doing research for paper

How To Write A Research Paper

Step-By-Step Tutorial With Examples + FREE Template

By: Derek Jansen (MBA) | Expert Reviewer: Dr Eunice Rautenbach | March 2024

For many students, crafting a strong research paper from scratch can feel like a daunting task – and rightly so! In this post, we’ll unpack what a research paper is, what it needs to do , and how to write one – in three easy steps. 🙂 

Overview: Writing A Research Paper

What (exactly) is a research paper.

• How to write a research paper
• Stage 1 : Topic & literature search
• Stage 2 : Structure & outline
• Stage 3 : Iterative writing
• Key takeaways

Let’s start by asking the most important question, “ What is a research paper? ”.

Simply put, a research paper is a scholarly written work where the writer (that’s you!) answers a specific question (this is called a research question ) through evidence-based arguments . Evidence-based is the keyword here. In other words, a research paper is different from an essay or other writing assignments that draw from the writer’s personal opinions or experiences. With a research paper, it’s all about building your arguments based on evidence (we’ll talk more about that evidence a little later).

Now, it’s worth noting that there are many different types of research papers , including analytical papers (the type I just described), argumentative papers, and interpretative papers. Here, we’ll focus on analytical papers , as these are some of the most common – but if you’re keen to learn about other types of research papers, be sure to check out the rest of the blog .

With that basic foundation laid, let’s get down to business and look at how to write a research paper .

Overview: The 3-Stage Process

While there are, of course, many potential approaches you can take to write a research paper, there are typically three stages to the writing process. So, in this tutorial, we’ll present a straightforward three-step process that we use when working with students at Grad Coach.

These three steps are:

• Finding a research topic and reviewing the existing literature
• Developing a provisional structure and outline for your paper, and
• Writing up your initial draft and then refining it iteratively

Let’s dig into each of these.

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Step 1: Find a topic and review the literature

As we mentioned earlier, in a research paper, you, as the researcher, will try to answer a question . More specifically, that’s called a research question , and it sets the direction of your entire paper. What’s important to understand though is that you’ll need to answer that research question with the help of high-quality sources – for example, journal articles, government reports, case studies, and so on. We’ll circle back to this in a minute.

The first stage of the research process is deciding on what your research question will be and then reviewing the existing literature (in other words, past studies and papers) to see what they say about that specific research question. In some cases, your professor may provide you with a predetermined research question (or set of questions). However, in many cases, you’ll need to find your own research question within a certain topic area.

Finding a strong research question hinges on identifying a meaningful research gap – in other words, an area that’s lacking in existing research. There’s a lot to unpack here, so if you wanna learn more, check out the plain-language explainer video below.

Once you’ve figured out which question (or questions) you’ll attempt to answer in your research paper, you’ll need to do a deep dive into the existing literature – this is called a “ literature search ”. Again, there are many ways to go about this, but your most likely starting point will be Google Scholar .

If you’re new to Google Scholar, think of it as Google for the academic world. You can start by simply entering a few different keywords that are relevant to your research question and it will then present a host of articles for you to review. What you want to pay close attention to here is the number of citations for each paper – the more citations a paper has, the more credible it is (generally speaking – there are some exceptions, of course).

Ideally, what you’re looking for are well-cited papers that are highly relevant to your topic. That said, keep in mind that citations are a cumulative metric , so older papers will often have more citations than newer papers – just because they’ve been around for longer. So, don’t fixate on this metric in isolation – relevance and recency are also very important.

Beyond Google Scholar, you’ll also definitely want to check out academic databases and aggregators such as Science Direct, PubMed, JStor and so on. These will often overlap with the results that you find in Google Scholar, but they can also reveal some hidden gems – so, be sure to check them out.

Once you’ve worked your way through all the literature, you’ll want to catalogue all this information in some sort of spreadsheet so that you can easily recall who said what, when and within what context. If you’d like, we’ve got a free literature spreadsheet that helps you do exactly that.

Step 2: Develop a structure and outline

With your research question pinned down and your literature digested and catalogued, it’s time to move on to planning your actual research paper .

It might sound obvious, but it’s really important to have some sort of rough outline in place before you start writing your paper. So often, we see students eagerly rushing into the writing phase, only to land up with a disjointed research paper that rambles on in multiple

Now, the secret here is to not get caught up in the fine details . Realistically, all you need at this stage is a bullet-point list that describes (in broad strokes) what you’ll discuss and in what order. It’s also useful to remember that you’re not glued to this outline – in all likelihood, you’ll chop and change some sections once you start writing, and that’s perfectly okay. What’s important is that you have some sort of roadmap in place from the start.

At this stage you might be wondering, “ But how should I structure my research paper? ”. Well, there’s no one-size-fits-all solution here, but in general, a research paper will consist of a few relatively standardised components:

• Introduction
• Literature review
• Methodology

Let’s take a look at each of these.

First up is the introduction section . As the name suggests, the purpose of the introduction is to set the scene for your research paper. There are usually (at least) four ingredients that go into this section – these are the background to the topic, the research problem and resultant research question , and the justification or rationale. If you’re interested, the video below unpacks the introduction section in more detail. 

The next section of your research paper will typically be your literature review . Remember all that literature you worked through earlier? Well, this is where you’ll present your interpretation of all that content . You’ll do this by writing about recent trends, developments, and arguments within the literature – but more specifically, those that are relevant to your research question . The literature review can oftentimes seem a little daunting, even to seasoned researchers, so be sure to check out our extensive collection of literature review content here .

With the introduction and lit review out of the way, the next section of your paper is the research methodology . In a nutshell, the methodology section should describe to your reader what you did (beyond just reviewing the existing literature) to answer your research question. For example, what data did you collect, how did you collect that data, how did you analyse that data and so on? For each choice, you’ll also need to justify why you chose to do it that way, and what the strengths and weaknesses of your approach were.

Now, it’s worth mentioning that for some research papers, this aspect of the project may be a lot simpler . For example, you may only need to draw on secondary sources (in other words, existing data sets). In some cases, you may just be asked to draw your conclusions from the literature search itself (in other words, there may be no data analysis at all). But, if you are required to collect and analyse data, you’ll need to pay a lot of attention to the methodology section. The video below provides an example of what the methodology section might look like.

By this stage of your paper, you will have explained what your research question is, what the existing literature has to say about that question, and how you analysed additional data to try to answer your question. So, the natural next step is to present your analysis of that data . This section is usually called the “results” or “analysis” section and this is where you’ll showcase your findings.

Depending on your school’s requirements, you may need to present and interpret the data in one section – or you might split the presentation and the interpretation into two sections. In the latter case, your “results” section will just describe the data, and the “discussion” is where you’ll interpret that data and explicitly link your analysis back to your research question. If you’re not sure which approach to take, check in with your professor or take a look at past papers to see what the norms are for your programme.

Alright – once you’ve presented and discussed your results, it’s time to wrap it up . This usually takes the form of the “ conclusion ” section. In the conclusion, you’ll need to highlight the key takeaways from your study and close the loop by explicitly answering your research question. Again, the exact requirements here will vary depending on your programme (and you may not even need a conclusion section at all) – so be sure to check with your professor if you’re unsure.

Step 3: Write and refine

Finally, it’s time to get writing. All too often though, students hit a brick wall right about here… So, how do you avoid this happening to you?

Well, there’s a lot to be said when it comes to writing a research paper (or any sort of academic piece), but we’ll share three practical tips to help you get started.

First and foremost , it’s essential to approach your writing as an iterative process. In other words, you need to start with a really messy first draft and then polish it over multiple rounds of editing. Don’t waste your time trying to write a perfect research paper in one go. Instead, take the pressure off yourself by adopting an iterative approach.

Secondly , it’s important to always lean towards critical writing , rather than descriptive writing. What does this mean? Well, at the simplest level, descriptive writing focuses on the “ what ”, while critical writing digs into the “ so what ” – in other words, the implications. If you’re not familiar with these two types of writing, don’t worry! You can find a plain-language explanation here.

Last but not least, you’ll need to get your referencing right. Specifically, you’ll need to provide credible, correctly formatted citations for the statements you make. We see students making referencing mistakes all the time and it costs them dearly. The good news is that you can easily avoid this by using a simple reference manager . If you don’t have one, check out our video about Mendeley, an easy (and free) reference management tool that you can start using today.

Recap: Key Takeaways

We’ve covered a lot of ground here. To recap, the three steps to writing a high-quality research paper are:

• To choose a research question and review the literature
• To plan your paper structure and draft an outline
• To take an iterative approach to writing, focusing on critical writing and strong referencing

Remember, this is just a b ig-picture overview of the research paper development process and there’s a lot more nuance to unpack. So, be sure to grab a copy of our free research paper template to learn more about how to write a research paper.

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How to write your first research paper.

Writing a research manuscript is an intimidating process for many novice writers in the sciences. One of the stumbling blocks is the beginning of the process and creating the first draft. This paper presents guidelines on how to initiate the writing process and draft each section of a research manuscript. The paper discusses seven rules that allow the writer to prepare a well-structured and comprehensive manuscript for a publication submission. In addition, the author lists different strategies for successful revision. Each of those strategies represents a step in the revision process and should help the writer improve the quality of the manuscript. The paper could be considered a brief manual for publication.

It is late at night. You have been struggling with your project for a year. You generated an enormous amount of interesting data. Your pipette feels like an extension of your hand, and running western blots has become part of your daily routine, similar to brushing your teeth. Your colleagues think you are ready to write a paper, and your lab mates tease you about your “slow” writing progress. Yet days pass, and you cannot force yourself to sit down to write. You have not written anything for a while (lab reports do not count), and you feel you have lost your stamina. How does the writing process work? How can you fit your writing into a daily schedule packed with experiments? What section should you start with? What distinguishes a good research paper from a bad one? How should you revise your paper? These and many other questions buzz in your head and keep you stressed. As a result, you procrastinate. In this paper, I will discuss the issues related to the writing process of a scientific paper. Specifically, I will focus on the best approaches to start a scientific paper, tips for writing each section, and the best revision strategies.

1. Schedule your writing time in Outlook

Whether you have written 100 papers or you are struggling with your first, starting the process is the most difficult part unless you have a rigid writing schedule. Writing is hard. It is a very difficult process of intense concentration and brain work. As stated in Hayes’ framework for the study of writing: “It is a generative activity requiring motivation, and it is an intellectual activity requiring cognitive processes and memory” [ 1 ]. In his book How to Write a Lot: A Practical Guide to Productive Academic Writing , Paul Silvia says that for some, “it’s easier to embalm the dead than to write an article about it” [ 2 ]. Just as with any type of hard work, you will not succeed unless you practice regularly. If you have not done physical exercises for a year, only regular workouts can get you into good shape again. The same kind of regular exercises, or I call them “writing sessions,” are required to be a productive author. Choose from 1- to 2-hour blocks in your daily work schedule and consider them as non-cancellable appointments. When figuring out which blocks of time will be set for writing, you should select the time that works best for this type of work. For many people, mornings are more productive. One Yale University graduate student spent a semester writing from 8 a.m. to 9 a.m. when her lab was empty. At the end of the semester, she was amazed at how much she accomplished without even interrupting her regular lab hours. In addition, doing the hardest task first thing in the morning contributes to the sense of accomplishment during the rest of the day. This positive feeling spills over into our work and life and has a very positive effect on our overall attitude.

Rule 1: Create regular time blocks for writing as appointments in your calendar and keep these appointments.

2. start with an outline.

Now that you have scheduled time, you need to decide how to start writing. The best strategy is to start with an outline. This will not be an outline that you are used to, with Roman numerals for each section and neat parallel listing of topic sentences and supporting points. This outline will be similar to a template for your paper. Initially, the outline will form a structure for your paper; it will help generate ideas and formulate hypotheses. Following the advice of George M. Whitesides, “. . . start with a blank piece of paper, and write down, in any order, all important ideas that occur to you concerning the paper” [ 3 ]. Use Table 1 as a starting point for your outline. Include your visuals (figures, tables, formulas, equations, and algorithms), and list your findings. These will constitute the first level of your outline, which will eventually expand as you elaborate.

The next stage is to add context and structure. Here you will group all your ideas into sections: Introduction, Methods, Results, and Discussion/Conclusion ( Table 2 ). This step will help add coherence to your work and sift your ideas.

Now that you have expanded your outline, you are ready for the next step: discussing the ideas for your paper with your colleagues and mentor. Many universities have a writing center where graduate students can schedule individual consultations and receive assistance with their paper drafts. Getting feedback during early stages of your draft can save a lot of time. Talking through ideas allows people to conceptualize and organize thoughts to find their direction without wasting time on unnecessary writing. Outlining is the most effective way of communicating your ideas and exchanging thoughts. Moreover, it is also the best stage to decide to which publication you will submit the paper. Many people come up with three choices and discuss them with their mentors and colleagues. Having a list of journal priorities can help you quickly resubmit your paper if your paper is rejected.

Rule 2: Create a detailed outline and discuss it with your mentor and peers.

3. continue with drafts.

After you get enough feedback and decide on the journal you will submit to, the process of real writing begins. Copy your outline into a separate file and expand on each of the points, adding data and elaborating on the details. When you create the first draft, do not succumb to the temptation of editing. Do not slow down to choose a better word or better phrase; do not halt to improve your sentence structure. Pour your ideas into the paper and leave revision and editing for later. As Paul Silvia explains, “Revising while you generate text is like drinking decaffeinated coffee in the early morning: noble idea, wrong time” [ 2 ].

Many students complain that they are not productive writers because they experience writer’s block. Staring at an empty screen is frustrating, but your screen is not really empty: You have a template of your article, and all you need to do is fill in the blanks. Indeed, writer’s block is a logical fallacy for a scientist ― it is just an excuse to procrastinate. When scientists start writing a research paper, they already have their files with data, lab notes with materials and experimental designs, some visuals, and tables with results. All they need to do is scrutinize these pieces and put them together into a comprehensive paper.

3.1. Starting with Materials and Methods

If you still struggle with starting a paper, then write the Materials and Methods section first. Since you have all your notes, it should not be problematic for you to describe the experimental design and procedures. Your most important goal in this section is to be as explicit as possible by providing enough detail and references. In the end, the purpose of this section is to allow other researchers to evaluate and repeat your work. So do not run into the same problems as the writers of the sentences in (1):

1a. Bacteria were pelleted by centrifugation. 1b. To isolate T cells, lymph nodes were collected.

As you can see, crucial pieces of information are missing: the speed of centrifuging your bacteria, the time, and the temperature in (1a); the source of lymph nodes for collection in (b). The sentences can be improved when information is added, as in (2a) and (2b), respectfully:

2a. Bacteria were pelleted by centrifugation at 3000g for 15 min at 25°C. 2b. To isolate T cells, mediastinal and mesenteric lymph nodes from Balb/c mice were collected at day 7 after immunization with ovabumin.

If your method has previously been published and is well-known, then you should provide only the literature reference, as in (3a). If your method is unpublished, then you need to make sure you provide all essential details, as in (3b).

3a. Stem cells were isolated, according to Johnson [23]. 3b. Stem cells were isolated using biotinylated carbon nanotubes coated with anti-CD34 antibodies.

Furthermore, cohesion and fluency are crucial in this section. One of the malpractices resulting in disrupted fluency is switching from passive voice to active and vice versa within the same paragraph, as shown in (4). This switching misleads and distracts the reader.

4. Behavioral computer-based experiments of Study 1 were programmed by using E-Prime. We took ratings of enjoyment, mood, and arousal as the patients listened to preferred pleasant music and unpreferred music by using Visual Analogue Scales (SI Methods). The preferred and unpreferred status of the music was operationalized along a continuum of pleasantness [ 4 ].

The problem with (4) is that the reader has to switch from the point of view of the experiment (passive voice) to the point of view of the experimenter (active voice). This switch causes confusion about the performer of the actions in the first and the third sentences. To improve the coherence and fluency of the paragraph above, you should be consistent in choosing the point of view: first person “we” or passive voice [ 5 ]. Let’s consider two revised examples in (5).

5a. We programmed behavioral computer-based experiments of Study 1 by using E-Prime. We took ratings of enjoyment, mood, and arousal by using Visual Analogue Scales (SI Methods) as the patients listened to preferred pleasant music and unpreferred music. We operationalized the preferred and unpreferred status of the music along a continuum of pleasantness. 5b. Behavioral computer-based experiments of Study 1 were programmed by using E-Prime. Ratings of enjoyment, mood, and arousal were taken as the patients listened to preferred pleasant music and unpreferred music by using Visual Analogue Scales (SI Methods). The preferred and unpreferred status of the music was operationalized along a continuum of pleasantness.

If you choose the point of view of the experimenter, then you may end up with repetitive “we did this” sentences. For many readers, paragraphs with sentences all beginning with “we” may also sound disruptive. So if you choose active sentences, you need to keep the number of “we” subjects to a minimum and vary the beginnings of the sentences [ 6 ].

Interestingly, recent studies have reported that the Materials and Methods section is the only section in research papers in which passive voice predominantly overrides the use of the active voice [ 5 , 7 , 8 , 9 ]. For example, Martínez shows a significant drop in active voice use in the Methods sections based on the corpus of 1 million words of experimental full text research articles in the biological sciences [ 7 ]. According to the author, the active voice patterned with “we” is used only as a tool to reveal personal responsibility for the procedural decisions in designing and performing experimental work. This means that while all other sections of the research paper use active voice, passive voice is still the most predominant in Materials and Methods sections.

Writing Materials and Methods sections is a meticulous and time consuming task requiring extreme accuracy and clarity. This is why when you complete your draft, you should ask for as much feedback from your colleagues as possible. Numerous readers of this section will help you identify the missing links and improve the technical style of this section.

Rule 3: Be meticulous and accurate in describing the Materials and Methods. Do not change the point of view within one paragraph.

3.2. writing results section.

For many authors, writing the Results section is more intimidating than writing the Materials and Methods section . If people are interested in your paper, they are interested in your results. That is why it is vital to use all your writing skills to objectively present your key findings in an orderly and logical sequence using illustrative materials and text.

Your Results should be organized into different segments or subsections where each one presents the purpose of the experiment, your experimental approach, data including text and visuals (tables, figures, schematics, algorithms, and formulas), and data commentary. For most journals, your data commentary will include a meaningful summary of the data presented in the visuals and an explanation of the most significant findings. This data presentation should not repeat the data in the visuals, but rather highlight the most important points. In the “standard” research paper approach, your Results section should exclude data interpretation, leaving it for the Discussion section. However, interpretations gradually and secretly creep into research papers: “Reducing the data, generalizing from the data, and highlighting scientific cases are all highly interpretive processes. It should be clear by now that we do not let the data speak for themselves in research reports; in summarizing our results, we interpret them for the reader” [ 10 ]. As a result, many journals including the Journal of Experimental Medicine and the Journal of Clinical Investigation use joint Results/Discussion sections, where results are immediately followed by interpretations.

Another important aspect of this section is to create a comprehensive and supported argument or a well-researched case. This means that you should be selective in presenting data and choose only those experimental details that are essential for your reader to understand your findings. You might have conducted an experiment 20 times and collected numerous records, but this does not mean that you should present all those records in your paper. You need to distinguish your results from your data and be able to discard excessive experimental details that could distract and confuse the reader. However, creating a picture or an argument should not be confused with data manipulation or falsification, which is a willful distortion of data and results. If some of your findings contradict your ideas, you have to mention this and find a plausible explanation for the contradiction.

In addition, your text should not include irrelevant and peripheral information, including overview sentences, as in (6).

6. To show our results, we first introduce all components of experimental system and then describe the outcome of infections.

Indeed, wordiness convolutes your sentences and conceals your ideas from readers. One common source of wordiness is unnecessary intensifiers. Adverbial intensifiers such as “clearly,” “essential,” “quite,” “basically,” “rather,” “fairly,” “really,” and “virtually” not only add verbosity to your sentences, but also lower your results’ credibility. They appeal to the reader’s emotions but lower objectivity, as in the common examples in (7):

7a. Table 3 clearly shows that … 7b. It is obvious from figure 4 that …

Another source of wordiness is nominalizations, i.e., nouns derived from verbs and adjectives paired with weak verbs including “be,” “have,” “do,” “make,” “cause,” “provide,” and “get” and constructions such as “there is/are.”

8a. We tested the hypothesis that there is a disruption of membrane asymmetry. 8b. In this paper we provide an argument that stem cells repopulate injured organs.

In the sentences above, the abstract nominalizations “disruption” and “argument” do not contribute to the clarity of the sentences, but rather clutter them with useless vocabulary that distracts from the meaning. To improve your sentences, avoid unnecessary nominalizations and change passive verbs and constructions into active and direct sentences.

9a. We tested the hypothesis that the membrane asymmetry is disrupted. 9b. In this paper we argue that stem cells repopulate injured organs.

Your Results section is the heart of your paper, representing a year or more of your daily research. So lead your reader through your story by writing direct, concise, and clear sentences.

Rule 4: Be clear, concise, and objective in describing your Results.

3.3. now it is time for your introduction.

Now that you are almost half through drafting your research paper, it is time to update your outline. While describing your Methods and Results, many of you diverged from the original outline and re-focused your ideas. So before you move on to create your Introduction, re-read your Methods and Results sections and change your outline to match your research focus. The updated outline will help you review the general picture of your paper, the topic, the main idea, and the purpose, which are all important for writing your introduction.

The best way to structure your introduction is to follow the three-move approach shown in Table 3 .

Adapted from Swales and Feak [ 11 ].

The moves and information from your outline can help to create your Introduction efficiently and without missing steps. These moves are traffic signs that lead the reader through the road of your ideas. Each move plays an important role in your paper and should be presented with deep thought and care. When you establish the territory, you place your research in context and highlight the importance of your research topic. By finding the niche, you outline the scope of your research problem and enter the scientific dialogue. The final move, “occupying the niche,” is where you explain your research in a nutshell and highlight your paper’s significance. The three moves allow your readers to evaluate their interest in your paper and play a significant role in the paper review process, determining your paper reviewers.

Some academic writers assume that the reader “should follow the paper” to find the answers about your methodology and your findings. As a result, many novice writers do not present their experimental approach and the major findings, wrongly believing that the reader will locate the necessary information later while reading the subsequent sections [ 5 ]. However, this “suspense” approach is not appropriate for scientific writing. To interest the reader, scientific authors should be direct and straightforward and present informative one-sentence summaries of the results and the approach.

Another problem is that writers understate the significance of the Introduction. Many new researchers mistakenly think that all their readers understand the importance of the research question and omit this part. However, this assumption is faulty because the purpose of the section is not to evaluate the importance of the research question in general. The goal is to present the importance of your research contribution and your findings. Therefore, you should be explicit and clear in describing the benefit of the paper.

The Introduction should not be long. Indeed, for most journals, this is a very brief section of about 250 to 600 words, but it might be the most difficult section due to its importance.

Rule 5: Interest your reader in the Introduction section by signalling all its elements and stating the novelty of the work.

3.4. discussion of the results.

For many scientists, writing a Discussion section is as scary as starting a paper. Most of the fear comes from the variation in the section. Since every paper has its unique results and findings, the Discussion section differs in its length, shape, and structure. However, some general principles of writing this section still exist. Knowing these rules, or “moves,” can change your attitude about this section and help you create a comprehensive interpretation of your results.

The purpose of the Discussion section is to place your findings in the research context and “to explain the meaning of the findings and why they are important, without appearing arrogant, condescending, or patronizing” [ 11 ]. The structure of the first two moves is almost a mirror reflection of the one in the Introduction. In the Introduction, you zoom in from general to specific and from the background to your research question; in the Discussion section, you zoom out from the summary of your findings to the research context, as shown in Table 4 .

Adapted from Swales and Feak and Hess [ 11 , 12 ].

The biggest challenge for many writers is the opening paragraph of the Discussion section. Following the moves in Table 1 , the best choice is to start with the study’s major findings that provide the answer to the research question in your Introduction. The most common starting phrases are “Our findings demonstrate . . .,” or “In this study, we have shown that . . .,” or “Our results suggest . . .” In some cases, however, reminding the reader about the research question or even providing a brief context and then stating the answer would make more sense. This is important in those cases where the researcher presents a number of findings or where more than one research question was presented. Your summary of the study’s major findings should be followed by your presentation of the importance of these findings. One of the most frequent mistakes of the novice writer is to assume the importance of his findings. Even if the importance is clear to you, it may not be obvious to your reader. Digesting the findings and their importance to your reader is as crucial as stating your research question.

Another useful strategy is to be proactive in the first move by predicting and commenting on the alternative explanations of the results. Addressing potential doubts will save you from painful comments about the wrong interpretation of your results and will present you as a thoughtful and considerate researcher. Moreover, the evaluation of the alternative explanations might help you create a logical step to the next move of the discussion section: the research context.

The goal of the research context move is to show how your findings fit into the general picture of the current research and how you contribute to the existing knowledge on the topic. This is also the place to discuss any discrepancies and unexpected findings that may otherwise distort the general picture of your paper. Moreover, outlining the scope of your research by showing the limitations, weaknesses, and assumptions is essential and adds modesty to your image as a scientist. However, make sure that you do not end your paper with the problems that override your findings. Try to suggest feasible explanations and solutions.

If your submission does not require a separate Conclusion section, then adding another paragraph about the “take-home message” is a must. This should be a general statement reiterating your answer to the research question and adding its scientific implications, practical application, or advice.

Just as in all other sections of your paper, the clear and precise language and concise comprehensive sentences are vital. However, in addition to that, your writing should convey confidence and authority. The easiest way to illustrate your tone is to use the active voice and the first person pronouns. Accompanied by clarity and succinctness, these tools are the best to convince your readers of your point and your ideas.

Rule 6: Present the principles, relationships, and generalizations in a concise and convincing tone.

4. choosing the best working revision strategies.

Now that you have created the first draft, your attitude toward your writing should have improved. Moreover, you should feel more confident that you are able to accomplish your project and submit your paper within a reasonable timeframe. You also have worked out your writing schedule and followed it precisely. Do not stop ― you are only at the midpoint from your destination. Just as the best and most precious diamond is no more than an unattractive stone recognized only by trained professionals, your ideas and your results may go unnoticed if they are not polished and brushed. Despite your attempts to present your ideas in a logical and comprehensive way, first drafts are frequently a mess. Use the advice of Paul Silvia: “Your first drafts should sound like they were hastily translated from Icelandic by a non-native speaker” [ 2 ]. The degree of your success will depend on how you are able to revise and edit your paper.

The revision can be done at the macrostructure and the microstructure levels [ 13 ]. The macrostructure revision includes the revision of the organization, content, and flow. The microstructure level includes individual words, sentence structure, grammar, punctuation, and spelling.

The best way to approach the macrostructure revision is through the outline of the ideas in your paper. The last time you updated your outline was before writing the Introduction and the Discussion. Now that you have the beginning and the conclusion, you can take a bird’s-eye view of the whole paper. The outline will allow you to see if the ideas of your paper are coherently structured, if your results are logically built, and if the discussion is linked to the research question in the Introduction. You will be able to see if something is missing in any of the sections or if you need to rearrange your information to make your point.

The next step is to revise each of the sections starting from the beginning. Ideally, you should limit yourself to working on small sections of about five pages at a time [ 14 ]. After these short sections, your eyes get used to your writing and your efficiency in spotting problems decreases. When reading for content and organization, you should control your urge to edit your paper for sentence structure and grammar and focus only on the flow of your ideas and logic of your presentation. Experienced researchers tend to make almost three times the number of changes to meaning than novice writers [ 15 , 16 ]. Revising is a difficult but useful skill, which academic writers obtain with years of practice.

In contrast to the macrostructure revision, which is a linear process and is done usually through a detailed outline and by sections, microstructure revision is a non-linear process. While the goal of the macrostructure revision is to analyze your ideas and their logic, the goal of the microstructure editing is to scrutinize the form of your ideas: your paragraphs, sentences, and words. You do not need and are not recommended to follow the order of the paper to perform this type of revision. You can start from the end or from different sections. You can even revise by reading sentences backward, sentence by sentence and word by word.

One of the microstructure revision strategies frequently used during writing center consultations is to read the paper aloud [ 17 ]. You may read aloud to yourself, to a tape recorder, or to a colleague or friend. When reading and listening to your paper, you are more likely to notice the places where the fluency is disrupted and where you stumble because of a very long and unclear sentence or a wrong connector.

Another revision strategy is to learn your common errors and to do a targeted search for them [ 13 ]. All writers have a set of problems that are specific to them, i.e., their writing idiosyncrasies. Remembering these problems is as important for an academic writer as remembering your friends’ birthdays. Create a list of these idiosyncrasies and run a search for these problems using your word processor. If your problem is demonstrative pronouns without summary words, then search for “this/these/those” in your text and check if you used the word appropriately. If you have a problem with intensifiers, then search for “really” or “very” and delete them from the text. The same targeted search can be done to eliminate wordiness. Searching for “there is/are” or “and” can help you avoid the bulky sentences.

The final strategy is working with a hard copy and a pencil. Print a double space copy with font size 14 and re-read your paper in several steps. Try reading your paper line by line with the rest of the text covered with a piece of paper. When you are forced to see only a small portion of your writing, you are less likely to get distracted and are more likely to notice problems. You will end up spotting more unnecessary words, wrongly worded phrases, or unparallel constructions.

After you apply all these strategies, you are ready to share your writing with your friends, colleagues, and a writing advisor in the writing center. Get as much feedback as you can, especially from non-specialists in your field. Patiently listen to what others say to you ― you are not expected to defend your writing or explain what you wanted to say. You may decide what you want to change and how after you receive the feedback and sort it in your head. Even though some researchers make the revision an endless process and can hardly stop after a 14th draft; having from five to seven drafts of your paper is a norm in the sciences. If you can’t stop revising, then set a deadline for yourself and stick to it. Deadlines always help.

Rule 7: Revise your paper at the macrostructure and the microstructure level using different strategies and techniques. Receive feedback and revise again.

5. it is time to submit.

It is late at night again. You are still in your lab finishing revisions and getting ready to submit your paper. You feel happy ― you have finally finished a year’s worth of work. You will submit your paper tomorrow, and regardless of the outcome, you know that you can do it. If one journal does not take your paper, you will take advantage of the feedback and resubmit again. You will have a publication, and this is the most important achievement.

What is even more important is that you have your scheduled writing time that you are going to keep for your future publications, for reading and taking notes, for writing grants, and for reviewing papers. You are not going to lose stamina this time, and you will become a productive scientist. But for now, let’s celebrate the end of the paper.

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Purdue Online Writing Lab Purdue OWL® College of Liberal Arts

Writing a Research Paper

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The pages in this section provide detailed information about how to write research papers including discussing research papers as a genre, choosing topics, and finding sources.

The Research Paper

There will come a time in most students' careers when they are assigned a research paper. Such an assignment often creates a great deal of unneeded anxiety in the student, which may result in procrastination and a feeling of confusion and inadequacy. This anxiety frequently stems from the fact that many students are unfamiliar and inexperienced with this genre of writing. Never fear—inexperience and unfamiliarity are situations you can change through practice! Writing a research paper is an essential aspect of academics and should not be avoided on account of one's anxiety. In fact, the process of writing a research paper can be one of the more rewarding experiences one may encounter in academics. What is more, many students will continue to do research throughout their careers, which is one of the reasons this topic is so important.

Becoming an experienced researcher and writer in any field or discipline takes a great deal of practice. There are few individuals for whom this process comes naturally. Remember, even the most seasoned academic veterans have had to learn how to write a research paper at some point in their career. Therefore, with diligence, organization, practice, a willingness to learn (and to make mistakes!), and, perhaps most important of all, patience, students will find that they can achieve great things through their research and writing.

The pages in this section cover the following topic areas related to the process of writing a research paper:

• Genre - This section will provide an overview for understanding the difference between an analytical and argumentative research paper.
• Choosing a Topic - This section will guide the student through the process of choosing topics, whether the topic be one that is assigned or one that the student chooses themselves.
• Identifying an Audience - This section will help the student understand the often times confusing topic of audience by offering some basic guidelines for the process.
• Where Do I Begin - This section concludes the handout by offering several links to resources at Purdue, and also provides an overview of the final stages of writing a research paper.

• How to Do Research for an Excellent Essay: The Complete Guide

One of the biggest secrets to writing a good essay is the Boy Scouts’ motto: ‘be prepared’. Preparing for an essay – by conducting effective research – lays the foundations for a brilliant piece of writing, and it’s every bit as important as the actual writing part. Many students skimp on this crucial stage, or sit in the library not really sure where to start; and it shows in the quality of their essays. This just makes it easier for you to get ahead of your peers, and we’re going to show you how. In this article, we take you through what you need to do in order to conduct effective research and use your research time to best effect.

Allow enough time

First and foremost, it’s vital to allow enough time for your research. For this reason, don’t leave your essay until the last minute . If you start writing without having done adequate research, it will almost certainly show in your essay’s lack of quality. The amount of research time needed will vary according to whether you’re at Sixth Form or university, and according to how well you know the topic and what teaching you’ve had on it, but make sure you factor in more time than you think you’ll need. You may come across a concept that takes you longer to understand than you’d expected, so it’s better to allow too much time than too little.

Read the essay question and thoroughly understand it

If you don’t have a thorough understanding of what the essay question is asking you to do, you put yourself at risk of going in the wrong direction with your research. So take the question, read it several times and pull out the key things it’s asking you to do. The instructions in the question are likely to have some bearing on the nature of your research. If the question says “Compare”, for example, this will set you up for a particular kind of research, during which you’ll be looking specifically for points of comparison; if the question asks you to “Discuss”, your research focus may be more on finding different points of view and formulating your own.

Begin with a brainstorm

Start your research time by brainstorming what you already know. Doing this means that you can be clear about exactly what you’re already aware of, and you can identify the gaps in your knowledge so that you don’t end up wasting time by reading books that will tell you what you already know. This gives your research more of a direction and allows you to be more specific in your efforts to find out certain things. It’s also a gentle way of introducing yourself to the task and putting yourself in the right frame of mind for learning about the topic at hand.

Achieve a basic understanding before delving deeper

If the topic is new to you and your brainstorm has yielded few ideas, you’ll need to acquire a basic understanding of the topic before you begin delving deeper into your research. If you don’t, and you start by your research by jumping straight in at the deep end, as it were, you’ll struggle to grasp the topic. This also means that you may end up being too swayed by a certain source, as you haven’t the knowledge to question it properly. You need sufficient background knowledge to be able to take a critical approach to each of the sources you read. So, start from the very beginning. It’s ok to use Wikipedia or other online resources to give you an introduction to a topic, though bear in mind that these can’t be wholly relied upon. If you’ve covered the topic in class already, re-read the notes you made so that you can refresh your mind before you start further investigation.

Working through your reading list

If you’ve been given a reading list to work from, be organised in how you work through each of the items on it. Try to get hold of as many of the books on it as you can before you start, so that you have them all easily to hand, and can refer back to things you’ve read and compare them with other perspectives. Plan the order in which you’re going to work through them and try to allocate a specific amount of time to each of them; this ensures that you allow enough time to do each of them justice and that focus yourself on making the most of your time with each one. It’s a good idea to go for the more general resources before honing in on the finer points mentioned in more specialised literature. Think of an upside-down pyramid and how it starts off wide at the top and becomes gradually narrower; this is the sort of framework you should apply to your research.

Ask a librarian

Library computer databases can be confusing things, and can add an extra layer of stress and complexity to your research if you’re not used to using them. The librarian is there for a reason, so don’t be afraid to go and ask if you’re not sure where to find a particular book on your reading list. If you’re in need of somewhere to start, they should be able to point you in the direction of the relevant section of the library so that you can also browse for books that may yield useful information.

Use the index

If you haven’t been given specific pages to read in the books on your reading list, make use of the index (and/or table of contents) of each book to help you find relevant material. It sounds obvious, but some students don’t think to do this and battle their way through heaps of irrelevant chapters before finding something that will be useful for their essay.

Taking notes

As you work through your reading, take notes as you go along rather than hoping you’ll remember everything you’ve read. Don’t indiscriminately write down everything – only the bits that will be useful in answering the essay question you’ve been set. If you write down too much, you risk writing an essay that’s full of irrelevant material and getting lower grades as a result. Be concise, and summarise arguments in your own words when you make notes (this helps you learn it better, too, because you actually have to think about how best to summarise it). You may want to make use of small index cards to force you to be brief with what you write about each point or topic. We’ve covered effective note-taking extensively in another article, which you can read here . Note-taking is a major part of the research process, so don’t neglect it. Your notes don’t just come in useful in the short-term, for completing your essay, but they should also be helpful when it comes to revision time, so try to keep them organised.

Research every side of the argument

Never rely too heavily on one resource without referring to other possible opinions; it’s bad academic practice. You need to be able to give a balanced argument in an essay, and that means researching a range of perspectives on whatever problem you’re tackling. Keep a note of the different arguments, along with the evidence in support of or against each one, ready to be deployed into an essay structure that works logically through each one. If you see a scholar’s name cropping up again and again in what you read, it’s worth investigating more about them even if you haven’t specifically been told to do so. Context is vital in academia at any level, so influential figures are always worth knowing about.

Keep a dictionary by your side

You could completely misunderstand a point you read if you don’t know what one important word in the sentence means. For that reason, it’s a good idea to keep a dictionary by your side at all times as you conduct your research. Not only does this help you fully understand what you’re reading, but you also learn new words that you might be able to use in your forthcoming essay or a future one . Growing your vocabulary is never a waste of time!

Start formulating your own opinion

As you work through reading these different points of view, think carefully about what you’ve read and note your own response to different opinions. Get into the habit of questioning sources and make sure you’re not just repeating someone else’s opinion without challenging it. Does an opinion make sense? Does it have plenty of evidence to back it up? What are the counter-arguments, and on balance, which sways you more? Demonstrating your own intelligent thinking will set your essay apart from those of your peers, so think about these things as you conduct your research.

Be careful with web-based research

Although, as we’ve said already, it’s fine to use Wikipedia and other online resources to give you a bit of an introduction to a topic you haven’t covered before, be very careful when using the internet for researching an essay. Don’t take Wikipedia as gospel; don’t forget, anybody can edit it! We wouldn’t advise using the internet as the basis of your essay research – it’s simply not academically rigorous enough, and you don’t know how out of date a particular resource might be. Even if your Sixth Form teachers may not question where you picked up an idea you’ve discussed in your essays, it’s still not a good habit to get into and you’re unlikely to get away with it at a good university. That said, there are still reliable academic resources available via the internet; these can be found in dedicated sites that are essentially online libraries, such as JSTOR. These are likely to be a little too advanced if you’re still in Sixth Form, but you’ll almost certainly come across them once you get to university.

Look out for footnotes

In an academic publication, whether that’s a book or a journal article, footnotes are a great place to look for further ideas for publications that might yield useful information. Plenty can be hidden away in footnotes, and if a writer is disparaging or supporting the ideas of another academic, you could look up the text in question so that you can include their opinion too, and whether or not you agree with them, for extra brownie points.

Don’t save doing all your own references until last

If you’re still in Sixth Form, you might not yet be required to include academic references in your essays, but for the sake of a thorough guide to essay research that will be useful to you in the future, we’re going to include this point anyway (it will definitely come in useful when you get to university, so you may as well start thinking about it now!). As you read through various books and find points you think you’re going to want to make in your essays, make sure you note down where you found these points as you go along (author’s first and last name, the publication title, publisher, publication date and page number). When you get to university you will be expected to identify your sources very precisely, so it’s a good habit to get into. Unfortunately, many students forget to do this and then have a difficult time of going back through their essay adding footnotes and trying to remember where they found a particular point. You’ll save yourself a great deal of time and effort if you simply note down your academic references as you go along. If you are including footnotes, don’t forget to add each publication to a main bibliography, to be included at the end of your essay, at the same time.

Putting in the background work required to write a good essay can seem an arduous task at times, but it’s a fundamental step that can’t simply be skipped. The more effort you put in at this stage, the better your essay will be and the easier it will be to write. Use the tips in this article and you’ll be well on your way to an essay that impresses!

To get even more prepared for essay writing you might also want to consider attending an Oxford Summer School .

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Basic Steps in the Research Process

The following steps outline a simple and effective strategy for writing a research paper. Depending on your familiarity with the topic and the challenges you encounter along the way, you may need to rearrange these steps.

Step 1: Identify and develop your topic

Selecting a topic can be the most challenging part of a research assignment. Since this is the very first step in writing a paper, it is vital that it be done correctly. Here are some tips for selecting a topic:

• Select a topic within the parameters set by the assignment. Many times your instructor will give you clear guidelines as to what you can and cannot write about. Failure to work within these guidelines may result in your proposed paper being deemed unacceptable by your instructor.
• Select a topic of personal interest to you and learn more about it. The research for and writing of a paper will be more enjoyable if you are writing about something that you find interesting.
• Select a topic for which you can find a manageable amount of information. Do a preliminary search of information sources to determine whether existing sources will meet your needs. If you find too much information, you may need to narrow your topic; if you find too little, you may need to broaden your topic.
• Be original. Your instructor reads hundreds of research papers every year, and many of them are on the same topics (topics in the news at the time, controversial issues, subjects for which there is ample and easily accessed information). Stand out from your classmates by selecting an interesting and off-the-beaten-path topic.
• Still can't come up with a topic to write about? See your instructor for advice.

Once you have identified your topic, it may help to state it as a question. For example, if you are interested in finding out about the epidemic of obesity in the American population, you might pose the question "What are the causes of obesity in America ?" By posing your subject as a question you can more easily identify the main concepts or keywords to be used in your research.

Step 2 : Do a preliminary search for information

Before beginning your research in earnest, do a preliminary search to determine whether there is enough information out there for your needs and to set the context of your research. Look up your keywords in the appropriate titles in the library's Reference collection (such as encyclopedias and dictionaries) and in other sources such as our catalog of books, periodical databases, and Internet search engines. Additional background information may be found in your lecture notes, textbooks, and reserve readings. You may find it necessary to adjust the focus of your topic in light of the resources available to you.

Step 3: Locate materials

With the direction of your research now clear to you, you can begin locating material on your topic. There are a number of places you can look for information:

If you are looking for books, do a subject search in One Search . A Keyword search can be performed if the subject search doesn't yield enough information. Print or write down the citation information (author, title,etc.) and the location (call number and collection) of the item(s). Note the circulation status. When you locate the book on the shelf, look at the books located nearby; similar items are always shelved in the same area. The Aleph catalog also indexes the library's audio-visual holdings.

Use the library's  electronic periodical databases  to find magazine and newspaper articles. Choose the databases and formats best suited to your particular topic; ask at the librarian at the Reference Desk if you need help figuring out which database best meets your needs. Many of the articles in the databases are available in full-text format.

Use search engines ( Google ,  Yahoo , etc.) and subject directories to locate materials on the Internet. Check the  Internet Resources  section of the NHCC Library web site for helpful subject links.

Step 4: Evaluate your sources

See the  CARS Checklist for Information Quality   for tips on evaluating the authority and quality of the information you have located. Your instructor expects that you will provide credible, truthful, and reliable information and you have every right to expect that the sources you use are providing the same. This step is especially important when using Internet resources, many of which are regarded as less than reliable.

Step 5: Make notes

Consult the resources you have chosen and note the information that will be useful in your paper. Be sure to document all the sources you consult, even if you there is a chance you may not use that particular source. The author, title, publisher, URL, and other information will be needed later when creating a bibliography.

Step 6: Write your paper

Begin by organizing the information you have collected. The next step is the rough draft, wherein you get your ideas on paper in an unfinished fashion. This step will help you organize your ideas and determine the form your final paper will take. After this, you will revise the draft as many times as you think necessary to create a final product to turn in to your instructor.

Step 7: Cite your sources properly

Give credit where credit is due; cite your sources.

Citing or documenting the sources used in your research serves two purposes: it gives proper credit to the authors of the materials used, and it allows those who are reading your work to duplicate your research and locate the sources that you have listed as references. The  MLA  and the  APA  Styles are two popular citation formats.

Failure to cite your sources properly is plagiarism. Plagiarism is avoidable!

Step 8: Proofread

The final step in the process is to proofread the paper you have created. Read through the text and check for any errors in spelling, grammar, and punctuation. Make sure the sources you used are cited properly. Make sure the message that you want to get across to the reader has been thoroughly stated.

Additional research tips:

• Work from the general to the specific -- find background information first, then use more specific sources.
• Don't forget print sources -- many times print materials are more easily accessed and every bit as helpful as online resources.
• The library has books on the topic of writing research papers at call number area LB 2369.
• If you have questions about the assignment, ask your instructor.
• If you have any questions about finding information in the library, ask the librarian.

Contact Information

Craig larson.

Librarian 763-424-0733 [email protected] Zoom:  myzoom   Available by appointment

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Doing Research

(11 reviews)

Celia Brinkerhoff, Kwantlen Polytechnic University

Copyright Year: 2019

Publisher: Kwantlen Polytechnic University

Language: English

Formats Available

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Learn more about reviews.

Reviewed by Kate Cammack, Associate Professor, University of the South on 11/20/23

A major benefit to this text is that it's not too dense, which enhances its approachability. The downside to this approach is that some key issues are discussed at a more general/introductory level, though this could likely be addressed with... read more

Comprehensiveness rating: 3 see less

A major benefit to this text is that it's not too dense, which enhances its approachability. The downside to this approach is that some key issues are discussed at a more general/introductory level, though this could likely be addressed with supplementary activities and/or resources. For instance, distinctions are made between popular & scholarly articles (p.34) and trade & professional sources (p.35); however, the terms/ideas of primary & secondary sources of information are not presented, which limits the reader's ability to consider the lens(es) through which the primary works are being discussed. As this is increasingly important in our information-dense era, I'd include supplementary sources around this issue if I were to use this text. Similarly, the chapter about starting a search with keywords (p.49) could be supplemented with an activity asking students to populate a short list of synonyms and running iterative searches where they trading out terms to see how the results differ. While most strategies are practical (e.g., adding filters to searches pg. 69-70; tracking citations, pg. 71-72), valuable theoretical frameworks (e.g., "Question authority" on pg. 84; "Who is the author?" on pg. 85; "Check for purpose" on pg. 89) are presented after much of the practical content in the first chapters. Given the importance of identifying, critiquing, and working with credible, reputable sources, I might have my students engage with some of this content a bit earlier.

Content Accuracy rating: 4

Content seemed accurate and error-free. There are a few specific references to certain search engines (e.g., Google Scholar) and resources (e.g., Wikipedia, news outlets), which seem like fairly neutral and appropriate examples.

Relevance/Longevity rating: 3

This text contains a short section on Wikipedia (p.17) that contains relevant information/perspectives on this online resources, including who can edit entries and the value of the external links/resources listed at the end of the entry. Other sites, such as Reddit and even AI-based resources (e.g., Claude, ChatGPT) are not included but could be addressed with supplemental information. Section 20 describes ways to engage with the library catalogue, which seems helpful (even if less likely to be used); Section 23 summarizes library research tools, which could be adapted with links/info relevant to your home institution. One of the more valuable and generalizable sections presents a way to evaluate sources of information, called the "SIFT Method" (pg. 91-96); it is followed by an infographic on "How to spot fake news" (pg. 97). As most students have heard of "fake news" (and are likely engaging with it!), this chapter could provide a valuable launching pad for critical thinking skills and discussions.

Clarity rating: 4

This text has a number of interactive activities, where readers can practice key concepts & ideas. For instance, there is a drag-and-drop activity where you can decide what type/source of information might best address a research question/topic.

Consistency rating: 4

Chapters & subsections follow a similar general structure. I liked the "key takeaway" and learning objectives listed at the top of each part. The learning objectives are primarily at lower-level Blooms but provide a good foundation from which to build.

Modularity rating: 4

Smaller sections make reading/engagement a very easy "lift"

Organization/Structure/Flow rating: 4

General flow/organization makes sense.

Interface rating: 4

The interface works fairly well. Pages 30-31 have a helpful-seeming table that is partially cut off and illegible in the pdf version; same with Pages 59-60. The two links on Part 8 (pg. 33) are described as going to different activities but directed me to the same activity. There are a few blank pages (e.g., pgs. 48, 59, 82) that aren't dividing chapters/sections in meaningful ways. The section on library research tools contains a table (pg. 74) with a few links that take you to .png images depicting those tools (rather than the actual websites/resources); again, this could be supplemented with resources relevant to your own institution.

Grammatical Errors rating: 5

Cultural Relevance rating: 3

There is no overtly insensitive content. However, there are many opportunities to provide important context/nuance to (a) how/why search engine algorithms are designed to highlight the articles/resources that they do and (b) ways that knowledge generation and information dissemination has privileged white male identities, at the exclusion of many minoritized/marginalized groups (e.g., BIPOC, women, LGBTQA+), and emphasized Western ideologies.

A valuable text to build on!

Reviewed by J. Renee Trombley, Assistant Professor, Metropolitan State University of Denver on 12/2/22

There is no index or glossary provided with this text. It is however marketed as a tutorial with 4 modules and the topics for each module are listed in the table of contents. This work does cover mostly everything a student would need to begin the... read more

Comprehensiveness rating: 4 see less

There is no index or glossary provided with this text. It is however marketed as a tutorial with 4 modules and the topics for each module are listed in the table of contents. This work does cover mostly everything a student would need to begin the research process and help them in understanding the process for developing a good research question. There are some additions I would add, specifically where to look in journal articles that can help them formulate a new research question and providing support for their own search for possible sources. Overall, this is a very good book.

The tutorial provides good information and covers everything I would have with my students and more on how to search for quality research. It also explains how to avoid bias in research. The only issue I have is that not all of the links provided in the text are working links, but that can be adjusted by providing new/different links or maybe not needed at all.

Relevance/Longevity rating: 5

The content used for this work is relevant and does not contain any dated information. All of the information provided will remain useful for years to come. However, this text was written for Canadian students, using a search engine I am not familiar with. I would need to provide my students that information and explain the names of the search engines our library system uses. This is not a huge issue, since the key information is how to use the search engine and that would remain the same.

Clarity rating: 5

This text does not use research language in the formal sense and does not need any extra explanation of terminology. It is also quite readable, and this makes it particularly useful for a wide range of students, and for use in both undergraduate and graduate level courses.

Consistency rating: 5

The framework provided is good and goes through the steps which can help the students understand that developing a research question is a process that can be circular in nature. It does not introduce terminology that is specific to a typical research methods course and that is a good thing because it could be overwhelming. The text does what it says it will and that focus is consistent throughout the text.

Modularity rating: 5

There are four modules presented in this text, with each one specific to the process in developing a good research question. These modules can also be easily broken up with specific page ranges used for assigned readings. Also, this is a short book and at 108 pages, broken up in four parts makes this book an easy read for students.

Organization/Structure/Flow rating: 5

The author did a good job of organizing the text both between and within the four modules. There is a logical layout, from thinking about a research topic and narrowing it down to finding and utilizing reputable sources. They also included a breakdown on how to do searches from a library database, and even though this comes from an author in Canada, it is still applicable, but you do need to let students know that although the process is the same, the names of the search engines are not.

Interface rating: 5

This text is free of any significant errors, and I did not see any minor errors as well in formatting/presentation. The only issue I did find is that some of the links did not work anymore, but there were many more that did.

This text is well written with no grammatical errors present.

Cultural Relevance rating: 5

From my perspective as a critical criminologist, there is no bias present in this text, and it is not culturally insensitive or offensive in any way.

Overall, this is a very good book and I do plan to use it in all of my classes where I am assigning a research paper. This book is well put together and offers a great resource for any discipline/course that asks students to conduct research for assignments. Some note to students can be added based on professor's own experiences as well as notes about links (whether working or not), and the different names associated with our library’s search engines (different than in the text). However, these are minor nuisances and do not distract from the overall usefulness of the text. I am looking forward to seeing what my students think.

Reviewed by Kimberly Reinhardt, Associate Professor, Texas A&M University-Corpus Christi on 11/9/22

One of the modules features how to select sources, which I think is very useful for an introduction course. read more

Comprehensiveness rating: 5 see less

One of the modules features how to select sources, which I think is very useful for an introduction course.

Content Accuracy rating: 5

This book is error-free.

The topics in this book flow well toward doing research, but it is specific to using particular library databases, which may not be applicable in your context.

This book is accessible.

This book leads one through the process of doing research on a variety of levels.

Excellent remix that developed the original book into modules.

The organization of the original book into modules offers a user friendly way to apply the book.

Each module has a clear objective and is easy to navigate.

no grammar errors.

This book uses current, relevant issues as examples in the selection process.

This book is an excellent remix. I plan to use it in my introduction to research class.

Reviewed by Omar Elizondo, Associate Professor of Practice, University of Texas Rio Grande Valley on 10/24/22

The book outlines and properly sequences the content in an order ideal for learning research. read more

The book outlines and properly sequences the content in an order ideal for learning research.

No issues were observed or discovered during the reading. All content was accurate, and all links were functional.

The author provides multiple examples of current databases and methods to search for resources for research.

The writing is clear and written very well. The author speaks professionally and with authority yet is relatable and understanding.

Many tips are available throughout the reading, serving as methods to condense the idea being taught. Each section begins with an estimated amount of time required to finish the reading, which is informational for the reader.

All reading a concise and does not overstay its welcome. The author is to the point but is never lacking in information.

Each section includes activities that are beneficial for the reader. The exercises are shot but impactful. Each chapter ends with a takeaway or summary of what was taught.

The book is very easy to navigate, and the flow of text to images and videos is easy to manage and understand.

No grammatical issues were observed for this review.

Cultural Relevance rating: 4

References are real-world and relatable. The author provides supportive videos which are concise and accessible online. The author provides multiple modes of retrieving information and resources. The author writes from a real-world experience but is not overbearing for students.

This book is excellent for introductory courses to research but is short enough to act as a refresher if necessary. In summary, the author has an excellent conceptual understanding of research and makes all concepts relate in terms easily understood. The author emphasizes the skills involved in proper research studies but is not overbearing on the reader. The book allows for easy access to research at a multitude of levels.

Reviewed by L. H., Faculty, Loyola Marymount University on 3/19/22

The book walks students new to research through the process of developing a research topic to finding and evaluating sources. It includes important digital literacy concepts such as “lateral research” and links to outside web sources that provide... read more

The book walks students new to research through the process of developing a research topic to finding and evaluating sources. It includes important digital literacy concepts such as “lateral research” and links to outside web sources that provide more context for such terminology. The book's strength is in its conciseness, practicality, and interactivity for the beginning researcher.

The information provided is accurate and provides a good foundation for future thinking about knowledge production.

The text is written with occasional references to the Kwantlen Polytechnic University’s (KPU) library and its Summon search tool, which can be distracting if the text is assigned at another institution. However, the information, process, and skills presented in the book are relevant to a wider audience and the distraction does not outweigh the textbook’s benefits.

The writing is lucid, concise, and accessible. Technical language is well explained and accessible to young researchers. Interactive activities are provided for visual, hands-on learners who benefit from seeing actual webpage and library database searches.

The text is internally consistent. Modules follow the same pattern and format.

The content is presented in four parts that are further divided into 34 sections total. Each of the sections are clearly labeled and can be used individually. Students will appreciate that the modules are focused and brief, with a total completion time of 15 minutes each.

The topics and subtopics are well organized and intuitive. Each of the four main sections begin with Key Takeaways and Learning Objectives. Short quizzes at the end of a section are useful and not overly burdensome to the reader. The interactive activities will be especially helpful to students who are new to research.

Interface rating: 3

Readers have the option to use the content box or arrows to navigate through the book. Arrows are more intuitive for me when working within a module, but this is one aspect of the online version that did not work for me. Navigational instructions are provided regularly, but the arrows themselves are placed outside of the frame of the text/page and on the bottom border. It takes me a minute to locate them each time I want to advance to the next page.

I did not see any grammatical errors.

I did not see content that was offensive.

While I found myself wanting a less value-free position on research, the book works very well as a primer on the practical steps for doing research. My students will benefit from it.

Reviewed by Dipti Mehta, Associate Librarian, Research and Instruction, Bridgewater State University on 6/25/21

The textbook is moderately comprehensive in that it provides a research process that both undergraduate and graduate students need to understand if they are to acquire information literacy skills for lifelong learning. The textbook creates... read more

The textbook is moderately comprehensive in that it provides a research process that both undergraduate and graduate students need to understand if they are to acquire information literacy skills for lifelong learning. The textbook creates awareness that research is a step by step approach rather than diving into searching databases with a topic in mind but no research process to go by with as covered in the text. Doing Research focuses on the ACRL information literacy standards which are integrated into the four modules and that helps students address an objective for their topic. The interactive addition of assignments for all modules creates a follow-up of an understanding of the learning objectives and research process.

Doing Research is a great research process textbook especially for students who are new to research and have no idea that library research is not a "thing", it really exists. This textbook represents the library research concepts and process accurately without bias. The content in this textbook helps students understand that research is not finding a topic and then writing a research paper, but it involves some accurate steps to achieving a good quality of research to write a paper or presentation purposes.

Each module has been developed around one of the core concepts of Association of College and Research Libraries (ACRL) Framework for Information Literacy for Higher Education, which was adopted by ACRL in 2016. Although the book has been written specifically for the Kwantlen Polytechnic University library, the 4 modules can be tweaked as per any academic library's resources. Most academic librarians address these modules in their instructions based on the assignment and nature of research. A relevant text book which puts a spin on traditional and modern way of doing research.

The textbook includes language that can easily be understood by college students. All the assignments, videos, images and tables presented in the modules are not too lengthy and therefore concisely and clearly stated. Students in the digital age have a difficult time with navigating, locating and evaluating academic library's' resource homepage, by breaking the guide up in different modules, there is more clarity when it comes to how to conduct research through a multi-step process. Though some of the images shared such as, "Comparing Google search terms" are blurry and not readable. When librarians are presenting information images need to be visually legible. The author should review and correct these images before publishing.

The content is at par with what any research or instruction librarian would deliver to college students. Some consistency is required in terms of formatting the text equitably with font and size to make it visually appealing as a textbook. There are a couple of blank pages within the textbook that could be removed. The terminology is consistent with the the title of the book and delivers the content on how to follow a research process systematically.

Doing Research presents the modules for the research process in order for students to effectively conduct and understand the research process to complete a particular assignment. These modules can be used individually depending on the need for the assignment . Also each module is not too long that a student get lost and deter from completing it.

Doing Research has definitely been organized as to how a research process ought to be conducted from a librarian's perspective. Research in itself is organization and the research process in this textbook gives an organization structure by way of the modules, to achieve a quality standard assignment or presentation if followed step by step. Every module has an ACRL Information literacy standard embedded with learning objectives that enforces students and faculty to understand the reason for teaching a certain module.

The "Comparing Google search terms" image and others are not clear but very blurry, so all images will have to have better resolutions. The textbook is available if four different formats: eBook, PDF, XML and Online. All hyperlinks for videos and activities work well and it is great that the author has included navigational instructions for all parts in the module.

There are no grammatical errors within the textbook.

The text pertains to all students and faculty of diverse backgrounds. Research is inclusive of all races, ethnicities and backgrounds. It may be done differently in various parts of the world and so having an open textbook on research makes it available culturally to all who can learn the right way of doing research.

I think that this open textbook is great resource for librarians, faculty and students in middle and high schools and of course colleges and universities. It does need to be formatted before finally being published. There is a lot of free white area with no text, images or anything. The interactive activities make the modules fun and appealing.

Reviewed by Carol Chester, adjunct English instructor, Worcester State University on 5/27/21

Doing Research is a concise yet comprehensive tutorial that will help undergraduate and graduate students choose a research topic, develop a research question, search for sources, and evaluate sources. read more

Doing Research is a concise yet comprehensive tutorial that will help undergraduate and graduate students choose a research topic, develop a research question, search for sources, and evaluate sources.

The tutorial, developed by Kwantlen Polytechnic University librarian Celia Binkerhoff, provides accurate, error-free, and unbiased information.

The tutorial, published in 2019, is relevant, applicable to all disciplines, and useful to students in the United States and Canada.

The tutorial is easy to understand, free of jargon and technical terms, and designed to be completed in a short period of time.

The tutorial consists of four modules that are set up the same way, beginning with Key Takeaway and Learning Objectives.

The tutorial has 34 lessons that are divided among four modules, a design that makes it easy to pick and choose which topics to explore.

The tutorial is very well organized and easy to navigate. A drop-down box provides a list of content, from which students can choose a topic to explore.

The tutorial consists of text, diagrams, tables, videos, and activities for the students to perform. The information is spaced far enough apart that students will not become confused or distracted.

While reading the text, I did not notice any grammatical errors.

While reading the text, I did not notice any material that might be offensive to a diverse student body.

Doing Research: A Student’s Guide to Finding & Using the Best Sources (2019) is a tutorial developed by Celia Binkerhoff, a librarian at Kwantlen Polytechnic University in Surrey, British Columbia, Canada, located in the metro Vancouver area near the U.S. border. The information in this tutorial is adapted from Choosing and Using Sources: A Guide to Academic Research, developed by Ohio State University Libraries.

The tutorial consists of four modules, each module designed to take approximately 15 minutes to complete. At the beginning of each module, students will find Key Takeaway--a statement about the main topic covered in the module—and Learning Objectives—a list of key concepts covered in the module.

The tutorial consists of text, tables, diagrams, videos, and activities to help students learn about choosing a research topic, developing a research question, locating sources, and evaluating sources.

Doing Research is a concise yet thorough guide that will help undergraduate and graduate students develop a strategy for planning and executing research projects.

Reviewed by Carol L. Chester, adjunct English instructor, Worcester State University, Worcester, Massachusetts, May 27, 2021.

Reviewed by Howard Pitler, Associate Professor, Emporia State University on 5/20/21

This book is designed to be a quick overview of the research process rather than a comprehensive dive into all aspects of qualitative and quantitative research. The book is presented in a series of 34 learning modules, each designed to take... read more

This book is designed to be a quick overview of the research process rather than a comprehensive dive into all aspects of qualitative and quantitative research. The book is presented in a series of 34 learning modules, each designed to take approximately 15 minutes, according to the author. Many of the modules have interactive activities, videos, or checks for understanding.

This is a useful guide for a student new to conducting library research. "Doing Research" accurately presents library research concepts to students and does so without any obvious bias.

This book is based on the Association of College and Research Libraries (ACRL) Framework for Information Literacy for Higher Education, which was adopted by ACRL in 2016. The short modules are designed in such a way that if the ACRL Framework is modified in the future, individual modules can be revised without changing the overall structure of the book. The book is written with the Kwantlen Polytechnic University library in mind. Therefore, some of the references may not be universally applicable but the information appears to be transferable. For example, a number of modules refer to the Summon search engine. While not all universities use that specific search engine, they should have a similar engine and the general query process would remain the same.

The book is written in a clear and accessible manner appropriate for entry-level college students. The videos that are embedded are also clear and easy to understand. The book is written for a Canadian university and uses Canadian spelling rather than American spelling, but that doesn't impact the content.

The book is very consistent in structure and terminology.

The book is designed to be worked through in a sequential manner, with each module building upon previous learning. That said, each module is also complete within itself and could be assigned as a review of a specific topic. For example, I might assign Module 12 if the topic we were studying was peer review.

The book is organized in a logical manner and guides the student through the research process from choosing a research topic and question through developing a search strategy and evaluating sources. The table of contents makes it easy to jump to a specific topic of interest. Each of the four parts of the book begins with a key takeaway and clear learning objectives.

The book is available as an eBook, PDF, online, and XML. I had no difficulty in navigating through the book using either Chrome or Firefox.

I found no major grammatical errors that detracted from the content.

The book did not contain any references that would be considered culturally offensive. There are very few references to different ethnicities. While the language is not always gender-neutral, I didn't find anything offensive.

I found the interactive activities to be both enjoyable and educational. They were presented in a non-threatening manner and provided immediate feedback and the opportunity to redo the activity. I may use parts or all of this book in one of my basic research classes.

Reviewed by Renee Bedard, Student Success Librarian, Community College of Aurora on 4/20/21

The text is very comprehensive of the elements of college-level assignment-based research. The content is clearly stated and organized in the expandable index, allowing for effective navigation to specific modules, and sections within modules.... read more

The text is very comprehensive of the elements of college-level assignment-based research. The content is clearly stated and organized in the expandable index, allowing for effective navigation to specific modules, and sections within modules. Each module includes explanations, examples, activities, and related concepts to provide a thorough introduction to research strategies. Some sections could be more developed; for example, section 15 includes explanations of how bias relates to creation of effective search terms, but does not provide examples or activities for readers to explore this concept.

The text provides accurate and error-free information on effective research strategies. Modules present current, useful approaches to information literacy that encourage and incorporate readers’ reflection on their own understandings and practices. Multiple sections refer readers to the “best” strategy or the “best” information. This designation fully incorporates the importance of context; for example, instead of stating that peer-reviewed sources are “best”, the Part 2: Recognize Types of Information module explores the types of situations in which you need or want information from peer-reviewed resources. Likewise, popular sources and social media or open web sources are shown to be useful in the context of specific information needs. Content is also specific to Library resource and database searching, providing accurate descriptions of search tools and strategies for reading and using these tools, such as filters and item records.

Content is relevant and up-to-date with current standards of information literacy. The text builds on the Framework for Information Literacy for Higher Education in a way that is relevant to the student audience and current strategies for searching with available tools and technology. Necessary updates will be relatively easy to implement due to the modular format of the text. Many of the examples are specific to the Library where the author works and their service region, but are still relevant to readers outside this area in how to approach and conduct assignment-based research. Example search topics are relevant to current issues as well as general research topics, and will likely remain relevant for some time.

Information about assignment-based academic research and the tasks involved is clearly and concisely presented. Useful examples and context are included to help make sense of different research concepts. Technical terminology is defined clearly and bolded or italicized for emphasis. The language is accessible to both beginner and experienced college-level researchers. Concepts are presented in multiple formats – tables, text, images, and video – which appeals to multiple learning styles without being overly repetitive. Activities are clearly aligned with the content. Some activities and figures could include more explanations or headers to support understanding of the concepts depicted. For example, source examples in the interactive table in section 8 don’t clarify the type of sources named which could confuse readers as to what they are examples of.

The text is extremely consistent in structure and terminology. Each section contains the same layout and flow of content while allowing for topic-specific differences in examples and activities. The language remains consistent as well, including definitions for new terms and an accessible style.

This text is modular by design, with 4 self-contained modules that provide key takeaways, learning outcomes, and topic-specific information. These modules could be easily assigned at different times throughout a course. While the modules build on each other, and make sense in the order the text presents them, individual modules could be used separate from the others to highlight that element of the research process for a particular class or assignment.

The organization of the text content is very clear, logical, and sequential. Information in later modules build on that from earlier ones and supports the progression of understanding the many elements of college-level assignment-based research. The flow between modules and sections is clear and seamless, with obvious transitions that allow for reflection. Key takeaways are presented at the beginning and end of modules to reinforce concepts learned.

The text is in four main formats: eBook, PDF, online, and XML. The text includes useful navigational instructions, such as how to interact with each of the activities, expected time to complete modules, what to click on, and how to return to the text after viewing an image or outside source. Videos and activities are embedded in the online version and all function well. Some figures are slightly distorted (Fig. 2.2 Types of Information Sources in section 7). Videos have closed captions in English.

I found one grammatical error, otherwise the text is error-free.

Content and examples in the text are not offensive or culturally insensitive. Some examples of search tools and topics are specific to Canada. Very few examples or content include identity information (gender, race, ethnicity) and when they do there is a variety of identities presented.

This text is a useful balance of content and practice. I enjoyed reading the text and engaging with the activities and can see it's many practical applications. I am interested in making an adaptation of this text, with slight edits for use at my institution in the Colorado, U.S.A.

Reviewed by Robin Ewing, Professor, St Cloud State University on 3/9/21, updated 6/1/21

Doing Research does not cover all aspects of research but it doesn't aspire to either. The creator is upfront that the modules are designed to take 15 minutes for students to complete. That goal is achieved. read more

Doing Research does not cover all aspects of research but it doesn't aspire to either. The creator is upfront that the modules are designed to take 15 minutes for students to complete. That goal is achieved.

Doing Research accurately presents library research concepts to students.

The modules are chunked in a way that will make changes easy to make.

Clarity rating: 3

I'd like to see more boxes with definitions for students. For example, a longer description of bias in Chapter 14.

I didn't notice any issues with consistency.

Doing Research is arranged in order by the steps in completing research for an assignment. However, each module can be assigned separately to students without completing earlier modules. For example, the first module on narrowing a topic and developing a research question might be skipped if topics are assigned to students. Students could then start with learning about the types of resources or how to develop a search strategy.

Doing Research walks students through finding research for course assignments in four modules in a logical order. Students first learn how to narrow a topic and develop a research question. Next, they learn about the different types of resources and when each type makes sense to use. The next module addresses how to search for resources. In the final module, students learn how to evaluate the information they find.

I'm not sure how accessible the images are that students are to analyze and respond to questions about.

I didn't notice any major grammatical errors.

Some inconsistency on using gender-neutral language.

In the introduction, the creator states this book is based on the Framework for Information Literacy for Higher Education but I didn't see the six frames clearly addressed. I'd suggest identifying which frames make the most sense for an introductory text to be emphasized. For example, the elements of Research as Inquiry could be integrated into the first module.

The activities in each chapter are good and it's a nice bonus that you can download the H5P files.

Reviewed by Kylie Quave, Assistant Professor, The George Washington University on 12/30/20

The author refers to this text as a “tutorial”; it is intended to introduce the student to library research methods. The text certainly does cover a broad spectrum of research methods topics. If students use the entire text, they will be taken... read more

The author refers to this text as a “tutorial”; it is intended to introduce the student to library research methods. The text certainly does cover a broad spectrum of research methods topics. If students use the entire text, they will be taken through modules showing them how to think critically about the larger process of refining a research topic, continuing to learn about it from different types of sources, and evaluating and curating the sources they find through library and other catalogues. Of course, there is much more to say on the subject of "doing research" than is said here but it is a succinct and accessible entry point for new researchers which lays out the most urgent and timely considerations of process and critical thought in research.

This is a useful and accurate introductory guide to asking the right kinds of questions in the research process. I did not find troubling biases; rather, the text is oriented to guiding students through how to recognize and mitigate their own biases in the research process. (There are some errors, but these are not in content; they are rather in formatting and numbering, as explained below.)

Relevance/Longevity rating: 4

This tutorial was developed followed the Association of College and Research Libraries’ (ACRL) Framework for Information Literacy for Higher Education, which was adopted by ACRL in 2016. The Framework represents recent work on ethical and responsible approaches to library-based research and, more broadly, information literacy. Throughout, there is an emphasis on evaluating sources and checking for bias in different ways and this is much needed for students across the board.

Though written specifically for students at KPU, this text could easily be used in a variety of higher ed writing and research classrooms. There are some references to specific KPU resources, but the instructor can replace these with their own institutional links and examples, such as a link to library reference books. It would be straightforward to implement changes and made additions to the text in its PDF form.

This text is clear, accessible, and interesting to read. The examples and activities clarify any doubts students may have in how to apply concepts to their individual research processes.

The author's technique of beginning each part of the text with a guide to the purpose fosters clarity for students in terms of what they should acquire in each section. And there is a friendly and inviting tone to the writing that makes it more accessible and less intimidating for students embarking on research projects. At the end of each section, summarizing language and activities solidify the clarity and accessibility of each of the four parts that make up the text.

Consistency rating: 2

There is some inconsistency in use of the term “module” and some errors in numbering sections. Instructors will want to ensure they are assigning numbered sections by checking both the table of contents and the text itself (and by differentiating how the online and PDF versions are numbered, which do not match). There are also minor errors in numbering of lists within the text.

The text is broken into four “Parts” that are organized by core concepts and practices (with learning objectives). Within each part, there are modules, which contain text, activities, and quizzes. Instructors can easily excerpt specific modules and assign them by URL or by excerpts from the PDF version of the text. Each module stands alone well.

There is an easily navigated, hyperlinked table of contents with headings broken down in digestible pieces. These headings reflect common questions or concepts that a student would want to consult, such as “Understanding Peer Review” and “But is it Relevant?”. Each of the four parts is organized progressively, though they are also easy to separate if an instructor wanted to use fewer than four. They can also be subdivided into their component Parts: each Part contains three to nine subheaded modules with activities, for a total of 26 modules.

Each Part begins with “Key Takeaways”, “Learning Objectives,” and a box on “Navigation: How to move around this tutorial.” At the end of each Part, a review quiz is helpful for ensuring students acquired to key takeaways from that section.

The text can be read as a PDF or as a web-based text. In case of using the former, there are hyperlinks that take the reader into the web-based interactive sections, including quizzes, videos (some made originally by the author), and other activities. The text is purportedly not compatible with Internet Explorer, but can be used with Chrome or Firefox. The quizzes helpfully offer an answer key in real time and can be taken multiple times. Quiz answers are checked and accompanied by an explanation of correct and incorrect responses.

There is integration with Moodle, but it appears to still be under development and I do not have a Moodle account to be able to try it out. Moodle will be the location for completion certificates and some activities, according to the text at the time of this review.

Within each Part, there are indications of how long each Part should take to complete, but confusingly these are referred to as “modules” again. Some of the hyperlinks to online activities are incorrect in the PDF version, but hopefully the author will update soon.

I did not find major or distracting grammatical errors.

There are a variety of examples of research themes and sources throughout the book that include multiple disciplines and social problems and some differences in cultural or national backgrounds. There is also use of the feminine gender when referencing authors in some places. There are minimal implicit references to different ethnicities in examples and cases.

The online activities are engaging and fun. They are challenging yet straightforward and students can assess their reading and concept comprehension through them. Overall, this is a fantastic introduction that can be adapted easily!

Table of Contents

• Introduction
• How to Use This Tutorial
• Acknowledgements
• I. Part 1. Get Started on Your Research
• II. Part 2. Recognize Types of Information
• III. Part 3. Develop your Search Strategy
• IV. Part 4. Evaluate your Sources

Ancillary Material

About the book.

A modules-based approach to learning research skills that emphasizes the reflective nature of information discovery, the contextual basis for evaluating that information, and a recognition that information has value.

About the Contributors

Celia Brinkerhoff , Kwantlen Polytechnic University

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College Info Geek

How to Do Research in 7 Simple Steps

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It’s 2 am, and you’re on your fifth cup of coffee (or was it your sixth?). You’re crouched at a table in some dark corner of the library surrounded by fifteen open books. Equally as many tabs are open on your laptop, and you still haven’t written a word of the paper that’s due in 7 hours.

Many things can explain how you got to this point, including procrastination , poor organization , and a messy schedule .

Very often, however, the problem is a lack of research skills .

And it’s not your fault. High school does a poor job of teaching you how to do research, and most college classes do little better. It feels like you’re expected to figure it out through trial and error.

I think we can do better than that, however. In this guide, I’m going to show you the 7-step process for researching everything from a 10-page term paper to a final presentation. Not only will you learn how to do better research; you’ll also learn how to research more efficiently.

What Is Research?

Before we go any further, what  is  research?

At its core, research is an attempt to answer a question. This could be anything from “How can we reduce infant mortality rates?” to “Why does salt make food taste good?”

To answer your question, you consult books, academic papers, newspaper articles, historical records, or anything else that could be helpful. The broad term for these things is “sources.”

And, usually, once you’ve done the research, you present or summarize it in some way. In many cases, this means writing an essay or another type of scholarly paper, but it could also mean giving a presentation or even creating a YouTube video.

Even if you have no interest in academia, research is an extremely useful skill to learn. When you know how to do research, it’s much easier to improve your life and work more effectively . Instead of having to ask someone every time you have a question, research will help you solve problems yourself (and help others in turn).

Note:  Research can also mean conducting surveys, performing experiments, or going on archaeological digs. While these activities are crucial for advancing human knowledge, I won’t be discussing them here. This article focuses on the research you can do with only a library and an internet connection.

The 7 Steps of the Research Process

Research can feel overwhelming, but it’s more manageable when you break it down into steps. In my experience, the research process has seven main steps:

• Find a topic
• Refine your topic
• Find key sources
• Take notes on your sources
• Create your paper or presentation
• Do additional research as necessary
• Cite your sources

Let’s look at each of these steps in more detail.

1. Find a Topic

If you don’t have a topic, your research will be undirected and inefficient. You’ll spend hours reading dozens of sources, all because you didn’t take a few minutes to develop a topic.

How do you come up with a topic? My number one suggestion is to create a mind map.

A mind map is a visual way to generate ideas. Here’s how it works:

• Get a piece of paper and a pen. Make sure the paper isn’t too small — you want lots of room for your ideas.
• Draw an oval in the center of the paper.
• Inside that oval, write a super vague topic. Start with whatever your professor has assigned you.
• Draw lines from the oval towards the edges of the paper.
• Draw smaller ovals connected to each of these lines.
• Inside the smaller ovals, write more specific ideas/topics related to the central one.
• Repeat until you’ve found 3-5 topic ideas.

When I write it out step by step, it sounds kind of strange. But trust me, it works . Anytime I’m stuck on a writing assignment, this method is my go-to. It’s basically magic.

To see what mind mapping looks like in practice, check out this clip:

Want to create a digital mind map like the one Thomas uses in the video? Check out Coggle .

2. Refine Your Topic

Okay, so now you have a list of 3-5 topics. They’re all still pretty general, and you need to narrow them down to one topic that you can research in depth.

To do this, spend 15 minutes doing some general research on each topic. Specifically, take each topic and plug it into your library’s catalog and database search tools.

The details of this process will vary from library to library. This is where consulting a librarian can be super helpful. They can show you how to use the tools I mentioned, as well as point you to some you probably don’t know about.

Furthermore, I suggest you ask your professor for recommendations. In some cases, they may even have created a resource page specifically for your assignment.

Once you’ve found out where to search, type in your topic. I like to use a mixture of the library catalog, a general academic database like EBSCO Host , and a search on Google Scholar .

What exactly are you trying to find? Basically, you’re trying to find a topic with a sufficient quantity and variety of sources.

Ideally, you want something with both journal articles and books, as this demonstrates that lots of scholars are seriously engaging with the topic.

Of course, in some cases (if the topic is very cutting edge, for example), you may be only able to find journal articles. That’s fine, so long as there are enough perspectives available.

Using this technique, you’ll be able to quickly eliminate some topics. Be ruthless. If you’re not finding anything after 15 minutes, move on. And don’t get attached to a topic.

Tip: If you find two topics with equal numbers of sources available, ask your professor to help you break the tie. They can give you insight into which topic is super common (and thus difficult to write about originally), as well as which they find more interesting.

Now that you have your topic, it’s time to narrow down your sources.

3. Find Key Sources

If you’ve picked a good topic, then you probably have lots of sources to work with. This is both a blessing and a curse. A variety of sources shows that there’s something worth saying about your topic, and it also gives you plenty of material to cite.

But this abundance can quickly turn into a nightmare in which you spend hours reading dense, mind-numbing material without getting any closer to actually producing a paper.

How do you keep this from happening? Choose 3–5  key sources and focus on them intently. Sure, you may end up needing more sources, especially if this is a long paper or if the professor requires it. But if you start out trying to read 15 sources, you’re likely to get overwhelmed and frustrated.

Focusing on a few key sources is powerful because it:

• Lets you engage deeply with each source.
• Gives you a variety of perspectives.
• Points you to further resources.
• Keeps you focused.

4. Read and Take Notes

But what do you do with these sources, exactly? You need to read them the right way . Follow these steps to effectively read academic books and articles:

Go through the article and look at the section headings. If any words or terms jump out at you, make note of them. Also, glance at the beginning sentences of each section and paragraph to get an overall idea of the author’s argument.

The goal here isn’t to comprehend deeply, but to prime your mind for effective reading .

Write down any questions you have after skimming the article, as well as any general questions you hope the article can answer. Always keep your topic in mind.

Read Actively

Now, start reading. But don’t just passively go through the information like you’re scrolling through Tumblr. Read with a pen or pencil in hand , underlining any unfamiliar terms or interesting ideas.

Make notes in the margins about other sources or concepts that come to mind. If you’re reading a library book, you can make notes on a separate piece of paper.

Once you’ve finished reading, take a short break. Have a cup of tea or coffee. Go for a walk around the library. Stretch. Just get your mind away from the research for a moment without resorting to distracting, low-density fun .

Now come back to the article and look at the things you underlined or noted. Gather these notes and transfer them to a program like Evernote .

If you need to look up a term, do that, and then add that definition to your notes. Also, make note of any sources the author cites that look helpful.

But what if I’m reading a book?   Won’t this take forever?  No, because you’re not going to read the entire book.

For most research you’ll do in college, reading a whole academic book is overkill . Just skim the table of contents and the book itself to find chapters or sections that look relevant.

Then, read each of those in the same way you would read an article. Also, be sure to glance at the book’s bibliography, which is a goldmine for finding additional sources.

Note: The above method is a variation on the classic SQ3R method , adapted slightly since we’re not interested in taking notes from textbooks .

5. Create Your Paper or Presentation

“You can’t turn in raw research.”

Research is crucial to crafting a great paper or presentation, but it’s also a great way to procrastinate. I had classmates in college who would spend 8 hours researching a 5-page paper. That’s way too much!

At some point, you need to stop researching and start writing (or whatever method you’re using to present your research).

How do you decide when to stop researching? There’s no strict rule, but in general I wouldn’t spend more than 30 minutes per page of the final paper.

So if the final paper is supposed to be 10 pages, don’t spend more than 5 hours researching it.

6. Do Additional Research (As Necessary)

Once you’ve started writing the draft of your paper, you’ll probably find a few gaps. Maybe you realize that one scholar’s argument isn’t relevant to your paper, or that you need more information for a particular section. In this case, you are free to return to researching as necessary.

But again, beware the trap of procrastination masquerading as productivity! Only do as much additional research as you need to answer your question. Don’t get pulled into rabbit holes or dragged off on tangents. Get in there, do your research, and get back to writing .

To keep yourself focused, I suggest keeping a separate document or piece of paper nearby to note points that need additional research.

Every time you encounter such a point, make note of it in the document and then keep writing. Only stop when you can’t get any further without additional research.

It’s much better to get a full draft done first. Otherwise, you risk suffering a cognitive switching penalty , making it harder to regain your focus.

7. Cite Your Sources

Whether you’re creating an oral presentation, essay, or video, you’ll need to cite your sources. Plagiarism is a serious offense, so don’t take any chances.

How to cite your sources depends on the subject and the professor’s expectations. Chicago, MLA, and APA are the most common citation formats to use in college, but there are thousands more.

Luckily, you don’t need to painstakingly type each of your citations by hand or slog through a style manual. Instead, you can use a tool like Zotero to track and generate your citations. To make things even easier, install the Zotero Connector browser extension. It can automatically pull citation information from entries in an online library catalog.

Once you’ve collected all of your sources, Zotero can generate a properly formatted works cited page or bibliography at just the click of a button.

For help setting up and using Zotero, read this guide . If you need further assistance, ask a librarian.

Go Research With Confidence

I hope you now understand how to do research with more confidence. If you follow the procedures I’ve covered in this article, you’ll waste less time, perform more effective research, and ultimately have the material for a winning essay.

Curious about how to use your research to write a great research paper? Check out this guide .

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How to Do Research

Last Updated: March 13, 2023 References

This article was co-authored by Matthew Snipp, PhD and by wikiHow staff writer, Jennifer Mueller, JD . C. Matthew Snipp is the Burnet C. and Mildred Finley Wohlford Professor of Humanities and Sciences in the Department of Sociology at Stanford University. He is also the Director for the Institute for Research in the Social Science’s Secure Data Center. He has been a Research Fellow at the U.S. Bureau of the Census and a Fellow at the Center for Advanced Study in the Behavioral Sciences. He has published 3 books and over 70 articles and book chapters on demography, economic development, poverty and unemployment. He is also currently serving on the National Institute of Child Health and Development’s Population Science Subcommittee. He holds a Ph.D. in Sociology from the University of Wisconsin—Madison. This article has been viewed 225,708 times.

The idea of doing research may seem daunting, but as long as you keep yourself organized and focus on the question you want to answer, you'll be fine. If you're curious and interested in the topic, you might even find it fun! We here at wikiHow have gathered answers to all your most common questions about how to do research, from finding a good topic to identifying the best sources and writing your final paper.

How do I find a topic to research?

• For example, if you're researching in the political science field, you might be interested in determining what leads people to believe that the 2020 US presidential election was illegitimate.

How do I get started on my research?

• For example, if you're researching the 2020 election, you might find that "absentee ballots" and "voting by mail" come up frequently. Those are issues you could look into further to figure out how they impacted the final election results.
• You don't necessarily have to use the overview articles you look at as resources in your actual paper. Even Wikipedia articles can be a good way to learn more about a topic and you can check the references for more reputable sources that might work for your paper.

What's the best way to keep track of my sources?

• Research papers typically discuss 2 or 3 separate things that work together to answer the research question. You might also want to make a note on the front of which thing that source relates to. That'll make it easier for you to organize your sources later.
• For example, if you're researching the 2020 election, you might have a section of your paper discussing voting by mail. For the sources that directly address that issue, write "voting by mail" in the corner.

What kind of notes should I be taking as I research?

• If you find something that you think would make a good quote, copy it out exactly with quote marks around it, then add the page number where it appears so you can correctly cite it in your paper without having to go back and hunt for it again.

How do I evaluate the quality of a source?

• Does the article discuss or reference another article? (If so, use that article instead.)
• What expertise or authority does the author have?
• When was the material written? (Is it the most up-to-date reference you could use?)
• Why was the article published? (Is it trying to sell you something or persuade you to adopt a certain viewpoint?)
• Are the research methods used consistent and reliable? (Appropriate research methods depend on what was studied.)

What if I'm having a hard time finding good sources?

• For example, if you're writing about the 2020 election, you might find tons of stories online, but very little that is reputable enough for you to use in your paper. Because the election happened so recently, it might be too soon for there to be a lot of solid academic research on it. Instead, you might focus on the 2016 election.
• You can also ask for help. Your instructor might be able to point you toward good sources. Research librarians are also happy to help you.

How do I organize my research for my paper?

• For example, if you're researching the effect of the COVID-19 pandemic on the 2020 election, you might have sections on social distancing and cleaning at in-person voting locations, the accessibility of mail-in ballots, and early voting.

What's the best way to start writing my paper?

• Include an in-text citation for everything that needs one, even in your initial rough draft. That'll help you make sure that you don't inadvertently misattribute or fail to cite something as you work your way through substantive drafts.
• Write your introduction and conclusion only after you're satisfied that the body of your paper is essentially what you want to turn in. Then, you can polish everything up for the final draft.

How can I make sure I'm not plagiarizing?

• If you have any doubt over whether you should cite something, go ahead and do it. You're better off to err on the side of over-citing than to look like you're taking credit for an idea that isn't yours.
• ↑ https://www.nhcc.edu/student-resources/library/doinglibraryresearch/basic-steps-in-the-research-process
• ↑ Matthew Snipp, PhD. Sociology Professor, Stanford University. Expert Interview. 26 March 2020.
• ↑ https://library.taylor.edu/eng-212/research-paper
• ↑ http://www.butte.edu/departments/cas/tipsheets/research/research_paper.html
• ↑ https://www.potsdam.edu/sites/default/files/documents/support/tutoring/cwc/6-Simple-Steps-for-Writing-a-Research-Paper.pdf
• ↑ https://www.umgc.edu/current-students/learning-resources/writing-center/online-guide-to-writing/tutorial/chapter4/ch4-05.html

Expert Q&A

You might also like.

About This Article

If you need to do research on a particular topic, start by searching the internet for any information you can find on the subject. In particular, look for sites that are sourced by universities, scientists, academic journals, and government agencies. Next, visit your local library and use the electric card catalog to research which books, magazines, and journals will have information on your topic. Take notes as you read, and write down all of the information you’ll need to cite your sources in your final project. To learn how interviewing a first-hand source can help you during your research, read on! Did this summary help you? Yes No

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15 Steps to Good Research

• Define and articulate a research question (formulate a research hypothesis). How to Write a Thesis Statement (Indiana University)
• Identify possible sources of information in many types and formats. Georgetown University Library's Research & Course Guides
• Judge the scope of the project.
• Reevaluate the research question based on the nature and extent of information available and the parameters of the research project.
• Select the most appropriate investigative methods (surveys, interviews, experiments) and research tools (periodical indexes, databases, websites).
• Plan the research project. Writing Anxiety (UNC-Chapel Hill) Strategies for Academic Writing (SUNY Empire State College)
• Retrieve information using a variety of methods (draw on a repertoire of skills).
• Refine the search strategy as necessary.
• Write and organize useful notes and keep track of sources. Taking Notes from Research Reading (University of Toronto) Use a citation manager: Zotero or Refworks
• Evaluate sources using appropriate criteria. Evaluating Internet Sources
• Synthesize, analyze and integrate information sources and prior knowledge. Georgetown University Writing Center
• Revise hypothesis as necessary.
• Use information effectively for a specific purpose.
• Understand such issues as plagiarism, ownership of information (implications of copyright to some extent), and costs of information. Georgetown University Honor Council Copyright Basics (Purdue University) How to Recognize Plagiarism: Tutorials and Tests from Indiana University
• Cite properly and give credit for sources of ideas. MLA Bibliographic Form (7th edition, 2009) MLA Bibliographic Form (8th edition, 2016) Turabian Bibliographic Form: Footnote/Endnote Turabian Bibliographic Form: Parenthetical Reference Use a citation manager: Zotero or Refworks

Adapted from the Association of Colleges and Research Libraries "Objectives for Information Literacy Instruction" , which are more complete and include outcomes. See also the broader "Information Literacy Competency Standards for Higher Education."

How to Write a Research Paper as a High School Student

By Carly Taylor

Senior at Stanford University

6 minute read

Read our guide to learn why you should write a research paper and how to do so, from choosing the right topic to outlining and structuring your argument.

What is a research paper?

A research paper poses an answer to a specific question and defends that answer using academic sources, data, and critical reasoning. Writing a research paper is an excellent way to hone your focus during a research project , synthesize what you’re learning, and explain why your work matters to a broader audience of scholars in your field.

The types of sources and evidence you’ll see used in a research paper can vary widely based on its field of study. A history research paper might examine primary sources like journals and newspaper articles to draw conclusions about the culture of a specific time and place, whereas a biology research paper might analyze data from different published experiments and use textbook explanations of cellular pathways to identify a potential marker for breast cancer.

However, researchers across disciplines must identify and analyze credible sources, formulate a specific research question, generate a clear thesis statement, and organize their ideas in a cohesive manner to support their argument. Read on to learn how this process works and how to get started writing your own research paper.

Choosing your topic

Tap into your passions.

A research paper is your chance to explore what genuinely interests you and combine ideas in novel ways. So don’t choose a subject that simply sounds impressive or blindly follow what someone else wants you to do – choose something you’re really passionate about! You should be able to enjoy reading for hours and hours about your topic and feel enthusiastic about synthesizing and sharing what you learn.

We've created these helpful resources to inspire you to think about your own passion project . Polygence also offers a passion exploration experience where you can dive deep into three potential areas of study with expert mentors from those fields.

Ask a difficult question

In the traditional classroom, top students are expected to always know the answers to the questions the teacher asks. But a research paper is YOUR chance to pose a big question that no one has answered yet, and figure out how to make a contribution to answering that question. So don’t be afraid if you have no idea how to answer your question at the start of the research process — this will help you maintain a motivational sense of discovery as you dive deeper into your research. If you need inspiration, explore our database of research project ideas .

Be as specific as possible

It’s essential to be reasonable about what you can accomplish in one paper and narrow your focus down to an issue you can thoroughly address. For example, if you’re interested in the effects of invasive species on ecosystems, it’s best to focus on one invasive species and one ecosystem, such as iguanas in South Florida , or one survival mechanism, such as supercolonies in invasive ant species . If you can, get hands on with your project.

You should approach your paper with the mindset of becoming an expert in this topic. Narrowing your focus will help you achieve this goal without getting lost in the weeds and overwhelming yourself.

Would you like to write your own research paper?

Polygence mentors can help you every step of the way in writing and showcasing your research paper

Preparing to write

Conduct preliminary research.

Before you dive into writing your research paper, conduct a literature review to see what’s already known about your topic. This can help you find your niche within the existing body of research and formulate your question. For example, Polygence student Jasmita found that researchers had studied the effects of background music on student test performance, but they had not taken into account the effect of a student’s familiarity with the music being played, so she decided to pose this new question in her research paper.

Pro tip: It’s a good idea to skim articles in order to decide whether they’re relevant enough to your research interest before committing to reading them in full. This can help you spend as much time as possible with the sources you’ll actually cite in your paper.

Skimming articles will help you gain a broad-strokes view of the different pockets of existing knowledge in your field and identify the most potentially useful sources. Reading articles in full will allow you to accumulate specific evidence related to your research question and begin to formulate an answer to it.

Draft a thesis statement

Your thesis statement is your succinctly-stated answer to the question you’re posing, which you’ll make your case for in the body of the paper. For example, if you’re studying the effect of K-pop on eating disorders and body image in teenagers of different races, your thesis may be that Asian teenagers who are exposed to K-pop videos experience more negative effects on their body image than Caucasian teenagers.

Pro Tip: It’s okay to refine your thesis as you continue to learn more throughout your research and writing process! A preliminary thesis will help you come up with a structure for presenting your argument, but you should absolutely change your thesis if new information you uncover changes your perspective or adds nuance to it.

Create an outline

An outline is a tool for sketching out the structure of your paper by organizing your points broadly into subheadings and more finely into individual paragraphs. Try putting your thesis at the top of your outline, then brainstorm all the points you need to convey in order to support your thesis.

Pro Tip : Your outline is just a jumping-off point – it will evolve as you gain greater clarity on your argument through your writing and continued research. Sometimes, it takes several iterations of outlining, then writing, then re-outlining, then rewriting in order to find the best structure for your paper.

Writing your paper

Introduction.

Your introduction should move the reader from your broad area of interest into your specific area of focus for the paper. It generally takes the form of one to two paragraphs that build to your thesis statement and give the reader an idea of the broad argumentative structure of your paper. After reading your introduction, your reader should know what claim you’re going to present and what kinds of evidence you’ll analyze to support it.

Topic sentences

Writing crystal clear topic sentences is a crucial aspect of a successful research paper. A topic sentence is like the thesis statement of a particular paragraph – it should clearly state the point that the paragraph will make. Writing focused topic sentences will help you remain focused while writing your paragraphs and will ensure that the reader can clearly grasp the function of each paragraph in the paper’s overall structure.

Transitions

Sophisticated research papers move beyond tacking on simple transitional phrases such as “Secondly” or “Moreover” to the start of each new paragraph. Instead, each paragraph flows naturally into the next one, with the connection between each idea made very clear. Try using specifically-crafted transitional phrases rather than stock phrases to move from one point to the next that will make your paper as cohesive as possible.

In her research paper on Pakistani youth in the U.S. , Polygence student Iba used the following specifically-crafted transition to move between two paragraphs: “Although the struggles of digital ethnography limited some data collection, there are also many advantages of digital data collection.” This sentence provides the logical link between the discussion of the limitations of digital ethnography from the prior paragraph and the upcoming discussion of this techniques’ advantages in this paragraph.

Your conclusion can have several functions:

To drive home your thesis and summarize your argument

To emphasize the broader significance of your findings and answer the “so what” question

To point out some questions raised by your thesis and/or opportunities for further research

Your conclusion can take on all three of these tasks or just one, depending on what you feel your paper is still lacking up to this point.

Citing sources

Last but not least, giving credit to your sources is extremely important. There are many different citation formats such as MLA, APA, and Chicago style. Make sure you know which one is standard in your field of interest by researching online or consulting an expert.

You have several options for keeping track of your bibliography:

Use a notebook to record the relevant information from each of your sources: title, author, date of publication, journal name, page numbers, etc.

Create a folder on your computer where you can store your electronic sources

Use an online bibliography creator such as Zotero, Easybib, or Noodletools to track sources and generate citations

You can read research papers by Polygence students under our Projects tab. You can also explore other opportunities for high school research .

If you’re interested in finding an expert mentor to guide you through the process of writing your own independent research paper, consider applying to be a Polygence scholar today!

Your research paper help even you to earn college credit , get published in an academic journal , contribute to your application for college , improve your college admissions chances !

Feeling Inspired?

Interested in doing an exciting research project? Click below to get matched with one of our expert mentors!

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

13.1 Formatting a Research Paper

Learning objectives.

• Identify the major components of a research paper written using American Psychological Association (APA) style.
• Apply general APA style and formatting conventions in a research paper.

In this chapter, you will learn how to use APA style , the documentation and formatting style followed by the American Psychological Association, as well as MLA style , from the Modern Language Association. There are a few major formatting styles used in academic texts, including AMA, Chicago, and Turabian:

• AMA (American Medical Association) for medicine, health, and biological sciences
• APA (American Psychological Association) for education, psychology, and the social sciences
• Chicago—a common style used in everyday publications like magazines, newspapers, and books
• MLA (Modern Language Association) for English, literature, arts, and humanities
• Turabian—another common style designed for its universal application across all subjects and disciplines

While all the formatting and citation styles have their own use and applications, in this chapter we focus our attention on the two styles you are most likely to use in your academic studies: APA and MLA.

If you find that the rules of proper source documentation are difficult to keep straight, you are not alone. Writing a good research paper is, in and of itself, a major intellectual challenge. Having to follow detailed citation and formatting guidelines as well may seem like just one more task to add to an already-too-long list of requirements.

Following these guidelines, however, serves several important purposes. First, it signals to your readers that your paper should be taken seriously as a student’s contribution to a given academic or professional field; it is the literary equivalent of wearing a tailored suit to a job interview. Second, it shows that you respect other people’s work enough to give them proper credit for it. Finally, it helps your reader find additional materials if he or she wishes to learn more about your topic.

Furthermore, producing a letter-perfect APA-style paper need not be burdensome. Yes, it requires careful attention to detail. However, you can simplify the process if you keep these broad guidelines in mind:

• Work ahead whenever you can. Chapter 11 “Writing from Research: What Will I Learn?” includes tips for keeping track of your sources early in the research process, which will save time later on.
• Get it right the first time. Apply APA guidelines as you write, so you will not have much to correct during the editing stage. Again, putting in a little extra time early on can save time later.
• Use the resources available to you. In addition to the guidelines provided in this chapter, you may wish to consult the APA website at http://www.apa.org or the Purdue University Online Writing lab at http://owl.english.purdue.edu , which regularly updates its online style guidelines.

General Formatting Guidelines

This chapter provides detailed guidelines for using the citation and formatting conventions developed by the American Psychological Association, or APA. Writers in disciplines as diverse as astrophysics, biology, psychology, and education follow APA style. The major components of a paper written in APA style are listed in the following box.

These are the major components of an APA-style paper:

Body, which includes the following:

• Headings and, if necessary, subheadings to organize the content
• In-text citations of research sources
• References page

All these components must be saved in one document, not as separate documents.

The title page of your paper includes the following information:

• Title of the paper
• Author’s name
• Name of the institution with which the author is affiliated
• Header at the top of the page with the paper title (in capital letters) and the page number (If the title is lengthy, you may use a shortened form of it in the header.)

List the first three elements in the order given in the previous list, centered about one third of the way down from the top of the page. Use the headers and footers tool of your word-processing program to add the header, with the title text at the left and the page number in the upper-right corner. Your title page should look like the following example.

The next page of your paper provides an abstract , or brief summary of your findings. An abstract does not need to be provided in every paper, but an abstract should be used in papers that include a hypothesis. A good abstract is concise—about one hundred fifty to two hundred fifty words—and is written in an objective, impersonal style. Your writing voice will not be as apparent here as in the body of your paper. When writing the abstract, take a just-the-facts approach, and summarize your research question and your findings in a few sentences.

In Chapter 12 “Writing a Research Paper” , you read a paper written by a student named Jorge, who researched the effectiveness of low-carbohydrate diets. Read Jorge’s abstract. Note how it sums up the major ideas in his paper without going into excessive detail.

Write an abstract summarizing your paper. Briefly introduce the topic, state your findings, and sum up what conclusions you can draw from your research. Use the word count feature of your word-processing program to make sure your abstract does not exceed one hundred fifty words.

Depending on your field of study, you may sometimes write research papers that present extensive primary research, such as your own experiment or survey. In your abstract, summarize your research question and your findings, and briefly indicate how your study relates to prior research in the field.

Margins, Pagination, and Headings

APA style requirements also address specific formatting concerns, such as margins, pagination, and heading styles, within the body of the paper. Review the following APA guidelines.

Use these general guidelines to format the paper:

• Set the top, bottom, and side margins of your paper at 1 inch.
• Use double-spaced text throughout your paper.
• Use a standard font, such as Times New Roman or Arial, in a legible size (10- to 12-point).
• Use continuous pagination throughout the paper, including the title page and the references section. Page numbers appear flush right within your header.
• Section headings and subsection headings within the body of your paper use different types of formatting depending on the level of information you are presenting. Additional details from Jorge’s paper are provided.

Begin formatting the final draft of your paper according to APA guidelines. You may work with an existing document or set up a new document if you choose. Include the following:

• Your title page
• The abstract you created in Note 13.8 “Exercise 1”
• Correct headers and page numbers for your title page and abstract

APA style uses section headings to organize information, making it easy for the reader to follow the writer’s train of thought and to know immediately what major topics are covered. Depending on the length and complexity of the paper, its major sections may also be divided into subsections, sub-subsections, and so on. These smaller sections, in turn, use different heading styles to indicate different levels of information. In essence, you are using headings to create a hierarchy of information.

The following heading styles used in APA formatting are listed in order of greatest to least importance:

• Section headings use centered, boldface type. Headings use title case, with important words in the heading capitalized.
• Subsection headings use left-aligned, boldface type. Headings use title case.
• The third level uses left-aligned, indented, boldface type. Headings use a capital letter only for the first word, and they end in a period.
• The fourth level follows the same style used for the previous level, but the headings are boldfaced and italicized.
• The fifth level follows the same style used for the previous level, but the headings are italicized and not boldfaced.

Visually, the hierarchy of information is organized as indicated in Table 13.1 “Section Headings” .

Table 13.1 Section Headings

A college research paper may not use all the heading levels shown in Table 13.1 “Section Headings” , but you are likely to encounter them in academic journal articles that use APA style. For a brief paper, you may find that level 1 headings suffice. Longer or more complex papers may need level 2 headings or other lower-level headings to organize information clearly. Use your outline to craft your major section headings and determine whether any subtopics are substantial enough to require additional levels of headings.

Working with the document you developed in Note 13.11 “Exercise 2” , begin setting up the heading structure of the final draft of your research paper according to APA guidelines. Include your title and at least two to three major section headings, and follow the formatting guidelines provided above. If your major sections should be broken into subsections, add those headings as well. Use your outline to help you.

Because Jorge used only level 1 headings, his Exercise 3 would look like the following:

Citation Guidelines

In-text citations.

Throughout the body of your paper, include a citation whenever you quote or paraphrase material from your research sources. As you learned in Chapter 11 “Writing from Research: What Will I Learn?” , the purpose of citations is twofold: to give credit to others for their ideas and to allow your reader to follow up and learn more about the topic if desired. Your in-text citations provide basic information about your source; each source you cite will have a longer entry in the references section that provides more detailed information.

In-text citations must provide the name of the author or authors and the year the source was published. (When a given source does not list an individual author, you may provide the source title or the name of the organization that published the material instead.) When directly quoting a source, it is also required that you include the page number where the quote appears in your citation.

This information may be included within the sentence or in a parenthetical reference at the end of the sentence, as in these examples.

Epstein (2010) points out that “junk food cannot be considered addictive in the same way that we think of psychoactive drugs as addictive” (p. 137).

Here, the writer names the source author when introducing the quote and provides the publication date in parentheses after the author’s name. The page number appears in parentheses after the closing quotation marks and before the period that ends the sentence.

Addiction researchers caution that “junk food cannot be considered addictive in the same way that we think of psychoactive drugs as addictive” (Epstein, 2010, p. 137).

Here, the writer provides a parenthetical citation at the end of the sentence that includes the author’s name, the year of publication, and the page number separated by commas. Again, the parenthetical citation is placed after the closing quotation marks and before the period at the end of the sentence.

As noted in the book Junk Food, Junk Science (Epstein, 2010, p. 137), “junk food cannot be considered addictive in the same way that we think of psychoactive drugs as addictive.”

Here, the writer chose to mention the source title in the sentence (an optional piece of information to include) and followed the title with a parenthetical citation. Note that the parenthetical citation is placed before the comma that signals the end of the introductory phrase.

David Epstein’s book Junk Food, Junk Science (2010) pointed out that “junk food cannot be considered addictive in the same way that we think of psychoactive drugs as addictive” (p. 137).

Another variation is to introduce the author and the source title in your sentence and include the publication date and page number in parentheses within the sentence or at the end of the sentence. As long as you have included the essential information, you can choose the option that works best for that particular sentence and source.

Citing a book with a single author is usually a straightforward task. Of course, your research may require that you cite many other types of sources, such as books or articles with more than one author or sources with no individual author listed. You may also need to cite sources available in both print and online and nonprint sources, such as websites and personal interviews. Chapter 13 “APA and MLA Documentation and Formatting” , Section 13.2 “Citing and Referencing Techniques” and Section 13.3 “Creating a References Section” provide extensive guidelines for citing a variety of source types.

Writing at Work

APA is just one of several different styles with its own guidelines for documentation, formatting, and language usage. Depending on your field of interest, you may be exposed to additional styles, such as the following:

• MLA style. Determined by the Modern Languages Association and used for papers in literature, languages, and other disciplines in the humanities.
• Chicago style. Outlined in the Chicago Manual of Style and sometimes used for papers in the humanities and the sciences; many professional organizations use this style for publications as well.
• Associated Press (AP) style. Used by professional journalists.

References List

The brief citations included in the body of your paper correspond to the more detailed citations provided at the end of the paper in the references section. In-text citations provide basic information—the author’s name, the publication date, and the page number if necessary—while the references section provides more extensive bibliographical information. Again, this information allows your reader to follow up on the sources you cited and do additional reading about the topic if desired.

The specific format of entries in the list of references varies slightly for different source types, but the entries generally include the following information:

• The name(s) of the author(s) or institution that wrote the source
• The year of publication and, where applicable, the exact date of publication
• The full title of the source
• For books, the city of publication
• For articles or essays, the name of the periodical or book in which the article or essay appears
• For magazine and journal articles, the volume number, issue number, and pages where the article appears
• For sources on the web, the URL where the source is located

The references page is double spaced and lists entries in alphabetical order by the author’s last name. If an entry continues for more than one line, the second line and each subsequent line are indented five spaces. Review the following example. ( Chapter 13 “APA and MLA Documentation and Formatting” , Section 13.3 “Creating a References Section” provides extensive guidelines for formatting reference entries for different types of sources.)

In APA style, book and article titles are formatted in sentence case, not title case. Sentence case means that only the first word is capitalized, along with any proper nouns.

Key Takeaways

• Following proper citation and formatting guidelines helps writers ensure that their work will be taken seriously, give proper credit to other authors for their work, and provide valuable information to readers.
• Working ahead and taking care to cite sources correctly the first time are ways writers can save time during the editing stage of writing a research paper.
• APA papers usually include an abstract that concisely summarizes the paper.
• APA papers use a specific headings structure to provide a clear hierarchy of information.
• In APA papers, in-text citations usually include the name(s) of the author(s) and the year of publication.
• In-text citations correspond to entries in the references section, which provide detailed bibliographical information about a source.

Writing for Success Copyright © 2015 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Choose Your Test

Sat / act prep online guides and tips, 113 great research paper topics.

General Education

One of the hardest parts of writing a research paper can be just finding a good topic to write about. Fortunately we've done the hard work for you and have compiled a list of 113 interesting research paper topics. They've been organized into ten categories and cover a wide range of subjects so you can easily find the best topic for you.

In addition to the list of good research topics, we've included advice on what makes a good research paper topic and how you can use your topic to start writing a great paper.

What Makes a Good Research Paper Topic?

Not all research paper topics are created equal, and you want to make sure you choose a great topic before you start writing. Below are the three most important factors to consider to make sure you choose the best research paper topics.

#1: It's Something You're Interested In

A paper is always easier to write if you're interested in the topic, and you'll be more motivated to do in-depth research and write a paper that really covers the entire subject. Even if a certain research paper topic is getting a lot of buzz right now or other people seem interested in writing about it, don't feel tempted to make it your topic unless you genuinely have some sort of interest in it as well.

#2: There's Enough Information to Write a Paper

Even if you come up with the absolute best research paper topic and you're so excited to write about it, you won't be able to produce a good paper if there isn't enough research about the topic. This can happen for very specific or specialized topics, as well as topics that are too new to have enough research done on them at the moment. Easy research paper topics will always be topics with enough information to write a full-length paper.

Trying to write a research paper on a topic that doesn't have much research on it is incredibly hard, so before you decide on a topic, do a bit of preliminary searching and make sure you'll have all the information you need to write your paper.

#3: It Fits Your Teacher's Guidelines

Don't get so carried away looking at lists of research paper topics that you forget any requirements or restrictions your teacher may have put on research topic ideas. If you're writing a research paper on a health-related topic, deciding to write about the impact of rap on the music scene probably won't be allowed, but there may be some sort of leeway. For example, if you're really interested in current events but your teacher wants you to write a research paper on a history topic, you may be able to choose a topic that fits both categories, like exploring the relationship between the US and North Korea. No matter what, always get your research paper topic approved by your teacher first before you begin writing.

113 Good Research Paper Topics

Below are 113 good research topics to help you get you started on your paper. We've organized them into ten categories to make it easier to find the type of research paper topics you're looking for.

Arts/Culture

• Discuss the main differences in art from the Italian Renaissance and the Northern Renaissance .
• Analyze the impact a famous artist had on the world.
• How is sexism portrayed in different types of media (music, film, video games, etc.)? Has the amount/type of sexism changed over the years?
• How has the music of slaves brought over from Africa shaped modern American music?
• How has rap music evolved in the past decade?
• How has the portrayal of minorities in the media changed?

Current Events

• What have been the impacts of China's one child policy?
• How have the goals of feminists changed over the decades?
• How has the Trump presidency changed international relations?
• Analyze the history of the relationship between the United States and North Korea.
• What factors contributed to the current decline in the rate of unemployment?
• What have been the impacts of states which have increased their minimum wage?
• How do US immigration laws compare to immigration laws of other countries?
• How have the US's immigration laws changed in the past few years/decades?
• How has the Black Lives Matter movement affected discussions and view about racism in the US?
• What impact has the Affordable Care Act had on healthcare in the US?
• What factors contributed to the UK deciding to leave the EU (Brexit)?
• What factors contributed to China becoming an economic power?
• Discuss the history of Bitcoin or other cryptocurrencies  (some of which tokenize the S&P 500 Index on the blockchain) .
• Do students in schools that eliminate grades do better in college and their careers?
• Do students from wealthier backgrounds score higher on standardized tests?
• Do students who receive free meals at school get higher grades compared to when they weren't receiving a free meal?
• Do students who attend charter schools score higher on standardized tests than students in public schools?
• Do students learn better in same-sex classrooms?
• How does giving each student access to an iPad or laptop affect their studies?
• What are the benefits and drawbacks of the Montessori Method ?
• Do children who attend preschool do better in school later on?
• What was the impact of the No Child Left Behind act?
• How does the US education system compare to education systems in other countries?
• What impact does mandatory physical education classes have on students' health?
• Which methods are most effective at reducing bullying in schools?
• Do homeschoolers who attend college do as well as students who attended traditional schools?
• Does offering tenure increase or decrease quality of teaching?
• How does college debt affect future life choices of students?
• Should graduate students be able to form unions?

• What are different ways to lower gun-related deaths in the US?
• How and why have divorce rates changed over time?
• Is affirmative action still necessary in education and/or the workplace?
• Should physician-assisted suicide be legal?
• How has stem cell research impacted the medical field?
• How can human trafficking be reduced in the United States/world?
• Should people be able to donate organs in exchange for money?
• Which types of juvenile punishment have proven most effective at preventing future crimes?
• Has the increase in US airport security made passengers safer?
• Analyze the immigration policies of certain countries and how they are similar and different from one another.
• Several states have legalized recreational marijuana. What positive and negative impacts have they experienced as a result?
• Do tariffs increase the number of domestic jobs?
• Which prison reforms have proven most effective?
• Should governments be able to censor certain information on the internet?
• Which methods/programs have been most effective at reducing teen pregnancy?
• What are the benefits and drawbacks of the Keto diet?
• How effective are different exercise regimes for losing weight and maintaining weight loss?
• How do the healthcare plans of various countries differ from each other?
• What are the most effective ways to treat depression ?
• What are the pros and cons of genetically modified foods?
• Which methods are most effective for improving memory?
• What can be done to lower healthcare costs in the US?
• What factors contributed to the current opioid crisis?
• Analyze the history and impact of the HIV/AIDS epidemic .
• Are low-carbohydrate or low-fat diets more effective for weight loss?
• How much exercise should the average adult be getting each week?
• Which methods are most effective to get parents to vaccinate their children?
• What are the pros and cons of clean needle programs?
• How does stress affect the body?
• Discuss the history of the conflict between Israel and the Palestinians.
• What were the causes and effects of the Salem Witch Trials?
• Who was responsible for the Iran-Contra situation?
• How has New Orleans and the government's response to natural disasters changed since Hurricane Katrina?
• What events led to the fall of the Roman Empire?
• What were the impacts of British rule in India ?
• Was the atomic bombing of Hiroshima and Nagasaki necessary?
• What were the successes and failures of the women's suffrage movement in the United States?
• What were the causes of the Civil War?
• How did Abraham Lincoln's assassination impact the country and reconstruction after the Civil War?
• Which factors contributed to the colonies winning the American Revolution?
• What caused Hitler's rise to power?
• Discuss how a specific invention impacted history.
• What led to Cleopatra's fall as ruler of Egypt?
• How has Japan changed and evolved over the centuries?
• What were the causes of the Rwandan genocide ?

• Why did Martin Luther decide to split with the Catholic Church?
• Analyze the history and impact of a well-known cult (Jonestown, Manson family, etc.)
• How did the sexual abuse scandal impact how people view the Catholic Church?
• How has the Catholic church's power changed over the past decades/centuries?
• What are the causes behind the rise in atheism/ agnosticism in the United States?
• What were the influences in Siddhartha's life resulted in him becoming the Buddha?
• How has media portrayal of Islam/Muslims changed since September 11th?

Science/Environment

• How has the earth's climate changed in the past few decades?
• How has the use and elimination of DDT affected bird populations in the US?
• Analyze how the number and severity of natural disasters have increased in the past few decades.
• Analyze deforestation rates in a certain area or globally over a period of time.
• How have past oil spills changed regulations and cleanup methods?
• How has the Flint water crisis changed water regulation safety?
• What are the pros and cons of fracking?
• What impact has the Paris Climate Agreement had so far?
• What have NASA's biggest successes and failures been?
• How can we improve access to clean water around the world?
• Does ecotourism actually have a positive impact on the environment?
• Should the US rely on nuclear energy more?
• What can be done to save amphibian species currently at risk of extinction?
• What impact has climate change had on coral reefs?
• How are black holes created?
• Are teens who spend more time on social media more likely to suffer anxiety and/or depression?
• How will the loss of net neutrality affect internet users?
• Analyze the history and progress of self-driving vehicles.
• How has the use of drones changed surveillance and warfare methods?
• Has social media made people more or less connected?
• What progress has currently been made with artificial intelligence ?
• Do smartphones increase or decrease workplace productivity?
• What are the most effective ways to use technology in the classroom?
• How is Google search affecting our intelligence?
• When is the best age for a child to begin owning a smartphone?
• Has frequent texting reduced teen literacy rates?

How to Write a Great Research Paper

Even great research paper topics won't give you a great research paper if you don't hone your topic before and during the writing process. Follow these three tips to turn good research paper topics into great papers.

#1: Figure Out Your Thesis Early

Before you start writing a single word of your paper, you first need to know what your thesis will be. Your thesis is a statement that explains what you intend to prove/show in your paper. Every sentence in your research paper will relate back to your thesis, so you don't want to start writing without it!

As some examples, if you're writing a research paper on if students learn better in same-sex classrooms, your thesis might be "Research has shown that elementary-age students in same-sex classrooms score higher on standardized tests and report feeling more comfortable in the classroom."

If you're writing a paper on the causes of the Civil War, your thesis might be "While the dispute between the North and South over slavery is the most well-known cause of the Civil War, other key causes include differences in the economies of the North and South, states' rights, and territorial expansion."

#2: Back Every Statement Up With Research

Remember, this is a research paper you're writing, so you'll need to use lots of research to make your points. Every statement you give must be backed up with research, properly cited the way your teacher requested. You're allowed to include opinions of your own, but they must also be supported by the research you give.

#3: Do Your Research Before You Begin Writing

You don't want to start writing your research paper and then learn that there isn't enough research to back up the points you're making, or, even worse, that the research contradicts the points you're trying to make!

Get most of your research on your good research topics done before you begin writing. Then use the research you've collected to create a rough outline of what your paper will cover and the key points you're going to make. This will help keep your paper clear and organized, and it'll ensure you have enough research to produce a strong paper.

What's Next?

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Christine graduated from Michigan State University with degrees in Environmental Biology and Geography and received her Master's from Duke University. In high school she scored in the 99th percentile on the SAT and was named a National Merit Finalist. She has taught English and biology in several countries.

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Fake academic papers are on the rise: why they’re a danger and how to stop them

Professor of Methodology and Integrity, Vrije Universiteit Amsterdam

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Lex Bouter is the founding chair of the World Conferences on Research Integrity Foundation and co-chair of the 8th WCRI in Athens, 2-5 June 2024.

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In the 1800s, British colonists in India set about trying to reduce the cobra population, which was making life and trade very difficult in Delhi. They began to pay a bounty for dead cobras. The strategy very quickly resulted in the widespread breeding of cobras for cash .

This danger of unintended consequences is sometimes referred to as the “ cobra effect ”. It can also be well summed up by Goodhardt’s Law , named after British economist Charles Goodhart. He stated that, when a measure becomes a target, it ceases to be a good measure.

The cobra effect has taken root in the world of research. The “publish or perish” culture, which values publications and citations above all, has resulted in its own myriad of “cobra breeding programmes”. That includes the widespread practice of questionable research practices, like playing up the impact of research findings to make work more attractive to publishers.

It’s also led to the rise of paper mills, criminal organisations that sell academic authorship. A report on the subject describes paper mills as (the)

process by which manufactured manuscripts are submitted to a journal for a fee on behalf of researchers with the purpose of providing an easy publication for them, or to offer authorship for sale.

These fake papers have serious consequences for research and its impact on society. Not all fake papers are retracted. And even those that are often still make their way into systematic literature reviews which are, in turn, used to draw up policy guidelines, clinical guidelines, and funding agendas.

How paper mills work

Paper mills rely on the desperation of researchers — often young, often overworked, often on the peripheries of academia struggling to overcome the high obstacles to entry — to fuel their business model.

They are frighteningly successful. The website of one such company based in Latvia advertises the publication of more than 12,650 articles since its launch in 2012. In an analysis of just two journals jointly conducted by the Committee on Publications Ethics and the International Association of Scientific, Technical and Medical Publishers, more than half of the 3440 article submissions over a two-year period were found to be fake.

It is estimated that all journals, irrespective of discipline, experience a steeply rising number of fake paper submissions. Currently the rate is about 2%. That may sound small. But, given the large and growing amount of scholarly publications it means that a lot of fake papers are published. Each of these can seriously damage patients, society or nature when applied in practice.

The fight against fake papers

Many individuals and organisations are fighting back against paper mills.

The scientific community is lucky enough to have several “fake paper detectives” who volunteer their time to root out fake papers from the literature. Elizabeth Bik , for instance, is a Dutch microbiologist turned science integrity consultant. She dedicates much of her time to searching the biomedical literature for manipulated photographic images or plagiarised text. There are others doing this work , too.

Organisations such as PubPeer and Retraction Watch also play vital roles in flagging fake papers and pressuring publishers to retract them.

These and other initiatives, like the STM Integrity Hub and United2Act , in which publishers collaborate with other stakeholders, are trying to make a difference.

But this is a deeply ingrained problem. The use of generative artificial intelligence like ChatGPT will help the detectives – but will also likely result in more fake papers which are now more easy to produce and more difficult or even impossible to detect.

Stop paying for dead cobras

They key to changing this culture is a switch in researcher assessment.

Researchers must be acknowledged and rewarded for responsible research practices: a focus on transparency and accountability, high quality teaching, good supervision, and excellent peer review. This will extend the scope of activities that yield “career points” and shift the emphasis of assessment from quantity to quality.

Fortunately, several initiatives and strategies already exist to focus on a balanced set of performance indicators that matter. The San Francisco Declaration on Research Assessment , established in 2012, calls on the research community to recognise and reward various research outputs, beyond just publication. The Hong Kong Principles , formulated and endorsed at the 6th World Conference in Research Integrity in 2019, encourage research evaluations that incentivise responsible research practices while minimise perverse incentives that drive practices like purchasing authorship or falsifying data.

These issues, as well as others related to protecting the integrity of research and building trust in it, will also be discussed during the 8th World Conference on Research Integrity in Athens, Greece in June this year.

Practices under the umbrella of “ Open Science ” will be pivotal to making the research process more transparent and researchers more accountable. Open Science is the umbrella term for a movement consisting of initiatives to make scholarly research more transparent and equitable, ranging from open access publication to citizen science.

Open Methods, for example, involves the pre-registration of a study design’s essential features before its start. A registered report containing the introduction and methods section is submitted to a journal before data collection starts. It is subsequently accepted or rejected based on the relevance of the research, as well as the methodology’s strength.

The added benefit of a registered report is that reviewer feedback on the methodology can still change the study methods, as the data collection hasn’t started. Research can then begin without pressure to achieve positive results, removing the incentive to tweak or falsify data.

Peer review

Peer reviewers are an important line of defence against the publication of fatally flawed or fake papers. In this system, quality assurance of a paper is done on a completely voluntary and often anonymous basis by an expert in the relevant field or subject.

However, the person doing the review work receives no credit or reward. It’s crucial that this sort of “invisible” work in academia be recognised, celebrated and included among the criteria for promotion. This can contribute substantially to detecting questionable research practices (or worse) before publication.

It will incentivise good peer review, so fewer suspect articles pass through the process, and it will also open more paths to success in academia – thus breaking up the toxic publish-or-perish culture.

This article is based on a presentation given by the lead author at Stellenbosch University, South Africa on 12 February 2024. Natalie Simon, a communications consultant specialising in research who is part of the communications team for the 8th World Conference on Research Integrity and is also currently completing an MPhil in Science and Technology Studies at Stellenbosch University, co-authored this article.

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Doing more, but learning less: the risks of ai in research.

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Artificial intelligence (AI) is widely heralded for its potential to enhance productivity in scientific research. But with that promise come risks that could narrow scientists’ ability to better understand the world, according to a new paper co-authored by a Yale anthropologist.

Some future AI approaches, the authors argue, could constrict the questions researchers ask, the experiments they perform, and the perspectives that come to bear on scientific data and theories.

All told, these factors could leave people vulnerable to “illusions of understanding” in which they believe they comprehend the world better than they do.

The paper published March 7 in Nature .

“ There is a risk that scientists will use AI to produce more while understanding less,” said co-author Lisa Messeri, an anthropologist in Yale’s Faculty of Arts and Sciences. “We’re not arguing that scientists shouldn’t use AI tools, but we’re advocating for a conversation about how scientists will use them and suggesting that we shouldn’t automatically assume that all uses of the technology, or the ubiquitous use of it, will benefit science.”

The paper, co-authored by Princeton cognitive scientist M. J. Crockett, sets a framework for discussing the risks involved in using AI tools throughout the scientific research process, from study design through peer review.

“ We hope this paper offers a vocabulary for talking about AI’s potential epistemic risks,” Messeri said.

Added Crockett: “To understand these risks, scientists can benefit from work in the humanities and qualitative social sciences.”

Messeri and Crockett classified proposed visions of AI spanning the scientific process that are currently creating buzz among researchers into four archetypes:

• In study design, they argue, “AI as Oracle” tools are imagined as being able to objectively and efficiently search, evaluate, and summarize massive scientific literatures, helping researchers to formulate questions in their project’s design stage.
• In data collection, “AI as Surrogate” applications, it is hoped, allow scientists to generate accurate stand-in data points, including as a replacement for human study participants, when data is otherwise too difficult or expensive to obtain.
• In data analysis, “AI as Quant” tools seek to surpass the human intellect’s ability to analyze vast and complex datasets.
• And “AI as Arbiter” applications aim to objectively evaluate scientific studies for merit and replicability, thereby replacing humans in the peer-review process.   

The authors warn against treating AI applications from these four archetypes as trusted partners, rather than simply tools , in the production of scientific knowledge. Doing so, they say, could make scientists susceptible to illusions of understanding, which can crimp their perspectives and convince them that they know more than they do.

The efficiencies and insights that AI tools promise can weaken the production of scientific knowledge by creating “monocultures of knowing,” in which researchers prioritize the questions and methods best suited to AI over other modes of inquiry, Messeri and Crockett state. A scholarly environment of that kind leaves researchers vulnerable to what they call “illusions of exploratory breadth,” where scientists wrongly believe that they are exploring all testable hypotheses, when they are only examining the narrower range of questions that can be tested through AI.

For example, “Surrogate” AI tools that seem to accurately mimic human survey responses could make experiments that require measurements of physical behavior or face-to-face interactions increasingly unpopular because they are slower and more expensive to conduct, Crockett said.

The authors also describe the possibility that AI tools become viewed as more objective and reliable than human scientists, creating a “monoculture of knowers” in which AI systems are treated as a singular, authoritative, and objective knower in place of a diverse scientific community of scientists with varied backgrounds, training, and expertise. A monoculture, they say, invites “illusions of objectivity” where scientists falsely believe that AI tools have no perspective or represent all perspectives when, in truth, they represent the standpoints of the computer scientists who developed and trained them.

“ There is a belief around science that the objective observer is the ideal creator of knowledge about the world,” Messeri said. “But this is a myth. There has never been an objective ‘knower,’ there can never be one, and continuing to pursue this myth only weakens science.”  

There is substantial evidence that human diversity makes science more robust and creative, the authors add.

“ Acknowledging that science is a social practice that benefits from including diverse standpoints will help us realize its full potential,” Crockett said. “Replacing diverse standpoints with AI tools will set back the clock on the progress we’ve made toward including more perspectives in scientific work.”

It is important to remember AI’s social implications, which extend far beyond the laboratories where it is being used in research, Messeri said.

“ We train scientists to think about technical aspects of new technology,” she said. “We don’t train them nearly as well to consider the social aspects, which is vital to future work in this domain.”

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Tim D. Smithies, Mark J. Campbell, … Adam J. Toth

Introduction

Association football, or simply football or soccer, is a widely popular and highly professionalised sport, in which two teams compete to score goals against each other. As each football team comprises up to 11 active players at all times and takes place on a very large pitch (also known as a soccer field), scoring goals tends to require a significant degree of strategic team-play. Under the rules codified in the Laws of the Game 1 , this competition has nurtured an evolution of nuanced strategies and tactics, culminating in modern professional football leagues. In today’s play, data-driven insights are a key driver in determining the optimal player setups for each game and developing counter-tactics to maximise the chances of success 2 .

When competing at the highest level the margins are incredibly tight, and it is increasingly important to be able to capitalise on any opportunity for creating an advantage on the pitch. To that end, top-tier clubs employ diverse teams of coaches, analysts and experts, tasked with studying and devising (counter-)tactics before each game. Several recent methods attempt to improve tactical coaching and player decision-making through artificial intelligence (AI) tools, using a wide variety of data types from videos to tracking sensors and applying diverse algorithms ranging from simple logistic regression to elaborate neural network architectures. Such methods have been employed to help predict shot events from videos 3 , forecast off-screen movement from spatio-temporal data 4 , determine whether a match is in-play or interrupted 5 , or identify player actions 6 .

The execution of agreed-upon plans by players on the pitch is highly dynamic and imperfect, depending on numerous factors including player fitness and fatigue, variations in player movement and positioning, weather, the state of the pitch, and the reaction of the opposing team. In contrast, set pieces provide an opportunity to exert more control on the outcome, as the brief interruption in play allows the players to reposition according to one of the practiced and pre-agreed patterns, and make a deliberate attempt towards the goal. Examples of such set pieces include free kicks, corner kicks, goal kicks, throw-ins, and penalties 2 .

Among set pieces, corner kicks are of particular importance, as an improvement in corner kick execution may substantially modify game outcomes, and they lend themselves to principled, tactical and detailed analysis. This is because corner kicks tend to occur frequently in football matches (with ~10 corners on average taking place in each match 7 ), they are taken from a fixed, rigid position, and they offer an immediate opportunity for scoring a goal—no other set piece simultaneously satisfies all of the above. In practice, corner kick routines are determined well ahead of each match, taking into account the strengths and weaknesses of the opposing team and their typical tactical deployment. It is for this reason that we focus on corner kick analysis in particular, and propose TacticAI, an AI football assistant for supporting the human expert with set piece analysis, and the development and improvement of corner kick routines.

TacticAI is rooted in learning efficient representations of corner kick tactics from raw, spatio-temporal player tracking data. It makes efficient use of this data by representing each corner kick situation as a graph—a natural representation for modelling relationships between players (Fig.  1 A, Table  2 ), and these player relationships may be of higher importance than the absolute distances between them on the pitch 8 . Such a graph input is a natural candidate for graph machine learning models 9 , which we employ within TacticAI to obtain high-dimensional latent player representations. In the Supplementary Discussion section, we carefully contrast TacticAI against prior art in the area.

A How corner kick situations are converted to a graph representation. Each player is treated as a node in a graph, with node, edge and graph features extracted as detailed in the main text. Then, a graph neural network operates over this graph by performing message passing; each node’s representation is updated using the messages sent to it from its neighbouring nodes. B How TacticAI processes a given corner kick. To ensure that TacticAI’s answers are robust in the face of horizontal or vertical reflections, all possible combinations of reflections are applied to the input corner, and these four views are then fed to the core TacticAI model, where they are able to interact with each other to compute the final player representations—each internal blue arrow corresponds to a single message passing layer from ( A ). Once player representations are computed, they can be used to predict the corner’s receiver, whether a shot has been taken, as well as assistive adjustments to player positions and velocities, which increase or decrease the probability of a shot being taken.

Uniquely, TacticAI takes advantage of geometric deep learning 10 to explicitly produce player representations that respect several symmetries of the football pitch (Fig.  1 B). As an illustrative example, we can usually safely assume that under a horizontal or vertical reflection of the pitch state, the game situation is equivalent. Geometric deep learning ensures that TacticAI’s player representations will be identically computed under such reflections, such that this symmetry does not have to be learnt from data. This proves to be a valuable addition, as high-quality tracking data is often limited—with only a few hundred matches played each year in every league. We provide an in-depth overview of how we employ geometric deep learning in TacticAI in the “Methods” section.

From these representations, TacticAI is then able to answer various predictive questions about the outcomes of a corner—for example, which player is most likely to make first contact with the ball, or whether a shot will take place. TacticAI can also be used as a retrieval system—for mining similar corner kick situations based on the similarity of player representations—and a generative recommendation system, suggesting adjustments to player positions and velocities to maximise or minimise the estimated shot probability. Through several experiments within a case study with domain expert coaches and analysts from Liverpool FC, the results of which we present in the next section, we obtain clear statistical evidence that TacticAI readily provides useful, realistic and accurate tactical suggestions.

To demonstrate the diverse qualities of our approach, we design TacticAI with three distinct predictive and generative components: receiver prediction, shot prediction, and tactic recommendation through guided generation, which also correspond to the benchmark tasks for quantitatively evaluating TacticAI. In addition to providing accurate quantitative insights for corner kick analysis with its predictive components, the interplay between TacticAI’s predictive and generative components allows coaches to sample alternative player setups for each routine of interest, and directly evaluate the possible outcomes of such alternatives.

We will first describe our quantitative analysis, which demonstrates that TacticAI’s predictive components are accurate at predicting corner kick receivers and shot situations on held-out test corners and that the proposed player adjustments do not strongly deviate from ground-truth situations. However, such an analysis only gives an indirect insight into how useful TacticAI would be once deployed. We tackle this question of utility head-on and conduct a comprehensive case study in collaboration with our partners at Liverpool FC—where we directly ask human expert raters to judge the utility of TacticAI’s predictions and player adjustments. The following sections expand on the specific results and analysis we have performed.

In what follows, we will describe TacticAI’s components at a minimal level necessary to understand our evaluation. We defer detailed descriptions of TacticAI’s components to the “Methods” section. Note that, all our error bars reported in this research are standard deviations.

Benchmarking TacticAI

We evaluate the three components of TacticAI on a relevant benchmark dataset of corner kicks. Our dataset consists of 7176 corner kicks from the 2020 to 2021 Premier League seasons, which we randomly shuffle and split into a training (80%) and a test set (20%). As previously mentioned, TacticAI operates on graphs. Accordingly, we represent each corner kick situation as a graph, where each node corresponds to a player. The features associated with each node encode the movements (velocities and positions) and simple profiles (heights and weights) of on-pitch players at the timestamp when the corresponding corner kick was being taken by the attacking kicker (see the “Methods” section), and no information of ball movement was encoded. The graphs are fully connected; that is, for every pair of players, we will include the edge connecting them in the graph. Each of these edges encodes a binary feature, indicating whether the two players are on opposing teams or not. For each task, we generated the relevant dataset of node/edge/graph features and corresponding labels (Tables  1 and 2 , see the “Methods” section). The components were then trained separately with their corresponding corner kick graphs. In particular, we only employ a minimal set of features to construct the corner kick graphs, without encoding the movements of the ball nor explicitly encoding the distances between players into the graphs. We used a consistent training-test split for all benchmark tasks, as this made it possible to benchmark not only the individual components but also their interactions.

Accurate receiver and shot prediction through geometric deep learning

One of TacticAI’s key predictive models forecasts the receiver out of the 22 on-pitch players. The receiver is defined as the first player touching the ball after the corner is taken. In our evaluation, all methods used the same set of features (see the “Receiver prediction” entry in Table  1 and the “Methods” section). We leveraged the receiver prediction task to benchmark several different TacticAI base models. Our best-performing model—achieving 0.782 ± 0.039 in top-3 test accuracy after 50,000 training steps—was a deep graph attention network 11 , 12 , leveraging geometric deep learning 10 through the use of D 2 group convolutions 13 . We supplement this result with a detailed ablation study, verifying that both our choice of base architecture and group convolution yielded significant improvements in the receiver prediction task (Supplementary Table  2 , see the subsection “Ablation study” in the “Methods” section). Considering that corner kick receiver prediction is a highly challenging task with many factors that are unseen by our model—including fatigue and fitness levels, and actual ball trajectory—we consider TacticAI’s top-3 accuracy to reflect a high level of predictive power, and keep the base TacticAI architecture fixed for subsequent studies. In addition to this quantitative evaluation with the evaluation dataset, we also evaluate the performance of TacticAI’s receiver prediction component in a case study with human raters. Please see the “Case study with expert raters” section for more details.

For shot prediction, we observe that reusing the base TacticAI architecture to directly predict shot events—i.e., directly modelling the probability \({\mathbb{P}}(\,{{\mbox{shot}}}| {{\mbox{corner}}}\,)\) —proved challenging, only yielding a test F 1 score of 0.52 ± 0.03, for a GATv2 base model. Note that here we use the F 1 score—the harmonic mean of precision and recall—as it is commonly used in binary classification problems over imbalanced datasets, such as shot prediction. However, given that we already have a potent receiver predictor, we decided to use its output to give us additional insight into whether or not a shot had been taken. Hence, we opted to decompose the probability of taking a shot as

where \({\mathbb{P}}(\,{{\mbox{receiver}}}| {{\mbox{corner}}}\,)\) are the probabilities computed by TacticAI’s receiver prediction system, and \({\mathbb{P}}(\,{{\mbox{shot}}}| {{\mbox{receiver}}},{{\mbox{corner}}}\,)\) models the conditional shot probability after a specific player makes first contact with the ball. This was implemented through providing an additional global feature to indicate the receiver in the corresponding corner kick (Table  1 ) while the architecture otherwise remained the same as that of receiver prediction (Supplementary Fig.  2 , see the “Methods” section). At training time, we feed the ground-truth receiver as input to the model—at inference time, we attempt every possible receiver, weighing their contributions using the probabilities given by TacticAI’s receiver predictor, as per Eq. ( 1 ). This two-phased approach yielded a final test F 1 score of 0.68 ± 0.04 for shot prediction, which encodes significantly more signal than the unconditional shot predictor, especially considering the many unobservables associated with predicting shot events. Just as for receiver prediction, this performance can be further improved using geometric deep learning; a conditional GATv2 shot predictor with D 2 group convolutions achieves an F 1 score of 0.71 ± 0.01.

Moreover, we also observe that, even just through predicting the receivers, without explicitly classifying any other salient features of corners, TacticAI learned generalisable representations of the data. Specifically, team setups with similar tactical patterns tend to cluster together in TacticAI’s latent space (Fig.  2 ). However, no clear clusters are observed in the raw input space (Supplementary Fig.  1 ). This indicates that TacticAI can be leveraged as a useful corner kick retrieval system, and we will present our evaluation of this hypothesis in the “Case study with expert raters” section.

We visualise the latent representations of attacking and defending teams in 1024 corner kicks using t -SNE. A latent team embedding in one corner kick sample is the mean of the latent player representations on the same attacking ( A – C ) or defending ( D ) team. Given the reference corner kick sample ( A ), we retrieve another corner kick sample ( B ) with respect to the closest distance of their representations in the latent space. We observe that ( A ) and ( B ) are both out-swing corner kicks and share similar patterns of their attacking tactics, which are highlighted with rectangles having the same colours, although they bear differences with respect to the absolute positions and velocities of the players. All the while, the latent representation of an in-swing attack ( C ) is distant from both ( A ) and ( B ) in the latent space. The red arrows are only used to demonstrate the difference between in- and out-swing corner kicks, not the actual ball trajectories.

Lastly, it is worth emphasising that the utility of the shot predictor likely does not come from forecasting whether a shot event will occur—a challenging problem with many imponderables—but from analysing the difference in predicted shot probability across multiple corners. Indeed, in the following section, we will show how TacticAI’s generative tactic refinements can directly influence the predicted shot probabilities, which will then corresponds to highly favourable evaluation by our expert raters in the “Case study with expert raters” section.

Controlled tactic refinement using class-conditional generative models

Equipped with components that are able to potently relate corner kicks with their various outcomes (e.g. receivers and shot events), we can explore the use of TacticAI to suggest adjustments of tactics, in order to amplify or reduce the likelihood of certain outcomes.

Specifically, we aim to produce adjustments to the movements of players on one of the two teams, including their positions and velocities, which would maximise or minimise the probability of a shot event, conditioned on the initial corner setup, consisting of the movements of players on both teams and their heights and weights. In particular, although in real-world scenarios both teams may react simultaneously to the movements of each other, in our study, we focus on moderate adjustments to player movements, which help to detect players that are not responding to a tactic properly. Due to this reason, we simplify the process of tactic refinement through generating the adjustments for only one team while keeping the other fixed. The way we train a model for this task is through an auto-encoding objective: we feed the ground-truth shot outcome (a binary indicator) as an additional graph-level feature to TacticAI’s model (Table  1 ), and then have it learn to reconstruct a probability distribution of the input player coordinates (Fig.  1 B, also see the “Methods” section). As a consequence, our tactic adjustment system does not depend on the previously discussed shot predictor—although we can use the shot predictor to evaluate whether the adjustments make a measurable difference in shot probability.

This autoencoder-based generative model is an individual component that separates from TacticAI’s predictive systems. All three systems share the encoder architecture (without sharing parameters), but use different decoders (see the “Methods” section). At inference time, we can instead feed in a desired shot outcome for the given corner setup, and then sample new positions and velocities for players on one team using this probability distribution. This setup, in principle, allows for flexible downstream use, as human coaches can optimise corner kick setups through generating adjustments conditioned on the specific outcomes of their interest—e.g., increasing shot probability for the attacking team, decreasing it for the defending team (Fig.  3 ) or amplifying the chance that a particular striker receives the ball.

TacticAI makes it possible for human coaches to redesign corner kick tactics in ways that help maximise the probability of a positive outcome for either the attacking or the defending team by identifying key players, as well as by providing temporally coordinated tactic recommendations that take all players into consideration. As demonstrated in the present example ( A ), for a corner kick in which there was a shot attempt in reality ( B ), TacticAI can generate a tactically-adjusted setting in which the shot probability has been reduced, by adjusting the positioning of the defenders ( D ). The suggested defender positions result in reduced receiver probability for attacking players 2–5 (see bottom row), while the receiver probability of Attacker 1, who is distant from the goalpost, has been increased ( C ). The model is capable of generating multiple such scenarios. Coaches can inspect the different options visually and additionally consult TacticAI’s quantitative analysis of the presented tactics.

We first evaluate the generated adjustments quantitatively, by verifying that they are indistinguishable from the original corner kick distribution using a classifier. To do this, we synthesised a dataset consisting of 200 corner kick samples and their corresponding conditionally generated adjustments. Specifically, for corners without a shot event, we generated adjustments for the attacking team by setting the shot event feature to 1, and vice-versa for the defending team when a shot event did happen. We found that the real and generated samples were not distinguishable by an MLP classifier, with an F 1 score of 0.53 ± 0.05, indicating random chance level accuracy. This result indicates that the adjustments produced by TacticAI are likely similar enough to real corner kicks that the MLP is unable to tell them apart. Note that, in spite of this similarity, TacticAI recommends player-level adjustments that are not negligible—in the following section we will illustrate several salient examples of this. To more realistically validate the practical indistinguishability of TacticAI’s adjustments from realistic corners, we also evaluated the realism of the adjustments in a case study with human experts, which we will present in the following section.

In addition, we leveraged our TacticAI shot predictor to estimate whether the proposed adjustments were effective. We did this by analysing 100 corner kick samples in which threatening shots occurred, and then, for each sample, generated one defensive refinement through setting the shot event feature to 0. We observed that the average shot probability significantly decreased, from 0.75 ± 0.14 for ground-truth corners to 0.69 ± 0.16 for adjustments ( z  = 2.62,  p  < 0.001). This observation was consistent when testing for attacking team refinements (shot probability increased from 0.18 ± 0.16 to 0.31 ± 0.26 ( z  = −4.46,  p  < 0.001)). Moving beyond this result, we also asked human raters to assess the utility of TacticAI’s proposed adjustments within our case study, which we detail next.

Case study with expert raters

Although quantitative evaluation with well-defined benchmark datasets was critical for the technical development of TacticAI, the ultimate test of TacticAI as a football tactic assistant is its practical downstream utility being recognised by professionals in the industry. To this end, we evaluated TacticAI through a case study with our partners at Liverpool FC (LFC). Specifically, we invited a group of five football experts: three data scientists, one video analyst, and one coaching assistant. Each of them completed four tasks in the case study, which evaluated the utility of TacticAI’s components from several perspectives; these include (1) the realism of TacticAI’s generated adjustments, (2) the plausibility of TacticAI’s receiver predictions, (3) effectiveness of TacticAI’s embeddings for retrieving similar corners, and (4) usefulness of TacticAI’s recommended adjustments. We provide an overview of our study’s results here and refer the interested reader to Supplementary Figs.  3 – 5 and the  Supplementary Methods for additional details.

We first simultaneously evaluated the realism of the adjusted corner kicks generated by TacticAI, and the plausibility of its receiver predictions. Going through a collection of 50 corner kick samples, we first asked the raters to classify whether a given sample was real or generated by TacticAI, and then they were asked to identify the most likely receivers in the corner kick sample (Supplementary Fig.  3 ).

On the task of classifying real and generated samples, first, we found that the raters’ average F 1 score of classifying the real vs. generated samples was only 0.60 ± 0.04, with individual F 1 scores ( \({F}_{1}^{A}=0.54,{F}_{1}^{B}=0.64,{F}_{1}^{C}=0.65,{F}_{1}^{D}=0.62,{F}_{1}^{E}=0.56\) ), indicating that the raters were, in many situations, unable to distinguish TacticAI’s adjustments from real corners.

The previous evaluation focused on analysing realism detection performance across raters. We also conduct a study that analyses realism detection across samples. Specifically, we assigned ratings for each sample—assigning +1 to a sample if it was identified as real by a human rater, and 0 otherwise—and computed the average rating for each sample across the five raters. Importantly, by studying the distribution of ratings, we found that there was no significant difference between the average ratings assigned to real and generated corners ( z  = −0.34,  p  > 0.05) (Fig.  4 A). Hence, the real and generated samples were assigned statistically indistinguishable average ratings by human raters.

In task 1, we tested the statistical difference between the real corner kick samples and the synthetic ones generated by TacticAI from two aspects: ( A.1 ) the distributions of their assigned ratings, and ( A.2 ) the corresponding histograms of the rating values. Analogously, in task 2 (receiver prediction), ( B.1 ) we track the distributions of the top-3 accuracy of receiver prediction using those samples, and ( B.2 ) the corresponding histogram of the mean rating per sample. No statistical difference in the mean was observed in either cases (( A.1 ) ( z  = −0.34,  p  > 0.05), and ( B.1 ) ( z  = 0.97,  p  > 0.05)). Additionally, we observed a statistically significant difference between the ratings of different raters on receiver prediction, with three clear clusters emerging ( C ). Specifically, Raters A and E had similar ratings ( z  = 0.66,  p  > 0.05), and Raters B and D also rated in similar ways ( z  = −1.84,  p  > 0.05), while Rater C responded differently from all other raters. This suggests a good level of variety of the human raters with respect to their perceptions of corner kicks. In task 3—identifying similar corners retrieved in terms of salient strategic setups—there were no significant differences among the distributions of the ratings by different raters ( D ), suggesting a high level of agreement on the usefulness of TacticAI’s capability of retrieving similar corners ( F 1,4  = 1.01,  p  > 0.1). Finally, in task 4, we compared the ratings of TacticAI’s strategic refinements across the human raters ( E ) and found that the raters also agreed on the general effectiveness of the refinements recommended by TacticAI ( F 1,4  = 0.45,  p  > 0.05). Note that the violin plots used in B.1 and C – E model a continuous probability distribution and hence assign nonzero probabilities to values outside of the allowed ranges. We only label y -axis ticks for the possible set of ratings.

For the task of identifying receivers, we rated TacticAI’s predictions with respect to a rater as +1 if at least one of the receivers identified by the rater appeared in TacticAI’s top-3 predictions, and 0 otherwise. The average top-3 accuracy among the human raters was 0.79 ± 0.18; specifically, 0.81 ± 0.17 for the real samples, and 0.77 ± 0.21 for the generated ones. These scores closely line up with the accuracy of TacticAI in predicting receivers for held-out test corners, validating our quantitative study. Further, after averaging the ratings for receiver prediction sample-wise, we found no statistically significant difference between the average ratings of predicting receivers over the real and generated samples ( z  = 0.97,  p  > 0.05) (Fig.  4 B). This indicates that TacticAI was equally performant in predicting the receivers of real corners and TacticAI-generated adjustments, and hence may be leveraged for this purpose even in simulated scenarios.

There is a notably high variance in the average receiver prediction rating of TacticAI. We hypothesise that this is due to the fact that different raters may choose to focus on different salient features when evaluating the likely receivers (or even the amount of likely receivers). We set out to validate this hypothesis by testing the pair-wise similarity of the predictions by the human raters through running a one-away analysis of variance (ANOVA), followed by a Tukey test. We found that the distributions of the five raters’ predictions were significantly different ( F 1,4  = 14.46,  p  < 0.001) forming three clusters (Fig.  4 C). This result indicates that different human raters—as suggested by their various titles at LFC—may often use very different leads when suggesting plausible receivers. The fact that TacticAI manages to retain a high top-3 accuracy in such a setting suggests that it was able to capture the salient patterns of corner kick strategies, which broadly align with human raters’ preferences. We will further test this hypothesis in the third task—identifying similar corners.

For the third task, we asked the human raters to judge 50 pairs of corners for their similarity. Each pair consisted of a reference corner and a retrieved corner, where the retrieved corner was chosen either as the nearest-neighbour of the reference in terms of their TacticAI latent space representations, or—as a feature-level heuristic—the cosine similarities of their raw features (Supplementary Fig.  4 ) in our corner kick dataset. We score the raters’ judgement of a pair as +1 if they considered the corners presented in the case to be usefully similar, otherwise, the pair is scored with 0. We first computed, for each rater, the recall with which they have judged a baseline- or TacticAI-retrieved pair as usefully similar—see description of Task 3 in the  Supplementary Methods . For TacticAI retrievals, the average recall across all raters was 0.59 ± 0.09, and for the baseline system, the recall was 0.36 ± 0.10. Secondly, we assess the statistical difference between the results of the two methods by averaging the ratings for each reference–retrieval pair, finding that the average rating of TacticAI retrievals is significantly higher than the average rating of baseline method retrievals ( z  = 2.34,  p  < 0.05). These two results suggest that TacticAI significantly outperforms the feature-space baseline as a method for mining similar corners. This indicates that TacticAI is able to extract salient features from corners that are not trivial to extract from the input data alone, reinforcing it as a potent tool for discovering opposing team tactics from available data. Finally, we observed that this task exhibited a high level of inter-rater agreement for TacticAI-retrieved pairs ( F 1,4  = 1.01,  p  > 0.1) (Fig.  4 D), suggesting that human raters were largely in agreement with respect to their assessment of TacticAI’s performance.

Finally, we evaluated TacticAI’s player adjustment recommendations for their practical utility. Specifically, each rater was given 50 tactical refinements together with the corresponding real corner kick setups—see Supplementary Fig.  5 , and the “Case study design” section in the  Supplementary Methods . The raters were then asked to rate each refinement as saliently improving the tactics (+1), saliently making them worse (−1), or offering no salient differences (0). We calculated the average rating assigned by each of the raters (giving us a value in the range [− 1, 1] for each rater). The average of these values across all five raters was 0.7 ± 0.1. Further, for 45 of the 50 situations (90%), the human raters found TacticAI’s suggestion to be favourable on average (by majority voting). Both of these results indicate that TacticAI’s recommendations are salient and useful to a downstream football club practitioner, and we set out to validate this with statistical tests.

We performed statistical significance testing of the observed positive ratings. First, for each of the 50 situations, we averaged its ratings across all five raters and then ran a t -test to assess whether the mean rating was significantly larger than zero. Indeed, the statistical test indicated that the tactical adjustments recommended by TacticAI were constructive overall ( \({t}_{49}^{{{{{{{{\rm{avg}}}}}}}}}=9.20,\, p \, < \, 0.001\) ). Secondly, we verified that each of the five raters individually found TacticAI’s recommendations to be constructive, running a t -test on each of their ratings individually. For all of the five raters, their average ratings were found to be above zero with statistical significance ( \({t}_{49}^{A}=5.84,\, {p}^{A} \, < \, 0.001;{t}_{49}^{B}=7.88,\; {p}^{B} \, < \, 0.001;{t}_{49}^{C}=7.00,\; {p}^{C} \, < \, 0.001;{t}_{49}^{D}=6.04,\; {p}^{D} \, < \, 0.001;{t}_{49}^{E}=7.30,\, {p}^{E} \, < \, 0.001\) ). In addition, their ratings also shared a high level of inter-agreement ( F 1,4  = 0.45,  p  > 0.05) (Fig.  4 E), suggesting a level of practical usefulness that is generally recognised by human experts, even though they represent different backgrounds.

Taking all of these results together, we find TacticAI to possess strong components for prediction, retrieval, and tactical adjustments on corner kicks. To illustrate the kinds of salient recommendations by TacticAI, in Fig.  5 we present four examples with a high degree of inter-rater agreement.

These examples are selected from our case study with human experts, to illustrate the breadth of tactical adjustments that TacticAI suggests to teams defending a corner. The density of the yellow circles coincides with the number of times that the corresponding change is recognised as constructive by human experts. Instead of optimising the movement of one specific player, TacticAI can recommend improvements for multiple players in one generation step through suggesting better positions to block the opposing players, or better orientations to track them more efficiently. Some specific comments from expert raters follow. In A , according to raters, TacticAI suggests more favourable positions for several defenders, and improved tracking runs for several others—further, the goalkeeper is positioned more deeply, which is also beneficial. In B , TacticAI suggests that the defenders furthest away from the corner make improved covering runs, which was unanimously deemed useful, with several other defenders also positioned more favourably. In C , TacticAI recommends improved covering runs for a central group of defenders in the penalty box, which was unanimously considered salient by our raters. And in D , TacticAI suggests substantially better tracking runs for two central defenders, along with a better positioning for two other defenders in the goal area.

We have demonstrated an AI assistant for football tactics and provided statistical evidence of its efficacy through a comprehensive case study with expert human raters from Liverpool FC. First, TacticAI is able to accurately predict the first receiver after a corner kick is taken as well as the probability of a shot as the direct result of the corner. Second, TacticAI has been shown to produce plausible tactical variations that improve outcomes in a salient way, while being indistinguishable from real scenarios by domain experts. And finally, the system’s latent player representations are a powerful means to retrieve similar set-piece tactics, allowing coaches to analyse relevant tactics and counter-tactics that have been successful in the past.

The broader scope of strategy modelling in football has previously been addressed from various individual angles, such as pass prediction 14 , 15 , 16 , shot prediction 3 or corner kick tactical classification 7 . However, to the best of our knowledge, our work stands out by combining and evaluating predictive and generative modelling of corner kicks for tactic development. It also stands out in its method of applying geometric deep learning, allowing for efficiently incorporating various symmetries of the football pitch for improved data efficiency. Our method incorporates minimal domain knowledge and does not rely on intricate feature engineering—though its factorised design naturally allows for more intricate feature engineering approaches when such features are available.

Our methodology requires the position and velocity estimates of all players at the time of execution of the corner and subsequent events. Here, we derive these from high-quality tracking and event data, with data availability from tracking providers limited to top leagues. Player tracking based on broadcast video would increase the reach and training data substantially, but would also likely result in noisier model inputs. While the attention mechanism of GATs would allow us to perform introspection of the most salient factors contributing to the model outcome, our method does not explicitly model exogenous (aleatoric) uncertainty, which would be valuable context for the football analyst.

While the empirical study of our method’s efficacy has been focused on corner kicks in association football, it readily generalises to other set pieces (such as throw-ins, which similarly benefit from similarity retrieval, pass and/or shot prediction) and other team sports with suspended play situations. The learned representations and overall framing of TacticAI also lay the ground for future research to integrate a natural language interface that enables domain-grounded conversations with the assistant, with the aim to retrieve particular situations of interest, make predictions for a given tactical variant, compare and contrast, and guide through an interactive process to derive tactical suggestions. It is thus our belief that TacticAI lays the groundwork for the next-generation AI assistant for football.

We devised TacticAI as a geometric deep learning pipeline, further expanded in this section. We process labelled spatio-temporal football data into graph representations, and train and evaluate on benchmarking tasks cast as classification or regression. These steps are presented in sequence, followed by details on the employed computational architecture.

Raw corner kick data

The raw dataset consisted of 9693 corner kicks collected from the 2020–21, 2021–22, and 2022–23 (up to January 2023) Premier League seasons. The dataset was provided by Liverpool FC and comprises four separate data sources, described below.

Our primary data source is spatio-temporal trajectory frames (tracking data), which tracked all on-pitch players and the ball, for each match, at 25 frames per second. In addition to player positions, their velocities are derived from position data through filtering. For each corner kick, we only used the frame in which the kick is being taken as input information.

Secondly, we also leverage event stream data, which annotated the events or actions (e.g., passes, shots and goals) that have occurred in the corresponding tracking frames.

Thirdly, the line-up data for the corresponding games, which recorded the players’ profiles, including their heights, weights and roles, is also used.

Lastly, we have access to miscellaneous game data, which contains the game days, stadium information, and pitch length and width in meters.

Graph representation and construction

We assumed that we were provided with an input graph \({{{{{{{\mathcal{G}}}}}}}}=({{{{{{{\mathcal{V}}}}}}}},\,{{{{{{{\mathcal{E}}}}}}}})\) with a set of nodes \({{{{{{{\mathcal{V}}}}}}}}\) and edges \({{{{{{{\mathcal{E}}}}}}}}\subseteq {{{{{{{\mathcal{V}}}}}}}}\times {{{{{{{\mathcal{V}}}}}}}}\) . Within the context of football games, we took \({{{{{{{\mathcal{V}}}}}}}}\) to be the set of 22 players currently on the pitch for both teams, and we set \({{{{{{{\mathcal{E}}}}}}}}={{{{{{{\mathcal{V}}}}}}}}\times {{{{{{{\mathcal{V}}}}}}}}\) ; that is, we assumed all pairs of players have the potential to interact. Further analyses, leveraging more specific choices of \({{{{{{{\mathcal{E}}}}}}}}\) , would be an interesting avenue for future work.

Additionally, we assume that the graph is appropriately featurised. Specifically, we provide a node feature matrix, \({{{{{{{\bf{X}}}}}}}}\in {{\mathbb{R}}}^{| {{{{{{{\mathcal{V}}}}}}}}| \times k}\) , an edge feature tensor, \({{{{{{{\bf{E}}}}}}}}\in {{\mathbb{R}}}^{| {{{{{{{\mathcal{V}}}}}}}}| \times | {{{{{{{\mathcal{V}}}}}}}}| \times l}\) , and a graph feature vector, \({{{{{{{\bf{g}}}}}}}}\in {{\mathbb{R}}}^{m}\) . The appropriate entries of these objects provide us with the input features for each node, edge, and graph. For example, \({{{{{{{{\bf{x}}}}}}}}}_{u}\in {{\mathbb{R}}}^{k}\) would provide attributes of an individual player \(u\in {{{{{{{\mathcal{V}}}}}}}}\) , such as position, height and weight, and \({{{{{{{{\bf{e}}}}}}}}}_{uv}\in {{\mathbb{R}}}^{l}\) would provide the attributes of a particular pair of players \((u,\, v)\in {{{{{{{\mathcal{E}}}}}}}}\) , such as their distance, and whether they belong to the same team. The graph feature vector, g , can be used to store global attributes of interest to the corner kick, such as the game time, current score, or ball position. For a simplified visualisation of how a graph neural network would process such an input, refer to Fig.  1 A.

To construct the input graphs, we first aligned the four data sources with respect to their game IDs and timestamps and filtered out 2517 invalid corner kicks, for which the alignment failed due to missing data, e.g., missing tracking frames or event labels. This filtering yielded 7176 valid corner kicks for training and evaluation. We summarised the exact information that was used to construct the input graphs in Table  2 . In particular, other than player heights (measured in centimeters (cm)) and weights (measured in kilograms (kg)), the players were anonymous in the model. For the cases in which the player profiles were missing, we set their heights and weights to 180 cm and 75 kg, respectively, as defaults. In total, we had 385 such occurrences out of a total of 213,246( = 22 × 9693) during data preprocessing. We downscaled the heights and weights by a factor of 100. Moreover, for each corner kick, we zero-centred the positions of on-pitch players and normalised them onto a 10 m × 10 m pitch, and their velocities were re-scaled accordingly. For the cases in which the pitch dimensions were missing, we used a standard pitch dimension of 110 m × 63 m as default.

We summarised the grouping of the features in Table  1 . The actual features used in different benchmark tasks may differ, and we will describe this in more detail in the next section. To focus on modelling the high-level tactics played by the attacking and defending teams, other than a binary indicator for ball possession—which is 1 for the corner kick taker and 0 for all other players—no information of ball movement, neither positions nor velocities, was used to construct the input graphs. Additionally, we do not have access to the player’s vertical movement, therefore only information on the two-dimensional movements of each player is provided in the data. We do however acknowledge that such information, when available, would be interesting to consider in a corner kick outcome predictor, considering the prevalence of aerial battles in corners.

Benchmark tasks construction

TacticAI consists of three predictive and generative models, which also correspond to three benchmark tasks implemented in this study. Specifically, (1) Receiver prediction, (2) Threatening shot prediction, and (3) Guided generation of team positions and velocities (Table  1 ). The graphs of all the benchmark tasks used the same feature space of nodes and edges, differing only in the global features.

For all three tasks, our models first transform the node features to a latent node feature matrix, \({{{{{{{\bf{H}}}}}}}}={f}_{{{{{{{{\mathcal{G}}}}}}}}}({{{{{{{\bf{X}}}}}}}},\, {{{{{{{\bf{E}}}}}}}},\, {{{{{{{\bf{g}}}}}}}})\) , from which we could answer queries: either about individual players—in which case we learned a relevant classifier or regressor over the h u vectors (the rows of H )—or about the occurrence of a global event (e.g. shot taken)—in which case we classified or regressed over the aggregated player vectors, ∑ u h u . In both cases, the classifiers were trained using stochastic gradient descent over an appropriately chosen loss function, such as categorical cross-entropy for classifiers, and mean squared error for regressors.

For different tasks, we extracted the corresponding ground-truth labels from either the event stream data or the tracking data. Specifically, (1) We modelled receiver prediction as a node classification task and labelled the first player to touch the ball after the corner was taken as the target node. This player could be either an attacking or defensive player. (2) Shot prediction was modelled as graph classification. In particular, we considered a next-ball-touch action by the attacking team as a shot if it was a direct corner, a goal, an aerial, hit on the goalposts, a shot attempt saved by the goalkeeper, or missing target. This yielded 1736 corners labelled as a shot being taken, and 5440 corners labelled as a shot not being taken. (3) For guided generation of player position and velocities, no additional label was needed, as this model relied on a self-supervised reconstruction objective.

The entire dataset was split into training and evaluation sets with an 80:20 ratio through random sampling, and the same splits were used for all tasks.

Graph neural networks

The central model of TacticAI is the graph neural network (GNN) 9 , which computes latent representations on a graph by repeatedly combining them within each node’s neighbourhood. Here we define a node’s neighbourhood, \({{{{{{{{\mathcal{N}}}}}}}}}_{u}\) , as the set of all first-order neighbours of node u , that is, \({{{{{{{{\mathcal{N}}}}}}}}}_{u}=\{v\,| \,(v,\, u)\in {{{{{{{\mathcal{E}}}}}}}}\}\) . A single GNN layer then transforms the node features by passing messages between neighbouring nodes 17 , following the notation of related work 10 , and the implementation of the CLRS-30 benchmark baselines 18 :

where \(\psi :{{\mathbb{R}}}^{k}\times {{\mathbb{R}}}^{k}\times {{\mathbb{R}}}^{l}\times {{\mathbb{R}}}^{m}\to {{\mathbb{R}}}^{{k}^{{\prime} }}\) and \(\phi :{{\mathbb{R}}}^{k}\times {{\mathbb{R}}}^{{k}^{{\prime} }}\to {{\mathbb{R}}}^{{k}^{{\prime} }}\) are two learnable functions (e.g. multilayer perceptrons), \({{{{{{{{\bf{h}}}}}}}}}_{u}^{(t)}\) are the features of node u after t GNN layers, and ⨁ is any permutation-invariant aggregator, such as sum, max, or average. By definition, we set \({{{{{{{{\bf{h}}}}}}}}}_{u}^{(0)}={{{{{{{{\bf{x}}}}}}}}}_{u}\) , and iterate Eq. ( 2 ) for T steps, where T is a hyperparameter. Then, we let \({{{{{{{\bf{H}}}}}}}}={f}_{{{{{{{{\mathcal{G}}}}}}}}}({{{{{{{\bf{X}}}}}}}},\, {{{{{{{\bf{E}}}}}}}},\, {{{{{{{\bf{g}}}}}}}})={{{{{{{{\bf{H}}}}}}}}}^{(T)}\) be the final node embeddings coming out of the GNN.

It is well known that Eq. ( 2 ) is remarkably general; it can be used to express popular models such as Transformers 19 as a special case, and it has been argued that all discrete deep learning models can be expressed in this form 20 , 21 . This makes GNNs a perfect framework for benchmarking various approaches to modelling player–player interactions in the context of football.

Different choices of ψ , ϕ and ⨁ yield different architectures. In our case, we utilise a message function that factorises into an attentional mechanism, \(a:{{\mathbb{R}}}^{k}\times {{\mathbb{R}}}^{k}\times {{\mathbb{R}}}^{l}\times {{\mathbb{R}}}^{m}\to {\mathbb{R}}\) :

yielding the graph attention network (GAT) architecture 12 . In our work, specifically, we use a two-layer multilayer perceptron for the attentional mechanism, as proposed by GATv2 11 :

where \({{{{{{{{\bf{W}}}}}}}}}_{1},\, {{{{{{{{\bf{W}}}}}}}}}_{2}\in {{\mathbb{R}}}^{k\times h}\) , \({{{{{{{{\bf{W}}}}}}}}}_{e}\in {{\mathbb{R}}}^{l\times h}\) , \({{{{{{{{\bf{W}}}}}}}}}_{g}\in {{\mathbb{R}}}^{m\times h}\) and \({{{{{{{\bf{a}}}}}}}}\in {{\mathbb{R}}}^{h}\) are the learnable parameters of the attentional mechanism, and LeakyReLU is the leaky rectified linear activation function. This mechanism computes coefficients of interaction (a single scalar value) for each pair of connected nodes ( u ,  v ), which are then normalised across all neighbours of u using the \({{{{{{{\rm{softmax}}}}}}}}\) function.

Through early-stage experimentation, we have ascertained that GATs are capable of matching the performance of more generic choices of ψ (such as the MPNN 17 ) while being more scalable. Hence, we focus our study on the GAT model in this work. More details can be found in the subsection “Ablation study” section.

Geometric deep learning

In spite of the power of Eq. ( 2 ), using it in its full generality is often prone to overfitting, given the large number of parameters contained in ψ and ϕ . This problem is exacerbated in the football analytics domain, where gold-standard data is generally very scarce—for example, in the English Premier League, only a few hundred games are played every season.

In order to tackle this issue, we can exploit the immense regularity of data arising from football games. Strategically equivalent game states are also called transpositions, and symmetries such as arriving at the same chess position through different move sequences have been exploited computationally since the 1960s 22 . Similarly, game rotations and reflections may yield equivalent strategic situations 23 . Using the blueprint of geometric deep learning (GDL) 10 , we can design specialised GNN architectures that exploit this regularity.

That is, geometric deep learning is a generic methodology for deriving mathematical constraints on neural networks, such that they will behave predictably when inputs are transformed in certain ways. In several important cases, these constraints can be directly resolved, directly informing neural network architecture design. For a comprehensive example of point clouds under 3D rotational symmetry, see Fuchs et al. 24 .

To elucidate several aspects of the GDL framework on a high level, let us assume that there exists a group of input data transformations (symmetries), \({\mathfrak{G}}\) under which the ground-truth label remains unchanged. Specifically, if we let y ( X ,  E ,  g ) be the label given to the graph featurised with X ,  E ,  g , then for every transformation \({\mathfrak{g}}\in {\mathfrak{G}}\) , the following property holds:

This condition is also referred to as \({\mathfrak{G}}\) -invariance. Here, by \({\mathfrak{g}}({{{{{{{\bf{X}}}}}}}})\) we denote the result of transforming X by \({\mathfrak{g}}\) —a concept also known as a group action. More generally, it is a function of the form \({\mathfrak{G}}\times {{{{{{{\mathcal{S}}}}}}}}\to {{{{{{{\mathcal{S}}}}}}}}\) for some state set \({{{{{{{\mathcal{S}}}}}}}}\) . Note that a single group element, \({\mathfrak{g}}\in {\mathfrak{G}}\) can easily produce different actions on different \({{{{{{{\mathcal{S}}}}}}}}\) —in this case, \({{{{{{{\mathcal{S}}}}}}}}\) could be \({{\mathbb{R}}}^{| {{{{{{{\mathcal{V}}}}}}}}| \times k}\) ( X ), \({{\mathbb{R}}}^{| {{{{{{{\mathcal{V}}}}}}}}| \times | {{{{{{{\mathcal{V}}}}}}}}| \times l}\) ( E ) and \({{\mathbb{R}}}^{m}\) ( g ).

It is worth noting that GNNs may also be derived using a GDL perspective if we set the symmetry group \({\mathfrak{G}}\) to \({S}_{| {{{{{{{\mathcal{V}}}}}}}}}|\) , the permutation group of \(| {{{{{{{\mathcal{V}}}}}}}}|\) objects. Owing to the design of Eq. ( 2 ), its outputs will not be dependent on the exact permutation of nodes in the input graph.

Frame averaging

A simple mechanism to enforce \({\mathfrak{G}}\) -invariance, given any predictor \({f}_{{{{{{{{\mathcal{G}}}}}}}}}({{{{{{{\bf{X}}}}}}}},\, {{{{{{{\bf{E}}}}}}}},\, {{{{{{{\bf{g}}}}}}}})\) , performs frame averaging across all \({\mathfrak{G}}\) -transformed inputs:

This ensures that all \({\mathfrak{G}}\) -transformed versions of a particular input (also known as that input’s orbit) will have exactly the same output, satisfying Eq. ( 5 ). A variant of this approach has also been applied in the AlphaGo architecture 25 to encode symmetries of a Go board.

In our specific implementation, we set \({\mathfrak{G}}={D}_{2}=\{{{{{{{{\rm{id}}}}}}}},\leftrightarrow,\updownarrow,\leftrightarrow \updownarrow \}\) , the dihedral group. Exploiting D 2 -invariance allows us to encode quadrant symmetries. Each element of the D 2 group encodes the presence of vertical or horizontal reflections of the input football pitch. Under these transformations, the pitch is assumed completely symmetric, and hence many predictions, such as which player receives the corner kick, or takes a shot from it, can be safely assumed unchanged. As an example of how to compute transformed features in Eq. ( 6 ), ↔( X ) horizontally reflects all positional features of players in X (e.g. the coordinates of the player), and negates the x -axis component of their velocity.

Group convolutions

While the frame averaging approach of Eq. ( 6 ) is a powerful way to restrict GNNs to respect input symmetries, it arguably misses an opportunity for the different \({\mathfrak{G}}\) -transformed views to interact while their computations are being performed. For small groups such as D 2 , a more fine-grained approach can be assumed, operating over a single GNN layer in Eq. ( 2 ), which we will write shortly as \({{{{{{{{\bf{H}}}}}}}}}^{(t)}={g}_{{{{{{{{\mathcal{G}}}}}}}}}({{{{{{{{\bf{H}}}}}}}}}^{(t-1)},\, {{{{{{{\bf{E}}}}}}}},\, {{{{{{{\bf{g}}}}}}}})\) . The condition that we need a symmetry-respecting GNN layer to satisfy is as follows, for all transformations \({\mathfrak{g}}\in {\mathfrak{G}}\) :

that is, it does not matter if we apply \({\mathfrak{g}}\) it to the input or the output of the function \({g}_{{{{{{{{\mathcal{G}}}}}}}}}\) —the final answer is the same. This condition is also referred to as \({\mathfrak{G}}\) -equivariance, and it has recently proved to be a potent paradigm for developing powerful GNNs over biochemical data 24 , 26 .

To satisfy D 2 -equivariance, we apply the group convolution approach 13 . Therein, views of the input are allowed to directly interact with their \({\mathfrak{G}}\) -transformed variants, in a manner very similar to grid convolutions (which is, indeed, a special case of group convolutions, setting \({\mathfrak{G}}\) to be the translation group). We use \({{{{{{{{\bf{H}}}}}}}}}_{{\mathfrak{g}}}^{(t)}\) to denote the \({\mathfrak{g}}\) -transformed view of the latent node features at layer t . Omitting E and g inputs for brevity, and using our previously designed layer \({g}_{{{{{{{{\mathcal{G}}}}}}}}}\) as a building block, we can perform a group convolution as follows:

Here, ∥ is the concatenation operation, joining the two node feature matrices column-wise; \({{\mathfrak{g}}}^{-1}\) is the inverse transformation to \({\mathfrak{g}}\) (which must exist as \({\mathfrak{G}}\) is a group); and \({{\mathfrak{g}}}^{-1}{\mathfrak{h}}\) is the composition of the two transformations.

Effectively, Eq. ( 8 ) implies our D 2 -equivariant GNN needs to maintain a node feature matrix \({{{{{{{{\bf{H}}}}}}}}}_{{\mathfrak{g}}}^{(t)}\) for every \({\mathfrak{G}}\) -transformation of the current input, and these views are recombined by invoking \({g}_{{{{{{{{\mathcal{G}}}}}}}}}\) on all pairs related together by applying a transformation \({\mathfrak{h}}\) . Note that all reflections are self-inverses, hence, in D 2 , \({\mathfrak{g}}={{\mathfrak{g}}}^{-1}\) .

It is worth noting that both the frame averaging in Eq. ( 6 ) and group convolution in Eq. ( 8 ) are similar in spirit to data augmentation. However, whereas standard data augmentation would only show one view at a time to the model, a frame averaging/group convolution architecture exhaustively generates all views and feeds them to the model all at once. Further, group convolutions allow these views to explicitly interact in a way that does not break symmetries. Here lies the key difference between the two approaches: frame averaging and group convolutions rigorously enforce the symmetries in \({\mathfrak{G}}\) , whereas data augmentation only provides implicit hints to the model about satisfying them. As a consequence of the exhaustive generation, Eqs. ( 6 ) and ( 8 ) are only feasible for small groups like D 2 . For larger groups, approaches like Steerable CNNs 27 may be employed.

Network architectures

While the three benchmark tasks we are performing have minor differences in the global features available to the model, the neural network models designed for them all have the same encoder–decoder architecture. The encoder has the same structure in all tasks, while the decoder model is tailored to produce appropriately shaped outputs for each benchmark task.

Given an input graph, TacticAI’s model first generates all relevant D 2 -transformed versions of it, by appropriately reflecting the player coordinates and velocities. We refer to the original input graph as the identity view, and the remaining three D 2 -transformed graphs as reflected views.

Once the views are prepared, we apply four group convolutional layers (Eq. ( 8 )) with a GATv2 base model (Eqs. ( 3 ) and ( 4 )) as the \({g}_{{{{{{{{\mathcal{G}}}}}}}}}\) function. Specifically, this means that, in Eqs. ( 3 ) and ( 4 ), every instance of \({{{{{{{{\bf{h}}}}}}}}}_{u}^{(t-1)}\) is replaced by the concatenation of \({({{{{{{{{\bf{h}}}}}}}}}_{{\mathfrak{h}}}^{(t-1)})}_{u}\parallel {({{{{{{{{\bf{h}}}}}}}}}_{{{\mathfrak{g}}}^{-1}{\mathfrak{h}}}^{(t-1)})}_{u}\) . Each GATv2 layer has eight attention heads and computes four latent features overall per player. Accordingly, once the four group convolutions are performed, we have a representation of \({{{{{{{\bf{H}}}}}}}}\in {{\mathbb{R}}}^{4\times 22\times 4}\) , where the first dimension corresponds to the four views ( \({{{{{{{{\bf{H}}}}}}}}}_{{{{{{{{\rm{id}}}}}}}}},\, {{{{{{{{\bf{H}}}}}}}}}_{\leftrightarrow },\, {{{{{{{{\bf{H}}}}}}}}}_{\updownarrow },\, {{{{{{{{\bf{H}}}}}}}}}_{\leftrightarrow \updownarrow }\in {{\mathbb{R}}}^{22\times 4}\) ), the second dimension corresponds to the players (eleven on each team), and the third corresponds to the 4-dimensional latent vector for each player node in this particular view. How this representation is used by the decoder depends on the specific downstream task, as we detail below.

For receiver prediction, which is a fully invariant function (i.e. reflections do not change the receiver), we perform simple frame averaging across all views, arriving at

and then learn a node-wise classifier over the rows of \({{{{{{{{\bf{H}}}}}}}}}^{{{{{{{{\rm{node}}}}}}}}}\in {{\mathbb{R}}}^{22\times 4}\) . We further decode H node into a logit vector \({{{{{{{\bf{O}}}}}}}}\in {{\mathbb{R}}}^{22}\) with a linear layer before computing the corresponding softmax cross entropy loss.

For shot prediction, which is once again fully invariant (i.e. reflections do not change the probability of a shot), we can further average the frame-averaged features across all players to get a global graph representation:

and then learn a binary classifier over \({{{{{{{{\bf{h}}}}}}}}}^{{{{{{{{\rm{graph}}}}}}}}}\in {{\mathbb{R}}}^{4}\) . Specifically, we decode the hidden vector into a single logit with a linear layer and compute the sigmoid binary cross-entropy loss with the corresponding label.

For guided generation (position/velocity adjustments), we generate the player positions and velocities with respect to a particular outcome of interest for the human coaches, predicted over the rows of the hidden feature matrix. For example, the model may adjust the defensive setup to decrease the shot probability by the attacking team. The model output is now equivariant rather than invariant—reflecting the pitch appropriately reflects the predicted positions and velocity vectors. As such, we cannot perform frame averaging, and take only the identity view’s features, \({{{{{{{{\bf{H}}}}}}}}}_{{{{{{{{\rm{id}}}}}}}}}\in {{\mathbb{R}}}^{22\times 4}\) . From this latent feature matrix, we can then learn a conditional distribution from each row, which models the positions or velocities of the corresponding player. To do this, we extend the backbone encoder with conditional variational autoencoder (CVAE 28 , 29 ). Specifically, for the u -th row of H id , h u , we first map its latent embedding to the parameters of a two-dimensional Gaussian distribution \({{{{{{{\mathcal{N}}}}}}}}({\mu }_{u}| {\sigma }_{u})\) , and then sample the coordinates and velocities from this distribution. At training time, we can efficiently propagate gradients through this sampling operation using the reparameterisation trick 28 : sample a random value \({\epsilon }_{u} \sim {{{{{{{\mathcal{N}}}}}}}}(0,1)\) for each player from the unit Gaussian distribution, and then treat μ u  +  σ u ϵ u as the sample for this player. In what follows, we omit edge features for brevity. For each corner kick sample X with the corresponding outcome o (e.g. a binary value indicating a shot event), we extend the standard VAE loss 28 , 29 to our case of outcome-conditional guided generation as

where h u is the player embedding corresponding to the u th row of H id , and \({\mathbb{KL}}\) is Kullback–Leibler (KL) divergence. Specifically, the first term is the generation loss between the real player input x u and the reconstructed sample decoded from h u with the decoder p ϕ . Using the KL term, the distribution of the latent embedding h u is regularised towards p ( h u ∣ o ), which is a multivariate Gaussian in our case.

A complete high-level summary of the generic encoder–decoder equivariant architecture employed by TacticAI can be summarised in Supplementary Fig.  2 . In the following section, we will provide empirical evidence for justifying these architectural decisions. This will be done through targeted ablation studies on our predictive benchmarks (receiver prediction and shot prediction).

Ablation study

We leveraged the receiver prediction task as a way to evaluate various base model architectures, and directly quantitatively assess the contributions of geometric deep learning in this context. We already see that the raw corner kick data can be better represented through geometric deep learning, yielding separable clusters in the latent space that could correspond to different attacking or defending tactics (Fig.  2 ). In addition, we hypothesise that these representations can also yield better performance on the task of receiver prediction. Accordingly, we ablate several design choices using deep learning on this task, as illustrated by the following four questions:

Does a factorised graph representation help? To assess this, we compare it against a convolutional neural network (CNN 30 ) baseline, which does not leverage a graph representation.

Does a graph structure help? To assess this, we compare against a Deep Sets 31 baseline, which only models each node in isolation without considering adjacency information—equivalently, setting each neighbourhood \({{{{{{{{\mathcal{N}}}}}}}}}_{u}\) to a singleton set { u }.

Are attentional GNNs a good strategy? To assess this, we compare against a message passing neural network 32 , MPNN baseline, which uses the fully potent GNN layer from Eq. ( 2 ) instead of the GATv2.

Does accounting for symmetries help? To assess this, we compare our geometric GATv2 baseline against one which does not utilise D 2 group convolutions but utilises D 2 frame averaging, and one which does not explicitly utilise any aspect of D 2 symmetries at all.

Each of these models has been trained for a fixed budget of 50,000 training steps. The test top- k receiver prediction accuracies of the trained models are provided in Supplementary Table  2 . As already discussed in the section “Results”, there is a clear advantage to using a full graph structure, as well as directly accounting for reflection symmetry. Further, the usage of the MPNN layer leads to slight overfitting compared to the GATv2, illustrating how attentional GNNs strike a good balance of expressivity and data efficiency for this task. Our analysis highlights the quantitative benefits of both graph representation learning and geometric deep learning for football analytics from tracking data. We also provide a brief ablation study for the shot prediction task in Supplementary Table  3 .

Training details

We train each of TacticAI’s models in isolation, using NVIDIA Tesla P100 GPUs. To minimise overfitting, each model’s learning objective is regularised with an L 2 norm penalty with respect to the network parameters. During training, we use the Adam stochastic gradient descent optimiser 33 over the regularised loss.

All models, including baselines, have been given an equal hyperparameter tuning budget, spanning the number of message passing steps ({1, 2, 4}), initial learning rate ({0.0001, 0.00005}), batch size ({128, 256}) and L 2 regularisation coefficient ({0.01, 0.005, 0.001, 0.0001, 0}). We summarise the chosen hyperparameters of each TacticAI model in Supplementary Table  1 .

Data availability

The data collected in the human experiments in this study have been deposited in the Zenodo database under accession code https://zenodo.org/records/10557063 , and the processed data which is used in the statistical analysis and to generate the relevant figures in the main text are available under the same accession code. The input and output data generated and/or analysed during the current study are protected and are not available due to data privacy laws and licensing restrictions. However, contact details of the input data providers are available from the corresponding authors on reasonable request.

Code availability

All the core models described in this research were built with the Graph Neural Network processors provided by the CLRS Algorithmic Reasoning Benchmark 18 , and their source code is available at https://github.com/google-deepmind/clrs . We are unable to release our code for this work as it was developed in a proprietary context; however, the corresponding authors are open to answer specific questions concerning re-implementations on request. For general data analysis, we used the following freely available packages: numpy v1.25.2 , pandas v1.5.3 , matplotlib v3.6.1 , seaborn v0.12.2 and scipy v1.9.3 . Specifically, the code of the statistical analysis conducted in this study is available at https://zenodo.org/records/10557063 .

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Acknowledgements

We gratefully acknowledge the support of James French, Timothy Waskett, Hans Leitert and Benjamin Hervey for their extensive efforts in analysing TacticAI’s outputs. Further, we are thankful to Kevin McKee, Sherjil Ozair and Beatrice Bevilacqua for useful technical discussions, and Marc Lanctôt and Satinder Singh for reviewing the paper prior to submission.

Author information

These authors contributed equally: Zhe Wang, Petar Veličković, Daniel Hennes.

Authors and Affiliations

Google DeepMind, 6-8 Handyside Street, London, N1C 4UZ, UK

Zhe Wang, Petar Veličković, Daniel Hennes, Nenad Tomašev, Laurel Prince, Michael Kaisers, Yoram Bachrach, Romuald Elie, Li Kevin Wenliang, Federico Piccinini, Jerome Connor, Yi Yang, Adrià Recasens, Mina Khan, Nathalie Beauguerlange, Pablo Sprechmann, Pol Moreno, Nicolas Heess & Demis Hassabis

Liverpool FC, AXA Training Centre, Simonswood Lane, Kirkby, Liverpool, L33 5XB, UK

William Spearman

Liverpool FC, Kirkby, UK

University of Alberta, Amii, Edmonton, AB, T6G 2E8, Canada

Michael Bowling

Google DeepMind, London, UK

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Contributions

Z.W., D. Hennes, L.P. and K.T. coordinated and organised the research effort leading to this paper. P.V. and Z.W. developed the core TacticAI models. Z.W., W.S. and I.G. prepared the Premier League corner kick dataset used for training and evaluating these models. P.V., Z.W., D. Hennes and N.T. designed the case study with human experts and Z.W. and P.V. performed the qualitative evaluation and statistical analysis of its outcomes. Z.W., P.V., D. Hennes, N.T., L.P., M. Kaisers, Y.B., R.E., L.K.W., F.P., W.S., I.G., N.H., M.B., D. Hassabis and K.T. contributed to writing the paper and providing feedback on the final manuscript. J.C., Y.Y., A.R., M. Khan, N.B., P.S. and P.M. contributed valuable technical and implementation discussions throughout the work’s development.

Corresponding authors

Correspondence to Zhe Wang , Petar Veličković or Karl Tuyls .

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Competing interests.

The authors declare no competing interests but the following competing interests: TacticAI was developed during the course of the Authors’ employment at Google DeepMind and Liverpool Football Club, as applicable to each Author.

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Wang, Z., Veličković, P., Hennes, D. et al. TacticAI: an AI assistant for football tactics. Nat Commun 15 , 1906 (2024). https://doi.org/10.1038/s41467-024-45965-x

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DoD enhances defense research with $17.6M funding to academic teams

O n 10 March the US Department of Defense (DoD) announced a move to enhance the United States' defence research capabilities, awarding $17.6m to 27 academic teams through the Defense Established Program to Stimulate Competitive Research (DEPSCoR). 

This initiative aims to enhance the basic research infrastructure across institutions of higher learning in states and territories that have historically been underutilised in this arena.

DEPSCoR is a strategic programme crafted to not only increase the number of academics engaging in research areas critical to the DoD but also to provide an enduring boost to the science and engineering capabilities of the institutions. 

Dr. Bindu Nair, the director of DoD's Basic Research Office, emphasised the importance of leveraging the diverse research talents found throughout the country. She stated: "The Department of Defense's science and technology mission relies on an ecosystem of creative and insightful researchers in every state."

The funding announcement follows the conclusion of two competitions held in Fiscal Year 2023: the DEPSCoR Research Collaboration competition and the DEPSCoR Capacity Building competition.

The Research Collaboration competition invited tenured and tenure-track faculty members from the 37 eligible states and territories to submit proposals that could potentially align with the DoD's research needs. This initiative aims to integrate new researchers into the DoD's research community by pairing them with experienced mentors who have previously collaborated with the department. 

Out of over 80 white paper submissions, 25 collaborative teams were selected , with principal investigators hailing from universities across 15 states. Each team is set to receive up to $600,000 over a three-year period to conduct science and engineering research pertinent to the DoD.

Winning teams include the University of Minnesota, Twin Cities, which, under principal investigator Marien Simeni, will explore ablation on hypersonic vehicle heat shielding by using pulse lasers. 

Principal investigator Nek Sharan, of Auburn university, was awarded the DoD grant money to investigate dynamic loads and flow structures on hovering rotor blades above inclined ground. 

Separately, the Capacity Building competition focuses on advancing the strategic objectives of higher education institutes. Its goal is to improve these institutions' standings as competitive centres for research and development. 

From over 15 submissions, two teams led by executive offices at Louisiana Tech University and the University of Tulsa were chosen. These teams are awarded up to $1.5m each over two years to engage in activities aimed at achieving excellence in basic research areas of importance to the DoD.

Louisiana Tech University will research the domestic manufacture of microelectronics for harsh environments ins next-generation nitride-based thin films, under principal investigator Zhi Liang.

Principal investigator Rose Gamble of the University of Tulsa will oversee research into ultra-high temperature materials for extreme environments.

"DoD enhances defense research with $17.6M funding to academic teams" was originally created and published by Army Technology , a GlobalData owned brand.

The information on this site has been included in good faith for general informational purposes only. It is not intended to amount to advice on which you should rely, and we give no representation, warranty or guarantee, whether express or implied as to its accuracy or completeness. You must obtain professional or specialist advice before taking, or refraining from, any action on the basis of the content on our site.

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Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Intermittent fasting linked to higher risk of cardiovascular death, research suggests

Intermittent fasting, a diet pattern that involves alternating between periods of fasting and eating, can lower blood pressure and help some people lose weight , past research has indicated.

But an analysis presented Monday at the American Heart Association’s scientific sessions in Chicago challenges the notion that intermittent fasting is good for heart health. Instead, researchers from Shanghai Jiao Tong University School of Medicine in China found that people who restricted food consumption to less than eight hours per day had a 91% higher risk of dying from cardiovascular disease over a median period of eight years, relative to people who ate across 12 to 16 hours.

It’s some of the first research investigating the association between time-restricted eating (a type of intermittent fasting) and the risk of death from cardiovascular disease.

The analysis — which has not yet been peer-reviewed or published in an academic journal — is based on data from the Centers for Disease Control and Prevention’s National Health and Nutrition Examination Survey collected between 2003 and 2018. The researchers analyzed responses from around 20,000 adults who recorded what they ate for at least two days, then looked at who had died from cardiovascular disease after a median follow-up period of eight years.

However, Victor Wenze Zhong, a co-author of the analysis, said it’s too early to make specific recommendations about intermittent fasting based on his research alone.

“Practicing intermittent fasting for a short period such as 3 months may likely lead to benefits on reducing weight and improving cardiometabolic health,” Zhong said via email. But he added that people “should be extremely cautious” about intermittent fasting for longer periods of time, such as years.

Intermittent fasting regimens vary widely. A common schedule is to restrict eating to a period of six to eight hours per day, which can lead people to consume fewer calories, though some eat the same amount in a shorter time. Another popular schedule is the "5:2 diet," which involves eating 500 to 600 calories on two nonconsecutive days of the week but eating normally for the other five.

Zhong said it’s not clear why his research found an association between time-restricted eating and a risk of death from cardiovascular disease. He offered an observation, though: People who limited their eating to fewer than eight hours per day had less lean muscle mass than those who ate for 12 to 16 hours. Low lean muscle mass has been linked to a higher risk of cardiovascular death .

Cardiovascular and nutrition experts who were not involved in the analysis offered several theories about what might explain the results.

Dr. Benjamin Horne, a research professor at Intermountain Health in Salt Lake City, said fasting can increase stress hormones such as cortisol and adrenaline, since the body doesn’t know when to expect food next and goes into survival mode. That added stress may raise the short-term risk of heart problems among vulnerable groups, he said, particularly elderly people or those with chronic health conditions.

Horne’s research has shown that fasting twice a week for four weeks, then once a week for 22 weeks may increase a person’s risk of dying after one year but decrease their 10-year risk of chronic disease.

“In the long term, what it does is reduces those risk factors for heart disease and reduces the risk factors for diabetes and so forth — but in the short term, while you’re actually doing it, your body is in a state where it’s at a higher risk of having problems,” he said.

Even so, Horne added, the analysis “doesn’t change my perspective that there are definite benefits from fasting, but it’s a cautionary tale that we need to be aware that there are definite, potentially major, adverse effects.” 

Intermittent fasting gained popularity about a decade ago, when the 5:2 diet was touted as a weight loss strategy in the U.K. In the years to follow, several celebrities espoused the benefits of an eight-hour eating window for weight loss, while some Silicon Valley tech workers believed that extreme periods of fasting boosted productivity . Some studies have also suggested that intermittent fasting might help extend people’s lifespans by warding off disease .

However, a lot of early research on intermittent fasting involved animals. In the last seven years or so, various clinical trials have investigated potential benefits for humans, including for heart health.

“The purpose of intermittent fasting is to cut calories, lose weight,” said Penny Kris-Etherton, emeritus professor of nutritional sciences at Penn State University and a member of the American Heart Association nutrition committee. “It’s really how intermittent fasting is implemented that’s going to explain a lot of the benefits or adverse associations.”

Dr. Francisco Lopez-Jimenez, a cardiologist at Mayo Clinic, said the timing of when people eat may influence the effects they see. 

“I haven’t met a single person or patient that has been practicing intermittent fasting by skipping dinner,” he said, noting that people more often skip breakfast, a schedule associated with an increased risk of heart disease and death .

The new research comes with limitations: It relies on people’s memories of what they consumed over a 24-hour period and doesn’t consider the nutritional quality of the food they ate or how many calories they consumed during an eating window.

So some experts found the analysis too narrow.

“It’s a retrospective study looking at two days’ worth of data, and drawing some very big conclusions from a very limited snapshot into a person’s lifestyle habits,” said Dr. Pam Taub, a cardiologist at UC San Diego Health.

Taub said her patients have seen “incredible benefits” from fasting regimens.

“I would continue doing it,” she said. “For people that do intermittent fasting, their individual results speak for themselves. Most people that do intermittent fasting, the reason they continue it is they see a decrease in their weight. They see a decrease in blood pressure. They see an improvement in their LDL cholesterol.” 

Kris-Etherton, however, urged caution: “Maybe consider a pause in intermittent fasting until we have more information or until the results of the study can be better explained,” she said.

Aria Bendix is the breaking health reporter for NBC News Digital.