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Where Can I Get Help Writing My Thesis Online?

quantitative masters dissertation

You’ve spent years preparing for your master’s degree or PhD. You’ve read, studied and spent hours of time and energy writing papers. Now you’ve arrived at the culmination of all this effort: writing your thesis. There are plenty of compelling stories about the time and energy that students have spent drafting their dissertations and theses.

The good news is that you’re not alone. While you certainly don’t want to hire someone to write your thesis for you, which goes against most institution policies and puts your academic integrity at risk, you can get plenty of help with certain aspects of your thesis online. Whether you’re looking for a little guidance or extensive assistance, various services can make writing or editing your thesis go smoothly.

Dissertation Editor

One of the greatest challenges of writing your thesis can be juggling your family or job responsibilities with your studies. The time that writing takes can add another layer of obligation to your already-packed schedule. Dissertation Editor is a company whose founder is a PhD-educated writer and professor, and it promises to help you complete your thesis or dissertation on time and in compliance with your university’s rules and regulations.

quantitative masters dissertation

Dissertation Editor’s primary function is to guide you along in the writing process and provide a helping hand in understanding everything you need to take care of. It places you with a writer who specializes in your area of study, and this individual can help you organize and analyze your research while making sure that your thesis fits your writing style and personality. This company also specializes in helping with any statistical analysis that you use in your thesis.

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quantitative masters dissertation

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Best Dissertation

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quantitative masters dissertation

Like most of the top thesis-assistance services, Best Dissertation employs writers with advanced degrees who specialize in various fields of study. What truly sets this company apart is the live support that it offers any time of the day or night. It claims to take the stress and strain out of writing your dissertation or thesis.

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quantitative masters dissertation

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  • Introduction

Quantitative Dissertations

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

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

Characteristics of quantitative dissertations

  • Types of quantitative dissertation: Replication, Data and Theory

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

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

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

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

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

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

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

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

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

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

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

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

Types of quantitative dissertation

Replication, data or theory.

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

Route #1: Replication-based dissertations

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

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

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

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

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

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

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

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

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

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

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

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

Choosing between routes

Which route should i choose.

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

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

Grad Coach

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 .

The results & analysis section in a dissertation

Overview: Quantitative Results Chapter

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

What exactly is the results chapter?

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

But how’s that different from the discussion chapter?

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

Let’s look at an example.

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

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

The results and discussion chapter are typically split

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.

There must be alignment between your research aims objectives and questions

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?

quantitative masters dissertation

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.

Your chapter must have a golden thread

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.

Communicate the 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.

Descriptive statistics

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 .

Dive into the inferential statistics

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.

make it easy for your reader to understand your quantitative results

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.

quantitative masters dissertation

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Master economics QEM

Master dissertation.

quantitative masters dissertation

To validate Erasmus Mundus Joint Master Degree QEM (EMJMD) , students are required to prepare a master's dissertation  during their fourth semester under the joint supervision of two advisors from the specialization universities. Students must then defend their dissertation in front of a joint committee composed of members from both universities.

Past Quantitative Economics Masters Dissertations

You can see the Dissertations defended by Quantitative Economics Master graduates in the following link  

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Hypothesis testing

Reliability and validity, approaches to quantitative research.

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Through quantitative research we seek to understand the relationships between variables. A variable will be a characteristic, value, attribute or behaviour that is of interest to the researcher. Some variables can be simple to measure, for example, height and weight. By contrast, others such as self-esteem or socio-economic status are more complex and therefore harder to measure. This is why it is important to operationalise your variables.

This essentially means being very clear about the way in which variables will be defined and measured in your study; this lends credibility to your methodology and helps the replicability of your research. It is important that you are detailed in your operational definition of any given variable because another researcher may define that variable differently from you. To illustrate, if a study examined memory ability, the researcher would specify exactly how this measure was generated: was it the number of words recalled in 60 seconds after reading a passage of text? Was it details about a picture? Defining your variables is an important of the research process as this will affect the reliability and validity of your study.

is the variable changed or manipulated by the researcher. Research generally seeks to establish whether the independent variable has an affect or influences the dependent variable in some way; this may be through a causal or non-causal relationship.

i s the variable that the researcher is trying to predict or explain through understanding its relationship with the independent variable. For example, if a researcher wants to establish if drinking coffee aids sporting performance, your independent variable would be the amount of coffee consumed (no coffee/1 cup/3 cups) and the dependent variable would be some operational definition of sporting performance (amount of weight lifted/vertical jump height/time taken to sprint 100m).

is  a variable that affects the strength of the relationship between the independent and dependent variable. For example, if you looked at the relationship between personality similarity in friendships (independent variable) and perceived friendship satisfaction (dependent variable), it might be that age is a moderating variable – e.g. the older you are, the weaker the relationship between personality similarity in a friendship and associated satisfaction with that friendship. From this you could make the tentative suggestion that similarity in personality becomes less important in a satisfying relationship as we become older. 

i s a variable that helps to explain the relationship between the independent and dependent variables. Consider the example above, we might discover that the number of shared activities also contributes to perceived friendship satisfaction . We could then remove this from our analysis and find that the relationship between personality similarity in friendships and perceived satisfaction in a friendship disappears - this would suggest that the relationship was mediated by the variable shared interests.

is any variable that is not the independent variable but may affect the results of the experiment. Examples can include; aspects of the environment (temperature/noise/lighting); differences between participants (mood/intellect/concentration); and experimenter effects (clues in an experiment which may convey that purpose of the research). It is important to minimise the influence of extraneous variables through the careful use of controls – for example, there are ways of minimising the effect of differences between participants through your experimental design (more on this later!)

  • Relationships are Complicated toolkit This short guide offers more information and examples of the types of relationships between variables.

A hypothesis is a predictive statement that can be tested through the collection of data. The data can be analysed and can either provide support for, or help to reject, a hypothesis; this in turn should allow a researcher to draw some conclusions about what they are investigating.

Null and alternative hypotheses

Hypothesis are classified by the way they describe the expected association/difference between variables. When we test our hypothesis/hypotheses it is important to remember we are testing it against the assumption that there isn’t an association/difference between the independent and dependent variables: we call this the null hypothesis. By testing this assumption, statistical tests can estimate how likely it is that any observed association/difference between variables is due to chance.

In addition to the null hypothesis we also have the alternative hypothesis. This hypothesis states that there is an association/difference between groups; this cannot be tested directly but can be accepted by rejecting the null hypothesis. This is achieved through statistical tests that can help to demonstrate that any observed association/differences are not due to chance. Once this is established, we can accept our alternative hypothesis and start to draw conclusions from our data.

Hypotheses can either be one-tailed or two-tailed:

  • One-tailed hypothesis –specifies the direction of the predicted association between the independent and dependent variable. For example, the higher an individual’s educational level, the more books they will read in a one-year period.
  • Two-tailed hypothesis – does not specify the direction of the predicted association between variables; only that an association exists. For example, there will a be difference in the number of books read in a one-year period, dependent on the level of an individual’s education.

Key things to remember when writing your hypothesis/hypotheses:

  • Your hypothesis should always be written as a statement and before any data are collected .
  • It should be simple and specific ; include the variables, using concise operational definitions, and the predicted relationship between these variables. If you have several predictor (independent) variables it would be better to write several simple hypotheses – think one predictor and one outcome variable.
  • Always keep your language clear and focused .

It is important that you show rigour within your research. This means demonstrating that you have given careful consideration to how you can enhance the quality of your research project. Within quantitative research this is achieved through examining reliability and validity.

  • Reliability – is a measure of how consistent, dependable and repeatable something is.
  • Validity – is the extent to which research measures the concept that it was designed to measure.

For example, if you had some scales that were always weighed an object as 5kg lighter than it actually is, this would be an example of a measure that was very reliable but not valid : the scales will always give you a consistent measure of weight, but this measure is not accurate.

There are several different types of reliability and validity that you should consider when planning, conducting and writing up your research project. For more information on the different types of reliability and validity have a look at the recommendations below:

  • Designing and Doing Survey Research (Andres, 2012) – see Chapter 7 .
  • Quantitative Health Research Issues and Methods (Curtis & Drennan, 2013) – see Chapter 16 .
  • Research Methods in Psychology (Howitt & Cramer, 2017) – see Chapter 16 .
  • Non-experimental
  • 'True' experiments
  • Quasi-experimental
  • Between-subjects
  • Within-subjects

Non-experimental research designs do not seek to establish cause and effect relationships. This is because the researcher does not manipulate the independent variable(s) to measure any effects on the dependent variable(s). Instead, researchers may use this type of design to begin exploring a topic where there is little current understanding, or to investigate the relationship between two (or more) variables.

  • Descriptive : these research designs help to understand the current state of a phenomenon and are often used when not much is known about a topic. Variables are not controlled, and data tends to be collected through observation or surveys. An example of this might be an investigation into the preferred news sources of 13-18-year olds.
  • Correlational :  these designs measure a relationship between two variables that are not controlled. As such, correlational designs cannot establish cause and effect – always remember correlation does not imply causation! This approach can be useful when there is a suspected relationship between variables, but it would be impractical or unethical to manipulate one of those variables. For example, you might hypothesize

True experiments seek to establish cause and effect relationships between a group of variables. Researchers control for all variables except for the variable(s) being manipulated, to establish its effect on the dependent variable.

These are similar to true experiments: the aim is to establish cause and effect relationships. Crucially however, assignment to groups is not random. This type of design is often used when it is not possible for the researcher to randomly assign participants to groups because they are interested in understanding a particular phenomenon in relation to naturally occurring differences between groups – an example of this could be an experiment where a researcher is interested in examining whether the effect of coffee consumption on sleep differs depending on age. In this example, it is impossible for the researcher to manipulate the age of participants, so instead group assignment would be made based on predetermined criteria e.g. under 40, 40+. As this assignment cannot be random this would be a quasi-experiment.

Between-subjects designs involve the assignments of participants to one of two (or more) conditions, with each participant experiencing only that condition. In its simplest form, a between-subjects design requires a control condition and a treatment condition. If the results of an experiment differ greatly between conditions, then it can be assumed that this due to the effect of the intervention or manipulation that has been applied in the treatment condition. To help minimise the affect of extraneous variables that might impact differences between the groups (and increase the likelihood that observed differences are due to the effect of the independent variable), participants in the control and treatment conditions might be matched for relevant characteristics.

For example, in an experiment to assess the effectiveness of two training programmes in improving athletic performance, participants might be matched for some key measures of fitness such as 100m sprint time, maximum squat etc. This would enable researchers to be more confident that any changes to athletic performance in the participants between the two groups were likely due to the training programme they undertook, rather than natural, pre-exiting differences in athletic performance.

Sometimes referred to as repeated measures , this approach involves obtaining more than one measure from each participant in a study. This means that participants take part in both the control and treatment condition(s). The primary advantage of this is that participants act as their own control; you can be more confident that any observed differences result from the treatment condition rather than naturally occurring differences between the groups. One problem, however, is that of order effects (sometimes called practice effects). These effects may occur because conditions are applied one after the other and this can lead to changes in performance that are not the result of the treatment but instead reflect some effect of the previous condition that a participant has experienced. For example, improvements in performance could be due to learning/practise and a decline in performance could be due to fatigue over experiencing two (or more) experimental conditions back-to-back.

One way to account for this problem is to counterbalance the order of your conditions. To do this, a researcher would ensure that each condition in the experiment is experienced 1 st for an equal number of participants:

10 participants experience condition A 1 st and condition B 2 nd 10 participants experience condition B 1 st and condition A 2 nd

Doing this helps to reduce the impact of order effects by ensuring that any effects are distributed evenly across all conditions.

Another option could be to create a long time between testing conditions to reduce any possible effects of learning and/or fatigue. It should be noted however, that creating distance between conditions isn’t always practical and it can be hard to know how long is sufficient to eliminate a potential order effect: this is particularly true for practice effects as it can be hard to accurately determine how long it takes for potential improvements in performance due to learning, to disappear.

The final type of design is used when a research design has one (or more) factors that is between subjects and one (or more) factors that is within subject. This is often used when for research that is looking at the effect on an intervention in relation to another factor that has a fixed effect.

For example, if a researcher was looking at the effectiveness of a drug for treating pain in those under 40 and over 40, age would be a between-subjects factor because a participant can’t be both under and over 40 at the same time. The drug that participants take would be the within-subjects factor. This type of design is particularly useful if you want to examine if the effect on an intervention is different dependent upon another factor. In the example above, it would be possible to establish if the effect of the drug was beneficial for all participants, or whether it was particularly effective/ineffective depending on the age of the participant – whether they were under or over 40. 

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MSc Mathematical & Computational Finance: sample dissertations

Below are some examples of MSc dissertations from previous years, which received high marks:

  • Optimal Strategies from forward versus classical utilities
  • Robust Pricing of Derivatives on Realised Variance
  • Log Mean-Variance Portfolio Theory and Time Inconsistency
  • Multilayer network valuation under bail-in
  • Topological Persistence in Market Micro Structure
  • Volatility is Rough
  • Deep learning approach to hedging (2019 prize for best Master’s Thesis in Quantitative Finance by Natixis Foundation for Research & Innovation.)
  • Risk Management with Generative Adversarial Networks.pdf  (Won award for best Masters Thesis in Quantitative Finance in Europe in 2020)
  • Hawkes Process-Driven Models for Limit Order Book Dynamics.pdf   (Won award for best Masters Thesis in Quantitative Finance in Europe in 2021)
  • Evaluating Credit Portfolios under IFRS 9 in the UK Economy

quantitative masters dissertation

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Conducting a Quantitative UK Dissertation Research Methodology – Step By Step Guide


Amassing information, documenting facts and attempting to find information in a rather unsystematic manner could be wrongfully deemed as research. On the contrary, research refers to an investigation that is appropriately structured and revolves around a phenomenon. The investigation here would be carried out with a particular reason which could be for verification of facts, and reaching a rational conclusion. While the sources for knowledge could be many, researchers in the social science domain depend on procedures organized to confirm social facts and this type of an organized structure is usually deemed as research methods (Mitra, 2021). Discussions revolving around the choice of research method have been a concern that keeps emerging, nonetheless, it is not restricted to the social science domain exclusively. On the contrary, it can be witnessed across every academic domain. Furthermore, this type of reemergence has been anticipated as a result of the importance associated with the selection of a research methodology .

When pursuing a master’s course in UK universities, students are frequently required to write their master’s dissertations and when there are dissertations, having knowledge about research methodologies are imminent. Research methodologies are mainly of three types and these include; qualitative, quantitative and mixed methods (Pawar, 2021). Taking it one step at a time, this piece provides information on developing your UK master’s dissertation using a quantitative research methodology.

Understanding and Tackling a Quantitative Research Method

Quantitative research is deemed as an organized inquiry to examine a phenomenon by collecting information in a numerical format and executing a statistical, mathematical or computational analysis (Apuke, 2017). Quantitative research methods are founded on the positivist paradigm that extends support to approaches deeply ingrained in statistical breakdown, while including several strategies like; structured protocols, mathematical exposition, hypotheses testing, blinding, questionnaires, quasi-experimental and experimental design, and randomization which have a restricted kind of preset responses. Objectives within quantitative research need to be such that it can be measured or at least split from variables and hypotheses. Variables here refer to notions that would fluctuate and are known to assume various values. However, hypotheses on the other hand are deemed as propositions of assumptions yet warranting testing of the link between variables. According to Igwenagu (2016), ex-post facto, experimental, survey and case study research are some of the commonly used quantitative research approaches while developing a UK dissertation.

quantitative masters dissertation

Figure 1. Key Points in Writing a Quantitative Research Methodology

Source; Tutors India, 2022

Techniques for Data Collection

Data collection in a quantitative research would hinge on probability sampling and the use of structured tools to gather data, which is apt for diverse experience within categories of response that are predefined. Outcomes obtained through quantitative methods of data collection produce results which render it easy to generalize, summarize and comparison (Eyisi, 2016). Data for quantitative research is carried out using tools like survey questionnaires.

Key Difference between Qualitative and Quantitative Approaches

In qualitative research, understanding and insights to a problem setting is provided and it usually exploratory and unstructured in nature wherein an investigation of a complex phenomenon is carried out. Qualitative methods are used when it is clear that the phenomenon cannot be elucidated with quantitative methods (Cropley, 2021). While quantitative research refers to a research type that relies on a natural science technique which produces numerical data and hard facts. The key purpose of a quantitative research is to establish a cause and effect relation among two variables using statistical, computational and mathematical techniques (Synopsis, 2017).

Justification for Using Quantitative Method

A research which is quantitative would be structured around a scientific technique. After getting an understanding about the phenomenon under investigation, the researcher delves into formulating hypotheses on which deductive reasoning is applied by predicting the manner in which the data needs to appear in case the hypotheses are true, following data collection and its analysis to reject or confirm the hypotheses. Furthermore, the findings derived from quantitative research methods are such that it can be generalized over a larger population This lends credibility to the research and is justification enough to use quantitative methods in a dissertation.

Considering the rate at which researches are being executed within academic realms and also in business sectors, choosing a particular method of research would depend largely on several factors. However, it has been stated by that the type of research problems hold the key in the choice of a research methodology. According to the tenets of quantitative research, there is a possibility that researchers are drawn to select a particular research approach as one which is most apt. Nevertheless, what is important is that any and every method of research is known to have its own set of strengths and weaknesses, and compatibility along with other strategies.

  • Apuke, O. D. (2017). Quantitative Research Methods : A Synopsis Approach. Kuwait Chapter of Arabian Journal of Business and Management Review , 6 (11), 40–47. https://doi.org/10.12816/0040336
  • Cropley, A. (2021). Introduction to qualitative research methods: A practice-oriented introduction for students of psychology and education. Open Access, 3rd Update .
  • Eyisi, D. (2016). The usefulness of qualitative and quantitative approaches and methods in researching problem-solving ability in science education curriculum. Journal of Education and Practice , 7 (15), 91–100. https://eric.ed.gov/?id=EJ1103224
  • Igwenagu, C. (2016). Fundamentals of research methodology and data collection [University of Nigeria, Nsukka]. http://staging-nodebb-uploads.s3.amazonaws.com/FundamentalsofResearchMethodologyandDataCollection-%25philu%25-6fae26dd-631d-4e66-9864-663ec2b30204.pdf
  • Mitra, A. (2021). TECHNO INDIA UNIVERSITY , WEST BENGAL Study Materials on ‘ Research Methodology ’ along with Sample Questions and Answers . October . https://www.researchgate.net/publication/355796104_Basics_of_Research_Methodology_RMpdf
  • Pawar, N. (2021). 6 . Type of Research and Type Research Design . June . https://www.researchgate.net/publication/352055750_6_Type_of_Research_and_Type_Research_Design
  • Synopsis, M. A. (2017). Arabian Journal of Business and Management Review ( Kuwait Chapter ) . 6 (10), 40–47. https://doi.org/10.12816/0040336
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Quantitative Data Analysis

Dissertation committees usually vigorously attack the way a study’s results are analyzed..

Dissertation committees usually vigorously attack the way a study’s results are analyzed; as such, data analysis can be extremely difficult and intimidating for students.

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Our statistical consultants and coaches can specifically help you with:

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How to use a case study in your masters dissertation

Prof martyn denscombe, author of “ the good research guide, 6th edition ”, gives expert advice on how to use a case study in your masters dissertation. .

There are two main examples for how to use a case study in your masters dissertation, namely quantitative and qualitative case studies.

First, a case study provides a platform that allows you to study a situation in depth and produce the level of academic inquiry that is expected in a master’s degree. In the context of any master’s programme the dissertation operates as something of a showcase for a student’s abilities.

It can easily make the difference between getting a merit and a distinction in the final award of degree. It is important, therefore, to base the work on an approach that allows things to be explored in sufficient depth and detail to warrant a good grade.

Second, case studies can be useful in a practical sense. It is possible to complete a case study in a relatively short period of intense study and so it is the kind of research that is feasible in terms of the kind of time constraints that face master’s students as they enter the final stages of their programme of study.

Added to which a case study can also be a rather convenient form of research, avoiding the time and costs of travel to multiple research sites. The use of case studies, then, would appear to be an attractive proposition. But it is not an approach that should be used naively without consideration of its limitations or potential pitfalls.

To be a good case study the research needs to consider certain key issues. If they are not addressed it will considerably lower the value of the master’s degree. For instance, a good case study needs to:

  • Be crystal clear about the purpose for which the research is being conducted
  • Justify the selection of the particular case being studied
  • Describe how the chosen case compares with others of its type
  • Explain the basis on which any generalizations can be made from the findings

This is where The Good Research Guide, 6th edition becomes so valuable. It not only identifies the key points that need to be addressed in order to conduct a competent questionnaire survey.

It gets right to the heart of the matter with plenty of practical guidance on how to deal with issues. Using plain language, this bestselling book covers a range of alternative strategies and methods for conducting small-scale social research projects. It outlines some of the main ways in which the data can be analysed.

Read Prof Martyn Denscombe’s advice on using a questionnaire survey for your postgraduate dissertation

Frequently Asked Questions

Can a case study be a thesis.

Yes, in fact a case study is a very good option in your dissertation. There are multiple ways to implement a case study in your thesis. For instance, one main study which is in depth and complex or you could feature multiple case studies.

What are case studies?

Case studies are a way to research a particular field, group, people and situation. The topic of research is studied deeply and thoroughly in order to solve a problem or uncover information. Case studies are a type of qualitative research.

If you are ready to find a masters course check out Masters Compare.


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