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How to Write a Dissertation Proposal | A Step-by-Step Guide

Published on 14 February 2020 by Jack Caulfield . Revised on 11 November 2022.

A dissertation proposal describes the research you want to do: what it’s about, how you’ll conduct it, and why it’s worthwhile. You will probably have to write a proposal before starting your dissertation as an undergraduate or postgraduate student.

A dissertation proposal should generally include:

• An introduction to your topic and aims
• A literature review  of the current state of knowledge
• An outline of your proposed methodology
• A discussion of the possible implications of the research
• A bibliography  of relevant sources

Dissertation proposals vary a lot in terms of length and structure, so make sure to follow any guidelines given to you by your institution, and check with your supervisor when you’re unsure.

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Table of contents

Step 1: coming up with an idea, step 2: presenting your idea in the introduction, step 3: exploring related research in the literature review, step 4: describing your methodology, step 5: outlining the potential implications of your research, step 6: creating a reference list or bibliography.

Before writing your proposal, it’s important to come up with a strong idea for your dissertation.

Find an area of your field that interests you and do some preliminary reading in that area. What are the key concerns of other researchers? What do they suggest as areas for further research, and what strikes you personally as an interesting gap in the field?

Once you have an idea, consider how to narrow it down and the best way to frame it. Don’t be too ambitious or too vague – a dissertation topic needs to be specific enough to be feasible. Move from a broad field of interest to a specific niche:

• Russian literature 19th century Russian literature The novels of Tolstoy and Dostoevsky
• Social media Mental health effects of social media Influence of social media on young adults suffering from anxiety

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Like most academic texts, a dissertation proposal begins with an introduction . This is where you introduce the topic of your research, provide some background, and most importantly, present your aim , objectives and research question(s) .

Try to dive straight into your chosen topic: What’s at stake in your research? Why is it interesting? Don’t spend too long on generalisations or grand statements:

• Social media is the most important technological trend of the 21st century. It has changed the world and influences our lives every day.
• Psychologists generally agree that the ubiquity of social media in the lives of young adults today has a profound impact on their mental health. However, the exact nature of this impact needs further investigation.

Once your area of research is clear, you can present more background and context. What does the reader need to know to understand your proposed questions? What’s the current state of research on this topic, and what will your dissertation contribute to the field?

If you’re including a literature review, you don’t need to go into too much detail at this point, but give the reader a general sense of the debates that you’re intervening in.

This leads you into the most important part of the introduction: your aim, objectives and research question(s) . These should be clearly identifiable and stand out from the text – for example, you could present them using bullet points or bold font.

Make sure that your research questions are specific and workable – something you can reasonably answer within the scope of your dissertation. Avoid being too broad or having too many different questions. Remember that your goal in a dissertation proposal is to convince the reader that your research is valuable and feasible:

• Does social media harm mental health?
• What is the impact of daily social media use on 18– to 25–year–olds suffering from general anxiety disorder?

Now that your topic is clear, it’s time to explore existing research covering similar ideas. This is important because it shows you what is missing from other research in the field and ensures that you’re not asking a question someone else has already answered.

You’ve probably already done some preliminary reading, but now that your topic is more clearly defined, you need to thoroughly analyse and evaluate the most relevant sources in your literature review .

Here you should summarise the findings of other researchers and comment on gaps and problems in their studies. There may be a lot of research to cover, so make effective use of paraphrasing to write concisely:

• Smith and Prakash state that ‘our results indicate a 25% decrease in the incidence of mechanical failure after the new formula was applied’.
• Smith and Prakash’s formula reduced mechanical failures by 25%.

The point is to identify findings and theories that will influence your own research, but also to highlight gaps and limitations in previous research which your dissertation can address:

• Subsequent research has failed to replicate this result, however, suggesting a flaw in Smith and Prakash’s methods. It is likely that the failure resulted from…

Next, you’ll describe your proposed methodology : the specific things you hope to do, the structure of your research and the methods that you will use to gather and analyse data.

You should get quite specific in this section – you need to convince your supervisor that you’ve thought through your approach to the research and can realistically carry it out. This section will look quite different, and vary in length, depending on your field of study.

You may be engaged in more empirical research, focusing on data collection and discovering new information, or more theoretical research, attempting to develop a new conceptual model or add nuance to an existing one.

Dissertation research often involves both, but the content of your methodology section will vary according to how important each approach is to your dissertation.

Empirical research

Empirical research involves collecting new data and analysing it in order to answer your research questions. It can be quantitative (focused on numbers), qualitative (focused on words and meanings), or a combination of both.

With empirical research, it’s important to describe in detail how you plan to collect your data:

• Will you use surveys ? A lab experiment ? Interviews?
• What variables will you measure?
• How will you select a representative sample ?
• If other people will participate in your research, what measures will you take to ensure they are treated ethically?
• What tools (conceptual and physical) will you use, and why?

It’s appropriate to cite other research here. When you need to justify your choice of a particular research method or tool, for example, you can cite a text describing the advantages and appropriate usage of that method.

Don’t overdo this, though; you don’t need to reiterate the whole theoretical literature, just what’s relevant to the choices you have made.

Moreover, your research will necessarily involve analysing the data after you have collected it. Though you don’t know yet what the data will look like, it’s important to know what you’re looking for and indicate what methods (e.g. statistical tests , thematic analysis ) you will use.

Theoretical research

You can also do theoretical research that doesn’t involve original data collection. In this case, your methodology section will focus more on the theory you plan to work with in your dissertation: relevant conceptual models and the approach you intend to take.

For example, a literary analysis dissertation rarely involves collecting new data, but it’s still necessary to explain the theoretical approach that will be taken to the text(s) under discussion, as well as which parts of the text(s) you will focus on:

• This dissertation will utilise Foucault’s theory of panopticism to explore the theme of surveillance in Orwell’s 1984 and Kafka’s The Trial…

Here, you may refer to the same theorists you have already discussed in the literature review. In this case, the emphasis is placed on how you plan to use their contributions in your own research.

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You’ll usually conclude your dissertation proposal with a section discussing what you expect your research to achieve.

You obviously can’t be too sure: you don’t know yet what your results and conclusions will be. Instead, you should describe the projected implications and contribution to knowledge of your dissertation.

First, consider the potential implications of your research. Will you:

• Develop or test a theory?
• Provide new information to governments or businesses?
• Challenge a commonly held belief?
• Suggest an improvement to a specific process?

Describe the intended result of your research and the theoretical or practical impact it will have:

Finally, it’s sensible to conclude by briefly restating the contribution to knowledge you hope to make: the specific question(s) you hope to answer and the gap the answer(s) will fill in existing knowledge:

Like any academic text, it’s important that your dissertation proposal effectively references all the sources you have used. You need to include a properly formatted reference list or bibliography at the end of your proposal.

Different institutions recommend different styles of referencing – commonly used styles include Harvard , Vancouver , APA , or MHRA . If your department does not have specific requirements, choose a style and apply it consistently.

A reference list includes only the sources that you cited in your proposal. A bibliography is slightly different: it can include every source you consulted in preparing the proposal, even if you didn’t mention it in the text. In the case of a dissertation proposal, a bibliography may also list relevant sources that you haven’t yet read, but that you intend to use during the research itself.

Check with your supervisor what type of bibliography or reference list you should include.

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Caulfield, J. (2022, November 11). How to Write a Dissertation Proposal | A Step-by-Step Guide. Scribbr. Retrieved 15 April 2024, from https://www.scribbr.co.uk/thesis-dissertation/proposal/

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How To Write The Results/Findings Chapter

For quantitative studies (dissertations & theses).

By: Derek Jansen (MBA). Expert Reviewed By: Kerryn Warren (PhD) | July 2021

So, you’ve completed your quantitative data analysis and it’s time to report on your findings. But where do you start? In this post, we’ll walk you through the results chapter (also called the findings or analysis chapter), step by step, so that you can craft this section of your dissertation or thesis with confidence. If you’re looking for information regarding the results chapter for qualitative studies, you can find that here .

Overview: Quantitative Results Chapter

• What exactly the results/findings/analysis chapter is
• What you need to include in your results chapter
• How to structure your results chapter
• A few tips and tricks for writing top-notch chapter

What exactly is the results chapter?

The results chapter (also referred to as the findings or analysis chapter) is one of the most important chapters of your dissertation or thesis because it shows the reader what you’ve found in terms of the quantitative data you’ve collected. It presents the data using a clear text narrative, supported by tables, graphs and charts. In doing so, it also highlights any potential issues (such as outliers or unusual findings) you’ve come across.

But how’s that different from the discussion chapter?

Well, in the results chapter, you only present your statistical findings. Only the numbers, so to speak – no more, no less. Contrasted to this, in the discussion chapter , you interpret your findings and link them to prior research (i.e. your literature review), as well as your research objectives and research questions . In other words, the results chapter presents and describes the data, while the discussion chapter interprets the data.

Let’s look at an example.

In your results chapter, you may have a plot that shows how respondents to a survey  responded: the numbers of respondents per category, for instance. You may also state whether this supports a hypothesis by using a p-value from a statistical test. But it is only in the discussion chapter where you will say why this is relevant or how it compares with the literature or the broader picture. So, in your results chapter, make sure that you don’t present anything other than the hard facts – this is not the place for subjectivity.

It’s worth mentioning that some universities prefer you to combine the results and discussion chapters. Even so, it is good practice to separate the results and discussion elements within the chapter, as this ensures your findings are fully described. Typically, though, the results and discussion chapters are split up in quantitative studies. If you’re unsure, chat with your research supervisor or chair to find out what their preference is.

What should you include in the results chapter?

Following your analysis, it’s likely you’ll have far more data than are necessary to include in your chapter. In all likelihood, you’ll have a mountain of SPSS or R output data, and it’s your job to decide what’s most relevant. You’ll need to cut through the noise and focus on the data that matters.

This doesn’t mean that those analyses were a waste of time – on the contrary, those analyses ensure that you have a good understanding of your dataset and how to interpret it. However, that doesn’t mean your reader or examiner needs to see the 165 histograms you created! Relevance is key.

How do I decide what’s relevant?

At this point, it can be difficult to strike a balance between what is and isn’t important. But the most important thing is to ensure your results reflect and align with the purpose of your study .  So, you need to revisit your research aims, objectives and research questions and use these as a litmus test for relevance. Make sure that you refer back to these constantly when writing up your chapter so that you stay on track.

As a general guide, your results chapter will typically include the following:

• Some demographic data about your sample
• Reliability tests (if you used measurement scales)
• Descriptive statistics
• Inferential statistics (if your research objectives and questions require these)
• Hypothesis tests (again, if your research objectives and questions require these)

We’ll discuss each of these points in more detail in the next section.

Importantly, your results chapter needs to lay the foundation for your discussion chapter . This means that, in your results chapter, you need to include all the data that you will use as the basis for your interpretation in the discussion chapter.

For example, if you plan to highlight the strong relationship between Variable X and Variable Y in your discussion chapter, you need to present the respective analysis in your results chapter – perhaps a correlation or regression analysis.

Need a helping hand?

How do I write the results chapter?

There are multiple steps involved in writing up the results chapter for your quantitative research. The exact number of steps applicable to you will vary from study to study and will depend on the nature of the research aims, objectives and research questions . However, we’ll outline the generic steps below.

Step 1 – Revisit your research questions

The first step in writing your results chapter is to revisit your research objectives and research questions . These will be (or at least, should be!) the driving force behind your results and discussion chapters, so you need to review them and then ask yourself which statistical analyses and tests (from your mountain of data) would specifically help you address these . For each research objective and research question, list the specific piece (or pieces) of analysis that address it.

At this stage, it’s also useful to think about the key points that you want to raise in your discussion chapter and note these down so that you have a clear reminder of which data points and analyses you want to highlight in the results chapter. Again, list your points and then list the specific piece of analysis that addresses each point. 

Next, you should draw up a rough outline of how you plan to structure your chapter . Which analyses and statistical tests will you present and in what order? We’ll discuss the “standard structure” in more detail later, but it’s worth mentioning now that it’s always useful to draw up a rough outline before you start writing (this advice applies to any chapter).

Step 2 – Craft an overview introduction

As with all chapters in your dissertation or thesis, you should start your quantitative results chapter by providing a brief overview of what you’ll do in the chapter and why . For example, you’d explain that you will start by presenting demographic data to understand the representativeness of the sample, before moving onto X, Y and Z.

This section shouldn’t be lengthy – a paragraph or two maximum. Also, it’s a good idea to weave the research questions into this section so that there’s a golden thread that runs through the document.

Step 3 – Present the sample demographic data

The first set of data that you’ll present is an overview of the sample demographics – in other words, the demographics of your respondents.

For example:

• What age range are they?
• How is gender distributed?
• How is ethnicity distributed?
• What areas do the participants live in?

The purpose of this is to assess how representative the sample is of the broader population. This is important for the sake of the generalisability of the results. If your sample is not representative of the population, you will not be able to generalise your findings. This is not necessarily the end of the world, but it is a limitation you’ll need to acknowledge.

Of course, to make this representativeness assessment, you’ll need to have a clear view of the demographics of the population. So, make sure that you design your survey to capture the correct demographic information that you will compare your sample to.

But what if I’m not interested in generalisability?

Well, even if your purpose is not necessarily to extrapolate your findings to the broader population, understanding your sample will allow you to interpret your findings appropriately, considering who responded. In other words, it will help you contextualise your findings . For example, if 80% of your sample was aged over 65, this may be a significant contextual factor to consider when interpreting the data. Therefore, it’s important to understand and present the demographic data.

 Step 4 – Review composite measures and the data “shape”.

Before you undertake any statistical analysis, you’ll need to do some checks to ensure that your data are suitable for the analysis methods and techniques you plan to use. If you try to analyse data that doesn’t meet the assumptions of a specific statistical technique, your results will be largely meaningless. Therefore, you may need to show that the methods and techniques you’ll use are “allowed”.

Most commonly, there are two areas you need to pay attention to:

#1: Composite measures

The first is when you have multiple scale-based measures that combine to capture one construct – this is called a composite measure .  For example, you may have four Likert scale-based measures that (should) all measure the same thing, but in different ways. In other words, in a survey, these four scales should all receive similar ratings. This is called “ internal consistency ”.

Internal consistency is not guaranteed though (especially if you developed the measures yourself), so you need to assess the reliability of each composite measure using a test. Typically, Cronbach’s Alpha is a common test used to assess internal consistency – i.e., to show that the items you’re combining are more or less saying the same thing. A high alpha score means that your measure is internally consistent. A low alpha score means you may need to consider scrapping one or more of the measures.

#2: Data shape

The second matter that you should address early on in your results chapter is data shape. In other words, you need to assess whether the data in your set are symmetrical (i.e. normally distributed) or not, as this will directly impact what type of analyses you can use. For many common inferential tests such as T-tests or ANOVAs (we’ll discuss these a bit later), your data needs to be normally distributed. If it’s not, you’ll need to adjust your strategy and use alternative tests.

To assess the shape of the data, you’ll usually assess a variety of descriptive statistics (such as the mean, median and skewness), which is what we’ll look at next.

Step 5 – Present the descriptive statistics

Now that you’ve laid the foundation by discussing the representativeness of your sample, as well as the reliability of your measures and the shape of your data, you can get started with the actual statistical analysis. The first step is to present the descriptive statistics for your variables.

For scaled data, this usually includes statistics such as:

• The mean – this is simply the mathematical average of a range of numbers.
• The median – this is the midpoint in a range of numbers when the numbers are arranged in order.
• The mode – this is the most commonly repeated number in the data set.
• Standard deviation – this metric indicates how dispersed a range of numbers is. In other words, how close all the numbers are to the mean (the average).
• Skewness – this indicates how symmetrical a range of numbers is. In other words, do they tend to cluster into a smooth bell curve shape in the middle of the graph (this is called a normal or parametric distribution), or do they lean to the left or right (this is called a non-normal or non-parametric distribution).
• Kurtosis – this metric indicates whether the data are heavily or lightly-tailed, relative to the normal distribution. In other words, how peaked or flat the distribution is.

A large table that indicates all the above for multiple variables can be a very effective way to present your data economically. You can also use colour coding to help make the data more easily digestible.

For categorical data, where you show the percentage of people who chose or fit into a category, for instance, you can either just plain describe the percentages or numbers of people who responded to something or use graphs and charts (such as bar graphs and pie charts) to present your data in this section of the chapter.

When using figures, make sure that you label them simply and clearly , so that your reader can easily understand them. There’s nothing more frustrating than a graph that’s missing axis labels! Keep in mind that although you’ll be presenting charts and graphs, your text content needs to present a clear narrative that can stand on its own. In other words, don’t rely purely on your figures and tables to convey your key points: highlight the crucial trends and values in the text. Figures and tables should complement the writing, not carry it .

Depending on your research aims, objectives and research questions, you may stop your analysis at this point (i.e. descriptive statistics). However, if your study requires inferential statistics, then it’s time to deep dive into those .

Step 6 – Present the inferential statistics

Inferential statistics are used to make generalisations about a population , whereas descriptive statistics focus purely on the sample . Inferential statistical techniques, broadly speaking, can be broken down into two groups .

First, there are those that compare measurements between groups , such as t-tests (which measure differences between two groups) and ANOVAs (which measure differences between multiple groups). Second, there are techniques that assess the relationships between variables , such as correlation analysis and regression analysis. Within each of these, some tests can be used for normally distributed (parametric) data and some tests are designed specifically for use on non-parametric data.

There are a seemingly endless number of tests that you can use to crunch your data, so it’s easy to run down a rabbit hole and end up with piles of test data. Ultimately, the most important thing is to make sure that you adopt the tests and techniques that allow you to achieve your research objectives and answer your research questions .

In this section of the results chapter, you should try to make use of figures and visual components as effectively as possible. For example, if you present a correlation table, use colour coding to highlight the significance of the correlation values, or scatterplots to visually demonstrate what the trend is. The easier you make it for your reader to digest your findings, the more effectively you’ll be able to make your arguments in the next chapter.

Step 7 – Test your hypotheses

If your study requires it, the next stage is hypothesis testing. A hypothesis is a statement , often indicating a difference between groups or relationship between variables, that can be supported or rejected by a statistical test. However, not all studies will involve hypotheses (again, it depends on the research objectives), so don’t feel like you “must” present and test hypotheses just because you’re undertaking quantitative research.

The basic process for hypothesis testing is as follows:

• Specify your null hypothesis (for example, “The chemical psilocybin has no effect on time perception).
• Specify your alternative hypothesis (e.g., “The chemical psilocybin has an effect on time perception)
• Set your significance level (this is usually 0.05)
• Calculate your statistics and find your p-value (e.g., p=0.01)
• Draw your conclusions (e.g., “The chemical psilocybin does have an effect on time perception”)

Finally, if the aim of your study is to develop and test a conceptual framework , this is the time to present it, following the testing of your hypotheses. While you don’t need to develop or discuss these findings further in the results chapter, indicating whether the tests (and their p-values) support or reject the hypotheses is crucial.

Step 8 – Provide a chapter summary

To wrap up your results chapter and transition to the discussion chapter, you should provide a brief summary of the key findings . “Brief” is the keyword here – much like the chapter introduction, this shouldn’t be lengthy – a paragraph or two maximum. Highlight the findings most relevant to your research objectives and research questions, and wrap it up.

Some final thoughts, tips and tricks

Now that you’ve got the essentials down, here are a few tips and tricks to make your quantitative results chapter shine:

• When writing your results chapter, report your findings in the past tense . You’re talking about what you’ve found in your data, not what you are currently looking for or trying to find.
• Structure your results chapter systematically and sequentially . If you had two experiments where findings from the one generated inputs into the other, report on them in order.
• Make your own tables and graphs rather than copying and pasting them from statistical analysis programmes like SPSS. Check out the DataIsBeautiful reddit for some inspiration.
• Once you’re done writing, review your work to make sure that you have provided enough information to answer your research questions , but also that you didn’t include superfluous information.

If you’ve got any questions about writing up the quantitative results chapter, please leave a comment below. If you’d like 1-on-1 assistance with your quantitative analysis and discussion, check out our hands-on coaching service , or book a free consultation with a friendly coach.

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• GETTING STARTED
• Introduction
• FUNDAMENTALS

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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Principles of Social Research Methodology pp 131–156 Cite as

Designing Research Proposal in Quantitative Approach

• Md. Rezaul Karim 4  
• First Online: 27 October 2022

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This chapter provides a comprehensive guideline for writing a research proposal in quantitative approach. It starts with the definition and purpose of writing a research proposal followed by a description of essential parts of a research proposal and subjects included in each part, organization of a research proposal, and guidelines for writing different parts of a research proposal including practical considerations and aims of a proposal that facilitate the acceptance of the proposal. Finally, an example of a quantitative research proposal has been presented. It is expected that research students and other interested researchers will be able to write their research proposal(s) using the guidelines presented in the chapter.

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http://libguides.usc.edu/writingguide/researchproposal .

University of Michigan. Research and Sponsored Projects. http://orsp.umich.edu/proposal-writers-guide-research-proposals-title-page .

Pajares, F. (n.d). The Elements of a Proposal. Emory University.

Wong, P.T. P. http://www.meaning.ca/archives/archive/art_how_to_write_P_Wong.htm .

https://www.scribd.com/document/40384531/Research-Proposal-1 .

Institute of International Studies. Dissertation Proposal Workshop, UC Berkeley, http://iis.berkeley.edu/node/424 .

For details of CSC see CARE Malawi. “The Community Score Card (CSC): A generic guide for implementing CARE’s CSC process to improve quality of services.” Cooperative for Assistance and Relief Everywhere, Inc., 2013. http://www.care.org/sites/default/files/documents/FP-2013-CARE_CommunityScoreCardToolkit.pdf

Institute of International Studies . Dissertation Proposal Workshop, UC Berkeley, http://iis.berkeley.edu/node/424 .

Bangladesh Bureau of Educational Information and Statistics

https://www.dhakatribune.com/uncategorized/2015/12/31/psc-pass-rate-98-52-ebtedayee-95-13 .

https://bdnews24.com/bangladesh/2018/12/24/jsc-jdc-pass-rate-85.83-gpa-5.0-rate-drops-sharply .

Arboleda, C. R. (1981). Communication research . Communication Foundation for Asia.

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Babbie, E. R. (2010). The practice of social research (12th ed.). Wadsworth Cengage.

BANBEIS (Bangladesh Bureau of Educational Information and Statistics). (2017). Bangladesh education statistics 2016. Bangladesh Bureau of Educational Information and Statistics (BANBEIS).

Borbasi, S., & Jackson, D. (2012). Navigating the maze of research . Mosby Elsevier.

Burns, N., Grove, S. K. (2009). The practice of nursing research: Appraisal, synthesis and generation of evidence. Saunders Elsevier.

Creswell, J. W. (1994). Research design: Qualitative & quantitative approaches . SAGE Publications.

Hasnat, M. A. (2017). School enrollment high but dropouts even higher. Dhaka Tribune September 8, 2017. https://www.Dhakatribune.com/Bangladesh/education/2017/09/08/school-enrollment-high-dropouts-even-higher .

Institute of International Studies. (n.d). Dissertation proposal workshop. Institute of International Studies. http://iis.berkeley.edu/node/424 .

Pajares, F. (n.d). The elements of a proposal. Emory University. Retrieved from http://www.uky.edu/~eushe2/Pajares/ElementsOfaProposal.pdf .

Przeworski, A., & Frank, S. (1995). On the art of writing proposals: some candid suggestions for applicants to social science research council competitions. Social Science Research Council. Retrieved from http://iis.berkeley.edu/sites/default/files/pdf/the_art_of_writing_proposals.pdf .

University of Michigan. (n.d). Research and sponsored projects. http://orsp.umich.edu/proposal-writers-guide-research-proposals-title-page .

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Karim, M.R. (2022). Designing Research Proposal in Quantitative Approach. In: Islam, M.R., Khan, N.A., Baikady, R. (eds) Principles of Social Research Methodology. Springer, Singapore. https://doi.org/10.1007/978-981-19-5441-2_10

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Writing a proposal for your dissertation : guidelines and examples

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• 1. Developing the Problem Statement for Your Dissertation Proposal :
• Introduction
• The Doctoral Experience
• The Problem Is the Problem
• Finding a Good Research Problem
• Characteristics of a Good Problem
• Writing the Problem Statement
• The Problem Statement as Part of a Dissertation Proposal
• Summary of Chapter One
• Do You Understand These Key Words and Phrases?
• Let's Start Writing Our Own Proposal
• 2. Writing Purpose Statements, Research Questions, and Hypotheses :
• The Quantitative Purpose Statement
• Purpose Statements for Qualitative Studies
• Defining and Describing a Research Question
• The Methodological Point of Departure
• Research Questions Will Ultimately Lead to the Study's Research Method
• Getting Back to Stating Our Research Question
• A Word of Caution!
• Putting It Together: Problem Statements, Purpose Statements, and Research Questions
• Problem Statements, Purpose Statements, and Research Questions in the Literature
• Stating Hypotheses for Your Research Study
• An Example of Stating Our Hypotheses
• Understanding the Four Basic Rules for Hypotheses
• The Direction of Hypotheses
• Hypotheses Must Be Testable via the Collection and Analysis of Data
• Research versus Null Hypotheses
• All Hypotheses Must Include the Word "Significant"
• Other Parts of Chapter 1 of the Dissertation
• Summary of Chapter Two
• Review Questions
• Progress Check for Chapter 1 of the Dissertation Proposal: The Introduction
• Let's Continue Writing Our Own Dissertation Proposal
• 3. Writing the Review of Literature for Your Study :
• What Is a Review of Literature and What Is Its Purpose?
• There Isn't a Magic Formula for Writing a Review of Literature
• Phase 1. Getting Ready to Write a Review of Literature
• Phase 2. Writing the Review of Literature
• Summary of Chapter Three
• Progress Check for Chapter 3 of the Dissertation Proposal: The Review of Literature
• 4. The First Part of Your Dissertation Research Method :
• Philosophy 101
• The Research Paradigm
• Nonrandom (Nonprobabalistic) Sampling
• Identifying the Population and a Sample for Your Study
• Summary of the Sampling Process
• Data Collection Instruments
• Instruments for Quantitative Research
• Instruments for Qualitative Research
• Reliability and Validity
• Plans for Data Analysis
• Ethical Considerations
• Plans for Presenting the Results
• Summary of Your Proposal
• Summary of Chapter Four: The First Part of Your Dissertation Research Method
• 5. Quantitative Research Methods :
• Different Types of Data
• Quantitative Research Designs
• Survey Research
• Correlational Research
• Causal-Comparative Research
• Hypothesis Testing
• Experimental Research
• The Validity of Your Study
• Threats to the Internal Validity of Your Study
• Threats to the External Validity of Your Study
• Experimental Research Designs
• Preexperimental Designs
• Quasi-Experimental Designs
• Experimental Designs
• Putting This All Together for the Quantitative Dissertation Proposal
• Chapter 3 of a Quantitative Dissertation Proposal
• Our First Example of Chapter 3 of a Proposal
• Summary of Chapter Five
• Progress Check for Chapter 3 of a Quantitative Dissertation Proposal
• Appendix 5.1. Example of a Descriptive Research Study
• Appendix 5.2. Example of a Correlational Research Study
• Appendix 5.3. Example of a Quasi-Experimental Research Study
• Appendix 5.4. Example of an Experimental Research Study
• Appendix 5.5. Threats to the Validity of an Experimental Study
• 6. Qualitative Research Methods :
• An Overview of Qualitative Methodologies
• The Role of the Researcher
• The Format of a Qualitative Dissertation Proposal
• Chapter 1 of a Qualitative Dissertation Proposal: The Introduction
• Chapter 3 of a Qualitative Dissertation Proposal: Research Methods
• Choosing the Right Qualitative Research Method
• Participants and Sampling
• Instruments
• Research Procedures
• The Validity and Reliability of a Qualitative Study
• Summary of Chapter Six
• Progress Check for Chapter 3 of a Qualitative Dissertation Proposal
• Appendix 6.1. Narrative Study Procedures: The Case of the Unfortunate Departure
• Appendix 6.2. Phenomenological Study Procedures: The Case of Sending Your Child to Safety
• Appendix 6.3. Ethnographic Study Procedures: The Case of Climbing the Mountain
• Appendix 6.4. Case Study Procedures: The Case of the Standardized Test
• Appendix 6.5. Grounded Theory Procedures: The Case of Homelessness
• Appendix 6.6. Content Analysis Procedures: The Case of the Eye Witness
• 7. Mixed Methods Research Designs :
• An Overview of Mixed Methods Research
• The Format of a Mixed Methods Proposal
• Chapter 1 of a Mixed Methods Study: The Introduction
• Background, Statement of the Problem, and Significance of the Study
• The Central Purpose of the Study
• Research Questions
• Hypotheses for Mixed Methods Studies
• Chapter 2 of a Mixed Methods Dissertation Proposal: The Review of Literature
• Chapter 3 of a Mixed Methods Dissertation Proposal: Research Methods
• The Mixed Methods Paradigm
• Research Design
• The Three Major Mixed Methods Designs
• Summary of Chapter Seven
• Progress Check for Chapter 3 of a Mixed Methods Dissertation Proposal: The Research Methods
• Appendix 7.1. Sequential Explanatory Design: The Case of the Tutors
• Appendix 7.2. Sequential Exploratory Design: The Case of the Academies
• Appendix 7.3. Convergent Design: The Case of Calling It In
• Epilogue: Have We Accomplished What We Set Out to Do?
• Appendix A. Progress Check for Chapter 1 of a Dissertation Proposal: The Introduction
• Appendix B. Progress Check for Chapter 2 of a Dissertation Proposal: The Review of Literature
• Appendix C. Progress Check for Chapter 3 of a Quantitative Dissertation Proposal
• Appendix D. Progress Check for Chapter 3 of a Qualitative Dissertation Proposal
• Appendix E. Progress Check for Chapter 3 of a Mixed Methods Dissertation Proposal.

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Methodology

• What Is Quantitative Research? | Definition, Uses & Methods

What Is Quantitative Research? | Definition, Uses & Methods

Published on June 12, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analyzing non-numerical data (e.g., text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

• What is the demographic makeup of Singapore in 2020?
• How has the average temperature changed globally over the last century?
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Table of contents

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, other interesting articles, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

• In descriptive research , you simply seek an overall summary of your study variables.
• In correlational research , you investigate relationships between your study variables.
• In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalized to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

Note that quantitative research is at risk for certain research biases , including information bias , omitted variable bias , sampling bias , or selection bias . Be sure that you’re aware of potential biases as you collect and analyze your data to prevent them from impacting your work too much.

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Once data is collected, you may need to process it before it can be analyzed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualize your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalizations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

First, you use descriptive statistics to get a summary of the data. You find the mean (average) and the mode (most frequent rating) of procrastination of the two groups, and plot the data to see if there are any outliers.

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardize data collection and generalize findings . Strengths of this approach include:

• Replication

Repeating the study is possible because of standardized data collection protocols and tangible definitions of abstract concepts.

• Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

• Large samples

Data from large samples can be processed and analyzed using reliable and consistent procedures through quantitative data analysis.

• Hypothesis testing

Using formalized and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

• Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

• Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

• Structural bias

Despite standardized procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

• Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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A Guide to Quantitative Research Proposals

In this essay, noted scholar Elizabeth Tipton elaborates on how to best articulate quantitative research design in grant proposals. This essay is a companion piece to our “ A Guide to Writing Successful Field Initiated Research Grant Proposals ,” which provides general information about the elements of grant writing.

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Dissertation Essentials

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The Dissertation Essentials area houses guides, manuals, and templates to assist you in your doctoral journey.  There is also a section specifically for rubrics for each of the chapters as well as the proposal and manuscript.  Along with these items, there are additional resources provided for the ASC, Library, technology, accessing published dissertations, and even some school specific resources.

• DSE Manual (Previously Handbook) Use this guide throughout the dissertation process to support you in understanding the courses, deliverables, and expectations of students and the dissertation committee.
• Dissertation Proposal/Manuscript Template You will use this templates to write all chapters of the dissertation.
• DSE Dissertation Revision Timeline Use this template to create a timeline for deliverable revisions in the dissertation.
• SOBE Best Practice Guide for Qualitative Research and Design Methods
• SOBE Best Practice Guide in Quantitative Research and Design Methods

If you are working on your CMP course, your course will provide information on how to format your prospectus/portfolio.

• DSE Chapter 1 Rubric Use this rubric to guide you when writing Chapter 1 of your dissertation.
• DSE Chapter 2 Rubric Use this rubric to guide you when writing Chapter 2 of your dissertation.
• DSE Chapter 3 Rubric Use this rubric to guide you when writing Chapter 3 of your dissertation.
• DSE Dissertation Proposal Rubric Use this rubric to guide you when combining Chapters 1-3 into the Dissertation Proposal.
• DSE Chapter 4 Rubric Use this rubric to guide you when writing Chapter 4 of your dissertation.
• DSE Chapter 5 Rubric Use this rubric to guide you when writing Chapter 5 of your dissertation.
• DSE Dissertation Manuscript Rubric Use this rubric to guide you when combing all five of your dissertation chapters to produce your Dissertation Manuscript.

Not yet at the Dissertation phase?  Getting ready for your CMP course?  Check out the CMP Course Frequently Asked Questions document below:

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Library Dissertation Toolbox Workshop Series

The  Library Dissertation Toolbox Workshop Series  consists of engaging, skill-building workshops designed specifically for doctoral students. Students will learn how to effectively locate, evaluate, and use information relating to their dissertation research topics. Each toolbox session features a new research focus- sign up for the entire series, or just those that most appeal to you:

• Research Process Guide by NU Library Outlines important steps in the research process and covers topics such as evaluating information.
• Managing and Writing the Doctoral Thesis or Dissertation Dr. Linda Bloomberg's newest edition Completing Your Qualitative Dissertation: A Road Map From Beginning to End is out now. This resource includes an interview between Methodspace and Dr. Bloomberg.

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• Last Updated: Mar 13, 2024 9:17 AM
• URL: https://resources.nu.edu/c.php?g=1005138

Welcome to the on-line version of the UNC dissertation proposal collection. The purpose of this collection is to provide examples of proposals for those of you who are thinking of writing a proposal of your own. I hope that this on-line collection proves to be more difficult to misplace than the physical collection that periodically disappears. If you are preparing to write a proposal you should make a point of reading the excellent document The Path to the Ph.D., written by James Coggins. It includes advice about selecting a topic, preparing a proposal, taking your oral exam and finishing your dissertation. It also includes accounts by many people about the process that each of them went through to find a thesis topic. Adding to the Collection This collection of proposals becomes more useful with each new proposal that is added. If you have an accepted proposal, please help by including it in this collection. You may notice that the bulk of the proposals currently in this collection are in the area of computer graphics. This is an artifact of me knowing more computer graphics folks to pester for their proposals. Add your non-graphics proposal to the collection and help remedy this imbalance! There are only two requirements for a UNC proposal to be added to this collection. The first requirement is that your proposal must be completely approved by your committee. If we adhere to this, then each proposal in the collection serves as an example of a document that five faculty members have signed off on. The second requirement is that you supply, as best you can, exactly the document that your committee approved. While reading over my own proposal I winced at a few of the things that I had written. I resisted the temptation to change the document, however, because this collection should truely reflect what an accepted thesis proposal looks like. Note that there is no requirement that the author has finished his/her Ph.D. Several of the proposals in the collection were written by people who, as of this writing, are still working on their dissertation. This is fine! I encourage people to submit their proposals in any form they wish. Perhaps the most useful forms at the present are Postscript and HTML, but this may not always be so. Greg Coombe has generously provided LaTeX thesis style files , which, he says, conform to the 2004-2005 stlye requirements.

Many thanks to everyone who contributed to this collection!

Greg Coombe, "Incremental Construction of Surface Light Fields" in PDF . Karl Hillesland, "Image-Based Modelling Using Nonlinear Function Fitting on a Stream Architecture" in PDF . Martin Isenburg, "Compressing, Streaming, and Processing of Large Polygon Meshes" in PDF . Ajith Mascarenhas, "A Topological Framework for Visualizing Time-varying Volumetric Datasets" in PDF . Josh Steinhurst, "Practical Photon Mapping in Hardware" in PDF . Ronald Azuma, "Predictive Tracking for Head-Mounted Displays," in Postscript Mike Bajura, "Virtual Reality Meets Computer Vision," in Postscript David Ellsworth, "Polygon Rendering for Interactive Scientific Visualization on Multicomputers," in Postscript Richard Holloway, "A Systems-Engineering Study of the Registration Errors in a Virtual-Environment System for Cranio-Facial Surgery Planning," in Postscript Victoria Interrante, "Uses of Shading Techniques, Artistic Devices and Interaction to Improve the Visual Understanding of Multiple Interpenetrating Volume Data Sets," in Postscript Mark Mine, "Modeling From Within: A Proposal for the Investigation of Modeling Within the Immersive Environment" in Postscript Steve Molnar, "High-Speed Rendering using Scan-Line Image Composition," in Postscript Carl Mueller, " High-Performance Rendering via the Sort-First Architecture ," in Postscript Ulrich Neumann, "Direct Volume Rendering on Multicomputers," in Postscript Marc Olano, "Programmability in an Interactive Graphics Pipeline," in Postscript Krish Ponamgi, "Collision Detection for Interactive Environments and Simulations," in Postscript Russell Taylor, "Nanomanipulator Proposal," in Postscript Greg Turk, " Generating Textures on Arbitrary Surfaces ," in HTML and Postscript Terry Yoo, " Statistical Control of Nonlinear Diffusion ," in Postscript

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From Proposal to Defense: Navigating the Challenges of Completing Your Dissertation

When conducting research, selecting an effective topic is crucial. this is the first step, and it can present serious challenges..

Completing a doctoral dissertation is a significant academic achievement representing the culmination of years of study and a testament to one's expertise in one's chosen field. 

Navigating the dissertation timeline, from the initial proposal to the rigorous defense, is demanding but rewarding. 

Therefore, you will need practical solutions to overcome common problems during dissertation writing. Moreover, you must clarify the thesis statement, select a research methodology that matches study objectives, and collaborate with supervisors for writing guidance and feedback. 

Therefore, this post will help you navigate these several challenges you might face when completing your dissertation.

Choosing The Topic

When conducting research, it's crucial to select an effective topic. Therefore, you must determine available resources to understand what topic fits best for your subject. 

Moreover, you need to read extensively on the topic, find a theoretical basis, and ensure the topic holds your interest. Also, find a niche where you can make a difference, allow yourself to shift gears, and fine-tune your topic based on input from others. 

Additionally, it's important to find a theoretical context for the results, as having an overarching theoretical context is crucial. 

Therefore, researchers can develop a well-rounded and impactful research project that increases their chances of success.

You can also seek help from professional services like  Ivory Research to find a unique topic that matches your field of expertise.

Developing The Proposal

Writing a research proposal is crucial for a student pursuing undergraduate or postgraduate studies. 

This document describes the research, methodology, and reasons behind the study. It should have an introduction, a literature review, a proposed method, research implications, and a bibliography of relevant sources. 

You must follow the guidelines set by the institution. However, you must consult your supervisor whenever you face a challenge.

The introduction should cover the topic, aims, and research question(s). The proposal then provides more background and context, emphasizing the importance of understanding the proposed questions, the current state of research, and the dissertation's contribution to the field. 

However, if a literature review is included, it should provide a general understanding of the debates the proposal addresses. 

Nevertheless, follow guidelines and consult with a supervisor when unsure is crucial.

Research & Data Collection

Once you’re clear about your topic demands, you must focus on effectively collecting theoretical and empirical data for your thesis.

Therefore, start by choosing a topic and gathering relevant information. 

You can find potential references through the following resources:

• Edited volumes.
• Monographs.
• Online databases like Google Scholar, Scopus or ERIC.
• Conference proceedings.
• Academic search engines. 

Thus, research theses on your topic to understand the approaches taken and aspects other writers have focused on.

Use content-sharing platforms like Medium, Issuu, Calameo, Scribd, and Slideshare to conduct a literature search for dissertations and final-year projects. 

Ensure the credibility of sources before relying on content. Therefore, choose qualitative and quantitative data for empirical data collection based on research outcomes and time constraints. 

Next, structure your thesis to avoid getting lost in the sea of information.

Understanding the differences between qualitative and quantitative research can help make informed decisions about the type of empirical data for your research.

Write & Revise

Dissertations are essential for a degree course, demonstrating research, data analysis, and clear argument writing. To achieve academic goals, acting on feedback, using supervision time, and demonstrating strong knowledge of the subject is crucial. 

To write an undergraduate, Masters, or PhD dissertation, follow these seven steps:

• Proofread your dissertation aloud to catch errors and change your environment. Focus on one thing at a time, such as grammar, spelling, or punctuation.
• Edit your dissertation by reviewing its structure and flow, ensuring well-organized arguments and logical presentation of ideas. 
• Check grammar, spelling, and punctuation carefully.
• Understand your university's reference style and ensure proper formatting of images, tables, and other materials.
• Seek feedback from your advisor or board members on specific issues while staying open to criticism.

What Is A Dissertation Defense?

A thesis defense is a crucial opportunity to present your research study to academic professionals who will evaluate your work. 

It can be akin to a cross-examination session, but being well-prepared is essential. In that light,  the  dissertation committee is a crucial decision for research students, guiding them through the proposal, writing, and revision process. 

Moreover, committee members serve as mentors, providing constructive feedback and guiding revision efforts. 

The committee is formed after academic coursework is completed, and it's essential to understand the expectations of committee members. 

However, some universities may allow an outside expert, such as a former professor or academic mentor, to serve on the committee. Choosing a faculty member who knows you and your research work is advisable.

How To Prepare  Dissertation Defense?

Thesis defense is a crucial process that requires careful preparation and preparation. It is not a one-time event but rather a series of steps that should be taken over several months.

1. Start Early

Start your preparation early by understanding the intricacies of your thesis and the reasons behind your research experiments. Attend open dissertation presentations at your university to learn about the process and the importance of defending your thesis.

2. Prepare The Slides

Prepare your slides properly, ensuring you have the right data and rephrasing your inferences to create a logical flow.

3. Structure Your Presentation

Structure your presentation by creating high-quality, well-structured slides that hold your audience's attention. Use smart art to create better slides.

4. Relax Before Presenting

Practice breathing techniques, such as controlling your breath and maintaining a steady pace. This will help you control your breathing and make your speech more fluent.

5. Plan A Unique Introduction

Create an impactful introduction, as the audience expects a lot from you. Your opening statement should enthrall the audience, and your thesis should make a good first impression.

6. Prepare Counter Questions

Maintain your list of questions while preparing for the presentation. Consider questions that could help you understand the topic better and practice answering them. Attending other candidates' open discussions can also help you assume the dissertation defense questions.

7. Practice A Million Times

Practice speech and  body language , focusing on your posture and movements while presenting your thesis.

Give a mock presentation, similar to your real defense, to gain experience and prepare for the actual defense.

8. Know How To Handle Mistakes

Learn how to handle mistakes, as everyone makes mistakes. Take a deep breath and move on to the next point.

Do not rush through the presentation; this can lead to anxiety and skip essential details, ultimately creating a fiasco.

Visualize The Moment

Visualize yourself defending your thesis each evening before going to sleep. This simple exercise takes commitment and persistence, but the results are worth it. Visualizing yourself doing the scary thing of defending your thesis will help you feel more confident and prepared for the process.

(Devdiscourse's journalists were not involved in the production of this article. The facts and opinions appearing in the article do not reflect the views of Devdiscourse and Devdiscourse does not claim any responsibility for the same.)

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