phd thesis negative results

Elisa Granato

Microbial ecology & evolution, phd tips – dealing with “failed” experiments.

“PhD tips” is an ongoing series of blog posts written by postdocs and aimed at graduate students at the University of Oxford (Department of Biology). I wrote this in April 2021.

[Update Dec 2022: this article is now published in The Microbiologist ]

Hi everyone!

I hope you are all doing well. This week, I want to talk about dealing with “failed” experiments.* I put “failed” in quotes because, as I will argue below, only a very small fraction of experiments perceived as failed have actually failed in the sense that they were completely pointless.

The first thing to realize is that the core of experimental science is an iterative process: you do a thing, it does something (or not), you think about it, you do it a little different next time. Crucially, this process works the same way whether the results made you happy or not (for whatever reason). You think about what happened, you change stuff, you do it again the same way, you do it again with a twist, or you do something else entirely. A very normal experience as a scientist is to – on average – be a little disappointed in the outcome of your experiment. See below for a list of outcomes that tend to make people unhappy. Again: this is normal and part of the process. The most “successful” projects are built on a foundation of “failed” or semi-failed attempts at doing something.**

The second thing to realize is that learning is at the core of this iterative process: a thing has to happen for us to better understand what we’re dealing with. This means that the essential goal of each experiment is to learn something, anything! And this “lesson” doesn’t usually come in the shape of a perfect plot that’s publication-ready on the first try. Instead, the next learning item usually comes from a mangled mess of an experiment or dataset.

What exactly you are learning from each attempt depends of course on the details of your project, but below I am giving a few real-life examples of typical experimental outcomes that tend to make people unhappy, and some suggestions on what one could learn from them.

• Not failed at all, this is a valuable negative result.
• Outcome: solid scientific insight.
• Not failed at all, this is valuable data we can learn from.
• Potential next step: figure out if noise is biological variance or technical measurement error, or both.
• Outcome: a better understanding of your system, solid scientific insight
• Example: forgot to wash cells before treating them.
• Semi-failed, this is valuable data we can learn from. By comparing the washed cells with the unwashed cells we can learn how this step in the experiment influences the final results. Can be useful for future troubleshooting.
• Outcome: an experimental protocol where we understand better what each step does, or where we realize one step is less important than previously thought; honing of lab skills.
• Example: positive/negative control didn’t yield the expected result, even though it has always worked before and the experiment was (to the best of your knowledge) executed as usual.
• Semi-failed. An opportunity to evaluate the reliability of the protocol/equipment/material.
• Potential next steps: introduce checklists and more note-taking to ensure little details are adhered to; evaluate assumptions e.g. is the control genotype actually this genotype, is something contaminated, is the machine still working, etc.
• Outcome: a more reliable experimental protocol, honing of lab skills
• Example: algae grow over all your aquatic plants and kill them
• Semi-failed. An opportunity to evaluate “housing” conditions for your organism.
• Potential next steps: pilot experiment to optimize housing conditions before doing “actual” experiments; investigate whether the contaminant did something interesting
• Outcome: a more reliable experimental protocol; chance of a random cool discovery with the contaminant
• Example: dropped tube with the sample on the floor. Cannot be recovered.
• Close to “actually failed”, but still an opportunity to introduce better safety measures / risk assessment.
• Potential next steps: introduce safety procedures to ensure better sample preservation, labelling etc.
• Outcome: a more reliable experimental protocol

The third thing to realize is that feelings of frustration, anger, sadness, fear, are all very common in the wake of a “failed” experiment, especially if it took a long time. And trying to think about the next steps while still frustrated can turn into a vicious cycle where you make mistakes because frustrated brains are bad at solving problems. So, instead, allow yourself to feel these feelings, get a good night’s sleep, and then start troubleshooting the next day with a fresh and relaxed mind.

So, to summarize: doing your PhD, and science in general, is about learning. Every experiment, no matter the outcome, can teach you something. Some of the lessons are perhaps a bit more appealing and easier to swallow than others, but they are all useful and necessary. So next time an experiment “fails”, try taking a nice little break if you can, and then list all the things you still learned about your experimental setup, or organism of interest.

Finally, I would like to share a little mental trick I developed over the years that has helped me with dealing and accepting experimental setbacks. I like to imagine that any given project will take me X hours to complete, including setbacks, trials by fire, random distractions, dead-ends etc. In science, X is usually unknown. But importantly, there is always an X. Every time I conduct an experiment that “fails”, and let’s say it took me 6 hours, I have now reduced X by 6 hours. Not bad! I am one step closer to finishing the project. It’s a bit tongue-in-cheek of course, but it still helps me see my work for what it is: I gave these 6 hours my best and the outcome doesn’t change that.

*If you work on theory you can probably replace “failed experiments” here with “theoretical approaches that didn’t work out” or something along those lines and maybe some of this might still be helpful.

**This is the science version of “The master has failed more times than the student has even tried”.

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How to Write About Negative (Or Null) Results in Academic Research

Researchers are often disappointed when their work yields "negative" results, meaning that the null hypothesis cannot be rejected. However, negative results are essential for research to progress. Negative results tell researchers that they are on the wrong path, or that their current techniques are ineffective. This is a natural and necessary part of discovering something that was previously unknown. Solving problems that lead to negative results is an integral part of being an effective researcher. Publishing negative results that are the result of rigorous research contributes to scientific progress.

There are three main reasons for negative results:

• The original hypothesis was incorrect
• The findings of a published report cannot be replicated
• Technical problems

Here, we will discuss how to write about negative results, first focusing on the most common reason: technical problems.

Writing about technical problems

Technical problems might include faulty reagents, inappropriate study design, and insufficient statistical power. Most researchers would prefer to resolve technical problems before presenting their work, and focus instead on their convincing results. In reality, researchers often need to present their work at a conference or to a thesis committee before some problems can be resolved.

When presenting at a conference, the objective should be to clearly describe your overall research goal and why it is important, your preliminary results, the current problem, and how previously published work is informing the steps you are taking to resolve the problem. Here, you want to take advantage of the collective expertise at the conference. By being straightforward about your difficulties, you increase the chance that someone can help you find a solution.

When presenting to a thesis committee, much of what you discuss will be the same (overall research goal and why it is important, results, problem(s) and possible solutions). Your primarily goal is to show that you are well prepared to move forward in your research career, despite the recent difficulties. The thesis defense is a defined stopping point, so most thesis students should write about solutions they would pursue if they were to continue the work. For example, "To resolve this problem, it would be advisable to increase the survey area by a factor of 4, and then…" In contrast, researchers who will be continuing their work should write about possible solutions using present and future tense. For example, "To resolve this problem, we are currently testing a wider variety of standards, and will then conduct preliminary experiments to determine…"

Putting the "re" in "research"

Whether you are presenting at a conference, defending a thesis, applying for funding, or simply trying to make progress in your research, you will often need to search through the academic literature to determine the best path forward. This is especially true when you get unexpected results—either positive or negative. When trying to resolve a technical problem, you should often find yourself carefully reading the materials and methods sections of papers that address similar research questions, or that used similar techniques to explore very different problems. For example, a single computer algorithm might be adapted to address research questions in many different fields.

In searching through published papers and less formal methods of communication—such as conference abstracts—you may come to appreciate the important details that good researchers will include when discussing technical problems or other negative results. For example, "We found that participants were more likely to complete the process when light refreshments were provided between the two sessions." By including this information, the authors may help other researchers save time and resources.

Thus, you are advised to be as thorough as possible in reviewing the relevant literature, to find the most promising solutions for technical problems. When presenting your work, show that you have carefully considered the possibilities, and have developed a realistic plan for moving forward. This will help a thesis committee view your efforts favorably, and can also convince possible collaborators or advisors to invest time in helping you.

Publishing negative results

Negative results due to technical problems may be acceptable for a conference presentation or a thesis at the undergraduate or master's degree level. Negative results due to technical problems are not sufficient for publication, a Ph.D. dissertation, or tenure. In those situations, you will need to resolve the technical problem and generate high quality results (either positive or negative) that stand up to rigorous analysis. Depending on the research field, high quality negative results might include multiple readouts and narrow confidence intervals.

Researchers are often reluctant to publish negative results, especially if their data don't support an interesting alternative hypothesis. Traditionally, journals have been reluctant to publish negative results that are not paired with positive results, even if the study is well designed and the results have sufficient statistical power. This is starting to change— especially for medical research —but publishing negative results can still be an uphill battle.

Not publishing high quality negative results is a disservice to the scientific community and the people who support it (including tax payers), since other scientists may need to repeat the work. For studies involving animal research or human tissue samples, not publishing would squander significant sacrifices. For research involving medical treatments—especially studies that contradict a published report—not publishing negative results leads to an inaccurate understanding of treatment efficacy.

So how can researchers write about negative results in a way that reflects its importance? Let's consider a common reason for negative results: the original hypothesis was incorrect.

Writing about negative results when the original hypothesis was incorrect

Researchers should be comfortable with being wrong some of the time, such as when results don't support an initial hypothesis. After all, research wouldn't be necessary if we already knew the answer to every possible question. The next step is usually to revise the hypothesis after reconsidering the available data, reading through the relevant literature, and consulting with colleagues.

Ideally, a revised hypothesis will lead to results that allow you to reject a (revised) null hypothesis. The negative results can then be reported alongside the positive results, possibly bolstering the significance of both. For example, "The DNA mutations in region A had a significant effect on gene expression, while the mutations outside of domain A had no effect. Don't forget to include important details about how you overcame technical problems, so that other researchers don't need to reinvent the wheel.

Unfortunately, it isn't always possible to pair negative results with related positive results. For example, imagine a year-long study on the effect of COVID-19 shelter-in-place orders on the mental health of avid video game players compared to people who don't play video games. Despite using well-established tools for measuring mental health, having a large sample size, and comparing multiple subpopulations (e.g. gamers who live alone vs. gamers who live with others), no significant differences were identified. There is no way to modify and repeat this study because the same shelter-in-place conditions no longer exist. So how can this research be presented effectively?

Writing when you only have negative results

When you write a scientific paper to report negative results, the sections will be the same as for any other paper: Introduction, Materials and Methods, Results and Discussion. In the introduction, you should prepare your reader for the possibility of negative results. You can highlight gaps or inconsistencies in past research, and point to data that could indicate an incomplete understanding of the situation.

In the example about video game players, you might highlight data showing that gamers are statistically very similar to large chunks of the population in terms of age, education, marital status, etc. You might discuss how the stigma associated with playing video games might be unfair and harmful to people in certain situations. You could discuss research showing the benefits of playing video games, and contrast gaming with engaging in social media, which is another modern hobby. Putting a positive spin on negative results can make the difference between a published manuscript and rejection.

In a paper that focuses on negative results—especially one that contradicts published findings—the research design and data analysis must be impeccable. You may need to collaborate with other researchers to ensure that your methods are sound, and apply multiple methods of data analysis.

As long as the research is rigorous, negative results should be used to inform and guide future experiments. This is how science improves our understanding of the world.

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• CAREER FEATURE
• 08 May 2024

Illuminating ‘the ugly side of science’: fresh incentives for reporting negative results

• Rachel Brazil 0

Rachel Brazil is a freelance journalist in London, UK.

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The editor-in-chief of the Journal of Trial & Error , Sarahanne Field wants to publish the messy, null and negative results sitting in researchers’ file drawers. Credit: Sander Martens

Editor-in-chief Sarahanne Field describes herself and her team at the Journal of Trial & Error as wanting to highlight the “ugly side of science — the parts of the process that have gone wrong”.

She clarifies that the editorial board of the journal, which launched in 2020 , isn’t interested in papers in which “you did a shitty study and you found nothing. We’re interested in stuff that was done methodologically soundly, but still yielded a result that was unexpected.” These types of result — which do not prove a hypothesis or could yield unexplained outcomes — often simply go unpublished, explains Field, who is also an open-science researcher at the University of Groningen in the Netherlands. Along with Stefan Gaillard, one of the journal’s founders, she hopes to change that.

Calls for researchers to publish failed studies are not new. The ‘file-drawer problem’ — the stacks of unpublished, negative results that most researchers accumulate — was first described in 1979 by psychologist Robert Rosenthal . He argued that this leads to publication bias in the scientific record: the gap of missing unsuccessful results leads to overemphasis on the positive results that do get published.

Careers Collection: Publishing

Over the past 30 years, the proportion of negative results being published has decreased further. A 2012 study showed that, from 1990 to 2007, there was a 22% increase in positive conclusions in papers; by 2007, 85% of papers published had positive results 1 . “People fail to report [negative] results, because they know they won’t get published — and when people do attempt to publish them, they get rejected,” says Field. A 2022 survey of researchers in France in chemistry, physics, engineering and environmental sciences showed that, although 81% had produced relevant negative results and 75% were willing to publish them, only 12.5% had the opportunity to do so 2 .

One factor that is leading some researchers to revisit the problem is the growing use of predictive modelling using machine-learning tools in many fields. These tools are trained on large data sets that are often derived from published work, and scientists have found that the absence of negative data in the literature is hampering the process. Without a concerted effort to publish more negative results that artificial intelligence (AI) can be trained on, the promise of the technology could be stifled.

“Machine learning is changing how we think about data,” says chemist Keisuke Takahashi at Hokkaido University in Japan, who has brought the issue to the attention of the catalysis-research community . Scientists in the field have typically relied on a mixture of trial and error and serendipity in their experiments, but there is hope that AI could provide a new route for catalyst discovery. Takahashi and his colleagues mined data from 1,866 previous studies and patents to train a machine-learning model to predict the best catalyst for the reaction between methane and oxygen to form ethane and ethylene, both of which are important chemicals used in industry 3 . But, he says, “over the years, people have only collected the good data — if they fail, they don’t report it”. This led to a skewed model that, in some cases, enhanced the predicted performance of a material, rather than realistically assessing its properties.

Synthetic organic chemist Felix Strieth-Kalthoff found that published data were too heavily biased toward positive results to effectively train an AI model to optimize chemical reaction yields. Credit: Cindy Huang

Alongside the flawed training of AI models, the huge gap of negative results in the scientific record continues to be a problem across all disciplines. In areas such as psychology and medicine, publication bias is one factor exacerbating the ongoing reproducibility crisis — in which many published studies are impossible to replicate. Without sharing negative studies and data, researchers could be doomed to repeat work that led nowhere. Many scientists are calling for changes in academic culture and practice — be it the creation of repositories that include positive and negative data, new publication formats or conferences aimed at discussing failure. The solutions are varied, but the message is the same: “To convey an accurate picture of the scientific process, then at least one of the components should be communicating all the results, [including] some negative results,” says Gaillard, “and even where you don’t end up with results, where it just goes wrong.”

Science’s messy side

Synthetic organic chemist Felix Strieth-Kalthoff, who is now setting up his own laboratory at the University of Wuppertal, Germany, has encountered positive-result bias when using data-driven approaches to optimize the yields of certain medicinal-chemistry reactions. His PhD work with chemist Frank Glorius at the University of Münster, Germany, involved creating models that could predict which reactants and conditions would maximize yields. Initially, he relied on data sets that he had generated from high-throughput experiments in the lab, which included results from both high- and low-yield reactions, to train his AI model. “Our next logical step was to do that based on the literature,” says Strieth-Kalthoff. This would allow him to curate a much larger data set to be used for training.

But when he incorporated real data from the reactions database Reaxys into the training process, he says, “[it] turned out they don’t really work at all”. Strieth-Kalthoff concluded the errors were due the lack of low-yield reactions 4 ; “All of the data that we see in the literature have average yields of 60–80%.” Without learning from the messy ‘failed’ experiments with low yields that were present in the initial real-life data, the AI could not model realistic reaction outcomes.

Although AI has the potential to spot relationships in complex data that a researcher might not see, encountering negative results can give experimentalists a gut feeling, says molecular modeller Berend Smit at the Swiss Federal Institute of Technology Lausanne. The usual failures that every chemist experiences at the bench give them a ‘chemical intuition’ that AI models trained only on successful data lack.

Smit and his team attempted to embed something similar to this human intuition into a model tasked with designing a metal-organic framework (MOF) with the largest known surface area for this type of material. A large surface area allows these porous materials to be used as reaction supports or molecular storage reservoirs. “If the binding [between components] is too strong, it becomes amorphous; if the binding is too weak, it becomes unstable, so you need to find the sweet spot,” Smit says. He showed that training the machine-learning model on both successful and unsuccessful reaction conditions created better predictions and ultimately led to one that successfully optimized the MOF 5 . “When we saw the results, we thought, ‘Wow, this is the chemical intuition we’re talking about!’” he says.

According to Strieth-Kalthoff, AI models are currently limited because “the data that are out there just do not reflect all of our knowledge”. Some researchers have sought statistical solutions to fill the negative-data gap. Techniques include oversampling, which means supplementing data with several copies of existing negative data or creating artificial data points, for example by including reactions with a yield of zero. But, he says, these types of approach can introduce their own biases.

Computer scientist Ella Peltonen helped to organize the first International Workshop on Negative Results in Pervasive Computing in 2022 to give researchers an opportunity to discuss failed experiments. Credit: University of Oulu

Capturing more negative data is now a priority for Takahashi. “We definitely need some sort of infrastructure to share the data freely.” His group has created a website for sharing large amounts of experimental data for catalysis reactions . Other organizations are trying to collect and publish negative data — but Takahashi says that, so far, they lack coordination, so data formats aren’t standardized. In his field, Strieth-Kalthoff says, there are initiatives such as the Open Reaction Database , launched in 2021 to share organic-reaction data and enable training of machine-learning applications. But, he says, “right now, nobody’s using it, [because] there’s no incentive”.

Smit has argued for a modular open-science platform that would directly link to electronic lab notebooks to help to make different data types extractable and reusable . Through this process, publication of negative data in peer-reviewed journals could be skipped, but the information would still be available for researchers to use in AI training. Strieth-Kalthoff agrees with this strategy in theory, but thinks it’s a long way off in practice, because it would require analytical instruments to be coupled to a third-party source to automatically collect data — which instrument manufacturers might not agree to, he says.

Publishing the non-positive

In other disciplines, the emphasis is still on peer-reviewed journals that will publish negative results. Gaillard, a science-studies PhD student at Radboud University in Nijmegen, the Netherlands, co-founded the Journal of Trial & Error after attending talks on how science can be made more open. Gaillard says that, although everyone whom they approached liked the idea of the journal, nobody wanted to submit articles at first. He and the founding editorial team embarked on a campaign involving cold calls and publicity at open-science conferences. “Slowly, we started getting our first submissions, and now we just get people sending things in [unsolicited],” he says. Most years the journal publishes one issue of about 8–14 articles, and it is starting to publish more special issues. It focuses mainly on the life sciences and data-based social sciences.

In 2008, David Alcantara, then a chemistry PhD student at the University of Seville in Spain who was frustrated by the lack of platforms for sharing negative results, set up The All Results journals, which were aimed at disseminating results regardless of the outcome . Of the four disciplines included at launch, only the biology journal is still being published. “Attracting submissions has always posed a challenge,” says Alcantara, now president at the consultancy and training organization the Society for the Improvement of Science in Seville.

But Alcantara thinks there has been a shift in attitudes: “More established journals [are] becoming increasingly open to considering negative results for publication.” Gaillard agrees: “I’ve seen more and more journals, like PLoS ONE , for example, that explicitly mentioned that they also publish negative results.” ( Nature welcomes submissions of replication studies and those that include null results, as described in this 2020 editorial .)

Journals might be changing their publication preferences, but there are still significant disincentives that stop researchers from publishing their file-drawer studies. “The current academic system often prioritizes high-impact publications and ground-breaking discoveries for career advancement, grants and tenure,” says Alcantara, noting that negative results are perceived as contributing little to nothing to these endeavours. Plus, there is still a stigma associated with any kind of failure . “People are afraid that this will look negative on their CV,” says Gaillard. Smit describes reporting failed experiments as a no-win situation: “It’s more work for [researchers], and they don’t get anything in return in the short term.” And, jokes Smit, what’s worse is that they could be providing data for an AI tool to take over their role.

Ultimately, most researchers conclude that publishing their failed studies and negative data is just not worth the time and effort — and there’s evidence that they judge others’ negative research more harshly than positive outcomes. In a study published in August, 500 researchers from top economics departments around the world were randomized to two groups and asked to judge a hypothetical research paper. Half of the participants were told that the study had a null conclusion, and the other half were told the results were sizeably significant. The null results were perceived to be 25% less likely to be published, of lower quality and less important than were the statistically significant findings 6 .

Some researchers have had positive experiences sharing their unsuccessful findings. For example, in 2021, psychologist Wendy Ross at the London Metropolitan University published her negative results from testing a hypothesis about human problem-solving in the Journal of Trial & Error 7 , and says the paper was “the best one I have published to date”. She adds, “Understanding the reasons for null results can really test and expand our theoretical understanding.”

Fields forging solutions

The field of psychology has introduced one innovation that could change publication biases — registered reports (RRs). These peer-reviewed reports , first published in 2014, came about largely as a response to psychology’s replication crisis, which began in around 2011. RRs set out the methodology of a study before the results are known, to try to prevent selective reporting of positive results. Daniël Lakens, who studies science-reward structures at Eindhoven University of Technology in the Netherlands, says there is evidence that RRs increase the proportion of negative results in the psychology literature.

In a 2021 study, Lakens analysed the proportion of published RRs whose results eventually support the primary hypothesis. In a random sample of hypothesis-testing studies from the standard psychology literature, 96% of the results were positive. In RRs, this fell to only 44% 8 . Lakens says the study shows “that if you offer this as an option, many more null results enter the scientific literature, and that is a desirable thing”. At least 300 journals, including Nature , are now accepting RRs, and the format is spreading to journals in biology, medicine and some social-science fields.

Yet another approach has emerged from the field of pervasive computing, the study of how computer systems are integrated into physical surroundings and everyday life. About four years ago, members of the community started discussing reproducibility, says computer scientist Ella Peltonen at the University of Oulu in Finland. Peltonen says that researchers realized that, to avoid the repetition of mistakes, there was a need to discuss the practical problems with studies and failed results that don’t get published. So in 2022, Peltonen and her colleagues held the first virtual International Workshop on Negative Results in Pervasive Computing (PerFail) , in conjunction with the field’s annual conference, the International Conference on Pervasive Computing and Communications.

Peltonen explains that PerFail speakers first present their negative results and then have the same amount of time for discussion afterwards, during which participants tease out how failed studies can inform future work. “It also encourages the community to showcase that things require effort and trial and error, and there is value in that,” she adds. Now an annual event, the organizers invite students to attend so they can see that failure is a part of research and that “you are not a bad researcher because you fail”, says Peltonen.

In the long run, Alcantara thinks a continued effort to persuade scientists to share all their results needs to be coupled with policies at funding agencies and journals that reward full transparency. “Criteria for grants, promotions and tenure should recognize the value of comprehensive research dissemination, including failures and negative outcomes,” he says. Lakens thinks funders could be key to boosting the RR format, as well. Funders, he adds, should say, “We want the research that we’re funding to appear in the scientific literature, regardless of the significance of the finding.”

There are some positive signs of change about sharing negative data: “Early-career researchers and the next generation of scientists are particularly receptive to the idea,” says Alcantara. Gaillard is also optimistic, given the increased interest in his journal, including submissions for an upcoming special issue on mistakes in the medical domain. “It is slow, of course, but science is a bit slow.”

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Note: * Ahmad HR. In: Medical Writing. Eds. SA Jawaid, MH Jafary & SJ Zuberi. PMJA, 1997 Ed II: 133-142.

PATIENT care and teaching are rather well established components of our medical career. However, with the passage of time a third component has started to influence our medical culture, namely research. 1 - 4 How to accept this challenge is a question. 5 Indeed, teaching and research form a dialectic unit, meaning that teaching without a research component is like a soup without salt. It is a well-established fact that the research activity of an institution is directly proportional to the number of qualified and committed PhD candidates. An inspiring infrastructure, laboratory facilities and libraries are pre-requisites for a research culture to grow. 6 - 8 This forms the basis of a generation cycle for an institution, so that research activity and its culture continues to grow from one generation to the next. The main objective of doctoral work in biomedical sciences is to develop a galaxy of scientist physicians and surgeons possessing high degree of humility, selflessness and ethical superiority. Such a programme will add a scholastic dimension to the clinical faculty.

Education in how to write a doctoral thesis or dissertation should be a part of the postgraduate curriculum, parallel to the laboratory work and Journal Club activities during the PhD studies and/or residency levels. 9 , 10 The overall structure of a doctoral thesis is internationally standardized. However, it varies in style and quality, depending upon how original the work is, and how much the author has understood the work. Therefore a thorough discussion with supervisor, colleagues and assistance from other authors through correspondence can be useful sources for consultation.

The choice of a topic for a doctoral thesis is a crucial step. It should be determined by scanning the literature whether the topic is original or similar work has already been done even a hundred years ago. It is the responsibility of both the supervisor and the PhD candidate to sort out this problem by continuous use of internet and a library. 11 The work leading to the PhD degree can originate from research in following spheres: 12

• b) Methodology
• c) Diagnostic
• d) Therapeutic and Management
• e) Epidemiology

The availability of internationally standardized methods, as well as research committed supervisors can enable physicians and surgeons to do PhD work in both basic and clinical health sciences. The importance of research in basic health sciences cannot be overemphasized. It is rather the base of the applied sciences. There are many instances where the elucidation of a mechanism involved in a process awaits the development of an adequate methodology. 13 In such a scenario; a new method is like a new eye. Research activity in the field of (a) and (b) illuminates the research directions for (c) (d) and (e). It is worth noting that sometimes important basic questions can come from (e) and stimulate research activity in the domain of basic health sciences. 14 , 15

Types of Doctoral Thesis

TYPE-I: Book Form: a classical style. The blueprint of this form is shown in Table-I .

Type-I: The Classical Book Form

TYPE-II: Cumulative Doctoral thesis: A modem but quite useful practice.

INTRODUCTION

A book containing the pearls of a PhD work has standardized divisions and formats, where the number of pages should be weighted in terms of content rather than container. The book includes summary, introduction, materials and methods, results, discussion, conclusions, references and acknowledgements.

Two exercises are mandatory before starting a PhD programme:

• Literature survey using a regular library hours and internet surfing
• Familiarization with the hands-on-experience of methodology involved in the work
• The importance of a continuous literature survey using library, internet and direct correspondence with authors across the globe in the same field cannot be over-emphasized. The main goal of this exercise is to pinpoint the unresolved problem in the literature. An attempt to solve this problem now becomes the topic of the PhD thesis. All the relevant references should be collected, and carefully preserved in the form of a card system arranged alphabetically according to themes and authors. The introduction of the thesis should be styled like a review article with a critical analysis of the work of authors in the literature. The aims of the present PhD work can then also be addressed in the form of questions. The objectives would then deal with how to achieve the aims of the proposed study.

MATERIALS / SUBJECTS AND METHODS

Now comes the most crucial and functional part of the doctoral work, the materials/subjects and methods section. This part can be considered as the motor of the PhD work. The reliability, sensitivity and specificity of the motor must be checked before embarking on a long journey. Controlling the controls is the best guide for a precise and authentic work. Usually materials and methods contain components such as a description of the species involved, their number, age, weight and anthropometric parameters, types of surgical procedures and anesthesia if applied, and a detailed description of methodology. Continuous or point measurements should be thoroughly described. However, a dynamic method should always be preferred to static one.

The experimental protocol should be designed after a small pilot study, which is especially advisable in research on human subjects. A detailed and well-thought experimental protocol forms the basis of conditions under which the results would be obtained. Any deviation from the experimental protocol will affect the outcome, and the interpretation of results. It may be noted that great discoveries are usually accidental and without a protocol, based merely on careful observation! However, for the sake of a publication, a protocol has to be designed after the discovery. After having described the different phases of the experimental protocol with the help of a schematic diagram e.g., showing variables, time period and interventions, the selection of a statistical method should be discussed. Negative results should not be disregarded because they represent the boundary conditions of positive results. Sometimes the negative results are the real results.

It is usual practice that most PhD candidates start writing the methodological components first. This is followed by writing the results. The pre-requisites for writing results are that all figures, tables, schematic diagrams of methods and a working model should be ready. They should be designed in such a way that the information content of each figure should, when projected as a frame be visually clear to audience viewing it from a distance of about fifty feet. It is often observed that the presenters themselves have difficulty in deciphering a frame of the Power-Point being projected in a conference.

The results of a doctoral thesis should be treated like a bride. The flow of writing results becomes easier if all figures and tables are well prepared. This promotes the train of thoughts required to analyze the data in a quantitative fashion. The golden rule of writing results of a thesis is to describe what the figure shows. No explanation is required. One should avoid writing anything which is not there in a figure. Before writing one should observe each diagram for some time and make a list of observations in the form of key words. The more one has understood the information content of a figure; the better will be the fluency of writing. The interruption of the flow in writing most often indicates that an author has not understood the results. Discussion with colleagues or reference to the literature is the only remedy, and it functions sometimes like a caesarean procedure.

Statistical methods are good devices to test the degree of authenticity and precision of results if appropriately applied. The application of statistical technique in human studies poses difficulties because of large standard deviations. Outliers must be discussed, if they are excluded for the sake of statistical significance. Large standard deviations can be minimized by increasing the number of observations. If a regression analysis is not weighted, it gives faulty information. The correlation coefficient value can change from 0.7 to 0.4 if the regression analysis is weighted using Fisher’s test. The dissection of effect from artifact should be analysed in such a way that the signal to noise ratio of a parameter should be considered. A competent statistician should always be consulted in order to avoid the danger of distortion of results.

The legend of a figure should be well written. It contains a title, a brief description of variables and interventions, the main effect and a concluding remark conveying the original message. The writing of PhD work is further eased by a well maintained collection of data in the form of log book, original recordings, analyzed references with summaries and compiling the virgin data of the study on master plan sheet to understand the original signals before submitting to the procedures of statistics. The original data belong to the laboratory of an institution where it came into being and should be preserved for 5-7 years in the archive for the sake of brevity.

This is the liveliest part of a thesis. Its main goal is to defend the work by staging a constructive debate with the literature. The golden rule of this written debate should be that a rigid explanation looks backward and a design looks forward. The object is to derive a model out of a jig-saw puzzle of information. It should be designed in such a way that the results of the present study and those of authors from the literature can be better discussed and interpreted. Agreement and disagreement can be better resolved if one considers under what experimental conditions the results were obtained by the various authors. It means that the boundary conditions for each result should be carefully analyzed and compared.

The discussion can be divided into the following parts:

• criticism of material/subjects and methods
• a list of important observations of the present study
• interpretation and comparison of results of other authors using a literature table
• design of a model
• claim of an original research work
• The criticism of the methodological procedure enables a candidate to demonstrate how precisely the research work has been carried out. The interpretation of results depends critically on the strict experimental protocol and methods. For example, an epidemiological work is a study of a population. However, if the population sampling is done regularly at a specific location; the question arises as to how a result derived from a localized place can be applied to the whole population.
• After having discussed at length the strong and weak points of material/subjects and methods, one should list in a telegraphic design the most important observations of the present study. This may form a good agenda to initiate interpretation, argument, reasoning and comparison with results of other authors. The outcome of this constructive debate should permit the design of a working model in the form of a block diagram. All statements should be very carefully referenced. The ratio of agreement and disagreement should indicate the ability of the author to reconcile conflicting data in an objective and quantitative way. Attempts should be made to design a solution out of the given quantum of information. It is also well known that most of the processes of human physiology can only be understood if their time course is known. The dynamic aspect of interpretation of results is therefore more powerful and superior to the static one. 16 Therefore a continuous record of variables should be preferred and sought to reveal the secrets hidden in the kinetics.
• Finally, the discussion should conclude how far the study was successful in answering the questions being posed at the end of the introduction part. Usually a doctoral thesis raises more questions than it answers. In this way research does not come to a standstill and does become a life time engagement for a committed scientist. Also it is important to note that all scientific theses should be quantifiable and falsifiable, otherwise they lose the spirit and fragrance of a scientific research.
• The author’s claim of original work is finally decided by the critical review of his research work by the literature and the number of times being cited. It can be easily read by a high rate of a citation index of a publication and invitation. When a methodological research clicks, one becomes a star overnight.

Type-II: CUMULATIVE DOCTORAL THESES

Another way of writing a doctoral work is a cumulative type of thesis. 11 It consists of a few original publications in refereed journals of repute. It is supplemented by a concise summary about the research work. This type of thesis is usually practiced in Sweden, Germany and other countries. It has the advantage of being doubly refereed by the journals and the faculty of health sciences. Additionally, papers are published during a doctoral work. A declaration has to be given to the faculty of science about the sharing of research work in publications, provided there are co-authors. The weightage should be in favour of the PhD candidate, so that the thesis can ethically be better defended before the team of august research faculty.

ACKNOWLEDGEMENTS

A critical review of this manuscript by Dr. Roger Sutton, Dr. Khalid Khan, Dr. Bukhtiar Shah and Dr. Satwat Hashmi is gratefully acknowledged.

Dedicated to the memory of Mr. Azim Kidwai for his exemplary academic commitment and devotion to the science journalism in Pakistan.

Writing a Postgraduate or Doctoral Thesis: A Step-by-Step Approach

• First Online: 01 October 2023

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• Usha Y. Nayak 4 ,
• Praveen Hoogar 5 ,
• Srinivas Mutalik 4 &
• N. Udupa 6  

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A key characteristic looked after by postgraduate or doctoral students is how they communicate and defend their knowledge. Many candidates believe that there is insufficient instruction on constructing strong arguments. The thesis writing procedure must be meticulously followed to achieve outstanding results. It should be well organized, simple to read, and provide detailed explanations of the core research concepts. Each section in a thesis should be carefully written to make sure that it transitions logically from one to the next in a smooth way and is free of any unclear, cluttered, or redundant elements that make it difficult for the reader to understand what is being tried to convey. In this regard, students must acquire the information and skills to successfully create a strong and effective thesis. A step-by-step description of the thesis/dissertation writing process is provided in this chapter.

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Nayak, U.Y., Hoogar, P., Mutalik, S., Udupa, N. (2023). Writing a Postgraduate or Doctoral Thesis: A Step-by-Step Approach. In: Jagadeesh, G., Balakumar, P., Senatore, F. (eds) The Quintessence of Basic and Clinical Research and Scientific Publishing. Springer, Singapore. https://doi.org/10.1007/978-981-99-1284-1_48

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How To Write The Results/Findings Chapter

For qualitative studies (dissertations & theses).

By: Jenna Crossley (PhD). Expert Reviewed By: Dr. Eunice Rautenbach | August 2021

So, you’ve collected and analysed your qualitative data, and it’s time to write up your results chapter. But where do you start? In this post, we’ll guide you through the qualitative results chapter (also called the findings chapter), step by step. 

Overview: Qualitative Results Chapter

• What (exactly) the qualitative results chapter is
• What to include in your results chapter
• How to write up your results chapter
• A few tips and tricks to help you along the way
• Free results chapter template

What exactly is the results chapter?

The results chapter in a dissertation or thesis (or any formal academic research piece) is where you objectively and neutrally present the findings of your qualitative analysis (or analyses if you used multiple qualitative analysis methods ). This chapter can sometimes be combined with the discussion chapter (where you interpret the data and discuss its meaning), depending on your university’s preference.  We’ll treat the two chapters as separate, as that’s the most common approach.

In contrast to a quantitative results chapter that presents numbers and statistics, a qualitative results chapter presents data primarily in the form of words . But this doesn’t mean that a qualitative study can’t have quantitative elements – you could, for example, present the number of times a theme or topic pops up in your data, depending on the analysis method(s) you adopt.

Adding a quantitative element to your study can add some rigour, which strengthens your results by providing more evidence for your claims. This is particularly common when using qualitative content analysis. Keep in mind though that qualitative research aims to achieve depth, richness and identify nuances , so don’t get tunnel vision by focusing on the numbers. They’re just cream on top in a qualitative analysis.

So, to recap, the results chapter is where you objectively present the findings of your analysis, without interpreting them (you’ll save that for the discussion chapter). With that out the way, let’s take a look at what you should include in your results chapter.

What should you include in the results chapter?

As we’ve mentioned, your qualitative results chapter should purely present and describe your results , not interpret them in relation to the existing literature or your research questions . Any speculations or discussion about the implications of your findings should be reserved for your discussion chapter.

In your results chapter, you’ll want to talk about your analysis findings and whether or not they support your hypotheses (if you have any). 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.

While you do need to present your analysis findings in some detail, you should avoid dumping large amounts of raw data in this chapter. Instead, focus on presenting the key findings and using a handful of select quotes or text extracts to support each finding . The reams of data and analysis can be relegated to your appendices.

While it’s tempting to include every last detail you found in your qualitative analysis, it is important to make sure that you report only that which is relevant to your research aims, objectives and research questions .  Always keep these three components, as well as your hypotheses (if you have any) front of mind when writing the chapter and use them as a filter to decide what’s relevant and what’s not.

Need a helping hand?

How do I write the results chapter?

Now that we’ve covered the basics, it’s time to look at how to structure your chapter. Broadly speaking, the results chapter needs to contain three core components – the introduction, the body and the concluding summary. Let’s take a look at each of these.

Section 1: Introduction

The first step is to craft a brief introduction to the chapter. This intro is vital as it provides some context for your findings. In your introduction, you should begin by reiterating your problem statement and research questions and highlight the purpose of your research . Make sure that you spell this out for the reader so that the rest of your chapter is well contextualised.

The next step is to briefly outline the structure of your results chapter. In other words, explain what’s included in the chapter and what the reader can expect. In the results chapter, you want to tell a story that is coherent, flows logically, and is easy to follow , so make sure that you plan your structure out well and convey that structure (at a high level), so that your reader is well oriented.

The introduction section shouldn’t be lengthy. Two or three short paragraphs should be more than adequate. It is merely an introduction and overview, not a summary of the chapter.

Pro Tip – To help you structure your chapter, it can be useful to set up an initial draft with (sub)section headings so that you’re able to easily (re)arrange parts of your chapter. This will also help your reader to follow your results and give your chapter some coherence.  Be sure to use level-based heading styles (e.g. Heading 1, 2, 3 styles) to help the reader differentiate between levels visually. You can find these options in Word (example below).

Section 2: Body

Before we get started on what to include in the body of your chapter, it’s vital to remember that a results section should be completely objective and descriptive, not interpretive . So, be careful not to use words such as, “suggests” or “implies”, as these usually accompany some form of interpretation – that’s reserved for your discussion chapter.

The structure of your body section is very important , so make sure that you plan it out well. When planning out your qualitative results chapter, create sections and subsections so that you can maintain the flow of the story you’re trying to tell. Be sure to systematically and consistently describe each portion of results. Try to adopt a standardised structure for each portion so that you achieve a high level of consistency throughout the chapter.

For qualitative studies, results chapters tend to be structured according to themes , which makes it easier for readers to follow. However, keep in mind that not all results chapters have to be structured in this manner. For example, if you’re conducting a longitudinal study, you may want to structure your chapter chronologically. Similarly, you might structure this chapter based on your theoretical framework . The exact structure of your chapter will depend on the nature of your study , especially your research questions.

As you work through the body of your chapter, make sure that you use quotes to substantiate every one of your claims . You can present these quotes in italics to differentiate them from your own words. A general rule of thumb is to use at least two pieces of evidence per claim, and these should be linked directly to your data. Also, remember that you need to include all relevant results , not just the ones that support your assumptions or initial leanings.

In addition to including quotes, you can also link your claims to the data by using appendices , which you should reference throughout your text. When you reference, make sure that you include both the name/number of the appendix , as well as the line(s) from which you drew your data.

As referencing styles can vary greatly, be sure to look up the appendix referencing conventions of your university’s prescribed style (e.g. APA , Harvard, etc) and keep this consistent throughout your chapter.

Section 3: Concluding summary

The concluding summary is very important because it summarises your key findings and lays the foundation for the discussion chapter . Keep in mind that some readers may skip directly to this section (from the introduction section), so make sure that it can be read and understood well in isolation.

In this section, you need to remind the reader of the key findings. That is, the results that directly relate to your research questions and that you will build upon in your discussion chapter. Remember, your reader has digested a lot of information in this chapter, so you need to use this section to remind them of the most important takeaways.

Importantly, the concluding summary should not present any new information and should only describe what you’ve already presented in your chapter. Keep it concise – you’re not summarising the whole chapter, just the essentials.

Tips for writing an A-grade results chapter

Now that you’ve got a clear picture of what the qualitative results chapter is all about, here are some quick tips and reminders to help you craft a high-quality chapter:

• Your results chapter should be written in the past tense . You’ve done the work already, so you want to tell the reader what you found , not what you are currently finding .
• Make sure that you review your work multiple times and check that every claim is adequately backed up by evidence . Aim for at least two examples per claim, and make use of an appendix to reference these.
• When writing up your results, make sure that you stick to only what is relevant . Don’t waste time on data that are not relevant to your research objectives and research questions.
• Use headings and subheadings to create an intuitive, easy to follow piece of writing. Make use of Microsoft Word’s “heading styles” and be sure to use them consistently.
• When referring to numerical data, tables and figures can provide a useful visual aid. When using these, make sure that they can be read and understood independent of your body text (i.e. that they can stand-alone). To this end, use clear, concise labels for each of your tables or figures and make use of colours to code indicate differences or hierarchy.
• Similarly, when you’re writing up your chapter, it can be useful to highlight topics and themes in different colours . This can help you to differentiate between your data if you get a bit overwhelmed and will also help you to ensure that your results flow logically and coherently.

If you have any questions, leave a comment below and we’ll do our best to help. If you’d like 1-on-1 help with your results chapter (or any chapter of your dissertation or thesis), check out our private dissertation coaching service here or book a free initial consultation to discuss how we can help you.

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20 Comments

This was extremely helpful. Thanks a lot guys

Hi, thanks for the great research support platform created by the gradcoach team!

I wanted to ask- While “suggests” or “implies” are interpretive terms, what terms could we use for the results chapter? Could you share some examples of descriptive terms?

I think that instead of saying, ‘The data suggested, or The data implied,’ you can say, ‘The Data showed or revealed, or illustrated or outlined’…If interview data, you may say Jane Doe illuminated or elaborated, or Jane Doe described… or Jane Doe expressed or stated.

I found this article very useful. Thank you very much for the outstanding work you are doing.

What if i have 3 different interviewees answering the same interview questions? Should i then present the results in form of the table with the division on the 3 perspectives or rather give a results in form of the text and highlight who said what?

I think this tabular representation of results is a great idea. I am doing it too along with the text. Thanks

That was helpful was struggling to separate the discussion from the findings

this was very useful, Thank you.

Very helpful, I am confident to write my results chapter now.

It is so helpful! It is a good job. Thank you very much!

Very useful, well explained. Many thanks.

Hello, I appreciate the way you provided a supportive comments about qualitative results presenting tips

I loved this! It explains everything needed, and it has helped me better organize my thoughts. What words should I not use while writing my results section, other than subjective ones.

Thanks a lot, it is really helpful

Thank you so much dear, i really appropriate your nice explanations about this.

Thank you so much for this! I was wondering if anyone could help with how to prproperly integrate quotations (Excerpts) from interviews in the finding chapter in a qualitative research. Please GradCoach, address this issue and provide examples.

what if I’m not doing any interviews myself and all the information is coming from case studies that have already done the research.

Very helpful thank you.

This was very helpful as I was wondering how to structure this part of my dissertation, to include the quotes… Thanks for this explanation

This is very helpful, thanks! I am required to write up my results chapters with the discussion in each of them – any tips and tricks for this strategy?

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The Final Chapter(s): Discussion, Conclusion, Limitations and Implications

Posted by Rene Tetzner | Oct 21, 2021 | PhD Success | 0 |

4.5 The Final Chapter(s): Discussion, Conclusion, Limitations and Implications

In some theses, the discussion of the study, its results and any concluding thoughts will fill more than one chapter: there may be, for instance, a chapter dedicated to discussion and a shorter conclusion chapter, or perhaps two chapters dedicated to discussing the results of different methodologies and a final chapter on relationships between those different results. In many theses, however, a single chapter will suffice for both. This final part of your thesis is a vital one: it tends to synthesise and analyse all elements of the thesis in order to evaluate and explain the overall significance of the research, and readers will often turn to the discussion (or conclusion) chapter first to obtain an overall picture of a study. The structure of this all-important final material varies considerably from thesis to thesis, yet it is essential that it be designed in a way that allows you to move through a great deal of potentially complicated, analytical and speculative information in an orderly fashion. Considerable reflection is therefore necessary before beginning to write, and not only to get the structure right. That structure depends, of course, on exactly what you intend to include in your discussion chapter(s), and deciding on exactly what to say will take some deep reflective thought on your part.

You may wish to address your plans for your discussion and conclusion chapter(s) with your supervisor and other committee members, and it is also a good idea to see if your department has any guidelines for this part of the thesis, especially if you are feeling unsure about how to proceed and could use some practical advice. In the final analysis, however, you are better acquainted with your research and thus better able to make sense of it than anyone else can be, so you are the only person truly qualified not only to write, but also to design the discussion and conclusion for that research. Besides, this final part of the thesis is your opportunity to express your own perspective and opinions on the significance of your doctoral research, based firmly, of course, on the topic, problem or phenomenon you investigated, your aims and objectives, the methods you used and the results you achieved.

Unfortunately, you may well feel rather exhausted by the experience of drafting your thesis by the time you arrive at your discussion chapter(s), but detailed analysis, intellectual vigour and creative connections are absolutely necessary for discussing and concluding your thesis, and a little passion never hurts either. Doing less than your best at this point would compromise all the effort you have already invested, so take a deep breath and dive into what can in fact be the most challenging and pleasurable part of writing a thesis. There is no simple or universal recipe for successfully discussing and concluding a doctoral thesis and its research, but, generally speaking, an inversion of your opening chapters takes place: in those chapters, you moved your reader from the more general background and scholarship associated with the topic, problem or phenomenon to focus on your particular perspective and approach; in your final chapter(s), you need to use the specific focus you adopted and the results you achieved to move your reader back out to the more general interpretations and implications of your research. In order to achieve this, there are a number of practical approaches and standard expectations that are worthy of serious consideration before forging your own unique path through the forest of possibilities.

Discussing and concluding a thesis effectively might and generally should involve:

• A brief introduction outlining the structure and content of the chapter(s). This can take the form of a single short paragraph, but because the discussion section of a thesis tends to be creative and is always unique, and it is essential that readers understand exactly what you intend, an opening paragraph of this sort can be extremely helpful for clarifying your approach and raising appropriate expectations.
• A brief but informative summary of the study or thesis as a whole. This need only highlight the major issues and can be done efficiently by moving through the individual chapters as outlined in the following four points.
• A return to the introduction of the thesis to restate the most important material introduced there. The research topic, problem or phenomenon should certainly be emphasised, as should its significance, your aims and objectives, and any research questions and hypotheses you introduced and used as guiding factors in your research. You will need to analyse how your research and results answer, resolve and contribute to knowledge in relation to these elements, so any or all of them can effectively be used to structure the chapter, especially if you have used them to organise your report of the results. You may also want to return to the essential aspects of the background and context of your research as discussed in the introduction chapter, but this tends to be necessary only if that background and context is immediately relevant to your discussion and concluding thoughts.
• A return to the literature review to highlight the most important aspects of previous research on your topic and any key theories related to your work in the thesis. Any gaps, misconceptions and limitations that you identified in the scholarship in your earlier literature review should be restated with the goal being to explain exactly how your research and its results have provided what was lacking, established new ways of looking at the problem(s) and thus moved beyond the traditional views and limitations. Your thesis should make an original contribution to knowledge and discussing it in relation to previous scholarship in the area is one of the most effective ways to identify, emphasise and explain that contribution.

• A return to the description of your methodology. This need not be long or detailed, but it sometimes is, and reviewing your main methods and approaches with an eye to evaluating their reliability, limitations and overall effectiveness is an important part of assessing your research. Your understanding of the appropriateness of your methodology may have changed since you first designed your study, particularly as you analysed and reported your results. Your methods may have exceeded or failed to live up to your expectations, or they may have presented limitations or complications you had not envisioned. You may have made errors while putting your methods into practice. Whatever the reality of your methodology proved to be should be explained accurately and objectively, so this is a place for complete if sometimes painful honesty.
• A return to the report of your results to summarise and highlight the key points. There is no need to go through your results in detail – the chapter(s) immediately preceding your discussion has already done that – but since you will need to use the results to discuss the implications and limitations of your research, mentioning the main trends is essential. You should focus particularly on the aspects of your results that you intend to discuss, but try not to leave out any significant or meaningful findings, including surprises, whether they support your research hypotheses or not. Focus and brevity can be achieved by making general statements about the nature of the results and following each statement with the most important examples and details to support it.
• An analysis and interpretation of your research as a whole and its results. This can be included in a step-by-step manner along with the summaries mentioned in the last four points above, or it can be tackled separately after the relevant information from previous chapters has been highlighted. Your approach, like your ideas, for this part of your thesis will require a great deal of reflection and you may find that you have to begin more than once before finding the right direction. The essential question is ‘What does it all mean?’ and the answer can be notoriously difficult to identify and explain both thoughtfully and accurately. It is likely that your research and results will have many meanings, but this is your chance to express your own views and opinions. These should not be whimsical, erratic, biassed or overly subjective, however, but should be solidly and logically based upon your findings. Your interpretations and conclusions should be plausible, insightful and comprehensive, and whenever your ideas are tentative or speculative (as they will necessarily be at times when discussing matters such as causality), your language should clearly indicate that with phrasing such as ‘it seems that,’ ‘these results appear to suggest,’ ‘it is probable that’ and ‘the likely explanation is.’ It is also important to explain how your research and results have resolved the problem you explored, answered your research questions, tested your hypotheses, filled gaps in the scholarship on the topic, revealed the limitations of earlier theories, proved the validity of your methodology and achieved your aims and objectives. If you have not met with success in any of these areas, that, too, should be indicated.

• An identification and analysis of the limitations of your study and its methodology. You may want to write this part along with the review of your main methods, but it can also work as a separate section of the chapter because limitations often extend beyond your methodology (to your findings, for instance). Identifying the limitations of your research and its results is a vital part of your thesis. You may prefer, of course, that there were no or very few limitations, but they are an inevitable aspect of any research, so recognising and discussing them not only demonstrates your intellectual maturity and critical skills, but also makes your research much more useful for future researchers in your area, including yourself. Limitations often stem from the nature of the research design and the exact qualities of the population or sample used, both of which might be called normal limitations that affect the generalisability of your research, but mistakes in controlling the relevant variables, defects in the instruments used or inadequate approaches to recording or analysing data will result in limitations (and perhaps unreliability) of a different kind. If any such limitations proved so significant that it was necessary to adjust your methods and/or repeat any of your research, a discussion of that process, the improvements you made and the ways in which the new results differed can be included as well, though you may want to consult your supervisor about this, as it might be more appropriate to discuss such changes in your methodology chapter(s) instead.
• A discussion of the implications of your research and its results. This will tie into your comments about the significance of your research project as expressed in your introduction to the thesis, but your understanding of that significance may well have changed since you began writing, and you can provide further details as well as more certain conclusions in this chapter based on the results you have presented. Implications, whether for practical action or intellectual reflection, should obviously be directly related to the topic, problem or phenomenon you explored (it can be helpful to imagine what you would tell an audience affected by the problem or phenomenon if you had only enough time to emphasise a few major points), and good use can be made of your research questions and hypotheses as well as your aims and objectives as you construct your discussion. The implications you identify should be considered in relation to the background and context of your research as well as previous scholarship in the area, including both trials and studies of a more theoretical nature – ask yourself how your results build on the literature you reviewed and how your study integrates with or overturns current theories. You may also want to comment on the implications of your methodology, especially if it has been innovative, problematic or extraordinarily successful. You will have a great deal of freedom in expressing your thoughts about your work – the factual reporter of your results chapter has been left behind for the authoritative thinker of your discussion – but be sure that the implications you identify and explain are based firmly and logically upon your research and its results, and when you are theorising, generalising and speculating, ensure that your language accurately reflects those tentative modes.
• One or more recommendations for future research in your area or discipline. Your recommendations for future research will be based to a large extent on the implications and limitations of your own research. It may be that some idealistic researchers start projects with the notion that their work will resolve all associated questions and problems, but this rarely (if ever) happens in reality, and often the answers that are derived from careful and extensive research complicate matters and open more doors on further research needs and possibilities. Your recommendations may involve repeating the kind of research you have done, but with changes that would eliminate the limitations you have identified (by adding controls, for instance, or choosing or developing different instruments, participants or means of analysis), or they may involve taking very different directions by conducting entirely different kinds of research (using entirely different methods perhaps, or focussing from a different perspective on the most unexpected or surprising trends of your results). Remember that your recommendations for future research can transform aspects of your work that may have proved disappointing into positive possibilities.
• A brief conclusion. Since conclusions about your study are a major part of this chapter, a conclusion to it may seem somewhat redundant, but it is important to end on a bright clear note. Sometimes an academic or scientific author will dedicate pages of text to describing, interpreting and speculating upon all the sound and revealing results he or she obtained only to turn at the end of the final chapter to the limitations of the study, which can have the effect of dumping all the wind out of the sails he or she has so carefully and convincingly filled. It may be that the guidelines provided by your university or department call for limitations to be reported at the end of the thesis, and, as an essential aspect of valuable research, limitations need not be negative, especially if they are combined with or (if possible) followed by recommendations for future research. However, after discussing the limitations of your study, it can be useful to return to the unquestionably positive results and conclusions of your research. You might choose, for instance, three main concepts or conclusions that you would like to highlight as you close your text, and these can certainly be qualified with hints of limitations and recommendations while still emphasising the significant contributions of your research. Keep in mind that a doctoral thesis should make a significant and original contribution to knowledge, so highlighting the main ways in which it does so in your final thoughts is not out of place. If there was ever a time to blow your own horn a little, this is it.

Why PhD Success?

To Graduate Successfully

This article is part of a book called "PhD Success" which focuses on the writing process of a phd thesis, with its aim being to provide sound practices and principles for reporting and formatting in text the methods, results and discussion of even the most innovative and unique research in ways that are clear, correct, professional and persuasive.

The assumption of the book is that the doctoral candidate reading it is both eager to write and more than capable of doing so, but nonetheless requires information and guidance on exactly what he or she should be writing and how best to approach the task. The basic components of a doctoral thesis are outlined and described, as are the elements of complete and accurate scholarly references, and detailed descriptions of writing practices are clarified through the use of numerous examples.

The basic components of a doctoral thesis are outlined and described, as are the elements of complete and accurate scholarly references, and detailed descriptions of writing practices are clarified through the use of numerous examples. PhD Success provides guidance for students familiar with English and the procedures of English universities, but it also acknowledges that many theses in the English language are now written by candidates whose first language is not English, so it carefully explains the scholarly styles, conventions and standards expected of a successful doctoral thesis in the English language.

Individual chapters of this book address reflective and critical writing early in the thesis process; working successfully with thesis supervisors and benefiting from commentary and criticism; drafting and revising effective thesis chapters and developing an academic or scientific argument; writing and formatting a thesis in clear and correct scholarly English; citing, quoting and documenting sources thoroughly and accurately; and preparing for and excelling in thesis meetings and examinations. 

Completing a doctoral thesis successfully requires long and penetrating thought, intellectual rigour and creativity, original research and sound methods (whether established or innovative), precision in recording detail and a wide-ranging thoroughness, as much perseverance and mental toughness as insight and brilliance, and, no matter how many helpful writing guides are consulted, a great deal of hard work over a significant period of time. Writing a thesis can be an enjoyable as well as a challenging experience, however, and even if it is not always so, the personal and professional rewards of achieving such an enormous goal are considerable, as all doctoral candidates no doubt realise, and will last a great deal longer than any problems that may be encountered during the process.

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Rene Tetzner

Rene Tetzner's blog posts dedicated to academic writing. Although the focus is on How To Write a Doctoral Thesis, many other important aspects of research-based writing, editing and publishing are addressed in helpful detail.

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Negative Capacitance for Ultra-low Power Computing

Asif islam khan, eecs department, university of california, berkeley, technical report no. ucb/eecs-2015-171, july 10, 2015, http://www2.eecs.berkeley.edu/pubs/techrpts/2015/eecs-2015-171.pdf.

Owing to the fundamental physics of the Boltzmann distribution, the ever-increasing power dissipation in nanoscale transistors threatens an end to the almost-four-decade-old cadence of continued performance improvement in complementary metal-oxide-semiconductor (CMOS) technology. It is now agreed that the introduction of new physics into the operation of field-effect transistors--in other words, ``reinventing the transistor''-- is required to avert such a bottleneck. In this dissertation, we present the experimental demonstration of a novel physical phenomenon, called the negative capacitance effect in ferroelectric oxides, which could dramatically reduce power dissipation in nanoscale transistors. It was theoretically proposed in 2008 that by introducing a ferroelectric negative capacitance material into the gate oxide of a metal-oxide-semiconductor field-effect transistor (MOSFET), the subthreshold slope could be reduced below the fundamental Boltzmann limit of 60 mV/dec, which, in turn, could arbitrarily lower the power supply voltage and the power dissipation. The research presented in this dissertation establishes the theoretical concept of ferroelectric negative capacitance as an experimentally verified fact.

The main results presented in this dissertation are threefold. To start, we present the first direct measurement of negative capacitance in isolated, single crystalline, epitaxially grown thin film capacitors of ferroelectric Pb(Zr0.2Ti0.8)O3. By constructing a simple resistor-ferroelectric capacitor series circuit, we show that, during ferroelectric switching, the ferroelectric voltage decreases, while the stored charge in it increases, which directly shows a negative slope in the charge-voltage characteristics of a ferroelectric capacitor. Such a situation is completely opposite to what would be observed in a regular resistor-positive capacitor series circuit. This measurement could serve as a canonical test for negative capacitance in any novel material system. Secondly, in epitaxially grown ferroelectric Pb(Zr0.2Ti0.8)O3-dielectric SrTiO3 heterostructure capacitors, we show that negative capacitance effect from the ferroelectric Pb(Zr0.2Ti0.8)O3 layer could result in an enhancement of the capacitance of bilayer heterostructure over that of the constituent dielectric SrTiO3 layer. This observation apparently violates the fundamental law of circuit theory which states that the equivalent capacitance of two capacitors connected in series is smaller than that of each of the constituent capacitors. Finally, we present a design framework for negative capacitance field-effect-transistors and project performance for such devices.

Advisors: Sayeef Salahuddin

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UT Electronic Theses and Dissertations

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This collection contains University of Texas at Austin electronic theses and dissertations (ETDs). The collection includes ETDs primarily from 2001 to the present. Some pre-2001 theses and dissertations have been digitized and added to this collection, but those are uncommon. The library catalog is the most comprehensive list of UT Austin theses and dissertations.

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