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How to Write a Research Paper Introduction (with Examples)

The research paper introduction section, along with the Title and Abstract, can be considered the face of any research paper. The following article is intended to guide you in organizing and writing the research paper introduction for a quality academic article or dissertation.

The research paper introduction aims to present the topic to the reader. A study will only be accepted for publishing if you can ascertain that the available literature cannot answer your research question. So it is important to ensure that you have read important studies on that particular topic, especially those within the last five to ten years, and that they are properly referenced in this section. 1 What should be included in the research paper introduction is decided by what you want to tell readers about the reason behind the research and how you plan to fill the knowledge gap. The best research paper introduction provides a systemic review of existing work and demonstrates additional work that needs to be done. It needs to be brief, captivating, and well-referenced; a well-drafted research paper introduction will help the researcher win half the battle.

The introduction for a research paper is where you set up your topic and approach for the reader. It has several key goals:

• Present your research topic
• Capture reader interest
• Summarize existing research
• Position your own approach
• Define your specific research problem and problem statement
• Highlight the novelty and contributions of the study
• Give an overview of the paper’s structure

The research paper introduction can vary in size and structure depending on whether your paper presents the results of original empirical research or is a review paper. Some research paper introduction examples are only half a page while others are a few pages long. In many cases, the introduction will be shorter than all of the other sections of your paper; its length depends on the size of your paper as a whole.

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Table of Contents

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The introduction in a research paper is placed at the beginning to guide the reader from a broad subject area to the specific topic that your research addresses. They present the following information to the reader

• Scope: The topic covered in the research paper
• Context: Background of your topic
• Importance: Why your research matters in that particular area of research and the industry problem that can be targeted

The research paper introduction conveys a lot of information and can be considered an essential roadmap for the rest of your paper. A good introduction for a research paper is important for the following reasons:

• It stimulates your reader’s interest: A good introduction section can make your readers want to read your paper by capturing their interest. It informs the reader what they are going to learn and helps determine if the topic is of interest to them.
• It helps the reader understand the research background: Without a clear introduction, your readers may feel confused and even struggle when reading your paper. A good research paper introduction will prepare them for the in-depth research to come. It provides you the opportunity to engage with the readers and demonstrate your knowledge and authority on the specific topic.
• It explains why your research paper is worth reading: Your introduction can convey a lot of information to your readers. It introduces the topic, why the topic is important, and how you plan to proceed with your research.
• It helps guide the reader through the rest of the paper: The research paper introduction gives the reader a sense of the nature of the information that will support your arguments and the general organization of the paragraphs that will follow. It offers an overview of what to expect when reading the main body of your paper.

What are the parts of introduction in the research?

A good research paper introduction section should comprise three main elements: 2

• What is known: This sets the stage for your research. It informs the readers of what is known on the subject.
• What is lacking: This is aimed at justifying the reason for carrying out your research. This could involve investigating a new concept or method or building upon previous research.
• What you aim to do: This part briefly states the objectives of your research and its major contributions. Your detailed hypothesis will also form a part of this section.

How to write a research paper introduction?

The first step in writing the research paper introduction is to inform the reader what your topic is and why it’s interesting or important. This is generally accomplished with a strong opening statement. The second step involves establishing the kinds of research that have been done and ending with limitations or gaps in the research that you intend to address. Finally, the research paper introduction clarifies how your own research fits in and what problem it addresses. If your research involved testing hypotheses, these should be stated along with your research question. The hypothesis should be presented in the past tense since it will have been tested by the time you are writing the research paper introduction.

The following key points, with examples, can guide you when writing the research paper introduction section:

• Highlight the importance of the research field or topic
• Describe the background of the topic
• Present an overview of current research on the topic

Example: The inclusion of experiential and competency-based learning has benefitted electronics engineering education. Industry partnerships provide an excellent alternative for students wanting to engage in solving real-world challenges. Industry-academia participation has grown in recent years due to the need for skilled engineers with practical training and specialized expertise. However, from the educational perspective, many activities are needed to incorporate sustainable development goals into the university curricula and consolidate learning innovation in universities.

• Reveal a gap in existing research or oppose an existing assumption
• Formulate the research question

Example: There have been plausible efforts to integrate educational activities in higher education electronics engineering programs. However, very few studies have considered using educational research methods for performance evaluation of competency-based higher engineering education, with a focus on technical and or transversal skills. To remedy the current need for evaluating competencies in STEM fields and providing sustainable development goals in engineering education, in this study, a comparison was drawn between study groups without and with industry partners.

• State the purpose of your study
• Highlight the key characteristics of your study
• Describe important results
• Highlight the novelty of the study.
• Offer a brief overview of the structure of the paper.

Example: The study evaluates the main competency needed in the applied electronics course, which is a fundamental core subject for many electronics engineering undergraduate programs. We compared two groups, without and with an industrial partner, that offered real-world projects to solve during the semester. This comparison can help determine significant differences in both groups in terms of developing subject competency and achieving sustainable development goals.

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With Paperpal Copilot, create a research paper introduction effortlessly. In this step-by-step guide, we’ll walk you through how Paperpal transforms your initial ideas into a polished and publication-ready introduction.

How to use Paperpal to write the Introduction section

Step 1: Sign up on Paperpal and click on the Copilot feature, under this choose Outlines > Research Article > Introduction

Step 2: Add your unstructured notes or initial draft, whether in English or another language, to Paperpal, which is to be used as the base for your content.

Step 3: Fill in the specifics, such as your field of study, brief description or details you want to include, which will help the AI generate the outline for your Introduction.

Step 4: Use this outline and sentence suggestions to develop your content, adding citations where needed and modifying it to align with your specific research focus.

Step 5: Turn to Paperpal’s granular language checks to refine your content, tailor it to reflect your personal writing style, and ensure it effectively conveys your message.

You can use the same process to develop each section of your article, and finally your research paper in half the time and without any of the stress.

The purpose of the research paper introduction is to introduce the reader to the problem definition, justify the need for the study, and describe the main theme of the study. The aim is to gain the reader’s attention by providing them with necessary background information and establishing the main purpose and direction of the research.

The length of the research paper introduction can vary across journals and disciplines. While there are no strict word limits for writing the research paper introduction, an ideal length would be one page, with a maximum of 400 words over 1-4 paragraphs. Generally, it is one of the shorter sections of the paper as the reader is assumed to have at least a reasonable knowledge about the topic. 2 For example, for a study evaluating the role of building design in ensuring fire safety, there is no need to discuss definitions and nature of fire in the introduction; you could start by commenting upon the existing practices for fire safety and how your study will add to the existing knowledge and practice.

When deciding what to include in the research paper introduction, the rest of the paper should also be considered. The aim is to introduce the reader smoothly to the topic and facilitate an easy read without much dependency on external sources. 3 Below is a list of elements you can include to prepare a research paper introduction outline and follow it when you are writing the research paper introduction. Topic introduction: This can include key definitions and a brief history of the topic. Research context and background: Offer the readers some general information and then narrow it down to specific aspects. Details of the research you conducted: A brief literature review can be included to support your arguments or line of thought. Rationale for the study: This establishes the relevance of your study and establishes its importance. Importance of your research: The main contributions are highlighted to help establish the novelty of your study Research hypothesis: Introduce your research question and propose an expected outcome. Organization of the paper: Include a short paragraph of 3-4 sentences that highlights your plan for the entire paper

Cite only works that are most relevant to your topic; as a general rule, you can include one to three. Note that readers want to see evidence of original thinking. So it is better to avoid using too many references as it does not leave much room for your personal standpoint to shine through. Citations in your research paper introduction support the key points, and the number of citations depend on the subject matter and the point discussed. If the research paper introduction is too long or overflowing with citations, it is better to cite a few review articles rather than the individual articles summarized in the review. A good point to remember when citing research papers in the introduction section is to include at least one-third of the references in the introduction.

The literature review plays a significant role in the research paper introduction section. A good literature review accomplishes the following: Introduces the topic – Establishes the study’s significance – Provides an overview of the relevant literature – Provides context for the study using literature – Identifies knowledge gaps However, remember to avoid making the following mistakes when writing a research paper introduction: Do not use studies from the literature review to aggressively support your research Avoid direct quoting Do not allow literature review to be the focus of this section. Instead, the literature review should only aid in setting a foundation for the manuscript.

Remember the following key points for writing a good research paper introduction: 4

• Avoid stuffing too much general information: Avoid including what an average reader would know and include only that information related to the problem being addressed in the research paper introduction. For example, when describing a comparative study of non-traditional methods for mechanical design optimization, information related to the traditional methods and differences between traditional and non-traditional methods would not be relevant. In this case, the introduction for the research paper should begin with the state-of-the-art non-traditional methods and methods to evaluate the efficiency of newly developed algorithms.
• Avoid packing too many references: Cite only the required works in your research paper introduction. The other works can be included in the discussion section to strengthen your findings.
• Avoid extensive criticism of previous studies: Avoid being overly critical of earlier studies while setting the rationale for your study. A better place for this would be the Discussion section, where you can highlight the advantages of your method.
• Avoid describing conclusions of the study: When writing a research paper introduction remember not to include the findings of your study. The aim is to let the readers know what question is being answered. The actual answer should only be given in the Results and Discussion section.

To summarize, the research paper introduction section should be brief yet informative. It should convince the reader the need to conduct the study and motivate him to read further. If you’re feeling stuck or unsure, choose trusted AI academic writing assistants like Paperpal to effortlessly craft your research paper introduction and other sections of your research article.

1. Jawaid, S. A., & Jawaid, M. (2019). How to write introduction and discussion. Saudi Journal of Anaesthesia, 13(Suppl 1), S18.

2. Dewan, P., & Gupta, P. (2016). Writing the title, abstract and introduction: Looks matter!. Indian pediatrics, 53, 235-241.

3. Cetin, S., & Hackam, D. J. (2005). An approach to the writing of a scientific Manuscript1. Journal of Surgical Research, 128(2), 165-167.

4. Bavdekar, S. B. (2015). Writing introduction: Laying the foundations of a research paper. Journal of the Association of Physicians of India, 63(7), 44-6.

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How to Write an Introduction for a Research Paper

How to write an introduction for a research paper? Eventually (and with practice) all writers will develop their own strategy for writing the perfect introduction for a research paper. Once you are comfortable with writing, you will probably find your own, but coming up with a good strategy can be tough for beginning writers.

The Purpose of an Introduction

Your opening paragraphs, phrases for introducing thesis statements, research paper introduction examples, using the introduction to map out your research paper.

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• First write your thesis.Your thesis should state the main idea in specific terms.
• After you have a working thesis, tackle the body of your paper before you write the rest of the introduction. Each paragraph in the body should explore one specific topic that proves, or summarizes your thesis. Writing is a thinking process. Once you have worked your way through that process by writing the body of the paper, you will have an intimate understanding of how you are supporting your thesis. After you have written the body paragraphs, go back and rewrite your thesis to make it more specific and to connect it to the topics you addressed in the body paragraph.
• Revise your introduction several times, saving each revision. Be sure your introduction previews the topics you are presenting in your paper. One way of doing this is to use keywords from the topic sentences in each paragraph to introduce, or preview, the topics in your introduction.This “preview” will give your reader a context for understanding how you will make your case.
• Experiment by taking different approaches to your thesis with every revision you make. Play with the language in the introduction. Strike a new tone. Go back and compare versions. Then pick the one that works most effectively with the body of your research paper.
• Do not try to pack everything you want to say into your introduction. Just as your introduction should not be too short, it should also not be too long. Your introduction should be about the same length as any other paragraph in your research paper. Let the content—what you have to say—dictate the length.

The first page of your research paper should draw the reader into the text. It is the paper’s most important page and, alas, often the worst written. There are two culprits here and effective ways to cope with both of them.

First, the writer is usually straining too hard to say something terribly BIG and IMPORTANT about the thesis topic. The goal is worthy, but the aim is unrealistically high. The result is often a muddle of vague platitudes rather than a crisp, compelling introduction to the thesis. Want a familiar example? Listen to most graduation speakers. Their goal couldn’t be loftier: to say what education means and to tell an entire football stadium how to live the rest of their lives. The results are usually an avalanche of clichés and sodden prose.

The second culprit is bad timing. The opening and concluding paragraphs are usually written late in the game, after the rest of the thesis is finished and polished. There’s nothing wrong with writing these sections last. It’s usually the right approach since you need to know exactly what you are saying in the substantive middle sections of the thesis before you can introduce them effectively or draw together your findings. But having waited to write the opening and closing sections, you need to review and edit them several times to catch up. Otherwise, you’ll putting the most jagged prose in the most tender spots. Edit and polish your opening paragraphs with extra care. They should draw readers into the paper.

After you’ve done some extra polishing, I suggest a simple test for the introductory section. As an experiment, chop off the first few paragraphs. Let the paper begin on, say, paragraph 2 or even page 2. If you don’t lose much, or actually gain in clarity and pace, then you’ve got a problem.

There are two solutions. One is to start at this new spot, further into the text. After all, that’s where you finally gain traction on your subject. That works best in some cases, and we occasionally suggest it. The alternative, of course, is to write a new opening that doesn’t flop around, saying nothing.

What makes a good opening? Actually, they come in several flavors. One is an intriguing story about your topic. Another is a brief, compelling quote. When you run across them during your reading, set them aside for later use. Don’t be deterred from using them because they “don’t seem academic enough.” They’re fine as long as the rest of the paper doesn’t sound like you did your research in People magazine. The third, and most common, way to begin is by stating your main questions, followed by a brief comment about why they matter.

Whichever opening you choose, it should engage your readers and coax them to continue. Having done that, you should give them a general overview of the project—the main issues you will cover, the material you will use, and your thesis statement (that is, your basic approach to the topic). Finally, at the end of the introductory section, give your readers a brief road map, showing how the paper will unfold. How you do that depends on your topic but here are some general suggestions for phrase choice that may help:

• This analysis will provide …
• This paper analyzes the relationship between …
• This paper presents an analysis of …
• This paper will argue that …
• This topic supports the argument that…
• Research supports the opinion that …
• This paper supports the opinion that …
• An interpretation of the facts indicates …
• The results of this experiment show …
• The results of this research show …

Comparisons/Contrasts

• A comparison will show that …
• By contrasting the results,we see that …
• This paper examines the advantages and disadvantages of …

Definitions/Classifications

• This paper will provide a guide for categorizing the following:…
• This paper provides a definition of …
• This paper explores the meaning of …
• This paper will discuss the implications of …
• A discussion of this topic reveals …
• The following discussion will focus on …

Description

• This report describes…
• This report will illustrate…
• This paper provides an illustration of …

Process/Experimentation

• This paper will identify the reasons behind…
• The results of the experiment show …
• The process revealed that …
• This paper theorizes…
• This paper presents the theory that …
• In theory, this indicates that …

Quotes, anecdotes, questions, examples, and broad statements—all of them can used successfully to write an introduction for a research paper. It’s instructive to see them in action, in the hands of skilled academic writers.

Let’s begin with David M. Kennedy’s superb history, Freedom from Fear: The American People in Depression and War, 1929–1945 . Kennedy begins each chapter with a quote, followed by his text. The quote above chapter 1 shows President Hoover speaking in 1928 about America’s golden future. The text below it begins with the stock market collapse of 1929. It is a riveting account of just how wrong Hoover was. The text about the Depression is stronger because it contrasts so starkly with the optimistic quotation.

“We in America today are nearer the final triumph over poverty than ever before in the history of any land.”—Herbert Hoover, August 11, 1928 Like an earthquake, the stock market crash of October 1929 cracked startlingly across the United States, the herald of a crisis that was to shake the American way of life to its foundations. The events of the ensuing decade opened a fissure across the landscape of American history no less gaping than that opened by the volley on Lexington Common in April 1775 or by the bombardment of Sumter on another April four score and six years later. The ratcheting ticker machines in the autumn of 1929 did not merely record avalanching stock prices. In time they came also to symbolize the end of an era. (David M. Kennedy, Freedom from Fear: The American People in Depression and War, 1929–1945 . New York: Oxford University Press, 1999, p. 10)

Kennedy has exciting, wrenching material to work with. John Mueller faces the exact opposite problem. In Retreat from Doomsday: The Obsolescence of Major War , he is trying to explain why Great Powers have suddenly stopped fighting each other. For centuries they made war on each other with devastating regularity, killing millions in the process. But now, Mueller thinks, they have not just paused; they have stopped permanently. He is literally trying to explain why “nothing is happening now.” That may be an exciting topic intellectually, it may have great practical significance, but “nothing happened” is not a very promising subject for an exciting opening paragraph. Mueller manages to make it exciting and, at the same time, shows why it matters so much. Here’s his opening, aptly entitled “History’s Greatest Nonevent”:

On May 15, 1984, the major countries of the developed world had managed to remain at peace with each other for the longest continuous stretch of time since the days of the Roman Empire. If a significant battle in a war had been fought on that day, the press would have bristled with it. As usual, however, a landmark crossing in the history of peace caused no stir: the most prominent story in the New York Times that day concerned the saga of a manicurist, a machinist, and a cleaning woman who had just won a big Lotto contest. This book seeks to develop an explanation for what is probably the greatest nonevent in human history. (John Mueller, Retreat from Doomsday: The Obsolescence of Major War . New York: Basic Books, 1989, p. 3)

In the space of a few sentences, Mueller sets up his puzzle and reveals its profound human significance. At the same time, he shows just how easy it is to miss this milestone in the buzz of daily events. Notice how concretely he does that. He doesn’t just say that the New York Times ignored this record setting peace. He offers telling details about what they covered instead: “a manicurist, a machinist, and a cleaning woman who had just won a big Lotto contest.” Likewise, David Kennedy immediately entangles us in concrete events: the stunning stock market crash of 1929. These are powerful openings that capture readers’ interests, establish puzzles, and launch narratives.

Sociologist James Coleman begins in a completely different way, by posing the basic questions he will study. His ambitious book, Foundations of Social Theory , develops a comprehensive theory of social life, so it is entirely appropriate for him to begin with some major questions. But he could just as easily have begun with a compelling story or anecdote. He includes many of them elsewhere in his book. His choice for the opening, though, is to state his major themes plainly and frame them as a paradox. Sociologists, he says, are interested in aggregate behavior—how people act in groups, organizations, or large numbers—yet they mostly examine individuals:

A central problem in social science is that of accounting for the function of some kind of social system. Yet in most social research, observations are not made on the system as a whole, but on some part of it. In fact, the natural unit of observation is the individual person…  This has led to a widening gap between theory and research… (James S. Coleman, Foundations of Social Theory . Cambridge, MA: Harvard University Press, 1990, pp. 1–2)

After expanding on this point, Coleman explains that he will not try to remedy the problem by looking solely at groups or aggregate-level data. That’s a false solution, he says, because aggregates don’t act; individuals do. So the real problem is to show the links between individual actions and aggregate outcomes, between the micro and the macro.

The major problem for explanations of system behavior based on actions and orientations at a level below that of the system [in this case, on individual-level actions] is that of moving from the lower level to the system level. This has been called the micro-to-macro problem, and it is pervasive throughout the social sciences. (Coleman, Foundations of Social Theory , p. 6)

Explaining how to deal with this “micro-to-macro problem” is the central issue of Coleman’s book, and he announces it at the beginning.

Coleman’s theory-driven opening stands at the opposite end of the spectrum from engaging stories or anecdotes, which are designed to lure the reader into the narrative and ease the path to a more analytic treatment later in the text. Take, for example, the opening sentences of Robert L. Herbert’s sweeping study Impressionism: Art, Leisure, and Parisian Society : “When Henry Tuckerman came to Paris in 1867, one of the thousands of Americans attracted there by the huge international exposition, he was bowled over by the extraordinary changes since his previous visit twenty years before.” (Robert L. Herbert, Impressionism: Art, Leisure, and Parisian Society . New Haven, CT: Yale University Press, 1988, p. 1.) Herbert fills in the evocative details to set the stage for his analysis of the emerging Impressionist art movement and its connection to Parisian society and leisure in this period.

David Bromwich writes about Wordsworth, a poet so familiar to students of English literature that it is hard to see him afresh, before his great achievements, when he was just a young outsider starting to write. To draw us into Wordsworth’s early work, Bromwich wants us to set aside our entrenched images of the famous mature poet and see him as he was in the 1790s, as a beginning writer on the margins of society. He accomplishes this ambitious task in the opening sentences of Disowned by Memory: Wordsworth’s Poetry of the 1790s :

Wordsworth turned to poetry after the revolution to remind himself that he was still a human being. It was a curious solution, to a difficulty many would not have felt. The whole interest of his predicament is that he did feel it. Yet Wordsworth is now so established an eminence—his name so firmly fixed with readers as a moralist of self-trust emanating from complete self-security—that it may seem perverse to imagine him as a criminal seeking expiation. Still, that is a picture we get from The Borderers and, at a longer distance, from “Tintern Abbey.” (David Bromwich, Disowned by Memory: Wordsworth’s Poetry of the 1790s . Chicago: University of Chicago Press, 1998, p. 1)

That’s a wonderful opening! Look at how much Bromwich accomplishes in just a few words. He not only prepares the way for analyzing Wordsworth’s early poetry; he juxtaposes the anguished young man who wrote it to the self-confident, distinguished figure he became—the eminent man we can’t help remembering as we read his early poetry.

Let us highlight a couple of other points in this passage because they illustrate some intelligent writing choices. First, look at the odd comma in this sentence: “It was a curious solution, to a difficulty many would not have felt.” Any standard grammar book would say that comma is wrong and should be omitted. Why did Bromwich insert it? Because he’s a fine writer, thinking of his sentence rhythm and the point he wants to make. The comma does exactly what it should. It makes us pause, breaking the sentence into two parts, each with an interesting point. One is that Wordsworth felt a difficulty others would not have; the other is that he solved it in a distinctive way. It would be easy for readers to glide over this double message, so Bromwich has inserted a speed bump to slow us down. Most of the time, you should follow grammatical rules, like those about commas, but you should bend them when it serves a good purpose. That’s what the writer does here.

The second small point is the phrase “after the revolution” in the first sentence: “Wordsworth turned to poetry after the revolution to remind himself that he was still a human being.” Why doesn’t Bromwich say “after the French Revolution”? Because he has judged his book’s audience. He is writing for specialists who already know which revolution is reverberating through English life in the 1790s. It is the French Revolution, not the earlier loss of the American colonies. If Bromwich were writing for a much broader audience—say, the New York Times Book Review—he would probably insert the extra word to avoid confusion.

The message “Know your audience” applies to all writers. Don’t talk down to them by assuming they can’t get dressed in the morning. Don’t strut around showing off your book learnin’ by tossing in arcane facts and esoteric language for its own sake. Neither will win over readers.

Bromwich, Herbert, and Coleman open their works in different ways, but their choices work well for their different texts. Your task is to decide what kind of opening will work best for yours. Don’t let that happen by default, by grabbing the first idea you happen upon. Consider a couple of different ways of opening your thesis and then choose the one you prefer. Give yourself some options, think them over, then make an informed choice.

Whether you begin with a story, puzzle, or broad statement, the next part of the introduction should pose your main questions and establish your argument. This is your thesis statement—your viewpoint along with the supporting reasons and evidence. It should be articulated plainly so readers understand full well what your paper is about and what it will argue.

After that, give your readers a road map of what’s to come. That’s normally done at the end of the introductory section (or, in a book, at the end of the introductory chapter). Here’s John J. Mearsheimer presenting such a road map in The Tragedy of Great Power Politics . He not only tells us the order of upcoming chapters, he explains why he’s chosen that order and which chapters are most important:

The Plan of the Book The rest of the chapters in this book are concerned mainly with answering the six big questions about power which I identified earlier. Chapter 2, which is probably the most important chapter in the book, lays out my theory of why states compete for power and why they pursue hegemony. In Chapters 3 and 4, I define power and explain how to measure it. I do this in order to lay the groundwork for testing my theory… (John J. Mearsheimer, The Tragedy of Great Power Politics . New York: W. W. Norton, 2001, p. 27)

As this excerpt makes clear, Mearsheimer has already laid out his “six big questions” in the introduction. Now he’s showing us the path ahead, the path to answering those questions.

At the end of the introduction, give your readers a road map of what’s to come. Tell them what the upcoming sections will be and why they are arranged in this particular order.

After having written your introduction it’s time to move to the biggest part: body of a research paper.

Back to How To Write A Research Paper .

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How to Write a Research Introduction

Last Updated: December 6, 2023 Fact Checked

This article was co-authored by Megan Morgan, PhD . Megan Morgan is a Graduate Program Academic Advisor in the School of Public & International Affairs at the University of Georgia. She earned her PhD in English from the University of Georgia in 2015. There are 7 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 2,653,888 times.

The introduction to a research paper can be the most challenging part of the paper to write. The length of the introduction will vary depending on the type of research paper you are writing. An introduction should announce your topic, provide context and a rationale for your work, before stating your research questions and hypothesis. Well-written introductions set the tone for the paper, catch the reader's interest, and communicate the hypothesis or thesis statement.

Introducing the Topic of the Paper

• In scientific papers this is sometimes known as an "inverted triangle", where you start with the broadest material at the start, before zooming in on the specifics. [2] X Research source
• The sentence "Throughout the 20th century, our views of life on other planets have drastically changed" introduces a topic, but does so in broad terms.
• It provides the reader with an indication of the content of the essay and encourages them to read on.

• For example, if you were writing a paper about the behaviour of mice when exposed to a particular substance, you would include the word "mice", and the scientific name of the relevant compound in the first sentences.
• If you were writing a history paper about the impact of the First World War on gender relations in Britain, you should mention those key words in your first few lines.

• This is especially important if you are attempting to develop a new conceptualization that uses language and terminology your readers may be unfamiliar with.

• If you use an anecdote ensure that is short and highly relevant for your research. It has to function in the same way as an alternative opening, namely to announce the topic of your research paper to your reader.
• For example, if you were writing a sociology paper about re-offending rates among young offenders, you could include a brief story of one person whose story reflects and introduces your topic.
• This kind of approach is generally not appropriate for the introduction to a natural or physical sciences research paper where the writing conventions are different.

Establishing the Context for Your Paper

• It is important to be concise in the introduction, so provide an overview on recent developments in the primary research rather than a lengthy discussion.
• You can follow the "inverted triangle" principle to focus in from the broader themes to those to which you are making a direct contribution with your paper.
• A strong literature review presents important background information to your own research and indicates the importance of the field.

• By making clear reference to existing work you can demonstrate explicitly the specific contribution you are making to move the field forward.
• You can identify a gap in the existing scholarship and explain how you are addressing it and moving understanding forward.

• For example, if you are writing a scientific paper you could stress the merits of the experimental approach or models you have used.
• Stress what is novel in your research and the significance of your new approach, but don't give too much detail in the introduction.
• A stated rationale could be something like: "the study evaluates the previously unknown anti-inflammatory effects of a topical compound in order to evaluate its potential clinical uses".

Specifying Your Research Questions and Hypothesis

• The research question or questions generally come towards the end of the introduction, and should be concise and closely focused.
• The research question might recall some of the key words established in the first few sentences and the title of your paper.
• An example of a research question could be "what were the consequences of the North American Free Trade Agreement on the Mexican export economy?"
• This could be honed further to be specific by referring to a particular element of the Free Trade Agreement and the impact on a particular industry in Mexico, such as clothing manufacture.
• A good research question should shape a problem into a testable hypothesis.

• If possible try to avoid using the word "hypothesis" and rather make this implicit in your writing. This can make your writing appear less formulaic.
• In a scientific paper, giving a clear one-sentence overview of your results and their relation to your hypothesis makes the information clear and accessible. [10] X Trustworthy Source PubMed Central Journal archive from the U.S. National Institutes of Health Go to source
• An example of a hypothesis could be "mice deprived of food for the duration of the study were expected to become more lethargic than those fed normally".

• This is not always necessary and you should pay attention to the writing conventions in your discipline.
• In a natural sciences paper, for example, there is a fairly rigid structure which you will be following.
• A humanities or social science paper will most likely present more opportunities to deviate in how you structure your paper.

Research Introduction Help

Community Q&A

• Use your research papers' outline to help you decide what information to include when writing an introduction. Thanks Helpful 0 Not Helpful 1
• Consider drafting your introduction after you have already completed the rest of your research paper. Writing introductions last can help ensure that you don't leave out any major points. Thanks Helpful 0 Not Helpful 0

• Avoid emotional or sensational introductions; these can create distrust in the reader. Thanks Helpful 50 Not Helpful 12
• Generally avoid using personal pronouns in your introduction, such as "I," "me," "we," "us," "my," "mine," or "our." Thanks Helpful 31 Not Helpful 7
• Don't overwhelm the reader with an over-abundance of information. Keep the introduction as concise as possible by saving specific details for the body of your paper. Thanks Helpful 24 Not Helpful 14

You Might Also Like

• ↑ https://library.sacredheart.edu/c.php?g=29803&p=185916
• ↑ https://www.aresearchguide.com/inverted-pyramid-structure-in-writing.html
• ↑ https://libguides.usc.edu/writingguide/introduction
• ↑ https://writing.wisc.edu/Handbook/PlanResearchPaper.html
• ↑ https://dept.writing.wisc.edu/wac/writing-an-introduction-for-a-scientific-paper/
• ↑ https://writing.wisc.edu/handbook/assignments/planresearchpaper/
• ↑ http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3178846/

About This Article

To introduce your research paper, use the first 1-2 sentences to describe your general topic, such as “women in World War I.” Include and define keywords, such as “gender relations,” to show your reader where you’re going. Mention previous research into the topic with a phrase like, “Others have studied…”, then transition into what your contribution will be and why it’s necessary. Finally, state the questions that your paper will address and propose your “answer” to them as your thesis statement. For more information from our English Ph.D. co-author about how to craft a strong hypothesis and thesis, keep reading! Did this summary help you? Yes No

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• If you are writing in a new discipline, you should always make sure to ask about conventions and expectations for introductions, just as you would for any other aspect of the essay. For example, while it may be acceptable to write a two-paragraph (or longer) introduction for your papers in some courses, instructors in other disciplines, such as those in some Government courses, may expect a shorter introduction that includes a preview of the argument that will follow.  
• In some disciplines (Government, Economics, and others), it’s common to offer an overview in the introduction of what points you will make in your essay. In other disciplines, you will not be expected to provide this overview in your introduction.  
• Avoid writing a very general opening sentence. While it may be true that “Since the dawn of time, people have been telling love stories,” it won’t help you explain what’s interesting about your topic.  
• Avoid writing a “funnel” introduction in which you begin with a very broad statement about a topic and move to a narrow statement about that topic. Broad generalizations about a topic will not add to your readers’ understanding of your specific essay topic.  
• Avoid beginning with a dictionary definition of a term or concept you will be writing about. If the concept is complicated or unfamiliar to your readers, you will need to define it in detail later in your essay. If it’s not complicated, you can assume your readers already know the definition.  
• Avoid offering too much detail in your introduction that a reader could better understand later in the paper.
• picture_as_pdf Introductions

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• 4. The Introduction
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

The introduction leads the reader from a general subject area to a particular topic of inquiry. It establishes the scope, context, and significance of the research being conducted by summarizing current understanding and background information about the topic, stating the purpose of the work in the form of the research problem supported by a hypothesis or a set of questions, explaining briefly the methodological approach used to examine the research problem, highlighting the potential outcomes your study can reveal, and outlining the remaining structure and organization of the paper.

Key Elements of the Research Proposal. Prepared under the direction of the Superintendent and by the 2010 Curriculum Design and Writing Team. Baltimore County Public Schools.

Importance of a Good Introduction

Think of the introduction as a mental road map that must answer for the reader these four questions:

• What was I studying?
• Why was this topic important to investigate?
• What did we know about this topic before I did this study?
• How will this study advance new knowledge or new ways of understanding?

According to Reyes, there are three overarching goals of a good introduction: 1) ensure that you summarize prior studies about the topic in a manner that lays a foundation for understanding the research problem; 2) explain how your study specifically addresses gaps in the literature, insufficient consideration of the topic, or other deficiency in the literature; and, 3) note the broader theoretical, empirical, and/or policy contributions and implications of your research.

A well-written introduction is important because, quite simply, you never get a second chance to make a good first impression. The opening paragraphs of your paper will provide your readers with their initial impressions about the logic of your argument, your writing style, the overall quality of your research, and, ultimately, the validity of your findings and conclusions. A vague, disorganized, or error-filled introduction will create a negative impression, whereas, a concise, engaging, and well-written introduction will lead your readers to think highly of your analytical skills, your writing style, and your research approach. All introductions should conclude with a brief paragraph that describes the organization of the rest of the paper.

Hirano, Eliana. “Research Article Introductions in English for Specific Purposes: A Comparison between Brazilian, Portuguese, and English.” English for Specific Purposes 28 (October 2009): 240-250; Samraj, B. “Introductions in Research Articles: Variations Across Disciplines.” English for Specific Purposes 21 (2002): 1–17; Introductions. The Writing Center. University of North Carolina; “Writing Introductions.” In Good Essay Writing: A Social Sciences Guide. Peter Redman. 4th edition. (London: Sage, 2011), pp. 63-70; Reyes, Victoria. Demystifying the Journal Article. Inside Higher Education.

Structure and Writing Style

I.  Structure and Approach

The introduction is the broad beginning of the paper that answers three important questions for the reader:

• What is this?
• Why should I read it?
• What do you want me to think about / consider doing / react to?

Think of the structure of the introduction as an inverted triangle of information that lays a foundation for understanding the research problem. Organize the information so as to present the more general aspects of the topic early in the introduction, then narrow your analysis to more specific topical information that provides context, finally arriving at your research problem and the rationale for studying it [often written as a series of key questions to be addressed or framed as a hypothesis or set of assumptions to be tested] and, whenever possible, a description of the potential outcomes your study can reveal.

These are general phases associated with writing an introduction: 1.  Establish an area to research by:

• Highlighting the importance of the topic, and/or
• Making general statements about the topic, and/or
• Presenting an overview on current research on the subject.

2.  Identify a research niche by:

• Opposing an existing assumption, and/or
• Revealing a gap in existing research, and/or
• Formulating a research question or problem, and/or
• Continuing a disciplinary tradition.

3.  Place your research within the research niche by:

• Stating the intent of your study,
• Outlining the key characteristics of your study,
• Describing important results, and
• Giving a brief overview of the structure of the paper.

NOTE:   It is often useful to review the introduction late in the writing process. This is appropriate because outcomes are unknown until you've completed the study. After you complete writing the body of the paper, go back and review introductory descriptions of the structure of the paper, the method of data gathering, the reporting and analysis of results, and the conclusion. Reviewing and, if necessary, rewriting the introduction ensures that it correctly matches the overall structure of your final paper.

II.  Delimitations of the Study

Delimitations refer to those characteristics that limit the scope and define the conceptual boundaries of your research . This is determined by the conscious exclusionary and inclusionary decisions you make about how to investigate the research problem. In other words, not only should you tell the reader what it is you are studying and why, but you must also acknowledge why you rejected alternative approaches that could have been used to examine the topic.

Obviously, the first limiting step was the choice of research problem itself. However, implicit are other, related problems that could have been chosen but were rejected. These should be noted in the conclusion of your introduction. For example, a delimitating statement could read, "Although many factors can be understood to impact the likelihood young people will vote, this study will focus on socioeconomic factors related to the need to work full-time while in school." The point is not to document every possible delimiting factor, but to highlight why previously researched issues related to the topic were not addressed.

Examples of delimitating choices would be:

• The key aims and objectives of your study,
• The research questions that you address,
• The variables of interest [i.e., the various factors and features of the phenomenon being studied],
• The method(s) of investigation,
• The time period your study covers, and
• Any relevant alternative theoretical frameworks that could have been adopted.

Review each of these decisions. Not only do you clearly establish what you intend to accomplish in your research, but you should also include a declaration of what the study does not intend to cover. In the latter case, your exclusionary decisions should be based upon criteria understood as, "not interesting"; "not directly relevant"; “too problematic because..."; "not feasible," and the like. Make this reasoning explicit!

NOTE:   Delimitations refer to the initial choices made about the broader, overall design of your study and should not be confused with documenting the limitations of your study discovered after the research has been completed.

ANOTHER NOTE: Do not view delimitating statements as admitting to an inherent failing or shortcoming in your research. They are an accepted element of academic writing intended to keep the reader focused on the research problem by explicitly defining the conceptual boundaries and scope of your study. It addresses any critical questions in the reader's mind of, "Why the hell didn't the author examine this?"

III.  The Narrative Flow

Issues to keep in mind that will help the narrative flow in your introduction :

• Your introduction should clearly identify the subject area of interest . A simple strategy to follow is to use key words from your title in the first few sentences of the introduction. This will help focus the introduction on the topic at the appropriate level and ensures that you get to the subject matter quickly without losing focus, or discussing information that is too general.
• Establish context by providing a brief and balanced review of the pertinent published literature that is available on the subject. The key is to summarize for the reader what is known about the specific research problem before you did your analysis. This part of your introduction should not represent a comprehensive literature review--that comes next. It consists of a general review of the important, foundational research literature [with citations] that establishes a foundation for understanding key elements of the research problem. See the drop-down menu under this tab for " Background Information " regarding types of contexts.
• Clearly state the hypothesis that you investigated . When you are first learning to write in this format it is okay, and actually preferable, to use a past statement like, "The purpose of this study was to...." or "We investigated three possible mechanisms to explain the...."
• Why did you choose this kind of research study or design? Provide a clear statement of the rationale for your approach to the problem studied. This will usually follow your statement of purpose in the last paragraph of the introduction.

IV.  Engaging the Reader

A research problem in the social sciences can come across as dry and uninteresting to anyone unfamiliar with the topic . Therefore, one of the goals of your introduction is to make readers want to read your paper. Here are several strategies you can use to grab the reader's attention:

• Open with a compelling story . Almost all research problems in the social sciences, no matter how obscure or esoteric , are really about the lives of people. Telling a story that humanizes an issue can help illuminate the significance of the problem and help the reader empathize with those affected by the condition being studied.
• Include a strong quotation or a vivid, perhaps unexpected, anecdote . During your review of the literature, make note of any quotes or anecdotes that grab your attention because they can used in your introduction to highlight the research problem in a captivating way.
• Pose a provocative or thought-provoking question . Your research problem should be framed by a set of questions to be addressed or hypotheses to be tested. However, a provocative question can be presented in the beginning of your introduction that challenges an existing assumption or compels the reader to consider an alternative viewpoint that helps establish the significance of your study. 
• Describe a puzzling scenario or incongruity . This involves highlighting an interesting quandary concerning the research problem or describing contradictory findings from prior studies about a topic. Posing what is essentially an unresolved intellectual riddle about the problem can engage the reader's interest in the study.
• Cite a stirring example or case study that illustrates why the research problem is important . Draw upon the findings of others to demonstrate the significance of the problem and to describe how your study builds upon or offers alternatives ways of investigating this prior research.

NOTE:   It is important that you choose only one of the suggested strategies for engaging your readers. This avoids giving an impression that your paper is more flash than substance and does not distract from the substance of your study.

Freedman, Leora  and Jerry Plotnick. Introductions and Conclusions. University College Writing Centre. University of Toronto; Introduction. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Introductions. The Writing Center. University of North Carolina; Introductions. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Introductions, Body Paragraphs, and Conclusions for an Argument Paper. The Writing Lab and The OWL. Purdue University; “Writing Introductions.” In Good Essay Writing: A Social Sciences Guide . Peter Redman. 4th edition. (London: Sage, 2011), pp. 63-70; Resources for Writers: Introduction Strategies. Program in Writing and Humanistic Studies. Massachusetts Institute of Technology; Sharpling, Gerald. Writing an Introduction. Centre for Applied Linguistics, University of Warwick; Samraj, B. “Introductions in Research Articles: Variations Across Disciplines.” English for Specific Purposes 21 (2002): 1–17; Swales, John and Christine B. Feak. Academic Writing for Graduate Students: Essential Skills and Tasks . 2nd edition. Ann Arbor, MI: University of Michigan Press, 2004 ; Writing Your Introduction. Department of English Writing Guide. George Mason University.

Writing Tip

Avoid the "Dictionary" Introduction

Giving the dictionary definition of words related to the research problem may appear appropriate because it is important to define specific terminology that readers may be unfamiliar with. However, anyone can look a word up in the dictionary and a general dictionary is not a particularly authoritative source because it doesn't take into account the context of your topic and doesn't offer particularly detailed information. Also, placed in the context of a particular discipline, a term or concept may have a different meaning than what is found in a general dictionary. If you feel that you must seek out an authoritative definition, use a subject specific dictionary or encyclopedia [e.g., if you are a sociology student, search for dictionaries of sociology]. A good database for obtaining definitive definitions of concepts or terms is Credo Reference .

Saba, Robert. The College Research Paper. Florida International University; Introductions. The Writing Center. University of North Carolina.

Another Writing Tip

When Do I Begin?

A common question asked at the start of any paper is, "Where should I begin?" An equally important question to ask yourself is, "When do I begin?" Research problems in the social sciences rarely rest in isolation from history. Therefore, it is important to lay a foundation for understanding the historical context underpinning the research problem. However, this information should be brief and succinct and begin at a point in time that illustrates the study's overall importance. For example, a study that investigates coffee cultivation and export in West Africa as a key stimulus for local economic growth needs to describe the beginning of exporting coffee in the region and establishing why economic growth is important. You do not need to give a long historical explanation about coffee exports in Africa. If a research problem requires a substantial exploration of the historical context, do this in the literature review section. In your introduction, make note of this as part of the "roadmap" [see below] that you use to describe the organization of your paper.

Introductions. The Writing Center. University of North Carolina; “Writing Introductions.” In Good Essay Writing: A Social Sciences Guide . Peter Redman. 4th edition. (London: Sage, 2011), pp. 63-70.

Yet Another Writing Tip

Always End with a Roadmap

The final paragraph or sentences of your introduction should forecast your main arguments and conclusions and provide a brief description of the rest of the paper [the "roadmap"] that let's the reader know where you are going and what to expect. A roadmap is important because it helps the reader place the research problem within the context of their own perspectives about the topic. In addition, concluding your introduction with an explicit roadmap tells the reader that you have a clear understanding of the structural purpose of your paper. In this way, the roadmap acts as a type of promise to yourself and to your readers that you will follow a consistent and coherent approach to addressing the topic of inquiry. Refer to it often to help keep your writing focused and organized.

Cassuto, Leonard. “On the Dissertation: How to Write the Introduction.” The Chronicle of Higher Education , May 28, 2018; Radich, Michael. A Student's Guide to Writing in East Asian Studies . (Cambridge, MA: Harvard University Writing n. d.), pp. 35-37.

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How to Write an Introduction For a Research Paper

Learn how to write a strong and efficient research paper introduction by following the suitable structure and avoiding typical errors.

An introduction to any type of paper is sometimes misunderstood as the beginning; yet, an introduction is actually intended to present your chosen subject to the audience in a way that makes it more appealing and leaves your readers thirsty for more information. After the title and abstract, your audience will read the introduction, thus it’s critical to get off to a solid start.  

This article includes instructions on how to write an introduction for a research paper that engages the reader in your research. You can produce a strong opening for your research paper if you stick to the format and a few basic principles.

What is An Introduction To a Research Paper?

An introduction is the opening section of a research paper and the section that a reader is likely to read first, in which the objective and goals of the subsequent writing are stated. 

The introduction serves numerous purposes. It provides context for your research, explains your topic and objectives, and provides an outline of the work. A solid introduction will establish the tone for the remainder of your paper, enticing readers to continue reading through the methodology, findings, and discussion. 

Even though introductions are generally presented at the beginning of a document, we must distinguish an introduction from the beginning of your research. An introduction, as the name implies, is supposed to introduce your subject without extending it. All relevant information and facts should be placed in the body and conclusion, not the introduction.

Structure Of An Introduction

Before explaining how to write an introduction for a research paper , it’s necessary to comprehend a structure that will make your introduction stronger and more straightforward.

A Good Hook

A hook is one of the most effective research introduction openers. A hook’s objective is to stimulate the reader’s interest to read the research paper.  There are various approaches you may take to generate a strong hook:  startling facts, a question, a brief overview, or even a quotation. 

Broad Overview

Following an excellent hook, you should present a wide overview of your major issue and some background information on your research. If you’re unsure about how to begin an essay introduction, the best approach is to offer a basic explanation of your topic before delving into specific issues. Simply said, you should begin with general information and then narrow it down to your relevant topics.

After offering some background information regarding your research’s main topic, go on to give readers a better understanding of what you’ll be covering throughout your research. In this section of your introduction, you should swiftly clarify your important topics in the sequence in which they will be addressed later, gradually introducing your thesis statement. You can use some  The following are some critical questions to address in this section of your introduction: Who? What? Where? When? How? And why is that?

Thesis Statement

The thesis statement, which must be stated in the beginning clause of your research since your entire research revolves around it, is the most important component of your research.

A thesis statement presents your audience with a quick overview of the research’s main assertion. In the body section of your work, your key argument is what you will expose or debate about it. An excellent thesis statement is usually very succinct, accurate, explicit, clear, and focused. Typically, your thesis should be at the conclusion of your introductory paragraph/section.

Tips for Writing a Strong Introduction

Aside from the good structure, here are a few tips to make your introduction strong and accurate:

• Keep in mind the aim of your research and make sure your introduction supports it.
• Use an appealing and relevant hook that catches the reader’s attention right away.
• Make it obvious to your readers what your stance is.
• Demonstrate your knowledge of your subject.
• Provide your readers with a road map to help them understand what you will address throughout the research.
• Be succinct – it is advised that your opening introduction consists of around 8-9 percent of the overall amount of words in your article (for example, 160 words for a 2000 words essay). 
• Make a strong and unambiguous thesis statement.
• Explain why the article is significant in 1-2 sentences.
• Remember to keep it interesting.

Mistakes to Avoid in Your Introduction

Check out what not to do and what to avoid now that you know the structure and how to write an introduction for a research paper .

• Lacking a feeling of direction or purpose.
• Giving out too much.
• Creating lengthy paragraphs.
• Excessive or insufficient background, literature, and theory.
• Including material that should be placed in the body and conclusion.
• Not writing enough or writing excessively.
• Using too many quotes.

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Home » Research Paper – Structure, Examples and Writing Guide

Research Paper – Structure, Examples and Writing Guide

Table of Contents

Research Paper

Definition:

Research Paper is a written document that presents the author’s original research, analysis, and interpretation of a specific topic or issue.

It is typically based on Empirical Evidence, and may involve qualitative or quantitative research methods, or a combination of both. The purpose of a research paper is to contribute new knowledge or insights to a particular field of study, and to demonstrate the author’s understanding of the existing literature and theories related to the topic.

Structure of Research Paper

The structure of a research paper typically follows a standard format, consisting of several sections that convey specific information about the research study. The following is a detailed explanation of the structure of a research paper:

The title page contains the title of the paper, the name(s) of the author(s), and the affiliation(s) of the author(s). It also includes the date of submission and possibly, the name of the journal or conference where the paper is to be published.

The abstract is a brief summary of the research paper, typically ranging from 100 to 250 words. It should include the research question, the methods used, the key findings, and the implications of the results. The abstract should be written in a concise and clear manner to allow readers to quickly grasp the essence of the research.

Introduction

The introduction section of a research paper provides background information about the research problem, the research question, and the research objectives. It also outlines the significance of the research, the research gap that it aims to fill, and the approach taken to address the research question. Finally, the introduction section ends with a clear statement of the research hypothesis or research question.

Literature Review

The literature review section of a research paper provides an overview of the existing literature on the topic of study. It includes a critical analysis and synthesis of the literature, highlighting the key concepts, themes, and debates. The literature review should also demonstrate the research gap and how the current study seeks to address it.

The methods section of a research paper describes the research design, the sample selection, the data collection and analysis procedures, and the statistical methods used to analyze the data. This section should provide sufficient detail for other researchers to replicate the study.

The results section presents the findings of the research, using tables, graphs, and figures to illustrate the data. The findings should be presented in a clear and concise manner, with reference to the research question and hypothesis.

The discussion section of a research paper interprets the findings and discusses their implications for the research question, the literature review, and the field of study. It should also address the limitations of the study and suggest future research directions.

The conclusion section summarizes the main findings of the study, restates the research question and hypothesis, and provides a final reflection on the significance of the research.

The references section provides a list of all the sources cited in the paper, following a specific citation style such as APA, MLA or Chicago.

How to Write Research Paper

You can write Research Paper by the following guide:

• Choose a Topic: The first step is to select a topic that interests you and is relevant to your field of study. Brainstorm ideas and narrow down to a research question that is specific and researchable.
• Conduct a Literature Review: The literature review helps you identify the gap in the existing research and provides a basis for your research question. It also helps you to develop a theoretical framework and research hypothesis.
• Develop a Thesis Statement : The thesis statement is the main argument of your research paper. It should be clear, concise and specific to your research question.
• Plan your Research: Develop a research plan that outlines the methods, data sources, and data analysis procedures. This will help you to collect and analyze data effectively.
• Collect and Analyze Data: Collect data using various methods such as surveys, interviews, observations, or experiments. Analyze data using statistical tools or other qualitative methods.
• Organize your Paper : Organize your paper into sections such as Introduction, Literature Review, Methods, Results, Discussion, and Conclusion. Ensure that each section is coherent and follows a logical flow.
• Write your Paper : Start by writing the introduction, followed by the literature review, methods, results, discussion, and conclusion. Ensure that your writing is clear, concise, and follows the required formatting and citation styles.
• Edit and Proofread your Paper: Review your paper for grammar and spelling errors, and ensure that it is well-structured and easy to read. Ask someone else to review your paper to get feedback and suggestions for improvement.
• Cite your Sources: Ensure that you properly cite all sources used in your research paper. This is essential for giving credit to the original authors and avoiding plagiarism.

Research Paper Example

Note : The below example research paper is for illustrative purposes only and is not an actual research paper. Actual research papers may have different structures, contents, and formats depending on the field of study, research question, data collection and analysis methods, and other factors. Students should always consult with their professors or supervisors for specific guidelines and expectations for their research papers.

Research Paper Example sample for Students:

Title: The Impact of Social Media on Mental Health among Young Adults

Abstract: This study aims to investigate the impact of social media use on the mental health of young adults. A literature review was conducted to examine the existing research on the topic. A survey was then administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO (Fear of Missing Out) are significant predictors of mental health problems among young adults.

Introduction: Social media has become an integral part of modern life, particularly among young adults. While social media has many benefits, including increased communication and social connectivity, it has also been associated with negative outcomes, such as addiction, cyberbullying, and mental health problems. This study aims to investigate the impact of social media use on the mental health of young adults.

Literature Review: The literature review highlights the existing research on the impact of social media use on mental health. The review shows that social media use is associated with depression, anxiety, stress, and other mental health problems. The review also identifies the factors that contribute to the negative impact of social media, including social comparison, cyberbullying, and FOMO.

Methods : A survey was administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The survey included questions on social media use, mental health status (measured using the DASS-21), and perceived impact of social media on their mental health. Data were analyzed using descriptive statistics and regression analysis.

Results : The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO are significant predictors of mental health problems among young adults.

Discussion : The study’s findings suggest that social media use has a negative impact on the mental health of young adults. The study highlights the need for interventions that address the factors contributing to the negative impact of social media, such as social comparison, cyberbullying, and FOMO.

Conclusion : In conclusion, social media use has a significant impact on the mental health of young adults. The study’s findings underscore the need for interventions that promote healthy social media use and address the negative outcomes associated with social media use. Future research can explore the effectiveness of interventions aimed at reducing the negative impact of social media on mental health. Additionally, longitudinal studies can investigate the long-term effects of social media use on mental health.

Limitations : The study has some limitations, including the use of self-report measures and a cross-sectional design. The use of self-report measures may result in biased responses, and a cross-sectional design limits the ability to establish causality.

Implications: The study’s findings have implications for mental health professionals, educators, and policymakers. Mental health professionals can use the findings to develop interventions that address the negative impact of social media use on mental health. Educators can incorporate social media literacy into their curriculum to promote healthy social media use among young adults. Policymakers can use the findings to develop policies that protect young adults from the negative outcomes associated with social media use.

References :

• Twenge, J. M., & Campbell, W. K. (2019). Associations between screen time and lower psychological well-being among children and adolescents: Evidence from a population-based study. Preventive medicine reports, 15, 100918.
• Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, J. B., … & James, A. E. (2017). Use of multiple social media platforms and symptoms of depression and anxiety: A nationally-representative study among US young adults. Computers in Human Behavior, 69, 1-9.
• Van der Meer, T. G., & Verhoeven, J. W. (2017). Social media and its impact on academic performance of students. Journal of Information Technology Education: Research, 16, 383-398.

Appendix : The survey used in this study is provided below.

Social Media and Mental Health Survey

• How often do you use social media per day?
• Less than 30 minutes
• 30 minutes to 1 hour
• 1 to 2 hours
• 2 to 4 hours
• More than 4 hours
• Which social media platforms do you use?
• Others (Please specify)
• How often do you experience the following on social media?
• Social comparison (comparing yourself to others)
• Cyberbullying
• Fear of Missing Out (FOMO)
• Have you ever experienced any of the following mental health problems in the past month?
• Do you think social media use has a positive or negative impact on your mental health?
• Very positive
• Somewhat positive
• Somewhat negative
• Very negative
• In your opinion, which factors contribute to the negative impact of social media on mental health?
• Social comparison
• In your opinion, what interventions could be effective in reducing the negative impact of social media on mental health?
• Education on healthy social media use
• Counseling for mental health problems caused by social media
• Social media detox programs
• Regulation of social media use

Thank you for your participation!

Applications of Research Paper

Research papers have several applications in various fields, including:

• Advancing knowledge: Research papers contribute to the advancement of knowledge by generating new insights, theories, and findings that can inform future research and practice. They help to answer important questions, clarify existing knowledge, and identify areas that require further investigation.
• Informing policy: Research papers can inform policy decisions by providing evidence-based recommendations for policymakers. They can help to identify gaps in current policies, evaluate the effectiveness of interventions, and inform the development of new policies and regulations.
• Improving practice: Research papers can improve practice by providing evidence-based guidance for professionals in various fields, including medicine, education, business, and psychology. They can inform the development of best practices, guidelines, and standards of care that can improve outcomes for individuals and organizations.
• Educating students : Research papers are often used as teaching tools in universities and colleges to educate students about research methods, data analysis, and academic writing. They help students to develop critical thinking skills, research skills, and communication skills that are essential for success in many careers.
• Fostering collaboration: Research papers can foster collaboration among researchers, practitioners, and policymakers by providing a platform for sharing knowledge and ideas. They can facilitate interdisciplinary collaborations and partnerships that can lead to innovative solutions to complex problems.

When to Write Research Paper

Research papers are typically written when a person has completed a research project or when they have conducted a study and have obtained data or findings that they want to share with the academic or professional community. Research papers are usually written in academic settings, such as universities, but they can also be written in professional settings, such as research organizations, government agencies, or private companies.

Here are some common situations where a person might need to write a research paper:

• For academic purposes: Students in universities and colleges are often required to write research papers as part of their coursework, particularly in the social sciences, natural sciences, and humanities. Writing research papers helps students to develop research skills, critical thinking skills, and academic writing skills.
• For publication: Researchers often write research papers to publish their findings in academic journals or to present their work at academic conferences. Publishing research papers is an important way to disseminate research findings to the academic community and to establish oneself as an expert in a particular field.
• To inform policy or practice : Researchers may write research papers to inform policy decisions or to improve practice in various fields. Research findings can be used to inform the development of policies, guidelines, and best practices that can improve outcomes for individuals and organizations.
• To share new insights or ideas: Researchers may write research papers to share new insights or ideas with the academic or professional community. They may present new theories, propose new research methods, or challenge existing paradigms in their field.

Purpose of Research Paper

The purpose of a research paper is to present the results of a study or investigation in a clear, concise, and structured manner. Research papers are written to communicate new knowledge, ideas, or findings to a specific audience, such as researchers, scholars, practitioners, or policymakers. The primary purposes of a research paper are:

• To contribute to the body of knowledge : Research papers aim to add new knowledge or insights to a particular field or discipline. They do this by reporting the results of empirical studies, reviewing and synthesizing existing literature, proposing new theories, or providing new perspectives on a topic.
• To inform or persuade: Research papers are written to inform or persuade the reader about a particular issue, topic, or phenomenon. They present evidence and arguments to support their claims and seek to persuade the reader of the validity of their findings or recommendations.
• To advance the field: Research papers seek to advance the field or discipline by identifying gaps in knowledge, proposing new research questions or approaches, or challenging existing assumptions or paradigms. They aim to contribute to ongoing debates and discussions within a field and to stimulate further research and inquiry.
• To demonstrate research skills: Research papers demonstrate the author’s research skills, including their ability to design and conduct a study, collect and analyze data, and interpret and communicate findings. They also demonstrate the author’s ability to critically evaluate existing literature, synthesize information from multiple sources, and write in a clear and structured manner.

Characteristics of Research Paper

Research papers have several characteristics that distinguish them from other forms of academic or professional writing. Here are some common characteristics of research papers:

• Evidence-based: Research papers are based on empirical evidence, which is collected through rigorous research methods such as experiments, surveys, observations, or interviews. They rely on objective data and facts to support their claims and conclusions.
• Structured and organized: Research papers have a clear and logical structure, with sections such as introduction, literature review, methods, results, discussion, and conclusion. They are organized in a way that helps the reader to follow the argument and understand the findings.
• Formal and objective: Research papers are written in a formal and objective tone, with an emphasis on clarity, precision, and accuracy. They avoid subjective language or personal opinions and instead rely on objective data and analysis to support their arguments.
• Citations and references: Research papers include citations and references to acknowledge the sources of information and ideas used in the paper. They use a specific citation style, such as APA, MLA, or Chicago, to ensure consistency and accuracy.
• Peer-reviewed: Research papers are often peer-reviewed, which means they are evaluated by other experts in the field before they are published. Peer-review ensures that the research is of high quality, meets ethical standards, and contributes to the advancement of knowledge in the field.
• Objective and unbiased: Research papers strive to be objective and unbiased in their presentation of the findings. They avoid personal biases or preconceptions and instead rely on the data and analysis to draw conclusions.

Advantages of Research Paper

Research papers have many advantages, both for the individual researcher and for the broader academic and professional community. Here are some advantages of research papers:

• Contribution to knowledge: Research papers contribute to the body of knowledge in a particular field or discipline. They add new information, insights, and perspectives to existing literature and help advance the understanding of a particular phenomenon or issue.
• Opportunity for intellectual growth: Research papers provide an opportunity for intellectual growth for the researcher. They require critical thinking, problem-solving, and creativity, which can help develop the researcher’s skills and knowledge.
• Career advancement: Research papers can help advance the researcher’s career by demonstrating their expertise and contributions to the field. They can also lead to new research opportunities, collaborations, and funding.
• Academic recognition: Research papers can lead to academic recognition in the form of awards, grants, or invitations to speak at conferences or events. They can also contribute to the researcher’s reputation and standing in the field.
• Impact on policy and practice: Research papers can have a significant impact on policy and practice. They can inform policy decisions, guide practice, and lead to changes in laws, regulations, or procedures.
• Advancement of society: Research papers can contribute to the advancement of society by addressing important issues, identifying solutions to problems, and promoting social justice and equality.

Limitations of Research Paper

Research papers also have some limitations that should be considered when interpreting their findings or implications. Here are some common limitations of research papers:

• Limited generalizability: Research findings may not be generalizable to other populations, settings, or contexts. Studies often use specific samples or conditions that may not reflect the broader population or real-world situations.
• Potential for bias : Research papers may be biased due to factors such as sample selection, measurement errors, or researcher biases. It is important to evaluate the quality of the research design and methods used to ensure that the findings are valid and reliable.
• Ethical concerns: Research papers may raise ethical concerns, such as the use of vulnerable populations or invasive procedures. Researchers must adhere to ethical guidelines and obtain informed consent from participants to ensure that the research is conducted in a responsible and respectful manner.
• Limitations of methodology: Research papers may be limited by the methodology used to collect and analyze data. For example, certain research methods may not capture the complexity or nuance of a particular phenomenon, or may not be appropriate for certain research questions.
• Publication bias: Research papers may be subject to publication bias, where positive or significant findings are more likely to be published than negative or non-significant findings. This can skew the overall findings of a particular area of research.
• Time and resource constraints: Research papers may be limited by time and resource constraints, which can affect the quality and scope of the research. Researchers may not have access to certain data or resources, or may be unable to conduct long-term studies due to practical limitations.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Home → Academic Writing → How to Write a Research Paper Introduction: Hook, Line, and Sinker

How to Write a Research Paper Introduction: Hook, Line, and Sinker

Jordan Kruszynski

• January 4, 2024

Want to know how to write a research paper introduction that dazzles?

Struggling to hook your reader in with your opening sentences?

Crafting a captivating research paper introduction can be the difference between a mediocre paper and an outstanding one. The introduction sets the tone for the entire paper, and if it fails to capture the reader’s attention, your hard work may go unnoticed. In this post, we’ll explore some techniques for crafting a compelling introduction that will hook your reader from the very beginning. From using statistics to posing thought-provoking questions, we’ll show you how to reel in your reader hook, line, and sinker.

So, grab your pen and paper, and let’s get started!

What Makes a Captivating Introduction?

When it comes to writing a research paper, the introduction is everything. It’s the first glimpse your audience gets of what’s to come and the determining factor as to whether they continue reading or move on. A captivating introduction should immediately grab the reader’s attention and draw them in, enticing them to learn more about your unique research. It should be thought-provoking, relevant and informative.

By connecting with your audience and allowing them to identify with your work, you create an emotional investment from the start. You might be thinking that a research paper introduction only needs to provide cold, hard information, but this is missing half of the picture. If you can blend quality information with skilful writing, you’ll ensure that your reader remains engaged and open to your argument throughout the entirety of your paper. So, when crafting your introduction, strive to be engaging and focus on making a strong impression.

Pre-Writing Strategies for Crafting an Effective Introduction

Crafting that quality introduction begins even before you put pen to paper (or finger to keyboard). Start planning mentally with the following tips:

• Try to ‘visualise’ your research from beginning to end. Your paper is your means of guiding the reader through that research. Imagine that you’re going to take the reader by the hand and walk them through it. What do they need to know before you set off? What’s going to convince them to take the journey? Thinking along these lines will set you in the right frame of mind for writing.
• Remember that your introduction acts as a roadmap, directing readers towards your key points and arguments and letting them know what to expect. Thinking in terms of providing a map will clarify your writing decisions.
• Think clearly and with confidence. If your introduction is vague, lacks sufficient information or is otherwise unconvincing, your reader may become disengaged from the outset.

How to Write a Research Paper Introduction with Clarity and Style

With your thoughts flowing, you can now turn to the act of writing your introduction, Each of the sections outlined below will typically take up one paragraph of your intro, with the exception of the literature review, which is likely to occupy several.

• Always keep in mind that anyone can read your paper, not just an academically literate audience. With this in mind, begin by introducing your subject generally, ideally in a way that a layperson could understand. If you overwhelm your reader with technical language from the outset, they may become frustrated and stop reading.
• Your subject introduction might include some historical context, or a brief overview of the significance of your field. Either way, prepare to narrow down that general overview to your specific research. Let the reader know what you’re working on.
• More importantly, explain why your research is important. Perhaps you’re seeking to fill in a gap in the historical record, or are working on medication that could help people with a specific illness. Be clear about why your research could make a difference and why the reader should pay attention to it.

Literature Review

• At this point, you can go into more detail on existing research efforts in your field with a literature review. Find out all about these and how to construct them in our complete guide . (Add link to lit. review post once it’s published)

Research Intention

• Here, go into detail on the intention of your research. If you have a hypothesis, state it, or if you’re approaching your work with a broader, more open research question, then set it out.
• Briefly discuss your research methods, keeping in mind that you’ll probably be writing a complete methodology section later.

Paper Overview

• In this optional section, provide a brief overview of your whole paper by section, outlining what you intend to do in each of them – for example ‘In Section 4 we describe our methodology in detail. In Section 5 we present our data without analysis. In Section 6 we conduct an analysis of the data.’

As we mentioned before, balancing quality information with skilful, engaging writing can grab your reader’s attention right from the start. One way to do this is through a hook. But what makes a good hook?

• It could be a statistic, taken either from your own research or elsewhere. Naturally, it should be relevant to your topic, as well as thought-provoking – a figure that makes your reader sit up and take notice of what you’re about to say. For example, if your paper focuses on marine plastics, then consider using a statistic to illustrate just how prevalent the problem is.
• It might be a reference to a current event that is garnering a lot of attention. If you can connect that event to your research, and prove its social relevance, you can potentially earn more readers than you might expect.
• You could even use a quotation, for example from a respected academic in your field. This can act as a point of inspiration for both you and your reader. There’s nothing stopping you from being creative in your introduction, and if your hook is directly relevant to your research, then it can take whatever shape you like.

Final Thoughts

The introductory paragraphs of your research paper are your chance to make a great first impression. By crafting a captivating introduction, you can draw your reader in and set the stage for an outstanding paper. From using powerful statistics to posing thought-provoking questions, there are many techniques you can use to hook your reader from the very beginning. So don’t be afraid to get creative and experiment with different approaches until you find one that works for you.

With these tips in mind, you’ll know how to write a research paper introduction that will leave your audience hooked, lined, and sunk!

Looking for introduction inspiration? Check out the array of papers available on Audemic , where you can listen to your heart’s content until you find the one that hits right!

Keep striving, researchers! ✨

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Excess mortality across countries in the Western World since the COVID-19 pandemic: ‘Our World in Data’ estimates of January 2020 to December 2022

Marcel Hoogland ,

Minke Huibers ,

Gertjan Kaspers .

https://doi.org/ 10.1136/bmjph-2023-000282

Introduction Excess mortality during the COVID-19 pandemic has been substantial. Insight into excess death rates in years following WHO’s pandemic declaration is crucial for government leaders and policymakers to evaluate their health crisis policies. This study explores excess mortality in the Western World from 2020 until 2022.

Methods All-cause mortality reports were abstracted for countries using the ‘Our World in Data’ database. Excess mortality is assessed as a deviation between the reported number of deaths in a country during a certain week or month in 2020 until 2022 and the expected number of deaths in a country for that period under normal conditions. For the baseline of expected deaths, Karlinsky and Kobak’s estimate model was used. This model uses historical death data in a country from 2015 until 2019 and accounts for seasonal variation and year-to-year trends in mortality.

Results The total number of excess deaths in 47 countries of the Western World was 3 098 456 from 1 January 2020 until 31 December 2022. Excess mortality was documented in 41 countries (87%) in 2020, 42 countries (89%) in 2021 and 43 countries (91%) in 2022. In 2020, the year of the COVID-19 pandemic onset and implementation of containment measures, records present 1 033 122 excess deaths (P-score 11.4%). In 2021, the year in which both containment measures and COVID-19 vaccines were used to address virus spread and infection, the highest number of excess deaths was reported: 1 256 942 excess deaths (P-score 13.8%). In 2022, when most containment measures were lifted and COVID-19 vaccines were continued, preliminary data present 808 392 excess deaths (P-score 8.8%).

Conclusions Excess mortality has remained high in the Western World for three consecutive years, despite the implementation of containment measures and COVID-19 vaccines. This raises serious concerns. Government leaders and policymakers need to thoroughly investigate underlying causes of persistent excess mortality.

What is already known on this topic

Excess mortality during the COVID-19 pandemic has been substantial. Insight into excess death rates in years following WHO’s pandemic declaration is crucial for government leaders and policymakers to evaluate their health crisis policies.

What this study adds

Excess mortality has remained high in the Western World for three consecutive years, despite the implementation of containment measures and COVID-19 vaccines. This raises serious concerns.

How this study might affect research, practice or policy

Government leaders and policymakers need to thoroughly investigate the underlying causes of persistent excess mortality.

• Introduction

Excess mortality is internationally recognised as an accurate measure for monitoring and comparing health crisis policies across geographic regions. 1–4 Excess mortality concerns the number of deaths from all causes during a humanitarian emergency, such as the COVID-19 pandemic, above the expected number of deaths under normal circumstances. 5–7 Since the outbreak of the COVID-19 pandemic, excess mortality thus includes not only deaths from SARS-CoV-2 infection but also deaths related to the indirect effects of the health strategies to address the virus spread and infection. 1–4 The burden of the COVID-19 pandemic on disease and death has been investigated from its beginning. Numerous studies expressed that SARS-CoV-2 infection was likely a leading cause of death among older patients with pre-existing comorbidities and obesity in the early phase of the pandemic, that various containment measures were effective in reducing viral transmission and that COVID-19 vaccines prevented severe disease, especially among the elderly population. 1 8–14 Although COVID-19 containment measures and COVID-19 vaccines were thus implemented to protect citizens from suffering morbidity and mortality by the COVID-19 virus, they may have detrimental effects that cause inferior outcomes as well. 1 2 15 It is noteworthy that excess mortality during a crisis points to a more extensive underlying burden of disease, disablement and human suffering. 16

On 11 March 2020, WHO declared the COVID-19 pandemic. 17 Countries in the Western World promptly implemented COVID-19 containment measures (such as lockdowns, school closures, physical distancing, travel restrictions, business closures, stay-at-home orders, curfews and quarantine measures with contact tracing) to limit virus spread and shield its residents from morbidity and mortality. 18 These non-pharmaceutical interventions however had adverse indirect effects (such as economic damage, limited access to education, food insecurity, child abuse, limited access to healthcare, disrupted health programmes and mental health challenges) that increased morbidity and mortality from other causes. 19 Vulnerable populations in need of acute or complex medical treatment, such as patients with cardiovascular disease, cerebrovascular conditions, diabetes and cancer, were hurt by these interventions due to the limited access to and delivery of medical services. Shortage of staff, reduced screening, delayed diagnostics, disrupted imaging, limited availability of medicines, postponed surgery, modified radiotherapy and restricted supportive care hindered protocol adherence and worsened the condition and prognosis of patients. 19–26 A recent study investigated excess mortality from some major non-COVID causes across 30 countries in 2020. Significant excess deaths were reported from ischaemic heart diseases (in 10 countries), cerebrovascular diseases (in 10 countries) and diabetes (in 19 countries). 27 On 14 October 2020, Professor Ioannidis from Stanford University published an overall Infection Fatality Rate of COVID-19 of 0.23%, and for people aged <70 years, the Infection Fatality Rate was 0.05%. 28 Governments in the Western World continued to impose lockdowns until the end of 2021.

In December 2020, the UK, the USA and Canada were the first countries in the Western World that started with the roll-out of the COVID-19 vaccines under emergency authorisation. 29–31 At the end of December 2020, a large randomised and placebo-controlled trial with 43 548 participants was published in the New England Journal of Medicine , which showed that a two-dose mRNA COVID-19 vaccine regimen provided an absolute risk reduction of 0.88% and relative risk reduction of 95% against laboratory-confirmed COVID-19 in the vaccinated group (8 COVID-19 cases/17 411 vaccine recipients) versus the placebo group (162 COVID-19 cases/17 511 placebo recipients). 32 33 At the beginning of 2021, most other Western countries followed with rolling out massive vaccination campaigns. 34–36 On 9 April 2021, the overall COVID-19 Infection Fatality Rate was reduced to 0.15% and expected to further decline with the widespread use of vaccinations, prior infections and the evolution of new and milder variants. 37 38

Although COVID-19 vaccines were provided to guard civilians from suffering morbidity and mortality by the COVID-19 virus, suspected adverse events have been documented as well. 15 The secondary analysis of the placebo-controlled, phase III randomised clinical trials of mRNA COVID-19 vaccines showed that the Pfizer trial had a 36% higher risk of serious adverse events in the vaccine group. The risk difference was 18.0 per 10 000 vaccinated (95% CI 1.2 to 34.9), and the risk ratio was 1.36 (95% CI 1.02 to 1.83). The Moderna trial had a 6% higher risk of serious adverse events among vaccine recipients. The risk difference was 7.1 per 10 000 vaccinated (95% CI −23.2 to 37.4), and the risk ratio was 1.06 (95% CI 0.84 to 1.33). 39 By definition, these serious adverse events lead to either death, are life-threatening, require inpatient (prolongation of) hospitalisation, cause persistent/significant disability/incapacity, concern a congenital anomaly/birth defect or include a medically important event according to medical judgement. 39–41 The authors of the secondary analysis point out that most of these serious adverse events concern common clinical conditions, for example, ischaemic stroke, acute coronary syndrome and brain haemorrhage. This commonality hinders clinical suspicion and consequently its detection as adverse vaccine reactions. 39 Both medical professionals and citizens have reported serious injuries and deaths following vaccination to various official databases in the Western World, such as VAERS in the USA, EudraVigilance in the European Union and Yellow Card Scheme in the UK. 42–48 A study comparing adverse event reports to VAERS and EudraVigilance following mRNA COVID-19 vaccines versus influenza vaccines observed a higher risk of serious adverse reactions for COVID-19 vaccines. These reactions included cardiovascular diseases, coagulation, haemorrhages, gastrointestinal events and thromboses. 39 49 Numerous studies reported that COVID-19 vaccination may induce myocarditis, pericarditis and autoimmune diseases. 50–57 Postmortem examinations have also ascribed myocarditis, encephalitis, immune thrombotic thrombocytopenia, intracranial haemorrhage and diffuse thrombosis to COVID-19 vaccinations. 58–67 The Food and Drug Administration noted in July 2021 that the following potentially serious adverse events of Pfizer vaccines deserve further monitoring and investigation: pulmonary embolism, acute myocardial infarction, immune thrombocytopenia and disseminated intravascular coagulation. 39 68

Insight into the excess death rates in the years following the declaration of the pandemic by WHO is crucial for government leaders and policymakers to evaluate their health crisis policies. 1–4 This study therefore explores excess mortality in the Western World from 1 January 2020 until 31 December 2022.

• Materials and methods

The Western World is primarily defined by culture rather than geography. It refers to various countries in Europe and to countries in Australasia (Australia, New Zealand) and North America (the USA, Canada) that are based on European cultural heritage. The latter countries were once British colonies that acquired Christianity and the Latin alphabet and whose populations comprised numerous descendants from European colonists or migrants. 69

Study design

All-cause mortality reports were abstracted for countries of the Western World using the ‘Our World in Data’ database. 12 Only countries that had all-cause mortality reports available for all three consecutive years (2020–2022) were included. If coverage of one of these years was missing, the country was excluded from the analysis.

The ‘Our World in Data’ database retrieves their reported number of deaths from both the Human Mortality Database (HMD) and the World Mortality Dataset (WMD). 5 HMD is sustained by research teams of both the University of California in the USA and the Max Planck Institute for Demographic Research in Germany. HMD recovers its data from Eurostat and national statistical agencies on a weekly basis. 5 70 The ‘Our World in Data’ database used HMD as their only data source until February 2021. 5 WMD is sustained by the researchers Karlinsky and Kobak. WMD recovers its data from HMD, Eurostat and national statistical agencies on a weekly basis. 5 71 The ‘Our World in Data’ database started to use WMD as a data source next to HMD since February 2021. 5

‘Excess mortality’ is assessed as the deviation between the reported number of deaths in a country during a certain week or month in 2020 until 2022 and the expected or projected number of deaths in a country for that period under normal conditions. 5 For the baseline of expected deaths, the estimate model of Karlinsky and Kobak was used. This linear regression model uses historical death data in a country from 2015 until 2019 and accounts for seasonal variation in mortality and year-to-year trends due to changing population structure or socioeconomic factors. 5 7

‘Excess mortality P-score’ concerns the percentage difference between the reported number of deaths and the projected number of deaths in a country. 5 This measure permits comparisons between various countries. Although presenting the raw number of excess deaths provides insight into the scale, it is less useful to compare countries because of their large population size variations. 5 The ‘Our World in Data’ database presents P-scores in a country during a certain week or month in 2020 until 2022. 5 These P-scores are calculated from both the reported number of deaths in HMD and WMD and the projected number of deaths using the estimate model of Karlinsky and Kobak in WMD. 5 7 70 71

For correct interpretation of excess mortality provided by the ‘Our World in Data’ database, the following needs to be taken into consideration: the reported number of deaths may not represent all deaths, as countries may lack the infrastructure and capacity to document and account for all deaths. 5 In addition, death reports may be incomplete due to delays. It may take weeks, months or years before a death is actually reported. The date of a reported death may refer to the actual death date or to its registration date. Sometimes, a death may be recorded but not the date of death. Countries that provide weekly death reports may use different start and end dates of the week. Most countries define the week from Monday until Sunday, but not all countries do. Weekly and monthly reported deaths may not be completely comparable, as excess mortality derived from monthly calculations inclines to be lower. 5 7

For our analysis, weekly all-cause mortality reports from the ‘Our World in Data’ database were converted to monthly reports. Subsequently, the monthly reports were converted to annual reports.

Patient and public involvement

Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

The ‘Our World in Data’ database contained all-cause mortality reports of 47 countries (96%) in the Western World for the years 2020, 2021 and 2022. Only Andorra and Gibraltar were excluded. Both countries lacked all-cause mortality reports for the year 2022. Most countries (n=36, 77%) present weekly all-cause mortality reports, whereas 11 countries (23%) report monthly. The latter countries include the following: Albania, Bosnia Herzegovina, Faeroe Islands, Greenland, Kosovo, Liechtenstein, Moldova, Monaco, North Macedonia, San Marino and Serbia.

The all-cause mortality reports were abstracted from the ‘Our World in Data’ database on 20 May 2023. At this date, four countries (9%) still lacked all-cause mortality reports for various periods: Canada (1 month), Liechtenstein (3 months), Monaco (3 months) and Montenegro (4 months). It is noteworthy that all-cause mortality reports are also still being updated for the other countries due to registration delays which may take weeks, months or even years.

Excess mortality

Online supplemental table 1 illustrates that the total number of excess deaths in the 47 countries of the Western World was 3 098 456 from 1 January 2020 until 31 December 2022. Excess mortality was documented in 41 countries (87%) in 2020, in 42 countries (89%) in 2021 and in 43 countries (91%) in 2022.

In 2020, the year of the COVID-19 pandemic and implementation of the containment measures, 1 033 122 excess deaths (P-score 11.4%) were recorded. In 2021, the year in which both COVID-19 containment measures and COVID-19 vaccines were used to address virus spread and infection, a total of 1 256 942 excess deaths (P-score 13.8%) were reported. In 2022, the year in which most containment measures were lifted and COVID-19 vaccines were continued, preliminary available data counts 808 392 excess deaths (P-score 8.8%).

Figure 1 presents the excess mortality and cumulative excess mortality in 47 countries of the Western World over the years 2020, 2021 and 2022. The linear excess mortality trendline is almost horizontal.

Excess mortality and cumulative excess mortality in the Western World (n=47 countries). Preliminary and incomplete all-cause mortality reports are available for 2022.

Excess mortality P-scores

Figure 2 shows the excess mortality P-scores per country in the Western World. Only Greenland had no excess deaths between 2020 and 2022. Among the other 46 countries with reported excess mortality, the percentage difference between the reported and projected number of deaths was highest in 13 countries (28%) during 2020, in 21 countries (46%) during 2021 and in 12 countries (26%) during 2022. Figure 3 exemplifies excess mortality P-score curves of the highest-populated country of North America (the USA), the four highest-populated countries of Europe (Germany, France, the UK and Italy) and the highest-populated country of Australasia (Australia).

Excess mortality P-scores per country in the Western World (n=47 countries). Preliminary and incomplete all-cause mortality reports are available for 2022.

Excess mortality P-score curves of six countries in the Western World. Preliminary and incomplete all-cause mortality reports are available for 2022.

Figure 4 highlights a map of excess mortality P-scores in the Western World over the years 2020, 2021 and 2022. 74 Table 1 presents a classification of excess mortality P-scores in the Western World.

Map of excess mortality P-scores in the Western World (n=47 countries). 74 Preliminary and incomplete all-cause mortality reports are available for 2022.

This study explored the excess all-cause mortality in 47 countries of the Western World from 2020 until 2022. The overall number of excess deaths was 3 098 456. Excess mortality was registered in 87% of countries in 2020, in 89% of countries in 2021 and in 91% of countries in 2022. During 2020, which was marked by the COVID-19 pandemic and the onset of mitigation measures, 1 033 122 excess deaths (P-score 11.4%) were to be regretted. 17 18 A recent analysis of seroprevalence studies in this prevaccination era illustrates that the Infection Fatality Rate estimates in non-elderly populations were even lower than prior calculations suggested. 37 At a global level, the prevaccination Infection Fatality Rate was 0.03% for people aged <60 years and 0.07% for people aged <70 years. 38 For children aged 0–19 years, the Infection Fatality Rate was set at 0.0003%. 38 This implies that children are rarely harmed by the COVID-19 virus. 19 38 During 2021, when not only containment measures but also COVID-19 vaccines were used to tackle virus spread and infection, the highest number of excess deaths was recorded: 1 256 942 excess deaths (P-score 13.8%). 26 37 Scientific consensus regarding the effectiveness of non-pharmaceutical interventions in reducing viral transmission is currently lacking. 75 76 During 2022, when most mitigation measures were negated and COVID-19 vaccines were sustained, preliminary available data count 808 392 excess deaths (P-score 8.8%). 39 The percentage difference between the documented and projected number of deaths was highest in 28% of countries during 2020, in 46% of countries during 2021, and in 26% of countries during 2022.

This insight into the overall all-cause excess mortality since the start of the COVID-19 pandemic is an important first step for future health crisis policy decision-making. 1–4 The next step concerns distinguishing between the various potential contributors to excess mortality, including COVID-19 infection, indirect effects of containment measures and COVID-19 vaccination programmes. Differentiating between the various causes is challenging. 16 National mortality registries not only vary in quality and thoroughness but may also not accurately document the cause of death. 1 19 The usage of different models to investigate cause-specific excess mortality within certain countries or subregions during variable phases of the pandemic complicates elaborate cross-country comparative analysis. 1 2 16 Not all countries provide mortality reports categorised per age group. 2 12 Also testing policies for COVID-19 infection differ between countries. 1 2 Interpretation of a positive COVID-19 test can be intricate. 77 Consensus is lacking in the medical community regarding when a deceased infected with COVID-19 should be registered as a COVID-19 death. 1 77 Indirect effects of containment measures have likely altered the scale and nature of disease burden for numerous causes of death since the pandemic. However, deaths caused by restricted healthcare utilisation and socioeconomic turmoil are difficult to prove. 1 78–81 A study assessing excess mortality in the USA observed a substantial increase in excess mortality attributed to non-COVID causes during the first 2 years of the pandemic. The highest number of excess deaths was caused by heart disease, 6% above baseline during both years. Diabetes mortality was 17% over baseline during the first year and 13% above it during the second year. Alzheimer’s disease mortality was 19% higher in year 1 and 15% higher in year 2. In terms of percentage, large increases were recorded for alcohol-related fatalities (28% over baseline during the first year and 33% during the second year) and drug-related fatalities (33% above baseline in year 1 and 54% in year 2). 82 Previous research confirmed profound under-reporting of adverse events, including deaths, after immunisation. 83 84 Consensus is also lacking in the medical community regarding concerns that mRNA vaccines might cause more harm than initially forecasted. 85 French studies suggest that COVID-19 mRNA vaccines are gene therapy products requiring long-term stringent adverse events monitoring. 85 86 Although the desired immunisation through vaccination occurs in immune cells, some studies report a broad biodistribution and persistence of mRNA in many organs for weeks. 85 87–90 Batch-dependent heterogeneity in the toxicity of mRNA vaccines was found in Denmark. 48 Simultaneous onset of excess mortality and COVID-19 vaccination in Germany provides a safety signal warranting further investigation. 91 Despite these concerns, clinical trial data required to further investigate these associations are not shared with the public. 92 Autopsies to confirm actual death causes are seldom done. 58 60 90 93–95 Governments may be unable to release their death data with detailed stratification by cause, although this information could help indicate whether COVID-19 infection, indirect effects of containment measures, COVID-19 vaccines or other overlooked factors play an underpinning role. 1 8–14 20–25 39–60 68 90 This absence of detailed cause-of-death data for certain Western nations derives from the time-consuming procedure involved, which entails assembling death certificates, coding diagnoses and adjudicating the underlying origin of death. Consequently, some nations with restricted resources assigned to this procedure may encounter delays in rendering prompt and punctual cause-of-death data. This situation existed even prior to the outbreak of the pandemic. 1 5

A critical challenge in excess mortality research is choosing an appropriate statistical method for calculating the projected baseline of expected deaths to which the observed deaths are compared. 96 Although the analyses and estimates in general are similar, the method can vary, for instance, per length of the investigated period, nature of available data, scale of geographic area, inclusion or exclusion of past influenza outbreaks, accounting for changes in population ageing and size and modelling trend over years or not. 7 96 Our analysis of excess mortality using the linear regression model of Karlinsky and Kobak varies thus to some extent from previous attempts to estimate excess deaths. For example, Islam et al conducted an age- and sex-disaggregated time series analysis of weekly mortality data in 29 high-income countries during 2020. 97 They used a more elaborate statistical approach, an overdispersed Poisson regression model, for estimating the baseline of expected deaths on historical death data from 2016 to 2019. In contrast to the model of Karlinsky and Kobak, their baseline is weighing down prior influenza outbreaks so that every novel outbreak evolves in positive excess mortality. 7 97 Islam’s study found that age-standardised excess death rates were higher in men than in women in nearly all nations. 97 Alicandro et al investigated sex- and age-specific excess total mortality in Italy during 2020 and 2021, using an overdispersed Poisson regression model that accounts for temporal trends and seasonal variability. Historical death data from 2011 to 2019 were used for the projected baseline. When comparing 2020 and 2021, an increased share of the total excess mortality was attributed to the working-age population in 2021. Excess deaths were higher in men than in women during both periods. 98 Msemburi et al provided WHO estimates of the global excess mortality for its 194 member states during 2020 and 2021. For most countries, the historical period 2015–2019 was used to determine the expected baseline of excess deaths. In locations missing comprehensive data, the all-cause deaths were forecasted employing an overdispersed Poisson framework that uses Bayesian inference techniques to measure incertitude. This study describes huge differences in excess mortality between the six WHO regions. 99 Paglino et al used a Bayesian hierarchical model trained on historical death data from 2015 to 2019 and provided spatially and temporally granular estimates of monthly excess mortality across counties in the USA during the first 2 years of the pandemic. The authors found that excess mortality decreased in large metropolitan counties but increased in non-metropolitan counties. 100 Ruhm examined the appropriateness of reported excess death estimates in the USA by four previous studies and concluded that these investigations have likely understated the projected baseline of excess deaths and therewith overestimated excess mortality and its attribution to non-COVID causes. Ruhm explains that the overstatement of excess deaths may partially be explained by the fact that the studies did not adequately take population growth and age structure into account. 96 101–104 Although all the above-mentioned studies used more elaborate statistical approaches for estimating baseline mortality, Karlinsky and Kobak argue that their method is a trade-off between suppleness and chasteness. 7 It is the simplest method to captivate seasonal fluctuation and annual trends and more transparent than extensive approaches. 7

This study has various significant limitations. Death reports may be incomplete due to delays. It may take weeks, months or years before a death is registered. 5 Four nations still lack all-cause mortality reports for 1–4 months. Some nations issue complete data with profound arrears, whereas other nations publish prompt, yet incomplete data. 5 7 The presented data, especially for 2022, are thus preliminary and subject to backward revisions. The more recent data are usually more incomplete and therefore can undergo upward revisions over time. This implies that several of the reported excess mortality estimates can be underestimations. 7 The completeness and reliability of death registration data can also differ per nation for other reasons. The recorded number of deaths may not depict all deaths accurately, as the resources, infrastructure and registration capacity may be limited in some nations. 5 7 Most countries report per week, but some per month. Weekly reports generally provide the date of death, whereas monthly reports often provide the date of registration. Weekly and monthly reports may not be entirely comparable. 5 7 Our data are collected at a country level and provide no detailed stratification for sociodemographic characteristics, such as age or gender. 5 7

In conclusion, excess mortality has remained high in the Western World for three consecutive years, despite the implementation of COVID-19 containment measures and COVID-19 vaccines. This is unprecedented and raises serious concerns. During the pandemic, it was emphasised by politicians and the media on a daily basis that every COVID-19 death mattered and every life deserved protection through containment measures and COVID-19 vaccines. In the aftermath of the pandemic, the same morale should apply. Every death needs to be acknowledged and accounted for, irrespective of its origin. Transparency towards potential lethal drivers is warranted. Cause-specific mortality data therefore need to be made available to allow more detailed, direct and robust analyses to determine the underlying contributors. Postmortem examinations need to be facilitated to allot the exact reason for death. Government leaders and policymakers need to thoroughly investigate underlying causes of persistent excess mortality and evaluate their health crisis policies.

Dissemination to participants and related patient and public communities

We will disseminate findings through a press release on publication and contact government leaders and policymakers to raise awareness about the need to investigate the underlying causes of persistent excess mortality.

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From Effects of Governance to Causes of Epistemic Change

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• Published: 29 May 2024

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• Jochen Gläser   ORCID: orcid.org/0000-0001-7356-9407 1 , 2  

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In this paper I argue that the attempts by science studies to identify epistemic effects of new governance instruments have largely failed. I suggest two main reasons for this failure. The first reason is that neither quantitative nor qualitative studies of effects of governance instruments meet the respective methodological standards for establishing causality. While much of this could be repaired, the second reason is more severe: given the complex causal web between governance and knowledge production and the multi-level nature of causation, a strategy that starts from a particular governance instrument and tries to identify its effects cannot work. I propose to reverse this strategy by starting from the observation of epistemic change and applying a strategy of “causal reconstruction” (Mayntz), which identifies the causes of this epistemic change and among them the contribution by governance. This approach has the advantage of starting from well-identified change. Challenges posed by the new approach include the empirical identification of epistemic change and the need to integrate sociological methods in science policy studies.

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Introduction: Do Governance Instruments Have Effects?

After almost 50 years of empirically investigating the introduction of new governance instruments, we are still unable to establish whether and how they affect the conduct and content of research. We assume that changes in governance affect epistemic change—understood here as change in the practices of producing new knowledge and in the outcomes of these practices—but cannot prove it according to the methodological standards of social science.

The field of science and higher education policy studies has strongly grown since the 1970s, not least due to significant changes in the governance of science and higher education in many OECD countries since then (Whitley 2010 ; Reale and Seeber 2013 ; Capano and Pritoni 2020 ). The introduction and sometimes rapid succession of policy reforms and new governance instruments called for description, comparison and assessment. Areas of interest include

changes in research funding, with an emphasis on the emergence of research councils as new important actors in the science system (Braun 1993, 1998; Rip 1994; Braun and Guston 2003; Nedeva 2013),

the realisation of political interests through particular funding programmes, with an emphasis on support for emerging fields (Molyneux-Hodgson and Meyer 2009; Bensaude-Vincent 2016) and on excellence funding (Laudel and Gläser 2014; Langfeldt et al. 2015; Möller et al. 2016), and

higher education reforms, with an emphasis on changing authority relations in the higher education sector and performance-based funding of higher education institutions (Schimank 2005; Whitley and Gläser 2007; Paradeise et al. 2009; Meier and Schimank 2010; Musselin 2014; Thomas et al. 2020).

One strand within this research has been devoted to the identification of epistemic effects of changes in the governance of science. This is not surprising given that the empirical objects of science policy studies – governance instruments – are designed to influence the conduct and content of science. Many science policy scholars have also been involved in advising science policy on the effectiveness of its instruments. Studying governance instruments remains incomplete without considering their intended and unintended effects.

However, several reviews have reported that convincing evidence of any causal links remains scarce (de Rijcke et al. 2016 ; Gläser and Laudel 2016 ; Thomas et al. 2020 ). Moreover, the possibility of establishing causality with our current approaches has been called into question (Gläser and Laudel 2016 ; Schneider et al. 2016 ; Aagaard and Schneider 2017 ; Gläser 2017 ; Thomas et al. 2020 ). Thomas et al. ( 2020 : 282-283) found that the literature on “performance-based research evaluation arrangements” is often descriptive, cannot establish effects on specific kinds of research, primarily focuses on micro-level changes, and does not apply comparative frameworks. They propose a new research agenda, whose key elements are the introduction of comparative analytical frameworks, complementing “efficiency concerns” (“whether arrangements have achieved what they set out to achieve”) with “effectiveness concerns” (“are the ‘right’ things being done in the science system?”), the causal attribution of effects, and the inclusion of effects on the structure of global knowledge communities and bodies of knowledge (ibid: 283; see also Gläser and Laudel 2016 : 156).

In this paper, I address the problem of causally attributing changes in the conduct and content of research to changes in governance. I demonstrate that studies that claim to have found effects of governance do not meet the methodological standards of causal analysis. However, as important as improving our methodologies is, it is unlikely to be sufficient for establishing causality due to the complexity of the causal processes involved. While the understanding of causality underlying the search for such effects is rarely explicated, the studies’ designs reflect attempts to solve a mono-causal problem, which is at odds with the multi-causal nature of social phenomena. This is why I suggest that we need to change our approach to establishing causality by abandoning the search for effects of governance and moving to the search for causes of changes in research content.

To develop this argument, I identify current dominant approaches to establishing causality and discuss their methodological shortcomings and principal limitations (2). Based on this analysis, I discuss reasons why tracing influences of governance instruments through the complex science and higher education system to the researchers whose behaviour they are assumed to change is unlikely ever to be successful (3). I then successively narrow my argument. First, I suggest that for establishing causality with qualitative methods, we need to reverse our analytical strategy by tracing causal processes backwards from observable epistemic change and determining the causal role of governance in these processes (4). Second, I turn to one particular kind of change and ask how we can identify epistemic change (5). Some conclusions about consequences of the new approach for science policy studies can be drawn (6).

Have We Demonstrated that Governance Causes Change in the Conduct and Content of Research?

In this section, I identify two dominant approaches that are used in science studies for causally ascribing changes in the conduct and content of research to governance instruments. Instead of duplicating the reviews listed in the introduction, I distinguish studies according to their empirical approach to collecting evidence supporting causal claims and discuss typical or particularly influential examples. I begin with bibliometric studies that try to causally attribute changes in publication behaviour or publication performance to a particular change in governance by considering it as a ‘treatment’ which is followed by ‘effects’. A second type of study attempts to establish causality by asking participants about changes in their research. Although a growing number of studies employs a ‘mixed methods’—approach, most studies use only one approach for establishing causality. At the end of this section, I consider the ‘causality narrative’, which is unfounded in the light of the methodological problems.

“Change after Treatment”—Bibliometric Studies of the Impact of Governance

Among the studies of effects of governance, one research tradition uses bibliometric methods to establish epistemic change on the country level, university level, or on the collective level of grant recipients. Bibliometric methods have three major advantages. They are unobtrusive, i.e., they do not require interactions with the units of analysis. They support the analysis of macro-level epistemic changes and of changes in researchers’ publication practices in national science systems. They can also be used to study research performance if it is validly reflected in publications or citations, a premise which is increasingly challenged.

Given this potential, bibliometric methods have so far been applied with a surprisingly narrow focus on publication behaviour, possibly because this is what most governance instruments under study target. Most studies include some aspect of research quality, e.g. by differentiating publications according to strata of journals or by including citation-based measures. To the extent to which these differences are assumed to reflect research performance, bibliometric studies address not only behavioural change but also epistemic change.

Bibliometric studies of effects of national research evaluation systems or grant funding apply before-after comparisons in which the introduction of a new governance measure is considered as treatment. Three prominent examples include:

1) A study by Butler (2002; 2003a; b; 2004) according to which the publication component of Australian formula-based funding of universities, which rewards publications regardless of their impact, caused a disproportionate increase in the number of publications in low-impact journals. Although the texts of Butler’s publications treat the causal claim as a “hypothesis” for which “support” is found, the titles of the publications “Explaining Australia’s increased share of ISI publications—the effects of a funding formula based on publication counts” (Butler 2003a) or “What Happens when Funding is Linked to Publication Counts?” (Butler 2004) promise to answer a causal question, as do some statements in the abstracts. Butler supplemented her argument by showing a different dynamics of publications in two control groups (the hospital sector and the government sector, Butler 2003a, b, 2004) and by presenting anecdotal evidence about links between different responses of universities to the introduction of the funding formula and publication dynamics (Butler 2003a). She also considered and dismissed some possible alternative causes of the publication dynamics (Butler 2003a: 149). Her study was received as demonstrating causality (Weingart 2005; Hicks 2009; Good et al. 2015; de Rijcke et al. 2016). It motivated a similar study of effects of the Norwegian funding system (Schneider et al. 2016), whose authors argued that the number of publications in low-impact journals did not rise in Norway because the Norwegian system takes the quality of publications into account. Although Schneider et al. were aware of the difficulties involved in establishing causality (ibid: 245), they nevertheless asked a causal question (ibid: 246) and answered it (ibid: 255). A year later, van den Besselaar et al. (2017) attempted to refute Butler’s argument by presenting data that showed “increased research quality” (ibid: 905), which they also causally attributed to the Australian funding formula. Several discussants of that paper pointed out that the available evidence does not enable causal claims either way (Aagaard and Schneider 2017; Gläser 2017 ; Hicks 2017). In a continuation of this discussion, Schneider et al. (2017) stated in a comment on their article from 2016 that they did not intend to make a causal claim.

2) The observation by Jimenez-Contreras et al. ( 2003 ) of an acceleration in the rate of Spanish publications indexed in the Web of Science from the end of the 1980s onwards, “which eventually became exponential” despite a levelling out of investment in science (ibid: 130-131). The authors reviewed possible explanations for this change and argued:

Because the factors reviewed above are not able in themselves to explain the change in the growth rate (although clearly they are of some relevance), the determining factor in this recent increase in the publication of Spanish research in international journals appears to be the introduction, in 1989, of mechanisms of evaluation of publicly-sponsored research activity. (ibid: 134, my emphasis)

Their study has been received as proving the effect of the governance scheme (Weingart 2005 ; Hicks 2012 ). Later, their conclusions were challenged by Osuna et al. ( 2011 ), who systematically considered possible alternative causes and compared participants in the sexenio to a control group. The authors argued that the causal ascription of changes in the number of international publications to the sexenio could not be upheld:

It seems clear enough from our analysis that simplistic approaches such as before and after measures, often used by politicians to legitimise their ‘narratives’, are not sufficient from a research point of view. (ibid: 589)

3) The analysis of a statistical association between institutional and individual incentive schemes, on the one hand, and an increase in submissions to and publications in the journal Science , on the other hand (Franzoni et al. 2011 ). The authors consider only very few alternative explanations (the variation of research inputs, the extent of international collaborations, and each country’s share in positions on the editorial board of Science ), which are controlled in their model. They write about association rather than causation throughout the paper but abandon this careful stance in the conclusions, where we read:

Incentives increased competition (with the US) from countries with latent capacity by altering the amount and apparent quality of the work that is submitted for scientific review and eventually published. (ibid: 703, my emphasis).

In the supporting online material, we also find a mix of causal and non-causal language. On page 4, we read “We also analyzed the impact of the incentive policies on the number of published papers (Table S7)” (my emphasis). On page 7, the authors state as one limitation that they “cannot test for causality”, and that there might be other explanations.

Bibliometric methods are also applied in the study of effects of funding schemes. Most empirical studies usually attempt to answer the question of whether a funding scheme achieves its intended aims and what side effects it has. For a discussion of these studies, I utilise the investigation of the impact of “effects” of grant funding by the Danish Council for Independent Research (Bloch 2020 ), which includes a detailed review of previous research (ibid: 457-460). According to this review, most studies compare grantees’ publication and citation performance to that of non-grantees, with the control group usually constructed from unsuccessful applicants. To exclude alternative causes, some studies apply matching techniques that eliminate ex-ante performance differences and other differences between the two groups. Other studies use regression discontinuity designs, which compare grantees to rejected applicants with assessment scores close to the threshold of funding. Results are inconsistent, with some studies finding significant correlations between funding and performance indicators, other finding significant correlations for publications but not citations, and yet others finding significant correlations for some fields but not others.

These bibliometric studies of evaluation systems and funding programmes illustrate the methodological problems involved in establishing effects of a ‘treatment’. Three minimum conditions for establishing causality (Aagaard and Schneider 2017 ) are only partially met by the studies’ designs. Footnote 1 A first condition is precedence: the cause must precede the effect. This can be easily established only for funding programmes. In the case of national evaluation systems, the ‘treatment’ is a process whose position and boundaries in time cannot be established because incentives can be anticipated by the actors involved (Butler 2017 ; Hicks 2017 ) but also “trickle down” (Aagaard 2015 ) at different speed in different parts of the science system. Footnote 2

The second condition is that correlation between treatment and effect must be established. This happened only in the study by Franzoni et al. ( 2011 ) and in some of the studies of funding programmes (with other such studies trying to but not finding correlations). Correlation is difficult to establish in studies of evaluation systems due to the impossibility to determine the time of the ‘treatment’ (Hicks 2017 ) and for other reasons listed by Aagaard and Schneider ( 2017 : 924).

The third condition is non-spuriousness. Establishing non-spuriousness would require the systematic exclusion of possible alternative causes of the observed phenomena, which the studies did only to a very limited extent and rarely with a convincing design. If studies of evaluation systems excluded possible alternative causes at all, these were introduced ad hoc . Some studies use untreated control groups, which are constructed from researchers who were not subject to an evaluation system or from various populations of non-grantees, respectively. Studies of funding programmes attempt to exclude alternative explanations by applying advanced matching techniques that minimise differences between grantees and non-grantees. However, this matching cannot exclude all alternative explanations because the conditions under which grantees and control group members work are not investigated.

Given these problems, it cannot be concluded that the studies of governance instruments were successful in identifying effects of governance instruments, i.e. changes in the conduct or content of research that can be at least partially ascribed to the causal influence of these instruments. This is not to say that governance instruments do not influence researchers or research. We just cannot conclude from existing studies that they do or how they do so. A few studies have at least plausibility on their side. Studies of the Czech evaluation system reported a strong increase in the number of published books that meet minimum criteria of the evaluation system, with universities and departments acting as publishers of their academics’ books (Broz and Stöckelová 2018 ), and a steep rise in the proportion of proceedings papers in the social sciences (Vanecek and Pecha 2020 ). In China, the cash-per-publication reward policy introduced strong direct incentives for researchers to publish more (Quan et al. 2017 ). These studies did not exclude alternative explanations of the observed changes in publication behaviour but have some plausibility because they link specific properties of evaluation systems to particularly drastic or unexpected changes in publication behaviour, which has the advantage that alternative explanations are difficult to imagine.

“How do you Think your Research has Changed?”—Survey-Based and Interview-Based Studies

The second main approach to establishing causation of epistemic change by governance instruments is based on asking researchers how their research has changed under the influence of these instruments. This approach utilises surveys (Harley and Lee 1997 ; Hammarfelt and de Rijcke 2015 ), interviews (Gläser et al. 2010 ; Leišytė et al. 2010 ; Linkova 2014 ; Cañibano et al. 2018 ; Neff 2018 ), or both methods in combination (McNay 1998 ; Good et al. 2015 ; Mouritzen and Opstrup 2020 ). In few cases, focus group discussions (McNay 1998 ; Linkova 2014 ) or observations (Linkova 2014 ) are included. Ethnographic studies are exceedingly rare and do not claim to establish causality (e.g. Lucas 2006 ).

In addition to evaluation-based funding schemes for universities, funding schemes for competitive grant funding have been studied with interviews and questionnaires. For example, Morris ( 2000 ) used interviews with researchers, administrators, and stakeholders from funding agencies to study the impact of grant funding on the work of biologists. She did not focus on a particular funding scheme. Hellström et al. ( 2018 ) also used interviews in their study of the Swedish funding programme for centres of excellence.

The aim of many of these studies was to identify effects of governance instruments. This interest is sometimes cast as studying “academics responses” to governance instruments (Linkova 2014 ; Leišytė et al. 2010 ). In other cases, an interest in “effects” or “the impact” of the governance instrument under investigation is clearly stated (Harley and Lee 1997 ; McNay 1998 ; Cañibano et al. 2018 ; Hellström et al. 2018 ; Mouritzen and Opstrup 2020 ). Hammarfelt and de Rijcke ( 2015 ) do not frame a causal question but leave the reader to wonder why one would investigate changes in publication practices of humanities scholars after the introduction of a performance evaluation system if not for finding out whether it made a difference.

Using researchers as sources of information about the impact of governance on their work has three distinct advantages compared to the unobtrusive bibliometric methods discussed in the previous section. Questioning researchers in their role of ‘obligatory passage points‘ for influences on research content (Gläser 2019 : 423) makes it possible to acquire first-hand knowledge about epistemic change, to consider overlaying influences, and to capture a wider range of possible changes than bibliometric indicators. Indeed, the studies listed above investigate not only changes in publication behaviour but also other behavioural change and a range of changes of the interviewees’ research including epistemic diversity and orientation towards the mainstream, the basic/applied character, interdisciplinarity, riskiness and time horizons of research.

At the same time, this approach faces methodological challenges when it comes to establishing causality. The first challenge is to avoid a methodological trap in the design of questions for interviews and surveys. The opportunity to ask researchers about changes in their research is sometimes used to also ask them what changes were caused by the governance instrument of interest. This practice of investigators ‘passing on’ their research question to participants instead of operationalising it is likely widespread but difficult to identify due to the woefully incomplete reporting on qualitative data collection.

Unfortunately, studies that adhere to higher standards of reporting their methodology need to serve as examples for the insufficient operationalisation of causal questions. In their study of Center of Excellence (CoE) funding, Hellström et al. ( 2018 : 75) reported to have asked interviewees the following question:

How has the Linnaeus CoE funding affected your research in terms of (a) organization (how research projects are run and related, teams etc.) and (b) the way that you pursue knowledge in your field?

Similar questions have been asked by Pinheiro et al. ( 2019 ) in their study of the impact of governance changes on performance in Nordic universities and by Leišytė ( 2007 : 382) in her study of the Dutch and English evaluation systems. Footnote 3 Questions of this kind are not neutral and exercises pressure on the participant to communicate effects (see e.g. Cairns-Lee et al. 2021 ). They also violate the principle of openness underlying qualitative research because the outcome of interest frames the approach to data collection. The validity of information obtained with them must therefore be doubted, which is why Mouritzen and Opstrup ( 2020 ) avoided this line of questioning (ibid: 108).

Answers to such leading questions are unsuitable for causal analysis. A question that casts the governance instrument as a cause presumes causation—that the instrument has effects—and asks interviewees to name these effects can only reconstruct the causality study participants believe to be at work. When participants describe changes in their behaviour or in the content of their research as effects of governance, they provide us with their holistic subjective theory of causes, assumed causal process, and effects. Probing questions that ask interviewees to explain their reasoning just force them to elaborate and thereby strengthen their theory by mobilising auxiliary hypotheses or constructing examples ad hoc . Instead of enabling a social-scientific causal analysis, research that passes on its research question can only collect participants’ subjective theories about that research question.

A correct operationalisation of the causal question would translate it in several interview questions that ask about conditions, actions, and outcomes of actions separately. For example, an interview schedule can be divided into a part that reconstructs decisions on research content and all the reasons why these decisions were made, a part in which necessary conditions for conducting this particular research are explored and a part in which perceptions of national and university evaluation schemes are explored. This separation and ordering avoid the framing of the reconstruction of decisions by the discussion of evaluations.

A second methodological challenge is the interpretation of self-reported behavioural change. Three examples illustrate the problem:

Cañibano and Corona ( 2018 ) compared statements of five historians who unambiguously reported to have changed their publication behaviour in response to an evaluation system to their publication histories and found no evidence for the self-reported behavioural change.

Hammarfelt and de Rijcke ( 2015 : 69) observe that the shares of articles and monographs in publication channels of the faculty of arts they investigated remained constant and on the same page quote a participant saying the opposite.

In their very careful mixed-methods study, Mouritzon and Opstrup ( 2020 :123) find that overall, “the introduction of the [evaluation system] does not seem to have resulted in major changes” but note that “…the many qualitative statements above almost exclusively emphasize unintended dysfunctional consequences of the [Bibliometric Research Indicator] for the production of scientific knowledge” (ibid: 124).

It is very likely that the interviewees in these studies believed their descriptions of their own or their colleagues’ responses to governance interventions to be correct. Nevertheless, their statements contradicted independently collected data about their behaviour.

The third challenge is the consideration of alternative explanations of observed change. Similar to the bibliometric studies discussed in the previous section, the strong focus of survey-based and interview-based studies on one governance instrument limits their openness for other partial causes or alternative explanations (Harley and Lee 1997 ; McNay 1998 ; Cañibano et al. 2018 ; Neff 2018 ). Studies that explore participants’ situations more fully find that the governance instrument in question is not the cause of behavioural change. For example, interview-based studies of the Australian, British and Dutch evaluation systems found the necessity of obtaining external grant funding to exercise a much stronger influence than research evaluations (Gläser et al. 2010 ; Leišytė et al. 2010 ).

These three methodological challenges can be addressed by increasing methodological rigour. A fourth challenge, which applies only to studies based on interviews, appears to be more obstinate. While micro-level change of research content can be identified and causally attributed to governance changes with qualitative approaches (Gläser et al. 2010 ; Hellström et al. 2018 ; Whitley et al. 2018 ), these micro-level observations do not currently enable conclusions about macro-level change. Such conclusions depend on the identification of mechanisms that aggregate micro-level epistemic change and of influences of overlapping processes of knowledge production. Neither task has yet been addressed by science studies. Footnote 4

Like the authors of studies discussed in the previous section, authors of survey-based and interview-based studies are ambiguous with regard to the question of having established causality. For example, Hammarfelt and de Rijcke make a causal argument in the title of their paper “Accountability in context: effects of research evaluation systems on publication practices, disciplinary norms, and individual working routines in the faculty of Arts at Uppsala University” (Hammarfelt and de Rijcke 2015 : 63) and in its abstract (ibid.) but later in the paper state: “We cannot make the causal claim that the implementation of evaluation models at the national and local level is solely or even mainly responsible for these changes” (ibid: 74). Neff ( 2018 ) claims causality with his title (“Publication incentives undermine the utility of science: Ecological research in Mexico”) but reports only his respondents’ opinions about effects.

Other studies move from the intention to establish causality to the accurate reporting of causality reported by participants . Cañibano et al. claim to have found causal relationships but ultimately present them as claims of their study participants, e.g. “According to our interviewees, the evaluation system encourages theoretical stagnation and repetitiveness” (Cañibano et al. 2018 : 787; see Neff 2018 for the same approach). Such statements can be validly derived from the empirical evidence but move the target from investigating effects of governance to reporting what study participants think about it.

Taking Stock: Methodological Challenges and Causality Narratives

The empirical studies discussed in the previous sections consider changes in research performance, researcher behaviour and in the content of research as effects of governance instruments. Most of them claim in one way or other to have established causality, at least by occasionally using suggestive causal language. None of them can be considered to have successfully established causality.

The authors of these studies appear to be aware of that problem and include disclaimers concerning causality that effect. Since causal language is nevertheless used repeatedly in many studies, readers are confronted by a curious mix of disclaimers saying causality could not be established and claims to have shown effects of governance instruments. Footnote 5 This resembles a practice termed “spin” in the biomedical literature, which is defined as “reporting practices that distort the interpretation of results and mislead readers so that results are viewed in a more favourable light” (Chiu et al. 2017 : 11). Spin is not necessarily applied intentionally. However, regardless of the reasons for publications applying ‘causality spin’ in the discussion of effects of governance, they feed a meta-narrative about governance instruments causing change in the conduct and content of research, e.g.

Studies that focused on effects of funding and evaluation systems on scientific output have indeed demonstrated goal displacement. Butler … analyzed the introduction of performance metrics in Australian research funding allocation. Her study revealed a sharp rise in ISI-ranked publications in all university fields (but not in other branches of research where this type of funding allocation is not present) when funding becomes linked with publications … Butler earlier demonstrated how this strategy, while leading to a rise of relative share of Australian publications, has also contributed to a decline of scientific impact (measured in citations) during the same period (Butler 2003a , b ). … Similar effects of the use of bibliometrics on the amount of publications have been found in Spain … Denmark, Flanders, and Norway …. (de Rijcke 2016 : 162-163). *** Numerous studies have analyzed changes in publication patterns in relation to the criteria that were set in subsequent national research assessment exercises in the UK, finding convincing evidence for a link between publication behavior and the conditions of assessment [...]. In these and other studies the effects (i.e. a rise in the number of publications) were seen to occur in systems where funding and scores on the metrics were directly linked. (Müller and de Rijcke 2017 : 159)

At the current state of our knowledge this narrative must be considered a myth. We need to be more careful.

The Nature of the Causal Problem

While most of the methodological problems discussed in the preceding section might be solved by more faithfully applying relevant methodological rules, this is unlikely to be sufficient. The studies appear to have set themselves an impossible task of causal analysis. The search for effects of a particular governance instrument and the disregard of alternative explanations of these effects frames a study as monocausal, which is clearly at odds with the complex structure of causal processes (Mackie 1965 ; Franzese 2007 ). I briefly recount an argument from the discussion about the counter-claim by van den Besselaar et al ( 2017 ) to Butler’s claim about effects of the publication component in Australia’s evaluation-based funding system (see above, section “Change after treatment”—bibliometric studies of the impact of governance ) in order to demonstrate that any possible influences of governance instruments are likely to get lost in translation, superposition and synthesis (Gläser 2017 ). The underlying problem is that the search for causation must be conducted in a vertically differentiated multi-level system (Fig. 1 ). Footnote 6 In these systems, the behaviour of lower-level elements is influenced by their embeddedness in higher level structures and at the same time generates higher-level processes (Mayntz 2009 : 91).

The causal web between governance instruments and macro-level epistemic change (simplified and generalised version of Figure 2 from Gläser 2017 : 930, solid arrows represent the commonly assumed causal path, dashed arrows represent major additional influences in the causal process)

The logic underlying current approaches to establishing effects of governance instruments starts from a governance instrument of interest and attempts to trace its influence to micro-level or macro-level change. Such a governance instrument may address researchers or research groups directly (as happens with funding programmes), or indirectly by communicating to universities the expectation that they make their researchers do more, better, or different research (as happens with national evaluation systems other policies). In both cases, actors that are not addressed by the governance instrument still observe it and thus may be influenced by it.

The influence of the governance instrument is overlaid by other influences from a variety of sources including other national or trans-national research governance instruments, other societal actors (e.g. commercial interests or civil society actors) and national and international scientific communities (which makes the conditions influencing meso-level actors and researchers field-specific). These influences contribute to shaping the situation of meso-level actors such as employment organisations or funders of research as well as the situation of a researcher.

Meso-level actors and their sub-units (see Mouritzen and Opstrup  2020 on the influence of university departments) thus face a situation shaped by overlapping influences from several actors, which are exercised through a variety of channels. They are organisations in an “evaluative landscape” (Brandtner 2017 ). They respond to this situation by influencing their researchers, thereby translating influences according to their own situations and interests. The influence exercised by meso-level actors is unlikely to be consistent. In most cases, a variety of expectations will be communicated and will be backed by different means for exercising influence. Together with influences from macro-level actors, these influences from meso-level actors shape the situations of researchers. It is important to note that this is not a superposition of equal influences. To maintain their identity as members of their scientific community, researchers need to produce contributions that meet the community’s standards of relevance and methodological conduct. National governance instruments are always overlaid by strong influences from scientific communities (Gläser 2019 ; Tirado et al. 2023 ).

Researchers respond to their situation by making epistemic choices about the content of their research. As a result, their research might change in accordance with the purposes of the governance instrument in question and/or in other ways. This micro-level epistemic change is overlaid by what other researchers in different situations do. The governance instrument in question might exercise an influence on these different situations, too. Researchers in other countries may experience a similar influence from a national governance instrument or different influences from different governance instruments.

The micro-level epistemic change is aggregated by mechanisms which are still scarcely understood. They can be assumed to be field-specific, which again complicates the causal analysis. The local and the communal levels of knowledge production are linked through processes like collaboration, peer review, controversies, and organisational as well as intellectual mobility of researchers. In each of these processes, community members influence their community’s knowledge production according to their social position and status in their community. The aggregation of micro-level epistemic change is in fact a synthesis that is accomplished by a community’s influential members in a complex process of negotiation and mutual adjustment that is still ill understood and has not yet been empirically investigated.

This brief account illustrates three problems of identifying behavioural or epistemic change and causally attributing it to a particular governance instrument. First, changes in the conduct of research (in decisions of researchers on topics, approaches, collaborations, or publications) or in the content of research including research performance (epistemic change) may not at all occur. This does not necessarily mean that the governance instrument had no effect. For example, it might have prevented epistemic change that would otherwise have occurred. Second, if change occurs, it inevitably has more than one cause because any governance instrument whose effects we are trying to establish is an INUS condition, i.e. an “ insufficient but necessary part of a condition which in itself is unnecessary but sufficient for the result” (Mackie 1965 : 245). Footnote 7 In other words, social phenomena are caused by more than one condition (multicausality), and several different sets of conditions can cause them (equifinality). This is why the effects of any single governance instrument are unlikely to be identifiable and unlikely to be causally attributable.

The distinction between multicausality and equifinality highlights an important tension of causal analysis. The search for alternative explanations and their exclusion addresses equifinality, i.e. the possible existence of a different unnecessary but sufficient condition that produces the same result. Such explanations must indeed be excluded for causal ascription to work. However, other causal factors identified in the investigation may also be partial causes in an explanatory account of the governance instrument under study as another partial cause. These factors are other parts of the same unnecessary but sufficient condition and must be included . When should additional causal factors be excluded because they are part of an alternative explanation, and when should they be included as candidate partial causes?

This dilemma in causal analyses points to a the third problem of identifying epistemic or behavioural change. The inclusion of other partial causes or alternative causal factors in the assessment of effects of governance instruments requires a middle-range theory that explains changes in the conduct and content of research by systematically linking conditions under which research takes place to processes triggered by these conditions and the outcomes of these processes. Without such a theory, we cannot know whether the governance instrument we are studying can have effects at all, which other conditions (other partial causes) must exist for the governance instrument to have an effect, or whether the effect might be produced without the governance instrument under study (alternative explanations). The difficulty of establishing causality of any kind in studies of the governance of science is largely due to the absence of a theory that could inform us what to look for. Currently, prior empirical research must take its place but as I demonstrated in the second section, this research is problematic.

These problems of causal analysis are inescapable implications of an analytical strategy that starts from a particular governance instrument or governance arrangement and tries to ‘forward-trace’ its influence on the conduct and content of research. This strategy faces the problem of a possible dilution of the governance instrument’s influence by translation, superposition, and synthesis in a causal network about which we have no theory yet, and is unlikely to lead to observations of change that can be causally attributed to governance. Researchers respond to a multitude of simultaneous influences, and their responses are overlaid by others’ responses to equally complex situations. Studies attempting to establish effects of governance instruments by searching for the change they cause set themselves an impossible task.

From Tracing Effects to Tracing Causes

Having discussed necessary methodological improvements and the complexity of the causal processes underlying eventual influences of governance on the conduct and content of research, I now turn to the question of how causal analysis could be improved. This question is somewhat optimistic because it presumes that a successful causal analysis is possible, while the preceding section could also be read as an argument against this very possibility. However, given the importance of causal analysis for theory building, particular in social studies of fields where creating change is a major concern of key actors, I believe we should keep trying. I first discuss three major approaches to causality in the social sciences and point out developments that could improve empirical analyses and then further develop the argument for qualitative studies by arguing that we need to turn the causal question around. Footnote 8 Instead of asking what change is produced by governance, we should ask how governance contributes to change by starting from observable change and find out how this change is brought about .

Three Ways of Establishing Causality in the Social Sciences

The literature on social science methodology and on the philosophy of social science discusses three ways in which causality can be established. Footnote 9 The first and most common way is to establish causal relationships , i.e. to identify social phenomena that can be considered as causes and effects. This is achieved by finding associations among variables through statistical analysis and providing grounds on which this correlation can be considered to represent causation (Pearl 2009 ). Footnote 10 The discussion about the difficulties of drawing causal conclusions from correlational analysis has not only contributed to the rising interest in causal mechanisms but also led to innovations in statistical methods that can better support causal analysis (ibid.). Still, it remains unclear how far innovative statistical methods can go without support from much deeper substantive knowledge of the empirical realm under study (Freedman 2010 ).

A second way of establishing causality, which has enjoyed increasing attention in the last three decades, is the search for causal mechanisms. A causal mechanism (or social mechanism) is understood here as a sequence of causally linked events that occur repeatedly in reality if certain conditions are given and which link specified initial conditions to a specific outcome (Gläser and Laudel 2019 : [4]). Footnote 11 The search for causal mechanisms is a response to approaches that identify causes and effects but black-box the process by which the causes produce the effect. Footnote 12 Establishing causality by providing the mechanism that produces effects from causes confronts social science with the task of identifying social mechanisms, which is currently discussed in the political science literature as process tracing (Mahoney 2000 : 412, 414). Unfortunately, the burgeoning literature on process tracing does not seem to converge on a coherent understanding of this approach (Trampusch and Palier 2016 ).

Deriving causal relationships from associations among variables and identifying causal mechanisms are complementary in some respects. Quantitative approaches support the identification of causal relationships within a population through empirical generalisation, i.e. generalisation from samples to the population. They thus provide information on the empirical scope of the causal relationship. In contrast, process tracing provides a generalised description of initial conditions that trigger and maintain the mechanism (causes), the mechanism itself, and the change it produces (effects). While it does not support an empirical generalisation, its abstract description of the conditions under which the mechanism is likely to operate can be considered as the mechanism's theoretical scope, i.e. a generalisation that provides a theoretical description of the conditions under which the mechanism is triggered and maintained. This information could be used to determine the empirical scope by determining the population in which the conditions exist.

The third approach combines the first two and thus creates a much higher bar for establishing causality. According to the Russo-Williamson Thesis (RWT), “a causal claim can be established only if it can be established that there is a difference-making relationship between the cause and the effect, and that there is a mechanism linking the cause and the effect that is responsible for such a difference-making relationship” (Ghiara 2022 : 2; see also Russo and Williamson 2007 ; Shan and Williamson 2021 ). The difference-making relationship is considered as causal relationship established through interpreting statistical association, while the causal mechanism is identified with qualitative methods. Thus, when

a non-spurious correlation between smoking rates and lung cancer rates is established that supports at least one of the following statements: “(i) intervening on smoking behaviours results in a decrease of cancer rates; (ii) when smoking rates decrease, lung cancer rates decrease too; (iii) smoking increases the probability of developing lung cancer” (Ghiara 2022 : 3)

there is “evidence of a sufficiently well understood biological mechanism made of entities (such as proteins and genes) and activities (such as protein expressions or genetic mutations) that links smoking and lung cancer” (ibid.),

the causal claim “smoking causes lung cancer” is supported.

Although research that successfully implements RWT appears to exist (Ghiara 2022 ), this approach seems very difficult to apply. It requires establishing the non-spuriousness of the difference-making relationship, which is a continuing problem in statistical analysis (Goldenberg 1998 ), it requires operationalising the same concepts for qualitative and quantitative studies (which appears to be much easier for the health sciences for which RWT was originally developed than for the social sciences), and it requires matching the empirical scope of the difference-making relationship to the theoretical scope of the mechanism(s).

If effects of governance are to be identified, doing so with quantitative methods would be based on the first approach to causality, while qualitative methods would need to adhere to the second. The fact that neither approach has been faithfully applied in empirical research on effects of governance instruments on the conduct and content of research points to the difficulties involved, which I outlined in section “ The nature of the causal problem ”. However, a possible way forward appears to be a specific approach to process tracing in the ‘mechanismic’ approach.

Causal Reconstruction

A small but consistent body of work suggests starting from an observed phenomenon and working backwards through the mechanism(s) producing it, thereby identifying the phenomenon as an effect of the conditions that trigger and maintain the mechanism(s). Van Evera identifies this approach as process tracing and sees its main utility in arriving at the prime cause:

The investigator traces backward the causal process that produces the case outcome, at each stage inferring from the context what caused each cause. If this backward process-trace succeeds, it leads the investigator back to a prime cause. (Van Evera 1997 : 70)

This aim seems to be a bit narrow in the light of the multi-causality already discussed, which is why more recent approaches to this version of process tracing appear to be more promising.

Mayntz ( 2004 : 238, 2009 , 2016 ) introduced the idea of “causal reconstruction” of macro phenomena, an approach that leads to a causal explanation via the reconstruction of causal processes that produce the phenomenon. This strategy is not only applicable to the explanation of macro phenomena. Beach and Pedersen include it both as “theory-building process tracing” for cases in which the outcome is known but the causes are not (Beach and Pedersen 2013 : 16) and as “Explaining-Outcome Process-Tracing” (Beach and Pedersen 2013 : 18-21; 2016 : 308-313). The basic idea of this approach to causal reconstruction is to take a phenomenon and conduct a comparative analysis of the conditions and processes producing it.

Applied to the problem of identifying causal links between governance and epistemic change, using causal reconstruction means that instead of taking any specific governance change and trying to ascertain its effects on knowledge production, we start from a specific epistemic change and try to ascertain how it was produced, and what role various governance instruments played in its causation. Considering all partial causes simultaneously is part of the conscious design of the study rather than done ad hoc .

The reconstruction of causal processes leading to a specified outcome is a well-established approach in the political sciences. The main advantage of causal reconstruction appears to be that it is less dependent on pre-existing theory because it does not require a priori knowledge about possible partial or alternative causes. Instead, partial causes (initial and operating conditions of mechanisms) are empirically identified with open qualitative methods for each link in the causal network.

Although causal reconstruction appears to have major advantages for the analysis of the role of governance for change in the conduct and content of research, it also has disadvantages. Any specific change in governance a policy researcher is interested in might turn out not to be a cause at all because the observed change cannot be traced back to it. However, this risk is not different from the current risk of not finding an effect when starting from a change in governance. It is also very unlikely that the reversal of the causal question will make governance disappear as a cause of change. Applications of causal reconstruction in a study of the development of scientific innovations demonstrated that governance practices do indeed constitute important causes of such change (Gläser et al. 2016 ; Whitley et al. 2018 ).

Reversing the strategy of causal analysis is not without problems. If observable change is to be the starting point of the causal analysis, the identification of theoretically significant or politically relevant change becomes crucial. This change needs to be identified and traced backwards to its causes at different levels of aggregation, which requires the integration of quantitative methods for identifying change with qualitative methods of process tracing. While this resembles the RWT approach in its combination of methods, it is much less demanding on empirical research and causal reasoning because no causal relationship needs to be established prior to the search for causal mechanisms.

On Identifying Epistemic Change

Causal reconstruction starts from the phenomenon that shall be explained, which in our case is epistemic change. The question then is how epistemic change, which I understand as change in the content and properties of knowledge production, can be empirically identified. This question must be answered for different levels of aggregation (Table 1 ).

At the level of international scientific communities, research content may change due to a re-distribution of effort over currently addressed topics or through the emergence of new topics through intellectual innovations. Innovations occur when new findings trigger sustained processes of change of research practices and purposes (Whitley et al. 2018 ). Examples include the transformation of cancer research by molecular-biological approaches (Fujimura 1988), the experimental realisation of Bose-Einstein condensation, the emergence of evolutionary developmental biology and international large-scale student assessment (Whitley et al. 2018 ).

Changes in the distribution of effort across topics in a scientific community are difficult to identify because the knowledge a scientific community works with must be delineated, topics be identified, and efforts on topics measured. Unfortunately, bibliometrics has not yet developed robust methods for delineating scientific communities and their knowledge (Held et al. 2021 ; Held 2022 ). International scientific communities and their topics are units of analysis that cannot currently be delineated with the necessary validity and reliability. Footnote 13 In contrast, intellectual innovations provide ideal sites for the causal reconstruction of processes leading to epistemic change because they are often highly visible on all levels of aggregation. Their development is based on researchers’ decisions to alter the trajectories of their work, which often leads to discontinuities and rapid change. These changes of individual research trajectories become integrated in the emergence and growth of new topics in universities, national sub-sections of scientific communities and international scientific communities. The discussion of findings triggering an innovation by scientific communities adds to their visibility. Changes of research trajectories often incur high costs in terms of resources and time for learning, which makes them susceptible to governance processes.

Although epistemic properties of fields have been discussed and compared in science studies for a long time, their measurement is still in its infancy. Epistemic diversity is a well-defined concept, but its bibliometric operationalisation is not yet settled (Abramo et al. 2018 : 1191-1192). The rate and mode of growth of knowledge might be measurable as the rate and extent of novel and original contributions but valid measures have yet to be developed. Various measures of novelty (e.g. Evans 2010 ; Azoulay et al. 2011 ; Wang et al. 2017 ), innovativeness (e.g. Klavans et al. 2014 ) and originality (e.g. Shibayama and Wang 2020 ) have been proposed but have not yet been sufficiently validated.

Assuming that epistemic change can be identified in international scientific communities, it is likely to be of interest mainly as a baseline against which epistemic change at lower levels of aggregation can be assessed. Governance is more likely to influence epistemic change in national sub-communities because major conditions for researchers such as access to positions and resources is provided by national institutions. Epistemic change on this level could occur because the provision of positions and resources is increasingly tied to expectations about the directions and performance levels of research. Therefore, epistemic change in national sub-communities is an interesting starting point for causal reconstruction.

While conceptually important for the study of governance, the level of national sub-communities is difficult to conceptualise sociologically because the knowledge production of many international scientific communities is tightly integrated on the international level and does not vary thematically between national sub-communities. Specific thematic foci—and thus nationally specific epistemic change—may occur in national subcommunities due to a community’s orientation towards applications (which are often developed in national contexts) and nationally specific research objects or research traditions.

National sub-communities are subject to the same kinds of epistemic change as international scientific communities, which pose the same problems of empirical identification. In addition, the contributions by a national sub-community to the international community’s knowledge production may change due to governance processes. Such change may be empirically identified if valid bibliometric indicators can be found. Quantitative indicators of research performance that are used so far measure the number and visibility of publications rather the amount and quality of relevant and reliable contributions to particular topics in the knowledge production of international scientific communities.

At the level of research groups and individual researchers, directions of knowledge production depend on research programmes as well as accessible empirical objects and methods. In addition to the properties already discussed, process-level epistemic properties like the epistemic uncertainty of research processes can be included. Such properties are potentially important because they may change if different research problems are chosen or due to external selection processes such as grant funding. Information about these properties can be obtained by qualitative methods. As discussed in the section “The nature of the causal problem”, the conceptual and empirical tools to aggregate these changes at the level of national sub-communities have yet to be developed.

The perspective on epistemic change within scientific communities must be complemented with a perspective on epistemic change in units of analysis that are created by governance. These units are multi-disciplinary because national science systems include all national sub-communities in a country and research organisations are comprised of research groups from different scientific communities. These units of analysis can thus be understood as maintaining multidisciplinary portfolios whose change constitutes a specific kind of epistemic change. Epistemic properties of these portfolios include their epistemic diversity and ‘performance’, i.e. the aggregate of contributions to the knowledge of various scientific communities (Table 2 ). Footnote 14

The identification of epistemic change in national science systems and public research organisations faces the challenge of comparing and aggregating field-specific epistemic changes. Comparing or aggregating epistemic change across fields seems currently impossible due to both missing conceptual foundations on epistemic properties of fields and a lack of methods for comparative measurement.

Given the methodological challenges involved in the empirical identification of epistemic change, utilising intellectual innovations in the sciences, social sciences and humanities as starting points for the causal reconstruction of influences on epistemic change appears to be the most promising approach. The epistemic change they bring about is easier to identify than changes in epistemic properties, occurs on all levels of aggregation from international scientific communities to individual researchers, and often involves high-cost decision situations that are particularly susceptible to governance.

Conclusions

Among the many fruitful analyses of changes in the governance of science that have been conducted in the last decades, one strand appears to have failed to achieve the goals it set itself. The studies that claim one way or other to have identified effects of governance on the content and conduct of research do not stand up to scrutiny. I identified some methodological problems that could be overcome but argued that the main problem is that current analytical approaches do not do justice to the complexity of the causal problem they need to solve. As a remedy for qualitative studies of governance, I propose to start from observable epistemic change and to apply a strategy of causal reconstruction that identifies the causal processes bringing about that change, the conditions that trigger these mechanisms, and the conditions under which they operate. In this causal reconstruction, the contribution of governance to change can be identified. Starting from observable change requires its empirical identification, which I discussed for the case of epistemic change.

Five conclusions can be drawn from this analysis. First, taking a governance instrument as analytical starting point and forward-tracing its effects is likely to lead to a study design that assumes mono-causality and cannot differentiate between additional partial causes (which need to be included due to the multi-causality of social phenomena) and alternative explanations (which need to be excluded to address the equifinality of causal social processes). These limitations are partly due to the lack of a middle-range theory that explains the contributions of governance to changes in science by linking specific conditions under which governance operates to changes in the conduct and content of research through mechanisms operating under these conditions.

Second, given the complex causal relationships and processes in which governance instruments are embedded, it may not be possible to identify consequences of governance for the conduct and content of research at all—even in the sense of partial, non-deterministic causality advanced in this paper. If this is the case, the field of science policy studies needs to rethink its agenda.

Third, for those who want to keep trying to establish causality, a possible alternative strategy for qualitative causal analysis appears to be the causal reconstruction of processes leading to change, i.e. starting from observable change and tracing it back to partial causes, which may or may not include governance. Although this strategy is not without problems, starting from change and putting governance in its place among the conditions and mechanisms leading to such change is likely to produce more information about the causal role of governance than our current strategy.

Fourth, identifying macro-level epistemic change and the causal mechanisms producing it requires a much higher level of integration of quantitative (bibliometric and survey-based) and qualitative methods than has been achieved so far. Both methods would need to operationalise the same concepts. This would require a theoretical and methodological integration far beyond the combination of methods in current ‘mixed method’ approaches.

Finally, identifying epistemic change and reconstructing the causal processes producing it requires sophisticated comparative strategies. Such studies would need to be simultaneously field-comparative in order to establish the causal role of epistemic and social-structural properties of scientific communities and country-comparative in order to establish the causal role of governance structures and processes. This requires larger teams, longer time horizons and more resources than are currently common in the sociology of science and science policy studies. Applying the new strategy of causal analysis would also require a significant change of research practices.

The statistical approach to studying the impact of evaluation systems on performance would be an interrupted time series analysis. However, this study design is rarely applied. Wang and Hicks (2013) apply interrupted time series analysis to the study of structural change in universities but observe that “structural change detection has not been used in the bibliometrics research community” (ibid: 260). The threats to validity of an interrupted time series analysis correspond to the minimum conditions put forward by Aagaard and Schneider (Shadish et al. 2002 : 179-181; Bernal et al. 2017 ).

In the language of interrupted time series analysis, this amounts to a “time-varying treatment effect” (Moraffah et al. 2021: 3059-3060).

For a counterexample with neutral questions, see Hammarfelt and Haddow (2018).

Nedeva et al. (2022) propose a framework for the study of field-level effects. However, while their empirical approach to the study of micro-level processes seems promising, their framework for the study of field-level effects does not address aggregation mechanisms and thus leaves the question of how findings about micro-level events will be aggregated to field-level effects unanswered. Their pilot study does not illustrate how this could be achieved because they studied high-energy-physics, where micro-level effects were missing to begin with.

A critical revision of my own publications on the subject shows that I am not innocent of this myself. While I maintain my claims to causality on the micro level, some papers with the phrase “the impact of ...” in the title do not claim causality in the main text.

See Mouritzen and Opstrup ( 2020 : 25) for a very similar causal model.

As Little (1991) pointed out, the “necessity” must be understood in a non-deterministic way as “causal relevance”.

The causal analysis with statistical methods is not further discussed here due to space restrictions. I look forward to suggestions how new approaches to causal analysis with quantitative methods could be applied to the problem at hand.

I exclude here the explanation of unique phenomena by reconstructing causal processes (of which causal mechanisms may be a part, Mayntz 2020 ) because it does not seem applicable to governance studies.

Similar approaches have been developed for qualitative research. Both analytic induction and qualitative comparative analysis are based on Mill’s method of agreement and disagreement. They establish causes as necessary conditions for effects to occur by comparing cases in which different sets of conditions are given and effects are or are not present (see Hammersley and Cooper 2012 for an insightful comparison of the two methods).

Gläser and Laudel adopted this from Mayntz ( 2004 : 241). It is consistent with earlier work on mechanisms, e.g. by Merton ( 1957 ) and Elster ( 1989 ).

The highly abstract language in which the 'mechanismic’ approach is presented is liable to two misunderstandings. It often seems to suggest a monocausal approach by discussing how a mechanism produces one outcome from one cause, and is equally often presented as deterministic. Neither aspect is a necessary property of causal mechanisms.

Topic modelling (e.g. Yau et al. 2014 ) might technically achieve a measurement of changes in “topics”. However, the method is not based on a theoretical definition of “topic”, is not sufficiently validated, and does not produce an outcome that can easily be interpreted across all fields of science.

The governance of science also creates temporary research organisations like collaborative research networks or centres of excellence, which are not considered here because they might be part of processes of epistemic change but usually are too short-lived to constitute environments in which epistemic change occurs.

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Acknowledgements

Writing this paper has taken a long time, and I have to thank too many colleagues who helped improving it to name them all. I am grateful to the participants in Georg Krücken’s Seminar at INCHER in Kassel, to participants of the KNOWSCIENCE closing conference, particularly to Merle Jacob and Tomas Hellström, and to the members of our sociology of science discussion group in Berlin. I experienced a very good review process with Minerva, with very helpful critical comments from both reviewers. Beate Krickel’s comments helped improve what I thought to be the last version of the paper and made me think about the next one. Not all colleagues will be happy about what I made of their comments. The remaining faults of this paper are of course my responsibility.

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Gläser, J. From Effects of Governance to Causes of Epistemic Change. Minerva (2024). https://doi.org/10.1007/s11024-024-09526-2

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