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How to Write the Dissertation Findings or Results – Steps & Tips
Published by Grace Graffin at August 11th, 2021 , Revised On February 27, 2023
Each part of the dissertation is unique, and some general and specific rules must be followed. The dissertation’s findings section presents the key results of your research without interpreting their meaning .
Theoretically, this is an exciting section of a dissertation because it involves writing what you have observed and found. However, it can be a little tricky if there is too much information to confuse the readers.
The goal is to include only the essential and relevant findings in this section. The results must be presented in an orderly sequence to provide clarity to the readers.
This section of the dissertation should be easy for the readers to follow, so you should avoid going into a lengthy debate over the interpretation of the results.
It is vitally important to focus only on clear and precise observations. The findings chapter of the dissertation is theoretically the easiest to write.
It includes statistical analysis and a brief write-up about whether or not the results emerging from the analysis are significant. This segment should be written in the past sentence as you describe what you have done in the past.
This article will provide detailed information about how to write the findings of a dissertation .
When to Write Dissertation Findings Chapter
As soon as you have gathered and analysed your data, you can start to write up the findings chapter of your dissertation paper. Remember that it is your chance to report the most notable findings of your research work and relate them to the research hypothesis or research questions set out in the introduction chapter of the dissertation .
You will be required to separately report your study’s findings before moving on to the discussion chapter if your dissertation is based on the collection of primary data or experimental work.
However, you may not be required to have an independent findings chapter if your dissertation is purely descriptive and focuses on the analysis of case studies or interpretation of texts.
- Always report the findings of your research in the past tense.
- The dissertation findings chapter varies from one project to another, depending on the data collected and analyzed.
- Avoid reporting results that are not relevant to your research questions or research hypothesis.
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1. Reporting Quantitative Findings
The best way to present your quantitative findings is to structure them around the research hypothesis or questions you intend to address as part of your dissertation project.
Report the relevant findings for each research question or hypothesis, focusing on how you analyzed them.
Analysis of your findings will help you determine how they relate to the different research questions and whether they support the hypothesis you formulated.
While you must highlight meaningful relationships, variances, and tendencies, it is important not to guess their interpretations and implications because this is something to save for the discussion and conclusion chapters.
Any findings not directly relevant to your research questions or explanations concerning the data collection process should be added to the dissertation paper’s appendix section.
Use of Figures and Tables in Dissertation Findings
Suppose your dissertation is based on quantitative research. In that case, it is important to include charts, graphs, tables, and other visual elements to help your readers understand the emerging trends and relationships in your findings.
Repeating information will give the impression that you are short on ideas. Refer to all charts, illustrations, and tables in your writing but avoid recurrence.
The text should be used only to elaborate and summarize certain parts of your results. On the other hand, illustrations and tables are used to present multifaceted data.
It is recommended to give descriptive labels and captions to all illustrations used so the readers can figure out what each refers to.
How to Report Quantitative Findings
Here is an example of how to report quantitative results in your dissertation findings chapter;
Two hundred seventeen participants completed both the pretest and post-test and a Pairwise T-test was used for the analysis. The quantitative data analysis reveals a statistically significant difference between the mean scores of the pretest and posttest scales from the Teachers Discovering Computers course. The pretest mean was 29.00 with a standard deviation of 7.65, while the posttest mean was 26.50 with a standard deviation of 9.74 (Table 1). These results yield a significance level of .000, indicating a strong treatment effect (see Table 3). With the correlation between the scores being .448, the little relationship is seen between the pretest and posttest scores (Table 2). This leads the researcher to conclude that the impact of the course on the educators’ perception and integration of technology into the curriculum is dramatic.
Paired samples correlation, paired samples test.
Also Read: How to Write the Abstract for the Dissertation.
2. Reporting Qualitative Findings
A notable issue with reporting qualitative findings is that not all results directly relate to your research questions or hypothesis.
The best way to present the results of qualitative research is to frame your findings around the most critical areas or themes you obtained after you examined the data.
In-depth data analysis will help you observe what the data shows for each theme. Any developments, relationships, patterns, and independent responses directly relevant to your research question or hypothesis should be mentioned to the readers.
Additional information not directly relevant to your research can be included in the appendix .
How to Report Qualitative Findings
Here is an example of how to report qualitative results in your dissertation findings chapter;
The last question of the interview focused on the need for improvement in Thai ready-to-eat products and the industry at large, emphasizing the need for enhancement in the current products being offered in the market. When asked if there was any particular need for Thai ready-to-eat meals to be improved and how to improve them in case of ‘yes,’ the males replied mainly by saying that the current products need improvement in terms of the use of healthier raw materials and preservatives or additives. There was an agreement amongst all males concerning the need to improve the industry for ready-to-eat meals and the use of more healthy items to prepare such meals. The females were also of the opinion that the fast-food items needed to be improved in the sense that more healthy raw materials such as vegetable oil and unsaturated fats, including whole-wheat products, to overcome risks associated with trans fat leading to obesity and hypertension should be used for the production of RTE products. The frozen RTE meals and packaged snacks included many preservatives and chemical-based flavouring enhancers that harmed human health and needed to be reduced. The industry is said to be aware of this fact and should try to produce RTE products that benefit the community in terms of healthy consumption.
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What to Avoid in Dissertation Findings Chapter
- Avoid using interpretive and subjective phrases and terms such as “confirms,” “reveals,” “suggests,” or “validates.” These terms are more suitable for the discussion chapter , where you will be expected to interpret the results in detail.
- Only briefly explain findings in relation to the key themes, hypothesis, and research questions. You don’t want to write a detailed subjective explanation for any research questions at this stage.
The DOs of Writing the Findings or Results Section
- Ensure you are not presenting results from other research studies in your findings.
- Observe whether or not your hypothesis is tested or research questions answered.
- Illustrations and tables present data and are labelled to help your readers understand what they relate to.
- Use software such as Excel, STATA, and SPSS to analyse results and important trends.
Essential Guidelines On How to Write Dissertation Findings
The dissertation findings chapter should provide the context for understanding the results. The research problem should be repeated, and the research goals should be stated briefly.
This approach helps to gain the reader’s attention toward the research problem. The first step towards writing the findings is identifying which results will be presented in this section.
The results relevant to the questions must be presented, considering whether the results support the hypothesis. You do not need to include every result in the findings section. The next step is ensuring the data can be appropriately organized and accurate.
You will need to have a basic idea about writing the findings of a dissertation because this will provide you with the knowledge to arrange the data chronologically.
Start each paragraph by writing about the most important results and concluding the section with the most negligible actual results.
A short paragraph can conclude the findings section, summarising the findings so readers will remember as they transition to the next chapter. This is essential if findings are unexpected or unfamiliar or impact the study.
Our writers can help you with all parts of your dissertation, including statistical analysis of your results . To obtain free non-binding quotes, please complete our online quote form here .
Be Impartial in Your Writing
When crafting your findings, knowing how you will organize the work is important. The findings are the story that needs to be told in response to the research questions that have been answered.
Therefore, the story needs to be organized to make sense to you and the reader. The findings must be compelling and responsive to be linked to the research questions being answered.
Always ensure that the size and direction of any changes, including percentage change, can be mentioned in the section. The details of p values or confidence intervals and limits should be included.
The findings sections only have the relevant parts of the primary evidence mentioned. Still, it is a good practice to include all the primary evidence in an appendix that can be referred to later.
The results should always be written neutrally without speculation or implication. The statement of the results mustn’t have any form of evaluation or interpretation.
Negative results should be added in the findings section because they validate the results and provide high neutrality levels.
The length of the dissertation findings chapter is an important question that must be addressed. It should be noted that the length of the section is directly related to the total word count of your dissertation paper.
The writer should use their discretion in deciding the length of the findings section or refer to the dissertation handbook or structure guidelines.
It should neither belong nor be short nor concise and comprehensive to highlight the reader’s main findings.
Ethically, you should be confident in the findings and provide counter-evidence. Anything that does not have sufficient evidence should be discarded. The findings should respond to the problem presented and provide a solution to those questions.
Structure of the Findings Chapter
The chapter should use appropriate words and phrases to present the results to the readers. Logical sentences should be used, while paragraphs should be linked to produce cohesive work.
You must ensure all the significant results have been added in the section. Recheck after completing the section to ensure no mistakes have been made.
The structure of the findings section is something you may have to be sure of primarily because it will provide the basis for your research work and ensure that the discussions section can be written clearly and proficiently.
One way to arrange the results is to provide a brief synopsis and then explain the essential findings. However, there should be no speculation or explanation of the results, as this will be done in the discussion section.
Another way to arrange the section is to present and explain a result. This can be done for all the results while the section is concluded with an overall synopsis.
This is the preferred method when you are writing more extended dissertations. It can be helpful when multiple results are equally significant. A brief conclusion should be written to link all the results and transition to the discussion section.
Numerous data analysis dissertation examples are available on the Internet, which will help you improve your understanding of writing the dissertation’s findings. Here is one such example.
Problems to Avoid When Writing Dissertation Findings
One of the problems to avoid while writing the dissertation findings is reporting background information or explaining the findings. This should be done in the introduction section .
You can always revise the introduction chapter based on the data you have collected if that seems an appropriate thing to do.
Raw data or intermediate calculations should not be added in the findings section. Always ask your professor if raw data needs to be included.
If the data is to be included, then use an appendix or a set of appendices referred to in the text of the findings chapter.
Do not use vague or non-specific phrases in the findings section. It is important to be factual and concise for the reader’s benefit.
The findings section presents the crucial data collected during the research process. It should be presented concisely and clearly to the reader. There should be no interpretation, speculation, or analysis of the data.
The significant results should be categorized systematically with the text used with charts, figures, and tables. Furthermore, avoiding using vague and non-specific words in this section is essential.
It is essential to label the tables and visual material properly. You should also check and proofread the section to avoid mistakes.
The dissertation findings chapter is a critical part of your overall dissertation paper. If you struggle with presenting your results and statistical analysis, our expert dissertation writers can help you get things right. Whether you need help with the entire dissertation paper or individual chapters, our dissertation experts can provide customized dissertation support .
FAQs About Findings of a Dissertation
How do i report quantitative findings.
The best way to present your quantitative findings is to structure them around the research hypothesis or research questions you intended to address as part of your dissertation project. Report the relevant findings for each of the research questions or hypotheses, focusing on how you analyzed them.
How do I report qualitative findings?
The best way to present the qualitative research results is to frame your findings around the most important areas or themes that you obtained after examining the data.
An in-depth analysis of the data will help you observe what the data is showing for each theme. Any developments, relationships, patterns, and independent responses that are directly relevant to your research question or hypothesis should be clearly mentioned for the readers.
Can I use interpretive phrases like ‘it confirms’ in the finding chapter?
No, It is highly advisable to avoid using interpretive and subjective phrases in the finding chapter. These terms are more suitable for the discussion chapter , where you will be expected to provide your interpretation of the results in detail.
Can I report the results from other research papers in my findings chapter?
NO, you must not be presenting results from other research studies in your findings.
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How To Write The Results/Findings Chapter
For quantitative studies (dissertations & theses).
By: Derek Jansen (MBA). Expert Reviewed By: Kerryn Warren (PhD) | July 2021
So, you’ve completed your quantitative data analysis and it’s time to report on your findings. But where do you start? In this post, we’ll walk you through the results chapter (also called the findings or analysis chapter), step by step, so that you can craft this section of your dissertation or thesis with confidence. If you’re looking for information regarding the results chapter for qualitative studies, you can find that here .
Overview: Quantitative Results Chapter
- What exactly the results/findings/analysis chapter is
- What you need to include in your results chapter
- How to structure your results chapter
- A few tips and tricks for writing top-notch chapter
What exactly is the results chapter?
The results chapter (also referred to as the findings or analysis chapter) is one of the most important chapters of your dissertation or thesis because it shows the reader what you’ve found in terms of the quantitative data you’ve collected. It presents the data using a clear text narrative, supported by tables, graphs and charts. In doing so, it also highlights any potential issues (such as outliers or unusual findings) you’ve come across.
But how’s that different from the discussion chapter?
Well, in the results chapter, you only present your statistical findings. Only the numbers, so to speak – no more, no less. Contrasted to this, in the discussion chapter , you interpret your findings and link them to prior research (i.e. your literature review), as well as your research objectives and research questions. In other words, the results chapter presents and describes the data, while the discussion chapter interprets the data.
Let’s look at an example.
In your results chapter, you may have a plot that shows how respondents to a survey responded: the numbers of respondents per category, for instance. You may also state whether this supports a hypothesis by using a p-value from a statistical test. But it is only in the discussion chapter where you will say why this is relevant or how it compares with the literature or the broader picture. So, in your results chapter, make sure that you don’t present anything other than the hard facts – this is not the place for subjectivity.
It’s worth mentioning that some universities prefer you to combine the results and discussion chapters. Even so, it is good practice to separate the results and discussion elements within the chapter, as this ensures your findings are fully described. Typically, though, the results and discussion chapters are split up in quantitative studies. If you’re unsure, chat with your research supervisor or chair to find out what their preference is.
What should you include in the results chapter?
Following your analysis, it’s likely you’ll have far more data than are necessary to include in your chapter. In all likelihood, you’ll have a mountain of SPSS or R output data, and it’s your job to decide what’s most relevant. You’ll need to cut through the noise and focus on the data that matters.
This doesn’t mean that those analyses were a waste of time – on the contrary, those analyses ensure that you have a good understanding of your dataset and how to interpret it. However, that doesn’t mean your reader or examiner needs to see the 165 histograms you created! Relevance is key.
How do I decide what’s relevant?
At this point, it can be difficult to strike a balance between what is and isn’t important. But the most important thing is to ensure your results reflect and align with the purpose of your study . So, you need to revisit your research aims, objectives and research questions and use these as a litmus test for relevance. Make sure that you refer back to these constantly when writing up your chapter so that you stay on track.
As a general guide, your results chapter will typically include the following:
- Some demographic data about your sample
- Reliability tests (if you used measurement scales)
- Descriptive statistics
- Inferential statistics (if your research objectives and questions require these)
- Hypothesis tests (again, if your research objectives and questions require these)
We’ll discuss each of these points in more detail in the next section.
Importantly, your results chapter needs to lay the foundation for your discussion chapter . This means that, in your results chapter, you need to include all the data that you will use as the basis for your interpretation in the discussion chapter.
For example, if you plan to highlight the strong relationship between Variable X and Variable Y in your discussion chapter, you need to present the respective analysis in your results chapter – perhaps a correlation or regression analysis.
Need a helping hand?
How do I write the results chapter?
There are multiple steps involved in writing up the results chapter for your quantitative research. The exact number of steps applicable to you will vary from study to study and will depend on the nature of the research aims, objectives and research questions . However, we’ll outline the generic steps below.
Step 1 – Revisit your research questions
The first step in writing your results chapter is to revisit your research objectives and research questions . These will be (or at least, should be!) the driving force behind your results and discussion chapters, so you need to review them and then ask yourself which statistical analyses and tests (from your mountain of data) would specifically help you address these . For each research objective and research question, list the specific piece (or pieces) of analysis that address it.
At this stage, it’s also useful to think about the key points that you want to raise in your discussion chapter and note these down so that you have a clear reminder of which data points and analyses you want to highlight in the results chapter. Again, list your points and then list the specific piece of analysis that addresses each point.
Next, you should draw up a rough outline of how you plan to structure your chapter . Which analyses and statistical tests will you present and in what order? We’ll discuss the “standard structure” in more detail later, but it’s worth mentioning now that it’s always useful to draw up a rough outline before you start writing (this advice applies to any chapter).
Step 2 – Craft an overview introduction
As with all chapters in your dissertation or thesis, you should start your quantitative results chapter by providing a brief overview of what you’ll do in the chapter and why . For example, you’d explain that you will start by presenting demographic data to understand the representativeness of the sample, before moving onto X, Y and Z.
This section shouldn’t be lengthy – a paragraph or two maximum. Also, it’s a good idea to weave the research questions into this section so that there’s a golden thread that runs through the document.
Step 3 – Present the sample demographic data
The first set of data that you’ll present is an overview of the sample demographics – in other words, the demographics of your respondents.
- 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 theoretical 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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Prize-Winning Thesis and Dissertation Examples
Published on September 9, 2022 by Tegan George . Revised on November 11, 2022.
It can be difficult to know where to start when writing your thesis or dissertation . One way to come up with some ideas or maybe even combat writer’s block is to check out previous work done by other students on a similar thesis or dissertation topic to yours.
This article collects a list of undergraduate, master’s, and PhD theses and dissertations that have won prizes for their high-quality research.
Table of contents
Award-winning undergraduate theses, award-winning master’s theses, award-winning ph.d. dissertations.
University : University of Pennsylvania Faculty : History Author : Suchait Kahlon Award : 2021 Hilary Conroy Prize for Best Honors Thesis in World History Title : “Abolition, Africans, and Abstraction: the Influence of the “Noble Savage” on British and French Antislavery Thought, 1787-1807”
University : Columbia University Faculty : History Author : Julien Saint Reiman Award : 2018 Charles A. Beard Senior Thesis Prize Title : “A Starving Man Helping Another Starving Man”: UNRRA, India, and the Genesis of Global Relief, 1943-1947
University: University College London Faculty: Geography Author: Anna Knowles-Smith Award: 2017 Royal Geographical Society Undergraduate Dissertation Prize Title: Refugees and theatre: an exploration of the basis of self-representation
University: University of Washington Faculty: Computer Science & Engineering Author: Nick J. Martindell Award: 2014 Best Senior Thesis Award Title: DCDN: Distributed content delivery for the modern web
University: University of Edinburgh Faculty: Informatics Author: Christopher Sipola Award: 2018 Social Responsibility & Sustainability Dissertation Prize Title: Summarizing electricity usage with a neural network
University: University of Ottawa Faculty: Education Author: Matthew Brillinger Award: 2017 Commission on Graduate Studies in the Humanities Prize Title: Educational Park Planning in Berkeley, California, 1965-1968
University: University of Ottawa Faculty: Social Sciences Author: Heather Martin Award: 2015 Joseph De Koninck Prize Title: An Analysis of Sexual Assault Support Services for Women who have a Developmental Disability
University : University of Ottawa Faculty : Physics Author : Guillaume Thekkadath Award : 2017 Commission on Graduate Studies in the Sciences Prize Title : Joint measurements of complementary properties of quantum systems
University: London School of Economics Faculty: International Development Author: Lajos Kossuth Award: 2016 Winner of the Prize for Best Overall Performance Title: Shiny Happy People: A study of the effects income relative to a reference group exerts on life satisfaction
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University : Stanford University Faculty : English Author : Nathan Wainstein Award : 2021 Alden Prize Title : “Unformed Art: Bad Writing in the Modernist Novel”
University : University of Massachusetts at Amherst Faculty : Molecular and Cellular Biology Author : Nils Pilotte Award : 2021 Byron Prize for Best Ph.D. Dissertation Title : “Improved Molecular Diagnostics for Soil-Transmitted Molecular Diagnostics for Soil-Transmitted Helminths”
University: Utrecht University Faculty: Linguistics Author: Hans Rutger Bosker Award: 2014 AVT/Anéla Dissertation Prize Title: The processing and evaluation of fluency in native and non-native speech
University: California Institute of Technology Faculty: Physics Author: Michael P. Mendenhall Award: 2015 Dissertation Award in Nuclear Physics Title: Measurement of the neutron beta decay asymmetry using ultracold neutrons
University: University of Illinois at Urbana-Champaign Faculty: Computer Science Author: John Criswell Award: 2014 Doctoral Dissertation Award Title: Secure Virtual Architecture: Security for Commodity Software Systems
University: Stanford University Faculty: Management Science and Engineering Author: Shayan O. Gharan Award: Doctoral Dissertation Award 2013 Title: New Rounding Techniques for the Design and Analysis of Approximation Algorithms
University: University of Minnesota Faculty: Chemical Engineering Author: Eric A. Vandre Award: 2014 Andreas Acrivos Dissertation Award in Fluid Dynamics Title: Onset of Dynamics Wetting Failure: The Mechanics of High-speed Fluid Displacement
University: Erasmus University Rotterdam Faculty: Marketing Author: Ezgi Akpinar Award: McKinsey Marketing Dissertation Award 2014 Title: Consumer Information Sharing: Understanding Psychological Drivers of Social Transmission
University: University of Washington Faculty: Computer Science & Engineering Author: Keith N. Snavely Award: 2009 Doctoral Dissertation Award Title: Scene Reconstruction and Visualization from Internet Photo Collections
University: University of Ottawa Faculty: Social Work Author: Susannah Taylor Award: 2018 Joseph De Koninck Prize Title: Effacing and Obscuring Autonomy: the Effects of Structural Violence on the Transition to Adulthood of Street Involved Youth
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The results chapter of a thesis or dissertation presents your research results concisely and objectively. In quantitative research, for each question or hypothesis, state: The type of analysis used; Relevant results in the form of descriptive and inferential statistics; Whether or not the alternative hypothesis was supported
Here is an example of how to report quantitative results in your dissertation findings chapter; Two hundred seventeen participants completed both the pretest and post-test and a Pairwise T-test was used for the analysis.
Naturally, the exact contents of your results chapter will depend on which qualitative analysis method (or methods) you use. For example, if you were to use thematic analysis, you’d detail the themes identified in your analysis, using extracts from the transcripts or text to support your claims.
See an example Award-winning Ph.D. dissertations University: Stanford University Faculty: English Author: Nathan Wainstein Award: 2021 Alden Prize Title: “Unformed Art: Bad Writing in the Modernist Novel” University: University of Massachusetts at Amherst Faculty: Molecular and Cellular Biology Author: Nils Pilotte