Start­ing your final year at uni­ver­si­ty is an excit­ing time – you’re coming to the end of this chapter of your life, and this year will hope­ful­ly equip you with the skills and con­fi­dence you need to begin any new adventures.

However, your final year can also be fraught with anxiety. Many psy­chol­o­gy degrees in the UK require you to take on a research project of your own and write an empir­i­cal dis­ser­ta­tion. This can be a par­tic­u­lar­ly daunt­ing prospect if this is your first time under­tak­ing your own orig­i­nal research.

I’ve been through this process myself and since I’ve (almost) fin­ished a PhD in cog­ni­tive psy­chol­o­gy. This required me to support final-year stu­dents as a tutor and super­vi­sor, from devel­op­ing a research ques­tion, to design­ing and setting up the experiment, doing the data col­lec­tion and analy­sis, as well as the actual writing of the dissertation.

Here are my top 10 tips for acing your research project work:


1. Don’t panic – create a plan!

Working on your dis­ser­ta­tion will involve some serious project man­age­ment skills, so get your cal­en­dar out and start plan­ning early.

  • A top tip for plan­ning work on any project is to plan back­wards. Instead of focus­ing on what you need to do right now, start by putting your dead­line in your cal­en­dar, and work your way back. Think about what needs to be done by what date before the final dead­line, e.g. when do you need to send a draft to your super­vi­sor to give them enough time to give you feed­back and to give your­self enough time to make those changes to your draft?
  • Define your major mile­stones: what are the big tasks you need to get done? This could be for example handing in a draft, fin­ish­ing the analy­sis, or fin­ish­ing data col­lec­tion. You can then break down those big tasks into smaller tasks and sub­tasks, e.g. writing the methods section, cre­at­ing a figure, running a certain sta­tis­ti­cal test for one of your hypothe­ses, etc. This way, the massive tasks will seem a lot less daunting.
  • Most tasks take twice as long as you think, so make sure you plan early and generously.
  • Gantt charts are a great way to visu­alise the time you need for each task. You can draw Gantt charts with a project man­age­ment soft­ware, using MS Excel, or even by hand. Start by marking out the time you have avail­able, working your way from your dead­line back­wards – this can be in months or in weeks. Then think about your mile­stones and your sub­tasks and mark out how long you esti­mate each of your tasks to take, start­ing with the most impor­tant tasks. This way you will be able to easily see when you’ll have time to spend on smaller tasks (e.g. for­mat­ting the doc­u­ment with appro­pri­ate margins, making your tables look aes­thet­i­cal­ly pleas­ing, etc).
  • Your Gantt chart will also help you set monthly or weekly goals so that you can pri­ori­tise the most urgent and impor­tant tasks.
  • To-do lists are great – but you need to actu­al­ly check them, so use tools that work for you. If you like to phys­i­cal­ly write down and cross out items on your to-do list, remem­ber that lists on indi­vid­ual pieces of paper can easily get lost. A more per­ma­nent option is to fix a list to your work­sta­tion, using a pin­board or white­board in a fixed loca­tion, or to use a note­book that you can carry around with you. Alter­na­tive­ly, there is a host of great options to keep track of your to-do lists dig­i­tal­ly. There are ded­i­cat­ed apps for list writing and task man­age­ment that allow you to sort your lists into dif­fer­ent cat­e­gories and set reminders. Wun­derlist, Todoist, or Trello, are just some of the apps available.
  • Struc­ture your to-do list if you can: what is urgent and impor­tant and needs to be done NOW? What is less urgent and should be done NEXT?
  • To make your weekly goals less daunt­ing and give your­self some struc­ture for working, you can use a tech­nique called cal­en­dar block­ing. Sit your­self down before the start of each week with a nice cup of tea, and struc­ture your working week: block out spe­cif­ic chunks of time in your day for a task. The key here is to be as spe­cif­ic as pos­si­ble so that when the time comes, you’ll know exactly what you must do and have no excuse to pro­cras­ti­nate. Avoid block­ing out 8 hours at a time for big and vague tasks such as “write the intro­duc­tion section”. Instead, try to sched­ule blocks of 1–2 hours at a time with very spe­cif­ic tasks like “write para­graph about topic X for the intro­duc­tion section”, or “run sta­tis­ti­cal test Y”.
  • Use pro­cras­ti­na­tion to your advan­tage. It’s often hard to keep focus on any one thing for too long. When you’re strug­gling to get into the “zone” for writing, for example, you can get other, less daunt­ing and cog­ni­tive­ly demand­ing tasks done in that time, e.g. double-check­ing your ref­er­ence list or making a figure look nice. This way you won’t be wasting your time. It’s a good idea to keep a list of tasks that you can do when­ev­er you’re not quite in “think­ing mode”, so that you always have some­thing to do that will move your project forward.


2. Get support

Working on your research project can be an iso­lat­ing expe­ri­ence. As you’ll likely be spend­ing a lot of time working on your project during your final year, it’s impor­tant to build up a good support network.

  • Choose your super­vi­sor wisely. The kind of rela­tion­ship you have with the aca­d­e­m­ic who will be guiding you through your research project will affect how much you enjoy your project, and, ulti­mate­ly, the quality of your work. If you have to choose between someone with a rep­u­ta­tion for bril­liance in the field, or someone who you know to be a kind person, go for the kind one. Do your research on your super­vi­sor – find out from pre­vi­ous stu­dents what they’re like. Are they sup­port­ive? Will they respond to student ques­tions in a timely manner and without being condescending?
  • Estab­lish a pro­fes­sion­al working rela­tion­ship with your super­vi­sor, making sure that you both know what you expect from each other through­out the project. For example, ThinkWell offer free down­loads of forms you can use to clarify your expec­ta­tions in your first super­vi­so­ry meeting.
  • Your uni­ver­si­ty might expect you to be very proac­tive in your super­vi­so­ry rela­tion­ship, e.g. it may be up to you to sched­ule regular meet­ings with your super­vi­sor. Don’t be afraid to take the lead a little bit, so you can ensure you stick with your project plan. Having regular meet­ings – and a clear agenda for each meeting – is impor­tant espe­cial­ly in the first stage of your project, where you will be final­is­ing your exper­i­men­tal design. Always take notes of what you’ve dis­cussed in your meeting!
  • Remem­ber that there are no “stupid ques­tions”; when you don’t know some­thing, ask!
  • If your super­vi­sor has a lab or a reading group that meets reg­u­lar­ly to discuss their ongoing research or current lit­er­a­ture, do join! Don’t be afraid to con­tribute to dis­cus­sions and ask ques­tions. You will benefit from the exper­tise of the other stu­dents and researchers in the group, and they will benefit from yours.
  • It is equally impor­tant to get support from your friends and fellow stu­dents while you’re working on your research project. You could sched­ule regular meetups or writing clubs where you do some work togeth­er and talk to each other about what you’ve learned from working on your projects so far. Exchange ideas and expe­ri­ences, or just vent about any dif­fi­cul­ties you’ve encountered.


3. Take a step back and ask your­self “Why?”

It’s easy to get lost in the details of design­ing your experiment, coding par­tic­i­pant respons­es, and analysing data. Every now and then, find a quiet moment to sit down armed with just a piece of paper and a pen, and try to answer one or more of the fol­low­ing ques­tions about your project:

  • If you had to, how would you sum­marise your research ques­tion and method in a single tweet?
  • In one sen­tence only, how would you con­vince someone that your research is important?
  • What do you expect the results of your experiment to be, and what will they mean?
  • What is the main message you want people to take away from your research?
  • How would you explain your dis­ser­ta­tion to someone with no sci­en­tif­ic knowledge?

Doing this repeat­ed­ly and at dif­fer­ent points during your project work will help you write a dis­ser­ta­tion with a clear main message and well-defined research question.


4. Don’t be too ambitious

It’s easy to get overly excited about doing your first research project by your­self, and you might find your­self wanting to answer ALL the ques­tions and test ALL the people. Remind your­self that you have limited time. Your final mark won’t depend on finding a par­tic­u­lar result, on the com­plex­i­ty of your design or the number of par­tic­i­pants you tested – so be tac­ti­cal and keep it as simple as pos­si­ble. If you are strug­gling with this, try some of the fol­low­ing steps:

  • Sim­pli­fy your research ques­tion or reduce the scope of your research to make it man­age­able. For example, rather than asking “What are the factors that deter­mine children’s devel­op­ment of Theory of Mind?”, ask a more con­tained research ques­tion like, “Does a child’s family struc­ture affect their Theory of Mind development?”.
  • Remem­ber that every­thing is likely to take twice as long as you think – that’s espe­cial­ly the case for data collection!
  • Ask your super­vi­sor early on whether you will have any help (e.g. from research assis­tants, other stu­dents, or your super­vi­sor) with data col­lec­tion, or whether you will you have to do it all by your­self. Plan accordingly.
  • Set a real­is­tic recruit­ment target for your par­tic­i­pant sample. If pos­si­ble, run a power analy­sis once you have finalised your design, or ask your super­vi­sor to help with this. A power analy­sis will help you figure out the minimum number of par­tic­i­pants you will need to recruit to make sure your sta­tis­ti­cal tests have enough sta­tis­ti­cal power to detect the effect that you’re after. A basic tuto­r­i­al for doing power analy­ses can be found here. Remem­ber that you won’t be marked down if you don’t recruit a certain number of par­tic­i­pants, so set your­self a rea­son­able minimum target.
  • Reduce the time that data col­lec­tion takes by testing par­tic­i­pants online rather than in the lab. Wher­ev­er pos­si­ble, online exper­i­ments are a viable alter­na­tive to lab-based exper­i­ments. Classic effects found in psy­chol­o­gy studies have been repli­cat­ed with online par­tic­i­pants, and recent research sug­gests that the quality of the data you can get from online samples isn’t any dif­fer­ent from the data quality from lab-tested student pop­u­la­tions (Peer, Brandi­marte, Samat, and Acquisti, 2017; Barn­hoorn, Haas­noot, Bocane­gra, & van Steen­ber­gen, 2015; de Leeuw & Motz, 2016; Reimers & Stewart, 2015). There are excel­lent tools avail­able to help you set up your experiment online. For example, you can use Gorilla to set up your experiment, send a direct link to poten­tial par­tic­i­pants or post it on social media to get people to do the experiment on their own device online. You can also use par­tic­i­pant recruit­ment plat­forms like your university’s par­tic­i­pant pool (e.g. Sona system), or Pro­lif­ic Aca­d­e­m­ic ( These tools are par­tic­u­lar­ly helpful if you need to recruit par­tic­i­pant pop­u­la­tions that are noto­ri­ous­ly hard to find, e.g. people with impair­ments, speak­ers of a spe­cif­ic lan­guage, or a par­tic­u­lar age range.
  • If it’s not pos­si­ble to run your experiment online, you could check whether your lab set up and exper­i­men­tal design allow you to test more than one par­tic­i­pant at the same time. You could also see if there are other stu­dents in your lab group or your cohort who are also recruit­ing from the same pop­u­la­tion as you. Could you combine efforts, and test the same people on two exper­i­ments at the same time?
  • Max­imise the quality of the data you’re col­lect­ing. It’s frus­trat­ing to test lots of par­tic­i­pants and end up with data that is unus­able because some people didn’t take the task seri­ous­ly. To avoid this, keep your experiment as short as pos­si­ble, and make sure it’s inter­est­ing and – if pos­si­ble – fun for your par­tic­i­pants. By doing this, you will increase the like­li­hood of people actu­al­ly com­plet­ing your task and putting appro­pri­ate effort into their performance.
  • Ask research ques­tions that you are con­fi­dent you can answer with the sta­tis­ti­cal tech­niques that you are famil­iar with. There will be not much time to learn any new fancy sta­tis­ti­cal methods, so it’s best to design your experiment with a par­tic­u­lar analy­sis method in mind that you are com­fort­able with.


5. Organ­ise your reading — ref­er­ence man­agers are your friends!

When you start out reading papers for your project, you may find that you manage quite well just with a folder full of pdf doc­u­ments called “64747ghdl.pdf” or “memory.pdf”. But as you down­load more and more arti­cles, you will soon find that this tech­nique won’t cut it.

Ref­er­ence man­agers may sound scary at first, but they will be super useful when it comes to organ­is­ing your reading list and making a bib­li­og­ra­phy for your dis­ser­ta­tion. Your uni­ver­si­ty may have sub­scrip­tions to EndNote or Mende­ley. A free, open-source alter­na­tive is Zotero.

As a first step, use your usual routes of finding rel­e­vant lit­er­a­ture. This could be:

  • Google Scholar
  • Subject-spe­cif­ic databases
  • Your uni­ver­si­ty library
  • From bib­li­ogra­phies of highly rel­e­vant papers, e.g. review papers

Take the time to quickly scan the abstracts of the papers you find to make sure they are rel­e­vant to your topic before you down­load the pdf files. It’s a good idea to have a system for naming your article pdfs; you can try out your own system, but one that might work for you is Author_etal_Year_JournalAbbreviation_AbbreviatedTitle.

You can then load the rel­e­vant papers into your ref­er­ence manager. If the pdf doc­u­ment is machine read­able, the pro­gramme will auto­mat­i­cal­ly also upload any cita­tion data from the doc­u­ment (i.e. authors, year, pub­li­ca­tion, page numbers etc). Within your ref­er­ence manager, you can organ­ise journal arti­cles into folders (e.g. by whether they are urgent or less urgent for you to read, or by topic), and use tags (e.g. for dif­fer­ent topics, methods or par­tic­i­pant groups). This allows you a very sys­tem­at­ic overview of the rel­e­vant lit­er­a­ture for your project. You can also mark papers that you have read, and those that still need reading.

You may also find that just reading a paper is not enough for you to remem­ber its content. Note-taking is an impor­tant part of the reading process. There are dif­fer­ent approach­es to note-taking; trial and error is usually a good way of finding one that works for you.

  • Ref­er­ence man­agers allow you to take notes direct­ly within the soft­ware. These notes will then be saved with the journal article.
  • You can keep a sep­a­rate word doc­u­ment, where you write short nar­ra­tive sum­maries of the papers you have read.
  • You may find it useful to keep an Excel sheet with basic infor­ma­tion about each paper (e.g. you can make columns for the main research ques­tion asked by the paper, the number of par­tic­i­pants, the type of task, the analy­sis method used, the main result, etc.). Excel is great for this because it allows you to easily sort and organ­ise the papers you’ve read by this information.
  • An ana­logue alter­na­tive to all of this is keeping a note­book for your reading. As hand­writ­ten note­books aren’t easily search­able like a word doc­u­ment, you may find it useful to number the pages, and include a table of con­tents, so you can easily find your notes on a par­tic­u­lar study again.

When it comes to writing up your dis­ser­ta­tion, ref­er­ence man­agers are an absolute godsend! Down­load the MS Word plugin for your ref­er­ence manager, and you’ll be able to auto­mat­i­cal­ly insert APA-for­mat­ted ref­er­ences in your text and create a bib­li­og­ra­phy in a mouse click. (Of course, tech­nol­o­gy doesn’t always get it right, so make sure to double-check your ref­er­ence list and in-text cita­tions when you edit your draft!). All in all, using a ref­er­ence manager will save you a lot of time and frustration.


6. Prepare with clear hypothe­ses and a detailed plan for analysis

Your inter­pre­ta­tion of your exper­i­men­tal results and your write-up will be much easier if you start out with clear hypothe­ses and a plan for your analy­sis. It’s impor­tant for sci­en­tists to be trans­par­ent in their work, for example by pre-reg­is­ter­ing their pre­dic­tions and analy­sis plans []. A pre-reg­is­tra­tion is a detailed plan that is usually sub­mit­ted to a web plat­form BEFORE a researcher embarks on data col­lec­tion. This way, other researchers can check that they haven’t run any funky analy­ses on the data that weren’t orig­i­nal­ly planned or that you haven’t have changed their hypothe­ses after they found out the results of the studies.

You can do your own pre-reg­is­tra­tion on a smaller scale. This will force you to think about your pre­dic­tions early on and to clarify your research ques­tion. It will also make the analy­sis stage a lot easier. Impor­tant­ly, a “pre-reg­is­tra­tion” doc­u­ment or a “research plan” will help you iden­ti­fy any flaws in your method BEFORE you’ve gone through all the trouble of col­lect­ing and analysing your data.

To create a “pre-reg­is­tra­tion” or a “research plan”, you can do the following:

  • Specify your research question.
  • Write a detailed descrip­tion of your method, e.g.
    • How many par­tic­i­pants are you plan­ning to recruit?
    • What char­ac­ter­is­tics will your par­tic­i­pants have, e.g. are you recruit­ing a par­tic­u­lar pop­u­la­tion? Are there any exclu­sion cri­te­ria, i.e. will you only allow people with par­tic­u­lar char­ac­ter­is­tics to take part?
    • What is your exper­i­men­tal design? Is it within- or between-sub­jects? What are your inde­pen­dent and your depen­dent variables?
    • What stimuli will you be using? Are you doing any coun­ter­bal­anc­ing or ran­domi­sa­tion? What is the task your par­tic­i­pants will be doing?
  • Write down what your hypothe­ses are and what you expect your data to look like if those hypothe­ses turn out to be correct. You could even draw a graph to show what your expect­ed results would look like.
  • Think about how you will treat your data: Will you exclude any trials e.g. because par­tic­i­pants have taken too little or too much time to press a button? How will you be able to tell whether your data is of good quality, or unus­able (e.g. because a par­tic­i­pant hasn’t under­stood the instructions)?
  • Make a detailed plan about the kinds of sta­tis­ti­cal tests you will need to use to answer your research question.


7. Use Gorilla to set up pro­fes­sion­al-looking exper­i­ments in no time

Setting up your experiment to work the way you want it to will be a major part of your project work. Researchers can pro­gramme their own exper­i­ments or use spe­cialised soft­ware. If you have no expe­ri­ence coding, don’t over­com­pli­cate things by trying to learn a pro­gram­ming lan­guage and pro­gram­ming your own experiment from scratch.

There are tools avail­able that make setting up psy­chol­o­gy exper­i­ments a breeze. For example, Gorilla is a great tool even for beginners.

With Gorilla’s intu­itive user inter­face, you can learn to set up a simple experiment in half an hour. The Task Builder allows you to create exper­i­ments with word, picture, or video stimuli, and record par­tic­i­pant respons­es and reac­tion times. You can also easily set up Ques­tion­naires and collect e.g. demo­graph­ic data or rating respons­es. The under­grad­u­ates I’ve taught have found Gorilla easy and intu­itive to use, and gen­er­al­ly required little help from me to set up their own experiments.

With Gorilla, you’ll be able to visu­alise your exper­i­men­tal design, and under­stand the logic behind impor­tant design fea­tures like coun­ter­bal­anc­ing and ran­domi­sa­tion. This is a very impor­tant skill if you’re think­ing about doing post­grad­u­ate study in psychology.

The great thing about Gorilla is that it can be used for both lab-based exper­i­ments and for online exper­i­ments, so you will have a lot of flex­i­bil­i­ty. Plus, your experiment will look neat and pro­fes­sion­al without you having to put a lot of effort in. You can make the experiment more appeal­ing to par­tic­i­pants by includ­ing fea­tures like a progress bar that lets them know how much more time they’ve got left on a task, or per­son­al­is­able response buttons that you can make to look like Smiley faces or pic­tures of cats if you fancy. The pos­si­bil­i­ties are (almost) endless!

Gorilla’s col­lab­o­ra­tion func­tion­al­i­ty also means that you can work on your experiment set up togeth­er with your super­vi­sor or other stu­dents, so you won’t need to do every­thing by your­self and get help when you need it.

You can also find simple how-to guides and videos on the support pages, and adapt example exper­i­ments to your own needs. If you ever run into any prob­lems with your experiment, you can always contact the support desk, and if your experiment is very com­pli­cat­ed, they also offer a con­sul­tan­cy service that will help you set it up accord­ing to your specifications.


8. Always double- and triple-check your experiments!

It can be very tempt­ing to quickly set up an experiment (espe­cial­ly if you’ve set it up with Gorilla!), and then jump straight into data col­lec­tion. However, it’s impor­tant to make sure that your method is sound, that the experiment runs without errors, and that it gives you the kind of data that you want.

It’s there­fore a good idea to test out (or “pilot”) your experiment before you embark on col­lect­ing data from real participants.

  • Firstly, test the experiment on your­self. Run through all the dif­fer­ent ver­sions of your task to make sure that stimuli are dis­played prop­er­ly, and any ran­domi­sa­tion you’ve set up works.
  • If you’ve set up your experiment with Gorilla, you can send a link with the experiment to a few friends or col­lab­o­ra­tors and get them to run through your experiment as well. For example, you could try out the experiment on 3–5 lab mates or other stu­dents in your cohort, and pilot on 5–7 friends who don’t do psy­chol­o­gy to make sure the task instruc­tions you’ve written are clear.
  • Ask your guinea pigs for feed­back after the experiment, e.g. whether the instruc­tions were easy to follow, whether they guessed what your hypoth­e­sis was, and whether the task ran smooth­ly, and they could see or hear the stimuli. With Gorilla, you can include all these ques­tions in a Ques­tion­naire tacked to the end of your experiment, and then delete that Ques­tion­naire once your pilot stage is completed.
  • Check the feed­back, and act on it. For example, you might have to make changes to any instruc­tions that were dif­fi­cult to under­stand or adapt the dif­fi­cul­ty level of your task if nec­es­sary. Make sure you discuss those changes with your supervisor!
  • Check that the output files from your pilot par­tic­i­pants give you the infor­ma­tion you need: Are you mea­sur­ing what you want to measure? Do you need any addi­tion­al data that you maybe hadn’t thought about?
  • It’s a good idea to run your data analy­sis plan on your pilot data to make sure you have all the data, and meta­da­ta you need. The spread­sheets you need to set up your Gorilla experiment can easily be pop­u­lat­ed with such meta­da­ta, and you may find it useful to include as much infor­ma­tion as you can in there, e.g. infor­ma­tion about which con­di­tion each trial was in, which answer was correct, etc.


9. Get to know a good sta­tis­tics soft­ware – but also learn to love Excel

Researchers often frown at Microsoft Excel but it’s going to be super useful for your data wran­gling (this is when you “wrangle” your data into the format you need for your analy­sis). Unless you’re already famil­iar with R and other pro­gram­ming lan­guages that allow you to get your data in the right format easily, it’s worth spend­ing some time getting famil­iar with Excel’s functions.

  • Always keep an unchanged raw data file and make changes only to copies of that orig­i­nal. This is par­tic­u­lar­ly impor­tant when working with Excel, where you don’t have a record of what changes you have made to the file. This way, you can always revert to the raw data in case some­thing went wrong (in par­tic­u­lar with the sort function!).
  • Use Excel’s filter func­tion to display only the rows you’re inter­est­ed in. This will give you an overview of what your data looks like.
  • You can also use the sort func­tion to sort your data by par­tic­u­lar vari­ables – just be careful to select ALL the columns you want to sort, so you don’t end up messing with whose data is whose.
  • You can use colour-coding to help you under­stand your spread­sheet in a visu­al­ly more intu­itive way.
  • Use arith­metic func­tions to cal­cu­late simple things like means, or stan­dard devi­a­tions from your data. This will help you get a quick overview.
  • Get famil­iar with Excel’s more advanced func­tions. For example, Pivot Tables are a great tool to sum­marise data quickly. You can use these to cal­cu­late means or stan­dard devi­a­tions grouped by dif­fer­ent con­di­tions. There are lots of tuto­ri­als on YouTube on how to use Pivot Tables, so just have a browse and find one that appeals to you.
  • You can also use Excel to create figures of your data (e.g. bar plots or scatter plots) in a rel­a­tive­ly pain-free way.

For data analy­sis, you should stick with what you know. Don’t attempt to learn how to use an entire­ly new sta­tis­tics soft­ware unless you have someone to help you.

Most psy­chol­o­gy degrees use SPSS; make sure you know your way around it. Unfor­tu­nate­ly, SPSS is not the most intu­itive software.

Andy Field’s website has great, easy-to-follow tuto­ri­als for SPSS. Alter­na­tive­ly, you may want to con­vince your super­vi­sor to let you try the more intu­itive (and free!) pack­ages JASP or JAMOVI.


10. Do your writing in chunks – and give your­self enough time to edit

It’s tempt­ing to leave your writing to the last minute, or to attempt to write your whole dis­ser­ta­tion in one go. This is def­i­nite­ly not an approach I would recommend.

Instead, remem­ber that although the dis­ser­ta­tion is a big piece of writing, it con­sists of dif­fer­ent sub­sec­tions. Writing a draft is a mile­stone task, but you can break it down into sub­tasks by think­ing about the dis­ser­ta­tion structure.

My main tips for your writing are:

  • Use Dropbox or some other cloud storage system to save your Word doc­u­ments. All your files will be backed up, so there will be no danger of losing your work. Oth­er­wise make sure you’ve got back-ups, e.g. store your drafts on an exter­nal hard drive as well as your laptop.
  • Try plan­ning your Intro­duc­tion section early on. This is where you set up the back­ground of your topic, and define your research ques­tion, so can make a broad outline from the start. Remem­ber that reading the lit­er­a­ture also counts towards your writing efforts, so sched­ule enough time for reading in your project plan. 
    • Use mind maps or other visual schemat­ics to plan your writing top-down from the start.
    • If you’re a very haptic person, you may find it helpful to use post-it notes to play around with dif­fer­ent struc­tures. On each post-it note, write down a main argu­ment or a brief summary of a paper you’ve read. Then group the post-its the­mat­i­cal­ly; can you iden­ti­fy indi­vid­ual sec­tions or para­graphs? You can then organ­ise your post-it groups into a logical, linear structure.
    • Think about your sec­tions and sub­sec­tions (e.g. the the­mat­ic groups of post-it notes) and give them a heading each. Write those head­ings into a Word doc­u­ment to help you organ­ise your sections.
    • See if you can esti­mate a word count for each section. How about each para­graph? When you have broken down your writing plan into sec­tions of only 200–500 words each, you will find that writing those indi­vid­ual sec­tions is a lot less daunt­ing than having to think about a whole Intro­duc­tion section.
    • Outline in a little bit more detail what each of your sec­tions or para­graphs will say: What’s the key message of the section? What papers will you need to cite here to support your argu­ment? How is the section linked to the pre­vi­ous and to the next section?
  • It’s a good idea to focus on dif­fer­ent sec­tions at dif­fer­ent points through­out the year. For example, it will be easier to write the Method section as you are setting up your experiment because you then won’t forget impor­tant details. You may want to do this as you’re writing your “pre-reg­is­tra­tion” or “research plan”. Sim­i­lar­ly, writing the Results section may be easiest to do along­side your data analysis.
  • Write the final ver­sions of your Intro­duc­tion and the Dis­cus­sion sec­tions in par­al­lel. This way you will avoid the common mistake of talking about some impor­tant concept in your Dis­cus­sion that you haven’t intro­duced in the begin­ning. Make clear how you’ve addressed the research ques­tion that you’ve set up in the Intro­duc­tion, and what the answer to that ques­tion is in your Discussion.
  • Use the tools that are avail­able in MS Word to your advan­tage. For example, the Styles feature allows you to specify dif­fer­ent levels of head­ings and set default formats for par­tic­u­lar sec­tions of text. When you go to the View tab and enable the Nav­i­ga­tion pane, you can then easily nav­i­gate your doc­u­ment to move between dif­fer­ent sec­tions and sub­sec­tions. You can even select a whole section and move it to a dif­fer­ent point in your document.
  • When it comes to actu­al­ly sitting down and craft­ing your dis­ser­ta­tion, I rec­om­mend you use the Pomodoro tech­nique. Giving your­self an unspec­i­fied amount of time to do your writing is often coun­ter­pro­duc­tive as you even­tu­al­ly get bored or hungry, and will end up pro­cras­ti­nat­ing. Instead, try to focus on writing for a spec­i­fied chunk of time (e.g. 20 minutes, or an hour), and set an alarm. At the alarm, take a break. This can be 5 minutes only, or an hour for lunch. Don’t try to cram your writing into long chunks of more than an hour at a time – this is exhaust­ing and will not be very productive.

Getting a first draft on paper is a great feeling. However, when you make your project plan, you will also need to con­sid­er that you’ll need enough time to go through your dis­ser­ta­tion draft again and edit your writing. Good editing is usually reward­ed by a sub­stan­tial increase in marks, so don’t skip this step!

It’s best to finish your draft, and then take a break of at least a couple of days. Do your editing only after you’ve taken that break so that you can look at your writing with fresh eyes and a clear mind. If you’re having a hard time spot­ting typos and con­cen­trat­ing during editing, try chang­ing the font style in your Word doc­u­ment. This makes it much easier to catch typos, and it will hope­ful­ly give you a fresh per­spec­tive on your writing. Try to reverse-outline your own writing. This means going through each para­graph and marking its key message. You can use this reverse-outline to check whether your struc­ture makes sense, e.g. do the para­graphs follow each other logically?

Finally, if your uni­ver­si­ty requires you to submit a hard copy of your dis­ser­ta­tion, set your print­ing and binding dead­line a few days before your hand-in dead­line. You never know what can happen!