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The PhD Experience

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Inspiration, motivation and the PhD: What are your 3 reasons?

Ellie Ralph |

When starting your PhD, as a bright-eyed and bushy-tailed student, you are full of motivation and passion for your research. However, we have all probably also been at the point when you have little to no motivation for continuing with your PhD and are contemplating if any of it is really ‘worth it’ anymore. The biggest piece of advice I can give in this circumstance is to remember the reason why you started. To help with this personally, I wrote a list of 3 of the top reasons why I am doing my PhD. I recommend to new PhD students to do this task at the start, but you can do this at any point in your PhD and use it as a tool to refer back to during those low points. Below I share my personal list of reasons, but it may help to spark motivation in others too:

My Grandpa – As a child, I was not a high-achiever in school and always felt like education wasn’t for me. It wasn’t until I started university and really found my passion that I started to enjoy learning again. Growing up, my Grandpa was always the person to express to me the importance of staying in school and achieving good grades. He always pushed for me to do my best, regardless of the result. In 2020, my Grandpa passed away. In the hospital when he was sick, he would tell the nurses and doctors that I was a professor (I wish!), and always talk of how proud he was of me and my achievements. He was always the first person I would call with any academic-related good news. I still find myself wanting to call him now, but I use this as a motivation tool, and like to think that I am making him proud.
My travel experience – My area of research mostly impacts people within Lebanon. I have been lucky enough to travel to Lebanon numerous times, for both work and personal travel, and have had direct contact with people that my research may one day impact. There is nothing that compares to travelling to the country your research is about, especially experiencing it in a ‘non-work-related way. I have also had some personal experiences with both Lebanese and Syrian nationals alike in the UK, and the experiences have always relit that spark within me to keep going.
Impact – This is one area in which I think everyone could add it to their list of 3. We have to remember that no matter how small our impact on the world may be, we are still making one. With your PhD, you are making a contribution to the wider sphere of knowledge. My support worker at university changed my perspective on this – that no matter how small your drop in the ocean may be, it is still a drop that wasn’t there before. Further to this, you don’t truly know the impact of your work, and you may be helping change the lives of people you will never meet.

I hope that sharing my experiences helps you to think of the reasons why you started, which may be the same reasons to keep going. It is normal to go through periods of low motivation and the crisis of ‘what am I doing?!’, but it is important to have a place you can refer back to when you’re feeling this way. Please feel free to share your 3 reasons in the comments below.

Photo by Colton Duke on Unsplash

Ellie Ralph is the Vice Chair for Pubs & Publications. She is a second year PhD student at Keele University in Politics and International Relations, exploring Lebanese local NGO management of the Syrian refugee crisis. You can find her on Twitter here.

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April 12, 2021

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Please note you do not have access to teaching notes, reasons, motives and motivations for completing a phd: a typology of doctoral studies as a quest.

Studies in Graduate and Postdoctoral Education

ISSN : 2398-4686

Article publication date: 16 November 2018

Issue publication date: 16 November 2018

This study aims to examine how PhD students with diverse profiles, intentions and expectations manage to navigate their doctoral paths within the same academic context under similar institutional conditions. Drawing on Giddens’ theory of structuration, this study explores how their primary reasons, motives and motivations for engaging in doctoral studies influence what they perceive as facilitating or constraining to progress, their strategies to face the challenges they encounter and their expectations regarding supervision.

Design/methodology/approach

Using a qualitative design, the analysis was conducted on a data subset from an instrumental case study (Stake, 2013) about PhD students’ persistence and progression. The focus is placed on semi-structured interviews carried out with 36 PhD students from six faculties in humanities and social sciences fields at a large Canadian university.

The analysis reveals three distinct scenarios regarding how these PhD students navigate their doctoral paths: the quest for the self; the intellectual quest; and the professional quest. Depending on their quest type, the nature and intensity of PhD students’ concerns and challenges, as well as their strategies and the support they expected, differed.

Originality/value

This study contributes to the discussion about PhD students’ challenges and persistence by offering a unique portrait of how diverse students’ profiles, intentions and expectations can concretely shape a doctoral experience.

Higher education
PhD experience
Doctoral education
Doctoral student learning
Doctoral students
Doctoral candidates
Doctoral experience
Doctoral trajectories
Higher education environment

Skakni, I. (2018), "Reasons, motives and motivations for completing a PhD: a typology of doctoral studies as a quest", Studies in Graduate and Postdoctoral Education , Vol. 9 No. 2, pp. 197-212. https://doi.org/10.1108/SGPE-D-18-00004

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Tress Academic

#35: PhD motivation running low? Here’s the cure!

December 10, 2019 by Tress Academic

Is it getting harder to be excited about your PhD? Perhaps you struggle to find the enthusiasm to start another work day – especially when nothing seems to be going your way. You might be suffering from one of the most common syndromes among PhD students: a lack of motivation. Although it may feel like your work is coming to standstill, DON’T BE FOOLED! There are many ways to get your motivation to come out of hiding, if you know what caused it disappear in the first place! We’ll help you to understand the causes and the cures for the motivational slumps, so you can stay on track and keep smiling until your PhD is in the bag!

Make no mistake, the PhD is a very demanding period of your life. You’re working on many difficult tasks, always aware that things can go wrong, with supervisors who all have high expectations, and dish out heavy criticism whenever they sense a momentary slip-up. Many different tasks demand your attention at any given moment; like working out your research project, experimenting, analysing data, and apart from all of that, you still must attend your graduate courses, present at conferences, and publish your results! That’s a lot to tackle alongside a high workload. So it comes as no surprise if this adds up to you feeling demotivated every now and then. Rest assured, no PhD student is super motivated and happy all the time. The ups and downs are just a part of the entire PhD process.

Motivation changes over time

It’s normal that motivational levels of PhD students naturally change over time. We see a lot of PhD students at the very beginning of our course “Completing your PhD successfully on time” that are walking on sunshine in the first weeks of their PhD! When they’re asked to rate their satisfaction with their PhD, they’re close to 100% because they’re just so happy that they got the chance to do a PhD, after receiving a grant or scholarship or successfully beat other competitors for a PhD position, that they feel a bit like they won the lottery!

Was it the same for you in the beginning? Well, then you also know that the feeling does not last. Because after a while, reality kicks in and you realise that not everything is as perfect as it seemed at first. This is often when one’s motivation starts to adjust to a normal level, but is still pretty stable. Later into the PhD, your motivation often continues to shrink. This is when there’s still an awful lot of work to do, with difficulties creeping up all around and no end in sight. But guess what? As the day of your submission approaches (even if it is still in the distant future), motivation often picks up again, once you start to gain confidence with the results of your research, or get your first papers published and a general feeling of – I’ll probably get through this one day – begins to sink in!

Don’t let low motivation drag you down

Apart from this usual fluctuation tendency in a PhD, low motivation is always a warning sign from your psyche telling you ‘uh oh something’s wrong here’ – so don’t ignore it.

It is very important to spot the early signs of low motivation, because at this stage, you can do a lot to get out of it quickly! And the sooner you take the necessary steps to get out of it, the better. In contrast, if you wait too long to act, then you might become really depressed and the situation is much more difficult to tackle.

With this blog post, we want to help you reflect on the reasons you might be feeling de-motivated – as this is often the key for improvement. As we put our heads together, to try and help you understand the problem, we also put together great tips on how to get out of a motivational low – all specific to the underlying reasons! Check out or free worksheet “How to get out of low PhD motivation?” So here’s the message: You don’t have to accept low motivation – it’s all within your power to change!

How to spot low motivation?

These are the typical signs of a PhD student who is at a motivational low:

You’re not as excited as usual to come to work, or when you think about your PhD.
It takes you a long time to get started and when you do, you postpone difficult or important tasks related to your project.
It takes you longer than usual and feels more difficult to finish something. You’re not happy with what you produce and your overall progress slows down.
You deliberately look for distractions. This might take shape as aimlessly browsing the web or social media platforms (for more on combatting social media addition, see our post #14 “Social media/www distractions at work: 5-step cure!” You might also distract yourself with work-related tasks that are not challenging but still give you the feeling of doing something, e.g. getting involved in the organisation of scientific events at your institute, or busying yourself cleaning up, sorting through emails or reorganising your workspace …

Whatever form it takes, we know that this behaviour always has a root cause. So we’ve broken down for you the five main reasons for low motivation that we see time and time again with PhD students:

Reason 1: Stuck in a boring routine

You may be in a situation where you have to do a tedious or boring task for a considerable amount of time. We know the typical routines: Maybe you are coding and you have nothing to do but coding for whole days, and you know it’ll go on like this for weeks on end. Or you’re spending seemingly endless hours in the lab, running gels, so your day is sliced into 15 or 20 min slots. Or you’re working with antibodies and have 2-3 h incubation times, which is not much better. Or you’re sorting through data to find a few meaningful correlations that will prove your PhD work as worthwhile … It’s no wonder that your motivation plummets and you can hardly pull yourself together to continue the slog.

Probably, you generally like working as a researcher, and most of the tasks come easy to you. But this type of routine would wear anyone down! So your motivation slips with certain repetitive tasks that you don’t like, are boing, or simply overwhelm you.

Reason 2: There’s no end in sight

Your research is in full swing and you thought by now you’d have more clarity and confidence about your project, but instead you are getting more uncertain and confused by the day. You may have some results already, but you are unsure which aspects of it to use for your dissertation, or if you can use them at all. You’ve no clue whether you are making progress with your PhD or not. All you see are loose ends everywhere: ideas that you did not follow up on, half-finished paper-drafts, and incomplete side-projects. It seems like you’ve lost track of it all, you’re going around in circles, with your head spinning, and your motivation is way down.

This type of motivation loss often hits home many months after you started the PhD. Your work gains complexity as you go, and not all results make sense. You may adjust and deviate from your original plan to follow different paths, but not all of them lead to success. Now you are in a phase where you are reading more and understanding better what others in your field did before you. But as you gain knowledge and insights, you also become much more critical of your own work and progress. For you, it feels like there is no clear win or breakthrough in sight that would give you the ‘green light’ so you finally know you’ll be able to manage it all and get your degree in the end.

Reason 3: Unacknowledged work

This has a lot to do with the nature of PhDs and the working culture in scientific institutes: Although you may be part of a team, most of what you do for your PhD in the end is done in isolation. That means you’re probably lacking positive feedback and stimulation. And because you’re still in research training and on a steep learning curve, you get the full brunt of criticism from colleagues. Your supervisors or PIs may be quick to point out any shortcomings or flaws in your work, but less practiced at giving out praise! Have you every heard anyone in your lab saying ‘Wow, you did an absolutely amazing job with this, congrats!’ Nope. This may lead you to think negatively of your own achievements, doubt your abilities, and be quite demotivating!

We have all experienced how this works: If we get positive feedback or a praise, we’re super happy and look forward to continuing with our work or even work harder. But if we are heavily criticised or if critique dominates and nothing positive is mentioned, we are hurt and demotivated. Sometimes this is so extreme that we’d rather stop working on a task and take on something else entirely.

Reason 4: Overworked and sleep deprived

It can happen to anyone: Your recent experiment or field campaign was much more time intensive than expected, there was a deadline for a conference paper that you wanted to submit, and you were also desperate to work on a proposal that would give you more funding for your PhD. As a result, you got into a habit of working very long days, even on weekends, and your last real break was a long time ago …

It’s no surprise that after weeks or months in ‘emergency overdrive’, you feel drained and exhausted. And although you initially thought you’ll just put in some extra hours temporarily, this has in fact become your standard mode of working. You got used to that high-intensity schedule and you had little to no time to recover! Demotivation creeps in, because – after all – you may be a PhD student, but you’re also a human being!

Reason 5: Uncertainty about the future

Do you get a funny feeling in your stomach when you imagine the time after your PhD completed? Do you feel the anxiety creeping up and freezing you to the spot? You’ve probably heard rumours from other PhD students who had difficulty finding a position afterwards and in your worst nightmares you picture yourself unemployed and broke…! So the thought of your ‘life-after-the-PhD’ and all the questions that come along with it are hanging over you, deflating your energy and shrinking your motivation to push ahead with your PhD – because what use is it?

Uncertainty about the future is one of the big recurring worries of PhD students. (Max-Planck survey link). As a PhD student, you have been within a university for such a long time that life beyond the ivory-tower is virtually beyond your imagination. Everything outside academia may seem scary and you have no clue which of your skills will be valued by employers. And even something familiar like continuing with a post-doc seems intangible and remains in the very distant future. Not surprising that your motivation to move on stalled. . .!

How to get out of it?

Help is around. For all these five possible reasons for your motivational low we come up with hands-on advice, tips and suggestions what you can do to overcome the motivational low and get your PhD back on track. Check out our free worksheet “How to get out of low PhD motivation?” for all the help that you need.

Conclusion:

It is normal to lose motivation at critical parts of your PhD. But it is also easy to combat if you recognize the signs early and treat yourself properly. Consider yourself another working part of your project that you may need to adjust as things move forward. You can’t always expect to get your best quality work if you are running on empty. So slow down, take stock, break up your routine now and then with something you love, get input from the people who care about you and rest!

If all our tips sound like we’re speaking a foreign language to you – you need to sit down and plan some changes in your week immediately! This time is always going to be a challenging one, so make it easier for yourself and take a moment of zen to see the past, present and future as part of an amazing journey that you can – no – will successfully finish! Our suggestions in our free worksheet “How to get out of low PhD motivation?” will definitely help you on your way!

Related resources:

Worksheet “How to get out of low PhD motivation?”
Smart Academics Blog #14 “Social media/www distractions at work: 5-step cure!”
Smart Academics Blog #37: 5 ways to boost your energy as a researcher!
Smart Academics Blog #55: 7 signs you need help with your PhD
Smart Academics Blog #59: Overwhelmed by PhD work? Here’s the way out!
Smart Academics Blog #72: 1000 things to do – no clue where to start
Smart Academics Blog #100: PhD success stories that motivate!
TRESS ACADEMIC course “Completing your PhD successfully on time”

More information:

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Photograph by Ethan Sykes at unsplash.com

#PhD, #Motivation, #MotivationalLow, #Demotivation, #DoctoralStudy,

How to Write a PhD Motivation Letter

Applying to a PhD

A PhD motivation letter is a document that describes your personal motivation and competence for a particular research project. It is usually submitted together with your academic CV to provide admissions staff with more information about you as an individual, to help them decide whether or not you are the ideal candidate for a research project.

A motivation letter has many similarities to a cover letter and a personal statement, and institutions will not ask you to submit all of these. However, it is a unique document and you should treat it as such. In the context of supporting a PhD application, the difference is nuanced; all three documents outline your suitability for PhD study. However, compared to a cover letter and personal statement, a motivation letter places more emphasis on your motivation for wanting to pursue the particular PhD position you are applying for.

Academic cover letters are more common in UK universities, while motivation letters are more common abroad.

A motivation letter can play a key part in the application process . It allows the admission committee to review a group of PhD applicants with similar academic backgrounds and select the ideal candidate based on their motivations for applying.

For admission staff, academic qualifications alone are not enough to indicate whether a student will be successful in their doctorate. In this sense, a motivational letter will allow them to judge your passion for the field of study, commitment to research and suitability for the programme, all of which better enables them to evaluate your potential.

How Should I Structure My Motivation Letter?

A strong motivation letter for PhD applications will include:

A concise introduction stating which programme you are applying for,
Your academic background and professional work experience,
Any key skills you possess and what makes you the ideal candidate,
Your interest and motivation for applying,
Concluding remarks and thanks.

This is a simplistic breakdown of what can be a very complicated document.

However, writing to the above structure will ensure you keep your letter of motivation concise and relevant to the position you are applying for. Remember, the aim of your letter is to show your enthusiasm and that you’re committed and well suited for the programme.

To help you write a motivation letter for a PhD application, we have outlined what to include in the start, main body, and closing sections.

How to Start a Motivation Letter

Introduction: Start with a brief introduction in which you clearly state your intention to apply for a particular programme. Think of this as describing what the document is to a stranger.

Education: State what you have studied and where. Your higher education will be your most important educational experience, so focus on this. Highlight any relevant modules you undertook as part of your studies that are relevant to the programme you are applying for. You should also mention how your studies have influenced your decision to pursue a PhD project, especially if it is in the same field you are currently applying to.

Work experience: Next summarise your professional work experience. Remember, you will likely be asked to submit your academic CV along with your motivation letter, so keep this section brief to avoid any unnecessary repetition. Include any other relevant experiences, such as teaching roles, non-academic experience, or charity work which demonstrates skills or shows your suitability for the research project and in becoming a PhD student.

Key skills: Outline your key skills. Remember the admissions committee is considering your suitability for the specific programme you are applying for, so mention skills relevant to the PhD course.

Motivation for applying: Show your enthusiasm and passion for the subject, and describe your long-term aspirations. Start with how you first became interested in the field, and how your interest has grown since. You should also mention anything else you have done which helps demonstrate your interest in your proposed research topic, for example:

Have you attended any workshops or seminars?
Do you have any research experience?
Have you taught yourself any aspects of the subject?
Have you read any literature within the research area?

Finally, describe what has convinced you to dedicate the next 3-4 years (assuming you are to study full time) of your life to research.

How to End a Motivation Letter

Concluding the motivation letter is where most people struggle. Typically, people can easily describe their academic background and why they want to study, but convincing the reader they are the best candidate for the PhD programme is often more challenging.

The concluding remarks of your motivation letter should highlight the impacts of your proposed research, in particular: the new contributions it will make to your field, the benefits it will have on society and how it fits in with your aspirations.

With this, conclude with your career goals. For example, do you want to pursue an academic career or become a researcher for a private organisation? Doing so will show you have put a lot of thought into your decision.

Remember, admissions into a PhD degree is very competitive, and supervisors invest a lot of time into mentoring their students. Therefore, supervisors naturally favour those who show the most dedication. Your conclusion should remind the reader that you are not only passionate about the research project, but that the university will benefit from having you.

Finally, thank the reader for considering your application.

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Motivation Letter Format

There are some basic rules to follow when writing a successful motivation letter. These will mimic the standard format for report writing that the supervisor will be familiar with:

Use a sans serif font (e.g. Arial or Times New Roman),
Use a standard font size (e.g. 12pt) and black font colour,
Keep your writing professional throughout and avoid the use of informal language,
Write in the first person,
Address your motivation letter to a named person such as the project supervisor, however, this could also be the person in charge of research admissions,
Structure your letter into paragraphs using the guidance above, such as introduction, academic history, motivation for research, and concluding remarks.

How Long Should a Motivation Letter Be?

A good rule of thumb for PhD motivation letters is to keep it to around one side of A4. A little longer than one page is acceptable, but two pages is generally considered too long. This equates to approximately 400-600 words.

Things to Avoid when Writing Your Motivational Letter

Your motivational letter will only be one of the several documents you’ll be asked to submit as part of your PhD application. You will almost certainly be asked to submit an Academic CV as well. Therefore, be careful not to duplicate any of the information.

It is acceptable to repeat the key points, such as what and where you have studied. However, while your CV should outline your academic background, your motivation letter should bring context to it by explaining why you have studied what you have, and where you hope to go with it. The simplest way to do this is to refer to the information in your CV and explain how it has led you to become interested in research.

Don’t try to include everything. A motivation letter should be short, so focus on the information most relevant to the programme and which best illustrates your passion for it. Remember, the academic committee will need to be critical in order to do their jobs effectively , so they will likely interpret an unnecessarily long letter as in indication that you have poor written skills and cannot communicate effectively.

You must be able to back up all of your statements with evidence, so don’t fabricate experiences or overstate your skills. This isn’t only unethical but is likely to be picked up by your proposed PhD supervisor or the admissions committee.

Whilst it is good to show you have an understanding of the field, don’t try to impress the reader with excessive use of technical terms or abbreviations.

PhD Motivation Letter Samples – A Word of Caution

There are many templates and samples of motivation letters for PhDs available online. A word of caution regarding these – although they can prove to be a great source of inspiration, you should refrain from using them as a template for your own motivation letter.

While there are no rules against them, supervisors will likely have seen a similar letter submitted to them in the past. This will not only prevent your application from standing out, but it will also reflect poorly on you by suggesting that you have put minimal effort into your application.

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How to Stay Motivated During Your PhD Programme

Motivation is a tricky thing. Even if you are committed to your goals, it can be acting as a roller coaster at times due to accumulating stress or losing faith in the result. With PhD thesis writing , such terms as ‘second-year blues’ as well as statistics of academic dropouts and mental health issues strongly suggest that staying on track may be much more difficult than you might think. The best solution here is to understand the existence of motivation problems, accept their inevitability, and plan your journey in a way minimising them. In this article, we will discuss a number of ways to stay motivated during your PhD programme.

1. Start Small

As noted by multiple experts, a PhD programme is a marathon rather than a sprint. If you choose to follow the same mentality used by Undergraduate and Master’s students, this will lead to inevitable burnout down the road. The infamous second-year blues usually occur because practitioners take more obligations than they can possibly meet. Unfortunately, this approach is actively promoted in academia:

Most supervisors expect you to invest all your spare time and resources into your PhD project.
Other students discuss the importance of ‘giving it all that you have’ during your first year.
Everyone is certain that ‘sacrificing something’ is the key to getting good things in life.

Surprisingly, the optimal strategy for staying motivated and productive throughout your PhD programme is the direct opposite of this approach. Do not be mistaken, you will definitely have some ‘crunch’ periods caused by unexpected circumstances while writing your thesis. However, going slow and steady is the best long-term strategy to follow most of the time due to the following reasons:

Motivation stems from overall satisfaction and good physical and mental health.
Balancing your work and social life is a good way of achieving this state.
The duration of your PhD project implies that you will not have time to recuperate.

The last thought is especially important. The length of your PhD project means that you will have to maintain your current productivity levels for several years without any breaks. If you intend to end a marathon successfully, you may choose to not exhaust yourself in the beginning.

2. Be Humble

If you have ever been to a gym, you have probably seen people coming to do some weightlifting exercises for the first time. In many cases, they use too much weight to ‘not look wimpy’. Unfortunately, this decision effectively ruins their technique and future progress. Any personal trainer will tell you to start with the smallest weights possible and add more as you progress. In line with the previous recommendation, this means that your PhD journey should proceed in accordance with the following routine:

Start with a minimal daily workload and experiment with several daily and weekly schedules.
Proceed with this arrangement and always maintain a leeway for emergencies.
Increase your daily/weekly workload if you feel that you can successfully maintain optimal work/life balance with the previous ‘setting’ for several weeks at a time.

While trying to ‘lift as much weight as you can’ may look ‘cool’ at first glance, this is simply not sustainable in a marathon setting. If you feel that you cannot manage your current workload while staying motivated and productive, this is a clear sign that you need to negotiate a more reasonable schedule with your supervisor. No athlete will continue lifting excessive weight after feeling chronic pain in their body. However, many PhD students see this as a viable long-term strategy for avoiding the necessary PhD programme extensions and end up losing more time due to stress accumulation and burnout.

Staying humble can also be compared with speeding up in your car. Most vehicles cannot start running at 100 miles per hour in a single second. You need to start slow and gradually ‘change gears’ while also observing the road situation. In many cases, you simply cannot proceed at the desired speed due to unexpected turns, pedestrians, and other obstacles. Driving slowly is always preferable to crashing your car and making a very long stop in your academic journey as a result.

3. Have a Plan

Progressing in small steps means that you should carefully plan each one of them to maximise your outputs. Motivation stems from measurable and manageable tasks that you complete successfully. Here are some ideas on how you can maintain it:

Set small and manageable tasks for each day (e.g. reading 5 articles or writing 300 words of your thesis);
If a task cannot be quantified, set it as ‘working on … for … minutes’;
Focus on the formal completion of the task rather than specific outputs or deliverables;
Keep track of your progress over time.

Keeping a diary is a must for staying motivated and productive during your PhD programme. Make sure to record the completion of individual tasks and your overall progress. This allows you to remind yourself about the substantial results you have already achieved in moments of doubt. A lack of such a diary leaves you one-on-one with your fears of underperforming and pushes you into the dreadful ‘sprinter’s mentality’ leading to burnout and academic failures.

Additionally, try to record non-quantifiable tasks as ‘time spent working on it’ instead of results. If you are looking for quality references in a particular field, you have no control over the actual existence of recent peer-reviewed articles in it. Hence, ending an hour of work with no quantitative results should still be recorded as progress and not a failure if you are willing to stay motivated and maintain an internal locus of control.

4. Stay Focused on the Bigger Picture

When you decided to enter a PhD programme, you were motivated by some long-term goals. They could include better employment perspectives, your in-depth interest in a certain field or your willingness to build a career in academia. Losing track of these objectives is one of the main reasons leading to poor productivity and low motivation. While your daily routine is probably filled with smaller tasks as suggested earlier, sticking a printed list of your long-term goals on your fridge may be a good way of reminding yourself why you are doing this in the first place.

In some cases, this ‘bigger picture’ needs to be adjusted over time. The COVID-19 pandemic has disrupted many PhD journeys and has substantially decreased the number of positions available in academia. If a certain student saw their long-term goal as a career in this sphere, this inevitably decreases their motivation at the moment. Effectively, their actions and progress are leading them nowhere according to the opinions of multiple experts and practitioners that they read.

If you find yourself involved in a disruptive trend like this one, you may need to make some hard decisions and reconsider your overall direction. The same is true for problems with a certain supervisor or not making progress with your initial topic. Biologically speaking, the loss of motivation is a physiological sign of not achieving your goals and losing interest in them. Reconsidering your objectives can be a better option than ignoring this increasing resistance.

5. Talk with Others

Networking is a powerful instrument for getting relevant information and minimising the amount of wasted effort. Make sure to ask a lot of questions during your meetings with your supervisor. This way, you can clarify their expectations and make sure that all your activities are rewarded with favourable outcomes afterwards. Not getting positive reinforcement for your efforts is a very short road to the loss of motivation.

Similarly, peer communication opens new opportunities for being productive and making better decisions. This can include writing articles with other PhD students, exchanging valuable information about your thesis-writing activities, and sharing your feelings and insights about your academic journeys. In many cases, this knowledge will help you set realistic goals and expectations and avoid a feeling of lagging behind your peers.

A good strategy here is to start up accounts on several popular online PhD forums. As opposed to social media, you can stay more or less anonymous, which protects you from your supervisor or your peers discovering your questions to community members. Such forums usually have hundreds of persons who lived through their PhD programmes and can share their stories or confirm your doubts. This will provide additional ‘reality checks’ for your ambitious plans and help you set realistic goals.

Staying motivated and focused for 3+ years of PhD writing is a challenging task. As stated earlier, some motivation problems in this sphere may stem from incorrect strategic choices made early on. Try to obtain multiple opinions and seek PhD help before you start your PhD programme. This way, you will know that you are working with a promising topic and a high-quality long-term plan for completing your PhD dissertation. If you feel like you are losing your overall direction and your supervisor is not providing sufficient help and support, contacting a reputable PhD writing service may be a good idea to get things under control. They can help ease the workload and help you stay motivated during your PhD programme.

What to do if you lack motivation in your PhD

May 4, 2020

Motivation is elusive. Some days you have it and others you don’t.

What gives?

Well, having fluctuations in your motivation is normal and to be expected. If you took ten PhD students, how many do you think would say that they’re highly motivated all the time? Not many, I imagine.

But it can also seem that motivation becomes harder and harder to find as you go through your PhD. With good reason. Studying for a PhD is an inherently lonely endeavour and the workload is considerable.

On top of that, the day to day routine can soon become boring, and you’re often undervalued, receive little acknowledgement for your expertise and frequently feel overwhelmed. Plus, the further you go on the PhD journey, the more uncertain you become about the quality of your work or where you’ll end up when you finally finish.

If you’re reading this and having trouble finding your own motivation, know that you aren’t alone. It’s okay to not always be highly motivated, and instead recognise that fluctuating motivation is a normal part of the PhD process.

Motivation is something you can control. Given the right tools, you can find motivation when it otherwise is missing. Here, I want to share with you a number of tips you can use to boost your motivation levels.

These tips have been shared by readers of this blog and from my own experience navigating my own PhD and coaching PhD students . Not all may be suitable for you, because everyone works in different ways. Instead, see them as a list you can pick from to suit your current situation.

Know that your lack of motivation is completely solvable. The first step in that process is changing your expectations.

Interested in group workshops, cohort-courses and a free PhD learning & support community?

The team behind The PhD Proofreaders have launched The PhD People, a free learning and community platform for PhD students. Connect, share and learn with other students, and boost your skills with cohort-based workshops and courses.

Stop expecting so much from yourself.

Ask whether you’re expecting too much from yourself. It’s fine to have goals and ambitions, but it’s not fine to expect 100% from yourself all day every day. You’re going to have days when you don’t feel up to the task, or where your heart really isn’t in it. If you expect 100%, these days are a problem. If instead you recognise that you’re human and humans have off days, these days aren’t such a big deal.

Try and lower your expectations for what’s possible within a given day and acknowledge that having a bad day every now and again isn’t the end of the world, it’s just part of the journey.

See the bigger picture

An effective way of managing your expectations is to see the bigger picture. Remind yourself why you started out on your PhD journey in the first place, what motivates you, and what your goal is with the thesis and beyond. Focusing on the bigger picture means you can see each day for what it is: a small component of that bigger objective. Having an off day and periods where you’re not motivated isn’t so important, as it’s just one tiny step in a much longer journey to get you where you want to go.

Focus on what you can control

But what about your daily habits? Have you formed effective daily routines that promote self-care? Do you make sure that your phone is turned off, you’re otherwise free from distraction as much as possible and that your place of work is the kind of place you could actually expect to get some deep concentration going?

Your PhD Thesis. On one page.

Make specific to-do lists.

Take it a step further and control the way you approach your day-to-day tasks. At the start of each day, you need to know clearly what it is you want to accomplish that day.

You need to be specific. Often a lack of motivation stems from not breaking down bigger tasks into smaller, more manageable components. If you wake up, look at your to-do list and all you see is ‘write literature review’, no sane person would be motivated to do that. Instead, if you saw ‘write the literature review introduction’ or ‘write 300 words of the literature review’, you’ve suddenly got something much more manageable on your hands.

On top of that, you’ve got clear, measurable deliverables. If your task is ‘write your literature review’ you aren’t going to finish it in a day so how will you know when you’re done for the day? If you instead write ‘write 300 words of the literature review’, you will know exactly where you stand.

So think to yourself: is this task broken down into small, more manageable components and am I being realistic about how many of those components I can achieve in one day?

Make your work place a place you actually want to work in

Once you’re sure you’ve broken down your tasks into manageable chunks, it’s time to think about how you actually sit down and work.

We’ve talked already about avoiding interruptions by doing things like turning your phone off. Your aim is for big chunks of uninterrupted time in which you can find your flow and focus on the job at hand.

Be realistic about how long you will be able to concentrate. A popular time management technique is the Pomodoro Technique . This simple productivity tool involves you setting a timer for twenty-five minutes, during which there’s no Facebook, no messages, no disruptions of any kind. At the end of that time, you take a five-minute break. You repeat that process four times (for two hours) before taking a longer, thirty-minute break.

Once you finish tasks, don’t just delete them off of your to-do list. Instead, shift them over to a ‘done’ list. That way, you can get a little motivational boost when you see how much you’re accomplishing in any one day. Also, because you’re working to a timer, you may find that you work more quickly because you want to get things wrapped up into neat twenty-five-minute packages.

Work out what’s important and urgent. Then work on that.

Choosing what to focus on in the first place is half the battle when it comes to increasing motivation. You need to bear in mind the distinction between something that is or isn’t important and something that is or isn’t urgent. You can have an urgent task that isn’t important, and an important task that isn’t urgent. Focus on what’s important and urgent first. Don’t waste your time on things that aren’t important and aren’t urgent.

This reflects the fact that 20% of your work is going to produce 80% of your outputs and outcomes in any given day. Spot what that 20% looks like and focus on that, as you’ll get the biggest bang for your buck. Don’t waste your time on the 80% of things that only lead to 20% of the outcomes.

The Eisenhower Matrix can help you understand what it is that is important or urgent and will help you better structure your workflow and to-do list.

Reward successes

Okay, so you’ve cleaned your desk, turned your phone off, set your timer and you’re moving stuff off your to-do lists. Good job. Here’s another important step.

Reward yourself. Life wouldn’t be any fun it is was all work, so be sure to reward yourself when you get things done, particularly if you’re doing things you didn’t particularly want to do in the first place.

There are two ways of doing this. On a day to day level, give yourself credit for getting stuff done. Have a slice of cake, take a long bath, do whatever it is you do to show yourself some love. On the grander scale, celebrate the successes. Each day adds up to the bigger goal you’ve set, so it isn’t enough just to celebrate getting through each day, you need to celebrate when you reach those goals. Get good feedback on a chapter? Celebrate! Got your fieldwork done? Celebrate! You get the idea.

Navigate Shit Valley

Inevitably though you are going to reach a stage where you can’t possibly face doing any more work. Everyone reaches this stage eventually. I call it Shit Valley .

In Shit Valley, everywhere you look is covered in shit and there doesn’t appear to be a way out. This stage normally comes about halfway through a PhD, when you’re about as far from a way out as it’s possible to be. You’re deep into your data, but you’re far away from the end of the tunnel. You still don’t really know what’s going on and you’re riddled with more self-doubt than you’ve ever had. It’s at this stage that motivation becomes a real struggle, as you’re too far invested to give up and too far away from the end to see what comes after.

Because the only way out of Shit Valley is to wade further through it, you need to really step up the techniques you use to foster motivation.

It’s at this stage that investing in your own health becomes particularly important. Resist the urge to eat junk and be lazy. Instead, eat well most of the time, eat junk only occasionally and make sure you’re moving around every day. Find something that suits you. Just move.

It’s also at this stage that having a life outside of your PhD becomes useful. Too many PhD students (myself included) make their PhD their entire life, at the expense of a sensible work-life balance and a healthy distraction away from your thesis. It’s important to cultivate your hobbies (or to find some if you don’t have any) and to maintain a friendship circle that isn’t full of PhD students. Having this external distraction may be the only thing that keeps you sane.

Now is also the time to frequently remind yourself why you are doing what you’re doing. Picture what it’s going to feel like once you’re done, when you’re graduating and when you’re able to move on with your life.

One day you’ll finish, and you’ll look back and be incredibly proud of what you have achieved. That long term perspective is a powerful one, and should make you reflect more kindly on yourself on the days where you’re not so motivated or where you’re not at 100%. Be kind to yourself, particularly when you’re not as motivated as you wish.

But also be proactive. When you’re not motivated, look at your current situation and ask yourself what it is about current arrangements that don’t lend themselves to productivity. What can you change? The advice and tips above are a good start. Explore them, see what works for you and slowly chip away until you start to find the routine and short-cuts that work for you.

Keep doing that and you’ll be calling yourself Doctor in no time.

Hello, Doctor…

Sounds good, doesn’t it? Be able to call yourself Doctor sooner with our five-star rated How to Write A PhD email-course. Learn everything your supervisor should have taught you about planning and completing a PhD.

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7 Super Simple PhD Student Motivation Hacks

Losing motivation during your PhD is very, after all, you are trying to work towards a single problem for many years. When things are not going your way, or you are just fed up of thinking about the same thing over and over again, you can very quickly lose motivation.

Keeping your motivation up during your PhD means understanding you need to focus on discipline and not necessarily motivation. However, remembering your “why”, eating healthily, and finding an energising hobby can help keep you motivated.

In this article, we will go over all of the things you need to know about keeping up your motivation as a PhD student and all of the things I learned throughout my 15 years in academia.

I was always surprised at how easy it was to get myself back on track if I found myself in a slump.

Check out my YouTube video if you want to know more about how to get your PhD motivation back. I summarised all of the most important and effective tricks:

Here are all of the little tricks you need to know.

It’s about discipline NOT motivation

It’s common to feel demotivated and lose your motivation during your PhD or when writing up your thesis, but there’s a simple fact that every successful person learns.

It’s not about motivation, it’s about discipline.

That’s what successful PhD students and academics understand.

They don’t wait to feel like doing something, they just do it. And they keep doing it, even when they don’t feel like it, because they know it’s important.

Successful PhD students are disciplined. They have the self-control to do what they need to do, even when they don’t want to do it. They know that if they’re not disciplined, they won’t achieve their goals.

Unfortunately, we often wait for too long for motivation to strike. In my experience, a lot of the time, this simply does not happen.

If you want to be successful during your PhD, you need to be disciplined. You need to have the self-control to do what’s necessary, even when you don’t feel like it. You need to keep going, even when you feel like giving up.

Discipline is the key to success in academia.

Sometimes, discipline is not enough on its own. If you are experiencing any of the low motivation symptoms, you can combat them relatively easily.

How to spot low motivation?

There are several ways to spot low motivation.

One way is to ask yourself how much pleasure you get from the activities you’re engaged in. If you’re not enjoying what you’re doing, it’s likely that your motivation is low.

Another way to tell if your motivation is low is to look at how much effort you’re putting into your studies.

If you find yourself procrastinating or not putting forth your best effort, it’s a sign that your motivation may be low.

Finally, take a look at your results. If you’re not seeing the progress you want, or if you’re seeing setbacks, it could be a sign that your motivation is lacking.

There are also some very specific PhD related symptoms that you should look for.

Not wanting to communicate with your supervisor

One of the first warning signs I look for in any of my students is any hesitation in communicating with their supervisors.

Students often avoid speaking with their supervisors if they are not producing results. This can happen when the PhD student feels like there is a massive hurdle in front of them that they cannot overcome.

Your supervisor should be able to help you find a simple experiment or study to do to start the ball rolling.

Never avoid or delay a supervisor meeting. The meetings will keep you accountable and help you on the path to completion.

Procrastination on thesis/writing

Writing is a massive pain in the bum.

I know that I would always procrastinate a lot when it came to writing up my thesis or peer-reviewed papers.

A lot of people find the academic writing process very tedious and painful. Finding the motivation to do just a few hundred words a day can also be very difficult.

Loss of enthusiasm

Burnout During your PhD, it is likely that you will feel overwhelmed and stressed at some point.

Your supervisor may not be able to help either, as they are usually busy with their own research and things.

Research is a notoriously competitive field, which means that there is a lot of pressure to succeed. This can lead to feelings of anxiety and stress, which can eventually lead to burnout.

If you find yourself feeling overwhelmed or burnt out, it is important to take a step back and assess your situation. Talk to your supervisor about your concerns and see if there is anything they can do to help you.

It may also be helpful to talk to other PhD students or academics who have been through the same thing. They will be able to offer advice and support.

In the end, it is up to you to manage your own stress levels and make sure you don’t end up burning out.

If you want to know more about combating burnout during your PhD check out my YouTube video below.

How do PhD students stay motivated?

There is no one answer to this question as different students have different motivators.

However, some ways that PhD students stay motivated include setting goals, breaking up their work into manageable tasks, staying organized, and seeking support from their peers and mentors.

Additionally, many students find it helpful to celebrate their small accomplishments along the way. This will help create a sense of momentum that can breed more motivation.

Here are the basic motivational tips including some simple actionable advice that you can use if you are feeling unmotivated.

Motivational Tips

1. The basics

First, try setting smaller goals that are more achievable. This will help you see progress and feel more successful, which can increase your motivation.

Second, make sure you’re taking care of yourself physically by getting enough sleep and exercise; both things can boost your energy and mood, which can in turn increase your motivation.

Finally, try speaking kindly to yourself and focusing on positive self-talk; this can help increase your confidence and self-belief, making it easier to stay motivated.

2. Remember your WHY

Throughout PhD it can be hard to remember why you actually started one in the first place. There is so much more you end up doing is a PhD student. You can actually forget your true purpose whilst busy with the admin, politics, and busywork that a PhD often presents.

Getting familiar with your motivations to do your PhD will certainly ground you, hopefully, help you remember why you decided to go down this path in the first place.

3. Focus on the bigger picture

Focusing on the bigger picture also helps me a lot.

Quite often we can get bogged down in the details of our research. However, connecting with the bigger picture and zooming out really helps boost motivation.

Remember questions such as:

who you’re doing this research for
why you did this in the first place
what the true benefits of your work are

can really help provide that small amount of inspiration when it is low.

4. Find an energizing hobby

Hobbies have been something that has provided a welcome distraction from my PhD and academic work.

They have allowed me to get away from work and take a break from the daily grind.

However, not all hobbies are made the same.

I would recommend finding a hobby in which you feel energised. Watching TV, reading a book, are great but often leave me feeling tired. Hobbies that include hanging out with other people and being active are often much better for keeping up my motivation and helping me feel energised and ready to tackle the issues by PhD threw up.

5. Eat well

It goes without saying that eating well throughout your PhD will help you feel better in many aspects of your life.

If you’re feeling unmotivated remember to go back to unprocessed and healthy food to kickstart your healthy eating habits again.

Stay away from highly processed foods and junk food – doing so has provided me with a huge boost in energy and therefore motivation.

6. Take time to step away from your work

Step away from your PhD every so often.

Take a moment to reconnect with friends, family and old acquaintances. It is actually okay to take some time for you.

Some PhD students need to step away from their work for much longer. Stepping away from your PhD for six months to a year can also help you regain the motivation you need to finish.

7. Focus on your achievements

In the daily grind of a PhD can be hard to focus on your achievements when all you can see are your failures or challenges.

Nothing motivates me more in my academic career than seeing what I have already achieved and what I can improve on.

Taking a moment to stop and reflect on your achievements will help you fine-tune your next step and will give you the energy to want to reproduce that successful experiment or study.

I like to keep a little list of my achievements nearby so that I can look at them whenever I am feeling flat.

Why Losing Motivation In Grad School Is Normal

Losing motivation in grad school is normal for a number of reasons.

First, the academic pressure can be intense and overwhelming at times.

Second, the process of getting a PhD or postdoc often takes much longer than students expect, which can lead to frustration and disappointment.

Third, many students are juggling multiple responsibilities (e.g., teaching, research, family) and simply don’t have the time or energy to devote to their studies.

Finally, it’s easy to become discouraged when you compare yourself to your peers and feel like you’re not making as much progress as they are.

If you’re feeling unmotivated, it’s important to remember that it’s normal and that you’re not alone.

Talk to your advisor or other trusted faculty member about how you’re feeling and see if they have any advice on how to get back on track.

Take some time for yourself outside of school and do things that make you happy. And finally, remind yourself why you’re doing this in the first place. Grad school is hard work, but it’s also an amazing opportunity to learn and grow as a person.

Wrapping up

This article is covered everything you need to know about keeping up your motivation as a PhD student.

The PhD is long, arduous, and can test even the most motivated of individuals. Focusing on discipline and execution every day will be the number one way you can build up momentum and keep moving forward.

When your willpower is depleted, make sure you are eating well, you take time to reconnect with friends and family and do an energising hobby.

Small steps every single day is what finishes a PhD. Take small steps and the rest of your PhD will follow.

Dr Andrew Stapleton has a Masters and PhD in Chemistry from the UK and Australia. He has many years of research experience and has worked as a Postdoctoral Fellow and Associate at a number of Universities. Although having secured funding for his own research, he left academia to help others with his YouTube channel all about the inner workings of academia and how to make it work for you.

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CAREER COLUMN
28 April 2020

Finding motivation while working from home as a PhD student during the coronavirus pandemic

Melina Papalampropoulou-Tsiridou 0

Melina Papalampropoulou-Tsiridou is a PhD and MBA candidate at Laval University in Quebec City, Canada, and conducts her PhD research in neuroscience at CERVO Brain Research Centre in Quebec City.

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At the moment, staying motivated can be tough. Many scientists have admitted this on social media or in online meetings. I’ve struggled to follow a consistent routine and to be productive, thinking twice about getting dressed in the morning while wondering, “What’s the point?” This is especially true when we’re surrounded by distractions at home — a place usually kept away from work.

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The Relationship Between Resilience and Motivation

First Online: 28 December 2018

Cite this chapter

Barbara Resnick 4

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Resilience refers to the capacity to spring back from a physical, emotional, financial, or social challenge and bounce forward. Being resilient indicates that the individual has the human ability to adapt in the face of tragedy, trauma, adversity, hardship, and ongoing significant life stressors. Motivation is different from resilience and is based on an inner urge rather than stimulated in response to adversity or challenge. Motivation refers to the need, drive, or desire to act in a certain way to achieve a certain end. Motivation is, however, related to resilience in that it requires motivation to be resilient. The characteristics of individuals who are motivated and those who are resilient are similar and can be developed over time. This chapter reviews the ways in which these two concepts are similar and different and provides theoretical and empirical support for the evidence that they are both critical to recovery following an acute event and to assure successful aging .

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Introduction—Setting the Scene

Introducing Resilience Outcome Expectations: New Avenues for Resilience Research and Practice

Resilience, Adapting to Change, and Healthy Aging

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Resnick, B. (2018). The Relationship Between Resilience and Motivation. In: Resnick, B., Gwyther, L., Roberto, K. (eds) Resilience in Aging. Springer, Cham. https://doi.org/10.1007/978-3-030-04555-5_12

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How to Write a PhD Motivation Letter with Samples and Expert Tips

Reading over some PhD motivation letter samples will give you an idea of how to make yours a strong, central component of your application to get into grad school . In addition to your grad school CV , a PhD motivation letter is a chance for you to demonstrate objectively why you are an excellent candidate for the faculty to which you are applying. Unlike a personal statement, a PhD motivation letter is distinct in its unique focus on your academic and research background with little mention of your personal story. This article will take you through the significance of the PhD motivation letter, describe what makes a stellar motivation letter, and provide examples.

>> Want us to help you get accepted? Schedule a free strategy call here . <<

Article Contents 11 min read

Do you need to write a phd motivation letter .

Yes, you must write a PhD motivation letter. It is mandatory for most, if not all, PhD programs, regardless of your field of study. Disciplines ranging from arts and humanities to physics and computer science all consider motivation letters (aka “statement of purpose” in some countries) a major component of your application.

Of course, you will also have to fulfill the other documentation requirements, like submitting your transcripts, CV, personal statement, and letters of recommendation, but a motivation letter has a specific intent: to summarize your academic achievements up to the present and what you plan to achieve in the future at this particular school.

The faculty who ultimately consider your application look for how you and your PhD topic match with the mission and values of their program. Personal details and other motivations are best left to your personal statement or letter of intent because the motivation letter is strictly an academic summary.

A great PhD motivation letter should highlight how and why you are prepared for the rigors of PhD-level work. It should include the details of your academic career that have propelled you further into your field of study, like an inspiring professor or undergraduate course that sparked interest in your field.

The following list will provide more insights, but you should remember that whatever you write must be backed up by a concrete, real-world demonstration. It is not enough to say, “I am interested in XYZ because of XYZ.” You must include specific events in your undergraduate and graduate studies where you excelled.

If you are applying for a PhD, that in itself suggests you have a bevy of academic and extracurricular experience to glean from, be it co-authoring a published paper, your time as a TA, or some type of academic recognition. Many stand-out motivation letters single out specific instances when you showed an outsized passion for your studies.

Dos and Don’ts in a PhD Motivation Letter

1. Gain Skills and Experiences

The track to obtaining a PhD degree is a long one, which is why anyone who wants to become a PhD should commit early on to what it entails. All PhD candidates must have both an undergraduate and a master's degree to even apply, so that means structuring your studies around those requirements.

You should gain as much experience in your field, learn new skills related to your studies (a new language, for example, or technical skills), and participate in as many extracurricular activities as possible. Gathering the necessary skills and experiences to enter a PhD program should be the first step, since they are a reflection of your commitment.

2. Start Writing Early

You should begin drafting your PhD motivation letter at least a few months before the deadline. Because it is one of the most important parts of your application, you want to give yourself time to refine it. Refining means going through multiple drafts, soliciting and receiving feedback from other candidates, getting professional grad school application help, and making changes as you go along.

3. Consider Your Audience

The people who will read your motivation letter are renowned academics who have devoted their lives to one particular subject. Your letter needs to reflect your respect not only for them, but for the field of study that you both share. You should write with genuine verve when talking about your topic. Remind them of why they committed so full-heartedly to their career by demonstrating how enthralled you are with your studies.

4. Use Active Voice

You should put “you” in your story. Avoid using the passive voice and hiding behind your achievements as if they spoke for themselves. The admissions committee members want to read about how you approached your studies and learn about your insights into the future of your field of interest. They do not want a cold recitation of your CV but a spirited defense or explanation of what you value most about your topic.

1. Don’t Forget About the Formatting

PhD admission requirements differ between the many programs out there, so be cognizant of how they ask you to format your paper. If the requirements state a two-page limit, then write two pages. The same goes for other criteria like font size, paragraph spacing, and word length. A rambling, incoherent letter is the last thing you want to submit, so make sure to keep it within the guidelines.

2. Don’t Include Personal Stories

A personal statement is the place for formative stories from the past, not your motivation letter. You can include personal thoughts and opinions about your field of study, even unfavorable ones, to show you have a unique perspective, but steer clear of using personal elements like early childhood experiences or anything unrelated to your program.

3. Don’t Ramble

Keep in mind that your writing and organizational skills are also on display when you submit your motivation letter, along with everything else about you (grades, college letter of intent , transcripts). Again, remember who you are writing for: professors with years of experience researching and writing. They, more than anyone, know what good writing looks like, so be concise and clear in your writing.

4. Don’t Shy Away from Failures

The collected experience of those reading your essay guarantees that they know a thing or two about failure. Whether it was an unpublished paper, or a failed experiment, showing your determination in the face of adversity paints a complete picture of who you are as a researcher and academic.

But, again, setbacks in your personal life should not be mentioned. Limit your story to problems you encountered during your undergrad, graduate, or research fellowships and how you sought to overcome them. Mention a class or subject you struggled with or a drop in your grades and how you improved them.

Structure of Your PhD Motivation Letter

The structure of a great motivation letter is easy to follow because its focus is so narrow. The body of your letter should only mention highlights from your academic career, in a very specific chronology starting with your undergrad and progressing from there. But the structure should also cover three main points:

You can adjust the structure based on the requirements of the PhD program you are applying to, but it should cover the reasons you want to commit yourself to this program, what you plan on achieving, and how you have prepared yourself to accomplish those goals. If you already went to grad school, then you can rework your college statement of purpose to use as a template.

PhD Motivation Letter Sample #1

Dear Members of the PhD Selection Committee,

My name is David White, and I am writing to you to express my interest in pursuing a PhD in the Migration Studies program at X University. I recently completed a Master of Ethnography at Y University with an emphasis on the cultural exchange between migrant communities and their adopted homelands viewed through the lens of shared trauma and memory.

In the media, migration is often described as a “crisis,” a designation that has always made me bristle. I assert that migration is one of the most fundamental aspects of our species, yet it has been flagrantly mislabeled to serve the political and socioeconomic interests of a few.

My research is centered around the ways that migrants form new identities based on their experiences. Conversely, I have also explored how an innate identity based on race, religion, gender, or sexual orientation impacts a migrant’s journey and how those markers expose them to further exploitation or, at the other end, fortify their resolve and inspire perseverance in the face of tremendous odds.

The need for further investigation into identity and the interplay of migration and culture came into focus for me during my second-year undergrad Political Science degree at XYZ University. I was influenced by the work of writers like Franz Fanon and Edward Said, who questioned the foundations of a post-colonial identity and whether it was ever possible for colonized people to form an identity separate from their colonizers. I took an anthropology course, The Nature of Humans, that impacted me greatly. It prompted a Cartesian examination of my own beliefs around identity, as it firmly associated the emergence of human societies with factors such as migration, evolution, adaptability, and diversity.

During my time as a graduate student, I secured a place on a research project headed by Prof. Mohamed Al-Nasseri, a diaspora studies expert. Professor Al-Nasseri's thesis was that policymakers were ignoring the psychological profiles of migrants when assessing their material needs and financial assistance levels.

Our four-person investigative team liaised with a local, non-profit resettlement agency who connected us with volunteer migrant families based in University Town. Under the supervision of Professor Al-Nasseri, we formulated a questionnaire based on the diagnostic criteria of the DSM-V for traumatic events, while taking into account the newly revised definitions.

Mindful of the possible triggering effect our questions could have, we invited a peer, fellow survivor/migrant, and, in some cases, a religious leader before we conducted the interviews or to sit-in on our interviews.

During the interviews, I felt both inspired and indignant. I maintained my composure and objectivity, but the fire within raged. Unfortunately, our findings were inconclusive and what we discovered in our interviews did not wholly support Dr. Al-Nasseri’s thesis. But the experience and motivation I took from the project were enough to fuel my desire to explore the topic of identity formation in migrant communities who have undergone severe trauma.

The Migration Studies program at your institution will provide what I consider the perfect research and support network to further my investigation of these topics. I have followed the work of the esteemed Dr. Ellerman whose research into the treatment of post-traumatic stress has informed the direction of my own research. Dr. Ellerman has opened new pathways for thinking about trauma that I wish to incorporate into my thesis project when the time comes.

Until then, I am grateful for the opportunity to apply to this institution and am ready to discuss my future with you should my candidacy prove successful.

David White

My name is Melanie Hicks, and I am writing this letter to fulfill the admission requirements of the Visual Arts PhD Program at Z University. I have already submitted my audiovisual portfolio, CV, and transcripts, along with three letters of recommendation from, respectively, my master’s degree supervisor, Dr. Dana Redmond, my thesis supervisor, Dr. Allan Lee, and my research colleague, Mark Fowler.

I would like to take this opportunity to expand further on the conceptual themes I have focused on in my artistic output over the past decade, contextualize the pieces I have submitted, and elaborate on the goals I have should my application to this program be successful.

My artistic career, from very early on, has been defined by modes of observation, the interplay of observation and reflection between subjects and objects within a sociopolitical realm, and the harnessing of Blackness as a form of radical self-interpretation – all of it couched within the media of still and moving images.

During my undergrad as a Fine Arts student at X University, I was lucky enough to be showcased at the Kepler Gallery for my series, Painted Faces, a collection of photographs I took while working as a freelance photographer for an independent newspaper in Chicago. My focus in that series was the effort and preparation female congregants of an all-Black church put into readying themselves for Sunday services.

After my undergrad, I traveled to Boston to volunteer in local after-school programs with children from minority backgrounds who had an interest in photography. All of them had grown up with easy access to a phone capable of taking crisp, digital images and had never taken film photographs, so it fell to me to show them how to develop prints in a darkroom.

As part of my portfolio, I have submitted photos I took during that time, along with selections from my Painted Faces series. I never constructed a specific narrative with the photos I took during my volunteer work, but they were informed by the social realist photographers and photojournalists who captured the Civil Rights Movement by participating in protests and documenting the unrest.

Gordon Parks is a major influence and part of the reason I am pursuing my PhD studies at this institution. Prof. Alys is a foremost expert on Parks’ work and curated the Parks Retrospective at the Local Museum. Parks himself said that the subject was always more important than the photographer, and I agreed with that statement for a long time, until I began reading Arthur Danto and his artist-centered philosophy of art. While many disagree with Danto’s definition of art as an elitist utopia, I would argue that he opens the gates to everyone, and that anyone can gain entry to the “artworld.”

There is no better exemplar, I think, of the democratization of the “artworld” first posited by Danto than Basquiat, who was not only “allowed” access to the “artworld” but redefined it, in his indomitable way. Basquiat’s quality of outsider-turned-insider and Danto’s liberating of the parameters of what defined art are central themes of my project to understand whether “outsider” artists still exist, given how new technologies and platforms have pushed Danto’s definitions beyond their logical boundaries, if not obliterated them completely.

I hope this program can help me refine my project while matching my urgency to further expand the definition of art and artists to be more inclusive of not only racial minorities, but non-binary and trans people, who are at the forefront of questioning the validity of assigned identities through the curation of their very genders or lack thereof.

I am grateful to this esteemed panel for considering my application, and I would like to close by expressing my profound admiration for the achievements in art, art theory, and the philosophy of art each of you has contributed to a long, continuing train of thought.

I would be honored to accept a place beside you as a PhD candidate.

Melanie Hicks

Motivation letters are used in areas other than academia, but a PhD motivation letter is different for several reasons. Regardless of your particular field of research, the letter should include important points about your academic achievements, research interests, and why you want to continue your research at the faculty to which you are applying.

Even though PhD motivation letters tend to be short – between 500 and 700 words – their length is often the most vexing thing about them. Because students have a hard time condensing their years of study and research into a few words, we hope this article will help you focus your writing and give you insight into what to include.

No, they are not the same. A motivation letter has many different applications but is primarily a summary of your academic and professional achievements. A personal statement is an essay explaining your personal reasons for wanting to enter a specific profession or academic institution.

You should focus only on concrete, real-world examples of how you performed, learned, or grew as the result of an event in your trajectory toward a PhD and how you plan on contributing something new to your field of study. You should also make sure to have enough material, in the form of experience or academic goals, to write a compelling letter.

PhD motivation letters are important because they let prospective PhD candidates distill their background and experience succinctly, so that selection committees can more easily judge their character, commitment, and potential.

Some people do find it challenging to write a letter about themselves without rambling or sounding incoherent. But if you prepare ahead of time, think honestly about your answer, and write several drafts, you should be able to write an above-average letter. If you are still struggling you can also get application help from professionals.

Programs tend to ask for either a one or two-page letter, between 700 and 900 words.

You can talk about anything that has do to with your past work to get to the PhD level, including aspects of your academic career, internships, independent or supervised research, fieldwork in a specific context, and any work experience you have related to your field of study.

You should not mention any personal motivations for wanting to pursue a PhD. You can write about your intrinsic motivations to become a doctor of philosophy in your personal statement, if you are asked to submit one with your application.

PhD programs around the world have various entry requirements that differ among schools. Some institutions ask for a motivation letter, while others ask for a personal statement or letter of recommendation and letter of intent, which has elements of a motivation letter but is not the same.

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P -Curve Analysis of the Köhler Motivation Gain Effect in Exercise Settings: A Demonstration of a Novel Technique to Estimate Evidential Value Across Multiple Studies

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Christopher R Hill, Stephen Samendinger, Amanda M Rymal, P -Curve Analysis of the Köhler Motivation Gain Effect in Exercise Settings: A Demonstration of a Novel Technique to Estimate Evidential Value Across Multiple Studies, Annals of Behavioral Medicine , Volume 55, Issue 6, June 2021, Pages 543–556, https://doi.org/10.1093/abm/kaaa080

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Practitioners and researchers may not always be able to adequately evaluate the evidential value of findings from a series of independent studies. This is partially due to the possibility of inflated effect size estimates for these findings as a result of researcher manipulation or selective reporting of analyses (i.e., p -hacking). In light of the possible overestimation of effect sizes in the literature, the p -curve analysis has been proposed as a worthwhile tool that may help identify bias across a series of studies focused on a single effect. The p -curve analysis provides a measure of the evidential value in the published literature and might highlight p -hacking practices.

Therefore, the purpose of this paper is to introduce the mechanics of the p -curve analysis to individuals researching phenomena in the psychosocial aspects of behavior and provide a substantive example of a p -curve analysis using findings from a series of studies examining a group dynamic motivation gain paradigm.

We performed a p -curve analysis on a sample of 13 studies that examined the Köhler motivation gain effect in exercise settings as a means to instruct readers how to conduct such an analysis on their own.

The p -curve for studies examining the Köhler effect demonstrated evidential value and that this motivation effect is likely not a byproduct of p -hacking. The p -curve analysis is explained, as well as potential limitations of the analysis, interpretation of the results, and other uses where a p -curve analysis could be implemented.

Recent meta-science researchers have noted that journals have a bias to publish results that reach a traditional level of statistical significance (e.g., p < .05) [ 1 , 2 ]. As a result of various incentives for active academics to publish in the scientific record, researchers may be tempted to engage in a variety of behaviors that will enhance the likelihood of their study results reaching statistical significance. P -hacking has previously been defined as selectively reporting analyses in a study that leads to statistically significant results [ 2 ]. Depending on the researcher’s rationale for doing so, this selective reporting of statistics could look very different in separate circumstances. Some types of p -hacking include a preliminary review of the data to see if a variable relationship is significant and then continuing to collect data if the significance threshold is not reached, selective reporting of dependent variables, and selectively excluding or including outliers [ 3 ]. For example, a researcher may peek at their data after a certain number of participants have completed the data collection process and either stop data collection, if a result reaches statistical significance, or continue to collect more data until a desired significance threshold is reached. Researchers could also run an analysis that leads to a result that does not reach thresholds of statistical significance and subsequently start to add covariates in until something reaches statistical significance [ 2 ]. In this situation, a researcher might choose to report the data analysis that maximizes the chances for publication. This aforementioned flexibility in data analysis is what we will term p -hacking for the purpose of this paper.

Engaging in p -hacking behaviors can lead to validity problems with the overall scientific record and can markedly change an established empirical relationship [ 4 ]. The risks of p -hacking are a particular concern with meta-analyses; one of the most valued forms of knowledge creation [ 5 ]. In a traditional meta-analysis, researchers aim to aggregate all of the findings focused on a single empirical relationship, and run one analysis that uncovers an approximation of what the “true” underlying empirical relationship looks like. Previous researchers have emphasized potential issues related to the impact of methodological decisions on the results and conclusions when employing meta-analytic techniques [ 5 ]. However, there is another issue that could be at stake if some of the results or effect sizes reported in the literature in a particular field are overestimated. Potentially, for every one study that shows there is a relationship between two variables, there are another three studies that are stuck in a file drawer that will not be published because the focal relationship was not statistically significant. This form of bias (i.e., not reporting findings on the presumption that statistical significance is required for a study to be published) could lead to the publication of select significant findings or many individual studies that only report significant findings. The consequence of this bias may be a misrepresentation of the evidence that overestimates variable relationships and contributes to meta-analyses that overestimate what the true effect size might be [ 6 ]. Of course, when reviewing a body of literature or meta-analysis findings, the reader can be alert for gross indicators of weaknesses in research methods or possible p -hacking. Signs of these biases may be inappropriate or insufficient rationale for excluding outliers or the use of multiple underpowered studies [ 7 ]. However, as we are aware that this overestimation of effect sizes exists in the literature, it would be worthwhile to examine a tool that could potentially provide a measure of the value in the reported evidence and provide more information about the underlying true effect in a sample of studies. There are an increasing number of meta-analyses and systematic reviews that are occurring in the behavioral sciences today. Incorporating techniques that will enhance and enrich the findings of the reviews will help further the understanding of the research literature in the behavioral sciences.

The p -curve analysis is a recent tool that has been introduced to help effectively evaluate the true effect of the evidence and potentially uncover a weakness in evidence reported as significant that may be a result of various types of p -hacking [ 8–10 ]. In order to function in this way, the p -curve analysis focuses on the distribution of statistically significant p -values in a body of published literature that has examined a particular effect. To understand the p -curve analysis, we must first dig into the properties of a p -value. By definition, “a p -value reflects the likelihood of observing at least as extreme an estimate if there is truly no effect ( d = 0)” [ 8 ]. Therefore, if there is truly no effect (null hypothesis is true) across studies, then a p -curve distribution of the study p -values would be expected to have a uniform shape. With a uniform p -curve distribution, we would expect that 5% of the study effects would fall below the alpha level set at .05 (the a priori accepted tolerance for error typically set by researchers). Likewise, if there is truly no effect and no p -hacking bias exists, we would expect that there is an equal chance of any significant p -values being observed. This all changes when a true effect exists (null hypothesis is rejected). If there is a true effect (with minimal or no p -hacking bias), the p -curve distribution will be increasingly right skewed. In other words, if an effect exists we would expect to see p -values reported more consistently at extremely low values (e.g., p < .01) and not all hovering around the probability threshold or equally distributed beyond the probability threshold (.05 in this example). In contrast, if a body of research suffers from p -hacking practices, the reported p -values are likely to not be as extreme because of a variety of methodological manipulations in which a significant result might have been “fished around for” (i.e., p -hacking). Once that significance threshold is satisfied, researchers are then likely to move forward and attempt publication because a purported meaningful finding can be reported. It is important to note here that obtaining results near p = .04 does not mean that the outcome of the study was p -hacked. However, if the majority of the findings, across a number of studies, rely on relatively large p -values (just barely statistically significant), the effect may be due to p -hacking practices and the value of the evidence may be in question. If a p -curve analysis indicates that there might be bias in the sample of studies, it does not explicitly indicate that p -hacking has occurred. It is plausible that the studies included were underpowered and barely identified effects, or that the effect is not as robust over many studies. Ultimately, this test provides more corroboration of evidential value across a single effect.

P -curve analysis also has the advantage of simplicity, in that it is calculated as a function of only sample size and effect size [ 9 ]. As such, if an experimental effect size remains constant but the sample size of a study increases, a more extreme p -value can be expected. Similarly, if the sample size is constant but the effect size increases, a more extreme p -value will be obtained. For a more thorough explanation on the relationship between effect size, sample size, and p -values see Simonsohn et al. [ 8 ].

Some researchers have questioned some of the utility of the application of p -curve analyses. Analytical approaches used by previous scholars have noted that the shape of a p -curve of two-tailed statistical tests is steeper than one-tailed statistical tests, which may complicate estimating the true effect [ 11 ] There have also been instances where a right-skewed p -curve (indicating evidential value) is generated due to instances of parallel p -hacking [ 12 ]. Parallel p -hacking is when researchers run multiple analyses and choose to report the test with the smallest p -value (i.e., most significant effects). In response to these criticisms, Simonsohn et al. [ 10 ] updated their p -curve analysis and discussed ways that journals, reviewers, and editors can promote appropriate reporting to mitigate these potential issues in future publications. Other researchers have noted restricting the p -curve to just statistically significant findings, even though there are more null results being reported in the literature today, can lead to a more challenging interpretation of the overall population effect [ 13 ]. However, when adhering to the original p -curve model assumptions (including careful study and p -value selection), researchers can expect the analysis to perform well [ 13 ]. Although not a panacea of detecting issues surrounding bias and p -hacking, the p -curve analysis is a useful tool that is likely to help detect bias and evaluate the value of an effect when conducting meta-analyses in the behavioral sciences. Like many research tools, a p -curve analysis and an understanding of the underlying concepts are good to have in a researcher’s tool belt. When evaluating a body of literature, it would be wise to report and discuss multiple indicators of bias assessment.

It is worth repeating that the p -curve analysis is dependent on using appropriate methodologies and following the steps outlined in this paper. If a p -curve will be used as supplemental information for a meta-analysis or systematic review, researchers should follow best practices associated with those techniques (e.g., PRISMA) [ 14 , 15 ]. However, because this paper is didactic in nature and is not designed as a meta-analysis, we did not apply the PRISMA guidelines beyond its recommendations for a study eligibility and search strategy.

Behavioral science researchers and practitioners may most often be confronted with the challenge to evaluate the true effect reported in the literature when relying on meta-analyses. Considering the recent boom in meta-analyses across disciplines, it makes sense to be cautious and carefully review the data utilized to aggregate findings [ 16 ]. Therefore, the purpose of this paper is threefold. First, we first wish to introduce the mechanics of the p -curve analysis to individuals researching phenomena in behavioral medicine. Secondly, we aim to provide a substantive demonstration of a p -curve analysis using findings from a series of studies examining a group dynamic psychosocial motivation gain paradigm. Specifically, we apply a p -curve analysis to a series of motivation-based physical activity studies in which researchers compared how the control condition (physical activity task without a partner) performed relative to a conjunctive condition (physical activity task with an interdependent superior partner). Finally, we apply a p -curve analysis to data that was created to demonstrate potential issues with p -hacking and discuss how to talk about those findings.

P -Curve Analysis Methods

The first step in conducting a p -curve analysis is to create a study selection rule—literally, a set of rules regarding how studies are chosen for inclusion. A simple study selection rule would entail creating some guideline that would garner a meaningful set of studies in order to evaluate the joint evidential value around a similar variable of interest. For the example motivation-based phenomena that is being used in this paper, we set the selection rule to identify studies that reported physical activity findings (intensity or persistence) examining the performance difference between people working in a conjunctive task group (i.e., the dyad’s physical activity score was defined as the score of the weakest team member’s score; the Köhler effect) compared with a control condition that performed the same activity without a partner. The Köhler effect is simply the term for the motivation gain that occurs during the conjunctive task condition where the team’s potential performance outcome is equal to the performance outcome of its least capable member [ 17 ]. The typical research design for the Köhler series of studies compare experimental partnered dyads to no-partner controls during single session exercise routines (e.g., abdominal exercises or aerobic cycling). A few studies have explored the effect in longitudinal designs utilizing aerobic cycling while continuing to compare performance outcomes between partnered and no-partner conditions. The simple exercises were chosen so that motivation could more easily be inferred from performance versus complex movement exercises that involve training, skill, and may rely on competency. In partnered conditions, a series of exercises were completed alone as a baseline and then, after a rest and further instruction, the exercises were repeated with a partner. To establish a conjunctive task group, the dyad’s performance was always based on the teammate who quit first (manipulated to always be the participant being studied). The participants were given information that the partner was more “in-shape” than they were but could fatigue like any exerciser. Participants were to perform the exercises until they could no longer continue but when one partner quit, the other must quit too, as they were yoked together for one team score based on the member who quit first. The Köhler motivation gain effect is thought to occur as a result of upward social comparison (desire to compete or improve to match superior partner) and team indispensability (yoked performance dependent on the weaker member). The superior partners were always presented to participants over a live video link from another lab, while they were actually human confederates previously recorded so that their performance could always be superior. The participants were always the weaker teammate for experimental purposes aimed at studying performance changes as a result of the Köhler paradigm. The recording was edited so that each participant was provided continuous feedback that implied the virtually presented partner was superior (i.e., not quitting first). In an attempt to bridge the gap from lab to real-world settings, some studies utilized superior software-generated partners that potentially solve practical use problems such as partner availability, exercise or partner adaptability, and social concerns of exercising with others. Thus, all studies included here examined the Köhler motivation gain (with either real human or software-generated partners) using similar research methods that included some physical activity behavior as the dependent variable. A meta-analysis examining motivation gains of inferior group members in nonexercise research reported evidence for a large effect size [ 18 ] but findings from research of this paradigm in physical activity settings have yet to be reviewed.

This type of simple p -curve analysis study selection rule is likely the most common type, but there are other options for creating a p -curve selection rule depending on the findings being evaluated. For example, a researcher could examine the most cited articles from a domain specific journal over a period of some years. By examining a journal’s most cited articles, a researcher may note a pattern that suggests a journal is encouraging scientists to meet some definition of statistical significance, via either overt or covert messages. For instance, it is possible that journals publishing high numbers of studies with findings that barely cross statistical significance thresholds are likely focused on publishing novel and exciting results over methodologically sound results. Using a p -curve analysis, a researcher might also choose to examine an entire field, subfield, or a particular researcher’s work. As such, the study selection rule is flexible but it must be applied consistently and should be set before any of the data analysis is conducted.

Step two of a p -curve analysis is to create a disclosure table that provides transparency related to the results that are going to be analyzed. The disclosure table is similar to a table that is common in a meta-analysis and includes information for each study pertaining to the original paper, including: the purpose of the study, the study design, and the statistical results that will be input into the p -curve calculator. If the p -curve analysis is concerned with the simple effect between two groups, there is no need to consider more complex analyses disclosure table. However, there are effects that may be of interest (e.g., interaction effects) and these are outlined in the supplementary material provided on the p -curve website ( http://p-curve.com/ ) for 3-cell designs, 2 × 2 designs, 3 × 2 designs, and 2 × 2 × 2 designs. If a researcher is interested in an interaction effect, the bulk of the work in building the table is the same. The main difference lies in the selection of the statistical test that is specifically examining the effect of multiple variables being studied.

The third step is to put the key results into the p -curve app ( p-curve.com ) or download the software code (for the R software program) that is freely available at the same website and plug in the requisite statistical tests. The p -curve analysis only requires the statistically significant effects so if results are input for a test statistic that is not statistically significant, the analysis will exclude that study.

The final step includes examining the output of the p -curve app and extracting the relevant statistical inferences. The output includes both a table of the results along with a graphical representation for visual inspection of the percentage of results at each threshold of statistical significance. If the p -curve is right skewed, it suggests that the set of study results has evidential value on the topic or variable of interest and the lack of a right skew suggests lack of evidential value [ 9 ]. As we describe the p -curve analysis below, readers will note some redundancy from the above information that we considered worthwhile in demonstrating how to conduct the analysis. See also Table 1 for a brief summary of these steps.

A brief review of the steps of a p -curve analysis

Applied p -Curve Example: The Köhler Motivation Gain Effect

During the first step (create and report a study selection rule), we selected studies that focused on motivation gains in exercise settings using a conjunctive task group paradigm. In a conjunctive group, the team member performance is linked or tied together during a physical activity task by the scoring function. That is, the team score is completely determined by the score of the weakest team member. As noted previously, this type of conjunctive task is most synonymous with the Köhler motivation gain effect. In this analysis, we focused on conjunctive studies in the exercise domain that utilized either real, robot, or virtual partners. So, the two key methodologic elements required for each study to be selected for this paper were: the presence of a partner in a conjunctive scenario and exercise-based task. Once the rule was established, a literature search was conducted to capture the studies that fit the inclusion criteria.

We conducted our literature search using PubMed, SportDiscus, and PsychInfo. We used the keywords “(Köhler OR Conjunctive OR social facilitation) AND (exercise OR physical activity)” in a title or abstract search and identified 191 articles for future inspection. The review of literature was conducted first in December 2018 and was updated for newer articles in April 2020. The first author reviewed all of the abstracts for the basic inclusion criteria and the first and second author reviewed the remaining 25 articles that were clearly testing the Köhler motivation gain effect in exercise settings. The reviews were conducted independently and then the two authors discussed any discrepancies. Our main focus during the search was whether a study assessed motivation gains in an exercise setting under conjunctive task demands. After assessing the abstracts, removing studies that did not fit the selection rule, and removing duplicate studies, 13 studies were identified as meeting the inclusion criteria for this p -curve analysis. A PRISMA flow diagram is in Fig. 1 .

PRISMA flow chart. Note : Studies were excluded during the screening process if they did not clearly test a conjunctive exercise condition against a control condition or the study did not use a physical task. Studies were excluded at the eligibility stage if they did not report a statistical test that was consistent with the disclosure rules for this analysis, the statistical test of interest was nonsignificant, or the task did not represent a specific form of exercise (e.g., arm trip wire task was not included as it does not represent exercise).

Although there is no set number of studies that are required to be included in a p -curve analysis, there are some considerations to note when conducting a p -curve analysis. The smaller the number of studies included in the analysis, the more likely a researcher is to find that those estimates are have no evidential value [ 9 , 10 ]. This also holds for studies that have few participants. With less information available to estimate the evidential value, the more likely it will look like evidential value is not present. P -curve analysis is much more precise when there are more studies, with larger sample sizes, or with larger effect sizes [ 9 ]. Previous simulation work highlights that when a true effect is present, as few as five studies will be sufficient to find the evidential value of the body of literature [ 9 , 10 ], but including more studies is always going to provide a better approximation of the underlying true effect.

The 13 studies can be found in the disclosure table, along with the abbreviated citation and concise details for the hypothesis prediction of interest, study design, and statistical result ( Table 2 ). When creating the disclosure table, we ensured consistency with original articles by directly copying the information from the original article. This type of disclosure table serves to make a p -curve analysis much more transparent and facilitate replication by anyone who has interest in the project. Of course, transparency is required for any review of a body of literature (i.e., tables regarding the included studies are often in meta-analytic reviews) and it can help contextualize effects from diverse fields.

Disclosure table

ANCOVA, analysis of covariance; ANOVA, analysis of variance; AVG, active video game; BMI, body mass index; SGP, software generated partner.

The results that we obtained from each study used in the p -curve analysis were specifically chosen because they match the hypothesis of interest for this analysis. All of the studies included reported multiple statistical tests, which presents one of the challenges in conducting the p -curve analysis—potentially picking the wrong statistical test. It is imperative that the researchers conducting a p -curve analysis carefully examine the specific test of interest and include only that test in the analysis. P -curve analyses should not include all of the p -values in each study, nor should it include p -values for anything other than the primary variable of interest [ 8 ]. The aforementioned reasons are also why it is important to be explicit in a p -curve disclosure table. The p -value and test that a p -curve analyst selects might not be central to the project’s main research question but instead, the test and p -value are chosen because they match the p -curve analysis selection rule. If a secondary analysis in a paper matches the p -curve analysis selection rule, it should be included in the analysis to obtain the most holistic view of the overarching research question. If a p -curve analysis researcher is examining a journal or a particular set of published works over a period of time, it would be wise to develop a clear and specific rule to abide by while gathering the studies.

Once all of the statistical tests and results were obtained for each Köhler study and reported in the disclosure table, each was input into the p -curve application ( p-curve.com ). Again, it is worth repeating that it is important to think about which statistical tests go into the application, as only statistically significant ( p ≤ .05) results will be used in the calculation of the p -curve analysis. If the app does recognize a p -value that is greater than .05, it will discard it from the analysis. The p -curve analysis was run directly from the application and produced a p -curve figure ( Fig. 2 ) with the results from both the binomial test and continuous test.

P -curve chart and output. Note : The observed p -curve includes 13 statistically significant ( p < .05) results, of which 13 are p < .025. There were no nonsignificant results entered.

Thirteen Köhler exercise studies (with 13 test statistics) where entered into the p -curve application to generate the p -curve ( Fig. 2 ). Descriptively, 92% of the statistical results entered had a p ≤ .01 and 8% of the results entered had a p = .02. There were no p s > .02, which resulted in a p -curve with an extreme right skew. Although we can never know what happens during the individual study statistical analysis processes (and cannot say anything to that effect), it does appear that the p -values reported in these studies are quite small and are likely not the outcome of p -hacking practices, from a descriptive point of view.

The first statistical test that is calculated in the p -curve analysis is a simple binomial test. In the binomial test, the researcher can compare the observed proportion of significant results based on a .05 significance midway cut-point (e.g., how many p -values are above and below .025). The number of results that are above and below .025 can be compared with what would be expected when there is no effect. This test is examining if the p -values observed are significantly right skewed by dichotomizing p -values as either higher than .025 or lower than .025. In the case of this p -curve analysis, all of the results are under the .025 threshold, therefore, it appears based on the simple binomial test, that there is evidential value in the series of study results examining the Köhler effect in exercise settings. However, because this test bins a significant p -value to under or over the threshold value, it ignores the variation in the observed p -values and is considered inefficient [ 10 ].

Another test included in the p -curve analysis is the continuous test, which is a function of both the full and half p -curve. The substantive difference between the full and half p -curve is the specific p -values that are included in the test. For the full p -curve, all p -values that are less than .05 are included in the curve where as for the half p -curve, all p -values less than .025 are included in the test. The half- p -curve is an attempt to test for even more extreme (smaller) values than the initial versions of the p -curve. Testing more extreme values might provide more information regarding the evidential value at even more robust threshold of evidence. The continuous test is conducted by computing so-called pp -values (the p -value of the p -value) for both the full and the half p -curve. The pp -value is the probability of at least as extreme p -value conditional of p < .05 when the null hypothesis is true [ 10 ]. These pp -values are then converted to Z -scores for each test and then all of the Z -scores are summed. The summed Z -scores are then divided by the square root of the number of tests included. This approach to dealing with Z -scores is referred to Stouffer’s method and decreases the p -curve analysis sensitivity to a few extreme values [ 10 ].

Both the binomial and continuous tests are one-sided tests that focus on whether the distribution is more right skewed than flat. When examining the results from our example set of studies for the binomial test, which is focused on the share of the results that report p -values of less than .025, we find that there is evidence in the Köhler studies of right skew ( p = .0002). Further, using the continuous test with the full p -curve, there appears to be evidence of right skew (Z = −13.20, p < .001). The half p -curve, testing for even more extreme values, also demonstrates that there is right skew in this distribution of p -values ( Z = −12.43, p < .001).

The binomial and continuous test can also be used to examine if the p -curve is flatter than a curve that represents studies at 33% power, which would indicate that the evidential value for these studies is inadequate and lack power (or an effect that is too small for the sample size). The binomial test for a curve flatter than 33% power was not significant for the Köhler studies ( p > .9999). The full p -curve continuous test ( Z = 9.96, p > .9999) and half p -curve continuous test ( Z = 10.9, p > .9999) both provide evidence that the Köhler effect studies chosen do not lack evidential value.

Finally, the p -curve analysis also provides an estimate of the power of the tests that are included in the p -curve. The statistical power estimate is calculated by comparing the p -curve that is obtained by the input study results to each value of power between 5% and 99%. This test evaluates the similarity of the curves using the overall Z -score that comes from the Stouffer method of aggregating the pp -values [ 10 ]. This useful test is possible because the pp -values themselves depend on the assumed level of power. For the Köhler effect studies, the power of the tests that were included in the p -curve was quite high (power = 99%, 90%, confidence interval 99%–99%).

Summarizing the results of our example p -curve analysis, after aggregating all of the findings from the 13 selected studies it appears that the Köhler effect does have evidential value, the observed power in the studies included is quite high, and the evidence for significant Köhler motivation gains in the studies we examined is likely not an artifact of p -hacking practices.

Applied p -Curve Example: Heterogenous p -Value Sample

The previous example demonstrated evidential value in a relatively unambiguous dataset. Since this is a paper to introduce a new technique, we wanted to provide an example with slightly more variation in p -values so that readers could see the contrast in interpretation between evidential value and no evidential value. To demonstrate how the p -curve analysis might perform with a more heterogenous set of p -values, we created a 10-test dataset. This dataset was intentionally created to have p -values that vary between .01 and .05 in order to evaluate how the p -curve test handles cases where there is more heterogeneity in the p -values than the actual example of studies presented above. It should be noted that this second sample of studies does not have high study-level heterogeneity (the effect sizes would be mostly consistent), but just more spread in the sampled p -values. Studies with high levels of overall study heterogeneity (meta-analysis with I 2 values above 75%; highly variable measurement techniques, samples, study designs, and analysis methods) should be cautious with their use of p -curve and it should be used more as a sensitivity analysis in those cases [ 13 ]. The data are supplied in Table 3 .

Calculations for each test entered into p -curve for the heterogenous p -value sample

The binomial test for right skew was not statistically significant ( p = .83). Both the full p -curve ( Z = .57, p = .71) and half p -curve ( Z = .44, p = .67) for the right skew test of evidential value were not significant. The binomial test for evidential value (i.e., power is flatter than 33%) was significant ( p = .04). The full p -curve for inadequate evidential value was significant ( Z = −2.19, p = .01) and the half p -curve for inadequate evidential was not significant ( Z = 1.58, p = .94). The estimate of statistical power included in the p -curve was 5% (90% confidence interval = 5%–19%). Evidential value occurs when the half p -curve for evidential value has a p < .05 or both the half and full p -curves of evidential value have a p < .1. In this simulation example, neither of those conditions are met so we can assume that there is no evidential value in these studies. The estimated power of these studies is also quite low at 5%. Therefore, if these studies represented a phenomenon in the behavioral sciences, we would not expect a direct replication of these studies to report a statistically significant finding [ 10 ].

The p -curve analysis tool was developed to assist researchers in identifying the evidential value in reported previous findings relevant to their hypothesis or effect of interest [ 8 ]. By critically evaluating the distribution curve of select p -values from these previous findings, researchers can make a judgment regarding the value of the evidence and gain some assurance that any true effect noted is not likely the result of p -hacking practices. This judgment is based on the premise that p -curve distributions are flat or uniform when the analysis suggests no true effect of the reported p -values but have a highly right-skewed distribution when there is likely a true underlying effect (dependent on sample size).

The p -curve analysis is rather straight forward and intuitive, but not fool-proof or conclusive. Researchers must use caution to carefully follow the rules mandated by the developers of the p -curve analysis [ 8 , 10 ]. As mentioned, when evaluating an effect across studies, a specific study selection rule must be established and followed to ensure that each single p -value selected from individual studies centers around a similar variable (testing a similar a priori hypothesis) and is statistically independent from other p -values. Careful selection of the p -values used in the analysis should be augmented with a disclosure table in which p -curve researchers summarize the studies, tests, and p -value chosen for the analysis. Any decision-making processes must be provided in the table for issues such as selecting one variable from several (likewise, isolating correlated variables), selecting variables with interaction terms, or managing means of multiple tests [ 8 ]. Finally, researchers should only include significant p -values as they analyze the p -curve data in the applicable software ( p-curve.com for R Code).

Interpreting the p -curve requires some caution as well. If researchers closely adhere to the proper guidelines during the p -curve analysis, there are still potential weaknesses that simply cannot be accounted for in the distribution of reported p -values. Some researchers (including the original developers) have noted instances in which the p -curve may not tell the whole story or result in a left-skewed p -curve. For example, there is no way to discern the amount of p -hacking that may occur in and across studies. In particular, researchers may hypothesize after the results are known (HARKing) [ 32 ] and, in doing so, may have manipulated their analyses by adding covariates or by altering their decision-making related to outliers and exclusions. Consistent HARKing will likely result in p -values that are higher, but there is no way to ensure that this type, or any type, of p -hacking will be detectable with a p -curve analysis. Similarly, the “best” (i.e., significant) test out of multiple statistical tests may be highlighted for publication and tied to the study’s post hoc hypothesis, while the original analysis or analysis of other variables are not reported. If these p -hacking practices occur enough that the reported p -values have been tweaked to fall just below the .05 threshold, the right-skewed p -curve may not be accurate and we should examine this unusual pattern of p -values [ 7 , 8 , 33 ]. Thus, even if the p -curve results in a right-skew distribution, it is difficult to determine the extent of p -hacking [ 33 ]. Simonsohn et al. [ 10 ] added the half-curve distribution (those with p < .025) in an attempt to make the p -curve analysis less susceptible to the threat of “moderately ambitious p -hacking” [ 10 ]. By excluding p -values that are near the .05 threshold, the binomial half curve analysis avoids including those studies that were manipulated to be just at the significant value. So, one of the strengths of the complete p -curve analysis is that it includes the full curve and the half curve subanalyses. As we mentioned earlier in our description of the binomial and continuous subanalyses, a set of studies (or p -values) is determined to be of evidential value if either the half p -curve has a p < .05 right-skew test, or both the full and half p -curves have p < .1 right-skew tests [ 10 ]. However, if researchers continue to engage in more and more “aggressive” p -hacking practices, it will be difficult to detect when these practices are occurring [ 33 ].

More recent studies have evaluated the efficacy of p -curves and when they can be effectively applied in meta-analytic reviews [ 7 ]. Based on a simulation study, it appears that the p -curve will reflect a true estimation of the effect as described in this paper whenever studies are largely homogenous or have small to moderate heterogeneity ( I 2 < .50) [ 7 ]. If heterogeneity is larger than moderate, researchers should strongly consider using studies that are more homogenous subgroups based on either theoretical or methodological considerations to better estimate the evidential value [ 7 ]. When studies are too heterogenous, p -curve analyses can be erratic and not as trustworthy [ 13 ].

A p -curve analysis can provide valuable insights to the evidential value of a collection of studies, however, we believe that it is best used in conjunction with other methodological tools. A p -curve would be a welcome addition, providing valuable information to support a meta-analysis. There are also other tools that are outside the scope of this paper that could be included to provide a well-rounded view of an effect of interest. A p -uniform analysis, a z -curve analysis or Hedges approach can provide similar insights with slightly different methodological approaches [ 7 ]. McShane et al. [ 13 ] make a strong argument for including Hedges approach when evaluating bias in systematic reviews and meta-analysis. We advocate that when used within the clear definitional bounds, the p -curve analysis is consistent with both Hedges approach and p -uniform analysis. This consistency is noted across simulation studies in the p -curve literature [ 10 ], and studies that have highlighted issues surrounding the utility of the p -curve [ 13 ]. If a meta-analysis has notedly high heterogeneity, it does appear that Hedges technique provides more accurate results. See McShane et al. for more details surrounding situations where the p -curve might not produce accurate results.

While the p -curve analysis is clearly a powerful tool, it is logical for researchers using the p -curve analysis to avoid overstatements or conclusive declarations of the p -curve interpretation. As the p -curve was not meant to determine the extent of publication bias or p -hacking or be sensitive enough to rule out the potential influence of all factors, it is best to interpret the p -curve results as an indication of whether there may be value in the reported findings or if there may be a problem in the reported p -values across the studies. P -hacking practices are increasingly common in many fields of science [ 3 ], and the behavioral sciences are not immune to individuals trying to “game” the system for personal gain. These researcher bias issues may have developed, in part, because many journals and reviewers value novelty of results over the method of the actual study. One major shift in research practice that would be of benefit is a shift to the registered report format, in which studies are evaluated on the merits of the methods and not solely based on the results [ 34 ]. Utilizing a registered report format, the study is published regardless of the novelty of the findings as long as the protocol is followed. The p -curve tool could be useful in helping researchers identify where a reported effect is actually nonexistent. This discrepancy may be a result of p -hacking practices and start discussions surrounding potential solutions that could make our scientific practices more robust. The p -curve analysis could also be a helpful accompanying analysis to include with meta-analytic techniques. For example, there are scenarios where a phenomenon could appear to have an appealing small to moderate effect size, but fail to demonstrate evidential value in a p -curve analysis (especially if the studies are under powered and barely reach traditional thresholds of statistical significance) [ 9 ]. Thus, we believe there are many applications of the p -curve analysis for researchers in behavioral sciences. Future work should continue to utilize the benefits of the p -curve tool while interpreting the findings from the tools with caution.

In this paper, the mechanics of the p -curve bias analysis are introduced and applied to a series of studies that examined the Köhler exercise motivation gain to highlight the practical use of this analysis in improving the interpretation of published evidence. A p -curve analysis demonstration was applied on a sample of 13 studies and resulted in a p -curve distribution suggesting that the reported motivation effect has evidential value and is likely not a byproduct of p -hacking. This paper provides a gentle introduction to the p -curve analysis and advocates for the use of the p -curve analysis in the behavioral sciences.

Authors’ Statement of Conflict of Interest and Adherence to Ethical Standards Authors Christopher R. Hill, Stephen Samendinger, and Amanda M. Rymal declare that they have no conflict of interest.

Authors’ Contributions

Ethical Approval

Informed Consent

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'Business knowledge is money, wealth and power'

The Motivation Curve. Peaks and Lows of Staying Motivated

BUSINESS MANAGEMENT , HUMAN RESOURCES and MOTIVATION

The Motivation Curve allows us to see a big picture to visualize where our motivation is at the certain moment in life. There will be highs and lows, but The Motivation Curve will always balance itself overtime.

With the world offering us so many different choices and countless growth opportunities, we can quickly get distracted. Once we feel fed up with something, bored or tired, we will stop doing it and switch to the next activity.

We will give up too easily on projects we have been working for weeks or months, because there are constantly new things happening around us. Using all the new technology, we expect to do things faster and more efficiently. We try to accomplish more and more by adding extra tasks into our already busy schedules. Unfortunately, when we fail on one of those tasks, we feel demotivated.

Another thing about feeling demotivated is when we do repetitive work on daily basis. Then, it is natural for us to be less enthusiastic about our job which may be simply boring. We may even feel ashamed that we lack ideas and inner strength to overcome those temporary energy shortfalls.

If we happen to teach or motivate others for a living, how can we do our job properly, when we cannot even motivate ourselves?

Lack of motivation happens to you and me, happens to everybody

Feeling less interested in the activity when getting halfway through it is something that every one of us can identify with. Just think about how many times you have given up a book after reading the first 50 pages, or you have stopped working out when the first month of your workout program has passed and you did not see expected results?

At the beginning of each new activity, task or a job, our motivation is high because we can see the progress in relation to the start, so does near completion as the end is in sight. The biggest problem with losing motivation is in the middle because we start to focus on how far there is still to go, while the results have not showed yet.

There are good days when we feel that we are the best in the world, and sometimes there are those bad days when nothing just goes well. For example, one day I am motivated enough to write three new articles for my business website, while the next day, I completely do not have any interesting ideas, hence every attempt to put together one sentence is a nightmare.

It is the same with working out or eating healthy – sometimes you just miss a work-out or do not have enough leafy veggies in your fridge and you feel it is too cold to go shopping.

There are moments in our lives that we feel enthusiastic and, of course, the moments when we just want to have a break from the whole world. It is totally normal. It is a part of human nature.

What is The Motivation Curve?

This is what The Motivation Curve looks like on the chart.

When our motivation is at its peak, we will be 100% dedicated to accomplish thousands of things, we will have a brilliant plan, we will believe that we can do it all and that nothing will stop us. The worst in all this is the fact that motivational highs will not last forever.

When the decline of motivation strikes, we will lack inspiration. This is something that we do not have control over. It destroys are plans and the rhythm of our days. The motivation lows, which happen every three to four months, are inevitable. Thus, the sooner one realizes this, the better for our mental health.

It is impossible to be perpetually at our best. Because the nature of our universe is that every time there is a growth, sooner or later there must be a decline. Balance is everything. We need the fall to take a grip and climb to the top again. We need to collapse over and over again to appreciate what had previously helped us to be the best.

The Motivation Curve guarantees constant development, hence continuous improvement of our lives. Because, if our motivation would be a straight line parallel to the axis, our life will be just average. That kind of life that people who do not have goals, desires and dreams have. People without ambitions, who do not live their lives thoughtfully, do not have the courage to climb to the top, improve things, get better because they are constantly afraid of falling.

If you do not come out of your comfort zone, you we will not be able to start fixing your life to live better than yesterday.

How to deal with lack of motivation?

You need to know that the decline in motivation will happen to you too. Because none of us have immunity against bad things, so learning how to live with it and working out a basic plan to deal with indifferent moments is necessary.

Every time you do something, check on progress more often and allow yourself frequent feedback. You can come up with visual representation of progress using The Motivation Curve to measure how much more time, money or effort, you will need to put into achieve your goal.

Try talking to other people who have more expertise and life experience than you in overcoming specific difficulties. They will give you lots of practical advice because they have already known all the details of the same situation that is happening to you right now.

Also, try to prevent the accumulation of half-finished goals because some redundant or unimportant tasks will drag you back, and you will not be able to move forward with what really matters or pursue those tasks that you really like.

The last piece of advice would be to become alert to the periods of time when motivation is very low. Do something you have not done in your life in order to seek new inspirations, e.g. read a book written by the author you have never read before, travel to a new country, run to a new place, cook a new dish and so on.

Change is inspirational. Change helps to stay motivated

Every time when the lack of motivation happens to you, do not be afraid of trying to change something in your life. It is a great opportunity to slow day, take a few deep breaths, think about the past experiences. Then, look for inspirations to improve your life.

And remember that life will always balance itself. Each time after the sad moments always come the joyous. It is never too late to start over.

I believe in balance deeply.

demotivation
inspirational change
lack of motivation
peaks and lows of motivation
staying motivated
staying productive
The Motivation Curve

CATEGORIES: BUSINESS MANAGEMENT , HUMAN RESOURCES AND MOTIVATION

Hi! I am Jerry. I am the owner and Editor-in-Chief of this website. I am experienced Lecturer and Researcher in Business Management, Head of Business and Economics, and IB Examiner for DP Business Management at International Baccalaureate (IB). I make business education accessible to everyone in the world by providing high-quality business resources for CEOs, directors, business managers, business owners, investors, entrepreneurs, business journalists, business teachers and business students. Privately, I live with my family in China from where I run a vlog Nie Te Chiny about my family life. MORE »

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In Canada and the United States, doctoral attrition rates are estimated to vary from 40% to 60%. Motivation has been proposed as a determinant of doctoral degree completion. The purpose of this study was to develop and validate a scale based on self-determination theory, to assess five types of regulation (intrinsic, integrated, identified, introjected, and external) toward PhD studies. Based ...
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#35: PhD motivation running low? Here’s the cure!

Motivation changes over time

Don’t let low motivation drag you down

How to spot low motivation?

Reason 1: Stuck in a boring routine

Reason 2: There’s no end in sight

Reason 3: Unacknowledged work

Reason 4: Overworked and sleep deprived

Reason 5: Uncertainty about the future

How to get out of it?

Conclusion:

Related resources:

How to Write a PhD Motivation Letter

How Should I Structure My Motivation Letter?

How to Start a Motivation Letter

How to End a Motivation Letter

Motivation Letter Format

How Long Should a Motivation Letter Be?

Things to Avoid when Writing Your Motivational Letter

PhD Motivation Letter Samples – A Word of Caution

Browse PhDs Now

How to Stay Motivated During Your PhD Programme

1. Start Small

2. Be Humble

3. Have a Plan

4. Stay Focused on the Bigger Picture

5. Talk with Others

What to do if you lack motivation in your PhD

Interested in group workshops, cohort-courses and a free PhD learning & support community?

The team behind The PhD Proofreaders have launched The PhD People, a free learning and community platform for PhD students. Connect, share and learn with other students, and boost your skills with cohort-based workshops and courses.

See the bigger picture

Focus on what you can control

Your PhD Thesis. On one page.

Make your work place a place you actually want to work in

Work out what’s important and urgent. Then work on that.

Reward successes

Navigate Shit Valley

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7 Super Simple PhD Student Motivation Hacks

It’s about discipline NOT motivation

How to spot low motivation?

Not wanting to communicate with your supervisor

Procrastination on thesis/writing

Loss of enthusiasm

How do PhD students stay motivated?

Motivational Tips

1. The basics

2. Remember your WHY

3. Focus on the bigger picture

4. Find an energizing hobby

5. Eat well

6. Take time to step away from your work

7. Focus on your achievements

Why Losing Motivation In Grad School Is Normal

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