A Practical Framework for Planning Your Dissertation (Full Transcript)

[00:00:00] Speaker 1: All right, today I want to talk about something that sounds simple, but actually causes tremendous problems for a lot of students and early stage researchers, and that's how do you plan a dissertation properly? And the problem is that a lot don't plan at all. You just kind of stumble into it, dive into it, maybe take on something their supervisor suggested would be good and hope for the best. And so, you know, whether you're doing a thesis as a master's student, undergrad or PhD student, you don't want to jump straight into reading papers or writing a lit review or collecting data if you've skipped the whole planning process. You don't have that architecture in place. Your project over time commonly starts to drift and it starts slow, but that drift happens to where all of a sudden you realize, wait a second, this isn't what I actually wanted to do. I don't, I don't like this project. This is going in circles. I feel like I'm not going anywhere. And these are all symptoms of the underlying common cause of not having a clear structure and clear planning in place. So as this dissertation season on Professor Stuckler, I want to give you my best tips of the essential ingredients you need to get in place today to avoid unnecessary hardship and time lost. It's actually the same framework we teach inside our Fast Track Dissertation Blueprint course. And I want to share that with you today for the first time. Obviously, I can't go into everything, but this has helped hundreds of students across the world finish their dissertations so much faster. So you get these pieces in place right from the beginning rather than try to do surgery later. The entire project just becomes dramatically easier. So let's go straight through. And I've sequenced this as a series of steps that you can take today. And the first two steps are actually the ones I see almost 90% of students don't do at all. So you may be surprised. I don't think you've seen these and be hard and be real with yourself if you've done these. If you haven't and you're doing a dissertation, do this immediately. Let's dive in. Step one, what we call a supervisor alignment check. Sometimes I think this comes out of doing grades where students have taken a class and supervisors assessing them. They think, well, my supervisor is just this neutral assessor. But in this case, no. Right. Your supervisors have research interests. They have their own preferences for what methods they like, what topics they find interesting. When your dissertation lands outside that neighborhood or that zone of the interests, their interest, you'll start to feel frictions. And that friction can show up like, oh, they're not responding to my emails. They don't answer me. They they're not helping very much. I get reports of students getting one hour a month with their supervisors, sometimes even less. And sometimes that's structural. That's the way the university set it up for undergrad theses. They don't want to dissipate. And the undergrad thesis is more about a grade, not really doing necessarily significant research. So they don't want to disadvantage others. But at the Ph.D. level, you actually need real mentorship. The Ph.D. is an apprenticeship system. Check out this video for why a Ph.D. is not what you think it is. Really important for you to understand that about the Ph.D. I won't be able to get into it here. But OK, so before you invest serious time into a topic, you need to do a supervisor alignment check. It's really simple. Go to their Google Scholar profile. If they don't have it, it's already kind of a yellowish red flag. But you could go to their website at the university. But go on this profile, look up their recent publication, last three to five years. What are they actually working on? What are they interested in? And also, if you're serious about publishing, if they haven't published much in the last few years or they haven't published in the kind of journals you're targeting for, you also need to think about if you're going to be able to get the mentorship that you need to get to the next level, if they're going to be able to transfer you knowledge and skills that they haven't demonstrated themselves. Also check in your supervisor alignment audit and check here. What methods are they using? Are they using quantitative, qualitative, mixed methods, systematic reviews? That really matters for you being able to have some resonance with them, to really synchronize with them so that you're kind of speaking the same language, right? If you're an anthropologist and you're working with a hardcore quant person, I just see people talking right past each other all the time. It's just going to create frictions. Also, you need to understand do they have funded grants or active projects your dissertation could plug into? That's another way to make sure you might get more investment out of them if there's a good fit there. And you also want to maybe just directly ask them if you can, hey, what are you excited about right now? And understand their agenda. And instead of coming to them as a mendicant or a beggar saying, please help me, see if you can find that symbiotic relationship, like how can you help advance their research goals? They're often under a lot of pressure to publish and get ahead and get grants. So ask them, show some actual regard for them rather than treating them like this neutral assessor who's there to give you a grade. So try, if you can, to land in a neighborhood where they're very comfortable. So, you know, this sounds basic, but it saves months of wasted effort. You know, simply like answer, have you spoken to your supervisor about your topic yet? Is there alignment in terms of topic and method and their active projects together? All right, suppose you've done this. Step two, and again, a lot of students come to me in a mess on their dissertations. They haven't done the first step, much less this step is there is that you're trying to finish a dissertation, but they don't really know what the bar they have to clear is. And all you need to do is look at successful past dissertations, ideally by your supervisor, if not in your department, if not in the wider university. So I don't want you to look at published papers. I want you to look at actual completed dissertations in your institution, because that's going to tell you what's achievable in your time frame, what the scope should look like and what your examiners have accepted before. And they're out there. They're actually easy to find. You can look at ProQuest dissertations. Your department or research librarian should tell you where to find them. I mean, I joked if somebody wants to get 20 pounds, I left a 20 pound note in my old dissertation I did when I was a Ph.D. student at Cambridge. It's probably gathering dust in the library somewhere, but they're they're out there. They're produced. They're on the shelf. So all you need to do is go find some dissertations, look at the structure, look at the scope, look at the type of data and analysis they use, how the question was framed. This is going to demystify the process and help you see and visualize you're here. I need to get to here instead of some vague, distant, obscure target in the future. And so many students actually massively overestimate what they need for a dissertation. And it feels daunting, scary and overwhelming. So demystify that process. Right. You don't need to solve an entire field. You don't need to win a Nobel Prize. And you just need to answer one clear research question. And again, I think you're going to feel a whole lot better after you've read three to five successful examples. And it's going to help you calibrate the whole process better than any advice that I could give you here. Let's go on to step three. And this is getting your topic right. Topic's super important. I mean, 95 percent of your ultimate success will come from getting the topic right. And people just gloss and dive into it and think, I got my topic. They haven't actually done the hard work. So here what we do is use a convergence method. You can see more on this convergence method in the video up here. And what I want to do is make sure we're getting you in the right neighborhood in this first phase of our approach of finding the right topic. And I want you to align and have this convergence, find the sweet spot in three things. So one is your passion. And yeah, absolutely. You're going to spend months on this. It needs to hold your attention. You need to get some energy talking about it. So passion needs to be there. There needs to be some debate in the literature that you can see there's activity. Right. If the final chapter has been written, all the ink's been spilled, there's nothing left to say. Well, you're wading into something that's kind of stale and crusty and medieval. I just don't recommend it. Jump into a live debate in your field. Something the field's actively arguing about hasn't been resolved. You can see this if you go into Google Scholar and you look in the area and you see a lot of citations coming for recent, recent papers. The other thing here is feasibility. I want to make sure that you're diving into space. If the whole field is using randomized control trials, well, and you're doing an undergrad thesis or master's thesis, that's going to be really hard to implement or do. So you need to look hard and say, well, the methods being used in my field, is this something I can achieve or have the knowledge about where I can quickly acquire it in a short period of time? I remember for me, I had this great idea. I wanted to look at some Russian data sets, which would have been fantastic, really nice gaps and nice value add there. But at the end, I realized, look, I've got three years from my thesis, not going to get to the level of fluency in Russia that I need to in a short period of time. And so I dispense with it as exciting as it was. So really be serious about feasibility here. So for this topic neighborhood, if you can get these three to overlap on that sweet spot, you've got a good topic neighborhood. And this can be space like it could be mental health and migrants, could be climate adaptation policy, digital health interventions, AI and education. Again, just land in the right neighborhood. We'll hone in and focus to sharpen it later because how we do that is going to depend on step four. With your dissertation, you need to define your end goal. And there's a fork in the road here, an important fork. And I want you to be honest about it because there's two different modes you can approach your dissertation in. Mode one is completion mode. You just want to pass. You want to tick the box, get your degree, treat it like a driver's license, and maybe move on to industry, next chapter of your life. That's completely valid. I say about half of the people who come and want to work with us, that's exactly the mode they're in. They just want to get this thing done and off their back. You don't need to change the world. You just need a solid, defensible topic your supervisor is on board with. Mode B, or the other road that you can take, is publication mode. You really want to give this a shot. You want to go down academic paths, not your ticket to a boring life. You want this work to matter, to mean something beyond your institution. You're doing this out of genuine drive and passion to make a knowledge impact and change the way we think and understand a phenomenon. If that's you, we need to go one step further because your dissertation needs to be genuinely publishable. Here, it's helpful that your supervisor approves, but that's not necessarily going to guarantee publishability. Check out our publishability formula here. That's going to help you understand if it is publishable. But in short, here you need to have a clear gap, a meaningful contribution, and a research question that's focused. So, just answer for yourself, which mode am I in? You know, am I just passing or I just want to publish? So, if you want to publish, then stick with me for this next step. If you don't care about that, then maybe skip forward a bit in the video. If you want to publish, I'm not going to be able to get into everything, but step five here for those taking this road, you need to have a genuine gap. Not just something no one has written about, right, but something the field actually needs. There's an important difference there. And let me list out three types of gaps. There's more. We've got more gap training specifically to help you find gap. Let me just tell you what some gaps are and where you can find them very quickly if you don't have this already. So, what is a population gap, for example, in study in the U.S., but not in sub-Saharan Africa? So, you could apply there. Temporal gap, maybe it's been looked at before 2010, but the world's changed, and so it needs an update, needs a refresh. It might not be the same. Methodological gap, everybody's using surveys, but they haven't used longitudinal methods that track people over time or administrative record linkages that avoid some kinds of problems with self-report. That's a contribution. Okay, there's more gaps out there, but not all these gaps are equally valuable. Bear that in mind, too. But, you know, if you don't have these gaps, pull 20, 30 papers from your research area and go straight, look in the topic neighborhood we defined before, go straight into future research and limitation of each of these. The authors will literally tell you where the gaps are. Another nice thing to do is to look in reviews, literature reviews, or systematic reviews, because that's part of the process. That's one of the big outputs they spit out. What's a future research agenda? They're literally laying it out for you. And if, you know, the other process is to do that lit review or systematic review yourself, because that will get you these gaps and help you identify low-hanging fruit. That can be a very good exercise for PhD students, something I recommend widely. But again, gap is not just, oh, no one's done this. If you want it to be publishable, it needs to be something the field is also asking for, right? Editors and reviewers are going to be looking for a valuable gap that they care about. One other quick note, if you aspire for publishability, you need to calibrate the value of the gap. So if, for example, you've got a population gap and you're like, well, this has been done in England, but not been done in Burkina Faso. Well, you know, in and of itself, that's not going to have a huge amount of publishability. It might be interesting to the residents in Burkina Faso, but why is that speaking to a wide international audience? So when you think about your gap and what the field is missing, you need to then spin it in a way that that's generally relevant to a wide audience. So maybe Burkina Faso is a rich, natural laboratory. They're doing something very innovative that actually the UK could learn from, or there's a reason why the mechanisms might break down in this special population. So we're going to study that. You need to have some bigger justification for why somebody internationally would care to read it. The same thing with a method, right? If you can go further and say, we're using longitudinal methods because this solves a bias of reverse causality. There was a chicken and egg problem we couldn't figure out. That's much better than just saying, oh, we're going to, we need longitudinal data. So make sure you can answer that. Why? Because what value that approach is going to, that you're going to use to address the gap is going to add to the field. All right. Stick with me here. We're going to get to a really important now step that whichever mode, if you're in completion or publishable mode, you need to be into. So right. If you're going to publish, you need a publishable gap. Even if you're doing regular mode, you still need some kind of gap in the literature, a specific real defensible, not necessarily publishable. If you're in mode a, we have a Northstar alignment sequence. And if you have this alignment in place, it's going to prevent drift, which is a big threat to thesis students over time as they get feedback and they get disjointed and pulled out of place and start wondering what, what am I even doing? And the whole edifice collapses like a house of cards. So here's our Northstar alignment sequence. Get this in place. As you're planning your dissertation, you'll avoid 90% of the problems that I see. So you've got a clear gap. Make sure that your thesis has a focused research question that speaks to that gap, right? So clear gap, clear research question. Make sure that lines up to then what you actually want to show, right? That's going to, it's really important that sometimes it's a claim or it's a hypothesis, but that's where your passion should be there. You should be passionate about this ideal. Make sure that's coming into contact with your research question. These three kind of consolidate into a thesis aim. One or two sentences that just explains what you're doing, why it matters. Like this thesis aims to explore attitudes of nurses to AI revolutions in healthcare in Sub-Saharan Africa, as AI has been used widely and integrated in healthcare in first world research systems. It's only starting to penetrate now in these, I don't know, something, but your thesis aim, you want to kind of integrate, have a clear statement that integrates those first three elements, your gap, your research question, what you want to show. This should then have a clear, this thesis aim should have a clear bridge to your methods. Your method should actually be able to deliver on this thesis aim. So for example, if you're aiming to establish causality, maybe a cross-sectional survey is not going to work. If you are aiming to understand why somebody thinks a certain way, maybe you need to do qualitative interviews and not just use kind of crude survey instruments. Your methods and this thesis aim really need to be aligned, come together. So the question to ask yourself is, if somebody reads my thesis aim, is it obvious why I'm using these methods? If yes, you're aligned, if not, something needs to change. One quick tip here for your methodological choices, tactically, is to choose the path of least resistance. So what I mean specifically is wherever possible, use secondary data, use existing data sets, administrative records, published databases, registered data, something already collected. And the reason why is because collecting new data is higher risk, is higher variance. Things can go wrong. You don't get a response rate. People don't show up. They don't answer the survey correctly. You need ethical approval to even go do that. You get 60% of the data you needed. You can't answer your questions. You did all this work and it's not publishable or won't tick the box. And it's not impossible. It's just higher risk. When if there's already an established, validated data set off the shelf that you can use or link up to something else to answer your research question, it's going to be faster. It's easier. It's just lower risk. You can focus your energy on the analysis, which is the fun, exciting part, rather than the painstaking data collection part, which saps your energy. Remember, what matters to the field is the contribution with valid, robust, believable data. You don't even get to the analysis if you didn't get past first base and the primary data are off. So it doesn't mean that primary data collection is wrong. Sometimes it is generally the best and only way to answer your research question. It's just when you have a choice, I would encourage you at this stage of your career, if you're doing a dissertation, to go with secondary data because it's almost always the lower risk path. All right, guys, be honest with yourself. Do you have these ingredients in place? They are indispensable for effective dissertation planning. So supervisor alignment, scope calibration by benchmarking against successful dissertations, getting in your topic neighborhood, having clarity about your end goal, making sure if you do want to publish, you've got a publishable gap, you get alignment from your gap to research question to what you want to show to your thesis aim to bridging to your methods. Our North Star alignment is going to be your best defense against drifting. And you have clear data strategy of how you're going to get data in a realistic, feasible way. Again, we emphasize secondary data. If you get these elements in place, the dissertations just stops being overwhelming and just becomes a series of clear-cut decisions. And listen, if you want to work through this with our support, we've got over 200 members in our mentorship communities internationally, worldwide, across fields, inside research groups, all working for the same goal to be the best researchers they can possibly be, supported by our training system, five workshops a week with real professors, experienced researchers, so you can get feedback. And I'd love to work together if we're a good fit. Check out the links below. See if the way we approach research resonates with you. And if you are going to be doing a dissertation and you do need to get your writing in place, check out this video I've got for you here. If you haven't been taught academic writing, this will transform the way you think and write immediately. Thank me later. See you in the next video.