research”, when in fact what they have done is:

• Started with a vague, poorly articulated question
• Not taken the time to see what research has already been done regarding the question
• Collected data and opinions that support their gut and undertaken a flimsy analysis
• Drawn a shaky conclusion, based on that analysis

If you want to see the perfect example of this in action, look out for the next Facebook post where someone claims they’ve done “research”… All too often, people consider reading a few blog posts to constitute research. Its no surprise then that what they end up with is an opinion piece, not research. Okay, okay – I’ll climb off my soapbox now.

The key takeaway here is that a dissertation (or thesis) is a formal piece of research, reflecting the research process. It’s not an opinion piece , nor a place to push your agenda or try to convince someone of your position. Writing a good dissertation involves asking a question and taking a systematic, rigorous approach to answering it.

If you understand this and are comfortable leaving your opinions or preconceived ideas at the door, you’re already off to a good start!

Step 2: Find a unique, valuable research topic

As we saw, the first step of the research process is to ask a specific, well-articulated question. In other words, you need to find a research topic that asks a specific question or set of questions (these are called research questions ). Sounds easy enough, right? All you’ve got to do is identify a question or two and you’ve got a winning research topic. Well, not quite…

A good dissertation or thesis topic has a few important attributes. Specifically, a solid research topic should be:

Let’s take a closer look at these:

Attribute #1: Clear

Your research topic needs to be crystal clear about what you’re planning to research, what you want to know, and within what context. There shouldn’t be any ambiguity or vagueness about what you’ll research.

Here’s an example of a clearly articulated research topic:

An analysis of consumer-based factors influencing organisational trust in British low-cost online equity brokerage firms.

As you can see in the example, its crystal clear what will be analysed (factors impacting organisational trust), amongst who (consumers) and in what context (British low-cost equity brokerage firms, based online).

Need a helping hand?

Attribute #2:   Unique

Your research should be asking a question(s) that hasn’t been asked before, or that hasn’t been asked in a specific context (for example, in a specific country or industry).

For example, sticking organisational trust topic above, it’s quite likely that organisational trust factors in the UK have been investigated before, but the context (online low-cost equity brokerages) could make this research unique. Therefore, the context makes this research original.

One caveat when using context as the basis for originality – you need to have a good reason to suspect that your findings in this context might be different from the existing research – otherwise, there’s no reason to warrant researching it.

Attribute #3: Important

Simply asking a unique or original question is not enough – the question needs to create value. In other words, successfully answering your research questions should provide some value to the field of research or the industry. You can’t research something just to satisfy your curiosity. It needs to make some form of contribution either to research or industry.

For example, researching the factors influencing consumer trust would create value by enabling businesses to tailor their operations and marketing to leverage factors that promote trust. In other words, it would have a clear benefit to industry.

So, how do you go about finding a unique and valuable research topic? We explain that in detail in this video post – How To Find A Research Topic . Yeah, we’ve got you covered 😊

Step 3: Write a convincing research proposal

Once you’ve pinned down a high-quality research topic, the next step is to convince your university to let you research it. No matter how awesome you think your topic is, it still needs to get the rubber stamp before you can move forward with your research. The research proposal is the tool you’ll use for this job.

So, what’s in a research proposal?

The main “job” of a research proposal is to convince your university, advisor or committee that your research topic is worthy of approval. But convince them of what? Well, this varies from university to university, but generally, they want to see that:

• You have a clearly articulated, unique and important topic (this might sound familiar…)
• You’ve done some initial reading of the existing literature relevant to your topic (i.e. a literature review)
• You have a provisional plan in terms of how you will collect data and analyse it (i.e. a methodology)

At the proposal stage, it’s (generally) not expected that you’ve extensively reviewed the existing literature , but you will need to show that you’ve done enough reading to identify a clear gap for original (unique) research. Similarly, they generally don’t expect that you have a rock-solid research methodology mapped out, but you should have an idea of whether you’ll be undertaking qualitative or quantitative analysis , and how you’ll collect your data (we’ll discuss this in more detail later).

Long story short – don’t stress about having every detail of your research meticulously thought out at the proposal stage – this will develop as you progress through your research. However, you do need to show that you’ve “done your homework” and that your research is worthy of approval .

So, how do you go about crafting a high-quality, convincing proposal? We cover that in detail in this video post – How To Write A Top-Class Research Proposal . We’ve also got a video walkthrough of two proposal examples here .

Step 4: Craft a strong introduction chapter

Once your proposal’s been approved, its time to get writing your actual dissertation or thesis! The good news is that if you put the time into crafting a high-quality proposal, you’ve already got a head start on your first three chapters – introduction, literature review and methodology – as you can use your proposal as the basis for these.

Handy sidenote – our free dissertation & thesis template is a great way to speed up your dissertation writing journey.

What’s the introduction chapter all about?

The purpose of the introduction chapter is to set the scene for your research (dare I say, to introduce it…) so that the reader understands what you’ll be researching and why it’s important. In other words, it covers the same ground as the research proposal in that it justifies your research topic.

What goes into the introduction chapter?

This can vary slightly between universities and degrees, but generally, the introduction chapter will include the following:

• A brief background to the study, explaining the overall area of research
• A problem statement , explaining what the problem is with the current state of research (in other words, where the knowledge gap exists)
• Your research questions – in other words, the specific questions your study will seek to answer (based on the knowledge gap)
• The significance of your study – in other words, why it’s important and how its findings will be useful in the world

As you can see, this all about explaining the “what” and the “why” of your research (as opposed to the “how”). So, your introduction chapter is basically the salesman of your study, “selling” your research to the first-time reader and (hopefully) getting them interested to read more.

How do I write the introduction chapter, you ask? We cover that in detail in this post .

Step 5: Undertake an in-depth literature review

As I mentioned earlier, you’ll need to do some initial review of the literature in Steps 2 and 3 to find your research gap and craft a convincing research proposal – but that’s just scratching the surface. Once you reach the literature review stage of your dissertation or thesis, you need to dig a lot deeper into the existing research and write up a comprehensive literature review chapter.

What’s the literature review all about?

There are two main stages in the literature review process:

Literature Review Step 1: Reading up

The first stage is for you to deep dive into the existing literature (journal articles, textbook chapters, industry reports, etc) to gain an in-depth understanding of the current state of research regarding your topic. While you don’t need to read every single article, you do need to ensure that you cover all literature that is related to your core research questions, and create a comprehensive catalogue of that literature , which you’ll use in the next step.

Reading and digesting all the relevant literature is a time consuming and intellectually demanding process. Many students underestimate just how much work goes into this step, so make sure that you allocate a good amount of time for this when planning out your research. Thankfully, there are ways to fast track the process – be sure to check out this article covering how to read journal articles quickly .

Literature Review Step 2: Writing up

Once you’ve worked through the literature and digested it all, you’ll need to write up your literature review chapter. Many students make the mistake of thinking that the literature review chapter is simply a summary of what other researchers have said. While this is partly true, a literature review is much more than just a summary. To pull off a good literature review chapter, you’ll need to achieve at least 3 things:

• You need to synthesise the existing research , not just summarise it. In other words, you need to show how different pieces of theory fit together, what’s agreed on by researchers, what’s not.
• You need to highlight a research gap that your research is going to fill. In other words, you’ve got to outline the problem so that your research topic can provide a solution.
• You need to use the existing research to inform your methodology and approach to your own research design. For example, you might use questions or Likert scales from previous studies in your your own survey design .

As you can see, a good literature review is more than just a summary of the published research. It’s the foundation on which your own research is built, so it deserves a lot of love and attention. Take the time to craft a comprehensive literature review with a suitable structure .

But, how do I actually write the literature review chapter, you ask? We cover that in detail in this video post .

Step 6: Carry out your own research

Once you’ve completed your literature review and have a sound understanding of the existing research, its time to develop your own research (finally!). You’ll design this research specifically so that you can find the answers to your unique research question.

There are two steps here – designing your research strategy and executing on it:

1 – Design your research strategy

The first step is to design your research strategy and craft a methodology chapter . I won’t get into the technicalities of the methodology chapter here, but in simple terms, this chapter is about explaining the “how” of your research. If you recall, the introduction and literature review chapters discussed the “what” and the “why”, so it makes sense that the next point to cover is the “how” –that’s what the methodology chapter is all about.

In this section, you’ll need to make firm decisions about your research design. This includes things like:

• Your research philosophy (e.g. positivism or interpretivism )
• Your overall methodology (e.g. qualitative , quantitative or mixed methods)
• Your data collection strategy (e.g. interviews , focus groups, surveys)
• Your data analysis strategy (e.g. content analysis , correlation analysis, regression)

If these words have got your head spinning, don’t worry! We’ll explain these in plain language in other posts. It’s not essential that you understand the intricacies of research design (yet!). The key takeaway here is that you’ll need to make decisions about how you’ll design your own research, and you’ll need to describe (and justify) your decisions in your methodology chapter.

2 – Execute: Collect and analyse your data

Once you’ve worked out your research design, you’ll put it into action and start collecting your data. This might mean undertaking interviews, hosting an online survey or any other data collection method. Data collection can take quite a bit of time (especially if you host in-person interviews), so be sure to factor sufficient time into your project plan for this. Oftentimes, things don’t go 100% to plan (for example, you don’t get as many survey responses as you hoped for), so bake a little extra time into your budget here.

Once you’ve collected your data, you’ll need to do some data preparation before you can sink your teeth into the analysis. For example:

• If you carry out interviews or focus groups, you’ll need to transcribe your audio data to text (i.e. a Word document).
• If you collect quantitative survey data, you’ll need to clean up your data and get it into the right format for whichever analysis software you use (for example, SPSS, R or STATA).

Once you’ve completed your data prep, you’ll undertake your analysis, using the techniques that you described in your methodology. Depending on what you find in your analysis, you might also do some additional forms of analysis that you hadn’t planned for. For example, you might see something in the data that raises new questions or that requires clarification with further analysis.

The type(s) of analysis that you’ll use depend entirely on the nature of your research and your research questions. For example:

• If your research if exploratory in nature, you’ll often use qualitative analysis techniques .
• If your research is confirmatory in nature, you’ll often use quantitative analysis techniques
• If your research involves a mix of both, you might use a mixed methods approach

Again, if these words have got your head spinning, don’t worry! We’ll explain these concepts and techniques in other posts. The key takeaway is simply that there’s no “one size fits all” for research design and methodology – it all depends on your topic, your research questions and your data. So, don’t be surprised if your study colleagues take a completely different approach to yours.

Step 7: Present your findings

Once you’ve completed your analysis, it’s time to present your findings (finally!). In a dissertation or thesis, you’ll typically present your findings in two chapters – the results chapter and the discussion chapter .

What’s the difference between the results chapter and the discussion chapter?

While these two chapters are similar, the results chapter generally just presents the processed data neatly and clearly without interpretation, while the discussion chapter explains the story the data are telling  – in other words, it provides your interpretation of the results.

For example, if you were researching the factors that influence consumer trust, you might have used a quantitative approach to identify the relationship between potential factors (e.g. perceived integrity and competence of the organisation) and consumer trust. In this case:

• Your results chapter would just present the results of the statistical tests. For example, correlation results or differences between groups. In other words, the processed numbers.
• Your discussion chapter would explain what the numbers mean in relation to your research question(s). For example, Factor 1 has a weak relationship with consumer trust, while Factor 2 has a strong relationship.

Depending on the university and degree, these two chapters (results and discussion) are sometimes merged into one , so be sure to check with your institution what their preference is. Regardless of the chapter structure, this section is about presenting the findings of your research in a clear, easy to understand fashion.

Importantly, your discussion here needs to link back to your research questions (which you outlined in the introduction or literature review chapter). In other words, it needs to answer the key questions you asked (or at least attempt to answer them).

For example, if we look at the sample research topic:

In this case, the discussion section would clearly outline which factors seem to have a noteworthy influence on organisational trust. By doing so, they are answering the overarching question and fulfilling the purpose of the research .

For more information about the results chapter , check out this post for qualitative studies and this post for quantitative studies .

Step 8: The Final Step Draw a conclusion and discuss the implications

Last but not least, you’ll need to wrap up your research with the conclusion chapter . In this chapter, you’ll bring your research full circle by highlighting the key findings of your study and explaining what the implications of these findings are.

What exactly are key findings? The key findings are those findings which directly relate to your original research questions and overall research objectives (which you discussed in your introduction chapter). The implications, on the other hand, explain what your findings mean for industry, or for research in your area.

Sticking with the consumer trust topic example, the conclusion might look something like this:

Key findings

This study set out to identify which factors influence consumer-based trust in British low-cost online equity brokerage firms. The results suggest that the following factors have a large impact on consumer trust:

While the following factors have a very limited impact on consumer trust:

Notably, within the 25-30 age groups, Factors E had a noticeably larger impact, which may be explained by…

Implications

The findings having noteworthy implications for British low-cost online equity brokers. Specifically:

The large impact of Factors X and Y implies that brokers need to consider….

The limited impact of Factor E implies that brokers need to…

As you can see, the conclusion chapter is basically explaining the “what” (what your study found) and the “so what?” (what the findings mean for the industry or research). This brings the study full circle and closes off the document.

Let’s recap – how to write a dissertation or thesis

You’re still with me? Impressive! I know that this post was a long one, but hopefully you’ve learnt a thing or two about how to write a dissertation or thesis, and are now better equipped to start your own research.

To recap, the 8 steps to writing a quality dissertation (or thesis) are as follows:

• Understand what a dissertation (or thesis) is – a research project that follows the research process.
• Find a unique (original) and important research topic
• Craft a convincing dissertation or thesis research proposal
• Write a clear, compelling introduction chapter
• Undertake a thorough review of the existing research and write up a literature review
• Undertake your own research
• Present and interpret your findings

Once you’ve wrapped up the core chapters, all that’s typically left is the abstract , reference list and appendices. As always, be sure to check with your university if they have any additional requirements in terms of structure or content.  

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

20 Comments

thankfull >>>this is very useful

Thank you, it was really helpful

unquestionably, this amazing simplified way of teaching. Really , I couldn’t find in the literature words that fully explicit my great thanks to you. However, I could only say thanks a-lot.

Great to hear that – thanks for the feedback. Good luck writing your dissertation/thesis.

This is the most comprehensive explanation of how to write a dissertation. Many thanks for sharing it free of charge.

Very rich presentation. Thank you

Thanks Derek Jansen|GRADCOACH, I find it very useful guide to arrange my activities and proceed to research!

Thank you so much for such a marvelous teaching .I am so convinced that am going to write a comprehensive and a distinct masters dissertation

It is an amazing comprehensive explanation

This was straightforward. Thank you!

I can say that your explanations are simple and enlightening – understanding what you have done here is easy for me. Could you write more about the different types of research methods specific to the three methodologies: quan, qual and MM. I look forward to interacting with this website more in the future.

Thanks for the feedback and suggestions 🙂

Hello, your write ups is quite educative. However, l have challenges in going about my research questions which is below; *Building the enablers of organisational growth through effective governance and purposeful leadership.*

Very educating.

Just listening to the name of the dissertation makes the student nervous. As writing a top-quality dissertation is a difficult task as it is a lengthy topic, requires a lot of research and understanding and is usually around 10,000 to 15000 words. Sometimes due to studies, unbalanced workload or lack of research and writing skill students look for dissertation submission from professional writers.

Thank you 💕😊 very much. I was confused but your comprehensive explanation has cleared my doubts of ever presenting a good thesis. Thank you.

thank you so much, that was so useful

Hi. Where is the excel spread sheet ark?

could you please help me look at your thesis paper to enable me to do the portion that has to do with the specification

my topic is “the impact of domestic revenue mobilization.

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Collecting Dissertation Data

Once you have successfully defended your dissertation proposal and have had your study approved by your university’s institutional review board, you are ready to start collecting data for your study. There are many data collection methods, but how you ultimately choose to collect data will depend on the design of your study. Below are some common methods of collecting dissertation data and the types of projects for which these methods are most appropriate.

Online Data Collection

Online data collection has become very popular. Compared to paper-and-pencil surveys, online data collection is much cheaper and less time consuming and allows researchers to recruit participants from a larger geographic area. There are many websites for data collection, such as Survey Monkey and Psych Data, which make the process of collecting data very easy. Given its nature, online dissertation data collection is really only appropriate for quantitative research projects.

Paper-and-Pencil Surveys

Like the name implies, paper-and-pencil surveys are hard copies of your questionnaires that are handed out to participants to complete and return. One advantage of using paper-and-pencil surveys is that participants are more likely to complete paper-and-pencil surveys if surveys are handed to participants and if participants are given time and space to complete the surveys (n.b., you can make use of all the undergraduate classes in which you and your fellow grad students are GAs). The drawback of paper-and-pencil surveys is that you will have to enter the data by hand, which you would not have to do for dissertation data collected online. However, you can always recruit eager undergraduates who want to get into grad school to help you enter the data. As with online data collection, paper-and-pencil surveys are only appropriate for quantitative research projects.

Interviews/Focus Groups

If your project is qualitative in nature, you will likely need to conduct interviews or focus groups to collect the dissertation data you need. Once you have conducted your interviews or focus groups, you will need to go back and transcribe them verbatim, which is also a rather time-consuming process. Again, you can enlist undergraduates to help you transcribe your interviews.

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Database Information in References

Database information is seldom provided in reference list entries. The reference provides readers with the details they will need to perform a search themselves if they want to read the work—in most cases, writers do not need to explain the path they personally used.

Think of it this way: When you buy a book at a bookstore or order a copy off the internet, you do not write the name of the (online) bookstore in the reference. And when you go to the library and get a book off the shelf, you do not write the name of the library in the reference. It is understood that readers will go to their bookstore or library of choice to find it.

The same is true for database information in references. Most periodicals and books are available through a variety of databases or platforms as well as in print. Different readers will have different methods or points of access, such as university library subscriptions. Most of the time, it does not matter what database you used, so it is not necessary to provide database information in references.

However, there are a few cases when it is necessary for readers to retrieve the cited work from a particular database or archive, either because the database publishes original, proprietary content or because the work is of limited circulation. This page explains how to write references for works from academic research databases and how to provide database information in references when it is necessary to do so.

Database information in references is covered in the seventh edition APA Style manuals in the Publication Manual Section 9.30 and the Concise Guide Section 9.30

Related handout

• Creating an APA Style Reference List (PDF, 179KB)

Works from academic research databases

Do not include database information for works obtained from most academic research databases or platforms because works in these resources are widely available. This includes journal articles, books, and book chapters from academic research databases.

• Examples of academic research databases and platforms include APA PsycNet, PsycInfo, Academic Search Complete, CINAHL, Ebook Central, EBSCOhost, Google Scholar, JSTOR (excluding its primary sources collection because these are works of limited distribution), MEDLINE, Nexis Uni, Ovid, ProQuest (excluding its dissertations and theses databases because dissertations and theses are works of limited circulation), PubMed Central (excluding authors’ final peer-reviewed manuscripts because these are works of limited circulation), ScienceDirect, Scopus, and Web of Science.
• When citing a work from one of these databases or platforms, do not include the database or platform name in the reference list entry unless the work falls under one of the exceptions described next ( databases with original, proprietary content and works of limited circulation ).
• Likewise, do not include URLs from these academic research databases in reference list entries because these URLs will not resolve for readers.
• Instead of a database URL, include a DOI if the work has one. If a widely available work (e.g., journal article, book, book chapter) from an academic research database does not have a DOI, treat the work as a print version. See the guidelines for how to include DOIs and URLs in references for more information.

The following example shows how to create a reference list entry for a journal article with a DOI from an academic research database.

Hallion, M., Taylor, A., Roberts, R., & Ashe, M. (2019). Exploring the association between physical activity participation and self-compassion in middle-aged adults. Sport, Exercise, and Performance Psychology , 8 (3), 305–316. https://doi.org/10.1037/spy0000150

• Parenthetical citation: (Hallion et al., 2019)
• Narrative citation: Hallion et al. (2019)

If the article did not have a DOI, the reference would simply end after the page range, the same as the reference for a print work.

Databases with original, proprietary content

Provide the name of the database or archive when it publishes original, proprietary works available only in that database or archive (e.g., UpToDate or the Cochrane Database of Systematic Reviews). Readers must retrieve the cited work from that exact database or archive, so include information about the database or archive in the reference list entry.

References for works from proprietary databases are similar to journal article references. The name of the database or archive is written in italic title case in the source element, the same as a periodical title, and followed by a period. After the database or archive information, also provide the DOI or URL of the work . If the URL is session-specific (meaning it will not resolve for readers), provide the URL of the database home page or login page instead.

The following example shows how to create a reference list entry for an article from the UpToDate database:

Stein, M. B., & Taylor, C. T. (2019). Approach to treating social anxiety disorder in adults. UpToDate . Retrieved September 13, 2019, from https://www.uptodate.com/contents/approach-to-treating-social-anxiety-disorder-in-adults

• Parenthetical citation: (Stein & Taylor, 2019)
• Narrative citation: Stein and Taylor (2019)

Works of limited circulation

Provide the name of the database or archive for works of limited circulation, such as dissertations and theses, manuscripts posted in a preprint archive, and monographs in ERIC. The database may also contain works of wide circulation, such as journal articles—only the works of limited circulation need database information in the reference.

References for works of limited circulation from databases or archives are similar to report references. The name of the database or archive is provided in the source element (in title case without italics ), the same as a publisher name, and followed by a period. After the database or archive information, also provide the DOI or URL of the work. If the URL is session-specific (meaning it will not resolve for readers), provide the URL of the database home page or login page instead.

The following are examples of works of limited circulation from databases or archives (for additional examples, see Section 9.30 of the Publication Manual ):

• dissertations and theses published in ProQuest Dissertations and Theses Global

Risto, A. (2014). The impact of social media and texting on students’ academic writing skills (Publication No. 3683242) [Doctoral dissertation, Tennessee State University]. ProQuest Dissertations and Theses Global.

• Parenthetical citation: (Risto, 2014)
• Narrative citation: Risto (2014)
• manuscripts posted in a preprint archive such as PsyArXiv

Inbar, Y., & Evers, E. R. K. (2019). Worse is bad: Divergent inferences from logically equivalent comparisons . PsyArXiv. https://doi.org/10.31234/osf.io/ueymx

• Parenthetical citation: (Inbar & Evers, 2014)
• Narrative citation: Inbar and Evers (2014)
• monographs published in ERIC

Riegelman, R. K., & Albertine, S. (2008). Recommendations for undergraduate public health education (ED504790). ERIC. https://files.eric.ed.gov/fulltext/ED504790.pdf

• Parenthetical citation: (Riegelman & Albertine, 2008)
• Narrative citation: Riegelman and Albertine (2008)

If you are in doubt as to whether to include database information in a reference, refer to the template for the reference type in question (see Chapter 10 of the Publication Manual ).

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Table of Contents

A dissertation and thesis serve similar purposes but have key differences that students should understand. Read on to learn everything from definitions to tips for completing these major academic papers.

Defining Dissertation and Thesis

A dissertation is a lengthy, original research document completed by doctoral candidates, typically over 18 months, to earn a PhD. Dissertations are generally between 50,000 and 100,000 words presenting the student’s findings and analysis.

A thesis is a shorter research paper completed by master’s degree candidates, usually between 40,000 and 60,000 words. These may contain original research but rely more heavily on existing studies than dissertations.

Contrasting the Purpose and Length 

The primary purpose of a dissertation is to contribute new knowledge or analysis as part of earning a doctoral degree. These also demonstrate expertise but focus on earning a master’s degree and do not necessarily require original research.

Dissertations are considerably longer as doctoral candidates are required to conduct extensive research over an extended period, often 18 months or more. Thesis are typically shorter since the research component is less intensive for a master’s program.

Structural and Research Variations

Both a dissertation and a thesis have a standard structure, but a dissertation has more required sections that are broader in scope. Dissertations include an extensive literature review of previous research, thoroughly explaining the chosen methodology, and discussing research findings. Dissertations always involve substantial original research conducted independently by the doctoral candidate to make a novel academic contribution and are rigorously reviewed by the dissertation committee.

Students may incorporate original research and analysis for a thesis but rely more heavily on synthesizing and building upon existing studies and theoretical frameworks. Thises are narrower in focus compared to dissertations. While original research is encouraged, it is not an absolute requirement in all master’s programs. Theses undergo review by advisors, but the process is less intensive than with doctoral dissertations subject to oral defense.

Expert Tips for Completing Your Paper

Successfully planning and completing a thesis or dissertation requires dedication and organization. Here are some tips:

• Create a realistic writing schedule with daily word count goals to help you progress consistently. Breaking the large project into smaller milestones can make it feel more manageable.
• Regularly consult your academic advisor or committee members regularly for guidance on your research and analysis. Their feedback can help you refine your approach and arguments.
• Carefully document all sources and information included to avoid unintentional plagiarism allegations. Develop strong note-taking skills early in the process.
• Avoid burnout by scheduling regular breaks during writing sessions and taking time away from the project daily. The long duration can lead to fatigue, so find ways to recharge.
• Be prepared to write multiple drafts over time. As you learn more, you may need to revise sections to strengthen your analysis, argumentation, flow, and structure.
• Craft your introduction once the body of the paper is complete. This allows you to comprehensively summarize and frame the content most effectively for readers.

Conclusion 

A dissertation and thesis have key differences that research students should be aware of in terms of length, purpose, structure, and research requirements. By understanding these distinctions, students can be better prepared to complete their culminating paper successfully.

For working professionals looking to earn a PhD degree and make original contributions to their field, upGrad offers online doctoral programs without requiring an 18 month on-campus residential requirement and necessary assignments like dissertation. 

1. What is the main difference between a dissertation and a thesis?

A dissertation is longer, requires extensive original research, and leads to a PhD. A thesis is shorter, may incorporate original research, and leads to a master’s degree.

2. What are the critical sections of a dissertation?

Key sections include literature review, methodology, findings, discussion, and conclusion.

3. Does a thesis require an oral defense?

No, an oral defense is only required for doctoral dissertations.

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Jobs report revision: US added 818,000 fewer jobs than believed

The labor market last year seemed to shrug off historically high interest rates and inflation , gaining well over 200,000 jobs a month.

Turns out the nation’s jobs engine wasn’t quite as invincible as it appeared.

The U.S. Bureau of Labor Statistics on Wednesday revised down its estimate of total employment in March 2024 by a whopping 818,000, the largest such downgrade in 15 years. That effectively means there were 818,000 fewer job gains than first believed from April 2023 through March 2024.

So, instead of adding a robust average of 242,000 jobs a month during that 12-month period, the nation gained a still solid 174,000 jobs monthly, according to the latest estimate.

The revision is based on the Quarterly Census of Employment and Wages, which draws from state unemployment insurance records that reflect actual payrolls, while the prior estimates come from monthly surveys. However, the estimate is preliminary and a final figure will be released early next year.

The largest downward revision was in professional and business services, with estimated payrolls lowered by 358,000, followed by a 150,000 downgrade in leisure and hospitality and 115,000 in manufacturing.

Is the Fed expected to lower interest rates?

The significantly cooler labor market depicted by the revisions could affect the thinking of Federal Reserve officials as they weigh when – and by how much – to lower interest rates now that inflation is easing. Many economists expect the Fed to reduce rates by a quarter percentage point next month, though some anticipated a half-point cut following a report early this month that showed just 114,000 job gains in July.

Wednesday’s revisions underscore that the labor market could have been softening for a much longer period than previously thought.

Is the US in recession right now?

Although the new estimates don't mean the nation is in a recession,  “it does signal we should expect monthly job growth to be more muted and put extra pressure on the Fed to cut rates,” economist Robert Frick of Navy Federal Credit Union wrote in a note to clients..

Some economists, however, are questioning the fresh figures. Goldman Sachs said the revision was likely overstated by as much as 400,000 to 600,000 because unemployment insurance records don’t include immigrants lacking permanent legal status, who have contributed dramatically to job growth the past couple of years.

Based on estimates before Wednesday's revisions, about 1 million jobs, or a third of those added last year, likely went to newly arrived immigrants, including many who entered the country illegally, RBC Capital Markets estimates.

Also, the Quarterly Census of Employment and Wages itself has been revised up every quarter since 2019 by an average of 100,000, Goldman says. In other words, Wednesday's downward revision could turn out to be notably smaller when the final figures are published early next year.

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CVE-2024-8005 Detail A vulnerability was found in demozx gf_cms 1.0/1.0.1. It has been classified as critical. This affects the function init of the file internal/logic/auth/auth.go of the component JWT Authentication. The manipulation leads to hard-coded credentials. It is possible to initiate the attack remotely. The exploit has been disclosed to the public and may be used. Upgrading to version 1.0.2 is able to address this issue. The patch is named be702ada7cb6fdabc02689d90b38139c827458a5. It is recommended to upgrade the affected component.    NVD   VulDB   CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:L/VI:L/VA:L/SC:N/SI:N/SA:N  NVD     CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H   VulDB     CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:L -->  NVD     VulDB     (AV:N/AC:L/Au:N/C:P/I:P/A:P) --> By selecting these links, you will be leaving NIST webspace. We have provided these links to other web sites because they may have information that would be of interest to you. No inferences should be drawn on account of other sites being referenced, or not, from this page. There may be other web sites that are more appropriate for your purpose. NIST does not necessarily endorse the views expressed, or concur with the facts presented on these sites. Further, NIST does not endorse any commercial products that may be mentioned on these sites. Please address comments about this page to . Hyperlink Resource Patch  Patch  Issue Tracking  Issue Tracking  Permissions Required  Third Party Advisory  VDB Entry  Third Party Advisory  VDB Entry  Third Party Advisory  VDB Entry  Weakness Enumeration CWE-ID CWE Name Source Use of Hard-coded Credentials VulDB   Known Affected Software Configurations Switch to CPE 2.2 Cpes loading, please wait.. Denotes Vulnerable Software Are we missing a CPE here? Please let us know . Change History Initial analysis by nist 8/21/2024 11:49:59 am. Action Type Old Value New Value Added CPE Configuration Added CVSS V3.1 Changed Reference Type Changed Reference Type Changed Reference Type Changed Reference Type Changed Reference Type Changed Reference Type Changed Reference Type New CVE Received by NIST 8/20/2024 10:15:11 AM Action Type Old Value New Value Added CVSS V2 Added CVSS V3.1 Added CVSS V4.0 Added CWE Added Description Added Reference Added Reference Added Reference Added Reference Added Reference Added Reference Added Reference InfoQ Software Architects' Newsletter A monthly overview of things you need to know as an architect or aspiring architect. View an example We protect your privacy. QCon San Francisco (Nov 18-22): Get assurance you’re adopting the right software practices. Register Now Facilitating the Spread of Knowledge and Innovation in Professional Software Development English edition Chinese edition Japanese edition French edition Back to login Login with: Don't have an infoq account, helpful links. About InfoQ InfoQ Editors Write for InfoQ About C4Media Choose your language Get clarity from senior software practitioners on today's critical dev priorities. Register Now. Level up your software skills by uncovering the emerging trends you should focus on. Register now. Discover emerging trends, insights, and real-world best practices in software development & tech leadership. Join now. Your monthly guide to all the topics, technologies and techniques that every professional needs to know about. Subscribe for free. InfoQ Homepage News AWS Introduces Logically Air-Gapped Vault for Enhanced Data Security AWS Introduces Logically Air-Gapped Vault for Enhanced Data Security Aug 21, 2024 2 min read Steef-Jan Wiggers AWS recently announced the public preview of AWS Backup logically air-gapped vault , a new type of vault that can be shared for recovery with other accounts using AWS Resource Access Manager (RAM). During a service restore, one key customer need is the ability to share recovery points stored in AWS Backup with other accounts, including cross-organization sharing, for quicker direct restore. Additionally, customers require maintaining access to the original AWS Key Management Service (AWS KMS) Customer Managed Key (CMK) used to encrypt the recovery point. The new AWS Backup logically air-gapped vault aims to fulfill these customer needs by providing a new type of vault that can be shared for recovery with other accounts using AWS RAM. The logically air-gapped vault from AWS Backup works by introducing immutable backup copies that are locked by default and further protected through encryption using AWS-owned keys . By employing an AWS Backup owned KMS key to encrypt recovery points, customers can mitigate the risk of accidental or unwanted deletions of customer managed keys. Moreover, the new feature simplifies sharing backup data with other accounts for restore purposes. Aabith Venkitachalapathy, an enterprise solutions architect at AWS, writes: Employing an AWS Backup owned KMS key to encrypt recovery points helps customers with accidental or unwanted deletions of customer managed keys. Customers can leverage AWS RAM to share the vault data with specific accounts, including cross-organization sharing, for faster direct restore. Once the vault is shared, backups can be directly restored, eliminating the step of copying backups into the destination account first. This capability reduces operational overhead, minimizes the time to recover from a data loss event, and lowers the cost of extra copies. To create a new logically air-gapped vault in AWS Backup, users can log in to the AWS Management Console, open the AWS Backup console, select "Create logically air-gapped vaults" from the Vaults menu, enter the required details, and then view the newly created vault under "Vaults owned by this account" using the "Search by vault name" filter. Other options for creating a new logically air-gapped vault in AWS Backup include using the API or CLI . Create a vault to complete the logically air-gapped vault creation process (Source: AWS News blog post ) Once the logically air-gapped vault has been created, users can find a recovery point to be copied into the new vault or use the new vault as a copy destination in a backup plan. They can also set up the logically air-gapped vault as the destination of a Copy operation using an AWS Backup plan rule to establish automated data protection strategies. In an AWS Storage blog post , the authors conclude: The key benefits of using an AWS Backup logically air-gapped vault are that it can significantly decrease recovery time and operational overhead and streamline recovery testing by providing the ability to share the vault across organizations and accounts. Furthermore, it offers heightened protection by automatically locking the vault in compliance mode and preventing accidental deletion of encryption keys by encrypting the vault using an AWS-owned key. These benefits are especially relevant as ransomware remains top of mind and can be used for highly sensitive workloads. Lastly, the AWS Backup support for logically air-gapped vaults is available in the various AWS regions . About the Author Rate this article, this content is in the cloud topic, related topics:. Development Architecture & Design Cloud Security Amazon Web Services Related Editorial Related sponsored content, fast, lean, and unbreakable: build cloud apps with akka and kubernetes, related sponsor. Build, Operate, and Secure Distributed Applications. Learn More . Related Content The infoq newsletter. A round-up of last week’s content on InfoQ sent out every Tuesday. Join a community of over 250,000 senior developers. 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Dissertations are generally between 50,000 and 100,000 words presenting the student's findings and analysis. Jobs report revision: US added 818,000 fewer jobs than believed The labor market last year seemed to shrug off historically high interest rates and inflation, gaining well over 200,000 jobs a month. Turns out the nation's jobs engine wasn't quite as ... SEC.gov The Securities and Exchange Commission today announced settled charges against New York-based registered transfer agent Equiniti Trust Company LLC, formerly known as American Stock Transfer & Trust Company LLC, for failing to assure that client securities and funds were protected against theft or misuse. Nvd National Vulnerability Database NVD. Vulnerabilities; NOTICE UPDATED - May, 29th 2024. The NVD has a new announcement page with status updates, news, and how to stay connected! CVE-2024-42568 Detail Description . School Management System commit bae5aa was discovered to contain a SQL injection vulnerability via the transport parameter at vehicle ... Nvd National Vulnerability Database NVD. Vulnerabilities; NOTICE UPDATED - May, 29th 2024. The NVD has a new announcement page with status updates, news, and how to stay connected! CVE-2024-8005 Detail Description . A vulnerability was found in demozx gf_cms 1.0/1.0.1. It has been classified as critical. Create a Sandbox For a Partial Copy sandbox, click Next, and then select the template you created to specify the data for your sandbox.If you haven't created a template for this Partial Copy sandbox, see Create or Edit Sandbox Templates.; For a Full sandbox click Next, and then decide how much data to include.. To include template-based data for a Full sandbox, select an existing sandbox template. AWS Introduces Logically Air-Gapped Vault for Enhanced Data AWS recently announced the public preview of AWS Backup logically air-gapped vault, a new type of vault that can be shared for recovery with other accounts using AWS Resource Access Manager (RAM). assignment essay homework paper phd resume review speech thesis