master thesis analysis

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 chapter is
• What you need to include in your chapter
• How to structure the chapter
• Tips and tricks for writing a top-notch chapter
• Free results chapter template

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

Getting to the main article

Choosing your route

Setting research questions/ hypotheses

Assessment point

Building the theoretical case

Setting your research strategy

Data collection

Data analysis

Data analysis techniques

In STAGE NINE: Data analysis , we discuss the data you will have collected during STAGE EIGHT: Data collection . However, before you collect your data, having followed the research strategy you set out in this STAGE SIX , it is useful to think about the data analysis techniques you may apply to your data when it is collected.

The statistical tests that are appropriate for your dissertation will depend on (a) the research questions/hypotheses you have set, (b) the research design you are using, and (c) the nature of your data. You should already been clear about your research questions/hypotheses from STAGE THREE: Setting research questions and/or hypotheses , as well as knowing the goal of your research design from STEP TWO: Research design in this STAGE SIX: Setting your research strategy . These two pieces of information - your research questions/hypotheses and research design - will let you know, in principle , the statistical tests that may be appropriate to run on your data in order to answer your research questions.

We highlight the words in principle and may because the most appropriate statistical test to run on your data not only depend on your research questions/hypotheses and research design, but also the nature of your data . As you should have identified in STEP THREE: Research methods , and in the article, Types of variables , in the Fundamentals part of Lærd Dissertation, (a) not all data is the same, and (b) not all variables are measured in the same way (i.e., variables can be dichotomous, ordinal or continuous). In addition, not all data is normal , nor is the data when comparing groups necessarily equal , terms we explain in the Data Analysis section in the Fundamentals part of Lærd Dissertation. As a result, you might think that running a particular statistical test is correct at this point of setting your research strategy (e.g., a statistical test called a dependent t-test ), based on the research questions/hypotheses you have set, but when you collect your data (i.e., during STAGE EIGHT: Data collection ), the data may fail certain assumptions that are important to such a statistical test (i.e., normality and homogeneity of variance ). As a result, you have to run another statistical test (e.g., a Wilcoxon signed-rank test instead of a dependent t-test ).

At this stage in the dissertation process, it is important, or at the very least, useful to think about the data analysis techniques you may apply to your data when it is collected. We suggest that you do this for two reasons:

REASON A Supervisors sometimes expect you to know what statistical analysis you will perform at this stage of the dissertation process

This is not always the case, but if you have had to write a Dissertation Proposal or Ethics Proposal , there is sometimes an expectation that you explain the type of data analysis that you plan to carry out. An understanding of the data analysis that you will carry out on your data can also be an expected component of the Research Strategy chapter of your dissertation write-up (i.e., usually Chapter Three: Research Strategy ). Therefore, it is a good time to think about the data analysis process if you plan to start writing up this chapter at this stage.

REASON B It takes time to get your head around data analysis

When you come to analyse your data in STAGE NINE: Data analysis , you will need to think about (a) selecting the correct statistical tests to perform on your data, (b) running these tests on your data using a statistics package such as SPSS, and (c) learning how to interpret the output from such statistical tests so that you can answer your research questions or hypotheses. Whilst we show you how to do this for a wide range of scenarios in the in the Data Analysis section in the Fundamentals part of Lærd Dissertation, it can be a time consuming process. Unless you took an advanced statistics module/option as part of your degree (i.e., not just an introductory course to statistics, which are often taught in undergraduate and master?s degrees), it can take time to get your head around data analysis. Starting this process at this stage (i.e., STAGE SIX: Research strategy ), rather than waiting until you finish collecting your data (i.e., STAGE EIGHT: Data collection ) is a sensible approach.

Final thoughts...

Setting the research strategy for your dissertation required you to describe, explain and justify the research paradigm, quantitative research design, research method(s), sampling strategy, and approach towards research ethics and data analysis that you plan to follow, as well as determine how you will ensure the research quality of your findings so that you can effectively answer your research questions/hypotheses. However, from a practical perspective, just remember that the main goal of STAGE SIX: Research strategy is to have a clear research strategy that you can implement (i.e., operationalize ). After all, if you are unable to clearly follow your plan and carry out your research in the field, you will struggle to answer your research questions/hypotheses. Once you are sure that you have a clear plan, it is a good idea to take a step back, speak with your supervisor, and assess where you are before moving on to collect data. Therefore, when you are ready, proceed to STAGE SEVEN: Assessment point .

• FindAMasters
• Researching and Writing a Masters Dissertation

Written by Mark Bennett

All Masters programmes include some form of extended individual project. Research-focussed programmes, such as an MRes , may include multiple independent research components. Taught courses usually culminate with a substantial research task, referred to as the Masters dissertation or thesis.

This article talks about how long a Masters dissertation is and the structure it follows.Before you get started on your dissertation, you'll usually need to write a proposal. Read our full guide to Masters dissertation proposals for more information on what this should include!

On this page

What’s the difference between a masters dissertation and an undergraduate dissertation.

The Masters thesis is a bridge between undergraduate study and higher level postgraduate degrees such as the PhD .

A postgraduate dissertation may not look that different to its undergraduate equivalent. You’ll likely have to produce a longer piece of work but the foundations remain the same.

After all, one of the purposes of an undergraduate dissertation or final year project is to prepare you for more in-depth research work as a postgraduate. That said, there are some important differences between the two levels.

So, how long is a Masters dissertation? A Masters dissertation will be longer than the undergraduate equivalent – usually it’ll be somewhere between 15,000 and 20,000 words, but this can vary widely between courses, institutions and countries.

To answer your overall research question comprehensively, you’ll be expected to identify and examine specific areas of your topic. This can be like producing a series of shorter pieces of work, similar to those required by individual modules. However, there’s the additional requirement that they collectively support a broader set of conclusions.

This more involved Masters dissertation structure will:

• Give you the scope to investigate your subject in greater detail than is possible at undergraduate level
• Challenge you to be effective at organising your work so that its individual components function as stages in a coherent and persuasive overall argument
• Allow you to develop and hone a suitable research methodology (for example, choosing between qualitative and quantitative methods)

If the individual topics within your overall project require you to access separate sources or datasets, this may also have an impact on your research process.

As a postgraduate, you’ll be expected to establish and assert your own critical voice as a member of the academic community associated with your field .

During your Masters thesis you’ll need to show that you are not just capable of analysing and critiquing original data or primary source material. You should also demonstrate awareness of the existing body of scholarship relating to your topic .

So, if you’ll excuse the pun, a ‘Masters’ degree really is about achieving ‘mastery’ of your particular specialism and the dissertation is where you’ll demonstrate this: showing off the scholarly expertise and research skills that you’ve developed across your programme.

What’s the difference between a dissertation and a thesis?

A dissertation is a long piece of (usually) written work on the same topic. A thesis is a little more specific: it usually means something that presents an original argument based on the interpretation of data, statistics or content.

So, a thesis is almost always presented as a dissertation, but not all dissertations present a thesis.

Masters dissertation structure

As you can probably imagine, no two dissertations follow the exact same structure, especially given the differences found between Masters programmes from university to university and country to country .

That said, there are several key components that make up the structure of a typical Masters dissertation

How long is a Masters dissertation?

Most dissertations will typically be between 15,000 and 20,000 words long, although this can vary significantly depending on the nature of the programme.

You should also check with your university exactly which sections of the dissertation count towards the final word count (the abstract, bibliography and appendices won’t usually be included in the total).

Usually around 300 words long, the abstract is meant to be a concise summary of your dissertation. It should briefly cover the question(s) you aim to answer, your primary argument and your conclusion.

Introduction

The purpose of the introduction is to provide context for the rest of the dissertation, setting out your aims and the scope of what you want to achieve with your research. The introduction should give a clear overview of the dissertation’s chapters and will usually be around 1,000 words long.

Literature review

This part of the dissertation should examine the scholarship that has already been published in your field, presenting various arguments and counter-arguments while situating your own research within this wider body of work.

You should analyse and evaluate other publications and explain how your dissertation will contribute to the existing literature in your subject area. The literature review sometimes forms part of the introduction or follows immediately on from it. Most literature reviews are up to 1,000 words long.

Research methodology

Not all dissertations will require a section covering research methodology (Arts and Humanities dissertations won’t normally undertake the kind of research that involves a set methodology). However, if you are using a particular method to collect information for your dissertation, you should make sure to explain the rationale behind your choice of methodology. The word count for this part of the dissertation is usually around the 1,500 mark.

Those in the Arts and Humanities will usually outline their theoretical perspectives and approaches as part of the introduction, rather than requiring a detailed explanation of the methodology for their data collection and analysis.

Results / findings

If your research involves some form of survey or experiment, this is where you’ll present the results of your work. Depending on the nature of the study, this might be in the form of graphs, tables or charts – or even just a written description of what the research entailed and what the findings were.

This section forms the bulk of your dissertation and should be carefully structured using a series of related chapters (and sub-chapters). There should be a logical progression from one chapter to the next, with each part building on the arguments of its predecessor.

It can be helpful to think of your Masters dissertation as a series of closely interlinked essays, rather than one overwhelming paper. The size of this section will depend on the overall word count for your dissertation. However, to give you a rough idea for a 15,000-word dissertation, the discussion part will generally be about 12,000 words long.

Here you should draw together the threads of the previous discussion chapters and make your final concluding statements, drawing on evidence and arguments that you’ve already explored over the course of the dissertation. Explain the significance of your findings and point towards directions that future research could follow. This section of the Masters thesis will be around 1,500 words long.

References / bibliography

While planning and writing your dissertation, you should keep an extensive, organised record of any papers, sources or books you’ve quoted (or referred to). This will be a lot easier than leaving all of it until the end and struggling to work out where a particular quotation is from!

Appendices won’t be necessary in many dissertations, but you may need to include supplementary material to support your argument. This could be interview transcripts or questionnaires. If including such content within the body of the dissertation won’t be feasible – i.e. there wouldn’t be enough space or it would break the flow of your writing – you should consult with your supervisor and consider attaching it in an appendix.

It’s worth bearing in mind that these sections won’t always be discretely labelled in every dissertation. For example, everything up to ‘discussion’ might be covered in introductory chapter (rather than as distinct sections). If you’re unsure about the structure of your Masters dissertation, your supervisor will be able to help you map it out.

How does supervision work for a Masters dissertation?

As a Masters student at the dissertation stage you’ll usually be matched with an academic within your institution who will be tasked with guiding your work. This might be someone who has already taught you, or it may be another scholar whose research interests and expertise align well with what you want to do. You may be able to request a particular supervisor, but taught postgraduates are more likely to be assigned them by their department.

Specific arrangements with your supervisor will vary depending on your institution and subject area. They will usually meet with you at the beginning of the dissertation period to discuss your project and agree a suitable schedule for its undertaking. This timetable will probably set dates for:

• Subsequent discussions and progress checks
• The submission of draft chapters or sections
• Feedback appointments

Though your supervisor is there to help and advise you, it is important to remember that your dissertation is a personal research project with associated expectations of you as an independent scholar.

As a rule of thumb, you can expect your supervisor to read each part of your dissertation once at the draft stage and to offer feedback. Most will not have time to look at lots of subsequent revisions, but may respond favourably to polite requests for exceptions (provided their own workload permits it).

Inundating your supervisor with emails or multiple iterations of draft material is best avoided; they will have their own research to manage (as well as other supervision assignments) and will be able to offer better quality feedback if you stick to an agreed schedule.

How is a Masters dissertation assessed and examined?

On most courses your dissertation will be assessed by an external examiner (as well as additional members of faculty within your university who haven’t been responsible for supervising you), but these will read and critique the work you submit without personally questioning and testing you on it.

Though this examination process is not as challenging as the oral defence or ‘ viva voce ’ required for a PhD thesis, the grading of your Masters dissertation is still a fundamental component of your degree.

On some programmes the result awarded to a student’s dissertation may determine the upper grade-band that can be awarded to their degree.

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Postgraduate study is often very flexible, with the option to study a Masters degree or other qualification part-time, online or through blended learning.

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Our guide tells you everything about the application process for studying a Masters in the USA.

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A complete guide to writing a master’s thesis

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If you’re reading this because you have been accepted onto a master’s programme that requires you to write a thesis – congratulations! This is a very exciting time in your life.

Hopefully what we’re about to tell you doesn’t come as too much of a surprise – as part of your master’s course you are going to be spending a considerable amount of time working on a single written submission. In fact, your master’s thesis may be the longest and most detailed piece of writing that you have ever been asked to complete.

With that in mind, getting some guidance before you begin is an important first step.

As you progress through your degree, you may have questions on exactly how to write a master’s thesis and are thus looking to obtain some thesis writing tips to help you on your way.

This post is designed to provide you with a comprehensive guide to writing a master’s thesis in the UK context. It will help you not just now, but will also be something you can refer back to throughout the duration of your master’s studies. If you haven’t already, we strongly recommend bookmarking this post so can you find it easily when you inevitably have questions during the thesis writing process.

In this post, we’ll clue you in to some strategies best suited to master’s students trying to complete a thesis. We’ll focus on what to expect at each stage, including creating a working plan, doing your reading, undertaking research, writing your master’s thesis, editing it, and leaving time to finish and proofread.

Hopefully, by the time you have finished reading, you will have the confidence and motivation to get going on a path that will lead to ultimate success in your thesis writing.

Start with a plan (and stick to it)

OK, we realise we may be teaching your grandma how to suck eggs here – starting with a plan is the obvious first step in any piece of academic writing.

And yet, as good as everyone’s intentions may be when students start writing a master’s thesis, circumstances (nearly) always arise that make sticking to the plan much more challenging.

So, first tip: when writing your thesis, make sure that your plan is flexible , and allows time for dealing with unexpected circumstances.

Next, reconsider your research proposal. It is likely you had to write one before you were accepted onto your master’s programme. If this was your first time producing a research proposal, you may read it back now and find it’s a little over-ambitious in its claims about what you planned to do. It’s a common trap to fall into, so don’t despair! Book some time with your research supervisor to determine whether your research proposal is actually realistic for your master’s thesis. If you claimed that you were going to do qualitative interviews of 200 participants across the UK, and you only have a year to complete your master’s, you might want to rethink your project and scale it to something that is achievable and not setting you up for failure.

Another tip on supervisors: make sure that you ask them questions about their expectations throughout the thesis writing process. Will they want to see drafts of your chapters as they’re written? If the answer is yes, finding out these dates will help you to develop a plan to achieve this without scrambling at the last minute.

Another tip for planning how to write your master’s thesis is to set yourself a goal of doing a little bit each day. Framing your thesis in your mind as a long-term project with a deadline very far away in the future will only encourage you to put off writing it. Then ‘far away in the future’ will all of a sudden be ‘next month’ and major panic will set in, and the lack of time at your disposal will make for rushed, compromised writing.

Set yourself milestones: a realistic plan for writing certain chapters by certain dates. Then within these milestones, commit to writing an amount of words per day, or per week. Then be disciplined and stick to your plan. Avoiding procrastination isn’t easy, but will very much work in your favour in the long run.

A final tip when devising your plan: it is easy to go back and delete words that you do not need during the editing process. Conversely, having to add thousands of words at the last minute will be stressful and sometimes impossible. Plan to start writing early, and budget for, say, 1000-2000 words every day. Not only will you then reach the full word count of your master’s thesis quickly, but you’ll leave yourself plenty of time to edit it, remove sections that aren’t working, and add more words that strengthen the overall assignment, all long before the deadline arrives.

Do your reading

The trick here is to find a balance between reading enough and not spending too much time doing so. There is so much reading to do and it can be easy to drift off topic.

Doing your reading and producing the final literature review are important components of a master’s thesis, but if you spend too much time reading there won’t be ample time for the data collection process and the writing up phase.

Here are some steps you can take to ensure that your reading process is both effective and efficient.

Step 1 – Understand your research questions

The first step in the reading phase of your master’s thesis is knowing what research questions you are trying to answer. Hopefully you have identified these questions with your supervisor before you started to work on your thesis. If you do not have a clear research question, your reading strategies will be severely hindered.

There are certain databases that are going to be more relevant to your area of study. Getting research from these databases is going to streamline the writing process for you to ensure that your project is focused within the context that it needs to be. A research librarian can likely help you focus this search, making the process significantly easier.

Step 2 – Make reading easier

There are several challenges associated with reading. First, it is easy to get distracted, especially if your reading material is lengthy and complex. So you want to keep your reading blocks short and sweet. ‘Chunk’ your reading. Spend 20 to 25 minutes reading without distraction (it hurts we know, but putting your phone on flight mode and leaving it in another room will ultimately help) and then take a 5 to 10 minute break (on your phone, if you must!) before starting up again.

Furthermore, whilst there is a lot of reading to do, it is unrealistic to spend your whole day doing it. Earmark just a portion of the day for reading, then make sure that you have other things that you can do with the rest of your time (like completing your ethics forms, or starting to create your research instruments). By dividing up your time, you are going to be able to keep your focus for longer, making you more productive and efficient overall.

Step 3 – Take good notes

It’s always worth remembering the forgetting curve – ah that’s a paradox if we ever saw one. The forgetting curve is the amount of information you will forget as time passes. It can be quite steep, and after a month passes you likely won’t remember much about what you have previously read. This could lead to disaster when writing up your literature review, so make sure that you take good, thorough notes throughout the reading process.

A good idea is to build out an excel spreadsheet or other list that documents your reading in a detailed and organised manner. You can keep track of key information, such as:

• Location of research
• Sample size
• Research methods
• Main findings

Not only will this help slow the curve of your inevitable forgetfulness, but crucially, it will also make referring back to your reading much easier when you move on to writing your overall literature review.

A note to remember: not everything that you read will end up in your literature review. The purpose of reading is to make sure that you, as a researcher, understand how your project is positioned within your area of study. The literature review explains this to the reader but in much simpler terms. So to reiterate, the reading process is for your own benefit, not solely to find studies to include in your literature review.

Do your research

Even if you did research as part of your undergraduate work, research for a master’s thesis is a whole different story. As an undergraduate, your project was likely quite small or it was significantly guided by a faculty member; as a master’s student, this is typically your first opportunity to do research on a topic that you have chosen to pursue. While this is an exciting step, it also means that you are accountable for your actions.

Your research type and tools

The first step in the research process is deciding on what type of research you will do. Is it going to be qualitative or quantitative? Maybe it will be a combination of the two. You have likely documented this in your research proposal, but your answer to this question will have implications about how you will organise and analyse your data once it is collected.

Regardless of what route you choose, you will need software to help you manage your data. Many universities have free data management software tools available, and if that is the case for your institution then use them –tools available otherwise can rack up quite a hefty bill.

The most common tools are SPSS, which deals primarily with quantitative data, or NVivo which focuses more on qualitative measures. There are numerous other software packages available, and your supervisor may have suggestions about which management tool is most suitable for your project.

Planning ahead for better outcomes

The second step in the process is to think about timing and distribution. If you are planning a qualitative study, perhaps using interviews, remember that you will need to transcribe all of the words that are contained in the interview. While some programmes allow speech-to-text translation, it is not always accurate. The process of transcription takes considerable time, and therefore, as a researcher, you should consider how many participants you are looking to have in your project.

While a quantitative project may not have the same level of detail in the data input process, there are likely to be more participants and a wider range of outcomes. As a researcher, you must recruit these participants and ensure that they meet the criteria for inclusion. Finding people who are willing to participate in this type of project (often volunteering their time for free) can be challenging, and so as a researcher, it can be useful to have a minimum number of participants that you believe (based on past research) will give you findings that can be reliable and valid within your context.

It is also worth mentioning that you will likely end up with a lot of data, much more than can actually be presented in your master’s thesis.

One of the challenging pieces of the research process is deciding which findings make the cut for your thesis and which get saved for a later date. While your data are probably very interesting to you, it is important that you do not overwhelm the reader or deviate from the research questions that you set out to answer.

To sum up, the process of actually carrying out research and distilling it for the writing part of your thesis takes time. You need to carefully plan your research steps to ensure not only that you cover everything you intend to, but that you also do it in good time, leaving yourself ample space in your schedule to write up your thesis.

Write your Master’s thesis: the right structure

It’s helpful to start here by going over the structure of a master’s thesis. The precise way that different master’s theses are structured is largely going to depend on the discipline area. But most of the time, empirical dissertations follow a format including:

• Table of contents
• List of tables/figures
• Introduction
• Literature review
• Methodology

Before you start writing

Before you start to write, draft an outline of your approach to each section including the word count you expect to have (total word counts also vary by discipline).

Within each section you should also include all the major subheadings that you plan to include in the final version.

Before writing any of the sections, meet with your supervisor to ensure that your outline generally conforms to their expectations. Supervisors are the experts in the field and have likely seen many master’s theses, so they will be able to tell you if you are on the right track.

Beginning to write

It’s worth noting here that the order in which you write all the sections of your master’s thesis can vary depending on your process and preferences.

Once you have a detailed outline, there is no rule that says you have to start with the introduction and end with the conclusion. While the reader will inevitably read your thesis this way, you are free to write the ‘easy’ sections first and then move on to ones that you find more challenging.

For many students, beginning with the methodology chapter makes the most sense, as this allows the project to be framed around the steps that you, as a researcher, will take. The methodology usually includes:

• The research question(s).
• Any hypotheses that you might have.
• Your theoretical framework, and the methods that you will use to collect your data.
• Often, but not always, it includes ethical considerations, especially if you are working with human participants.

For many writers, the methodology chapter is written prior to the collection of data, whereas other chapters may be written after the data have been collected and analysed.

The same can be said for writing the literature review . For some writers, the literature review begins to take shape early in the project, but others choose to leave the writing until after data collection has occurred.

Both strategies have value. Writing the literature review early can give a researcher a clear indication of what data already exists and how this could relate to the potential project. The downside is that if the findings from the current project do not match the historical findings from the literature, the whole chapter may need to be revised to better align with the current findings.

Leaving the literature review until after the data collection means a bigger gap between when the reading was actually done for the project and the writing up period, meaning that the sources may need to be consulted repeatedly. In addition, leaving all the writing to the end of the project may seem tedious for some writers.

Another element that you will need to consider is how to present your findings . For some researchers, combining the findings and discussion sections makes logical sense, whereas for others, this presentation makes the chapter unwieldy and difficult to read.

Staying on track

There is no universal approach to writing a master’s thesis, but there are a lot of people out there who are willing to help you along the way. You will put yourself in a really good place if you seek advice at multiple stages in the process and from multiple different sources.

Your university library is going to be a useful source for research and reference, whereas your supervisor can give more discipline-specific advice on writing. Your university will likely have a writing centre too that can offer suggestions on how to improve your writing and make sure that you are staying on track. Making appointments at your writing centre can also help with accountability, as you will have to actually complete parts of your writing in order to discuss them with others.

Finishing and proofreading

When you write those last few words of your conclusion and you have made it to the end of your thesis (hopefully in one piece – you, not the thesis), there may be a sense of finality. It’s a huge feat you’ve just overcome and for that, you deserve a pat on the back.

But finishing writing your master’s thesis is a little like reaching Camp 4 on an Everest summit trek. Without wanting to sound too ominous, there is still a considerable amount of work to do – chiefly, putting the finishing touches on your thesis through editing and proofreading .

Hopefully, during the process of writing your thesis, you sent drafts to your supervisor for review. These drafts may have included individual chapters or various sections within the data set that required clarification. Your supervisor would have provided feedback on these drafts either through written or verbal comments. It is essential that you keep track of these comments, as they will become crucial for the final stages prior to submission.

There are two ways that you can approach the editing of your master’s thesis. Both have value and it depends on how you view the process of writing. These are:

• Individually edit sections as they are returned from the supervisor.
• Edit at the very end, so that the editing can be consistent across sections.

With the first strategy, the editing process is broken up into manageable chunks, but at the end you will have to go back and re-edit sections to improve the clarity and flow.

With the second strategy, you may be able to achieve better flow, but the number of edits at the end may seem overwhelming and take up considerable time.

These challenges bring us back to the importance of a timeline. Leaving several weeks for the editing process is necessary because editing can take longer than you think . Also, once you have made these necessary edits, you will need to go through and proofread your document to make sure that the fine details are consistent across chapters. This includes things like making sure acronyms are clearly defined, tables are appropriately numbered/titled, that punctuation and syntax are accurate, and that formatting and alignment is consistent.

Something you may find challenging during the finishing process is knowing when to stop. With writing there are always changes that can be made – ideas or sentences that can be written just a little bit better or slightly more clearly. You could spend years (really!) refining your work – writing and rewriting sections to make them exactly how you want them – but the simple fact is: you do not have time for that.

Use the time that you do have for editing your thesis to the best of your ability, but also be willing to say “this is good enough” and submit your work.

Handing something in that you have worked diligently on for a long time is a truly satisfying feeling, so try to cherish that moment when it comes.

Also, it goes without saying but is always worth the reminder: the expert editors we have on board here at Oxbridge Editing can not only relieve a phenomenal amount of effort in this final hurdle of your assignment, but, thanks to their experience and skill, they will also ensure your thesis is flawless and truly ready for submission. You can find out more about thesis editing here .

Final words

Hopefully, by reading this post you have identified some tips for writing your master’s thesis that you can apply in your own context.

While the finished product will vary by discipline, the strategies listed above can apply across a wide range of contexts.

Above all else: start early and stick to the plan.

There are many examples of master’s dissertations that you can refer to for guidance so that you can identify the appropriate thesis structure for your project. By doing a little bit each day and by keeping track of your reading, you can ensure that you remain organised and efficient with your work.

Remember that writing your master’s thesis is your first opportunity to demonstrate to the academic community that you are a proficient scholar in your field. A UK master’s dissertation is no easy task, but there are lots of people and resources available to help you. Take guidance from your supervisor and use the facilities that exist on your university campus, including the writing centre and the library.

Best of luck!

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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.

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What is a thesis | A Complete Guide with Examples

Table of Contents

A thesis is a comprehensive academic paper based on your original research that presents new findings, arguments, and ideas of your study. It’s typically submitted at the end of your master’s degree or as a capstone of your bachelor’s degree.

However, writing a thesis can be laborious, especially for beginners. From the initial challenge of pinpointing a compelling research topic to organizing and presenting findings, the process is filled with potential pitfalls.

Therefore, to help you, this guide talks about what is a thesis. Additionally, it offers revelations and methodologies to transform it from an overwhelming task to a manageable and rewarding academic milestone.

What is a thesis?

A thesis is an in-depth research study that identifies a particular topic of inquiry and presents a clear argument or perspective about that topic using evidence and logic.

Writing a thesis showcases your ability of critical thinking, gathering evidence, and making a compelling argument. Integral to these competencies is thorough research, which not only fortifies your propositions but also confers credibility to your entire study.

Furthermore, there's another phenomenon you might often confuse with the thesis: the ' working thesis .' However, they aren't similar and shouldn't be used interchangeably.

A working thesis, often referred to as a preliminary or tentative thesis, is an initial version of your thesis statement. It serves as a draft or a starting point that guides your research in its early stages.

As you research more and gather more evidence, your initial thesis (aka working thesis) might change. It's like a starting point that can be adjusted as you learn more. It's normal for your main topic to change a few times before you finalize it.

While a thesis identifies and provides an overarching argument, the key to clearly communicating the central point of that argument lies in writing a strong thesis statement.

What is a thesis statement?

A strong thesis statement (aka thesis sentence) is a concise summary of the main argument or claim of the paper. It serves as a critical anchor in any academic work, succinctly encapsulating the primary argument or main idea of the entire paper.

Typically found within the introductory section, a strong thesis statement acts as a roadmap of your thesis, directing readers through your arguments and findings. By delineating the core focus of your investigation, it offers readers an immediate understanding of the context and the gravity of your study.

Furthermore, an effectively crafted thesis statement can set forth the boundaries of your research, helping readers anticipate the specific areas of inquiry you are addressing.

Different types of thesis statements

A good thesis statement is clear, specific, and arguable. Therefore, it is necessary for you to choose the right type of thesis statement for your academic papers.

Thesis statements can be classified based on their purpose and structure. Here are the primary types of thesis statements:

Argumentative (or Persuasive) thesis statement

Purpose : To convince the reader of a particular stance or point of view by presenting evidence and formulating a compelling argument.

Example : Reducing plastic use in daily life is essential for environmental health.

Analytical thesis statement

Purpose : To break down an idea or issue into its components and evaluate it.

Example : By examining the long-term effects, social implications, and economic impact of climate change, it becomes evident that immediate global action is necessary.

Expository (or Descriptive) thesis statement

Purpose : To explain a topic or subject to the reader.

Example : The Great Depression, spanning the 1930s, was a severe worldwide economic downturn triggered by a stock market crash, bank failures, and reduced consumer spending.

Cause and effect thesis statement

Purpose : To demonstrate a cause and its resulting effect.

Example : Overuse of smartphones can lead to impaired sleep patterns, reduced face-to-face social interactions, and increased levels of anxiety.

Compare and contrast thesis statement

Purpose : To highlight similarities and differences between two subjects.

Example : "While both novels '1984' and 'Brave New World' delve into dystopian futures, they differ in their portrayal of individual freedom, societal control, and the role of technology."

When you write a thesis statement , it's important to ensure clarity and precision, so the reader immediately understands the central focus of your work.

What is the difference between a thesis and a thesis statement?

While both terms are frequently used interchangeably, they have distinct meanings.

A thesis refers to the entire research document, encompassing all its chapters and sections. In contrast, a thesis statement is a brief assertion that encapsulates the central argument of the research.

Here’s an in-depth differentiation table of a thesis and a thesis statement.

Now, to craft a compelling thesis, it's crucial to adhere to a specific structure. Let’s break down these essential components that make up a thesis structure

15 components of a thesis structure

Navigating a thesis can be daunting. However, understanding its structure can make the process more manageable.

Here are the key components or different sections of a thesis structure:

Your thesis begins with the title page. It's not just a formality but the gateway to your research.

Here, you'll prominently display the necessary information about you (the author) and your institutional details.

• Title of your thesis
• Your full name
• Your department
• Your institution and degree program
• Your submission date
• Your Supervisor's name (in some cases)
• Your Department or faculty (in some cases)
• Your University's logo (in some cases)
• Your Student ID (in some cases)

In a concise manner, you'll have to summarize the critical aspects of your research in typically no more than 200-300 words.

This includes the problem statement, methodology, key findings, and conclusions. For many, the abstract will determine if they delve deeper into your work, so ensure it's clear and compelling.

Acknowledgments

Research is rarely a solitary endeavor. In the acknowledgments section, you have the chance to express gratitude to those who've supported your journey.

This might include advisors, peers, institutions, or even personal sources of inspiration and support. It's a personal touch, reflecting the humanity behind the academic rigor.

Table of contents

A roadmap for your readers, the table of contents lists the chapters, sections, and subsections of your thesis.

By providing page numbers, you allow readers to navigate your work easily, jumping to sections that pique their interest.

List of figures and tables

Research often involves data, and presenting this data visually can enhance understanding. This section provides an organized listing of all figures and tables in your thesis.

It's a visual index, ensuring that readers can quickly locate and reference your graphical data.

Introduction

Here's where you introduce your research topic, articulate the research question or objective, and outline the significance of your study.

• Present the research topic : Clearly articulate the central theme or subject of your research.
• Background information : Ground your research topic, providing any necessary context or background information your readers might need to understand the significance of your study.
• Define the scope : Clearly delineate the boundaries of your research, indicating what will and won't be covered.
• Literature review : Introduce any relevant existing research on your topic, situating your work within the broader academic conversation and highlighting where your research fits in.
• State the research Question(s) or objective(s) : Clearly articulate the primary questions or objectives your research aims to address.
• Outline the study's structure : Give a brief overview of how the subsequent sections of your work will unfold, guiding your readers through the journey ahead.

The introduction should captivate your readers, making them eager to delve deeper into your research journey.

Literature review section

Your study correlates with existing research. Therefore, in the literature review section, you'll engage in a dialogue with existing knowledge, highlighting relevant studies, theories, and findings.

It's here that you identify gaps in the current knowledge, positioning your research as a bridge to new insights.

To streamline this process, consider leveraging AI tools. For example, the SciSpace literature review tool enables you to efficiently explore and delve into research papers, simplifying your literature review journey.

Methodology

In the research methodology section, you’ll detail the tools, techniques, and processes you employed to gather and analyze data. This section will inform the readers about how you approached your research questions and ensures the reproducibility of your study.

Here's a breakdown of what it should encompass:

• Research Design : Describe the overall structure and approach of your research. Are you conducting a qualitative study with in-depth interviews? Or is it a quantitative study using statistical analysis? Perhaps it's a mixed-methods approach?
• Data Collection : Detail the methods you used to gather data. This could include surveys, experiments, observations, interviews, archival research, etc. Mention where you sourced your data, the duration of data collection, and any tools or instruments used.
• Sampling : If applicable, explain how you selected participants or data sources for your study. Discuss the size of your sample and the rationale behind choosing it.
• Data Analysis : Describe the techniques and tools you used to process and analyze the data. This could range from statistical tests in quantitative research to thematic analysis in qualitative research.
• Validity and Reliability : Address the steps you took to ensure the validity and reliability of your findings to ensure that your results are both accurate and consistent.
• Ethical Considerations : Highlight any ethical issues related to your research and the measures you took to address them, including — informed consent, confidentiality, and data storage and protection measures.

Moreover, different research questions necessitate different types of methodologies. For instance:

• Experimental methodology : Often used in sciences, this involves a controlled experiment to discern causality.
• Qualitative methodology : Employed when exploring patterns or phenomena without numerical data. Methods can include interviews, focus groups, or content analysis.
• Quantitative methodology : Concerned with measurable data and often involves statistical analysis. Surveys and structured observations are common tools here.
• Mixed methods : As the name implies, this combines both qualitative and quantitative methodologies.

The Methodology section isn’t just about detailing the methods but also justifying why they were chosen. The appropriateness of the methods in addressing your research question can significantly impact the credibility of your findings.

Results (or Findings)

This section presents the outcomes of your research. It's crucial to note that the nature of your results may vary; they could be quantitative, qualitative, or a mix of both.

Quantitative results often present statistical data, showcasing measurable outcomes, and they benefit from tables, graphs, and figures to depict these data points.

Qualitative results , on the other hand, might delve into patterns, themes, or narratives derived from non-numerical data, such as interviews or observations.

Regardless of the nature of your results, clarity is essential. This section is purely about presenting the data without offering interpretations — that comes later in the discussion.

In the discussion section, the raw data transforms into valuable insights.

Start by revisiting your research question and contrast it with the findings. How do your results expand, constrict, or challenge current academic conversations?

Dive into the intricacies of the data, guiding the reader through its implications. Detail potential limitations transparently, signaling your awareness of the research's boundaries. This is where your academic voice should be resonant and confident.

Practical implications (Recommendation) section

Based on the insights derived from your research, this section provides actionable suggestions or proposed solutions.

Whether aimed at industry professionals or the general public, recommendations translate your academic findings into potential real-world actions. They help readers understand the practical implications of your work and how it can be applied to effect change or improvement in a given field.

When crafting recommendations, it's essential to ensure they're feasible and rooted in the evidence provided by your research. They shouldn't merely be aspirational but should offer a clear path forward, grounded in your findings.

The conclusion provides closure to your research narrative.

It's not merely a recap but a synthesis of your main findings and their broader implications. Reconnect with the research questions or hypotheses posited at the beginning, offering clear answers based on your findings.

Reflect on the broader contributions of your study, considering its impact on the academic community and potential real-world applications.

Lastly, the conclusion should leave your readers with a clear understanding of the value and impact of your study.

References (or Bibliography)

Every theory you've expounded upon, every data point you've cited, and every methodological precedent you've followed finds its acknowledgment here.

In references, it's crucial to ensure meticulous consistency in formatting, mirroring the specific guidelines of the chosen citation style .

Proper referencing helps to avoid plagiarism , gives credit to original ideas, and allows readers to explore topics of interest. Moreover, it situates your work within the continuum of academic knowledge.

To properly cite the sources used in the study, you can rely on online citation generator tools  to generate accurate citations!

Here’s more on how you can cite your sources.

Often, the depth of research produces a wealth of material that, while crucial, can make the core content of the thesis cumbersome. The appendix is where you mention extra information that supports your research but isn't central to the main text.

Whether it's raw datasets, detailed procedural methodologies, extended case studies, or any other ancillary material, the appendices ensure that these elements are archived for reference without breaking the main narrative's flow.

For thorough researchers and readers keen on meticulous details, the appendices provide a treasure trove of insights.

Glossary (optional)

In academics, specialized terminologies, and jargon are inevitable. However, not every reader is versed in every term.

The glossary, while optional, is a critical tool for accessibility. It's a bridge ensuring that even readers from outside the discipline can access, understand, and appreciate your work.

By defining complex terms and providing context, you're inviting a wider audience to engage with your research, enhancing its reach and impact.

Remember, while these components provide a structured framework, the essence of your thesis lies in the originality of your ideas, the rigor of your research, and the clarity of your presentation.

As you craft each section, keep your readers in mind, ensuring that your passion and dedication shine through every page.

Thesis examples

To further elucidate the concept of a thesis, here are illustrative examples from various fields:

Example 1 (History): Abolition, Africans, and Abstraction: the Influence of the ‘Noble Savage’ on British and French Antislavery Thought, 1787-1807 by Suchait Kahlon.

Example 2 (Climate Dynamics): Influence of external forcings on abrupt millennial-scale climate changes: a statistical modelling study by Takahito Mitsui · Michel Crucifix

Checklist for your thesis evaluation

Evaluating your thesis ensures that your research meets the standards of academia. Here's an elaborate checklist to guide you through this critical process.

Content and structure

• Is the thesis statement clear, concise, and debatable?
• Does the introduction provide sufficient background and context?
• Is the literature review comprehensive, relevant, and well-organized?
• Does the methodology section clearly describe and justify the research methods?
• Are the results/findings presented clearly and logically?
• Does the discussion interpret the results in light of the research question and existing literature?
• Is the conclusion summarizing the research and suggesting future directions or implications?

Clarity and coherence

• Is the writing clear and free of jargon?
• Are ideas and sections logically connected and flowing?
• Is there a clear narrative or argument throughout the thesis?

Research quality

• Is the research question significant and relevant?
• Are the research methods appropriate for the question?
• Is the sample size (if applicable) adequate?
• Are the data analysis techniques appropriate and correctly applied?
• Are potential biases or limitations addressed?

Originality and significance

• Does the thesis contribute new knowledge or insights to the field?
• Is the research grounded in existing literature while offering fresh perspectives?

Formatting and presentation

• Is the thesis formatted according to institutional guidelines?
• Are figures, tables, and charts clear, labeled, and referenced in the text?
• Is the bibliography or reference list complete and consistently formatted?
• Are appendices relevant and appropriately referenced in the main text?

Grammar and language

• Is the thesis free of grammatical and spelling errors?
• Is the language professional, consistent, and appropriate for an academic audience?
• Are quotations and paraphrased material correctly cited?

Feedback and revision

• Have you sought feedback from peers, advisors, or experts in the field?
• Have you addressed the feedback and made the necessary revisions?

Overall assessment

• Does the thesis as a whole feel cohesive and comprehensive?
• Would the thesis be understandable and valuable to someone in your field?

Ensure to use this checklist to leave no ground for doubt or missed information in your thesis.

After writing your thesis, the next step is to discuss and defend your findings verbally in front of a knowledgeable panel. You’ve to be well prepared as your professors may grade your presentation abilities.

Preparing your thesis defense

A thesis defense, also known as "defending the thesis," is the culmination of a scholar's research journey. It's the final frontier, where you’ll present their findings and face scrutiny from a panel of experts.

Typically, the defense involves a public presentation where you’ll have to outline your study, followed by a question-and-answer session with a committee of experts. This committee assesses the validity, originality, and significance of the research.

The defense serves as a rite of passage for scholars. It's an opportunity to showcase expertise, address criticisms, and refine arguments. A successful defense not only validates the research but also establishes your authority as a researcher in your field.

Here’s how you can effectively prepare for your thesis defense .

Now, having touched upon the process of defending a thesis, it's worth noting that scholarly work can take various forms, depending on academic and regional practices.

One such form, often paralleled with the thesis, is the 'dissertation.' But what differentiates the two?

Dissertation vs. Thesis

Often used interchangeably in casual discourse, they refer to distinct research projects undertaken at different levels of higher education.

To the uninitiated, understanding their meaning might be elusive. So, let's demystify these terms and delve into their core differences.

Here's a table differentiating between the two.

Wrapping up

From understanding the foundational concept of a thesis to navigating its various components, differentiating it from a dissertation, and recognizing the importance of proper citation — this guide covers it all.

As scholars and readers, understanding these nuances not only aids in academic pursuits but also fosters a deeper appreciation for the relentless quest for knowledge that drives academia.

It’s important to remember that every thesis is a testament to curiosity, dedication, and the indomitable spirit of discovery.

Good luck with your thesis writing!

Frequently Asked Questions

A thesis typically ranges between 40-80 pages, but its length can vary based on the research topic, institution guidelines, and level of study.

A PhD thesis usually spans 200-300 pages, though this can vary based on the discipline, complexity of the research, and institutional requirements.

To identify a thesis topic, consider current trends in your field, gaps in existing literature, personal interests, and discussions with advisors or mentors. Additionally, reviewing related journals and conference proceedings can provide insights into potential areas of exploration.

The conceptual framework is often situated in the literature review or theoretical framework section of a thesis. It helps set the stage by providing the context, defining key concepts, and explaining the relationships between variables.

A thesis statement should be concise, clear, and specific. It should state the main argument or point of your research. Start by pinpointing the central question or issue your research addresses, then condense that into a single statement, ensuring it reflects the essence of your paper.

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How do I make a simple literature analysis in a bachelor's or master's thesis?

A literature analysis for the bachelor’s or master’s thesis is the systematic research, acquisition and examination of scientific texts and reliable publications including papers in regard to the specific questions of the topic.

A literature analysis is the systematic research, procurement and analysis of scientific literature including papers and their contents with regard to specific issues.

The result is a well-structured and detailed collection and overview of answers to the leading question of the thesis in the form of relevant data, arguments and descriptions of the topic.

What is the purpose of a literature analysis or review?

The purpose is to understand and describe the current state of research as precisely as possible in order to discover the research gap, the research question and the detailed questions. Therefore, both the procedure and the results must be documented accurately. This is the only way the discovery of the gap and questions  can be understood.

What are the advantages of a literature analysis compared to other methods?

This question is irrelevant because you have to complete this evaluation for your thesis in any case. There's no way around it. Without the evaluation of sources, the building material (base) for the text is ultimately missing... But let's look at what would happen if this was the only method used and no further empirical analysis was done. These are the advantages:

• No dependence on others
• Plenty of sources
• Close orientation to literature
• Easy to plan if the sources are available
• Ability to write quickly
• Less effort
• No wasted time waiting for data
• Fewer risks and therefore fewer surprises.

What are the disadvantages of a simple literature analysis?

The disadvantages are:

• You can spend forever searching and reading
• Danger of getting lost in literature
• Methodology is difficult to grasp because it is mainly mental operations that often have strange names.
• Without adequate preparation (leading question, detailed questions, formulation of objectives in the proposal and then in the introduction) you don't know exactly when you will be finished. This puts the whole project in danger.

Under what conditions can I do a simple literature analysis?

• You need a clear guiding question.
• You must find many good and abundant sources.
• The sources must contain the information and arguments for the answers.

BUT: sometimes you must do a simple literature evaluation because you cannot or may not do empiricism.

Which mistakes must I avoid and how?

• Under no circumstances should you read for weeks. Reading without aim only confuses you.
• You must always take notes, otherwise you will not find anything.
• You should not start without a framework of questions with guiding questions and detailed questions.
• It is essential that you first define the terms related to the topic to make your way to the work’s goal.
• Only reputable and productive sources are suitable.
• Avoid plagiarism otherwise it was all for nothing. To do this, follow these simple rules.

Which tools can I use?

• Questions are your most important tools.
• A guide to evaluating sources is very important.
• You need a procedure for researching sources.

Examples will help you find answers to your questions with the help of literature. You can find all the tools in the Aristolo Thesis Guide.

What's the best place to start?

Start with the research question, the goal and the detailed questions. Then deal with the terms. Proceed as follows:

• Clarify your terms with the help of sources. Write out the definitions with references to the source.
• Search for models in scientific sources such as reference books and studies.
• Find other good sources with relevant content.
• Look for the answers to your detailed questions in the literature. If you find some, the question is acceptable, if not, then revise it.
• Work through your entire question list.

How does the Aristolo Thesis Guide help you with a literature analysis for the Bachelor’s or Master’s Thesis?

The Thesis Guide provides approaches to simple literature evaluations so you can learn how to formulate questions, find the appropriate sources and analyze them to create your own thesis.

Good luck writing your text! Silvio and the Aristolo Team

PS: Check out the Thesis-ABC and the Thesis Guide for writing a bachelor or master thesis in 31 days.

Dissertation, Doctoral Project, and Thesis Information & Templates

Note: Forms required for the submission of theses and dissertations are available on the  Academic Forms  page.

Important Notes for Dissertation, Doctoral Project & Thesis Writers

• Information is available in Section IV.B.2 Research on Human Subjects of the  Graduate Bulletin   (from the  Resources and Policies page ).
• Additional information and forms are available on the   IRB website . Your IRB approval number must be included on the Thesis or Dissertation Proposal Form.
• Consult the  Guidelines for Dissertation, Doctoral Project and Thesis Writers  before beginning your thesis or dissertation.
• Download a template to assist with formatting your work. The templates are unlocked and can be edited (links to the template can be found in the “Submission Procedures” sections below).
• Check the Resources & Guidelines section of the ProQuest website for instructions on using the site. The Library has created a very informative series of  short videos  about the choices you must make on the ProQuest site.
• Additional information on copyright, publishing options and other topics is available on  Lauinger’s Scholarly Communication  website.
• More information about the requirements for dissertations, doctoral projects and theses can be found in the  Graduate Bulletin .

Submission of the Thesis, Doctoral Project or Dissertation

Information on the forms required leading up to a defense and also afterward appear on Submission of Thesis  and  Submission of Dissertation or Doctoral Project .

Download a Thesis / Doctoral Project / Dissertation Template

(for Master’s and Doctoral candidates) We recommend that you download a Thesis / Doctoral Project / Dissertation Template using Mozilla Firefox, Safari, or Google Chrome browsers. There are some reported issues for students trying to download using Internet Explorer. The download links are shown below:

• The combined  Master’s Thesis / Doctoral Project / Doctoral Dissertation Template  for MS-Word for Windows is available at: Thesis/Project/Dissertation Template-PC
• The  Master’s   Thesis Template  for Word for Mac is available at:  Thesis Template-MAC
• The  Doctoral Template  for Word for Mac is available at  Dissertation Template-MAC
• If you use the LaTeX markup language, you can download a ZIP file folder containing several template and style documents, as well as an extensive tutorial manual, at this link:  Thesis/Dissertation Template-LaTeX . An updated .sty file was uploaded in June 2020.

LaTeX users please note: These LaTeX template materials are provided for the use of those who are already proficient in the use of LaTeX. Neither the Graduate School nor the faculty who helped develop this template are able to provide support or training in the use of this specialty software.

A note for better Understanding of Thesis vs Dissertation

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Methodology

• How to Do Thematic Analysis | Step-by-Step Guide & Examples

How to Do Thematic Analysis | Step-by-Step Guide & Examples

Published on September 6, 2019 by Jack Caulfield . Revised on June 22, 2023.

Thematic analysis is a method of analyzing qualitative data . It is usually applied to a set of texts, such as an interview or transcripts . The researcher closely examines the data to identify common themes – topics, ideas and patterns of meaning that come up repeatedly.

There are various approaches to conducting thematic analysis, but the most common form follows a six-step process: familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up. Following this process can also help you avoid confirmation bias when formulating your analysis.

This process was originally developed for psychology research by Virginia Braun and Victoria Clarke . However, thematic analysis is a flexible method that can be adapted to many different kinds of research.

Table of contents

When to use thematic analysis, different approaches to thematic analysis, step 1: familiarization, step 2: coding, step 3: generating themes, step 4: reviewing themes, step 5: defining and naming themes, step 6: writing up, other interesting articles.

Thematic analysis is a good approach to research where you’re trying to find out something about people’s views, opinions, knowledge, experiences or values from a set of qualitative data – for example, interview transcripts , social media profiles, or survey responses .

Some types of research questions you might use thematic analysis to answer:

• How do patients perceive doctors in a hospital setting?
• What are young women’s experiences on dating sites?
• What are non-experts’ ideas and opinions about climate change?
• How is gender constructed in high school history teaching?

To answer any of these questions, you would collect data from a group of relevant participants and then analyze it. Thematic analysis allows you a lot of flexibility in interpreting the data, and allows you to approach large data sets more easily by sorting them into broad themes.

However, it also involves the risk of missing nuances in the data. Thematic analysis is often quite subjective and relies on the researcher’s judgement, so you have to reflect carefully on your own choices and interpretations.

Pay close attention to the data to ensure that you’re not picking up on things that are not there – or obscuring things that are.

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Once you’ve decided to use thematic analysis, there are different approaches to consider.

There’s the distinction between inductive and deductive approaches:

• An inductive approach involves allowing the data to determine your themes.
• A deductive approach involves coming to the data with some preconceived themes you expect to find reflected there, based on theory or existing knowledge.

Ask yourself: Does my theoretical framework give me a strong idea of what kind of themes I expect to find in the data (deductive), or am I planning to develop my own framework based on what I find (inductive)?

There’s also the distinction between a semantic and a latent approach:

• A semantic approach involves analyzing the explicit content of the data.
• A latent approach involves reading into the subtext and assumptions underlying the data.

Ask yourself: Am I interested in people’s stated opinions (semantic) or in what their statements reveal about their assumptions and social context (latent)?

After you’ve decided thematic analysis is the right method for analyzing your data, and you’ve thought about the approach you’re going to take, you can follow the six steps developed by Braun and Clarke .

The first step is to get to know our data. It’s important to get a thorough overview of all the data we collected before we start analyzing individual items.

This might involve transcribing audio , reading through the text and taking initial notes, and generally looking through the data to get familiar with it.

Next up, we need to code the data. Coding means highlighting sections of our text – usually phrases or sentences – and coming up with shorthand labels or “codes” to describe their content.

Let’s take a short example text. Say we’re researching perceptions of climate change among conservative voters aged 50 and up, and we have collected data through a series of interviews. An extract from one interview looks like this:

In this extract, we’ve highlighted various phrases in different colors corresponding to different codes. Each code describes the idea or feeling expressed in that part of the text.

At this stage, we want to be thorough: we go through the transcript of every interview and highlight everything that jumps out as relevant or potentially interesting. As well as highlighting all the phrases and sentences that match these codes, we can keep adding new codes as we go through the text.

After we’ve been through the text, we collate together all the data into groups identified by code. These codes allow us to gain a a condensed overview of the main points and common meanings that recur throughout the data.

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Next, we look over the codes we’ve created, identify patterns among them, and start coming up with themes.

Themes are generally broader than codes. Most of the time, you’ll combine several codes into a single theme. In our example, we might start combining codes into themes like this:

At this stage, we might decide that some of our codes are too vague or not relevant enough (for example, because they don’t appear very often in the data), so they can be discarded.

Other codes might become themes in their own right. In our example, we decided that the code “uncertainty” made sense as a theme, with some other codes incorporated into it.

Again, what we decide will vary according to what we’re trying to find out. We want to create potential themes that tell us something helpful about the data for our purposes.

Now we have to make sure that our themes are useful and accurate representations of the data. Here, we return to the data set and compare our themes against it. Are we missing anything? Are these themes really present in the data? What can we change to make our themes work better?

If we encounter problems with our themes, we might split them up, combine them, discard them or create new ones: whatever makes them more useful and accurate.

For example, we might decide upon looking through the data that “changing terminology” fits better under the “uncertainty” theme than under “distrust of experts,” since the data labelled with this code involves confusion, not necessarily distrust.

Now that you have a final list of themes, it’s time to name and define each of them.

Defining themes involves formulating exactly what we mean by each theme and figuring out how it helps us understand the data.

Naming themes involves coming up with a succinct and easily understandable name for each theme.

For example, we might look at “distrust of experts” and determine exactly who we mean by “experts” in this theme. We might decide that a better name for the theme is “distrust of authority” or “conspiracy thinking”.

Finally, we’ll write up our analysis of the data. Like all academic texts, writing up a thematic analysis requires an introduction to establish our research question, aims and approach.

We should also include a methodology section, describing how we collected the data (e.g. through semi-structured interviews or open-ended survey questions ) and explaining how we conducted the thematic analysis itself.

The results or findings section usually addresses each theme in turn. We describe how often the themes come up and what they mean, including examples from the data as evidence. Finally, our conclusion explains the main takeaways and shows how the analysis has answered our research question.

In our example, we might argue that conspiracy thinking about climate change is widespread among older conservative voters, point out the uncertainty with which many voters view the issue, and discuss the role of misinformation in respondents’ perceptions.

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.

• Normal distribution
• Measures of central tendency
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Discourse analysis
• Cohort study
• Peer review
• Ethnography

Research bias

• Implicit bias
• Cognitive bias
• Conformity bias
• Hawthorne effect
• Availability heuristic
• Attrition bias
• Social desirability bias

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Home > Dissertations, Theses & Capstones Projects by Program > Data Analysis & Visualization Master’s Theses and Capstone Projects

Data Analysis & Visualization Master’s Theses and Capstone Projects

Dissertations/theses/capstones from 2024 2024.

Assessing Job Vulnerability and Employment Growth in the Era of Large Language Models (LLMs) , Prudence P. Brou

The Charge Forward: An Assessment of Electric Vehicle Charging Infrastructure in New York City , Christopher S. Cali

Visualizing a Life, Uprooted: An Interactive, Web-Map and Scroll-Driven Exploration of the Oral History of my Great-Grandfather – from Ottoman Cilicia to Lebanon and Beyond , Alyssa Campbell

Examining the Health Risks of Particulate Matter 2.5 in New York City: How it Affects Marginalized Groups and the Steps Needed to Reduce Air Pollution , Freddy Castro

Clustering of Patients with Heart Disease , Mukadder Cinar

Modeling of COVID-19 Clinical Outcomes in Mexico: An Analysis of Demographic, Clinical, and Chronic Disease Factors , Livia Clarete

The Complete Sight and Sound Greatest Films of All Time Database , Katie Donia

Wrapped Insights: A Data-Driven Approach to Personalizing User Experiences in a Digital Tipping Platform , Hamza Habeeb

The Efficacy of Using Machine Learning Techniques for Identifying and Classifying “Fake News” , Muhammad Islam

Invisible Hand of Socioeconomic Factors in Rising Trend of Maternal Mortality Rates in the U.S. , Disha Kanada

Factors that Impact New York City Public High School Graduation: Finding Barriers to Education through Data Analysis and Visualization , Kyoung Kang

Multi-Perspective Analysis for Derivative Financial Product Prediction with Stacked Recurrent Neural Networks, Natural Language Processing and Large Language Model , Ethan Lo

What Does One Billion Dollars Look Like?: Visualizing Extreme Wealth , William Mahoney Luckman

Making Sense of Making Parole in New York , Alexandra McGlinchy

Employment Outcomes in Higher Education , Yunxia Wei

Dissertations/Theses/Capstones from 2023 2023

Phantom Shootings , Allan Ambris

Naming Venus: An Exploration of Goddesses, Heroines, and Famous Women , Kavya Beheraj

Social Impacts of Robotics on the Labor and Employment Market , Kelvin Espinal

Fighting the Invisibility of Domestic Violence , Yesenny Fernandez

Navigating Through World’s Military Spending Data with Scroll-Event Driven Visualization , Hong Beom Hur

Evocative Visualization of Void and Fluidity , Tomiko Karino

Analyzing Relationships with Machine Learning , Oscar Ko

Analyzing ‘Fight the Power’ Part 1: Music and Longevity Across Evolving Marketing Eras , Shokolatte Tachikawa

Stand-up Comedy Visualized , Berna Yenidogan

Dissertations/Theses/Capstones from 2022 2022

El Ritmo del Westside: Exploring the Musical Landscape of San Antonio’s Historic Westside , Valeria Alderete

A Comparison of Machine Learning Techniques for Validating Students’ Proficiency in Mathematics , Alexander Avdeev

A Machine Learning Approach to Predicting the Onset of Type II Diabetes in a Sample of Pima Indian Women , Meriem Benarbia

Disrepair, Displacement and Distress: Finding Housing Stories Through Data Visualizations , Jennifer Cheng

Blockchain: Key Principles , Nadezda Chikurova

Data for Power: A Visual Tool for Organizing Unions , Shay Culpepper

Happiness From a Different Perspective , Suparna Das

Happiness and Policy Implications: A Sociological View , Sarah M. Kahl

Heating Fire Incidents in New York City , Merissa K. Lissade

NYC vs. Covid-19: The Human and Financial Resources Deployed to Fight the Most Expensive Health Emergency in History in NYC during the Year 2020 , Elmer A. Maldonado Ramirez

Slices of the Big Apple: A Visual Explanation and Analysis of the New York City Budget , Joanne Ramadani

The Value of NFTs , Angelina Tham

Air Pollution, Climate Change, and Our Health , Kathia Vargas Feliz

Peru's Fishmeal Industry: Its Societal and Environmental Impact , Angel Vizurraga

Why, New York City? Gauging the Quality of Life Through the Thoughts of Tweeters , Sheryl Williams

Dissertations/Theses/Capstones from 2021 2021

Data Analysis and Visualization to Dismantle Gender Discrimination in the Field of Technology , Quinn Bolewicki

Remaking Cinema: Black Hollywood Films, Filmmakers, and Finances , Kiana A. Carrington

Detecting Stance on Covid-19 Vaccine in a Polarized Media , Rodica Ceslov

Dota 2 Hero Selection Analysis , Zhan Gong

An Analysis of Machine Learning Techniques for Economic Recession Prediction , Sheridan Kamal

Black Women in Romance , Vianny C. Lugo Aracena

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