What this handout is about.
This handout provides a general guide to writing reports about scientific research you’ve performed. In addition to describing the conventional rules about the format and content of a lab report, we’ll also attempt to convey why these rules exist, so you’ll get a clearer, more dependable idea of how to approach this writing situation. Readers of this handout may also find our handout on writing in the sciences useful.
Why do we write research reports.
You did an experiment or study for your science class, and now you have to write it up for your teacher to review. You feel that you understood the background sufficiently, designed and completed the study effectively, obtained useful data, and can use those data to draw conclusions about a scientific process or principle. But how exactly do you write all that? What is your teacher expecting to see?
To take some of the guesswork out of answering these questions, try to think beyond the classroom setting. In fact, you and your teacher are both part of a scientific community, and the people who participate in this community tend to share the same values. As long as you understand and respect these values, your writing will likely meet the expectations of your audience—including your teacher.
So why are you writing this research report? The practical answer is “Because the teacher assigned it,” but that’s classroom thinking. Generally speaking, people investigating some scientific hypothesis have a responsibility to the rest of the scientific world to report their findings, particularly if these findings add to or contradict previous ideas. The people reading such reports have two primary goals:
Your job as a writer, then, is to fulfill these two goals.
Good question. Here is the basic format scientists have designed for research reports:
This format, sometimes called “IMRAD,” may take slightly different shapes depending on the discipline or audience; some ask you to include an abstract or separate section for the hypothesis, or call the Discussion section “Conclusions,” or change the order of the sections (some professional and academic journals require the Methods section to appear last). Overall, however, the IMRAD format was devised to represent a textual version of the scientific method.
The scientific method, you’ll probably recall, involves developing a hypothesis, testing it, and deciding whether your findings support the hypothesis. In essence, the format for a research report in the sciences mirrors the scientific method but fleshes out the process a little. Below, you’ll find a table that shows how each written section fits into the scientific method and what additional information it offers the reader.
states your hypothesis | explains how you derived that hypothesis and how it connects to previous research; gives the purpose of the experiment/study | |
details how you tested your hypothesis | clarifies why you performed your study in that particular way | |
provides raw (i.e., uninterpreted) data collected | (perhaps) expresses the data in table form, as an easy-to-read figure, or as percentages/ratios | |
considers whether the data you obtained support the hypothesis | explores the implications of your finding and judges the potential limitations of your experimental design |
Thinking of your research report as based on the scientific method, but elaborated in the ways described above, may help you to meet your audience’s expectations successfully. We’re going to proceed by explicitly connecting each section of the lab report to the scientific method, then explaining why and how you need to elaborate that section.
Although this handout takes each section in the order in which it should be presented in the final report, you may for practical reasons decide to compose sections in another order. For example, many writers find that composing their Methods and Results before the other sections helps to clarify their idea of the experiment or study as a whole. You might consider using each assignment to practice different approaches to drafting the report, to find the order that works best for you.
The best way to prepare to write the lab report is to make sure that you fully understand everything you need to about the experiment. Obviously, if you don’t quite know what went on during the lab, you’re going to find it difficult to explain the lab satisfactorily to someone else. To make sure you know enough to write the report, complete the following steps:
Once you’ve completed these steps as you perform the experiment, you’ll be in a good position to draft an effective lab report.
How do i write a strong introduction.
For the purposes of this handout, we’ll consider the Introduction to contain four basic elements: the purpose, the scientific literature relevant to the subject, the hypothesis, and the reasons you believed your hypothesis viable. Let’s start by going through each element of the Introduction to clarify what it covers and why it’s important. Then we can formulate a logical organizational strategy for the section.
The inclusion of the purpose (sometimes called the objective) of the experiment often confuses writers. The biggest misconception is that the purpose is the same as the hypothesis. Not quite. We’ll get to hypotheses in a minute, but basically they provide some indication of what you expect the experiment to show. The purpose is broader, and deals more with what you expect to gain through the experiment. In a professional setting, the hypothesis might have something to do with how cells react to a certain kind of genetic manipulation, but the purpose of the experiment is to learn more about potential cancer treatments. Undergraduate reports don’t often have this wide-ranging a goal, but you should still try to maintain the distinction between your hypothesis and your purpose. In a solubility experiment, for example, your hypothesis might talk about the relationship between temperature and the rate of solubility, but the purpose is probably to learn more about some specific scientific principle underlying the process of solubility.
For starters, most people say that you should write out your working hypothesis before you perform the experiment or study. Many beginning science students neglect to do so and find themselves struggling to remember precisely which variables were involved in the process or in what way the researchers felt that they were related. Write your hypothesis down as you develop it—you’ll be glad you did.
As for the form a hypothesis should take, it’s best not to be too fancy or complicated; an inventive style isn’t nearly so important as clarity here. There’s nothing wrong with beginning your hypothesis with the phrase, “It was hypothesized that . . .” Be as specific as you can about the relationship between the different objects of your study. In other words, explain that when term A changes, term B changes in this particular way. Readers of scientific writing are rarely content with the idea that a relationship between two terms exists—they want to know what that relationship entails.
Not a hypothesis:
“It was hypothesized that there is a significant relationship between the temperature of a solvent and the rate at which a solute dissolves.”
Hypothesis:
“It was hypothesized that as the temperature of a solvent increases, the rate at which a solute will dissolve in that solvent increases.”
Put more technically, most hypotheses contain both an independent and a dependent variable. The independent variable is what you manipulate to test the reaction; the dependent variable is what changes as a result of your manipulation. In the example above, the independent variable is the temperature of the solvent, and the dependent variable is the rate of solubility. Be sure that your hypothesis includes both variables.
You need to do more than tell your readers what your hypothesis is; you also need to assure them that this hypothesis was reasonable, given the circumstances. In other words, use the Introduction to explain that you didn’t just pluck your hypothesis out of thin air. (If you did pluck it out of thin air, your problems with your report will probably extend beyond using the appropriate format.) If you posit that a particular relationship exists between the independent and the dependent variable, what led you to believe your “guess” might be supported by evidence?
Scientists often refer to this type of justification as “motivating” the hypothesis, in the sense that something propelled them to make that prediction. Often, motivation includes what we already know—or rather, what scientists generally accept as true (see “Background/previous research” below). But you can also motivate your hypothesis by relying on logic or on your own observations. If you’re trying to decide which solutes will dissolve more rapidly in a solvent at increased temperatures, you might remember that some solids are meant to dissolve in hot water (e.g., bouillon cubes) and some are used for a function precisely because they withstand higher temperatures (they make saucepans out of something). Or you can think about whether you’ve noticed sugar dissolving more rapidly in your glass of iced tea or in your cup of coffee. Even such basic, outside-the-lab observations can help you justify your hypothesis as reasonable.
This part of the Introduction demonstrates to the reader your awareness of how you’re building on other scientists’ work. If you think of the scientific community as engaging in a series of conversations about various topics, then you’ll recognize that the relevant background material will alert the reader to which conversation you want to enter.
Generally speaking, authors writing journal articles use the background for slightly different purposes than do students completing assignments. Because readers of academic journals tend to be professionals in the field, authors explain the background in order to permit readers to evaluate the study’s pertinence for their own work. You, on the other hand, write toward a much narrower audience—your peers in the course or your lab instructor—and so you must demonstrate that you understand the context for the (presumably assigned) experiment or study you’ve completed. For example, if your professor has been talking about polarity during lectures, and you’re doing a solubility experiment, you might try to connect the polarity of a solid to its relative solubility in certain solvents. In any event, both professional researchers and undergraduates need to connect the background material overtly to their own work.
Most of the time, writers begin by stating the purpose or objectives of their own work, which establishes for the reader’s benefit the “nature and scope of the problem investigated” (Day 1994). Once you have expressed your purpose, you should then find it easier to move from the general purpose, to relevant material on the subject, to your hypothesis. In abbreviated form, an Introduction section might look like this:
“The purpose of the experiment was to test conventional ideas about solubility in the laboratory [purpose] . . . According to Whitecoat and Labrat (1999), at higher temperatures the molecules of solvents move more quickly . . . We know from the class lecture that molecules moving at higher rates of speed collide with one another more often and thus break down more easily [background material/motivation] . . . Thus, it was hypothesized that as the temperature of a solvent increases, the rate at which a solute will dissolve in that solvent increases [hypothesis].”
Again—these are guidelines, not commandments. Some writers and readers prefer different structures for the Introduction. The one above merely illustrates a common approach to organizing material.
As with any piece of writing, your Methods section will succeed only if it fulfills its readers’ expectations, so you need to be clear in your own mind about the purpose of this section. Let’s review the purpose as we described it above: in this section, you want to describe in detail how you tested the hypothesis you developed and also to clarify the rationale for your procedure. In science, it’s not sufficient merely to design and carry out an experiment. Ultimately, others must be able to verify your findings, so your experiment must be reproducible, to the extent that other researchers can follow the same procedure and obtain the same (or similar) results.
Here’s a real-world example of the importance of reproducibility. In 1989, physicists Stanley Pons and Martin Fleischman announced that they had discovered “cold fusion,” a way of producing excess heat and power without the nuclear radiation that accompanies “hot fusion.” Such a discovery could have great ramifications for the industrial production of energy, so these findings created a great deal of interest. When other scientists tried to duplicate the experiment, however, they didn’t achieve the same results, and as a result many wrote off the conclusions as unjustified (or worse, a hoax). To this day, the viability of cold fusion is debated within the scientific community, even though an increasing number of researchers believe it possible. So when you write your Methods section, keep in mind that you need to describe your experiment well enough to allow others to replicate it exactly.
With these goals in mind, let’s consider how to write an effective Methods section in terms of content, structure, and style.
Sometimes the hardest thing about writing this section isn’t what you should talk about, but what you shouldn’t talk about. Writers often want to include the results of their experiment, because they measured and recorded the results during the course of the experiment. But such data should be reserved for the Results section. In the Methods section, you can write that you recorded the results, or how you recorded the results (e.g., in a table), but you shouldn’t write what the results were—not yet. Here, you’re merely stating exactly how you went about testing your hypothesis. As you draft your Methods section, ask yourself the following questions:
Describe the control in the Methods section. Two things are especially important in writing about the control: identify the control as a control, and explain what you’re controlling for. Here is an example:
“As a control for the temperature change, we placed the same amount of solute in the same amount of solvent, and let the solution stand for five minutes without heating it.”
Organization is especially important in the Methods section of a lab report because readers must understand your experimental procedure completely. Many writers are surprised by the difficulty of conveying what they did during the experiment, since after all they’re only reporting an event, but it’s often tricky to present this information in a coherent way. There’s a fairly standard structure you can use to guide you, and following the conventions for style can help clarify your points.
Increasingly, especially in the social sciences, using first person and active voice is acceptable in scientific reports. Most readers find that this style of writing conveys information more clearly and concisely. This rhetorical choice thus brings two scientific values into conflict: objectivity versus clarity. Since the scientific community hasn’t reached a consensus about which style it prefers, you may want to ask your lab instructor.
Here’s a paradox for you. The Results section is often both the shortest (yay!) and most important (uh-oh!) part of your report. Your Materials and Methods section shows how you obtained the results, and your Discussion section explores the significance of the results, so clearly the Results section forms the backbone of the lab report. This section provides the most critical information about your experiment: the data that allow you to discuss how your hypothesis was or wasn’t supported. But it doesn’t provide anything else, which explains why this section is generally shorter than the others.
Before you write this section, look at all the data you collected to figure out what relates significantly to your hypothesis. You’ll want to highlight this material in your Results section. Resist the urge to include every bit of data you collected, since perhaps not all are relevant. Also, don’t try to draw conclusions about the results—save them for the Discussion section. In this section, you’re reporting facts. Nothing your readers can dispute should appear in the Results section.
Most Results sections feature three distinct parts: text, tables, and figures. Let’s consider each part one at a time.
This should be a short paragraph, generally just a few lines, that describes the results you obtained from your experiment. In a relatively simple experiment, one that doesn’t produce a lot of data for you to repeat, the text can represent the entire Results section. Don’t feel that you need to include lots of extraneous detail to compensate for a short (but effective) text; your readers appreciate discrimination more than your ability to recite facts. In a more complex experiment, you may want to use tables and/or figures to help guide your readers toward the most important information you gathered. In that event, you’ll need to refer to each table or figure directly, where appropriate:
“Table 1 lists the rates of solubility for each substance”
“Solubility increased as the temperature of the solution increased (see Figure 1).”
If you do use tables or figures, make sure that you don’t present the same material in both the text and the tables/figures, since in essence you’ll just repeat yourself, probably annoying your readers with the redundancy of your statements.
Feel free to describe trends that emerge as you examine the data. Although identifying trends requires some judgment on your part and so may not feel like factual reporting, no one can deny that these trends do exist, and so they properly belong in the Results section. Example:
“Heating the solution increased the rate of solubility of polar solids by 45% but had no effect on the rate of solubility in solutions containing non-polar solids.”
This point isn’t debatable—you’re just pointing out what the data show.
As in the Materials and Methods section, you want to refer to your data in the past tense, because the events you recorded have already occurred and have finished occurring. In the example above, note the use of “increased” and “had,” rather than “increases” and “has.” (You don’t know from your experiment that heating always increases the solubility of polar solids, but it did that time.)
You shouldn’t put information in the table that also appears in the text. You also shouldn’t use a table to present irrelevant data, just to show you did collect these data during the experiment. Tables are good for some purposes and situations, but not others, so whether and how you’ll use tables depends upon what you need them to accomplish.
Tables are useful ways to show variation in data, but not to present a great deal of unchanging measurements. If you’re dealing with a scientific phenomenon that occurs only within a certain range of temperatures, for example, you don’t need to use a table to show that the phenomenon didn’t occur at any of the other temperatures. How useful is this table?
As you can probably see, no solubility was observed until the trial temperature reached 50°C, a fact that the text part of the Results section could easily convey. The table could then be limited to what happened at 50°C and higher, thus better illustrating the differences in solubility rates when solubility did occur.
As a rule, try not to use a table to describe any experimental event you can cover in one sentence of text. Here’s an example of an unnecessary table from How to Write and Publish a Scientific Paper , by Robert A. Day:
As Day notes, all the information in this table can be summarized in one sentence: “S. griseus, S. coelicolor, S. everycolor, and S. rainbowenski grew under aerobic conditions, whereas S. nocolor and S. greenicus required anaerobic conditions.” Most readers won’t find the table clearer than that one sentence.
When you do have reason to tabulate material, pay attention to the clarity and readability of the format you use. Here are a few tips:
It’s a little tough to see the trends that the author presumably wants to present in this table. Compare this table, in which the data appear vertically:
The second table shows how putting like elements in a vertical column makes for easier reading. In this case, the like elements are the measurements of length and height, over five trials–not, as in the first table, the length and height measurements for each trial.
1058 |
432 |
7 |
Although tables can be useful ways of showing trends in the results you obtained, figures (i.e., illustrations) can do an even better job of emphasizing such trends. Lab report writers often use graphic representations of the data they collected to provide their readers with a literal picture of how the experiment went.
Remember the circumstances under which you don’t need a table: when you don’t have a great deal of data or when the data you have don’t vary a lot. Under the same conditions, you would probably forgo the figure as well, since the figure would be unlikely to provide your readers with an additional perspective. Scientists really don’t like their time wasted, so they tend not to respond favorably to redundancy.
If you’re trying to decide between using a table and creating a figure to present your material, consider the following a rule of thumb. The strength of a table lies in its ability to supply large amounts of exact data, whereas the strength of a figure is its dramatic illustration of important trends within the experiment. If you feel that your readers won’t get the full impact of the results you obtained just by looking at the numbers, then a figure might be appropriate.
Of course, an undergraduate class may expect you to create a figure for your lab experiment, if only to make sure that you can do so effectively. If this is the case, then don’t worry about whether to use figures or not—concentrate instead on how best to accomplish your task.
Figures can include maps, photographs, pen-and-ink drawings, flow charts, bar graphs, and section graphs (“pie charts”). But the most common figure by far, especially for undergraduates, is the line graph, so we’ll focus on that type in this handout.
At the undergraduate level, you can often draw and label your graphs by hand, provided that the result is clear, legible, and drawn to scale. Computer technology has, however, made creating line graphs a lot easier. Most word-processing software has a number of functions for transferring data into graph form; many scientists have found Microsoft Excel, for example, a helpful tool in graphing results. If you plan on pursuing a career in the sciences, it may be well worth your while to learn to use a similar program.
Computers can’t, however, decide for you how your graph really works; you have to know how to design your graph to meet your readers’ expectations. Here are some of these expectations:
The discussion section is probably the least formalized part of the report, in that you can’t really apply the same structure to every type of experiment. In simple terms, here you tell your readers what to make of the Results you obtained. If you have done the Results part well, your readers should already recognize the trends in the data and have a fairly clear idea of whether your hypothesis was supported. Because the Results can seem so self-explanatory, many students find it difficult to know what material to add in this last section.
Basically, the Discussion contains several parts, in no particular order, but roughly moving from specific (i.e., related to your experiment only) to general (how your findings fit in the larger scientific community). In this section, you will, as a rule, need to:
Let’s look at some dos and don’ts for each of these objectives.
This statement is usually a good way to begin the Discussion, since you can’t effectively speak about the larger scientific value of your study until you’ve figured out the particulars of this experiment. You might begin this part of the Discussion by explicitly stating the relationships or correlations your data indicate between the independent and dependent variables. Then you can show more clearly why you believe your hypothesis was or was not supported. For example, if you tested solubility at various temperatures, you could start this section by noting that the rates of solubility increased as the temperature increased. If your initial hypothesis surmised that temperature change would not affect solubility, you would then say something like,
“The hypothesis that temperature change would not affect solubility was not supported by the data.”
Note: Students tend to view labs as practical tests of undeniable scientific truths. As a result, you may want to say that the hypothesis was “proved” or “disproved” or that it was “correct” or “incorrect.” These terms, however, reflect a degree of certainty that you as a scientist aren’t supposed to have. Remember, you’re testing a theory with a procedure that lasts only a few hours and relies on only a few trials, which severely compromises your ability to be sure about the “truth” you see. Words like “supported,” “indicated,” and “suggested” are more acceptable ways to evaluate your hypothesis.
Also, recognize that saying whether the data supported your hypothesis or not involves making a claim to be defended. As such, you need to show the readers that this claim is warranted by the evidence. Make sure that you’re very explicit about the relationship between the evidence and the conclusions you draw from it. This process is difficult for many writers because we don’t often justify conclusions in our regular lives. For example, you might nudge your friend at a party and whisper, “That guy’s drunk,” and once your friend lays eyes on the person in question, she might readily agree. In a scientific paper, by contrast, you would need to defend your claim more thoroughly by pointing to data such as slurred words, unsteady gait, and the lampshade-as-hat. In addition to pointing out these details, you would also need to show how (according to previous studies) these signs are consistent with inebriation, especially if they occur in conjunction with one another. To put it another way, tell your readers exactly how you got from point A (was the hypothesis supported?) to point B (yes/no).
You need to take these exceptions and divergences into account, so that you qualify your conclusions sufficiently. For obvious reasons, your readers will doubt your authority if you (deliberately or inadvertently) overlook a key piece of data that doesn’t square with your perspective on what occurred. In a more philosophical sense, once you’ve ignored evidence that contradicts your claims, you’ve departed from the scientific method. The urge to “tidy up” the experiment is often strong, but if you give in to it you’re no longer performing good science.
Sometimes after you’ve performed a study or experiment, you realize that some part of the methods you used to test your hypothesis was flawed. In that case, it’s OK to suggest that if you had the chance to conduct your test again, you might change the design in this or that specific way in order to avoid such and such a problem. The key to making this approach work, though, is to be very precise about the weakness in your experiment, why and how you think that weakness might have affected your data, and how you would alter your protocol to eliminate—or limit the effects of—that weakness. Often, inexperienced researchers and writers feel the need to account for “wrong” data (remember, there’s no such animal), and so they speculate wildly about what might have screwed things up. These speculations include such factors as the unusually hot temperature in the room, or the possibility that their lab partners read the meters wrong, or the potentially defective equipment. These explanations are what scientists call “cop-outs,” or “lame”; don’t indicate that the experiment had a weakness unless you’re fairly certain that a) it really occurred and b) you can explain reasonably well how that weakness affected your results.
If, for example, your hypothesis dealt with the changes in solubility at different temperatures, then try to figure out what you can rationally say about the process of solubility more generally. If you’re doing an undergraduate lab, chances are that the lab will connect in some way to the material you’ve been covering either in lecture or in your reading, so you might choose to return to these resources as a way to help you think clearly about the process as a whole.
This part of the Discussion section is another place where you need to make sure that you’re not overreaching. Again, nothing you’ve found in one study would remotely allow you to claim that you now “know” something, or that something isn’t “true,” or that your experiment “confirmed” some principle or other. Hesitate before you go out on a limb—it’s dangerous! Use less absolutely conclusive language, including such words as “suggest,” “indicate,” “correspond,” “possibly,” “challenge,” etc.
We’ve been talking about how to show that you belong in a particular community (such as biologists or anthropologists) by writing within conventions that they recognize and accept. Another is to try to identify a conversation going on among members of that community, and use your work to contribute to that conversation. In a larger philosophical sense, scientists can’t fully understand the value of their research unless they have some sense of the context that provoked and nourished it. That is, you have to recognize what’s new about your project (potentially, anyway) and how it benefits the wider body of scientific knowledge. On a more pragmatic level, especially for undergraduates, connecting your lab work to previous research will demonstrate to the TA that you see the big picture. You have an opportunity, in the Discussion section, to distinguish yourself from the students in your class who aren’t thinking beyond the barest facts of the study. Capitalize on this opportunity by putting your own work in context.
If you’re just beginning to work in the natural sciences (as a first-year biology or chemistry student, say), most likely the work you’ll be doing has already been performed and re-performed to a satisfactory degree. Hence, you could probably point to a similar experiment or study and compare/contrast your results and conclusions. More advanced work may deal with an issue that is somewhat less “resolved,” and so previous research may take the form of an ongoing debate, and you can use your own work to weigh in on that debate. If, for example, researchers are hotly disputing the value of herbal remedies for the common cold, and the results of your study suggest that Echinacea diminishes the symptoms but not the actual presence of the cold, then you might want to take some time in the Discussion section to recapitulate the specifics of the dispute as it relates to Echinacea as an herbal remedy. (Consider that you have probably already written in the Introduction about this debate as background research.)
This information is often the best way to end your Discussion (and, for all intents and purposes, the report). In argumentative writing generally, you want to use your closing words to convey the main point of your writing. This main point can be primarily theoretical (“Now that you understand this information, you’re in a better position to understand this larger issue”) or primarily practical (“You can use this information to take such and such an action”). In either case, the concluding statements help the reader to comprehend the significance of your project and your decision to write about it.
Since a lab report is argumentative—after all, you’re investigating a claim, and judging the legitimacy of that claim by generating and collecting evidence—it’s often a good idea to end your report with the same technique for establishing your main point. If you want to go the theoretical route, you might talk about the consequences your study has for the field or phenomenon you’re investigating. To return to the examples regarding solubility, you could end by reflecting on what your work on solubility as a function of temperature tells us (potentially) about solubility in general. (Some folks consider this type of exploration “pure” as opposed to “applied” science, although these labels can be problematic.) If you want to go the practical route, you could end by speculating about the medical, institutional, or commercial implications of your findings—in other words, answer the question, “What can this study help people to do?” In either case, you’re going to make your readers’ experience more satisfying, by helping them see why they spent their time learning what you had to teach them.
We consulted these works while writing this handout. This is not a comprehensive list of resources on the handout’s topic, and we encourage you to do your own research to find additional publications. Please do not use this list as a model for the format of your own reference list, as it may not match the citation style you are using. For guidance on formatting citations, please see the UNC Libraries citation tutorial . We revise these tips periodically and welcome feedback.
American Psychological Association. 2010. Publication Manual of the American Psychological Association . 6th ed. Washington, DC: American Psychological Association.
Beall, Herbert, and John Trimbur. 2001. A Short Guide to Writing About Chemistry , 2nd ed. New York: Longman.
Blum, Deborah, and Mary Knudson. 1997. A Field Guide for Science Writers: The Official Guide of the National Association of Science Writers . New York: Oxford University Press.
Booth, Wayne C., Gregory G. Colomb, Joseph M. Williams, Joseph Bizup, and William T. FitzGerald. 2016. The Craft of Research , 4th ed. Chicago: University of Chicago Press.
Briscoe, Mary Helen. 1996. Preparing Scientific Illustrations: A Guide to Better Posters, Presentations, and Publications , 2nd ed. New York: Springer-Verlag.
Council of Science Editors. 2014. Scientific Style and Format: The CSE Manual for Authors, Editors, and Publishers , 8th ed. Chicago & London: University of Chicago Press.
Davis, Martha. 2012. Scientific Papers and Presentations , 3rd ed. London: Academic Press.
Day, Robert A. 1994. How to Write and Publish a Scientific Paper , 4th ed. Phoenix: Oryx Press.
Porush, David. 1995. A Short Guide to Writing About Science . New York: Longman.
Williams, Joseph, and Joseph Bizup. 2017. Style: Lessons in Clarity and Grace , 12th ed. Boston: Pearson.
You may reproduce it for non-commercial use if you use the entire handout and attribute the source: The Writing Center, University of North Carolina at Chapel Hill
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This review covers the basic elements of a research report. This is a general guide for what you will see in journal articles or dissertations. This format assumes a mixed methods study, but you can leave out either quantitative or qualitative sections if you only used a single methodology.
This review is divided into sections for easy reference. There are five MAJOR parts of a Research Report:
1. Introduction 2. Review of Literature 3. Methods 4. Results 5. Discussion
As a general guide, the Introduction, Review of Literature, and Methods should be about 1/3 of your paper, Discussion 1/3, then Results 1/3.
Section 1 : Cover Sheet (APA format cover sheet) optional, if required.
Section 2: Abstract (a basic summary of the report, including sample, treatment, design, results, and implications) (≤ 150 words) optional, if required.
Section 3 : Introduction (1-3 paragraphs) • Basic introduction • Supportive statistics (can be from periodicals) • Statement of Purpose • Statement of Significance
Section 4 : Research question(s) or hypotheses • An overall research question (optional) • A quantitative-based (hypotheses) • A qualitative-based (research questions) Note: You will generally have more than one, especially if using hypotheses.
Section 5: Review of Literature ▪ Should be organized by subheadings ▪ Should adequately support your study using supporting, related, and/or refuting evidence ▪ Is a synthesis, not a collection of individual summaries
Section 6: Methods ▪ Procedure: Describe data gathering or participant recruitment, including IRB approval ▪ Sample: Describe the sample or dataset, including basic demographics ▪ Setting: Describe the setting, if applicable (generally only in qualitative designs) ▪ Treatment: If applicable, describe, in detail, how you implemented the treatment ▪ Instrument: Describe, in detail, how you implemented the instrument; Describe the reliability and validity associated with the instrument ▪ Data Analysis: Describe type of procedure (t-test, interviews, etc.) and software (if used)
Section 7: Results ▪ Restate Research Question 1 (Quantitative) ▪ Describe results ▪ Restate Research Question 2 (Qualitative) ▪ Describe results
Section 8: Discussion ▪ Restate Overall Research Question ▪ Describe how the results, when taken together, answer the overall question ▪ ***Describe how the results confirm or contrast the literature you reviewed
Section 9: Recommendations (if applicable, generally related to practice)
Section 10: Limitations ▪ Discuss, in several sentences, the limitations of this study. ▪ Research Design (overall, then info about the limitations of each separately) ▪ Sample ▪ Instrument/s ▪ Other limitations
Section 11: Conclusion (A brief closing summary)
Section 12: References (APA format)
About research rundowns.
Research Rundowns was made possible by support from the Dewar College of Education at Valdosta State University .
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Chapter 11: Presenting Your Research
Learning Objectives
In this section, we look at how to write an APA-style empirical research report , an article that presents the results of one or more new studies. Recall that the standard sections of an empirical research report provide a kind of outline. Here we consider each of these sections in detail, including what information it contains, how that information is formatted and organized, and tips for writing each section. At the end of this section is a sample APA-style research report that illustrates many of these principles.
Title page and abstract.
An APA-style research report begins with a title page . The title is centred in the upper half of the page, with each important word capitalized. The title should clearly and concisely (in about 12 words or fewer) communicate the primary variables and research questions. This sometimes requires a main title followed by a subtitle that elaborates on the main title, in which case the main title and subtitle are separated by a colon. Here are some titles from recent issues of professional journals published by the American Psychological Association.
Below the title are the authors’ names and, on the next line, their institutional affiliation—the university or other institution where the authors worked when they conducted the research. As we have already seen, the authors are listed in an order that reflects their contribution to the research. When multiple authors have made equal contributions to the research, they often list their names alphabetically or in a randomly determined order.
In some areas of psychology, the titles of many empirical research reports are informal in a way that is perhaps best described as “cute.” They usually take the form of a play on words or a well-known expression that relates to the topic under study. Here are some examples from recent issues of the Journal Psychological Science .
Individual researchers differ quite a bit in their preference for such titles. Some use them regularly, while others never use them. What might be some of the pros and cons of using cute article titles?
For articles that are being submitted for publication, the title page also includes an author note that lists the authors’ full institutional affiliations, any acknowledgments the authors wish to make to agencies that funded the research or to colleagues who commented on it, and contact information for the authors. For student papers that are not being submitted for publication—including theses—author notes are generally not necessary.
The abstract is a summary of the study. It is the second page of the manuscript and is headed with the word Abstract . The first line is not indented. The abstract presents the research question, a summary of the method, the basic results, and the most important conclusions. Because the abstract is usually limited to about 200 words, it can be a challenge to write a good one.
The introduction begins on the third page of the manuscript. The heading at the top of this page is the full title of the manuscript, with each important word capitalized as on the title page. The introduction includes three distinct subsections, although these are typically not identified by separate headings. The opening introduces the research question and explains why it is interesting, the literature review discusses relevant previous research, and the closing restates the research question and comments on the method used to answer it.
The opening , which is usually a paragraph or two in length, introduces the research question and explains why it is interesting. To capture the reader’s attention, researcher Daryl Bem recommends starting with general observations about the topic under study, expressed in ordinary language (not technical jargon)—observations that are about people and their behaviour (not about researchers or their research; Bem, 2003 [1] ). Concrete examples are often very useful here. According to Bem, this would be a poor way to begin a research report:
Festinger’s theory of cognitive dissonance received a great deal of attention during the latter part of the 20th century (p. 191)
The following would be much better:
The individual who holds two beliefs that are inconsistent with one another may feel uncomfortable. For example, the person who knows that he or she enjoys smoking but believes it to be unhealthy may experience discomfort arising from the inconsistency or disharmony between these two thoughts or cognitions. This feeling of discomfort was called cognitive dissonance by social psychologist Leon Festinger (1957), who suggested that individuals will be motivated to remove this dissonance in whatever way they can (p. 191).
After capturing the reader’s attention, the opening should go on to introduce the research question and explain why it is interesting. Will the answer fill a gap in the literature? Will it provide a test of an important theory? Does it have practical implications? Giving readers a clear sense of what the research is about and why they should care about it will motivate them to continue reading the literature review—and will help them make sense of it.
Breaking the Rules
Researcher Larry Jacoby reported several studies showing that a word that people see or hear repeatedly can seem more familiar even when they do not recall the repetitions—and that this tendency is especially pronounced among older adults. He opened his article with the following humourous anecdote:
A friend whose mother is suffering symptoms of Alzheimer’s disease (AD) tells the story of taking her mother to visit a nursing home, preliminary to her mother’s moving there. During an orientation meeting at the nursing home, the rules and regulations were explained, one of which regarded the dining room. The dining room was described as similar to a fine restaurant except that tipping was not required. The absence of tipping was a central theme in the orientation lecture, mentioned frequently to emphasize the quality of care along with the advantages of having paid in advance. At the end of the meeting, the friend’s mother was asked whether she had any questions. She replied that she only had one question: “Should I tip?” (Jacoby, 1999, p. 3)
Although both humour and personal anecdotes are generally discouraged in APA-style writing, this example is a highly effective way to start because it both engages the reader and provides an excellent real-world example of the topic under study.
Immediately after the opening comes the literature review , which describes relevant previous research on the topic and can be anywhere from several paragraphs to several pages in length. However, the literature review is not simply a list of past studies. Instead, it constitutes a kind of argument for why the research question is worth addressing. By the end of the literature review, readers should be convinced that the research question makes sense and that the present study is a logical next step in the ongoing research process.
Like any effective argument, the literature review must have some kind of structure. For example, it might begin by describing a phenomenon in a general way along with several studies that demonstrate it, then describing two or more competing theories of the phenomenon, and finally presenting a hypothesis to test one or more of the theories. Or it might describe one phenomenon, then describe another phenomenon that seems inconsistent with the first one, then propose a theory that resolves the inconsistency, and finally present a hypothesis to test that theory. In applied research, it might describe a phenomenon or theory, then describe how that phenomenon or theory applies to some important real-world situation, and finally suggest a way to test whether it does, in fact, apply to that situation.
Looking at the literature review in this way emphasizes a few things. First, it is extremely important to start with an outline of the main points that you want to make, organized in the order that you want to make them. The basic structure of your argument, then, should be apparent from the outline itself. Second, it is important to emphasize the structure of your argument in your writing. One way to do this is to begin the literature review by summarizing your argument even before you begin to make it. “In this article, I will describe two apparently contradictory phenomena, present a new theory that has the potential to resolve the apparent contradiction, and finally present a novel hypothesis to test the theory.” Another way is to open each paragraph with a sentence that summarizes the main point of the paragraph and links it to the preceding points. These opening sentences provide the “transitions” that many beginning researchers have difficulty with. Instead of beginning a paragraph by launching into a description of a previous study, such as “Williams (2004) found that…,” it is better to start by indicating something about why you are describing this particular study. Here are some simple examples:
Another example of this phenomenon comes from the work of Williams (2004).
Williams (2004) offers one explanation of this phenomenon.
An alternative perspective has been provided by Williams (2004).
We used a method based on the one used by Williams (2004).
Finally, remember that your goal is to construct an argument for why your research question is interesting and worth addressing—not necessarily why your favourite answer to it is correct. In other words, your literature review must be balanced. If you want to emphasize the generality of a phenomenon, then of course you should discuss various studies that have demonstrated it. However, if there are other studies that have failed to demonstrate it, you should discuss them too. Or if you are proposing a new theory, then of course you should discuss findings that are consistent with that theory. However, if there are other findings that are inconsistent with it, again, you should discuss them too. It is acceptable to argue that the balance of the research supports the existence of a phenomenon or is consistent with a theory (and that is usually the best that researchers in psychology can hope for), but it is not acceptable to ignore contradictory evidence. Besides, a large part of what makes a research question interesting is uncertainty about its answer.
The closing of the introduction—typically the final paragraph or two—usually includes two important elements. The first is a clear statement of the main research question or hypothesis. This statement tends to be more formal and precise than in the opening and is often expressed in terms of operational definitions of the key variables. The second is a brief overview of the method and some comment on its appropriateness. Here, for example, is how Darley and Latané (1968) [2] concluded the introduction to their classic article on the bystander effect:
These considerations lead to the hypothesis that the more bystanders to an emergency, the less likely, or the more slowly, any one bystander will intervene to provide aid. To test this proposition it would be necessary to create a situation in which a realistic “emergency” could plausibly occur. Each subject should also be blocked from communicating with others to prevent his getting information about their behaviour during the emergency. Finally, the experimental situation should allow for the assessment of the speed and frequency of the subjects’ reaction to the emergency. The experiment reported below attempted to fulfill these conditions. (p. 378)
Thus the introduction leads smoothly into the next major section of the article—the method section.
The method section is where you describe how you conducted your study. An important principle for writing a method section is that it should be clear and detailed enough that other researchers could replicate the study by following your “recipe.” This means that it must describe all the important elements of the study—basic demographic characteristics of the participants, how they were recruited, whether they were randomly assigned, how the variables were manipulated or measured, how counterbalancing was accomplished, and so on. At the same time, it should avoid irrelevant details such as the fact that the study was conducted in Classroom 37B of the Industrial Technology Building or that the questionnaire was double-sided and completed using pencils.
The method section begins immediately after the introduction ends with the heading “Method” (not “Methods”) centred on the page. Immediately after this is the subheading “Participants,” left justified and in italics. The participants subsection indicates how many participants there were, the number of women and men, some indication of their age, other demographics that may be relevant to the study, and how they were recruited, including any incentives given for participation.
After the participants section, the structure can vary a bit. Figure 11.1 shows three common approaches. In the first, the participants section is followed by a design and procedure subsection, which describes the rest of the method. This works well for methods that are relatively simple and can be described adequately in a few paragraphs. In the second approach, the participants section is followed by separate design and procedure subsections. This works well when both the design and the procedure are relatively complicated and each requires multiple paragraphs.
What is the difference between design and procedure? The design of a study is its overall structure. What were the independent and dependent variables? Was the independent variable manipulated, and if so, was it manipulated between or within subjects? How were the variables operationally defined? The procedure is how the study was carried out. It often works well to describe the procedure in terms of what the participants did rather than what the researchers did. For example, the participants gave their informed consent, read a set of instructions, completed a block of four practice trials, completed a block of 20 test trials, completed two questionnaires, and were debriefed and excused.
In the third basic way to organize a method section, the participants subsection is followed by a materials subsection before the design and procedure subsections. This works well when there are complicated materials to describe. This might mean multiple questionnaires, written vignettes that participants read and respond to, perceptual stimuli, and so on. The heading of this subsection can be modified to reflect its content. Instead of “Materials,” it can be “Questionnaires,” “Stimuli,” and so on.
The results section is where you present the main results of the study, including the results of the statistical analyses. Although it does not include the raw data—individual participants’ responses or scores—researchers should save their raw data and make them available to other researchers who request them. Several journals now encourage the open sharing of raw data online.
Although there are no standard subsections, it is still important for the results section to be logically organized. Typically it begins with certain preliminary issues. One is whether any participants or responses were excluded from the analyses and why. The rationale for excluding data should be described clearly so that other researchers can decide whether it is appropriate. A second preliminary issue is how multiple responses were combined to produce the primary variables in the analyses. For example, if participants rated the attractiveness of 20 stimulus people, you might have to explain that you began by computing the mean attractiveness rating for each participant. Or if they recalled as many items as they could from study list of 20 words, did you count the number correctly recalled, compute the percentage correctly recalled, or perhaps compute the number correct minus the number incorrect? A third preliminary issue is the reliability of the measures. This is where you would present test-retest correlations, Cronbach’s α, or other statistics to show that the measures are consistent across time and across items. A final preliminary issue is whether the manipulation was successful. This is where you would report the results of any manipulation checks.
The results section should then tackle the primary research questions, one at a time. Again, there should be a clear organization. One approach would be to answer the most general questions and then proceed to answer more specific ones. Another would be to answer the main question first and then to answer secondary ones. Regardless, Bem (2003) [3] suggests the following basic structure for discussing each new result:
Notice that only Step 3 necessarily involves numbers. The rest of the steps involve presenting the research question and the answer to it in words. In fact, the basic results should be clear even to a reader who skips over the numbers.
The discussion is the last major section of the research report. Discussions usually consist of some combination of the following elements:
The discussion typically begins with a summary of the study that provides a clear answer to the research question. In a short report with a single study, this might require no more than a sentence. In a longer report with multiple studies, it might require a paragraph or even two. The summary is often followed by a discussion of the theoretical implications of the research. Do the results provide support for any existing theories? If not, how can they be explained? Although you do not have to provide a definitive explanation or detailed theory for your results, you at least need to outline one or more possible explanations. In applied research—and often in basic research—there is also some discussion of the practical implications of the research. How can the results be used, and by whom, to accomplish some real-world goal?
The theoretical and practical implications are often followed by a discussion of the study’s limitations. Perhaps there are problems with its internal or external validity. Perhaps the manipulation was not very effective or the measures not very reliable. Perhaps there is some evidence that participants did not fully understand their task or that they were suspicious of the intent of the researchers. Now is the time to discuss these issues and how they might have affected the results. But do not overdo it. All studies have limitations, and most readers will understand that a different sample or different measures might have produced different results. Unless there is good reason to think they would have, however, there is no reason to mention these routine issues. Instead, pick two or three limitations that seem like they could have influenced the results, explain how they could have influenced the results, and suggest ways to deal with them.
Most discussions end with some suggestions for future research. If the study did not satisfactorily answer the original research question, what will it take to do so? What new research questions has the study raised? This part of the discussion, however, is not just a list of new questions. It is a discussion of two or three of the most important unresolved issues. This means identifying and clarifying each question, suggesting some alternative answers, and even suggesting ways they could be studied.
Finally, some researchers are quite good at ending their articles with a sweeping or thought-provoking conclusion. Darley and Latané (1968) [4] , for example, ended their article on the bystander effect by discussing the idea that whether people help others may depend more on the situation than on their personalities. Their final sentence is, “If people understand the situational forces that can make them hesitate to intervene, they may better overcome them” (p. 383). However, this kind of ending can be difficult to pull off. It can sound overreaching or just banal and end up detracting from the overall impact of the article. It is often better simply to end when you have made your final point (although you should avoid ending on a limitation).
The references section begins on a new page with the heading “References” centred at the top of the page. All references cited in the text are then listed in the format presented earlier. They are listed alphabetically by the last name of the first author. If two sources have the same first author, they are listed alphabetically by the last name of the second author. If all the authors are the same, then they are listed chronologically by the year of publication. Everything in the reference list is double-spaced both within and between references.
Appendices, tables, and figures come after the references. An appendix is appropriate for supplemental material that would interrupt the flow of the research report if it were presented within any of the major sections. An appendix could be used to present lists of stimulus words, questionnaire items, detailed descriptions of special equipment or unusual statistical analyses, or references to the studies that are included in a meta-analysis. Each appendix begins on a new page. If there is only one, the heading is “Appendix,” centred at the top of the page. If there is more than one, the headings are “Appendix A,” “Appendix B,” and so on, and they appear in the order they were first mentioned in the text of the report.
After any appendices come tables and then figures. Tables and figures are both used to present results. Figures can also be used to illustrate theories (e.g., in the form of a flowchart), display stimuli, outline procedures, and present many other kinds of information. Each table and figure appears on its own page. Tables are numbered in the order that they are first mentioned in the text (“Table 1,” “Table 2,” and so on). Figures are numbered the same way (“Figure 1,” “Figure 2,” and so on). A brief explanatory title, with the important words capitalized, appears above each table. Each figure is given a brief explanatory caption, where (aside from proper nouns or names) only the first word of each sentence is capitalized. More details on preparing APA-style tables and figures are presented later in the book.
Figures 11.2, 11.3, 11.4, and 11.5 show some sample pages from an APA-style empirical research report originally written by undergraduate student Tomoe Suyama at California State University, Fresno. The main purpose of these figures is to illustrate the basic organization and formatting of an APA-style empirical research report, although many high-level and low-level style conventions can be seen here too.
Key Takeaways
Figure 11.1 long description: Table showing three ways of organizing an APA-style method section.
In the simple method, there are two subheadings: “Participants” (which might begin “The participants were…”) and “Design and procedure” (which might begin “There were three conditions…”).
In the typical method, there are three subheadings: “Participants” (“The participants were…”), “Design” (“There were three conditions…”), and “Procedure” (“Participants viewed each stimulus on the computer screen…”).
In the complex method, there are four subheadings: “Participants” (“The participants were…”), “Materials” (“The stimuli were…”), “Design” (“There were three conditions…”), and “Procedure” (“Participants viewed each stimulus on the computer screen…”). [Return to Figure 11.1]
A type of research article which describes one or more new empirical studies conducted by the authors.
The page at the beginning of an APA-style research report containing the title of the article, the authors’ names, and their institutional affiliation.
A summary of a research study.
The third page of a manuscript containing the research question, the literature review, and comments about how to answer the research question.
An introduction to the research question and explanation for why this question is interesting.
A description of relevant previous research on the topic being discusses and an argument for why the research is worth addressing.
The end of the introduction, where the research question is reiterated and the method is commented upon.
The section of a research report where the method used to conduct the study is described.
The main results of the study, including the results from statistical analyses, are presented in a research article.
Section of a research report that summarizes the study's results and interprets them by referring back to the study's theoretical background.
Part of a research report which contains supplemental material.
Research Methods in Psychology - 2nd Canadian Edition Copyright © 2015 by Paul C. Price, Rajiv Jhangiani, & I-Chant A. Chiang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.
A research report is a document in which a researcher presents the results of an original study. In the past, research reports were published as PDFs. But as you will see from the examples in this guide, the best research reports today are published as highly visual, interactive web pages.
Indeed — over the last five years, we’ve seen an explosion of research reports and white papers from businesses and NGOs.
Take, for example, this recent report on Green Mortgages from IMLA, made with the assistance of digital agency Rostrum. It’s a beautifully designed report, rich with infographics and data visualisations .
The biggest SaaS companies are also investing in reports, including Slack , Twilio , and Atlassian .
It's not only businesses publishing reports. The white paper below, from the Publishers Association, dives into an initiative on the future role of artificial intelligence in the publishing industry.
Join the BBC, Penguin, and the University of Cambridge. Craft stunning, interactive web content with Shorthand. Publish your first story for free — no code or web design skills required. Sign up now.
A research report is an in-depth document that contains the results of a research project. It includes information about the research topic, the research question, the methodology used to collect data from respondents, the results of the research, and the conclusion of the researcher.
The report also includes information about the funding source or partnerships for the project, if applicable. The purpose of a research report is to communicate the findings of research studies to a wider audience. The report should be clear, concise, and well-organised so that readers can easily understand the information presented.
Many research reports are formally structured, with headings and — for PDFs — page numbers,
As mentioned above, research reports have traditionally been published as PDFs , but are increasingly moving to interactive content .
Why are these organisations investing in research reports and white papers?
Most of these teams aren’t filled with scientists or academics, and their readers aren’t usually trawling research databases for help with their work.
The reason is — let’s be blunt — most content published on the web underwhelms.
Even on the most well-attended blogs, organic traffic and dwell-time generally remains flat. CTAs are stubbornly un-clicked. The common fate of most content is to gather dust almost immediately after publication.
There are many reasons for this. Search has clearly become much more competitive. It’s difficult for most organisations to get their ordinary blog content ranked anywhere near the first page.
Social media, too, has long been a ‘pay-to-play’ environment, with only extremely brave (or foolish) content teams banking on their posts going viral.
To meet these challenges, the most successful content teams have committed to producing high-quality content. Rather than pumping out content-for-content’s-sake — which, to be frank, few humans actually want to read — these teams produce content that helps, informs, and delights their readers.
High quality content takes a range of forms, including ebooks, longform content , all-encompassing ‘skyscraper’ guides, and feature stories . It's often highly visual, immersive, and multimedia, and can include elements like audio, video, and interactive infographics .
Because this content is produced to genuinely help the reader, it’s much more likely to be read, shared, and — critically for SEO — linked. Readers tend to stay on the page for longer, another key metric for SEO. They’re also more likely to click calls-to-action.
This is borne out by our customers at Shorthand. After nine months using Shorthand as an investment in producing high quality content, Imperial College London’s feature stories saw 142% higher average unique pageviews and 50% higher average time on page.
Honda, too, saw the average site dwell time increase by 85% after transitioning to publishing immersive, high quality digital stories (again, built with Shorthand).
Clearly, quality content gets better results. But it isn’t easy to make. It requires investment, dedication, and clear goals.
In this guide, we focus on one of the best-performing genres of quality content — the research report, which has become the not-so-secret weapon for the world’s leading content teams.
For most organisations, research reports will sit somewhere between marketing and academia.
On the one hand, they need to be as rigorous, scientific, and statistically literate as any published research paper. There's no point — and real reputational risk — in publishing a sloppy, factually inaccurate report.
On the other hand, most research reports outside of academia are published to support sales and marketing efforts. For some companies, such as market research firms, these reports are the product itself. Research reports need to be beautifully produced, clearly written, and have clear takeaways for the reader.
But unlike academia, there's also no one-size-fits-all structure. With that in mind, here are some common sections to keep in mind when writing a research report.
Navigation . For print reports and PDFs, it's common to include a table of contents after the title page. But if you're producing your report natively for the web — which we highly recommend — then it's worth giving your reader a way to jump back and forth. At Shorthand, we make it simple to create a custom top navigation, which allows readers to easily browse through longer content.
Introduction section . For research reports, your introduction is a good opportunity to outline the scope of your work; note your research questions, research design, and research methods; establish context and significance; and add any background information you think might be relevant.
Literature review . These take a specific form in academic research, but outside of academia, it might pay to show some awareness of other research that has been conducted in your space.
Research Methodology . Again, this doesn't need to have all the rigour of an academic journal article. But to establish credibility, it pays to outline how you produced and analysed the qualitative or quantitative data at the heart of your report. For example, if you collected your data from an online questionnaire, it pays to point this out.
Research findings . The most important part of your report will be your results section, covering of your findings. As we discuss below, for quantitative research, this section should be rich with data visualisations and infographics . This will likely be the most compelling part of the report for your readers.
Discussion section . This is where you can contextualise the results, and offer an argument about the significance of the data. In many research reports for brands, this section and the 'research findings' sections are merged.
Conclusion. This is where you can pull the various threads of your research report together. This will also allow you to carefully advance an argument about the significance of the research, and what it suggests about the future.
Craft stunning, interactive research reports with Shorthand. Publish your first story for free — no code or web design skills required. Sign up now.
More than any other genre of content, research reports require consistent — and persistent — project management. Unlike blog posts or case studies, a research report can't be turned around in a week or two.
This can be daunting for teams that haven't published research before — and daunting projects have a way of getting postponed.
As with any large project, the best thing to do is create a realistic plan. This plan will need to include all the different stakeholders — including writers, designers, and management — and factor in their likely contribution. Part of this will involve taking a realistic look at their future commitments.
Plan time for data collection, drafting, data visualisation, design, editing , and writing. This will all take longer than you think.
Once you've established your plan — and once it's been signed off by all relevant stakeholders — stick to it. Trust it. Try not to deviate too much from the process.
Data is the core of any research report. It will be the stuff that gets quoted and highlighted, and it will be what earns your report any backlinks or extra addition.
Without fresh data, your report is just another bunch of unsupported assertions — and there’s more than enough of those on the web already.
The way you get fresh data will depend on what exactly you’re researching. You might be analysing usage patterns in software. You might be interviewing your customers or a professional cohort.
It could be anything — but whatever it is, make sure it’s fresh and unique to your report.
If you're looking for more examples and inspiration, check out our guide on how to get started with data storytelling , as well as our post on 8 examples of powerful data stories .
(or anything else).
Odds are, your research report isn’t going to be peer reviewed, and it won’t be published in a scientific journal. But this doesn't give you an excuse to cut corners.
A research report is a form of ‘anchor’ content. It is specifically produced to earn attention for your brand.
But attention can swing both ways. If people notice mistakes or major errors in your report, then this will impact the reputation of your organisation.
What are the most common mistakes for research reports?
The most common areas where research reports fall down are in data collection, data analysis, and data visualisation. Make sure you have someone sufficiently numerate to double-check your process and results.
A research report is an invaluable way to establish your brand as a leader in your field. This is important for SEO and engagement. But it’s also important for the buying process — whatever it is your organisation is trying to sell.
Simply put, potential leads are much more likely to take action with organisations that they trust. This is true for businesses — but it is also true for NGOs, universities, and government agencies.
You can read more in our guide to brand storytelling .
The most effective research reports are presented as a neutral interpretation of data — without any embellishment or sales flourishes.
Ideally, you want your readers to engage with your report as an accurate representation of the world. You want them to trust it — and trust you. Anything that betrays your agenda will weaken this trust, and make the report less effective.
Obviously, your report isn’t neutral. It’s an investment in a piece of content. And, like all content you publish, you have an end goal in mind.
But, done well, a professionally produced report will accomplish those goals — including better engagement, reputational gain, and lead generation — without you needing to aggressively sell your product or service.
After collecting data and analysing your findings, you need to consider your data visualisations. This includes any relevant charts, graphs, and maps.
Your data visualisations will be the centrepiece of your report. They will likely be the parts of your report that readers skip to. They’re also likely to be the information readers retain and share.
With this in mind, they're worth doing right. There are many different data visualisation tools out there, and there's no single best approach.
Read more about data visualisation in our guide to effective data journalism .
Some reports will benefit from a chart or map that readers can click and interrogate in the browser. Others will benefit from scroll-based animation, as used in this story from the Council of the European Union.
One constant across the best research reports on the web, though, is the use of interactive data visualisation. While it was common in the past to use static images of charts and graphs — usually recycling visual assets used in the PDF version of the report — this approach is gradually being supplanted by more advanced techniques.
Some of these data visualisation techniques will require web design and developer resource. Others — like Shorthand itself — will be easier to use out-of-the-box.
PDFs are an extraordinarily common method of publishing research reports — even today. Indeed, some organisations publish their reports as ‘PDF-first,’ with any web publication treated as a poor cousin.
This is the wrong approach. And with the rise of new web technologies and more powerful web browsers, it’s also extremely outdated. For better results, we recommend producing reports first and foremost for the web.
Web-based reports have many distinct advantages over the PDF, including:
If you want you to read more about the problems with the PDF, check out our guide on why the PDF is falling out of favour .
With the rise of digital storytelling platforms, the calibre of published research reports on the web has improved markedly. That means that there are plenty of excellent reports to check out for inspiration.
At Shorthand, we’ve collected some of the best reports — including thought-leadership reports, annual reports for businesses and NGOs, and original research — in our collection of featured stories .
Craft sumptuous content at speed. No code required.
Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.
Section 1- Evidence-based practice (EBP)
Components of a research report.
Partido, B.B.
Elements of research report
Introduction | What is the issue? |
Methods | What methods have been used to investigate the issue? |
Results | What was found? |
Discussion | What are the implications of the findings? |
The research report contains four main areas:
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Saul Mcleod, PhD
Editor-in-Chief for Simply Psychology
BSc (Hons) Psychology, MRes, PhD, University of Manchester
Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.
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Olivia Guy-Evans, MSc
Associate Editor for Simply Psychology
BSc (Hons) Psychology, MSc Psychology of Education
Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.
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In psychology, a lab report outlines a study’s objectives, methods, results, discussion, and conclusions, ensuring clarity and adherence to APA (or relevant) formatting guidelines.
A typical lab report would include the following sections: title, abstract, introduction, method, results, and discussion.
The title page, abstract, references, and appendices are started on separate pages (subsections from the main body of the report are not). Use double-line spacing of text, font size 12, and include page numbers.
The report should have a thread of arguments linking the prediction in the introduction to the content of the discussion.
This must indicate what the study is about. It must include the variables under investigation. It should not be written as a question.
Title pages should be formatted in APA style .
The abstract provides a concise and comprehensive summary of a research report. Your style should be brief but not use note form. Look at examples in journal articles . It should aim to explain very briefly (about 150 words) the following:
The abstract comes at the beginning of your report but is written at the end (as it summarises information from all the other sections of the report).
The purpose of the introduction is to explain where your hypothesis comes from (i.e., it should provide a rationale for your research study).
Ideally, the introduction should have a funnel structure: Start broad and then become more specific. The aims should not appear out of thin air; the preceding review of psychological literature should lead logically into the aims and hypotheses.
There should be a logical progression of ideas that aids the flow of the report. This means the studies outlined should lead logically to your aims and hypotheses.
Do be concise and selective, and avoid the temptation to include anything in case it is relevant (i.e., don’t write a shopping list of studies).
USE THE FOLLOWING SUBHEADINGS:
The reference section lists all the sources cited in the essay (alphabetically). It is not a bibliography (a list of the books you used).
In simple terms, every time you refer to a psychologist’s name (and date), you need to reference the original source of information.
If you have been using textbooks this is easy as the references are usually at the back of the book and you can just copy them down. If you have been using websites then you may have a problem as they might not provide a reference section for you to copy.
References need to be set out APA style :
Author, A. A. (year). Title of work . Location: Publisher.
Author, A. A., Author, B. B., & Author, C. C. (year). Article title. Journal Title, volume number (issue number), page numbers
A simple way to write your reference section is to use Google scholar . Just type the name and date of the psychologist in the search box and click on the “cite” link.
Next, copy and paste the APA reference into the reference section of your essay.
Once again, remember that references need to be in alphabetical order according to surname.
Quantitative paper template.
Quantitative professional paper template: Adapted from “Fake News, Fast and Slow: Deliberation Reduces Belief in False (but Not True) News Headlines,” by B. Bago, D. G. Rand, and G. Pennycook, 2020, Journal of Experimental Psychology: General , 149 (8), pp. 1608–1613 ( https://doi.org/10.1037/xge0000729 ). Copyright 2020 by the American Psychological Association.
Qualitative professional paper template: Adapted from “‘My Smartphone Is an Extension of Myself’: A Holistic Qualitative Exploration of the Impact of Using a Smartphone,” by L. J. Harkin and D. Kuss, 2020, Psychology of Popular Media , 10 (1), pp. 28–38 ( https://doi.org/10.1037/ppm0000278 ). Copyright 2020 by the American Psychological Association.
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A research paper is a piece of academic writing that provides analysis, interpretation, and argument based on in-depth independent research.
Research papers are similar to academic essays , but they are usually longer and more detailed assignments, designed to assess not only your writing skills but also your skills in scholarly research. Writing a research paper requires you to demonstrate a strong knowledge of your topic, engage with a variety of sources, and make an original contribution to the debate.
This step-by-step guide takes you through the entire writing process, from understanding your assignment to proofreading your final draft.
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Understand the assignment, choose a research paper topic, conduct preliminary research, develop a thesis statement, create a research paper outline, write a first draft of the research paper, write the introduction, write a compelling body of text, write the conclusion, the second draft, the revision process, research paper checklist, free lecture slides.
Completing a research paper successfully means accomplishing the specific tasks set out for you. Before you start, make sure you thoroughly understanding the assignment task sheet:
Carefully consider your timeframe and word limit: be realistic, and plan enough time to research, write, and edit.
Professional editors proofread and edit your paper by focusing on:
See an example
There are many ways to generate an idea for a research paper, from brainstorming with pen and paper to talking it through with a fellow student or professor.
You can try free writing, which involves taking a broad topic and writing continuously for two or three minutes to identify absolutely anything relevant that could be interesting.
You can also gain inspiration from other research. The discussion or recommendations sections of research papers often include ideas for other specific topics that require further examination.
Once you have a broad subject area, narrow it down to choose a topic that interests you, m eets the criteria of your assignment, and i s possible to research. Aim for ideas that are both original and specific:
Note any discussions that seem important to the topic, and try to find an issue that you can focus your paper around. Use a variety of sources , including journals, books, and reliable websites, to ensure you do not miss anything glaring.
Do not only verify the ideas you have in mind, but look for sources that contradict your point of view.
In this stage, you might find it helpful to formulate some research questions to help guide you. To write research questions, try to finish the following sentence: “I want to know how/what/why…”
A thesis statement is a statement of your central argument — it establishes the purpose and position of your paper. If you started with a research question, the thesis statement should answer it. It should also show what evidence and reasoning you’ll use to support that answer.
The thesis statement should be concise, contentious, and coherent. That means it should briefly summarize your argument in a sentence or two, make a claim that requires further evidence or analysis, and make a coherent point that relates to every part of the paper.
You will probably revise and refine the thesis statement as you do more research, but it can serve as a guide throughout the writing process. Every paragraph should aim to support and develop this central claim.
A research paper outline is essentially a list of the key topics, arguments, and evidence you want to include, divided into sections with headings so that you know roughly what the paper will look like before you start writing.
A structure outline can help make the writing process much more efficient, so it’s worth dedicating some time to create one.
Your first draft won’t be perfect — you can polish later on. Your priorities at this stage are as follows:
You do not need to start by writing the introduction. Begin where it feels most natural for you — some prefer to finish the most difficult sections first, while others choose to start with the easiest part. If you created an outline, use it as a map while you work.
Do not delete large sections of text. If you begin to dislike something you have written or find it doesn’t quite fit, move it to a different document, but don’t lose it completely — you never know if it might come in useful later.
Paragraphs are the basic building blocks of research papers. Each one should focus on a single claim or idea that helps to establish the overall argument or purpose of the paper.
George Orwell’s 1946 essay “Politics and the English Language” has had an enduring impact on thought about the relationship between politics and language. This impact is particularly obvious in light of the various critical review articles that have recently referenced the essay. For example, consider Mark Falcoff’s 2009 article in The National Review Online, “The Perversion of Language; or, Orwell Revisited,” in which he analyzes several common words (“activist,” “civil-rights leader,” “diversity,” and more). Falcoff’s close analysis of the ambiguity built into political language intentionally mirrors Orwell’s own point-by-point analysis of the political language of his day. Even 63 years after its publication, Orwell’s essay is emulated by contemporary thinkers.
It’s also important to keep track of citations at this stage to avoid accidental plagiarism . Each time you use a source, make sure to take note of where the information came from.
You can use our free citation generators to automatically create citations and save your reference list as you go.
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The research paper introduction should address three questions: What, why, and how? After finishing the introduction, the reader should know what the paper is about, why it is worth reading, and how you’ll build your arguments.
What? Be specific about the topic of the paper, introduce the background, and define key terms or concepts.
Why? This is the most important, but also the most difficult, part of the introduction. Try to provide brief answers to the following questions: What new material or insight are you offering? What important issues does your essay help define or answer?
How? To let the reader know what to expect from the rest of the paper, the introduction should include a “map” of what will be discussed, briefly presenting the key elements of the paper in chronological order.
The major struggle faced by most writers is how to organize the information presented in the paper, which is one reason an outline is so useful. However, remember that the outline is only a guide and, when writing, you can be flexible with the order in which the information and arguments are presented.
One way to stay on track is to use your thesis statement and topic sentences . Check:
Be aware of paragraphs that seem to cover the same things. If two paragraphs discuss something similar, they must approach that topic in different ways. Aim to create smooth transitions between sentences, paragraphs, and sections.
The research paper conclusion is designed to help your reader out of the paper’s argument, giving them a sense of finality.
Trace the course of the paper, emphasizing how it all comes together to prove your thesis statement. Give the paper a sense of finality by making sure the reader understands how you’ve settled the issues raised in the introduction.
You might also discuss the more general consequences of the argument, outline what the paper offers to future students of the topic, and suggest any questions the paper’s argument raises but cannot or does not try to answer.
You should not :
There are four main considerations when it comes to the second draft.
The goal during the revision and proofreading process is to ensure you have completed all the necessary tasks and that the paper is as well-articulated as possible. You can speed up the proofreading process by using the AI proofreader .
Check the content of each paragraph, making sure that:
Next, think about sentence structure , grammatical errors, and formatting . Check that you have correctly used transition words and phrases to show the connections between your ideas. Look for typos, cut unnecessary words, and check for consistency in aspects such as heading formatting and spellings .
Finally, you need to make sure your paper is correctly formatted according to the rules of the citation style you are using. For example, you might need to include an MLA heading or create an APA title page .
Scribbr’s professional editors can help with the revision process with our award-winning proofreading services.
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I have followed all instructions in the assignment sheet.
My introduction presents my topic in an engaging way and provides necessary background information.
My introduction presents a clear, focused research problem and/or thesis statement .
My paper is logically organized using paragraphs and (if relevant) section headings .
Each paragraph is clearly focused on one central idea, expressed in a clear topic sentence .
Each paragraph is relevant to my research problem or thesis statement.
I have used appropriate transitions to clarify the connections between sections, paragraphs, and sentences.
My conclusion provides a concise answer to the research question or emphasizes how the thesis has been supported.
My conclusion shows how my research has contributed to knowledge or understanding of my topic.
My conclusion does not present any new points or information essential to my argument.
I have provided an in-text citation every time I refer to ideas or information from a source.
I have included a reference list at the end of my paper, consistently formatted according to a specific citation style .
I have thoroughly revised my paper and addressed any feedback from my professor or supervisor.
I have followed all formatting guidelines (page numbers, headers, spacing, etc.).
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This chart lists the major biomedical research reporting guidelines that provide advice for reporting research methods and findings. They usually "specify a minimum set of items required for a clear and transparent account of what was done and what was found in a research study, reflecting, in particular, issues that might introduce bias into the research" (Adapted from the EQUATOR Network Resource Centre ). The chart also includes editorial style guides for writing research reports or other publications.
See the details of the search strategy. More research reporting guidelines are at the EQUATOR Network Resource Centre .
American Medical Association
| A manuscript style guide for medical science. |
American Psychological Association | Used in social and behavioral science studies. |
Animal Research: Reporting of In Vivo Experiments | For reporting animal research and peer-reviewers of animal research studies.
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ASSERT: A Standard for the Scientific and Ethical Review of Trials | Research ethics committees use this guideline to review and monitor randomized clinical trials.
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reporting guidelines for CAse REports | Evidence-based, minimum recommendations for case reports. The CARE guidelines provide early signs of what may work for patients.
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Common Data Elements | Common data elements are standardized terms for the collection and exchange of data. CDEs are metadata; they describe the type of data collected, not the data itself. An example of metadata is the question presented on a form, "Patient Name," whereas an example of data would be "Jane Smith."
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Clinical Data Interchange Standards Consortium | Standards supporting the "acquisition, exchange, submission and archive of clinical research data and metadata."
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Consolidated Health Economic Evaluation Reporting Standards Statement | Used to report "economic evaluations of health interventions."
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Citation of BioResources in journal Articles | Developed by members of the journal editors’ subgroup of the Bioresource Research Impact Factor (BRIF) for citing bioresources, such as biological samples, data, and databases.
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Consolidated Standards of Reporting Trials | Evidence-based, 25-item checklist containing the minimum recommendations for reporting Randomized Clinical Trials (RCTs). |
Committee on Publication Ethics | Forum for editors of peer-reviewed journals to discuss issues related to the integrity of the scientific record. Asks editors to report, record, and initiate investigations into ethical problems in the publication process. All Elsevier journals are COPE members.
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Consolidated criteria for reporting qualitative research | A "32-item checklist for interviews and focus groups."
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Council of Science Editors | Authority on scientific communication issues. |
European Association of Science Editors | Remain aware of trends in traditional or electronic scientific publishing.
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Enhancing the QUAlity and Transparency Of health Research | Reporting guidelines developers, medical journal editors and peer reviewers, research funding bodies, and other partners work to improve the quality of research. |
(formerly, Biosharing) | "A curated, informative and educational resource on data and metadata standards, inter-related to databases and data policies."
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Editorial Guidelines: Forum for African Medical Editors | 68-page guidelines includes the and the Helsinki Declaration.
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Guidelines for Neuro-Oncology: Standards for Investigational Studies | Guidelines to standardize reports of surgically-based Phase 1 and Phase 2 neuro-oncology trials.
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Good Publication Practice | Guidelines for the results of clinical trials sponsored by pharmaceutical companies. (To download a PDF of GPP 2022 from the Annals of Internal Medicine website, if you do not have a journal subscription.)
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Grey Literature International Steering Committee | Guidelines to produce scientific and technical reports and writing/distributing grey literature.
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Mulford Library, University of Toledo HSL | Lists in alphabetical order. Contains publishing guidelines for some journals. Indicates which journals follow CONSORT and/or other guidelines.
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International Committee of Medical Journal Editors | Uniform Requirements for Manuscripts Submitted to Biomedical Journals (also called the Vancouver Style)
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The aim is to improve the quality and credibility of scientific peer review and publication and to help advance the efficiency, effectiveness, and equitability of the dissemination of biomedical information throughout the world.
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International Academy of Nursing Editors | To promote best practices in the nursing literature.
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Mayfield Handbook Investigation/Study/Assay (ISA) tab-delimited (TAB) format | "a general purpose framework with which to collect and communicate complex metadata (i.e. sample characteristics, technologies used, type of measurements made) from 'omics-based' experiments employing a combination of technologies."
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Minimum Information About a Microarray Experiment | The MIAME guideline is in Appendix B of Applications of Toxicogenomic Technologies to Predictive Toxicology and Risk Assessment (2007) at . Describes the basic data needed to enable the unambiguous interpretation of the results and to possibly replicate the experiment.
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Minimum Information for Biological and Biomedical Investigations | Portal of almost 40 checklists can use when reporting biological and biomedical science research.
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Meta-analysis of Observational Studies in Epidemiology | To report the meta-analyses of observational studies in epidemiology.
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Guidelines for Transparent Reporting of Outbreak Reports and Intervention studies Of Nosocomial infection | A 22-item checklist showing items to include when reporting an outbreak or intervention study of a nosocomial organism. Endorsed by professional special interest groups and societies, including the Association of Medical Microbiologists (AMM), British Society for Antimicrobial Chemotherapy (BSAC) & the Infection Control Nurses' Association (ICNA) Research and Development Group.
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PRIMER Collaboration: PRESENTATION AND INTERPRETATION OF MEDICAL RESEARCH | Group that aims to improve the design of studies, their presentation, interpretation of results and translation into practice.
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, National Institutes of Health (NIH) | NIH held a joint workshop in June 2014 with the Nature Publishing Group and Science on the issue of reproducibility and rigor of research findings, with journal editors representing over 30 basic/preclinical science journals in which NIH-funded investigators have most often published.
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Preferred Reporting Items for Systematic Reviews and Meta-Analyses (formerly, the QUOROM statement) | The is to help authors improve the reporting of systematic reviews and meta-analyses. It has “focused on randomized trials, but PRISMA can also be used as a basis for reporting systematic reviews of other types of research, particularly evaluations of interventions. PRISMA may also be useful for critical appraisal of published systematic reviews, although it is not a quality assessment instrument to gauge the quality of a systematic review.”
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QUOROM: QUality Of Reporting Of Meta-analyses (Renamed PRISMA in 2009) | Checklist that describes the preferred way to present the abstract, introduction, methods, results, and discussion sections of a report of a meta-analysis.
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Reporting Data on Homeopathic Treatments (A CONSORT Supplement) | Eight-item checklist to use by authors and editors when publishing reports of homeopathic clinical trials.
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Reporting guidElines For randomized controLled trials for livEstoCk and food safeTy | Evidence-based minimum set of items for trials reporting production, health, and food-safety outcomes. (22-item checklist)
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REporting recommendations for tumor MARKer prognostic studies | Guidelines for reporting of tumor marker studies.
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"Reporting practice guidelines in health care"
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Sex and Gender Equity in Research | How to report sex and gender information in a study’s design, data analyses, results, and interpretation of findings.
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Standard Metabolic Reporting Structures | Recommendations for standardizing and reporting of metabolic analyses.
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Standard Protocol Items: Recommendations for Interventional Trials | The SPIRIT 2013 Statement is a 33-item checklist that recommend a minimum set of data to include in a clinical trial protocol.
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Revised Standards for Quality Improvement Reporting Excellence | The SQUIRE Guidelines help authors write usable articles about quality improvement in healthcare so that results are findable and widely distributed.
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Standards for reporting qualitative research: a synthesis of recommendations | How to report qualitative research.
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STAndards for the Reporting of Diagnostic accuracy | Aims to improve the accuracy and completeness of reporting of studies of diagnostic accuracy, to allow readers to assess the potential for bias in the study (internal validity) and to evaluate its generalizability. Checklist contains 34-items.
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Statement on Reporting of Evaluation Studies in Health Informatics | Used to report health informatics evaluation studies.
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STrengthening the REporting of Genetic Associations | To promote reporting of genetic association studies.
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: STandards for Reporting Interventions in Controlled Trials of Acupuncture (A CONSORT Supplement) | Designed as a supplement to CONSORT, which has led to improved reporting of trial design and conduct in general. Current plans are to revise STRICTA in collaboration with the CONSORT Group, such that STRICTA becomes an "official" extension to CONSORT.
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STrengthening the Reporting of OBservational studies in Epidemiology | Aims to establish a of items to include in articles reporting observational research.
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, National Library of Medicine (NLM) | Description of structured abstracts and how MEDLINE formats them.
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Updated guidance for reporting clinical prediction models that use regression or machine learning methods | "Reporting of studies that develop a prediction model or evaluate its performance."
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World Association of Medical Editors | Editors of peer-reviewed medical journals |
Last Reviewed: April 14, 2023
Understanding research reports, financial analyst research reports, research report impact, conflicts of interest.
James Chen, CMT is an expert trader, investment adviser, and global market strategist.
A research report is a document prepared by an analyst or strategist who is a part of the investment research team in a stock brokerage or investment bank . A research report may focus on a specific stock or industry sector, a currency, commodity or fixed-income instrument, or on a geographic region or country. Research reports generally, but not always, have actionable recommendations such as investment ideas that investors can act upon.
Research reports are produced by a variety of sources, ranging from market research firms to in-house departments at large organizations. When applied to the investment industry, the term usually refers to sell-side research, or investment research produced by brokerage houses.
Such research is disseminated to the institutional and retail clients of the brokerage that produces it. Research produced by the buy-side, which includes pension funds, mutual funds, and portfolio managers , is usually for internal use only and is not distributed to external parties.
Financial analysts may produce research reports for the purpose of supporting a particular recommendation, such as whether to buy or sell a particular security or whether a client should consider a particular financial product. For example, an analyst may create a report in regards to a new offering being proposed by a company. The report could include relevant metrics regarding the company itself, such as the number of years they have been in operation as well as the names of key stakeholders , along with statistics regarding the current state of the market in which the company participates. Information regarding overall profitability and the intended use of the funds can also be included.
Enthusiasts of the Efficient Market Hypothesis (EMH) might insist that the value of professional analysts' research reports is suspect and that investors likely place too much confidence in the conclusions such analysts make. While a definitive conclusion about this topic is difficult to make because comparisons are not exact, some research papers do exist which claim empirical evidence supporting the value of such reports.
One such paper studied the market for India-based investments and analysts who cover them. The paper was published in the March 2014 edition of the International Research Journal of Business and Management. Its authors concluded that analyst recommendations do have an impact and are beneficial to investors at least in short-term decisions.
While some analysts are functionally unaffiliated, others may be directly or indirectly affiliated with the companies for which they produce reports. Unaffiliated analysts traditionally perform independent research to determine an appropriate recommendation and may have a limited concern regarding the outcome.
Affiliated analysts may feel best served by ensuring any research reports portray clients in a favorable light. Additionally, if an analyst is also an investor in the company on which the report is based, he may have a personal incentive to avoid topics that may result in a lowered valuation of the securities in which he has invested.
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Emma l. henderson.
1 School of Psychology, University of Surrey, Guildford, Surrey, United Kingdom
2 Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff, United Kingdom
Introduction.
Registered Reports are an increasingly popular publishing format that is currently offered in more than 300 journals. Because the process of writing and submitting a Registered Report is different to that of standard manuscripts, we felt it important to create this “10 Simple Rules” guide for writing a more open and useful manuscript.
Registered Reports are a form of research article where the study protocol is reviewed before the study is undertaken. They are designed to reduce publication bias and various forms of reporting bias by using a 2-stage writing and peer review process. Before the research is conducted, authors submit a Stage 1 manuscript that includes an introduction (with hypotheses where relevant) and detailed methods and analysis plans. Following peer review and revision, the decision to publish is made based on evaluation of the research question and the rigour of the methods, and is therefore results-agnostic. If the article is accepted, authors receive an “in-principle acceptance” that commits the journal or platform to publishing the final research regardless of the outcome . Authors then conduct their research as outlined in their Stage 1 and complete a Stage 2 manuscript in which the results and discussion sections are added to the approved Stage 1 protocol. The completed manuscript undergoes a second round of peer review focusing on compliance with the Stage 1 plans and assessing whether the conclusions are valid given the results. Following possible revisions, the Registered Report is published.
Registered Reports bring a wealth of benefits both for the research community and for the individual researcher. A key benefit is that they provide a powerful antidote for publication bias, see [ 1 , 2 ]: The decision to publish is results-agnostic because it is taken pre-study and results-blind. This principle not only ensures that both “positive” and “negative” results are equally likely to be published, but also guarantees publication independent of outcome (as long as you follow your Stage 1 plans, see rule 8 ) while releasing the pressure on authors to present “positive,” ground-breaking, or novel results [ 3 ]. Thus, this format not only alleviates the aforementioned biases, but also the stress on researchers navigating their way through a “publish or perish” culture.
Receiving the commitment to publish your research before the study is run (in-principle acceptance at Stage 1) means that you can add the paper to your CV as a concrete output (i.e., you can include the in-principle acceptance date and journal/platform) much earlier than for a standard article where you have to wait until the study is completed and accepted. This is particularly vital for early career researchers (ECRs) applying for their first jobs and grants and is perhaps among the reasons why the majority of published Registered Reports are first authored by ECRs [ 3 ].
A further salient benefit for the individual researcher is that Stage 1 peer review occurs when it matters most—before any primary research is conducted (in some cases, pilot work may have been completed) and at a time when the authors can improve the quality of their research by adjusting their plans.
To date, the majority of the over 700 (at time of publication) published Registered Reports report confirmatory, experimental work. However, as the format develops, Registered Reports are increasingly being used for more diverse types of research. The following formats currently exist (note not all journals that offer Registered Reports support all formats, so check your target journal early, see rule 3 ):
Confirmatory: So called “primary Registered Reports” report hypothesis-testing (confirmatory) research using newly generated data and currently make up the bulk of published Registered Reports. These papers may include a single study (e.g., [ 4 ]) or several prespecified studies (e.g., [ 5 ]).
Existing data: So called “secondary Registered Reports” use data that already exist to answer a research question (e.g., [ 6 ]). If there is a potential risk of bias because the data have already been observed, you will need to address this risk (see here for further information, including a level-based taxonomy of bias control for Registered Reports involving existing data).
Meta-analyses, systematic reviews, and systematic maps: Protocols for research synthesis studies are often publicly registered, but the Registered Reports format has the added benefits of protocol peer review and in-principle acceptance (e.g., systematic review and meta-analysis: [ 7 ]; systematic map: [ 8 ]).
Qualitative : Many aspects of qualitative research can be specified a priori (e.g., [ 9 ]). Authors can refer to guidance on qualitative preregistration [ 10 ].
Incremental: You can add a new study to an accepted Registered Report. This option is appropriate where studies are interdependent, for example, the results of study 1 inform the design of study 2, or where an important exploratory finding warrants a second study within the same article (more information here , no examples at the time of publication).
Programmatic: For larger or longer-term projects, programmatic Registered Reports offer the option to publish several Stage 2 manuscripts from a single approved Stage 1 (e.g., [ 11 ]).
The rules below detail practical recommendations to help researchers with both experimental and non-experimental research. Rules 1 to 6 relate to the steps leading up to Stage 1 in-principle acceptance and 7 to 10 to post acceptance.
Use the period of writing your Stage 1 manuscript to learn before you conduct your study, so that when you come to run it, you have already anticipated potential pitfalls and know how you will handle, analyse, and interpret your data. Starting work on your Registered Report from as early a stage as possible will help guide your focus and learning. Most journals or platforms that offer Registered Reports have clear guidelines on their statistical (e.g., conducting a statistical sampling plan) and methodological requirements, some of which you may not be familiar with. Knowing the parameters against which your work will be judged before you design it allows you to learn the right things at the right time or to seek out collaborators with appropriate expertise as necessary. Ensure your collaborators are familiar with the Registered Reports format. All coauthors should understand the primary aim of Registered Reports to reduce bias, which requires critical design and analysis decisions to be made before conducting the research, and the Stage 1 manuscript to remain largely unchanged in the final paper. For introductory guides to Registered Reports, see [ 3 , 12 ].
The format also front-loads important decisions to the start of the research project, when you’re motivated and excited about the study. You will receive reviewer feedback when it’s most useful—before you start your research—allowing you to improve the design in ways that would be impossible had the study already been run as with traditional peer review. So when you start data collection (or analysis in the case of secondary data), you will have everything ready to complete your study, including a detailed, peer-reviewed study protocol.
Without an empirically valid question, you do not have the basis of a Registered Report: The philosophy behind Registered Reports is that what gives research its value is the question being asked and the quality of the methods used, not the results. Valid research questions are usually derived from theory, applications, or gaps in knowledge. At Stage 1 peer review, the editor and reviewers will evaluate the empirical validity of your research question(s), and some journals may also assess its subjective importance. You should use your introduction to explain why the question needs to be answered and how the study will be informative regardless of the outcome (e.g., whether the hypothesis is supported or not). To do this, you should describe the logic and rationale for your research question(s); your hypotheses (where applicable) should follow directly from your research question(s), be precisely stated, and translate theoretical predictions into observable outcomes.
The second key criteria reviewers assess at Stage 1 is the soundness and feasibility of your methodology and analysis plan to test your questions. In terms of soundness, you should consider design features that maximise the rigour and informativeness of your study (regardless of outcome) such as sample size, blinding, randomisation, participant recruitment criteria, prespecification and justification of inclusion and exclusion criteria, validity (see [ 13 ]), generalisability (see [ 14 ]), and outcome-neutral checks (also known as “control checks,” “positive controls,” “manipulation checks,” “tests of intervention fidelity,” or “sanity checks”) that confirm that the study is sufficiently well designed to be capable of answering the research questions. Outcome-neutral checks test the auxiliary assumptions in your design, for example that your independent variable manipulates what it intends to, by targeting a variable (other than the dependent variable of interest) that the independent variable would be expected to influence. Such checks show you and reviewers that the study worked as intended, and a “negative” result therefore cannot be ascribed to a failed manipulation. For more information, see [ 15 ].
In the event of a failed outcome-neutral check, the study may still be informative in showing that a procedure does not perform as intended, perhaps even challenging the status of an assumed reality check [ 15 ]. In such cases, the commitment to publishing the Stage 2 manuscript is likely to be maintained provided there are additional indicators that the study was undertaken to a sufficiently high standard. In rare, severe cases, where outcome-neutral checks and all other critical quality checks fail, the article may be rejected at Stage 2. However, a more likely outcome in that case is that authors would be given the opportunity to redesign the study. This would be treated as an incremental registration in which the authors add a study to the approved submission, and the new study undergoes Stage 1 peer review.
For Registered Reports involving hypothesis testing, reviewers are evaluating the extent to which your study minimises false positives (i.e., incorrectly concluding that an effect exists) and false negatives (i.e., incorrectly concluding that there is no effect). Again, these factors maximise the informativeness of the study regardless of outcome. Prespecifying your analysis plan constrains researcher degrees of freedom and helps minimise false positives. You should prespecify your sampling plan (e.g., statistical power analysis) for each hypothesis including the reasoning for your effect size, the rationale for any specified statistical priors, cut offs, collapsing analyses across groups, etc. You should also consider designing your analysis plan to maximise the informativeness of null results by using equivalence testing [ 16 ] or Bayesian analyses to support claims of invariance between conditions [ 17 ].
Pilot data, though not a requirement of Registered Reports, is especially useful to test and show reviewers that your planned design is feasible (pilot data is not typically useful for calculating power analyses as it introduces bias; see [ 18 ] for an explanation and [ 19 ] for recommendations). As well as allowing you to check feasibility, pilot data may reveal unanticipated exclusion criteria for example, and will give you data to plan your data analysis steps in order to write your Stage 1 protocol and code (code is preferable to narrative explanations because it is more precise). If you are writing a meta-analysis, systematic review, or systematic map, you should pilot your searches to ensure that you will have sufficient studies included to provide a meaningful answer to your research question(s). This level of planning is one of the benefits of Registered Reports over vanilla preregistration; you do not just specify the topline design, but also detail all the processes and steps behind that design, so you won’t end up wedded to a design that is infeasible in practice. Any research conducted prior to your Stage 1 submission should be noted as such (e.g., “All steps in this search term identification section were completed prior to submitting the Stage 1 Registered Report”).
The template in S1 Appendix provides further information on the criteria necessary for designing a rigorous Registered Report.
While developing your study, plan where you’re going to submit your work. There are 2 main options: submit to a single journal that offers Registered Reports or to the supra-journal platform Peer Community In Registered Reports (PCI RR).
For the former option, refer to the current list of journals that offer Registered Reports . Beyond checking the disciplinary scope of your target journal, also check that the journal accepts the type of study that you’re designing. All journals that currently offer Registered Reports accept confirmatory hypothesis testing research and a subset accept qualitative studies, systematic reviews, or meta-analyses as Registered Reports.
The alternative option is submitting to the free, supra-journal platform PCI RR ( https://rr.peercommunityin.org/ ), where Registered Reports from any research field are reviewed and accepted as preprints. Once accepted, the reviews and editorial recommendation are published on the PCI RR website and authors have the choice to keep their Stage 2 article as a peer-reviewed Registered Report preprint (with DOI) or to submit it as a Registered Report to one of several “ PCI RR-friendly journals ” that have committed to publishing Registered Reports accepted by PCI RR without further peer review. That is, when you receive in-principle acceptance from PCI RR, you automatically receive in-principle acceptance from all eligible journals. PCI RR accepts a wide range of Registered Reports including quantitative and qualitative studies, systematic reviews, systematic maps, scoping reviews, and meta-analyses.
Check the author guidelines of your intended outlet for more details. Identify submission requirements (e.g., any minimum requirements for power or Bayes Factors, requirements to openly share data) and ensure that you can adhere to them with your design and resources. If you’re initially submitting to PCI RR but ultimately have a journal planned for publication, you’ll need to make sure you comply with your intended journal’s conditions for Registered Reports submissions. These do differ among PCI RR-friendly journals, so make sure you plan ahead.
As with standard manuscripts, you may wish to send the editor a pre-submission enquiry to check the suitability of your manuscript for the journal. However, unlike standard manuscripts, you could also use this enquiry to discuss any study-specific concerns, for example, any constraints you have on the timing of data collection.
If your research requires ethical approval from your institution, you will need to consider when to apply for it (see Fig 1 ). Check both your target outlet’s policy regarding ethical approval and also your institution’s policy on accepting amendments or allowing flexibility for minor deviations. If the journal or platform requires ethical approval at Stage 1 submission and your institution allows some flexibility, obtain ethical approval prior to Stage 1 submission and then once you have received in-principle acceptance, check with your review board that any changes are within the limits of their flexibility. If no flexibility is allowed, and your study plans have changed as a result of the peer review process, you’ll need to resubmit your ethics once you have your in-principle acceptance and a firm study plan.
EC, ethics committee; IPA, in-principle acceptance; IRB, institutional review board.
When you submit your Stage 1 Registered Report, you will typically be asked to provide an anticipated timescale for your Stage 2 submission. If you need to seek ethical approval after in-principle acceptance, don’t forget to factor this into your timescale.
In your Stage 1 Registered Report, provide clear links between your research question(s), hypotheses (if applicable), sampling plan (if applicable), analyses, through to interpretation. This linking ensures that any predictions made in the introduction are transparently connected to the analysis in the results section and the conclusions inferred from the pattern of results. There are a couple of ways that you can make sure that these elements are explicitly connected. First, number each research question (Q1, Q2 …) and/or hypothesis (H1, H2 …) and add this suffix to corresponding analyses and interpretations (for an example, see [ 4 ]).
Second, an unambiguous way to report this critical information (and this also helps with planning the study) is to create a design summary table listing each research question, hypothesis, sampling plan, the analyses that will test those hypotheses and the results that will confirm or disconfirm each prediction. See S1 Appendix for a blank design summary table and S2 Appendix for examples of completed tables that have been published in peer-reviewed Stage 1 preprints and awarded in-principle acceptance by PCI RR. If your analysis plan depends on the results (e.g., parametric versus nonparametric tests), then specify the contingencies for making different choices using IF-THEN statements.
Registered Reports aim to reduce various forms of reporting bias by eliminating undisclosed flexibility in the procedures and analyses. Your Stage 1 manuscript should therefore be precise and comprehensive in its level of detail; an independent researcher in your field should be able to replicate your research without seeking additional information [ 12 ]. This level of clarity demands an attention to detail beyond that of standard papers, requiring the inclusion of every procedural specific no matter how mundane or apparently inconsequential [ 12 ]. In practice this means, for example, listing the order in which exclusion criteria will be applied, as well as details of the exclusions themselves. Pilot data may prove useful here, see rule 2 . In addition to specifying what you will do (e.g., “We will contact authors and request details of unpublished studies.”), you should also specify what you will not do (e.g., “If after 2 attempts to contact the authors there is no response, we will not contact them again.”).
To meet the requirement to provide precise and exhaustive detail, the method section of Registered Reports is often longer than those of traditional papers. Journals sometimes avoid imposing word limits for Registered Reports, but where space is limited, use appendices to supplement the main text. Using a design summary table (see rule 5 ) will help improve the clarity of your analysis plans. Authors of meta-analyses, systematic reviews, or systematic maps should also consult guidelines such as PRISMA [ 20 ] and NIRO [ 21 ] for the type and level of detail to be included in the Stage 1 manuscript (see https://www.equator-network.org/ for a list of reporting guidelines by study type).
At the point you receive in-principle acceptance of your Stage 1 manuscript, you should preregister it, either under embargo (you may wish to embargo your Stage 1 manuscript, for example, to avoid potential participants reading your plans and hypotheses) or publicly, in a public repository like the Open Science Framework. Select “Registered Report Protocol Preregistration”, this is very brief and designed specifically for Registered Reports (you do not need to write a separate new preregistration). It asks for details of the in-principle acceptance date, journal, and a PDF of your Stage 1 Registered Report and associated materials. You should also include everything that forms part of your approved Stage 1, such as any pilot data and/or simulations and related analyses, and all your materials and code. At some journals/platforms, such as Cortex and PCI RR, the editorial team will preregister the accepted Stage 1 manuscript for you.
Once you have received in-principle acceptance and preregistered, you can conduct your study in the knowledge that it will be published regardless of the outcome. Ensure that you run your study in line with the Stage 1 protocol.
Unanticipated developments or events outside your control may necessitate a change to your methods post-Stage 1 approval (e.g., an additional exclusion criteria, procedural deviation, or technical error). Any such change must be recorded and transparently reported in the Stage 2 manuscript as a deviation from the approved protocol (for example, see Table 2 in [ 4 ], where non-preregistered exclusion criteria are marked with an asterisk). If the change is substantial (i.e., has the potential to change the type or validity of inferences that can be drawn), you should immediately seek the approval of the editor, who may obtain input from the Stage 1 reviewers. What is deemed substantial will vary based on your research design. Examples include a change to equipment, materials, participant population, inferential analyses, or coding scheme. If you are unsure whether or not to contact the editor, it is better to err on the side of caution and seek editorial approval rather than risk Stage 2 rejection due an unauthorised deviation from protocol. Failure to do so could result in the Stage 2 manuscript being rejected (when reviewers are focused on assessing adherence to the Stage 1 plans). Remind the editor of any approved changes in your Stage 2 cover letter.
You can also transparently document any such changes as they occur , either by noting them in a time-stamped, open lab book (for an example see https://osf.io/jcvue/ ), or in the case of substantial changes, by updating your preregistration. In the Open Science Framework repository, unexpected changes can be appended to the original preregistration by selecting “update” on your original registration.
Your introduction and method sections in your Stage 2 paper should not deviate unnecessarily from your approved Stage 1 manuscript, other than changing from future to past tense (e.g., “we will test” changes to “we tested”) or correcting any factual errors or misunderstandings. If pertinent new research is published in the meantime, this should not be added to your introduction because it did not motivate the research. Instead, it can be included in your discussion. Equally should your understanding of the topic evolve post Stage 1 acceptance, this can be added to the discussion. If having such information upfront is essential to the understanding of the paper, it can be added to the introduction as a footnote that explicitly states the additional information is a deviation from the accepted Stage 1. You should explain why having this information upfront is essential.
Registered Reports constrain the space for post hoc decisions, therefore for hypothesis-driven studies, all confirmatory (i.e., hypothesis testing) analyses should be included in the analysis plan in the Stage 1 manuscript and must be reported in the final paper (unless, for example, a fatal flaw is detected in the analysis and the omission is agreed with the editor and reviewers: In such cases, the omission should be noted in the final manuscript). At Stage 2, additional exploratory, data-dependent (i.e., hypothesis generating) analyses are welcome provided that they are justified in the text and clearly distinguished as exploratory (i.e., in a section labelled “Exploratory Analyses”). The distinction is critical because the unbounded nature of exploratory research makes it susceptible to undesirable outcomes such as the effects of bias, inflated alpha levels, and low power. Results based on exploratory analyses should be considered tentative and in need of verification via further confirmatory research. When interpreting such results in your discussion, make it clear that your exploratory analyses are generating hypotheses, not testing them.
For non-hypothesis testing research, such as qualitative research, systematic reviews, or systematic maps, all research questions should be defined in the Stage 1 manuscript. Additional questions that arise from the data should be explicitly noted as such.
Like other forms of open research, Registered Reports are about transparency. When your Stage 2 manuscript is accepted, you should make your Stage 1 preregistered manuscript publicly available (many journals/platforms will require you to do so anyway). This will allow readers of your published Stage 2 manuscript to compare the 2 versions. At the same time, you should make your data, code, and materials as open as possible within ethical and legal constraints; indeed, many journals make such transparency a mandatory requirement.
Registered Reports encourage methodological rigour (see [ 22 ]) and transparent planning and reporting, while reducing biases and likely representing a less distorted and selective picture of research than standard papers (see [ 2 , 3 ]). These 10 simple rules provide guidance for writing and submitting a Registered Report. The authors of this paper are also happy to answer questions directly. For more information on Registered Reports, see the central Registered Report hub ( https://www.cos.io/initiatives/registered-reports ).
S1 appendix, s2 appendix, funding statement.
The author(s) received no specific funding for this work.
Heroin is an illegal, highly addictive drug processed from morphine, a naturally occurring substance extracted from the seed pod of certain varieties of poppy plants. It is typically sold as a white or brownish powder that is "cut" with sugars, starch, powdered milk, or quinine. Pure heroin is a white powder with a bitter taste that predominantly originates in South America and, to a lesser extent, from Southeast Asia, and dominates U.S. markets east of the Mississippi River. 3 Highly pure heroin can be snorted or smoked and may be more appealing to new users because it eliminates the stigma associated with injection drug use. "Black tar" heroin is sticky like roofing tar or hard like coal and is predominantly produced in Mexico and sold in U.S. areas west of the Mississippi River. 3 The dark color associated with black tar heroin results from crude processing methods that leave behind impurities. Impure heroin is usually dissolved, diluted, and injected into veins, muscles, or under the skin.
If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.
This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.
Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.
Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.
Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).
Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.
Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.
The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.
Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.
As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.
Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).
Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.
In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.
Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.
Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.
The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.
Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.
Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.
To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.
What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.
Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.
In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.
The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.
Alex Singla and Alexander Sukharevsky are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall is an associate partner in the Washington, DC, office.
They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.
This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.
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Pastor Views | Lifeway Research | Jun 4, 2024
Most Protestant pastors remain opposed to same-sex marriage, and the supporting percentage isn’t growing any larger.
By Aaron Earls
Almost a decade after the Supreme Court legalized same-sex marriage across the country, most pastors remain opposed, and the supporting percentage isn’t growing any larger.
One in 5 U.S. Protestant pastors (21%) say they see nothing wrong with two people of the same gender getting married, according to a Lifeway Research study. Three in 4 (75%) are opposed, including 69% who strongly disagree with same-sex marriage. Another 4% say they aren’t sure.
Previous Lifeway Research studies found growing support among pastors. In 2010 , 15% of U.S. Protestant pastors had no moral issues with the practice. The percentage in favor grew to 24% in 2019 . Today, support is statistically unchanged at 21%.
“Debates continue within denominations at national and judicatory levels on the morality of same-sex marriage, yet the overall number of Protestant pastors who support same-sex marriage is not growing,” said Scott McConnell, executive director of Lifeway Research. “The previous growth was seen most clearly among mainline pastors, and that level did not rise in our latest survey.”
Pastors are slightly more supportive of legal civil unions between two people of the same gender, but most still disagree. Currently, 28% back such arrangements, statistically unchanged from the 32% in 2019 and 28% in 2018.
For most pastors, this remains a somewhat theoretical issue. Almost 9 in 10 say they’ve never been asked to perform a same-sex ceremony, according to a 2022 Lifeway Research study .
The previous growth in clergy support of same-sex marriages was driven by U.S. mainline Protestant pastors. In 2010, a third (32%) were in favor. By 2019, almost half (47%) saw nothing wrong. Current support among self-identified mainline pastors remains at similar levels (46%).
Evangelical pastors have been consistently opposed to same-sex marriage. Fewer than 1 in 10 have expressed support for the practice since 2010. Today, 7% of self-identified U.S. evangelical Protestant pastors say they see nothing wrong with two people of the same gender getting married.
A similar divide exists regarding civil unions between two people of the same gender. Most mainline pastors (54%) are supportive, while only 14% of evangelical pastors agree.
Methodists (53%), Presbyterian/Reformed (36%) and Lutherans (34%) are more likely to be supportive of same-sex marriage than Restorationist Movement (8%), non-denominational (5%), Baptist (4%) or Pentecostal (1%) pastors.
Additionally, female pastors (42%), who are more common among mainline denominations , are far more likely than their male counterparts (16%) to back same-sex marriage.
Other demographic groups also have varying degrees of support, though none as drastic as the denominational differences.
Younger pastors are more likely to be supportive than the oldest pastors. Protestant pastors 18 to 44 (27%) and 55 to 64 (22%) are more likely than pastors 65 and older (15%) to see nothing wrong with same-sex marriage.
“The moral and doctrinal beliefs of individuals do not tend to move very often or very far, so we wouldn’t expect pastors’ positions to change much,” said McConnell. “However, the differences we see by age make it noteworthy that the higher numbers of young pastors seeing nothing wrong with same-sex marriage is not yet having much of an impact on overall numbers.”
Those with more education are more supportive. Pastors with a master’s (30%) or doctoral degree (26%) are more likely than those with no college degree (9%) or a bachelor’s degree (7%) to say they’re OK with same-sex marriage.
Pastors in the Northeast (27%), where same-sex marriage was first legalized in the U.S., and the Midwest (25%), are more likely than those in the South (18%) to be supportive.
Those leading smaller churches are more likely to see nothing wrong with two people of the same gender getting married. Pastors at churches with fewer than 50 in attendance (27%) and those at congregations of 50 to 99 (25%) are more likely than those at churches with attendance between 100 and 249 (11%) and 250 or more (8%) to be in favor of same-sex marriage.
“Because fewer pastors in mid- and large-size churches are open to same-sex marriage morally, an even larger majority of Protestant churchgoers are in churches in which their pastor does not support same-sex marriages or civil unions,” said McConnell.
Many of the differences between various types of pastors exist for civil unions as well. Younger pastors are more likely to be supportive than older pastors. Pastors with more formal education are more likely to back civil unions. Those in the Northeast and Midwest tend to be more in favor than those in the South. Pastors at the smallest churches are more likely to see nothing wrong with civil unions between two people of the same gender than those at larger churches.
Lifeway Research studies can be used and referenced in news articles freely. This news release can also be republished in its entirety on other websites and in other publications without obtaining permission.
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Aaron is the senior writer at Lifeway Research.
For more information, view the complete report .
Methodology
The phone survey of 1,004 Protestant pastors was conducted Aug. 29, 2023 – Sept. 20, 2023. The calling list was a stratified random sample, drawn from a list of all Protestant churches. Quotas were used for church size. Each interview was conducted with the senior pastor, minister or priest at the church. Responses were weighted by region and church size to reflect the population more accurately. The completed sample is 1,004 surveys. The sample provides 95% confidence that the sampling error does not exceed plus or minus 3.2%. This margin of error accounts for the effect of weighting. Margins of error are higher in sub-groups.
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In the new report, foundation models dominate, benchmarks fall, prices skyrocket, and on the global stage, the U.S. overshadows.
This year’s AI Index — a 500-page report tracking 2023’s worldwide trends in AI — is out.
The index is an independent initiative at the Stanford Institute for Human-Centered Artificial Intelligence (HAI), led by the AI Index Steering Committee, an interdisciplinary group of experts from across academia and industry. This year’s report covers the rise of multimodal foundation models, major cash investments into generative AI, new performance benchmarks, shifting global opinions, and new major regulations.
Don’t have an afternoon to pore through the findings? Check out the high level here.
This past year, organizations released 149 foundation models, more than double the number released in 2022. Of these newly released models, 65.7% were open-source (meaning they can be freely used and modified by anyone), compared with only 44.4% in 2022 and 33.3% in 2021.
Closed-source models still outperform their open-sourced counterparts. On 10 selected benchmarks, closed models achieved a median performance advantage of 24.2%, with differences ranging from as little as 4.0% on mathematical tasks like GSM8K to as much as 317.7% on agentic tasks like AgentBench.
Industry dominates AI, especially in building and releasing foundation models. This past year Google edged out other industry players in releasing the most models, including Gemini and RT-2. In fact, since 2019, Google has led in releasing the most foundation models, with a total of 40, followed by OpenAI with 20. Academia trails industry: This past year, UC Berkeley released three models and Stanford two.
If you needed more striking evidence that corporate AI is the only player in the room right now, this should do it. In 2023, industry accounted for 72% of all new foundation models.
One of the reasons academia and government have been edged out of the AI race: the exponential increase in cost of training these giant models. Google’s Gemini Ultra cost an estimated $191 million worth of compute to train, while OpenAI’s GPT-4 cost an estimated $78 million. In comparison, in 2017, the original Transformer model, which introduced the architecture that underpins virtually every modern LLM, cost around $900.
At least in terms of notable machine learning models, the United States vastly outpaced other countries in 2023, developing a total of 61 models in 2023. Since 2019, the U.S. has consistently led in originating the majority of notable models, followed by China and the UK.
As of 2023, AI has hit human-level performance on many significant AI benchmarks, from those testing reading comprehension to visual reasoning. Still, it falls just short on some benchmarks like competition-level math. Because AI has been blasting past so many standard benchmarks, AI scholars have had to create new and more difficult challenges. This year’s index also tracked several of these new benchmarks, including those for tasks in coding, advanced reasoning, and agentic behavior.
While AI private investment has steadily dropped since 2021, generative AI is gaining steam. In 2023, the sector attracted $25.2 billion, nearly ninefold the investment of 2022 and about 30 times the amount from 2019 (call it the ChatGPT effect). Generative AI accounted for over a quarter of all AI-related private investments in 2023.
And again, in 2023 the United States dominates in AI private investment. In 2023, the $67.2 billion invested in the U.S. was roughly 8.7 times greater than the amount invested in the next highest country, China, and 17.8 times the amount invested in the United Kingdom. That lineup looks the same when zooming out: Cumulatively since 2013, the United States leads investments at $335.2 billion, followed by China with $103.7 billion, and the United Kingdom at $22.3 billion.
More companies are implementing AI in some part of their business: In surveys, 55% of organizations said they were using AI in 2023, up from 50% in 2022 and 20% in 2017. Businesses report using AI to automate contact centers, personalize content, and acquire new customers.
Globally, most people expect AI to change their jobs, and more than a third expect AI to replace them. Younger generations — Gen Z and millennials — anticipate more substantial effects from AI compared with older generations like Gen X and baby boomers. Specifically, 66% of Gen Z compared with 46% of boomer respondents believe AI will significantly affect their current jobs. Meanwhile, individuals with higher incomes, more education, and decision-making roles foresee AI having a great impact on their employment.
When asked in a survey about whether AI products and services make you nervous, 69% of Aussies and 65% of Brits said yes. Japan is the least worried about their AI products at 23%.
More American regulatory agencies are passing regulations to protect citizens and govern the use of AI tools and data. For example, the Copyright Office and the Library of Congress passed copyright registration guidance concerning works that contained material generated by AI, while the Securities and Exchange Commission developed a cybersecurity risk management strategy, governance, and incident disclosure plan. The agencies to pass the most regulation were the Executive Office of the President and the Commerce Department.
The AI Index was first created to track AI development. The index collaborates with such organizations as LinkedIn, Quid, McKinsey, Studyportals, the Schwartz Reisman Institute, and the International Federation of Robotics to gather the most current research and feature important insights on the AI ecosystem.
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The 2024 Open Enrollment Period (OEP) for the Health Insurance Marketplaces ran between November 1, 2023 and January 16, 2024 for the 32 states that used HealthCare.gov (HC.gov).
For the 19 State-based Marketplaces (SBMs) using their own platforms, the reporting period reflects plan selection and Marketplace activity from the beginning of the OEP on November 1, 2023 (except Idaho, which has a reporting period beginning on October 15, 2023) to the end of each SBM’s respective OEP and any run-out period that captures remaining in-line applications and post-OE cleanup activities. Any renewals processed before November 1, 2023 are also included. Data for each SBM are provided through the following dates: California (1/31/2024), Colorado (1/17/2024), Connecticut (1/15/2024), District of Columbia (2/2/2024), Idaho (12/15/2023), Kentucky (1/17/2024), Maine (1/16/2024), Maryland (1/15/2024), Massachusetts (1/28/2024), Minnesota (1/15/2024), Nevada (1/20/2024), New Jersey (1/31/2024), New Mexico (1/16/2024), New York (2/3/2024), Pennsylvania (1/19/2024), Rhode Island (1/31/2024), Vermont 1/19/2024), and Washington (1/15/2024).
The PUFs contain data on individual Marketplace activity, including health insurance applications, Qualified Health Plan (QHP) selections, and stand-alone dental plan (SADP) selections. The PUFs also include demographic characteristics of consumers who made a plan selection.
CMS has prepared a number of Public Use Files (PUFs) summarizing plan selection activity during the applicable OEPs.
The data are available in downloadable CSV and/or Excel files below. For information on the methodology and the metric definitions used in the PUFs please refer to the accompanying documents.
The related Health Insurance Marketplaces 2024 Open Enrollment Report summarizes health plan selections through the individual Marketplaces during the 2024 OEP, including demographic breakouts, premium and financial assistance trends, and estimated savings due to the American Rescue Plan (ARP) and Inflation Reduction Act (IRA).
2024 OEP State-Level Public Use File (ZIP)
2024 OEP State, Metal Level, and Enrollment Status Public Use File (ZIP)
2024 OEP County-Level Public Use File (ZIP)
2024 OEP Zip Code-Level Public Use File (ZIP)
2024 OEP Snapshot Public Use File (ZIP)
2014-2024 OEP Plan Design Public Use File (ZIP)
2024 Public Use Files Definitions (PDF)
2024 Public Use Files FAQs (PDF)
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preparation of a good research report is not a trivial task. This article discusses the common sections of a research report along with frequently made mistakes. While the emphasis here is on reports prepared for scholarly, peer-reviewed publication, these points are applicable to other forms of research reports.
A typical lab report would include the following sections: title, abstract, introduction, method, results, and discussion. The title page, abstract, references, and appendices are started on separate pages (subsections from the main body of the report are not). Use double-line spacing of text, font size 12, and include page numbers.
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If 2023 was the year the world discovered generative AI (gen AI), 2024 is the year organizations truly began using—and deriving business value from—this new technology.In the latest McKinsey Global Survey on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago.
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This year's AI Index — a 500-page report tracking 2023's worldwide trends in AI — is out.. The index is an independent initiative at the Stanford Institute for Human-Centered Artificial Intelligence (HAI), led by the AI Index Steering Committee, an interdisciplinary group of experts from across academia and industry. This year's report covers the rise of multimodal foundation models ...
The Expensive Get More Expensive: Home Value Growth Tops in Highest-Price Markets (March 2024 Market Report) Monthly appreciation spikes in expensive West Coast metros with meager options, stays cooler in areas where inventory has returned. Skylar Olsen • Apr 12 2024.
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