scientific research report examples

Scientific Reports

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.

Background and pre-writing

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:

• They want to gather the information presented.
• They want to know that the findings are legitimate.

Your job as a writer, then, is to fulfill these two goals.

How do I do that?

Good question. Here is the basic format scientists have designed for research reports:

• Introduction

Methods and Materials

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.

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.

What should I do before drafting the lab report?

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:

• What are we going to do in this lab? (That is, what’s the procedure?)
• Why are we going to do it that way?
• What are we hoping to learn from this experiment?
• Why would we benefit from this knowledge?
• Consult your lab supervisor as you perform the lab. If you don’t know how to answer one of the questions above, for example, your lab supervisor will probably be able to explain it to you (or, at least, help you figure it out).
• Plan the steps of the experiment carefully with your lab partners. The less you rush, the more likely it is that you’ll perform the experiment correctly and record your findings accurately. Also, take some time to think about the best way to organize the data before you have to start putting numbers down. If you can design a table to account for the data, that will tend to work much better than jotting results down hurriedly on a scrap piece of paper.
• Record the data carefully so you get them right. You won’t be able to trust your conclusions if you have the wrong data, and your readers will know you messed up if the other three people in your group have “97 degrees” and you have “87.”
• Consult with your lab partners about everything you do. Lab groups often make one of two mistakes: two people do all the work while two have a nice chat, or everybody works together until the group finishes gathering the raw data, then scrams outta there. Collaborate with your partners, even when the experiment is “over.” What trends did you observe? Was the hypothesis supported? Did you all get the same results? What kind of figure should you use to represent your findings? The whole group can work together to answer these questions.
• Consider your audience. You may believe that audience is a non-issue: it’s your lab TA, right? Well, yes—but again, think beyond the classroom. If you write with only your lab instructor in mind, you may omit material that is crucial to a complete understanding of your experiment, because you assume the instructor knows all that stuff already. As a result, you may receive a lower grade, since your TA won’t be sure that you understand all the principles at work. Try to write towards a student in the same course but a different lab section. That student will have a fair degree of scientific expertise but won’t know much about your experiment particularly. Alternatively, you could envision yourself five years from now, after the reading and lectures for this course have faded a bit. What would you remember, and what would you need explained more clearly (as a refresher)?

Once you’ve completed these steps as you perform the experiment, you’ll be in a good position to draft an effective lab report.

Introductions

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.

Justify your hypothesis

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.

Background/previous research

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.

Organization of this section

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.

How do I write a strong Materials and Methods section?

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:

• How much detail? Be precise in providing details, but stay relevant. Ask yourself, “Would it make any difference if this piece were a different size or made from a different material?” If not, you probably don’t need to get too specific. If so, you should give as many details as necessary to prevent this experiment from going awry if someone else tries to carry it out. Probably the most crucial detail is measurement; you should always quantify anything you can, such as time elapsed, temperature, mass, volume, etc.
• Rationale: Be sure that as you’re relating your actions during the experiment, you explain your rationale for the protocol you developed. If you capped a test tube immediately after adding a solute to a solvent, why did you do that? (That’s really two questions: why did you cap it, and why did you cap it immediately?) In a professional setting, writers provide their rationale as a way to explain their thinking to potential critics. On one hand, of course, that’s your motivation for talking about protocol, too. On the other hand, since in practical terms you’re also writing to your teacher (who’s seeking to evaluate how well you comprehend the principles of the experiment), explaining the rationale indicates that you understand the reasons for conducting the experiment in that way, and that you’re not just following orders. Critical thinking is crucial—robots don’t make good scientists.
• Control: Most experiments will include a control, which is a means of comparing experimental results. (Sometimes you’ll need to have more than one control, depending on the number of hypotheses you want to test.) The control is exactly the same as the other items you’re testing, except that you don’t manipulate the independent variable-the condition you’re altering to check the effect on the dependent variable. For example, if you’re testing solubility rates at increased temperatures, your control would be a solution that you didn’t heat at all; that way, you’ll see how quickly the solute dissolves “naturally” (i.e., without manipulation), and you’ll have a point of reference against which to compare the solutions you did heat.

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.”

Structure and style

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.

• Subsections: Occasionally, researchers use subsections to report their procedure when the following circumstances apply: 1) if they’ve used a great many materials; 2) if the procedure is unusually complicated; 3) if they’ve developed a procedure that won’t be familiar to many of their readers. Because these conditions rarely apply to the experiments you’ll perform in class, most undergraduate lab reports won’t require you to use subsections. In fact, many guides to writing lab reports suggest that you try to limit your Methods section to a single paragraph.
• Narrative structure: Think of this section as telling a story about a group of people and the experiment they performed. Describe what you did in the order in which you did it. You may have heard the old joke centered on the line, “Disconnect the red wire, but only after disconnecting the green wire,” where the person reading the directions blows everything to kingdom come because the directions weren’t in order. We’re used to reading about events chronologically, and so your readers will generally understand what you did if you present that information in the same way. Also, since the Methods section does generally appear as a narrative (story), you want to avoid the “recipe” approach: “First, take a clean, dry 100 ml test tube from the rack. Next, add 50 ml of distilled water.” You should be reporting what did happen, not telling the reader how to perform the experiment: “50 ml of distilled water was poured into a clean, dry 100 ml test tube.” Hint: most of the time, the recipe approach comes from copying down the steps of the procedure from your lab manual, so you may want to draft the Methods section initially without consulting your manual. Later, of course, you can go back and fill in any part of the procedure you inadvertently overlooked.
• Past tense: Remember that you’re describing what happened, so you should use past tense to refer to everything you did during the experiment. Writers are often tempted to use the imperative (“Add 5 g of the solid to the solution”) because that’s how their lab manuals are worded; less frequently, they use present tense (“5 g of the solid are added to the solution”). Instead, remember that you’re talking about an event which happened at a particular time in the past, and which has already ended by the time you start writing, so simple past tense will be appropriate in this section (“5 g of the solid were added to the solution” or “We added 5 g of the solid to the solution”).
• Active: We heated the solution to 80°C. (The subject, “we,” performs the action, heating.)
• Passive: The solution was heated to 80°C. (The subject, “solution,” doesn’t do the heating–it is acted upon, not acting.)

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.

How do I write a strong Results section?

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:

• Number your table. Then, when you refer to the table in the text, use that number to tell your readers which table they can review to clarify the material.
• Give your table a title. This title should be descriptive enough to communicate the contents of the table, but not so long that it becomes difficult to follow. The titles in the sample tables above are acceptable.
• Arrange your table so that readers read vertically, not horizontally. For the most part, this rule means that you should construct your table so that like elements read down, not across. Think about what you want your readers to compare, and put that information in the column (up and down) rather than in the row (across). Usually, the point of comparison will be the numerical data you collect, so especially make sure you have columns of numbers, not rows.Here’s an example of how drastically this decision affects the readability of your table (from A Short Guide to Writing about Chemistry , by Herbert Beall and John Trimbur). Look at this table, which presents the relevant data in horizontal rows:

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.

• Make sure to include units of measurement in the tables. Readers might be able to guess that you measured something in millimeters, but don’t make them try.

• Don’t use vertical lines as part of the format for your table. This convention exists because journals prefer not to have to reproduce these lines because the tables then become more expensive to print. Even though it’s fairly unlikely that you’ll be sending your Biology 11 lab report to Science for publication, your readers still have this expectation. Consequently, if you use the table-drawing option in your word-processing software, choose the option that doesn’t rely on a “grid” format (which includes vertical lines).

How do I include figures in my report?

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.

When should you use a figure?

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:

• Keep it as simple as possible. You may be tempted to signal the complexity of the information you gathered by trying to design a graph that accounts for that complexity. But remember the purpose of your graph: to dramatize your results in a manner that’s easy to see and grasp. Try not to make the reader stare at the graph for a half hour to find the important line among the mass of other lines. For maximum effectiveness, limit yourself to three to five lines per graph; if you have more data to demonstrate, use a set of graphs to account for it, rather than trying to cram it all into a single figure.
• Plot the independent variable on the horizontal (x) axis and the dependent variable on the vertical (y) axis. Remember that the independent variable is the condition that you manipulated during the experiment and the dependent variable is the condition that you measured to see if it changed along with the independent variable. Placing the variables along their respective axes is mostly just a convention, but since your readers are accustomed to viewing graphs in this way, you’re better off not challenging the convention in your report.
• Label each axis carefully, and be especially careful to include units of measure. You need to make sure that your readers understand perfectly well what your graph indicates.
• Number and title your graphs. As with tables, the title of the graph should be informative but concise, and you should refer to your graph by number in the text (e.g., “Figure 1 shows the increase in the solubility rate as a function of temperature”).
• Many editors of professional scientific journals prefer that writers distinguish the lines in their graphs by attaching a symbol to them, usually a geometric shape (triangle, square, etc.), and using that symbol throughout the curve of the line. Generally, readers have a hard time distinguishing dotted lines from dot-dash lines from straight lines, so you should consider staying away from this system. Editors don’t usually like different-colored lines within a graph because colors are difficult and expensive to reproduce; colors may, however, be great for your purposes, as long as you’re not planning to submit your paper to Nature. Use your discretion—try to employ whichever technique dramatizes the results most effectively.
• Try to gather data at regular intervals, so the plot points on your graph aren’t too far apart. You can’t be sure of the arc you should draw between the plot points if the points are located at the far corners of the graph; over a fifteen-minute interval, perhaps the change occurred in the first or last thirty seconds of that period (in which case your straight-line connection between the points is misleading).
• If you’re worried that you didn’t collect data at sufficiently regular intervals during your experiment, go ahead and connect the points with a straight line, but you may want to examine this problem as part of your Discussion section.
• Make your graph large enough so that everything is legible and clearly demarcated, but not so large that it either overwhelms the rest of the Results section or provides a far greater range than you need to illustrate your point. If, for example, the seedlings of your plant grew only 15 mm during the trial, you don’t need to construct a graph that accounts for 100 mm of growth. The lines in your graph should more or less fill the space created by the axes; if you see that your data is confined to the lower left portion of the graph, you should probably re-adjust your scale.
• If you create a set of graphs, make them the same size and format, including all the verbal and visual codes (captions, symbols, scale, etc.). You want to be as consistent as possible in your illustrations, so that your readers can easily make the comparisons you’re trying to get them to see.

How do I write a strong Discussion section?

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:

Explain whether the data support your hypothesis

• Acknowledge any anomalous data or deviations from what you expected

Derive conclusions, based on your findings, about the process you’re studying

• Relate your findings to earlier work in the same area (if you can)

Explore the theoretical and/or practical implications of your findings

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).

Acknowledge any anomalous data, or deviations from what you expected

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.

Relate your findings to previous work in the field (if possible)

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.

Works consulted

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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Home » Research Report – Example, Writing Guide and Types

Research Report – Example, Writing Guide and Types

Table of Contents

Research Report

Definition:

Research Report is a written document that presents the results of a research project or study, including the research question, methodology, results, and conclusions, in a clear and objective manner.

The purpose of a research report is to communicate the findings of the research to the intended audience, which could be other researchers, stakeholders, or the general public.

Components of Research Report

Components of Research Report are as follows:

Introduction

The introduction sets the stage for the research report and provides a brief overview of the research question or problem being investigated. It should include a clear statement of the purpose of the study and its significance or relevance to the field of research. It may also provide background information or a literature review to help contextualize the research.

Literature Review

The literature review provides a critical analysis and synthesis of the existing research and scholarship relevant to the research question or problem. It should identify the gaps, inconsistencies, and contradictions in the literature and show how the current study addresses these issues. The literature review also establishes the theoretical framework or conceptual model that guides the research.

Methodology

The methodology section describes the research design, methods, and procedures used to collect and analyze data. It should include information on the sample or participants, data collection instruments, data collection procedures, and data analysis techniques. The methodology should be clear and detailed enough to allow other researchers to replicate the study.

The results section presents the findings of the study in a clear and objective manner. It should provide a detailed description of the data and statistics used to answer the research question or test the hypothesis. Tables, graphs, and figures may be included to help visualize the data and illustrate the key findings.

The discussion section interprets the results of the study and explains their significance or relevance to the research question or problem. It should also compare the current findings with those of previous studies and identify the implications for future research or practice. The discussion should be based on the results presented in the previous section and should avoid speculation or unfounded conclusions.

The conclusion summarizes the key findings of the study and restates the main argument or thesis presented in the introduction. It should also provide a brief overview of the contributions of the study to the field of research and the implications for practice or policy.

The references section lists all the sources cited in the research report, following a specific citation style, such as APA or MLA.

The appendices section includes any additional material, such as data tables, figures, or instruments used in the study, that could not be included in the main text due to space limitations.

Types of Research Report

Types of Research Report are as follows:

Thesis is a type of research report. A thesis is a long-form research document that presents the findings and conclusions of an original research study conducted by a student as part of a graduate or postgraduate program. It is typically written by a student pursuing a higher degree, such as a Master’s or Doctoral degree, although it can also be written by researchers or scholars in other fields.

Research Paper

Research paper is a type of research report. A research paper is a document that presents the results of a research study or investigation. Research papers can be written in a variety of fields, including science, social science, humanities, and business. They typically follow a standard format that includes an introduction, literature review, methodology, results, discussion, and conclusion sections.

Technical Report

A technical report is a detailed report that provides information about a specific technical or scientific problem or project. Technical reports are often used in engineering, science, and other technical fields to document research and development work.

Progress Report

A progress report provides an update on the progress of a research project or program over a specific period of time. Progress reports are typically used to communicate the status of a project to stakeholders, funders, or project managers.

Feasibility Report

A feasibility report assesses the feasibility of a proposed project or plan, providing an analysis of the potential risks, benefits, and costs associated with the project. Feasibility reports are often used in business, engineering, and other fields to determine the viability of a project before it is undertaken.

Field Report

A field report documents observations and findings from fieldwork, which is research conducted in the natural environment or setting. Field reports are often used in anthropology, ecology, and other social and natural sciences.

Experimental Report

An experimental report documents the results of a scientific experiment, including the hypothesis, methods, results, and conclusions. Experimental reports are often used in biology, chemistry, and other sciences to communicate the results of laboratory experiments.

Case Study Report

A case study report provides an in-depth analysis of a specific case or situation, often used in psychology, social work, and other fields to document and understand complex cases or phenomena.

Literature Review Report

A literature review report synthesizes and summarizes existing research on a specific topic, providing an overview of the current state of knowledge on the subject. Literature review reports are often used in social sciences, education, and other fields to identify gaps in the literature and guide future research.

Research Report Example

Following is a Research Report Example sample for Students:

Title: The Impact of Social Media on Academic Performance among High School Students

This study aims to investigate the relationship between social media use and academic performance among high school students. The study utilized a quantitative research design, which involved a survey questionnaire administered to a sample of 200 high school students. The findings indicate that there is a negative correlation between social media use and academic performance, suggesting that excessive social media use can lead to poor academic performance among high school students. The results of this study have important implications for educators, parents, and policymakers, as they highlight the need for strategies that can help students balance their social media use and academic responsibilities.

Introduction:

Social media has become an integral part of the lives of high school students. With the widespread use of social media platforms such as Facebook, Twitter, Instagram, and Snapchat, students can connect with friends, share photos and videos, and engage in discussions on a range of topics. While social media offers many benefits, concerns have been raised about its impact on academic performance. Many studies have found a negative correlation between social media use and academic performance among high school students (Kirschner & Karpinski, 2010; Paul, Baker, & Cochran, 2012).

Given the growing importance of social media in the lives of high school students, it is important to investigate its impact on academic performance. This study aims to address this gap by examining the relationship between social media use and academic performance among high school students.

Methodology:

The study utilized a quantitative research design, which involved a survey questionnaire administered to a sample of 200 high school students. The questionnaire was developed based on previous studies and was designed to measure the frequency and duration of social media use, as well as academic performance.

The participants were selected using a convenience sampling technique, and the survey questionnaire was distributed in the classroom during regular school hours. The data collected were analyzed using descriptive statistics and correlation analysis.

The findings indicate that the majority of high school students use social media platforms on a daily basis, with Facebook being the most popular platform. The results also show a negative correlation between social media use and academic performance, suggesting that excessive social media use can lead to poor academic performance among high school students.

Discussion:

The results of this study have important implications for educators, parents, and policymakers. The negative correlation between social media use and academic performance suggests that strategies should be put in place to help students balance their social media use and academic responsibilities. For example, educators could incorporate social media into their teaching strategies to engage students and enhance learning. Parents could limit their children’s social media use and encourage them to prioritize their academic responsibilities. Policymakers could develop guidelines and policies to regulate social media use among high school students.

Conclusion:

In conclusion, this study provides evidence of the negative impact of social media on academic performance among high school students. The findings highlight the need for strategies that can help students balance their social media use and academic responsibilities. Further research is needed to explore the specific mechanisms by which social media use affects academic performance and to develop effective strategies for addressing this issue.

Limitations:

One limitation of this study is the use of convenience sampling, which limits the generalizability of the findings to other populations. Future studies should use random sampling techniques to increase the representativeness of the sample. Another limitation is the use of self-reported measures, which may be subject to social desirability bias. Future studies could use objective measures of social media use and academic performance, such as tracking software and school records.

Implications:

The findings of this study have important implications for educators, parents, and policymakers. Educators could incorporate social media into their teaching strategies to engage students and enhance learning. For example, teachers could use social media platforms to share relevant educational resources and facilitate online discussions. Parents could limit their children’s social media use and encourage them to prioritize their academic responsibilities. They could also engage in open communication with their children to understand their social media use and its impact on their academic performance. Policymakers could develop guidelines and policies to regulate social media use among high school students. For example, schools could implement social media policies that restrict access during class time and encourage responsible use.

References:

• Kirschner, P. A., & Karpinski, A. C. (2010). Facebook® and academic performance. Computers in Human Behavior, 26(6), 1237-1245.
• Paul, J. A., Baker, H. M., & Cochran, J. D. (2012). Effect of online social networking on student academic performance. Journal of the Research Center for Educational Technology, 8(1), 1-19.
• Pantic, I. (2014). Online social networking and mental health. Cyberpsychology, Behavior, and Social Networking, 17(10), 652-657.
• Rosen, L. D., Carrier, L. M., & Cheever, N. A. (2013). Facebook and texting made me do it: Media-induced task-switching while studying. Computers in Human Behavior, 29(3), 948-958.

Note*: Above mention, Example is just a sample for the students’ guide. Do not directly copy and paste as your College or University assignment. Kindly do some research and Write your own.

Applications of Research Report

Research reports have many applications, including:

• Communicating research findings: The primary application of a research report is to communicate the results of a study to other researchers, stakeholders, or the general public. The report serves as a way to share new knowledge, insights, and discoveries with others in the field.
• Informing policy and practice : Research reports can inform policy and practice by providing evidence-based recommendations for decision-makers. For example, a research report on the effectiveness of a new drug could inform regulatory agencies in their decision-making process.
• Supporting further research: Research reports can provide a foundation for further research in a particular area. Other researchers may use the findings and methodology of a report to develop new research questions or to build on existing research.
• Evaluating programs and interventions : Research reports can be used to evaluate the effectiveness of programs and interventions in achieving their intended outcomes. For example, a research report on a new educational program could provide evidence of its impact on student performance.
• Demonstrating impact : Research reports can be used to demonstrate the impact of research funding or to evaluate the success of research projects. By presenting the findings and outcomes of a study, research reports can show the value of research to funders and stakeholders.
• Enhancing professional development : Research reports can be used to enhance professional development by providing a source of information and learning for researchers and practitioners in a particular field. For example, a research report on a new teaching methodology could provide insights and ideas for educators to incorporate into their own practice.

How to write Research Report

Here are some steps you can follow to write a research report:

• Identify the research question: The first step in writing a research report is to identify your research question. This will help you focus your research and organize your findings.
• Conduct research : Once you have identified your research question, you will need to conduct research to gather relevant data and information. This can involve conducting experiments, reviewing literature, or analyzing data.
• Organize your findings: Once you have gathered all of your data, you will need to organize your findings in a way that is clear and understandable. This can involve creating tables, graphs, or charts to illustrate your results.
• Write the report: Once you have organized your findings, you can begin writing the report. Start with an introduction that provides background information and explains the purpose of your research. Next, provide a detailed description of your research methods and findings. Finally, summarize your results and draw conclusions based on your findings.
• Proofread and edit: After you have written your report, be sure to proofread and edit it carefully. Check for grammar and spelling errors, and make sure that your report is well-organized and easy to read.
• Include a reference list: Be sure to include a list of references that you used in your research. This will give credit to your sources and allow readers to further explore the topic if they choose.
• Format your report: Finally, format your report according to the guidelines provided by your instructor or organization. This may include formatting requirements for headings, margins, fonts, and spacing.

Purpose of Research Report

The purpose of a research report is to communicate the results of a research study to a specific audience, such as peers in the same field, stakeholders, or the general public. The report provides a detailed description of the research methods, findings, and conclusions.

Some common purposes of a research report include:

• Sharing knowledge: A research report allows researchers to share their findings and knowledge with others in their field. This helps to advance the field and improve the understanding of a particular topic.
• Identifying trends: A research report can identify trends and patterns in data, which can help guide future research and inform decision-making.
• Addressing problems: A research report can provide insights into problems or issues and suggest solutions or recommendations for addressing them.
• Evaluating programs or interventions : A research report can evaluate the effectiveness of programs or interventions, which can inform decision-making about whether to continue, modify, or discontinue them.
• Meeting regulatory requirements: In some fields, research reports are required to meet regulatory requirements, such as in the case of drug trials or environmental impact studies.

When to Write Research Report

A research report should be written after completing the research study. This includes collecting data, analyzing the results, and drawing conclusions based on the findings. Once the research is complete, the report should be written in a timely manner while the information is still fresh in the researcher’s mind.

In academic settings, research reports are often required as part of coursework or as part of a thesis or dissertation. In this case, the report should be written according to the guidelines provided by the instructor or institution.

In other settings, such as in industry or government, research reports may be required to inform decision-making or to comply with regulatory requirements. In these cases, the report should be written as soon as possible after the research is completed in order to inform decision-making in a timely manner.

Overall, the timing of when to write a research report depends on the purpose of the research, the expectations of the audience, and any regulatory requirements that need to be met. However, it is important to complete the report in a timely manner while the information is still fresh in the researcher’s mind.

Characteristics of Research Report

There are several characteristics of a research report that distinguish it from other types of writing. These characteristics include:

• Objective: A research report should be written in an objective and unbiased manner. It should present the facts and findings of the research study without any personal opinions or biases.
• Systematic: A research report should be written in a systematic manner. It should follow a clear and logical structure, and the information should be presented in a way that is easy to understand and follow.
• Detailed: A research report should be detailed and comprehensive. It should provide a thorough description of the research methods, results, and conclusions.
• Accurate : A research report should be accurate and based on sound research methods. The findings and conclusions should be supported by data and evidence.
• Organized: A research report should be well-organized. It should include headings and subheadings to help the reader navigate the report and understand the main points.
• Clear and concise: A research report should be written in clear and concise language. The information should be presented in a way that is easy to understand, and unnecessary jargon should be avoided.
• Citations and references: A research report should include citations and references to support the findings and conclusions. This helps to give credit to other researchers and to provide readers with the opportunity to further explore the topic.

Advantages of Research Report

Research reports have several advantages, including:

• Communicating research findings: Research reports allow researchers to communicate their findings to a wider audience, including other researchers, stakeholders, and the general public. This helps to disseminate knowledge and advance the understanding of a particular topic.
• Providing evidence for decision-making : Research reports can provide evidence to inform decision-making, such as in the case of policy-making, program planning, or product development. The findings and conclusions can help guide decisions and improve outcomes.
• Supporting further research: Research reports can provide a foundation for further research on a particular topic. Other researchers can build on the findings and conclusions of the report, which can lead to further discoveries and advancements in the field.
• Demonstrating expertise: Research reports can demonstrate the expertise of the researchers and their ability to conduct rigorous and high-quality research. This can be important for securing funding, promotions, and other professional opportunities.
• Meeting regulatory requirements: In some fields, research reports are required to meet regulatory requirements, such as in the case of drug trials or environmental impact studies. Producing a high-quality research report can help ensure compliance with these requirements.

Limitations of Research Report

Despite their advantages, research reports also have some limitations, including:

• Time-consuming: Conducting research and writing a report can be a time-consuming process, particularly for large-scale studies. This can limit the frequency and speed of producing research reports.
• Expensive: Conducting research and producing a report can be expensive, particularly for studies that require specialized equipment, personnel, or data. This can limit the scope and feasibility of some research studies.
• Limited generalizability: Research studies often focus on a specific population or context, which can limit the generalizability of the findings to other populations or contexts.
• Potential bias : Researchers may have biases or conflicts of interest that can influence the findings and conclusions of the research study. Additionally, participants may also have biases or may not be representative of the larger population, which can limit the validity and reliability of the findings.
• Accessibility: Research reports may be written in technical or academic language, which can limit their accessibility to a wider audience. Additionally, some research may be behind paywalls or require specialized access, which can limit the ability of others to read and use the findings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How To Write A Research Paper

Step-By-Step Tutorial With Examples + FREE Template

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

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

Overview: Writing A Research Paper

What (exactly) is a research paper.

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

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

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

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

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

Overview: The 3-Stage Process

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

These three steps are:

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

Let’s dig into each of these.

Need a helping hand?

Step 1: Find a topic and review the literature

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

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

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

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

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

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

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

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

Step 2: Develop a structure and outline

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

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

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

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

• Introduction
• Literature review
• Methodology

Let’s take a look at each of these.

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

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

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

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

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

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

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

Step 3: Write and refine

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

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

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

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

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

Recap: Key Takeaways

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

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

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

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Can you help me with a full paper template for this Abstract:

Background: Energy and sports drinks have gained popularity among diverse demographic groups, including adolescents, athletes, workers, and college students. While often used interchangeably, these beverages serve distinct purposes, with energy drinks aiming to boost energy and cognitive performance, and sports drinks designed to prevent dehydration and replenish electrolytes and carbohydrates lost during physical exertion.

Objective: To assess the nutritional quality of energy and sports drinks in Egypt.

Material and Methods: A cross-sectional study assessed the nutrient contents, including energy, sugar, electrolytes, vitamins, and caffeine, of sports and energy drinks available in major supermarkets in Cairo, Alexandria, and Giza, Egypt. Data collection involved photographing all relevant product labels and recording nutritional information. Descriptive statistics and appropriate statistical tests were employed to analyze and compare the nutritional values of energy and sports drinks.

Results: The study analyzed 38 sports drinks and 42 energy drinks. Sports drinks were significantly more expensive than energy drinks, with higher net content and elevated magnesium, potassium, and vitamin C. Energy drinks contained higher concentrations of caffeine, sugars, and vitamins B2, B3, and B6.

Conclusion: Significant nutritional differences exist between sports and energy drinks, reflecting their intended uses. However, these beverages’ high sugar content and calorie loads raise health concerns. Proper labeling, public awareness, and responsible marketing are essential to guide safe consumption practices in Egypt.

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How to Write a Scientific Paper: Practical Guidelines

Edgard delvin.

1 Centre de recherche, CHU Sainte-Justine

2 Département de Biochimie, Université de Montréal, Montréal, Canada

Tahir S. Pillay

3 Department of Chemical Pathology, Faculty of Health Sciences, University of Pretoria

4 Division of Chemical Pathology, University of Cape Town

5 National Health Laboratory Service, CTshwane Academic Division, Pretoria, South Africa

Anthony Newman

6 Life Sciences Department, Elsevier, Amsterdam, The Netherlands

Precise, accurate and clear writing is essential for communicating in health sciences, as publication is an important component in the university criteria for academic promotion and in obtaining funding to support research. In spite of this, the development of writing skills is a subject infrequently included in the curricula of faculties of medicine and allied health sciences. Therefore clinical investigators require tools to fill this gap. The present paper presents a brief historical background to medical publication and practical guidelines for writing scientific papers for acceptance in good journals.

INTRODUCTION

A scientific paper is the formal lasting record of a research process. It is meant to document research protocols, methods, results and conclusions derived from an initial working hypothesis. The first medical accounts date back to antiquity. Imhotep, Pharaoh of the 3 rd Dynasty, could be considered the founder of ancient Egyptian medicine as he has been credited with being the original author of what is now known as the Edwin Smith Papyrus ( Figure 1 ). The Papyrus, by giving some details on cures and anatomical observations, sets the basis of the examination, diagnosis, treatment, and prognosis of numerous diseases. Closer to the Common Era, in 460 BCE, Hippocrates wrote 70 books on medicine. In 1020, the Golden age of the Muslim Culture, Ibn Sina, known as Avicenna ( Figure 2a ), recorded the Canon of medicine that was to become the most used medical text in Europe and Middle East for almost half a millennium. This was followed in the beginning of the 12 th Century bytheextensivetreatiseofMaimonides( Figure 2b ) (Moses ben Maimon) on Greek and Middle Eastern medicine. Of interest, by the end of the 11 th Century Trotula di Ruggiero, a woman physician, wrote several influential books on women’s ailment. A number of other hallmark treatises also became more accessible, thanks to the introduction of the printing press that allowed standardization of the texts. One example is the De Humani Corporis Fabrica by Vesalius which contains hundreds of illustrations of human dissection. Thomas A Lang provides an excellent concise history of scientific publications [ 1 ]. These were the days when writing and publishing scientific or philosophical works were the privilege of the few and hence there was no or little competition and no recorded peer reviewing system. Times have however changed, and contemporary scientists have to compose with an increasingly harsh competition in attracting editors and publishers attention. As an example, the number of reports and reviews on obesity and diabetes has increased from 400 to close to 4000/year and 50 to 600/year respectively over a period of 20 years ( Figure 3 ). The present article, essentially based on TA Lang’s guide for writing a scientific paper [ 1 ], will summarize the steps involved in the process of writing a scientific report and in increasing the likelihood of its acceptance.

The Edwin Smith Papyrus (≈3000 BCE)

Avicenna and Maimonides

Annual publication load in the field of obesity and diabetes over 20 years.

Reasons for publishing are varied. One may write to achieve a post-graduate degree, to obtain funding for pursuing research or for academic promotion. While all 3 reasons are perfectly legitimate, one must ask whether they are sufficient to be considered by editors, publishers and reviewers. Why then should the scientist write? The main reason is to provide to the scientific community data based on hypotheses that are innovative and thus to advance the understanding in a specific domain. One word of caution however, is that if a set of experiments has not been done or reported, it does not mean that it should be. It may simply reflect a lack of interest in it.

DECIDING ON PUBLISHING AND TARGETING THE JOURNAL

In order to assist with the decision process, pres-ent your work orally first to colleagues in your field who may be more experienced in publishing. This step will help you in gauging whether your work is publishable and in shaping the paper.

Targeting the journal, in which you want to present your data, is also a critical step and should be done before starting to write. One hint is to look for journals that have published similar work to yours, and that aims readers most likely to be interested in your research. This will allow your article to be well read and cited. These journals are also those that you are most likely to read on a regular basis and to cite abundantly. The next step is to decide whether you submit your manuscript to a top-ranking impact factor journal or to a journal of lower prestige. Although it is tempting to test the waters, or to obtain reviewers comments, be realistic about the contribution your work provides and submit to a journal with an appropriate rank.

Do not forget that each rejection delays publication and that the basin of reviewers within your specialty is shallow. Thus repeated submission to different journals could likely result in having your work submitted for review to the same re-viewer.

DECIDING ON THE TYPE OF MANUSCRIPT

There are several types of scientific reports: observational, experimental, methodological, theoretical and review. Observational studies include 1) single-case report, 2) collective case reports on a series of patients having for example common signs and symptoms or being followed-up with similar protocols, 3) cross-sectional, 4) cohort studies, and 5) case-control studies. The latter 3 could be perceived as epidemiological studies as they may help establishing the prevalence of a condition, and identify a defined population with and without a particular condition (disease, injury, surgical complication). Experimental reports deal with research that tests a research hypothesis through an established protocol, and, in the case of health sciences, formulate plausible explanations for changes in biological systems. Methodological reports address for example advances in analytical technology, statistical methods and diagnostic approach. Theoretical reports suggest new working hypotheses and principles that have to be supported or disproved through experimental protocols. The review category can be sub-classified as narrative, systematic and meta-analytic. Narrative reviews are often broad overviews that could be biased as they are based on the personal experience of an expert relying on articles of his or her own choice. Systematic reviews and meta-analyses are based on reproducible procedures and on high quality data. Researchers systematically identify and analyze all data collected in articles that test the same working hypothesis, avoiding selection bias, and report the data in a systematic fashion. They are particularly helpful in asking important questions in the field of healthcare and are often the initial step for innovative research. Rules or guidelines in writing such report must be followed if a quality systematic review is to be published.

For clinical research trials and systematic reviews or meta-analyses, use the Consort Statement (Consolidated Standards Of Reporting Trials) and the PRISMA Statement (Preferred Reporting Items for Systematic reviews and Meta-Analyses) respectively [ 2 , 3 ]. This assures the editors and the reviewers that essential elements of the trials and of the reviews were tackled. It also speeds the peer review process. There are several other Statements that apply to epidemiological studies [ 4 ], non-randomized clinical trials [ 5 ], diagnostic test development ( 6 ) and genetic association studies ( 7 ). The Consortium of Laboratory Medicine Journal Editors has also published guidelines for reporting industry-sponsored laboratory research ( 8 ).

INITIAL STEPS IN THE PROCESS OF WRITING A SCIENTIFIC DOCUMENT

Literature review is the initial and essential step before starting your study and writing the scientific report based on it. In this process use multiple databases, multiple keyword combinations. It will allow you to track the latest development in your field and thus avoid you to find out that someone else has performed the study before you, and hence decrease the originality of your study. Do not forget that high-ranking research journals publish results of enough importance and interest to merit their publication.

Determining the authorship and the order of authorship, an ethical issue, is the second essential step, and is unfortunately often neglected. This step may avoid later conflicts as, despite existing guidelines, it remains a sensitive issue owing to personal biases and the internal politics of institutions. The International Committee of Medical Editors has adopted the following guidelines for the biomedical sciences ( 9 ).

“Authorship credit should be based only on: 1) Substantial contributions to the conception and design, or acquisition of data, or analysis and interpretation of data; 2) Drafting the article or revising it critically for important intellectual content; and 3) Final approval of the version to be published. Conditions 1, 2 and 3 must be all met. Acquisition of funding, the collections of data, or general supervision of the research group, by themselves, do not justify authorship.” ( 9 , 10 )

The order of authorship should reflect the individual contribution to the research and to the publication, from most to least ( 11 ). The first author usually carries out the lead for the project reported. However the last author is often mistakenly perceived as the senior author. This is perpetuated from the European tradition and is discouraged. As there are divergent conventions among journals, the order of authorship order may or may not reflect the individual contributions; with the exception that the first author should be the one most responsible for the work.

WRITING EFFECTIVELY

Effective writing requires that the text helps the readers 1) understand the content and the context, 2) remember what the salient points are, 3) find the information rapidly and, 4) use or apply the information given. These cardinal qualities should be adorned with the precise usage of the language, clarity of the text, inclu-siveness of the information, and conciseness. Effective writing also means that you have to focus on the potential readers’ needs. Readers in science are informed individuals who are not passive, and who will formulate their own opinion of your writing whether or not the meaning is clear. Therefore you need to know who your audience is. The following 4 questions should help you writing a reader-based text, meaning written to meet the information needs of readers [ 12 ].

What do you assume your readers already know? In other words, which terms and concepts can you use without explanation, and which do you have to define?

What do they want to know? Readers in science will read only if they think they will learn something of value.

What do they need to know? Your text must contain all the information necessary for the reader to understand it, even if you think this information id obvious to them.

What do they think they know that is not so? Correcting misconceptions can be an important function of communication, and persuading readers to change their minds can be a challenging task.

WRITING THE SCIENTIFIC PAPER

Babbs and Tacker ’ s advice to write as much of the paper before performing the research project or experimental protocol may, at first sight, seem unexpected and counterintuitive [ 13 ], but in fact it is exactly what is being done when writing a research grant application. It will allow you to define the authorship alluded to before. The following section will briefly review the structure of the different sections of a manuscript and describe their purpose.

Reading the instructions to authors of the Journal you have decided to submit your manuscript is the first important step. They provide you with the specific requirements such as the way of listing the authors, type of abstract, word, figure or table limits and citation style. The Mulford Library of University of Toledo website contains instructions to authors for over 3000 journals ( http://mulford.meduoiho.edu/instr/ ).

The general organization of an article follows the IMRAD format (Introduction, Methods, Results, and Discussion). These may however vary. For instance, in clinical research or epidemiology studies, the methods section will include details on the subjects included, and there will be a statement of the limitation of the study. Although conclusions may not always be part of the structure, we believe that it should, even in methodological reports.

The tile page provides essential information so that the editor, reviewers, and readers will identify the manuscript and the authors at a glance as well as enabling them to classify the field to which the article pertains.

The title page must contain the following:

• The tile of the article – it is an important part of the manuscript as it is the most often read and will induce the interested readers to pursue further. Therefore the title should be precise, accurate, specific and truthful;
• Each author’s given name (it may be the full name or initials) and family name;
• Each author’s affiliation;
• Some journals ask for highest academic degree;
• A running title that is usually limited to a number of characters. It must relate to the full title;
• Key words that will serve for indexing;
• For clinical studies, the trial’s registration number;
• The name of the corresponding author with full contact information.

The abstract is also an important section of your manuscript. Importantly, the abstract is the part of the article that your peers will see when consulting publication databases such as PubMed. It is the advertisement to your work and will strongly influence the editor deciding whether it will be submitted to reviewers or not. It will also help the readers decide to read the full article. Hence it has to be comprehensible on its own. Writing an abstract is challenging. You have to carefully select the content and, while being concise, assure to deliver the essence of your manuscript.

Without going into details, there are 3 types of abstracts: descriptive, informative and structured. The descriptive abstract is particularly used for theoretical, methodological or review articles. It usually consists of a single paragraph of 150 words or less. The informative abstract, the most common one, contains specific information given in the article and, are organized with an introduction (background, objectives), methods, results and discussion with or without conclusion. They usually are 150 to 250 words in length. The structured abstract is in essence an informative abstract with sections labeled with headings. They may also be longer and are limited to 250 to 300 words. Recent technology also allows for graphical or even video abstracts. The latter are interesting in the context of cell biology as they enable the investigator to illustrate ex vivo experiment results (phagocytosis process for example).

Qualities of abstracts:

• Understood without reading the full paper. Shoul dcontain no abbreviations.lf abbreviations are used, they must be defined. This however removes space for more important information;
• Contains information consistent with the full report. Conclusions in the abstract must match those given in the full report;
• Is attractive and contains information needed to decide whether to read the full report.

Introduction

The introduction has 3 main goals: to establish the need and importance of your research, to indicate how you have filled the knowledge gap in your field and to give your readers a hint of what they will learn when reading your paper. To fulfil these goals, a four-part introduction consisting of a background statement, a problem statement, an activity statement and a forecasting statement, is best suited. Poorly defined background information and problem setting are the 2 most common weaknesses encountered in introductions. They stem from the false perception that peer readers know what the issue is and why the study to solve it is necessary. Although not a strict rule, the introduction in clinical science journals should target only references needed to establish the rationale for the study and the research protocol. This differ from more basic science or cell biology journals, for which a longer and elaborate introduction may be justified because the research at hand consists of several approaches each requiring background and justification.

The 4-part introduction consists of:

• A background statement that provides the context and the approach of the research;
• A problem statement that describes the nature, scope and importance of the problem or the knowledge gap;
• An activity statement, that details the research question, sets the hypothesis and actions undertaken for the investigation;
• A forecasting statement telling the readers whattheywillfìndwhen readingyourarticle [ 14 ].

Methods section

This section may be named “Materials and Methods”, “Experimental section” or “Patients and Methods” depending upon the type of journal. Its purpose to allow your readers to provide enough information on the methods used for your research and to judge on their adequacy. Although clinical and “basic” research protocols differ, the principles involved in describing the methods share similar features. Hence, the breadth of what is being studied and how the study can be performed is common to both. What differ are the specific settings. For example, when a study is conducted on humans, you must provide, up front, assurance that it has received the approval of you Institution Ethics Review Board (IRB) and that participants have provided full and informed consent. Similarly when the study involves animals, you must affirm that you have the agreement from your Institutional Animal Care and Use Committee (IACUC). These are too often forgotten, and Journals (most of them) abiding to the rules of the Committee on Publication Ethics (COPE) and World Association of Medical Editors (WAME) will require such statement. Although journals publishing research reports in more fundamental science may not require such assurance, they do however also follow to strict ethics rules related to scientific misconduct or fraud such as data fabrication, data falsification. For clinical research papers, you have to provide information on how the participants were selected, identify the possible sources of bias and confounding factors and how they were diminished.

In terms of the measurements, you have to clearly identify the materials used as well as the suppliers with their location. You should also be unambiguous when describing the analytical method. If the method has already been published, give a brief account and refer to the original publication (not a review in which the method is mentioned without a description). If you have modified it, you have to provide a detailed account of the modifications and you have to validate its accuracy, precision and repeatability. Mention the units in which results are reported and, if necessary, include the conversion factors [mass units versus “système international” (S.I.)]. In clinical research, surrogate end-points are often used as biomarkers. Under those circumstances, you must show their validity or refer to a study that has already shown that are valid.

In cases of clinical trials, the Methods section should include the study design, the patient selection mode, interventions, type of outcomes.

Statistics are important in assuring the quality of the research project. Hence, you should consult a biostatistician at the time of devising the research protocol and not after having performed the experiments or the clinical trial.

The components of the section on statistics should include:

• The way the data will be reported (mean, median, centiles for continuous data);
• Details on participant assignments to the different groups (random allocation, consecutive entry);
• Statistical comparison tools (parametric or non parametric statistics, paired or unpaired t-tests for normally distributed data and so on);
• The statistical power calculation when determining the sample size to obtain valid and significant comparisons together with the a level;
• The statistical software package used in the analysis.

Results section

The main purpose of the results section is to report the data that were collected and their relationship. It should also provide information on the modifications that have taken place because of unforeseen events leading to a modification of the initial protocol (loss of participants, reagent substitution, loss of data).

• Report results as tables and figures whenever possible, avoid duplication in the text. The text should summarize the findings;
• Report the data with the appropriate descriptive statistics;
• Report any unanticipated events that could affect the results;
• Report a complete account of observations and explanations for missing data (patient lost).

The discussion should set your research in context, reinforce its importance and show how your results have contributed to the further understanding of the problem posed. This should appear in the concluding remarks. The following organization could be helpful.

• Briefly summarize the main results of your study in one or two paragraphs, and how they support your working hypothesis;
• Provide an interpretation of your results and show how they logically fit in an overall scheme (biological or clinical);
• Describe how your results compare with those of other investigators, explain the differences observed;
• Discuss how your results may lead to a new hypothesis and further experimentation, or how they could enhance the diagnostic procedures.
• Provide the limitations of your study and steps taken to reduce them. This could be placed in the concluding remarks.

Acknowledgements

The acknowledgements are important as they identify and thank the contributors to the study, who do not meet the criteria as co-authors. They also include the recognition of the granting agency. In this case the grant award number and source is usually included.

Declaration of competing interests

Competing interests arise when the author has more than one role that may lead to a situation where there is a conflict of interest. This is observed when the investigator has a simultaneous industrial consulting and academic position. In that case the results may not be agreeable to the industrial sponsor, who may impose a veto on publication or strongly suggest modifications to the conclusions. The investigator must clear this issue before starting the contracted research. In addition, the investigator may own shares or stock in the company whose product forms the basis of the study. Such conflicts of interest must be declared so that they are apparent to the readers.

Acknowledgments

The authors thank Thomas A Lang, for his advice in the preparation of this manuscript.

How to write a scientific report at university

David foster, professor in science and engineering at the university of manchester, explains the best way to write a successful scientific report.

David H Foster

At university, you might need to write scientific reports for laboratory experiments, computing and theoretical projects, and literature-based studies – and some eventually as research dissertations. All have a similar structure modelled on scientific journal articles. Their special format helps readers to navigate, understand and make comparisons across the research field.

Scientific report structure

The main components are similar for many subject areas, though some sections might be optional.

If you can choose a title, make it informative and not more than around 12 words. This is the average for scientific articles. Make every word count.  

The abstract summarises your report’s content in a restricted word limit. It might be read separately from your full report, so it should contain a micro-report, without references or personalisation.  

Usual elements you can include:  

• Some background to the research area.
• Reason for the work.
• Main results.
• Any implications.

Ensure you omit empty statements such as “results are discussed”, as they usually are.  

Introduction  

The introduction should give enough background for readers to assess your work without consulting previous publications.  

It can be organised along these lines:  

• An opening statement to set the context.  
• A summary of relevant published research.
• Your research question, hypothesis or other motivation.  
• The purpose of your work.
• An indication of methodology.
• Your outcome.

Choose citations to any previous research carefully. They should reflect priority and importance, not necessarily recency. Your choices signal your grasp of the field.  

Literature review  

Dissertations and literature-based studies demand a more comprehensive review of published research than is summarised in the introduction. Fortunately, you don’t need to examine thousands of articles. Just proceed systematically.  

• Use two to three published reviews to familiarise yourself with the field.  
• Use authoritative databases such as Scopus or Web of Science to find the most frequently cited articles.  
• Read these articles, noting key points. Experiment with their order and then turn them into sentences, in your own words.  
• Get advice about expected review length and database usage from your individual programme.

Aims and objectives  

Although the introduction describes the purpose of your work, dissertations might require something more accountable, with distinct aims and objectives.

The aim or aims represent the overall goal (for example, to land people on the moon). The objectives are the individual tasks that together achieve this goal (build rocket, recruit volunteers, launch rocket and so on).

The method section must give enough detail for a competent researcher to repeat your work. Technical descriptions should be accessible, so use generic names for equipment with proprietary names in parentheses (model, year, manufacturer, for example). Ensure that essential steps are clear, especially any affecting your conclusions.

The results section should contain mainly data and analysis. Start with a sentence or two to orient your reader. For numeric data, use graphs over tables and try to make graphs self-explanatory. Leave any interpretations for the discussion section.

The purpose of the discussion is to say what your results mean. Useful items to include:  

• A reminder of the reason for the work.
• A review of the results. Ensure you are not repeating the results themselves; this should be more about your thoughts on them.
• The relationship between your results and the original objective.
• Their relationship to the literature, with citations.  
• Any limitations of your results.  
• Any knowledge you gained, new questions or longer-term implications.

The last item might form a concluding paragraph or be placed in a separate conclusion section. If your report is an internal document, ensure you only refer to your future research plans.  

Try to finish with a “take-home” message complementing the opening of your introduction. For example: “This analysis has shown the process is feasible, but cost will decide its acceptability.”  

Five common mistakes to avoid when writing your doctoral dissertation   9 tips to improve your academic writing Five resources to help students with academic writing

Acknowledgements  

If appropriate, thank colleagues for advice, reading your report and technical support. Make sure that you secure their agreement first. Thank any funding agency. Avoid emotional declarations that you might later regret. That is all that is required in this section.  

Referencing  

Giving references ensures other authors’ ideas, procedures, results and inferences are credited. Use Web of Science or Scopus as mentioned earlier. Avoid databases giving online sources without journal publication details because they might be unreliable.

Don’t refer to Wikipedia. It isn’t a citable source.  

Use one referencing style consistently and make sure it matches the required style of your degree or department. Choose either numbers or author and year to refer to the full references listed near the end of your report. Include all publication details, not just website links. Every reference should be cited in the text.  

Figures and tables  

Each figure should have a caption below with a label, such as “Fig. 1”, with a title and a sentence or two about what it shows. Similarly for tables, except that the title appears above. Every figure and table should be cited in the text.

Theoretical studies  

More flexibility is possible with theoretical reports, but extra care is needed with logical development and mathematical presentation. An introduction and discussion are still needed, and possibly a literature review.

Final steps

Check that your report satisfies the formatting requirements of your department or degree programme. Check for grammatical errors, misspellings, informal language, punctuation, typos and repetition or omission.

Ask fellow students to read your report critically. Then rewrite it. Put it aside for a few days and read it afresh, making any new edits you’ve noticed. Keep up this process until you are happy with the final report. 

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Chapter 11: Presenting Your Research

Writing a Research Report in American Psychological Association (APA) Style

Learning Objectives

• Identify the major sections of an APA-style research report and the basic contents of each section.
• Plan and write an effective APA-style research report.

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.

Sections of a Research Report

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.

• Sex Differences in Coping Styles and Implications for Depressed Mood
• Effects of Aging and Divided Attention on Memory for Items and Their Contexts
• Computer-Assisted Cognitive Behavioural Therapy for Child Anxiety: Results of a Randomized Clinical Trial
• Virtual Driving and Risk Taking: Do Racing Games Increase Risk-Taking Cognitions, Affect, and Behaviour?

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 .

• “Smells Like Clean Spirit: Nonconscious Effects of Scent on Cognition and Behavior”
• “Time Crawls: The Temporal Resolution of Infants’ Visual Attention”
• “Scent of a Woman: Men’s Testosterone Responses to Olfactory Ovulation Cues”
• “Apocalypse Soon?: Dire Messages Reduce Belief in Global Warming by Contradicting Just-World Beliefs”
• “Serial vs. Parallel Processing: Sometimes They Look Like Tweedledum and Tweedledee but They Can (and Should) Be Distinguished”
• “How Do I Love Thee? Let Me Count the Words: The Social Effects of Expressive Writing”

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.

Introduction

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

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.

The Literature Review

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

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:

• Remind the reader of the research question.
• Give the answer to the research question in words.
• Present the relevant statistics.
• Qualify the answer if necessary.
• Summarize the 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:

• Summary of the research
• Theoretical implications
• Practical implications
• Limitations
• Suggestions for future research

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

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.

Sample APA-Style Research Report

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

• An APA-style empirical research report consists of several standard sections. The main ones are the abstract, introduction, method, results, discussion, and references.
• The introduction consists of an opening that presents the research question, a literature review that describes previous research on the topic, and a closing that restates the research question and comments on the method. The literature review constitutes an argument for why the current study is worth doing.
• The method section describes the method in enough detail that another researcher could replicate the study. At a minimum, it consists of a participants subsection and a design and procedure subsection.
• The results section describes the results in an organized fashion. Each primary result is presented in terms of statistical results but also explained in words.
• The discussion typically summarizes the study, discusses theoretical and practical implications and limitations of the study, and offers suggestions for further research.
• Practice: Look through an issue of a general interest professional journal (e.g.,  Psychological Science ). Read the opening of the first five articles and rate the effectiveness of each one from 1 ( very ineffective ) to 5 ( very effective ). Write a sentence or two explaining each rating.
• Practice: Find a recent article in a professional journal and identify where the opening, literature review, and closing of the introduction begin and end.
• Practice: Find a recent article in a professional journal and highlight in a different colour each of the following elements in the discussion: summary, theoretical implications, practical implications, limitations, and suggestions for future research.

Long Descriptions

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]

• Bem, D. J. (2003). Writing the empirical journal article. In J. M. Darley, M. P. Zanna, & H. R. Roediger III (Eds.),  The compleat academic: A practical guide for the beginning social scientist  (2nd ed.). Washington, DC: American Psychological Association. ↵
• Darley, J. M., & Latané, B. (1968). Bystander intervention in emergencies: Diffusion of responsibility.  Journal of Personality and Social Psychology, 4 , 377–383. ↵

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.

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Lab Report Format: Step-by-Step Guide & Examples

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.

Learn about our Editorial Process

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.

On This Page:

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:

• Start with a one/two sentence summary, providing the aim and rationale for the study.
• Describe participants and setting: who, when, where, how many, and what groups?
• Describe the method: what design, what experimental treatment, what questionnaires, surveys, or tests were used.
• Describe the major findings, including a mention of the statistics used and the significance levels, or simply one sentence summing up the outcome.
• The final sentence(s) outline the study’s “contribution to knowledge” within the literature. What does it all mean? Mention the implications of your findings if appropriate.

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).

Introduction

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.

• Start with general theory, briefly introducing the topic. Define the important key terms.
• Explain the theoretical framework.
• Summarise and synthesize previous studies – What was the purpose? Who were the participants? What did they do? What did they find? What do these results mean? How do the results relate to the theoretical framework?
• Rationale: How does the current study address a gap in the literature? Perhaps it overcomes a limitation of previous research.
• Aims and hypothesis. Write a paragraph explaining what you plan to investigate and make a clear and concise prediction regarding the results you expect to find.

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:

Participants

• How many participants were recruited?
• Say how you obtained your sample (e.g., opportunity sample).
• Give relevant demographic details (e.g., gender, ethnicity, age range, mean age, and standard deviation).
• State the experimental design .
• What were the independent and dependent variables ? Make sure the independent variable is labeled and name the different conditions/levels.
• For example, if gender is the independent variable label, then male and female are the levels/conditions/groups.
• How were the IV and DV operationalized?
• Identify any controls used, e.g., counterbalancing and control of extraneous variables.
• List all the materials and measures (e.g., what was the title of the questionnaire? Was it adapted from a study?).
• You do not need to include wholesale replication of materials – instead, include a ‘sensible’ (illustrate) level of detail. For example, give examples of questionnaire items.
• Include the reliability (e.g., alpha values) for the measure(s).
• Describe the precise procedure you followed when conducting your research, i.e., exactly what you did.
• Describe in sufficient detail to allow for replication of findings.
• Be concise in your description and omit extraneous/trivial details, e.g., you don’t need to include details regarding instructions, debrief, record sheets, etc.
• Assume the reader has no knowledge of what you did and ensure that he/she can replicate (i.e., copy) your study exactly by what you write in this section.
• Write in the past tense.
• Don’t justify or explain in the Method (e.g., why you chose a particular sampling method); just report what you did.
• Only give enough detail for someone to replicate the experiment – be concise in your writing.
• The results section of a paper usually presents descriptive statistics followed by inferential statistics.
• Report the means, standard deviations, and 95% confidence intervals (CIs) for each IV level. If you have four to 20 numbers to present, a well-presented table is best, APA style.
• Name the statistical test being used.
• Report appropriate statistics (e.g., t-scores, p values ).
• Report the magnitude (e.g., are the results significant or not?) as well as the direction of the results (e.g., which group performed better?).
• It is optional to report the effect size (this does not appear on the SPSS output).
• Avoid interpreting the results (save this for the discussion).
• Make sure the results are presented clearly and concisely. A table can be used to display descriptive statistics if this makes the data easier to understand.
• DO NOT include any raw data.
• Follow APA style.

Use APA Style

• Numbers reported to 2 d.p. (incl. 0 before the decimal if 1.00, e.g., “0.51”). The exceptions to this rule: Numbers which can never exceed 1.0 (e.g., p -values, r-values): report to 3 d.p. and do not include 0 before the decimal place, e.g., “.001”.
• Percentages and degrees of freedom: report as whole numbers.
• Statistical symbols that are not Greek letters should be italicized (e.g., M , SD , t , X 2 , F , p , d ).
• Include spaces on either side of the equals sign.
• When reporting 95%, CIs (confidence intervals), upper and lower limits are given inside square brackets, e.g., “95% CI [73.37, 102.23]”
• Outline your findings in plain English (avoid statistical jargon) and relate your results to your hypothesis, e.g., is it supported or rejected?
• Compare your results to background materials from the introduction section. Are your results similar or different? Discuss why/why not.
• How confident can we be in the results? Acknowledge limitations, but only if they can explain the result obtained. If the study has found a reliable effect, be very careful suggesting limitations as you are doubting your results. Unless you can think of any c onfounding variable that can explain the results instead of the IV, it would be advisable to leave the section out.
• Suggest constructive ways to improve your study if appropriate.
• What are the implications of your findings? Say what your findings mean for how people behave in the real world.
• Suggest an idea for further research triggered by your study, something in the same area but not simply an improved version of yours. Perhaps you could base this on a limitation of your study.
• Concluding paragraph – Finish with a statement of your findings and the key points of the discussion (e.g., interpretation and implications) in no more than 3 or 4 sentences.

Reference Page

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.

Journal Articles

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.

Psychology Lab Report Example

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 paper template

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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Library Guides

Report writing: scientific reports.

• Scientific Reports
• Business Reports

Scientific and lab reports

A good scientific report has a clear organisational structure that is divided into headings and sub-headings. The outline below details typical sections of a standard scientific report.

The structure and scientific conventions you should use in your report will be based on your department or subject field requirements. Therefore, it is always best to check your departmental guidelines or module/assignment instructions first.

Scientific reports often adopt the  IMRaD  format: I ntroduction, M ethods, R esults, and D iscussion.

The summary below outlines the standard components of a scientific report:  

The abstract is a short summary of your project. Here, you should state your research questions and aims and provide a brief description of your methodology. It also includes an overview of your most significant findings. It is best to write this last after finalising the report. 

• Introduction

This is where you set the scene for your report. The introduction should clearly articulate the purpose and aim (and, possibly, objectives) of the report, along with providing the background context for the report's topic and area of research. A scientific report may have an hypothesis in addition or in stead of aims and objectives. It may also provide any definitions or explanations for the terms used in the report or theoretical underpinnings of the research so that the reader has a clear understanding of what the research is based upon. It may be useful to also indicate any limitations to the scope of the report and identify the parameters of the research.

The methods section includes any information on the methods, tools and equipment used to get the data and evidence for your report. You should justify your method (that is, explain why your method was chosen), acknowledge possible problems encountered during the research, and present the limitations of your methodology.

If you are required to have a separate results and discussion section, then the results section should only include a summary of the findings, rather than an analysis of them - leave the critical analysis of the results for the discussion section. Presenting your results may take the form of graphs, tables, or any necessary diagrams of the gathered data. It is best to present your results in a logical order, making them as clear and understandable as possible through concise titles, brief summaries of the findings, and what the diagrams/charts/graphs or tables are showing to the reader.

This section is where the data gathered and your results are truly put to work. It is the main body of your report in which you should critically analyse what the results mean in relation to the aims and objectives (and/or, in scientific writing, hypotheses) put forth at the beginning of the report. You should follow a logical order, and can structure this section in sub-headings.

The conclusion should not include any new material but instead show a summary of your main arguments and findings. It is a chance to remind the reader of the key points within your report, the significance of the findings and the most central issues or arguments raised from the research. The conclusion may also include recommendations for further research, or how the present research may be carried out more effectively in future.

Similar to your essays, a report still requires a bibliography of all the published resources you have referenced within your report. Check your module handbook for the referencing style you should use as there are different styles depending on your degree. If it is the standard Westminster Harvard Referencing style, then follow these guidelines and remember to be consistent.

Scientific Writing Style

Scientific report/lab writing and essay writing differ in style. Compared to essay writing styles, scientific report writing styles expect the following:

• A lean and direct approach to the words chosen: do not use words unnecessarily, be concise, and always consider the purpose of each and every word.
• Each sentence must serve a purpose , so treat each sentence as important in the role it performs within the report.  
• The focus is on measurement and observation, and conveying the evidence with clarity , we therefore want to avoid using our opinions or suppositions : be objective and avoid the use of superlatives, emotive language, or wishy washy phrases, such as 'somewhat,' 'potentially,' 'possibly,' 'nearly,' and 'may be.' 
• It is important to not only begin with a question, but also the method by which you will answer that question: pre-plan and be sure of the methods you're using so that your approach is organised and systematic. Your way of answering the question must be reproducible in order to check the validity of the results and conclusions, and produce 'intersubjectively accessible knowledge.
• It is important to show your evidence , as this is what your conclusions will be based on. Be critical of the evidence, don't just tell the reader, but show the reader what it means by questioning how the evidence supports the answer to the question. 
• Maintain a rigid structure to your writing that reflects the scientific method that underlines the report: check the specific guidelines of the assignment and thoroughly follow these. If, however, you are not provided with a required structure, consider following the IMRaD structure and adapt where needed.

Recommendation: Check out the further resources for more advice, AND also take a look through scientific articles and research - use your reading effectively ! 

Reading scientific papers is an excellent way of not only developing your knowledge of a subject, but also developing your scientific writing practices and gaining a greater understanding of what is to be expected. When reading, be sure to keep in mind the author's use of language and phrases, ways of presenting and discussing evidence, and ways of organising, structuring, and formatting material, as you may wish to emulate or imitate (NOT plagiarise or copy) the styles you read.

Further Resources

Science Writing Resources for Learning by The University of British Columbia

Scientific Writing Resource by the Duke Graduate School

Scientific Writing by the Royal Literary Fund

Successful Scientific Writing  by Janice R. Matthews, John M. Bowen and Robert W. Matthews

Writing for Science Students (Palgrave Study Skills) by Jennifer Boyle

The Scientist's Guide to Writing: How to Write More Easily and Effectively Throughout Your Scientific Career by Stephen B. Heard

Writing for Biomedical Sciences Students (Macmillan Study Skills)  by Harry Witchel

Successful Scientific Writing: A Step-By-Step Guide for the Biological and Medical Sciences  by Janice R. Matthews

Date Handling and Analysis (Fundamentals of Biomedical Science)  by Andrew Blann

How to Write a Scientific Paper: An Academic Self-Help Guide for PhD Students  by Jari Saramäki

Free and Purchasable Courses:

Writing in the Sciences run by Coursera

Science Writing run by The University of Cambridge Institute of Continuing Education

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Writing a scientific report

Before you begin writing a scientific paper, it is useful to know how to read one. Being familiar with the language and structure will not only improve your reading skills but it will help you to construct your own reports more effectively.

Reading a scientific paper

I think the important thing in reading a scientific paper is understanding the structure. Once you understand the structure, it’s easy to select information because you know where to look. - Dennis Farrugia Language and Learning Adviser.

Choose credible and reliable sources

Before your first reading of a scientific paper, check that the information you are reading is  credible, reliable and accurate .

Check with your tutor if you need further assistance with choosing appropriate sources. Also, explore the Deakin Library's  library guides in your discipline.

Reading and taking notes

Identifying the different parts of a scientific text can help you read more effectively and find relevant information for your assignments. In this video, a language and learning adviser discusses the importance of understanding structure when reading a scientific paper.

Pay attention to the different sections commonly used in a journal article to better understand the research being presented. You can then use your knowledge of these sections to scan papers and to extract key information for your own notes. The different sections include:

• Abstract - overview of paper.
• Introduction - background, context (resolving a problem or gap in the research), the aims of the research.
• Methodology – how the research was conducted, how the data was collected and analysed, sample sizes.
• Results  or  Findings - results only (no discussion).
• Discussion - discussion of results and how it relates to other research and the implications of this.
• Conclusion - explores implications for further research.

After you have gained some understanding of the research, start thinking critically about the information by:

• comparing this research to other studies on the same topic
• judging whether there was enough data to support the claims being made
• suggesting what the limitations of the data might mean for the results.

Reading and analysing scientific texts (PDF, 70.0KB)

Guide to writing lab and field reports (PDF, 91.0KB)

Writing strategies

Scientific writing is very precise, so it’s important to make sure you’re as concise and clear as possible. Being clear with your purpose helps you stay focused on what you’re writing about. - Dennis Farrugia Language and Learning Adviser

In this video, a language and learning adviser provides some useful language tips for writing a scientific paper. In summary, these tips are:

• Be clear about the purpose of the paper.
• Use precise language.
• Be aware of your use of verb tense (past tense is often used, as you are reporting on past events in the lab/field).
• Use  cautious/tentative and objective language.
• Order your ideas logically, using the appropriate structure.

Defining terms

An important feature of science writing is knowing how to define terms. As you read, take note of how scientific terms are explained and categorised. Once you have identified some of these models of writing you can then apply them in your own work.

For more information on using tentative language, classifying, listing and reporting results, visit the  Manchester academic phrasebank .

Download the  guide to writing lab and field reports (PDF) for further examples of the characteristics of scientific writing.

When you are asked to write a report on investigations you carry out in labs or when you go on fieldwork, it is important to recognise that these reports are structured differently from other types of research reports and essays.

Lab or fieldwork reports are based on detailed observations of the aims, methods and procedures of your experiments or fieldwork investigations, so it is important to keep very precise and detailed notes when you are out in the field or working in the lab.

Download the  Guide to writing lab and field reports (PDF, 91.0KB) on this page for an overview of the structure of reports, as well as some language tips for each section of the report.

Note: Always follow the assessment instructions provided in your unit. This guide provides general advice only.

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This document originally came from the Journal of Mammalogy courtesy of Dr. Ronald Barry, a former editor of the journal.

Writing an Introduction for a Scientific Paper

Dr. michelle harris, dr. janet batzli, biocore.

This section provides guidelines on how to construct a solid introduction to a scientific paper including background information, study question , biological rationale, hypothesis , and general approach . If the Introduction is done well, there should be no question in the reader’s mind why and on what basis you have posed a specific hypothesis.

Broad Question : based on an initial observation (e.g., “I see a lot of guppies close to the shore. Do guppies like living in shallow water?”). This observation of the natural world may inspire you to investigate background literature or your observation could be based on previous research by others or your own pilot study. Broad questions are not always included in your written text, but are essential for establishing the direction of your research.

Background Information : key issues, concepts, terminology, and definitions needed to understand the biological rationale for the experiment. It often includes a summary of findings from previous, relevant studies. Remember to cite references, be concise, and only include relevant information given your audience and your experimental design. Concisely summarized background information leads to the identification of specific scientific knowledge gaps that still exist. (e.g., “No studies to date have examined whether guppies do indeed spend more time in shallow water.”)

Testable Question : these questions are much more focused than the initial broad question, are specific to the knowledge gap identified, and can be addressed with data. (e.g., “Do guppies spend different amounts of time in water <1 meter deep as compared to their time in water that is >1 meter deep?”)

Biological Rationale : describes the purpose of your experiment distilling what is known and what is not known that defines the knowledge gap that you are addressing. The “BR” provides the logic for your hypothesis and experimental approach, describing the biological mechanism and assumptions that explain why your hypothesis should be true.

The biological rationale is based on your interpretation of the scientific literature, your personal observations, and the underlying assumptions you are making about how you think the system works. If you have written your biological rationale, your reader should see your hypothesis in your introduction section and say to themselves, “Of course, this hypothesis seems very logical based on the rationale presented.”

• A thorough rationale defines your assumptions about the system that have not been revealed in scientific literature or from previous systematic observation. These assumptions drive the direction of your specific hypothesis or general predictions.
• Defining the rationale is probably the most critical task for a writer, as it tells your reader why your research is biologically meaningful. It may help to think about the rationale as an answer to the questions— how is this investigation related to what we know, what assumptions am I making about what we don’t yet know, AND how will this experiment add to our knowledge? *There may or may not be broader implications for your study; be careful not to overstate these (see note on social justifications below).
• Expect to spend time and mental effort on this. You may have to do considerable digging into the scientific literature to define how your experiment fits into what is already known and why it is relevant to pursue.
• Be open to the possibility that as you work with and think about your data, you may develop a deeper, more accurate understanding of the experimental system. You may find the original rationale needs to be revised to reflect your new, more sophisticated understanding.
• As you progress through Biocore and upper level biology courses, your rationale should become more focused and matched with the level of study e ., cellular, biochemical, or physiological mechanisms that underlie the rationale. Achieving this type of understanding takes effort, but it will lead to better communication of your science.

***Special note on avoiding social justifications: You should not overemphasize the relevance of your experiment and the possible connections to large-scale processes. Be realistic and logical —do not overgeneralize or state grand implications that are not sensible given the structure of your experimental system. Not all science is easily applied to improving the human condition. Performing an investigation just for the sake of adding to our scientific knowledge (“pure or basic science”) is just as important as applied science. In fact, basic science often provides the foundation for applied studies.

Hypothesis / Predictions : specific prediction(s) that you will test during your experiment. For manipulative experiments, the hypothesis should include the independent variable (what you manipulate), the dependent variable(s) (what you measure), the organism or system , the direction of your results, and comparison to be made.

If you are doing a systematic observation , your hypothesis presents a variable or set of variables that you predict are important for helping you characterize the system as a whole, or predict differences between components/areas of the system that help you explain how the system functions or changes over time.

Experimental Approach : Briefly gives the reader a general sense of the experiment, the type of data it will yield, and the kind of conclusions you expect to obtain from the data. Do not confuse the experimental approach with the experimental protocol . The experimental protocol consists of the detailed step-by-step procedures and techniques used during the experiment that are to be reported in the Methods and Materials section.

Some Final Tips on Writing an Introduction

• As you progress through the Biocore sequence, for instance, from organismal level of Biocore 301/302 to the cellular level in Biocore 303/304, we expect the contents of your “Introduction” paragraphs to reflect the level of your coursework and previous writing experience. For example, in Biocore 304 (Cell Biology Lab) biological rationale should draw upon assumptions we are making about cellular and biochemical processes.
• Be Concise yet Specific: Remember to be concise and only include relevant information given your audience and your experimental design. As you write, keep asking, “Is this necessary information or is this irrelevant detail?” For example, if you are writing a paper claiming that a certain compound is a competitive inhibitor to the enzyme alkaline phosphatase and acts by binding to the active site, you need to explain (briefly) Michaelis-Menton kinetics and the meaning and significance of Km and Vmax. This explanation is not necessary if you are reporting the dependence of enzyme activity on pH because you do not need to measure Km and Vmax to get an estimate of enzyme activity.
• Another example: if you are writing a paper reporting an increase in Daphnia magna heart rate upon exposure to caffeine you need not describe the reproductive cycle of magna unless it is germane to your results and discussion. Be specific and concrete, especially when making introductory or summary statements.

Where Do You Discuss Pilot Studies? Many times it is important to do pilot studies to help you get familiar with your experimental system or to improve your experimental design. If your pilot study influences your biological rationale or hypothesis, you need to describe it in your Introduction. If your pilot study simply informs the logistics or techniques, but does not influence your rationale, then the description of your pilot study belongs in the Materials and Methods section.  

How will introductions be evaluated? The following is part of the rubric we will be using to evaluate your papers.

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Teaching Resources & Guides > How to Teach Science Tips > Writing a Science Report  

Writing a Science Report

With science fair season coming up as well as many end of the year projects, students are often required to write a research paper or a report on their project. Use this guide to help you in the process from finding a topic to revising and editing your final paper.

Brainstorming Topics

Sometimes one of the largest barriers to writing a research paper is trying to figure out what to write about. Many times the topic is supplied by the teacher, or the curriculum tells what the student should research and write about. However, this is not always the case. Sometimes the student is given a very broad concept to write a research paper on, for example, water. Within the category of water, there are many topics and subtopics that would be appropriate. Topics about water can include anything from the three states of water, different water sources, minerals found in water, how water is used by living organisms, the water cycle, or how to find water in the desert. The point is that “water” is a very large topic and would be too broad to be adequately covered in a typical 3-5 page research paper.

When given a broad category to write about, it is important to narrow it down to a topic that is much more manageable. Sometimes research needs to be done in order to find the best topic to write about. (Look for searching tips in “Finding and Gathering Information.”) Listed below are some tips and guidelines for picking a suitable research topic:

• Pick a topic within the category that you find interesting. It makes it that much easier to research and write about a topic if it interests you.
• You may find while researching a topic that the details of the topic are very boring to you. If this is the case, and you have the option to do this, change your topic.
• Pick a topic that you are already familiar with and research further into that area to build on your current knowledge.
• When researching topics to do your paper on, look at how much information you are finding. If you are finding very little information on your topic or you are finding an overwhelming amount, you may need to rethink your topic.
• If permissible, always leave yourself open to changing your topic. While researching for topics, you may come across one that you find really interesting and can use just as well as the previous topics you were searching for.
• Most importantly, does your research topic fit the guidelines set forth by your teacher or curriculum?

Finding and Gathering Information

There are numerous resources out there to help you find information on the topic selected for your research paper. One of the first places to begin research is at your local library. Use the Dewey Decimal System or ask the librarian to help you find books related to your topic. There are also a variety of reference materials, such as encyclopedias, available at the library.

A relatively new reference resource has become available with the power of technology – the Internet. While the Internet allows the user to access a wealth of information that is often more up-to-date than printed materials such as books and encyclopedias, there are certainly drawbacks to using it. It can be hard to tell whether or not a site contains factual information or just someone’s opinion. A site can also be dangerous or inappropriate for students to use.

You may find that certain science concepts and science terminology are not easy to find in regular dictionaries and encyclopedias. A science dictionary or science encyclopedia can help you find more in-depth and relevant information for your science report. If your topic is very technical or specific, reference materials such as medical dictionaries and chemistry encyclopedias may also be good resources to use.

If you are writing a report for your science fair project, not only will you be finding information from published sources, you will also be generating your own data, results, and conclusions. Keep a journal that tracks and records your experiments and results. When writing your report, you can either write out your findings from your experiments or display them using graphs or charts .

*As you are gathering information, keep a working bibliography of where you found your sources. Look under “Citing Sources” for more information. This will save you a lot of time in the long run!

Organizing Information

Most people find it hard to just take all the information they have gathered from their research and write it out in paper form. It is hard to get a starting point and go from the beginning to the end. You probably have several ideas you know you want to put in your paper, but you may be having trouble deciding where these ideas should go. Organizing your information in a way where new thoughts can be added to a subtopic at any time is a great way to organize the information you have about your topic. Here are two of the more popular ways to organize information so it can be used in a research paper:

• Graphic organizers such as a web or mind map . Mind maps are basically stating the main topic of your paper, then branching off into as many subtopics as possible about the main topic. Enchanted Learning has a list of several different types of mind maps as well as information on how to use them and what topics fit best for each type of mind map and graphic organizer.
• Sub-Subtopic: Low temperatures and adequate amounts of snow are needed to form glaciers.
• Sub-Subtopic: Glaciers move large amounts of earth and debris.
• Sub-Subtopic: Two basic types of glaciers: valley and continental.
• Subtopic: Icebergs – large masses of ice floating on liquid water

Different Formats For Your Paper

Depending on your topic and your writing preference, the layout of your paper can greatly enhance how well the information on your topic is displayed.

1. Process . This method is used to explain how something is done or how it works by listing the steps of the process. For most science fair projects and science experiments, this is the best format. Reports for science fairs need the entire project written out from start to finish. Your report should include a title page, statement of purpose, hypothesis, materials and procedures, results and conclusions, discussion, and credits and bibliography. If applicable, graphs, tables, or charts should be included with the results portion of your report.

2. Cause and effect . This is another common science experiment research paper format. The basic premise is that because event X happened, event Y happened.

3. Specific to general . This method works best when trying to draw conclusions about how little topics and details are connected to support one main topic or idea.

4. Climatic order . Similar to the “specific to general” category, here details are listed in order from least important to most important.

5. General to specific . Works in a similar fashion as the method for organizing your information. The main topic or subtopic is stated first, followed by supporting details that give more information about the topic.

6. Compare and contrast . This method works best when you wish to show the similarities and/or differences between two or more topics. A block pattern is used when you first write about one topic and all its details and then write about the second topic and all its details. An alternating pattern can be used to describe a detail about the first topic and then compare that to the related detail of the second topic. The block pattern and alternating pattern can also be combined to make a format that better fits your research paper.

Citing Sources

When writing a research paper, you must cite your sources! Otherwise you are plagiarizing (claiming someone else’s ideas as your own) which can cause severe penalties from failing your research paper assignment in primary and secondary grades to failing the entire course (most colleges and universities have this policy). To help you avoid plagiarism, follow these simple steps:

• Find out what format for citing your paper your teacher or curriculum wishes you to use. One of the most widely used and widely accepted citation formats by scholars and schools is the Modern Language Association (MLA) format. We recommended that you do an Internet search for the most recent format of the citation style you will be using in your paper.
• Keep a working bibliography when researching your topic. Have a document in your computer files or a page in your notebook where you write down every source that you found and may use in your paper. (You probably will not use every resource you find, but it is much easier to delete unused sources later rather than try to find them four weeks down the road.) To make this process even easier, write the source down in the citation format that will be used in your paper. No matter what citation format you use, you should always write down title, author, publisher, published date, page numbers used, and if applicable, the volume and issue number.
• When collecting ideas and information from your sources, write the author’s last name at the end of the idea. When revising and formatting your paper, keep the author’s last name attached to the end of the idea, no matter where you move that idea. This way, you won’t have to go back and try to remember where the ideas in your paper came from.
• There are two ways to use the information in your paper: paraphrasing and quotes. The majority of your paper will be paraphrasing the information you found. Paraphrasing is basically restating the idea being used in your own words.   As a general rule of thumb, no more than two of the original words should be used in sequence when paraphrasing information, and similes should be used for as many of the words as possible in the original passage without changing the meaning of the main point. Sometimes, you may find something stated so well by the original author that it would be best to use the author’s original words in your paper. When using the author’s original words, use quotation marks only around the words being directly quoted and work the quote into the body of your paper so that it makes sense grammatically. Search the Internet for more rules on paraphrasing and quoting information.

Revising and Editing Your Paper

Revising your paper basically means you are fixing grammatical errors or changing the meaning of what you wrote. After you have written the rough draft of your paper, read through it again to make sure the ideas in your paper flow and are cohesive. You may need to add in information, delete extra information, use a thesaurus to find a better word to better express a concept, reword a sentence, or just make sure your ideas are stated in a logical and progressive order.

After revising your paper, go back and edit it, correcting the capitalization, punctuation, and spelling errors – the mechanics of writing. If you are not 100% positive a word is spelled correctly, look it up in a dictionary. Ask a parent or teacher for help on the proper usage of commas, hyphens, capitalization, and numbers. You may also be able to find the answers to these questions by doing an Internet search on writing mechanics or by checking you local library for a book on writing mechanics.

It is also always a good idea to have someone else read your paper. Because this person did not write the paper and is not familiar with the topic, he or she is more likely to catch mistakes or ideas that do not quite make sense. This person can also give you insights or suggestions on how to reword or format your paper to make it flow better or convey your ideas better.

More Information:

• Quick Science Fair Guide
• Science Fair Project Ideas

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• 10 Research Question Examples to Guide Your Research Project

10 Research Question Examples to Guide your Research Project

Published on October 30, 2022 by Shona McCombes . Revised on October 19, 2023.

The research question is one of the most important parts of your research paper , thesis or dissertation . It’s important to spend some time assessing and refining your question before you get started.

The exact form of your question will depend on a few things, such as the length of your project, the type of research you’re conducting, the topic , and the research problem . However, all research questions should be focused, specific, and relevant to a timely social or scholarly issue.

Once you’ve read our guide on how to write a research question , you can use these examples to craft your own.

Note that the design of your research question can depend on what method you are pursuing. Here are a few options for qualitative, quantitative, and statistical research questions.

Other interesting articles

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

Methodology

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, October 19). 10 Research Question Examples to Guide your Research Project. Scribbr. Retrieved July 1, 2024, from https://www.scribbr.com/research-process/research-question-examples/

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Writing a scientific paper.

• Writing a lab report
• INTRODUCTION

Writing a "good" results section

Figures and Captions in Lab Reports

"Results Checklist" from: How to Write a Good Scientific Paper. Chris A. Mack. SPIE. 2018.

Additional tips for results sections.

• LITERATURE CITED
• Bibliography of guides to scientific writing and presenting
• Peer Review
• Presentations
• Lab Report Writing Guides on the Web

This is the core of the paper. Don't start the results sections with methods you left out of the Materials and Methods section. You need to give an overall description of the experiments and present the data you found.

• Factual statements supported by evidence. Short and sweet without excess words
• Present representative data rather than endlessly repetitive data
• Discuss variables only if they had an effect (positive or negative)
• Use meaningful statistics
• Avoid redundancy. If it is in the tables or captions you may not need to repeat it

A short article by Dr. Brett Couch and Dr. Deena Wassenberg, Biology Program, University of Minnesota

• Present the results of the paper, in logical order, using tables and graphs as necessary.
• Explain the results and show how they help to answer the research questions posed in the Introduction. Evidence does not explain itself; the results must be presented and then explained. 
• Avoid: presenting results that are never discussed;  presenting results in chronological order rather than logical order; ignoring results that do not support the conclusions; 
• Number tables and figures separately beginning with 1 (i.e. Table 1, Table 2, Figure 1, etc.).
• Do not attempt to evaluate the results in this section. Report only what you found; hold all discussion of the significance of the results for the Discussion section.
• It is not necessary to describe every step of your statistical analyses. Scientists understand all about null hypotheses, rejection rules, and so forth and do not need to be reminded of them. Just say something like, "Honeybees did not use the flowers in proportion to their availability (X2 = 7.9, p<0.05, d.f.= 4, chi-square test)." Likewise, cite tables and figures without describing in detail how the data were manipulated. Explanations of this sort should appear in a legend or caption written on the same page as the figure or table.
• You must refer in the text to each figure or table you include in your paper.
• Tables generally should report summary-level data, such as means ± standard deviations, rather than all your raw data.  A long list of all your individual observations will mean much less than a few concise, easy-to-read tables or figures that bring out the main findings of your study.  
• Only use a figure (graph) when the data lend themselves to a good visual representation.  Avoid using figures that show too many variables or trends at once, because they can be hard to understand.

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How to Write a Good Conclusion For a Lab Report

Writing a good conclusion for your science lab report can be the difference between a good grade and a great one. It's your last chance to show you understand the experiment and why it matters. This article will help you learn how to write a lab conclusion that sums up your work and shows your teacher that you understood what you did.

What Should Be in Your Lab Report Conclusion?

A good lab report conclusion wraps up your lab work in a neat package. When you're thinking about how to write a conclusion for a lab report, focus on four main things. First, remind everyone in a sentence or two of your experiment objectives. Then, quickly mention how you did the experiment and what you found out, but don't introduce new ideas.

Next, talk about the most important things you learned from your experiment. Show how what you found out connects to what you initially tried to do. Lastly, think briefly about what your work means or any limitations you faced during the process. You may include suggestions for further investigation but refrain from proposing solutions.

How to Write a Lab Report Conclusion

To write a good lab conclusion, follow these steps:

• Remind the reader why you did the experiment and its aims. 
• Describe how you did the experiment and what tools you used.
• Briefly discuss the samples used and the results obtained.
• Provide a short analysis, including your arguments and assumptions.
• Relate your findings to the broader scientific context of your discipline.

Important: Keep your conclusion short and easy to understand. A lab conclusion should be about 200-300 words or one paragraph. But if your experiment was really complex, you might need up to 500 words.

Remember, your lab conclusion is part of a bigger report. Always make sure your whole report is well-organized, with a title, introduction, how you did things, what you found, what it means, conclusion, and a list of where you got your information. If you have a lot of numbers or calculations, put them at the end in a separate section to make your report easier to read.

A Sample Lab Report Conclusion

Here's an example of how to write a scientific conclusion for a plant experiment:

The experiment examined how various light wavelengths impact tomato seedling growth. Our findings revealed that blue light (450-495 nm) significantly enhanced stem elongation and leaf surface area in tomato seedlings compared to red (620-750 nm) or full-spectrum white light. Throughout the 4-week study, seedlings exposed to blue light achieved an average height of 15.3 cm, surpassing those exposed to red (10.7 cm) and white light (12.1 cm).  These results align with our hypothesis that blue light promotes more vigorous vegetative growth in tomato seedlings, potentially due to its activation of phototropins and cryptochromes. While these outcomes provide valuable insights into early-stage tomato plant development, additional research is necessary to determine the long-term effects on fruit production and quality. This study contributes to our understanding of optimizing light conditions for improved seedling growth in controlled agricultural environments.

This example shows the important parts of a good lab conclusion: it reminds us what the experiment was for, tells how it was done, shares the results, and explains what it all means.

Useful Tips for Improving Your Lab Conclusion

To make your conclusion lab report better, try these tips:

• Review your grading rubric to ensure you meet all requirements.
• Maintain an appropriate tone (explanatory, descriptive, or process-oriented).
• Keep your notes nearby so you can check your facts.
• Use your own words to say what you were trying to do; don't just copy from your lab instructions.
• Use passive voice and past tense , typically avoiding first-person perspective. Most lab reports are written in the third person.

When writing a discussion lab report, focus on clarity and sticking to what's important. Don't add new information or discuss things that aren't part of your experiment.

Making Your Scientific Conclusion Clear and Impactful

Writing a great lab report conclusion doesn't have to be hard. With the tips we've discussed on writing a scientific conclusion, you can now write good summaries of your science work. Remember, when writing your discussion lab report, stay focused on your experiment and what you found out. Don't talk about things that aren't related or say things you can't prove. Instead, explain your results, their meaning, and why they matter in science.

Need a little extra help polishing your scientific writing? Aithor might be just what you're looking for. This nifty AI writing tool will streamline your essay and report writing processes. It keeps your original ideas intact while giving your work a professional shine. Whether tackling a tricky lab report or a complex essay, this tool can help you craft well-structured, engaging content in no time. 

Give Aithor a try and see the difference it can make in your academic work.

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A Chinese lunar probe returns to Earth with the world’s first samples from the far side of the moon

FILE -This China National Space Administration (CNSA) handout image released by Xinhua News Agency, shows the lander-ascender combination of Chang’e-6 probe taken by a mini rover after it landed on the moon surface, June 4, 2024. China’s Chang’e 6 probe returned on Earth on Tuesday with rock and soil samples from the little-explored far side of the moon in a global first. The probe landed in northern China on Tuesday afternoon in the Inner Mongolian region. (CNSA/Xinhua via AP, File)

FILE -In this photo provided by China’s Xinhua News Agency, a Long March-5 rocket, carrying the Chang’e-6 spacecraft, blasts off from its launchpad at the Wenchang Space Launch Site in Wenchang, south China’s Hainan Province, May 3, 2024. China’s Chang’e 6 probe returned on Earth on Tuesday with rock and soil samples from the little-explored far side of the moon in a global first.The probe landed in northern China on Tuesday afternoon in the Inner Mongolian region. (Guo Cheng/Xinhua via AP, File)

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BANGKOK (AP) — China’s Chang’e 6 probe returned on Earth with rock and soil samples from the little-explored far side of the moon in a global first.

The probe landed in the Inner Mongolian region in northern China on Tuesday afternoon.

“I now declare that the Chang’e 6 Lunar Exploration Mission achieved complete success,” Zhang Kejian, Director of the China National Space Administration, said in a televised news conference after the landing.

Chinese scientists anticipate the returned samples will include 2.5 million-year-old volcanic rock and other material that scientists hope will answer questions about geographic differences on the moon’s two sides.

The near side is what is seen from Earth, and the far side faces outer space. The far side is also known to have mountains and impact craters, contrasting with the relatively flat expanses visible on the near side.

The probe had landed in the moon’s South Pole-Aitken Basin, an impact crater created more than 4 billion years ago. The samples scientists are expecting will likely come from different layers of the basin, which will bear traces of the different geological events across its long chronology, such as when the moon was younger and had an active inside that could produce volcanic rock.

While past U.S. and Soviet missions have collected samples from the moon’s near side, the Chinese mission was the first that has collected samples from the far side.

“This is a global first in the sense that it’s the first time anyone has been able to take off from the far side of the moon and bring back samples,” said Richard de Grijs, a professor of astrophysics at Macquarie University in Australia.

The moon program is part of a growing rivalry with the U.S. — still the leader in space exploration — and others, including Japan and India. China has put its own space station in orbit and regularly sends crews there.

China’s leader Xi Jinping sent a message of congratulations to the Chang’e team, saying that it was a “landmark achievement in our country’s efforts at becoming a space and technological power.”

The probe left earth on May 3, and its journey lasted 53 days . The probe has drilled into the core and scooped rocks from the surface.

The samples “are expected to answer one of the most fundamental scientific questions in lunar science research: what geologic activity is responsible for the differences between the two sides?” said Zongyu Yue, a geologist at the Chinese Academy of Sciences, in a statement issued in the Innovation Monday, a journal published in partnership with the Chinese Academy of Sciences.

China in recent years has launched multiple successful missions to the moon, collecting samples from the moon’s near side with the Chang’e 5 probe previously.

They are also hoping that the probe will return with material that bear traces of meteorite strikes from the moon’s past. That material could shed light on the solar system’s early days. There’s a theory that the moon acted as a vaccum cleaner of sorts, attracting all the meteorites and debris in the system’s earlier era so that they didn’t hit Earth, said de Grijs, who is also executive director at the International Space Science Institute — Beijing.

China has said it plans to share the samples with international scientists, although it did not say exactly in which countries.

AP video producer Olivia Zhang contributed to this report.

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• Published: 01 July 2024

Exploration and verification a 13-gene diagnostic framework for ulcerative colitis across multiple platforms via machine learning algorithms

• Jing Wang 1 ,
• Lin Li 1   na1 ,
• Pingbo Chen 2   na1 ,
• Chiyi He 1 &
• Xiaoping Niu 1  

Scientific Reports volume  14 , Article number:  15009 ( 2024 ) Cite this article

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• Computational biology and bioinformatics
• Medical research
• Predictive medicine

Ulcerative colitis (UC) is a chronic inflammatory bowel disease with intricate pathogenesis and varied presentation. Accurate diagnostic tools are imperative to detect and manage UC. This study sought to construct a robust diagnostic model using gene expression profiles and to identify key genes that differentiate UC patients from healthy controls. Gene expression profiles from eight cohorts, encompassing a total of 335 UC patients and 129 healthy controls, were analyzed. A total of 7530 gene sets were computed using the GSEA method. Subsequent batch correction, PCA plots, and intersection analysis identified crucial pathways and genes. Machine learning, incorporating 101 algorithm combinations, was employed to develop diagnostic models. Verification was done using four external cohorts, adding depth to the sample repertoire. Evaluation of immune cell infiltration was undertaken through single-sample GSEA. All statistical analyses were conducted using R (Version: 4.2.2), with significance set at a P value below 0.05. Employing the GSEA method, 7530 gene sets were computed. From this, 19 intersecting pathways were discerned to be consistently upregulated across all cohorts, which pertained to cell adhesion, development, metabolism, immune response, and protein regulation. This corresponded to 83 unique genes. Machine learning insights culminated in the LASSO regression model, which outperformed others with an average AUC of 0.942. This model's efficacy was further ratified across four external cohorts, with AUC values ranging from 0.694 to 0.873 and significant Kappa statistics indicating its predictive accuracy. The LASSO logistic regression model highlighted 13 genes, with LCN2, ASS1, and IRAK3 emerging as pivotal. Notably, LCN2 showcased significantly heightened expression in active UC patients compared to both non-active patients and healthy controls (P < 0.05). Investigations into the correlation between these genes and immune cell infiltration in UC highlighted activated dendritic cells, with statistically significant positive correlations noted for LCN2 and IRAK3 across multiple datasets. Through comprehensive gene expression analysis and machine learning, a potent LASSO-based diagnostic model for UC was developed. Genes such as LCN2, ASS1, and IRAK3 hold potential as both diagnostic markers and therapeutic targets, offering a promising direction for future UC research and clinical application.

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Identification of diagnostic biomarks and immune cell infiltration in ulcerative colitis

Meta-analysis of gene expression disease signatures in colonic biopsy tissue from patients with ulcerative colitis

Introduction.

Ulcerative colitis (UC) is indeed an inflammatory bowel disease (IBD) that predominantly impacts the mucosal and submucosal layers of the colon and rectum, manifesting as a chronic condition characterized by inflammation and the formation of ulcers in the lining of the colon and rectum. Simultaneously, prolonged UC results in structural damage, amplifying the susceptibility to conditions such as colon cancer and extraintestinal malignancies 1 , 2 . However, the pathogenesis of UC remains a complex and not fully elucidated process. It is currently understood that UC predominantly affects individuals with genetic susceptibility, while factors such as epithelial barrier defects, dysbiosis, and dysregulated immune responses play significant roles in its pathogenesis 3 , 4 , 5 . Epidemiologically, the incidence and prevalence of UC have been a dramatic rise in recent years. Globally, the highest incidence and prevalence are in Northern Europe, 505 per 100,000 in Norway, followed by North America, 286 per 100,000 in the USA 6 . The annual incidence of UC in Europe has surged to 24.3 cases per 100,000 individuals, and there is a clear upward trajectory in both the prevalence and incidence of UC over time 7 . It’s worth noting that in many emerging industrialized countries in South America, Asia, and Africa, although the prevalence is still low, the number of new UC diagnoses is increasing, and the prevalence is expected to rise in the future 8 . This presents a substantial challenge for healthcare systems on a global scale.

A potential pathogenesis of UC could be immune system dysfunction. When the immune system works hard to resist invading viruses or bacteria, an abnormal immune response can cause the immune system to also attack cells in the digestive tract, leading to chronic intestinal inflammation or mucosal damage. Genetics also play a role as UC is more common in people with family members who have the disease 9 . In the past, UC was commonly managed with 5-aminosalicylates, steroids, and thiopurines. However, despite these treatment options, UC continues to significantly affect patients' quality of life and is associated with a high morbidity rate 10 . Procedures such as ileo-pouch-anal anastomosis and colectomy come with the potential risks of infertility, compromised pouch function, and the development of capsulitis 11 . In recent years, targeted therapeutic agents like tumor necrosis factor (TNF) inhibitors and interleukin inhibitors have garnered increased attention in clinical practice. With ongoing advancements in drug development, there has been a substantial decrease in UC-related mortality, enhancing the overall prognosis for patients with UC 12 . Nonetheless, there is undeniably substantial room for enhancement in the management of UC, as indicated by existing studies that report remission rates (Based on clinical improvements in stool frequency, rectal bleeding, and mucosal appearance on endoscopy, Mayo score) typically falling below 20–30% 12 .

The diagnosis of UC primarily rests on a combination of clinical symptoms, endoscopic findings, histological examination, and exclusion of other causes of colitis, such as infections 13 , 14 . Serological markers and fecal calprotectin can assist in differentiating UC from other gastrointestinal disorders, but they are not definitive. Looking ahead, there is growing interest in the realm of genetics for diagnostic insights. Recent advancements in genome-wide association studies (GWAS) have identified numerous genetic loci associated with UC susceptibility 15 , 16 . The clinical symptoms might also correlate with genetic alterations, gene expression profiles in symptomatic controls, from whom inflammatory bowel disease (IBD) had been excluded, resembled those of IBD patients and diverged from healthy controls. The gene expression signatures of these IBD-excluded samples were related to their symptomatic status 17 . Crooke et al. detected the transcript levels of a total of 45 genes in blood by quantitative real-time polymerase chain reaction, and then used ratio score and support vector machine methods to distinguish UC from several types of gastro-intestinal diseases 18 . Recent years, next-generation sequencing is widely applied in disease diagnostic and precision treatment 19 , 20 . As our understanding of the genetic architecture of UC deepens, it is anticipated that genetic markers could serve as adjunct diagnostic tools, offering more precise disease categorization and personalized therapeutic strategies. This burgeoning area of research holds the promise of reshaping the diagnostic landscape of UC in the future.

The objective of current study is to explore the potential of gene expression profiles in enhancing the accuracy and early detection of UC, particularly in cases where traditional diagnostic methods may be inconclusive. While traditional diagnostics are indeed effective and cost-efficient, gene expression profiling offers several distinct advantages. These include the ability to identify molecular changes at an early stage, which may precede clinical symptoms, thus enabling earlier intervention and potentially improving patient outcomes. In this study, we incorporated soft tissue sequencing data from a cohort of 259 UC patients and 60 individuals without the condition. From this dataset, we identified six key genes and developed a predictive model with a high degree of accuracy for UC diagnosis.

Patients’ summary

We collected a total of eight cohorts contains both health controls and UC patients for the current study. The training datasets derived from mucosal tissue samples included GSE87466 with 21 normal and 87 UC patients, GSE59071 with 11 normal and 97 UC patients, GSE47908 with 15 normal and 45 UC patients, and GSE38713 with 13 normal and 30 UC patients. For validation, the mucosal tissue cohorts comprised GSE53306, which had 12 normal controls, 16 patients in the active UC category and 12 in the inactive UC category. Similarly, GSE13367 had 8 inflamed and 9 non-inflamed UC patients, compared with 10 controls. GSE48958 also from mucosal tissue had 7 active UC and 6 inactive UC patients, accompany with 8 controls. Finally, the GSE126124 dataset, derived from peripheral whole blood, included 39 normal and 18 UC patients (Table 1 ).

Mitigating batch effects

Batch effects represent the non-biological discrepancies observed across multiple datasets. To ensure analytical consistency and mitigate biases introduced by such effects, we employed the ComBat algorithms from the "sva" package. This methodology was instrumental in harmonizing the transcriptional profiles of the training cohorts (GSE87466, GSE59071, GSE47908, GSE38713), thus effectively offsetting the intrinsic batch differences among them. For the validation cohort, we abstained from this procedure, as our intent was to further authenticate the diagnostic across diverse platforms.

Calculation of the scores of signaling pathways

Gene Set Enrichment Analysis (GSEA) is a computational approach ascertaining whether a designated gene set exhibits statistically significant deviations between two groups. We implemented GSEA to initially contrast the various activated signaling pathways between UC patients and healthy controls. The backdrop file of molecular signature gene sets was procured from MSigDB, C5: Biological Process, comprising a total of 7530 gene sets 21 , 22 .

An integrative diagnostic model leveraging machine learning techniques

To craft a unified model possessing robust accuracy and stability in distinguishing between UC patients and healthy individuals, we amalgamated 10 machine learning algorithms, yielding 101 algorithmic combinations. The ensemble of algorithms comprised Elastic Net (Enet), Lasso, Ridge, Stepglm[both], Stepglm[backward], glmBoost, Latent Dirichlet Allocation (LDA), NaiveBayes, plsRglm, Random Forest (RF), and Support Vector Machine (SVM). The signature derivation protocol entailed: (1) Isolating the most prominently activated pathways in UC patients across the four GEO cohorts; (2) Subsequently, the 101 algorithmic combinations were executed on the genes curated from these prominently activated pathways; (3) All models underwent training within the GSE55235 dataset and validation in the remaining three cohorts, which remained untouched during pathway filtration; (4) For every model, the AUC metric was ascertained across all participating cohorts.

Evaluation immunocytes infiltration

Through single-sample gene set enrichment analysis (ssGSEA), the infiltration of immune cells was discerned and evaluated using transcriptional data. The gene collections representing 28 immune cell types were sourced from the research undertaken by Charoentong et al 23 .

Tatistical analyses were conducted using R (Version: 4.2.2). For continuous variables, the Student's t-test and the two-sample Mann–Whitney test were employed for comparisons between two groups if data exhibited a normal distribution, whereas the Wilson rank test was invoked otherwise. A Pearson correlation analysis was employed for continuous datasets. Pertinent pathways were delineated using a heatmap, facilitated by the R package "pheatmap". The Kappa Statistic serves as a metric for contrasting predictive versus actual subtypes. For comparisons across more than two groups, the Kruskal–Wallis test was utilized, and for pairwise assessments, the Wilcoxon test was applied 24 . A two-tailed P value below 0.05 was considered to indicate statistical significance.

Summarize of the process

In this study, transcriptomic data from four cohorts, encompassing Ulcerative Colitis (UC) patients and healthy controls, were evaluated to identify key signaling pathways associated with UC. The gene expression profiles underwent batch correction to ensure uniformity and mitigate batch effects. Using Gene Set Enrichment Analysis (GSEA), over 7500 gene sets were computed, each representing a unique cellular signaling pathway. Machine learning techniques were then employed, with the LASSO regression model emerging as the most efficient diagnostic tool with an average AUC value of 0.942. The robustness of this model was validated using external cohorts. From the diagnostic model, 13 characteristic genes were identified and assessed for their expression differences. Three of these genes, LCN2, ASS1, and IRAK3, were particularly noteworthy as they exhibited elevated expression in UC patients. The study further examined the relationship between these genes and immune cell infiltration, establishing their correlation with activated dendritic cells. These findings reinforce the role of immune system dysregulation in UC and introduce potential biomarkers for diagnostic and therapeutic applications. The flowchart of the current study is displayed in Fig.  1 .

Flowchart illustrating the step-by-step methodology of the current study. Starting from transcriptomic data acquisition from four cohorts, through data preprocessing, gene set enrichment analysis, machine learning diagnostics, and concluding with the identification of characteristic genes and their association with immune cell infiltration.

Identifying key signaling pathways reflecting UC

As delineated in the methods section, our study incorporated samples from four cohorts, encompassing both UC patients and healthy controls. To ensure uniformity of the transcriptomic data before further analysis, we initially subjected the gene expression profiles from all four cohorts to batch correction. Prior to this correction, the PCA plot exhibited pronounced disparities among the four cohorts (Fig.  2 A). However, post-correction, batch effect variations in gene expression distribution across all cohorts were effectively nullified (Fig.  2 B). Subsequently, employing the GSEA method, we computed 7530 gene sets, each reflecting the activation status of distinct cellular signaling pathways; each sample included in the analysis garnered a score across these 7530 pathways. The distribution of scores for these pathways across samples in the different cohorts is illustrated in Fig.  2 C.

Batch correction and gene set enrichment analysis outcomes. ( A ) Principal component analysis (PCA) plot showing gene expression disparities among the four cohorts prior to batch correction. ( B ) PCA plot post batch correction showcasing uniform gene expression distribution across all cohorts. ( C ) Distribution of scores across 7530 signaling pathways, based on Gene Set Enrichment Analysis (GSEA), for samples in the different cohorts.

Subsequent to this, within each cohort, we discerned signaling pathways that were differentially activated between UC patients and healthy controls (Fig.  3 A). In the GSE38713 cohort, 79 pathways were upregulated in UC patients; in the GSE47908 cohort, 428 pathways were upregulated; in the GSE59071 cohort, 107 pathways were upregulated, and in the GSE87466 cohort, 3,609 pathways saw upregulation in UC patients. By extracting the intersecting upregulated pathways across the four cohorts, a total of 19 pathways were finalized (Fig.  3 B). These 19 pathways pertained to cell adhesion and development, cell respiration and metabolism, immune response and signaling, as well as regulation of protein activity and secretion (Fig.  3 C). Excluding the redundant genes within these pathways, a total of 83 unique genes remained.

Differentially activated signaling pathways in Ulcerative Colitis (UC) patients versus healthy controls for each cohort. ( A ) Visualization of pathways upregulated in UC patients across the four cohorts. ( B ) Venn diagram illustrating the 19 common upregulated pathways identified across all cohorts. ( C ) List of the names of the 19 upregulated pathways.

Machine learning constructs a model for identifying patients with UC

The predictors used as input for the ML models are the gene expression levels of the 83 identified genes. These variables are continuous, representing the expression levels of each gene. Through the iterative analysis of the selected 83 genes across 101 algorithm combinations, 40 combination models were successfully generated. These models displayed their predictive capabilities across different cohorts using AUC values, with the average AUC value across four cohorts also being computed (Fig.  4 A). Ultimately, the LASSO regression model demonstrated superior diagnostic capabilities (Average AUC = 0.942). The prediction score can be calculated with the formula: Score = 0.03328012 × SYK + 0.51625614 × CALR − 0.14331840 × GATA5 + 1.29808010 × FLRT2 + 0.80143919 × IRAK3 − 0.59448664 × DUSP26 + 0.85254969 × SPINK5 + 0.25364614 × PTPN6 + 0.44029637 × LCN2 + 0.70178103 × ASS1 + 0.20803807 × BAK1 + 0.70268334 × VCP + 0.27895531 × ACTN3.

Machine learning-based diagnostic model evaluation. ( A ) AUC values of the 40 types of machine learning model across the four cohorts. ( B – E ) Kappa statistics for GSE38713 ( B ), GSE47908 ( C ), GSE59071 ( D ), and GSE87466 ( E ) comparing predicted outcomes with actual UC statuses.

Based on the LASSO model, the AUC values for the GSE87466, GSE38713, GSE59071, and GSE47908 cohorts were 1, 0.903, 0.963, and 0.902, respectively. Further, the Kappa statistic was employed to evaluate the heterogeneity between predicted and actual outcomes, revealing that the novel diagnostic model exhibited robust predictive power across all four cohorts (GSE87466: Kappa = 1, P < 0.001; GSE38713: Kappa = 0.652, P < 0.001; GSE59071: Kappa = 0.544, P < 0.001; GSE47908: Kappa = 0.623, P < 0.001; Fig.  4 B–E).

Verifying the efficacy of the diagnostic model in external cohorts

To further ascertain the diagnostic capabilities of the model, we included four external cohorts: GSE53306, GSE13367, GSE48958, and GSE126124. The samples from the first three cohorts were derived from intestinal mucosal tissue, while the GSE126124 cohort utilized peripheral blood samples from patients and healthy controls. Using the same methodology, we computed the predictive results of the four external cohorts across the 40 models. Ultimately, the LASSO-based diagnostic model consistently showcased commendable diagnostic prowess (Fig.  5 A) with the following results: GSE53306 (AUC = 0.798, Kappa = 0.360, P = 0.024, Fig.  5 B), GSE13367 (AUC = 0.782, Kappa = 0.340, P = 0.006, Fig.  5 C), GSE48958 (AUC = 0.873, Kappa = 0.529, P = 0.007, Fig.  5 D). For the GSE126124 cohort, although the AUC value was only 0.694, considering that these samples were derived from peripheral blood, its predictive capability near 0.7 remains a valuable asset for clinical diagnosis (Kappa = 0.272, P = 0.003, Fig.  5 E).

Assessing of the LASSO-based diagnostic model on external cohorts. ( A ) Overall diagnostic performance across the four external cohorts. ( B – E ) Detailed diagnostic metrics including AUC, Kappa, and P-values for external cohort, GSE53306 ( B ), GSE13367 ( C ), GSE48958 ( D ), and GSE126124 ( E ).

Expression of 13 characteristic genes in UC

The LASSO logistic regression analysis incorporated 13 genes into the model, namely SYK, CALR, GATA5, FLRT2, IRAK3, DUSP26, SPINK5, PTPN6, LCN2, ASS1, BAK1, VCP, and ACTN3. To elucidate the conditions of these 13 genes, their expression differences between UC patients and healthy controls in a training cohort amalgamated from four cohorts were initially assessed. Notably, 11 out of these 13 genes exhibited significantly heightened expression in UC patients, while DUSP26 manifested diminished expression and ACTN3 showcased no significant difference (Fig.  6 A). We selected three significantly upregulated genes in UC, namely LCN2, ASS1, and IRAK3, for further validation in external cohorts. In the GSE13367 dataset, the expression of three genes was notably elevated in UC patients compared to healthy controls. Although these genes exhibited higher expression in inflamed UC patients, there was no statistically significant difference when compared to non-inflamed patients (Fig.  6 B). In the GSE48958 dataset, the expression trends of these genes mirrored the previously described patterns, with LCN2 showing the highest expression in active UC patients (Fig.  6 C). In the GSE53360 dataset, we observed that LCN2 also had the highest expression in active UC patients, with significant differences when compared both to non-active patients (P < 0.05) and to healthy controls (P < 0.05) (Fig.  6 D). These findings indicate that LCN2, ASS1, and IRAK3 are crucial markers distinguishing between healthy controls and UC patients.

Expression profiles of the 13 characteristic genes. ( A ) Expression differences between UC patients and healthy controls for the identified genes in a merged training cohort. ( B – D ) Validation of expression patterns of LCN2, ASS1, and IRAK3 in three external datasets, GSE13367 ( B ), GSE48958 ( C ), and GSE53360 ( D ).

Correlation between key biomarkers and immune cell infiltration

A plethora of research concurs that immune system dysregulation is a critical factor precipitating the onset of UC. Consequently, a comparison was made between all included normal controls and UC patients to discern differences in immune cell distribution. It was discerned that the majority of immune cells exhibited pronounced expression elevation in UC patients, most notably myeloid-derived suppressor cell (MDSC), Neutrophil, and central memory CD4 T cells (Fig.  7 A). Subsequent investigations evaluated the relationship between LCN2, ASS1, IRAK3, and immune cell infiltration in all UC patients. All three genes exhibited positive correlations with the majority of immune cells, with the strongest associations found with activated dendritic cells, neutrophils, and immature dendritic cells (Fig.  7 B–D). Additionally, correlations were established between LCN2 and Effector memory CD8 T cells as well as Gamma delta T cells (Fig.  7 B); ASS1 and Type 17T helper cells (Fig.  7 C); and IRAK3 with Type 1T helper cells and Gamma delta T cells (Fig.  7 D).

Analysis of immune cell infiltration in UC and its relationship with LCN2, ASS1, and IRAK3. ( A ) Differences in immune cell distribution between UC patients and normal controls. ( B – D ) Correlation plots showcasing associations between LCN2 ( B ), ASS1 ( C ), and IRAK3 ( D ) and various immune cells.

It was observed that all three genes had a pronounced positive correlation with activated dendritic cells. Therefore, further analysis delved into the relationship between these genes and different UC disease statuses. In the GSE13367 cohort, the strongest correlations in active UC patients with activated dendritic cells were noted (LCN2: R = 0.72, P = 0.0024; ASS1: R = 0.61, P = 0.014; IRAK3: R = 0.71, P = 0.0029; Fig.  8 A). In the GSE48958 cohort, only IRAK3 exhibited a positive correlation with activated dendritic cells in active UC patients (R = 0.82, P = 0.034, Fig.  8 B).

Correlation of LCN2, ASS1, and IRAK3 with activated dendritic cells across different UC disease statuses. ( A ) Correlations in the GSE13367 cohort for three genes and UC patients. ( B ) Correlation in the GSE48958 cohort for three genes and UC patients.

UC remains a focal point in gastroenterological research due to its multifaceted etiological profile and the intricacies associated with its management 25 , 26 . Developing a robust diagnostic model that can accurately differentiate between UC patients and healthy individuals could offer a paradigm shift in the management of this condition. The application of machine learning in biomedical research has surged exponentially in recent years, with its prowess in data handling and pattern recognition being especially transformative for complex datasets 27 , 28 , 29 . The present study exemplifies this paradigm shift by utilizing machine learning to sift through intricate gene expression profiles, leading to the elucidation of a diagnostic model for UC.

In the current study, four training cohorts were utilized to identify key pathways and genes, leading to the construction of the prediction model in GSE87466, followed by internal validation and subsequent external validation. GSE87466, comprising the largest sample size, was selected for model construction. We did not amalgamate all four training cohorts into a single extensive dataset due to the potential substantial batch effects within the cohorts. For the external validation cohort, GSE126124 comprises samples from peripheral whole blood, whereas the training cohort GSE87466 includes samples from mucosa. In summary, this study encompasses training, internal validation, external validation, and further validation with peripheral whole blood samples to ensure the diagnostic model's robustness and credibility. Central to our findings is the delineation of specific cellular pathways and genes that are distinctly altered in UC patients. Notably, the pathways identified in our study encompass a broad spectrum of cellular processes, ranging from cell adhesion to immune signaling, reinforcing the notion of UC as a systemic ailment with widespread cellular repercussions 30 , 31 . In subsequently study, the iterative analysis of 83 genes across 101 algorithm combinations is testament to this capability. It is noteworthy that out of these numerous combinations, a set of 40 viable diagnostic models emerged, showcasing the flexibility and rigor of machine learning in generating a suite of models tailored to the data's nuances. The Average AUC value of 0.942 achieved by the LASSO model, and its robust predictive power across all four cohorts, underscore its efficacy. In addition, the model demonstrating remarkable diagnostic precision across multiple external validation cohorts. The strength of the model, as evidenced by its high average AUC value, suggests that gene expression profiling can serve as a formidable tool in the diagnostic arsenal against UC. Furthermore, the robustness of this model, even when applied to peripheral blood samples, underscores its potential versatility and broad applicability in clinical settings.

The incorporation of machine learning also allowed for the identification of 13 key genes, which upon further validation, revealed LCN2, ASS1, and IRAK3 as pivotal markers distinguishing between healthy individuals and UC patients. It is well-established that UC is characterized by chronic inflammation of the colon, predominantly driven by an aberrant immune response 32 . In this study, the robust correlation observed between the expression levels of LCN2, ASS1, and IRAK3 and specific immune cell populations, particularly activated dendritic cells, highlights the intertwined relationship between these genes and immune cell activity in UC 33 . Dendritic cells are known to play a pivotal role in antigen presentation and initiation of adaptive immune responses, their activation could subsequently lead to the recruitment and activation of other immune cells, perpetuating the inflammatory cascade observed in UC 34 , 35 . Notably, LCN2 has been previously documented to play a role in innate immunity, being associated with neutrophil function and acting as a bacteriostatic agent by sequestering iron, which in turn limits bacterial growth 36 , 37 , 38 . Although we observed that IRAK3 is correlated with the infiltration of activated dendritic cell, however, it can not distinguish the disease status of UC, the potential reason is that in the UC cases, inflammation and tissue remodeling of uninflamed (inactive) regions similar to inflamed (active) regions, they all have the increased expression of TGF -β, vimentin, and α-SMA 39 .

Combining various methods in a multi-faceted research setup presents a range of benefits and drawbacks. One significant advantage is the increased robustness and reliability of the results. By integrating different techniques, such as machine learning algorithms and gene expression analyses, researchers can cross-validate findings, reducing the likelihood of false positives and enhancing the overall confidence in the results. Additionally, the flexibility in combining methods can facilitate the discovery of novel biomarkers and therapeutic targets, providing a holistic view of disease mechanisms and potential intervention points. However, there are inherent drawbacks to this approach. The complexity of managing and integrating diverse datasets and methodologies can be challenging, requiring advanced computational skills and substantial computational resources. The risk of overfitting increases with the use of multiple machine learning models, where a model may perform exceptionally well on training data but poorly on unseen data, thus limiting its generalizability. Furthermore, while combining methods can highlight potential biomarkers or pathways, it often does not provide mechanistic insights into their roles, necessitating further functional studies to elucidate their contributions to disease pathogenesis. Therefore, while the integration of multiple methods can significantly advance our understanding and management of diseases like UC, it requires careful consideration of these potential limitations.

While the advantages of machine learning are manifold, it is vital to approach its results with a measure of caution, and there are several limitations for the current study. First, this study utilized a relatively small cohort of patients. Larger and more varied cohorts are necessary to validate the diagnostic model across different demographic groups. Second, the external validation cohorts primarily consisted of mucosal tissue samples, with only one cohort (GSE126124) derived from peripheral blood. The diagnostic model's performance in blood samples was lower (AUC = 0.694) compared to mucosal samples, indicating the need for further refinement and validation in non-invasive sample types like blood. Third, while the study identified several key genes and pathways associated with UC, it did not provide detailed mechanistic insights into how these genes contribute to the disease's pathogenesis. Functional studies are necessary to elucidate the biological roles of these genes and their potential as therapeutic targets.

In conclusion, our research epitomizes the transformative potential of machine learning in the realm of UC research, offering hope for more accurate and early diagnosis. As we stand on the cusp of a new era in personalized medicine, integrating machine learning insights with traditional biomedical research could pave the way for novel therapeutic avenues and improved patient outcomes. Future studies should prioritize external validation of these models in diverse populations and delve deeper into the functional roles of identified biomarkers.

Data availability

All the datasets presented in this study can be obtained from the GEO ( http://www.ncbi.nlm.nih.gov/geo ) database, and details listed in Table 1 . Data is provided within the manuscript or supplementary information files and it is available upon request from the corresponding author.

Jess, T., Rungoe, C. & Peyrin-Biroulet, L. Risk of colorectal cancer in patients with ulcerative colitis: A meta-analysis of population-based cohort studies. Clin. Gastroenterol. Hepatol. 10 , 639–645. https://doi.org/10.1016/j.cgh.2012.01.010 (2012).

Article   PubMed   Google Scholar  

Lo, B., Zhao, M., Vind, I. & Burisch, J. The risk of extraintestinal cancer in inflammatory bowel disease: A systematic review and meta-analysis of population-based cohort studies. Clin. Gastroenterol. Hepatol. 19 , 1117–1138. https://doi.org/10.1016/j.cgh.2020.08.015 (2021).

Dinallo, V. et al. Neutrophil extracellular traps sustain inflammatory signals in ulcerative colitis. J. Crohns Colitis 13 , 772–784. https://doi.org/10.1093/ecco-jcc/jjy215 (2019).

Eckburg, P. B. et al. Diversity of the human intestinal microbial flora. Science 308 , 1635–1638. https://doi.org/10.1126/science.1110591 (2005).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Neurath, M. F. Cytokines in inflammatory bowel disease. Nat. Rev. Immunol. 14 , 329–342. https://doi.org/10.1038/nri3661 (2014).

Article   CAS   PubMed   Google Scholar  

Ng, S. C. et al. Worldwide incidence and prevalence of inflammatory bowel disease in the 21st century: A systematic review of population-based studies. Lancet 390 , 2769–2778. https://doi.org/10.1016/S0140-6736(17)32448-0 (2017).

Molodecky, N. A. et al. Increasing incidence and prevalence of the inflammatory bowel diseases with time, based on systematic review. Gastroenterology 142 , 46–54. https://doi.org/10.1053/j.gastro.2011.10.001 (2012).

Du, L. & Ha, C. Epidemiology and pathogenesis of ulcerative colitis. Gastroenterol. Clin. N. Am. 49 , 643–654. https://doi.org/10.1016/j.gtc.2020.07.005 (2020).

Article   Google Scholar  

Orholm, M. et al. Familial occurrence of inflammatory bowel disease. N. Engl. J. Med. 324 , 84–88. https://doi.org/10.1056/NEJM199101103240203 (1991).

Benchimol, E. I. et al. The impact of inflammatory bowel disease in Canada 2018: A scientific report from the canadian gastro-intestinal epidemiology consortium to crohn’s and colitis Canada. J. Can. Assoc. Gastroenterol. 2 , S1–S5. https://doi.org/10.1093/jcag/gwy052 (2019).

Khanna, R., Chande, N. & Marshall, J. K. Ozanimod for the treatment of ulcerative colitis. Gastroenterology 162 , 2104–2106. https://doi.org/10.1053/j.gastro.2022.01.033 (2022).

Alsoud, D., Verstockt, B., Fiocchi, C. & Vermeire, S. Breaking the therapeutic ceiling in drug development in ulcerative colitis. Lancet Gastroenterol. Hepatol. 6 , 589–595. https://doi.org/10.1016/S2468-1253(21)00065-0 (2021).

Gros, B. & Kaplan, G. G. Ulcerative colitis in adults: A review. JAMA 330 , 951–965. https://doi.org/10.1001/jama.2023.15389 (2023).

Dinca, R. & Sturniolo, G. Biomarkers in IBD: What to utilize for the diagnosis? Diagnostics (Basel) 13 , 1. https://doi.org/10.3390/diagnostics13182931 (2023).

Saurabh, R., Fouodo, C. J. K., Konig, I. R., Busch, H. & Wohlers, I. A survey of genome-wide association studies, polygenic scores and UK Biobank highlights resources for autoimmune disease genetics. Front. Immunol. 13 , 972107. https://doi.org/10.3389/fimmu.2022.972107 (2022).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Caliendo, G. et al. Biological, genetic and epigenetic markers in ulcerative colitis. Adv. Med. Sci. 68 , 386–395. https://doi.org/10.1016/j.advms.2023.09.010 (2023).

Vatn, S. S. et al. Mucosal gene transcript signatures in treatment naive inflammatory bowel disease: A comparative analysis of disease to symptomatic and healthy controls in the European IBD-character cohort. Clin. Exp. Gastroenterol. 15 , 5–25. https://doi.org/10.2147/CEG.S343468 (2022).

Crooke, P. S. et al. Using gene expression data to identify certain gastro-intestinal diseases. J. Clin. Bioinf. 2 , 20. https://doi.org/10.1186/2043-9113-2-20 (2012).

Article   CAS   Google Scholar  

Huo, Y. et al. Subpathway analysis of transcriptome profiles reveals new molecular mechanisms of acquired chemotherapy resistance in breast cancer. Cancers (Basel) 14 . https://doi.org/10.3390/cancers14194878 (2022).

Satam, H. et al. Next-generation sequencing technology: Current trends and advancements. Biology 12 , 1. https://doi.org/10.3390/biology12070997 (2023).

Hanzelmann, S., Castelo, R. & Guinney, J. GSVA: Gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14 , 7. https://doi.org/10.1186/1471-2105-14-7 (2013).

Article   PubMed   PubMed Central   Google Scholar  

Subramanian, A. et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. U S A 102 , 15545–15550. https://doi.org/10.1073/pnas.0506580102 (2005).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Charoentong, P. et al. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep. 18 , 248–262. https://doi.org/10.1016/j.celrep.2016.12.019 (2017).

Hazra, A. & Gogtay, N. Biostatistics series module 3: comparing groups: Numerical variables. Indian J. Dermatol. 61 , 251–260. https://doi.org/10.4103/0019-5154.182416 (2016).

Siegel, C. A. & Bernstein, C. N. Identifying patients with inflammatory bowel diseases at high vs low risk of complications. Clin. Gastroenterol. Hepatol. 18 , 1261–1267. https://doi.org/10.1016/j.cgh.2019.11.034 (2020).

Nadeem, M. S., Kumar, V., Al-Abbasi, F. A., Kamal, M. A. & Anwar, F. Risk of colorectal cancer in inflammatory bowel diseases. Semin. Cancer Biol. 64 , 51–60. https://doi.org/10.1016/j.semcancer.2019.05.001 (2020).

Bokma, W. A. et al. Predicting the naturalistic course in anxiety disorders using clinical and biological markers: A machine learning approach. Psychol. Med. 52 , 57–67. https://doi.org/10.1017/S0033291720001658 (2022).

Sundar, R. et al. Machine-learning model derived gene signature predictive of paclitaxel survival benefit in gastric cancer: Results from the randomised phase III SAMIT trial. Gut 71 , 676–685. https://doi.org/10.1136/gutjnl-2021-324060 (2022).

Mc Ardle, A. et al. Identification and evaluation of serum protein biomarkers that differentiate psoriatic arthritis from rheumatoid arthritis. Arthritis Rheumatol. 74 , 81–91. https://doi.org/10.1002/art.41899 (2022).

Samoila, I., Dinescu, S. & Costache, M. Interplay between cellular and molecular mechanisms underlying inflammatory bowel diseases development-a focus on ulcerative colitis. Cells 9 , 1. https://doi.org/10.3390/cells9071647 (2020).

Kaur, A. & Goggolidou, P. Ulcerative colitis: Understanding its cellular pathology could provide insights into novel therapies. J. Inflamm. 17 , 15. https://doi.org/10.1186/s12950-020-00246-4 (2020).

Zou, J., Liu, C., Jiang, S., Qian, D. & Duan, J. Cross talk between gut microbiota and intestinal mucosal immunity in the development of ulcerative colitis. Infect. Immun. 89 , e0001421. https://doi.org/10.1128/IAI.00014-21 (2021).

Penrose, H. M. et al. Ulcerative colitis immune cell landscapes and differentially expressed gene signatures determine novel regulators and predict clinical response to biologic therapy. Sci. Rep. 11 , 9010. https://doi.org/10.1038/s41598-021-88489-w (2021).

Yang, Z. J. et al. Functions of dendritic cells and its association with intestinal diseases. Cells 10 , 1. https://doi.org/10.3390/cells10030583 (2021).

Article   ADS   CAS   Google Scholar  

Bates, J. & Diehl, L. Dendritic cells in IBD pathogenesis: An area of therapeutic opportunity?. J. Pathol. 232 , 112–120. https://doi.org/10.1002/path.4277 (2014).

Singh, V. et al. Microbiota-inducible innate immune, siderophore binding protein lipocalin 2 is critical for intestinal homeostasis. Cell Mol. Gastroenterol. Hepatol. 2 , 482–498. https://doi.org/10.1016/j.jcmgh.2016.03.007 (2016).

Bachman, M. A., Miller, V. L. & Weiser, J. N. Mucosal lipocalin 2 has pro-inflammatory and iron-sequestering effects in response to bacterial enterobactin. PLoS Pathog. 5 , e1000622. https://doi.org/10.1371/journal.ppat.1000622 (2009).

Xiao, X., Yeoh, B. S. & Vijay-Kumar, M. Lipocalin 2: An emerging player in iron homeostasis and inflammation. Annu. Rev. Nutr. 37 , 103–130. https://doi.org/10.1146/annurev-nutr-071816-064559 (2017).

Jun, Y. K. et al. Molecular activity of inflammation and epithelial-mesenchymal transition in the microenvironment of ulcerative colitis. Gut Liver https://doi.org/10.5009/gnl230283 (2024).

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Acknowledgements

The authors acknowledge support from 2023 Wan-nan Medical College Scientific Research Project (No. WK2023ZQNZ52), the Key Research Project of Wan-nan Medical College (No. WK2022ZF03) and Wuhu City Science and Technology Project (No. 2021cg36). Thanks to ChatGPT for polishing the language and grammar of the article.

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Department of Gastroenterology, The First Affiliated Hospital of Wan-Nan Medical College, Wuhu, 241001, China

Jing Wang, Lin Li, Chiyi He & Xiaoping Niu

Department of Joint-Orthopedics, The First Affiliated Hospital of Wan-Nan Medical College, Wuhu, 241001, China

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Jing Wang: Conceptualization, methodology, formal analysis, writing—original draft preparation; Lin Li: Data curation, software, validation, visualization; Pingbo Chen: Supervision, funding acquisition, project administration, writing—reviewing and editing; Chiyi He: Supervision, writing—reviewing and editing; Xiaoping Niu: Supervision, funding acquisition, writing—reviewing and editing. All authors read and approved the final manuscript.

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Wang, J., Li, L., Chen, P. et al. Exploration and verification a 13-gene diagnostic framework for ulcerative colitis across multiple platforms via machine learning algorithms. Sci Rep 14 , 15009 (2024). https://doi.org/10.1038/s41598-024-65481-8

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Research Trends in STEM Clubs: A Content Analysis

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To identify the research trends in studies related to STEM Clubs, 56 publications that met the inclusion and extraction criteria were identified from the online databases ERIC and WoS in this study. These studies were analysed by using the descriptive content analysis research method based on the Paper Classification Form (PCF), which includes publishing years, keywords, research methods, sample levels and sizes, data collection tools, data analysis methods, durations, purposes, and findings. The findings showed that, the keywords in the studies were used under six different categories: disciplines, technological concepts, academic community, learning experiences, core elements of education, and psychosocial factors (variables). Case studies were frequently employed, with middle school students serving as the main participants in sample groups ranging from 11–15, 16–20, and 201–250. Surveys, questionnaires, and observations were the primary methods of data collection, and descriptive analysis was commonly used for data analysis. STEM Clubs had sessions ranging from 2 to 16 weeks, with each session commonly lasting 60 to 120 min. The study purposes mainly focused on four themes: the impact of participation on various aspects such as attitudes towards STEM disciplines, career paths, STEM major selection, and academic achievement; the development and implementation of a sample STEM Club program, including challenges and limitations; the examination of students' experiences, perceptions, and factors influencing their involvement and choice of STEM majors; the identification of some aspects such as attitudinal effects and non-academic skills; and the comparison of STEM experiences between in-school and out-of-school settings. The study results mainly focused on three themes: the increase in various aspects such as academic achievement, STEM major choice, engagement in STEM clubs, identity, interest in STEM, collaboration-communication skills; the design of STEM Clubs, including sample implementations, design principles, challenges, and factors affecting their success and sustainability; and the identification of factors influencing participation, motivation, and barriers. Overall, this study provides a comprehensive understanding of STEM Clubs, leading the way for more targeted and informed future research endeavours.

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Introduction

Worldwide, STEM education, which integrates the disciplines of science, technology, engineering, and math, is gaining popularity in K-12 settings due to its capacity to enhance 21st-century skills such as adaptability, problem-solving, and creative thinking (National Research Council [NRC], 2015 ). In STEM lessons, students are frequently guided by the engineering design process, which involves identifying problems or technical challenges and creating and developing solutions. Furthermore, higher achievement in STEM education has been linked to increased enrolment in post-secondary STEM fields, offering students greater opportunities to pursue careers in these domains (Merrill & Daugherty, 2010 ). However, STEM activities require dedicated time and the restructuring of integrated curricula, necessitating careful organization of lessons. Recognizing the complexity of developing 21st-century STEM proficiency, schools are not expected to tackle this challenge alone. In addition to regular STEM classes, there exists a diverse range of extended education programs, activities, and out-of-school learning environments (Baran et al., 2016 ; Kalkan & Eroglu, 2017 ; Schweingruber et al., 2014 ). In this paper, out-of-school learning environments, informal learning environments, extended education, and afterschool programs were used synonymously. It is worth noting that the literature lacks a universally accepted definition for out-of-school learning environments, leading to the use of various interchangeable terms (Donnelly et al., 2019 ). Some of these terms include informal learning environments, extended education, afterschool programs, all-day school, extracurricular activities, out-of-school time learning, extended schools, expanded learning, and leisure-time activities. These terms refer to optional programs and clubs offered by schools that exist outside of the standard academic curriculum (Baran et al., 2016 ; Cooper, 2011 ; Kalkan & Eroglu, 2017 ; Schweingruber et al., 2014 ).

Out-of-school learning, in contrast to traditional in-school learning, offers greater flexibility in terms of time and space, as it is not bound by the constraints of the school schedule, national or state standards, and standardized tests (Cooper, 2011 ). Out-of-school learning experiences typically involve collaborative engagement, the use of tools, and immersion in authentic environments, while school environments often emphasize individual performance, independent thinking, symbolic representations, and the acquisition of generalized skills and knowledge (Resnick, 1987 ). They encompass everyday activities such as family discussions, pursuing hobbies, and engaging in daily conversations, as well as designed environments like museums, science centres, and afterschool programs (Civil, 2007 ; Hein, 2009 ). On the other hand, extended education refers to intentionally structured learning and development programs and activities that are not part of regular classes. These programs are typically offered before and after school, as well as at locations outside the school (Bae, 2018 ). As a result, out-of-school learning environments encompass a wide range of experiences, including social, cultural, and technical excursions around the school, field studies at museums, zoos, nature centres, aquariums, and planetariums, project-based learning, sports activities, nature training, and club activities (Civil, 2007 ; Donnelly et al., 2019 ; Hein, 2009 ). At this point, STEM clubs are a specialized type of extracurricular activity that engage students in hands-on projects, experiments, and learning experiences related to scientific, technological, engineering, and mathematical disciplines. STEM Clubs, described as flexible learning environments unconstrained by time or location, offer an effective approach to conducting STEM studies outside of school (Blanchard et al., 2017 ; Cooper, 2011 ; Dabney et al., 2012 ).

Out-of-school learning environments, extended education or afterschool programs, hold tremendous potential for enhancing student learning and providing them with a diverse and enriching educational experience (Robelen, 2011 ). Extensive research supports the notion that these alternative educational programs not only contribute to students' academic growth but also foster their social, emotional, and intellectual development (NRC, 2015 ). Studies have consistently shown that after-school programs play a vital role in boosting students' achievement levels (Casing & Casing, 2024 ; Pastchal-Temple, 2012 ; Shernoff & Vandell, 2007 ), and contributing to positive emotional development, including improved self-esteem, positive attitudes, and enhanced social behaviour (Afterschool Alliance, 2015 ; Durlak & Weissberg, 2007 ; Lauer et al., 2006 ; Little et al., 2008 ). Moreover, engaging in various activities within these programs allows students to develop meaningful connections, expand their social networks, enhance leadership skills (Lipscomb et al., 2017 ), and cultivate cooperation, effective communication, and innovative problem-solving abilities (Mahoney et al., 2007 ).

Implementing STEM activities in out-of-school learning environments not only supports students in making career choices and fostering meaningful learning and interest in science, but also facilitates deep learning experiences (Bybee, 2001 ; Dabney et al., 2012 ; Sahin et al., 2018 ). Furthermore, STEM Clubs enhance students' emotional skills, such as a sense of belonging and peer-to-peer communication, while also fostering 21st-century skills, facilitating the acquisition of current content, and promoting career awareness and interest in STEM professions (Blanchard et al., 2017 ). In summary, engaging in STEM activities through social club activities not only addresses time constraints but also complements formal education and contributes to students' overall development. Hence, STEM Clubs, which are part of extended education, can be defined as dynamic and flexible learning environments that provide an effective approach to conducting STEM studies beyond traditional classroom settings. These clubs offer flexibility in terms of time and location, with intentionally structured programs and activities that take place outside of regular classes. They provide students with unique opportunities to explore and deepen their understanding of STEM subjects through collaborative engagement, hands-on use of tools, and immersive experiences in authentic environments (Bae, 2018 ; Blanchard, et al., 2017 ; Bybee, 2001 ; Cooper, 2011 ; Dabney et al., 2012 ). STEM Clubs have gained immense popularity worldwide, providing students with invaluable opportunities to explore and cultivate their interests and knowledge in these crucial fields (Adams et al., 2014 ; Bell et al., 2009 ). According to America After 3PM, nearly 75% of afterschool program participants, around 5,740,836 children, have access to STEM learning opportunities (Afterschool Alliance, 2015 ).

STEM Clubs as after-school programs come in various forms and provide diverse tutoring and instructional opportunities. For instance, the Boys and Girls Club of America (BGCA) operates in numerous cities across the United States, annually serving 4.73 million students (Boys and Girls Club of America, 2019 ). This program offers students the chance to engage in activities like sports, art, dance, field trips, and addresses the underrepresentation of African Americans in STEM. Another example is the Science Club for Girls (SCFG), established by concerned parents in Cambridge to address gender inequity in math, science, and technology courses and careers. SCFG brings together girls from grades K–7 through free after-school or weekend clubs, science explorations during vacations, and community science fairs, with approximately 800 to 1,000 students participating each year. The primary goal of these clubs is to increase STEM literacy and self-confidence among K–12 girls from underrepresented groups in these fields. More examples can be found in the literature, such as the St. Jude STEM Club (SJSC), where students conducted a 10-week paediatric cancer research project using accurate data (Ayers et al., 2020 ), and After School Matters, based in Chicago, offers project-based learning that enhances students' soft skills and culminates in producing a final project based on their activities (Hirsch, 2011 ).

The Purpose of The Study

The literature on STEM Clubs indicates a diverse range of such clubs located worldwide, catering to different student groups, operating on varying schedules, implementing diverse activities, and employing various strategies, methodologies, experiments, and assessments (Ayers et al., 2020 ; Blanchard et al., 2017 ; Boys and Girls Club of America, 2019 ; Hirsch, 2011 ; Sahin et al., 2018 ). However, it was previously unknown which specific sample groups were most commonly studied, which analytical methods were used frequently, and which results were primarily reported, even though the overall topic of STEM Clubs has gained significant attention. Therefore, organizing and categorizing this expansive body of literature is necessary to gain deeper insights into the current state of knowledge and practices in STEM Clubs. By systematically reviewing and synthesizing the diverse range of studies on this topic, we can develop a clearer understanding of the focus areas, methodologies, and key findings that have emerged from the existing research (Fraenkel et al., 2012 ). At this point, using a content analysis method is appropriate for this purpose because this method is particularly useful for examining trends and patterns in documents (Stemler, 2000 ). Similarly, some previous research on STEM education has conducted content analyses to examine existing studies and construct holistic patterns to understand trends (Bozkurt et al., 2019 ; Chomphuphra et al., 2019 ; Irwanto et al., 2022 ; Li et al., 2020 ; Lin et al., 2019 ; Martín-Páez et al., 2019 ; Noris et al., 2023 ). However, there is a lack of content analysis specifically focused on studies of STEM Clubs in the literature and showing the trends in this topic. Analysing research trends in STEM Clubs can help build upon existing knowledge, identify gaps, explore emerging topics, and highlight successful methodologies and strategies (Fraenkel et al., 2012 ; Noris et al., 2023 ; Stemler, 2000 ). This information can be valuable for researchers, educators, and policymakers to stay up-to-date and make informed decisions regarding curriculum design (Bozkurt et al., 2019 ; Chomphuphra et al., 2019 ; Irwanto et al., 2022 ; Li et al., 2020 ; Lin et al., 2019 ; Martín-Páez et al., 2019 ; Noris et al., 2023 ), the development of effective STEM Club programs, resource allocation, and policy formulation (Blanchard et al., 2017 ; Cooper, 2011 ; Dabney et al., 2012 ). Therefore, the identification of research trends in STEM Clubs was the aim of this study.

To identify research trends, studies commonly analysed documents by considering the dimensions of articles such as keywords, publishing years, research designs, purposes, sample levels, sample sizes, data collection tools, data analysis methods, and findings (Bozkurt et al., 2019 ; Chomphuphra et al., 2019 ; Irwanto et al., 2022 ; Li et al., 2020 ; Sozbilir et al., 2012 ). Using these dimensions as a framework is a useful and common approach in content analysis because this framework allows researchers to systematically examine the key aspects of existing studies and uncover patterns, relationships, and trends within the research data (Sozbilir et al., 2012 ). Hence, since the aim of this study is to identify and analyse research trends in STEM Clubs, it focused on publishing years, keywords, research designs, purposes, sample levels, sample sizes, data collection tools, data analysis methods, and findings of the studies on STEM Clubs.

As a conclusion, the main problem of this study is “What are the characteristics of the studies on STEM Clubs?”. The following sub-questions are addressed in this study:

What is the distribution of studies on STEM Clubs by year?

What are the frequently used keywords in studies on STEM Clubs?

What are the commonly employed research designs in studies on STEM Clubs?

What are the typical purposes explored in studies on STEM Clubs?

What are the commonly observed sample levels in studies on STEM Clubs?

What are the commonly observed sample sizes in studies on STEM Clubs?

What are the commonly utilized data collection tools in studies on STEM Clubs?

What are the commonly utilized data analysis methods in studies on STEM Clubs?

What are the typical durations reported in studies on STEM Clubs?

What are the commonly reported findings in studies on STEM Clubs?

In this study, the descriptive content analysis research method was employed, which allows for a systematic and objective examination of the content within articles, and description of the general trends and research results in a particular subject matter (Lin et al., 2014 ; Suri & Clarke, 2009 ; Sozbilir et al., 2012 ; Stemler, 2000 ). Given the aim of examining research trends in STEM Clubs, the utilization of this method was appropriate, as it provides a structured approach to identify patterns and trends (Gay et al., 2012 ). To implement the content analysis method, this study followed the three main phases proposed by Elo and Kyngäs ( 2008 ): preparation, organizing, and reporting. In the preparation phase, the unit of analysis, such as a word or theme, is selected as the starting point. So, in this study, the topic of STEM Clubs was carefully selected. During the organizing process, the researcher strives to make sense of the data and to learn "what is going on" and obtain a sense of the whole. So, in this study, during the analysis process, the content analysis framework (sample levels, sample sizes, data collection tools, research designs, etc.) was used to question the collected studies. Finally, in the reporting phase, the analyses are presented in a meaningful and coherent manner. So, the analyses were presented meaningfully with visual representations such as tables, graphs, etc. By adopting the content analysis research method and following the suggested phases, this study aimed to gain insights into research trends in STEM Clubs, identify recurring themes, and provide a comprehensive analysis of the collected data.

Search and Selection Process

The online databases ERIC and Web of Science were searched using keywords derived from a database thesaurus. These databases were chosen because of their widespread recognition and respect in the fields of education and academic research, and they offer a substantial amount of high-quality, peer-reviewed literature. The search process involved several steps. Firstly, titles, abstracts, and keywords were searched using Boolean operators for the keywords "STEM Clubs," "STEAM Clubs," "science-technology-engineering-mathematics clubs," "after school STEM program" and "extracurricular STEM activities" in the databases (criterion-1). Secondly, studies were collected beginning from November to the end of December 2023. So, the studies published until the end of December 2023 were included in the search, without a specific starting date restriction (criterion-2). Thirdly, the search was limited to scientific journal articles, book chapters, proceedings, and theses, excluding publications such as practices, letters to editors, corrections, and (guest) editorials (criterion-3). Fourthly, studies published in languages other than English were excluded, focusing exclusively on English language publications (criterion-4). Fifthly, duplicate articles found in both databases were identified and removed. Next, the author read the contents of all the studies, including those without full articles, with a particular focus on the abstract sections. After that, studies related to after school program and extracurricular activities that did not specifically involve the terms STEM or clubs were excluded, even though “extracurricular STEM activities” and “after school STEM program” were used in the search process, and there were studies related to after school program or extracurricular activities but not STEM (criterion-5). Additionally, studies conducted in formal and informal settings within STEM clubs were included, while studies conducted in settings such as museums or trips were excluded (criterion-6). Because STEM Clubs are a subset of informal STEM education settings, which also include museums and field trips, the main focus of this study is to show the trends specifically related to STEM Clubs. Moreover, studies focusing solely on technology without incorporating other STEM components were also excluded (criterion-7). Finally, 56 publications that met the inclusion and extraction criteria were identified. These publications comprised two dissertations, seven proceedings, and 47 articles from 36 different journals. By applying these criteria, the search process aimed to ensure the inclusion of relevant studies while excluding those that did not meet the specified criteria as shown in Fig.  1 .

Flowchart of article process selection

Data Analysing Process

Two different approaches were followed in the content analysis process of this study. In the first part, deductive content analysis was used, and a priori coding was conducted as the categories were established prior to the analysis. The categorization matrix was created based on the Paper Classification Form (PCF) developed by Sozbilir et al. ( 2012 ). The coding scheme devised consisted of eight classification groups for the sections of publication years, keywords, research designs, sample levels, sample sizes, data collection tools, data analysis methods, and durations, with sub-categories for each section. For example, under the research designs section, the sub-categories included qualitative and quantitative methods, case study, design-case study, comparative-case study, ethnographic study, phenomenological study, survey study, experimental study, mixed and longitudinal study, and literature review study. These sub-categories were identified prior to the analysis. Coding was then applied to the data using spreadsheets in the Excel program, based on the categorization matrix. Frequencies for the codes and categories created were calculated and presented in the findings section with tables. Line charts were used for the publication years section, while word clouds, which visually represent word frequency, were used for the keywords section. Word clouds display the most frequently used words in different sizes and colours based on their frequencies (DePaolo & Wilkinson, 2014 ). So, in this part, the analysis was certain since the studies mostly provided related information in their contents.

In the second part, open coding and the creation of categories and abstraction phases were followed for the purposes and findings sections. Firstly, the stated purposes and findings of the studies were written as text. The written text was then carefully reviewed, and any necessary terms were written down in the margins to describe all aspects of the content. Following this open coding, the lists of categories were grouped under higher order headings, taking into consideration their similarities or dissimilarities. Each category was named using content-characteristic words. The abstraction process was repeated to the extent that was reasonable and possible. In this coding process, two individuals independently reviewed ten studies, considering the coding scheme for the first part and conducting open coding for the second part. They then compared their notes and resolved any differences that emerged during their initial checklists. Inter-rater reliability was calculated as 0.84 using Cohen's kappa analysis. Once coding reliability was ensured, the remaining articles were independently coded by the author. After completing the coding process, consensus was reached through discussions regarding any disagreements among the researchers regarding the codes, as well as the codes and categories constructed for the purpose and findings sections. At this point, there were mostly agreements in the coding process since the studies had already clearly stated their key characteristics, such as research design, sample size, sample level, and data collection tools. Additionally, when coding the studies' stated purposes and results, the researchers closely referred to the original sentences in the studies, which led to a high level of consistency in the coded content between the two raters.

Studies related to the STEM Clubs were initially conducted in 2009 (Fig.  2 ). The noticeable increase in the number of studies conducted each year is remarkable. It can be seen that the majority of the 47 articles that were examined (56 articles) were published after 2015, despite a decrease in the year 2018. Additionally, it was observed that the articles were most frequently published (8) in the years 2019 and 2022, least frequently (1) in the years 2009, 2010, and 2014, and there were no publications in 2012.

Number of articles by years

Word clouds were utilized to present the most frequently used keywords in the articles, as shown in Fig.  3 . However, due to the lack of reported keywords in the ERIC database, only 30 articles were included for these analyses. The keywords that exist in these studies were represented in a word cloud in Fig.  3 . The most frequently appearing keywords, such as "STEM," "education" and "learning" were identified. Additionally, by using a content analysis method, these keywords were categorized into six different groups: disciplines, technological concepts, academic community, learning experiences, core elements of education, and psychosocial factors (variables) in Table  1 .

Word cloud of the keywords used in articles

The purposes of the identified studies identified were classified into six main themes: “effects of participation in STEM Clubs on” (25), “evolution of a sample program for STEM Clubs and its implementation” (25), “examination of” (11), “identification of” (3), “comparison of in-school and out-school STEM experiences” (2) and “others” (6). Table 2 presents the distribution of the articles’ purposes based on the classification regarding these themes. Therefore, it can be seen that purposes of “effects of participation in STEM Clubs on,” and “evolution of a sample program for STEM Clubs and its implementation” were given the highest and equal consideration, while the purposes related to "identification of" (3) and "comparison of in-school and out-of-school STEM experiences" (2) were given the least consideration among them.

Within the theme of "effects of participation in STEM Clubs on" there are 11 categories. The aims of the studies in this section are to examine the effect of participation in STEM Clubs on various aspects such as attitudes towards STEM disciplines or career paths, STEM major choice/career aspiration, achievement in math, science, STEM disciplines, or content knowledge, perception of scientists, strategies used, value of clubs, STEM career paths, enjoyment of physics, use of complex and scientific language, interest in STEM, creativity, critical thinking about STEM texts, images of mathematics, or climate-change beliefs/literacy. It is evident that the majority of research in this section focuses on the effects of participation in STEM Clubs on STEM major choice/career aspiration (5), achievement (4), perception of something (4), and interest in STEM (3).

Within the theme of "evolution of a sample program for STEM Clubs and its implementation" there are three categories: development of program/curriculum/activity (14), identification of program's challenges and limitations (3), and implementation of program/activity (8). The studies in this section aim to develop a sample program for STEM Clubs and describe its implementation. It can be seen that the most preferred purpose among them is the development of program/curriculum/activity (14), while the least preferred purpose is the identification of program's challenges and limitations (3). In addition, studies that focus on the development of the program, curriculum, or activity were classified under the "general" category (10). Sub-categories were created for studies specifically expressing the development of the program with a focus on a particular area, such as the maker movement or Arduino-assisted robotics and coding. Similarly, studies that explicitly mentioned the development of the program based on presented ideas and experiences formed another sub-category. Furthermore, the category related to the implementation of program/activity was divided into eight sub-categories, each indicating the specific centre of implementation, such as problem-based learning-centred and representation of blacks-centred.

The theme of "examination of" refers to studies that aim to examine certain aspects, such as the experiences and perceptions of students (7) and the factors influencing specific subjects (4). Studies focusing on examining the experiences and perceptions of students were labelled as "general" (4), while studies exploring their experiences and perceptions regarding specific content, such as influences and challenges to participation in STEM clubs (2) and assessment (1), were labelled accordingly. Additionally, studies that focused on examining factors affecting the choice of STEM majors (2), participation in STEM clubs (1), and motivation to develop interest in STEM (1) were categorized in line with their respective focuses. As shown in Table  2 , it is evident that studies focusing on examining the experiences and perceptions of students (7) were more frequently conducted compared to studies focusing on examining the factors affecting specific subjects (4).

The theme of "identification of" refers to studies that aim to identify certain aspects, such as the types of attitudinal effects (1), types of changes in affect toward engineering (1), and non-academic skills (1). Additionally, the theme of "comparison of in-school and out-of-school STEM experiences" (2) refers to studies that aim to compare STEM experiences within school and outside of school. Lastly, studies that did not fit into the aforementioned categories were included in the "others" theme (6) as no clear connection could be identified among them.

Research Designs

The research designs employed in the examined articles were identified as follows: qualitative methods (36), including case study (20), design-case study (6), comparative-case study (4), ethnographic study (2), phenomenological study (2), and survey study (2); quantitative methods (7), including survey study (4) and experimental study (3); mixed methods and longitudinal studies (10); and literature review (3), as illustrated in Table  3 . It can be observed that among these methods, case study was the most commonly utilized. Furthermore, it is evident that quantitative methods (7) and literature reviews (3) were employed less frequently compared to qualitative (36) and mixed methods (10). Additionally, survey studies were utilized in both quantitative and qualitative studies.

Sample Levels

The frequencies and percentages of sample levels in the examined articles are presented in Table  4 . The studies involved participants at different educational levels, including elementary school (8), middle school (23), high school (14), pre-service teachers or undergraduate students (6), teachers (4), parents (3), and others (1). It is apparent that middle school students (23) were the most commonly utilized sample among them, while high school students (14) were more frequently chosen compared to elementary school students (8). It should be noted that while grade levels were specified for both elementary and middle school students, separate grade levels were not identified for high school students in these studies. Additionally, studies that involved mixed groups were labelled as 3-5th and 6-8th grades. However, when the mixed groups included participants from different educational levels such as elementary, middle, or high school, teachers, parents, etc., they were counted as separate levels. Furthermore, the studies conducted with participants such as pre-service teachers, undergraduates, teachers, and parents were less frequently employed compared to K-12 students.

Sample Sizes

The frequencies of sample sizes in the examined articles are presented in Table  5 . It was observed that in 15 studies, the number of sample sizes was not provided. The intervals for the sample size were not equally separated; instead, they were arranged with intervals of 5, 10, 50, and 100. This choice was made to allow for a more detailed analysis of smaller samples, as smaller intervals can provide a more granular examination of data instead of cumulative amounts. The analysis reveals that the studies primarily prioritized sample groups with 11–15 (f:8) participants, followed by groups of 16–20 (f:4) and 201–250 (f:4). Additionally, it is evident that sample sizes of 6–10, 21–25, 41–50, 50–100, and more than 2000 (f:1) were the least commonly studied.

Data Collection Tools

The frequencies and percentages of data collection tools in the examined articles are presented in Table  6 . The analysis reveals that the studies primarily employed survey or questionnaires (31.6%) and observations (30.5%) as data collection methods, followed by interviews (15.8%), documents (13.7%), tests (4.2%), and field notes (4.2%). Regarding survey/questionnaires, Likert-type scales (f:23) were more commonly employed compared to open-ended questions (f:7). Tests were predominantly used as achievement tests (f:2) and assessments (f:2), representing the least preferred data collection tools. Furthermore, the table illustrates that multiple data collection tools were frequently employed, as the total number of tools (95) is nearly twice the number of studies (56).

Data Analysing Methods

The frequencies and percentages of data analysing methods in the examined articles are presented in Table  7 . The table reveals that the studies predominantly employed descriptive analysis (f:33, 41.25%), followed by inferential statistics (f:16, 20%), descriptive statistics (f:15, 18.75%), content analysis (f:14, 17.5%), and the constant-comparative method (f:2, 2.5%). It is notable that qualitative methods (f:49, 61.25%) were preferred more frequently than quantitative methods (f:31, 38.75%) in the examined studies related to STEM Clubs. Within the qualitative methods, descriptive analysis (f:33) was utilized nearly twice as often as content analysis (f:14), while within the quantitative methods, descriptive statistics (f:15) and inferential statistics (f:16), including t-tests, ANOVA, regression, and other methods, were used with comparable frequency.

The durations of STEM Clubs in the examined studies are presented in Table  8 . Based on the analysis, there are more studies (f:37) that do not state the duration of STEM Clubs than studies (f:19) that do provide information on the durations. Additionally, among the studies that do state the durations, there is no common period of time for STEM Clubs, as they were implemented for varying numbers of weeks and sessions, with session durations ranging from several minutes. Therefore, it can be observed that STEM Clubs were conducted over the course of 3 semesters (academic year and summer), 5 months, 2 to 16 weeks, with session durations ranging from 60 to 120 min. Furthermore, the durations of "3 semesters," "10 weeks with 90-min sessions per week," and "unknown weeks with 60-min sessions per week" were used more than once in the studies.

The content analysis of the findings of the identified examined articles are presented by their frequencies in Table  9 . Although the studies cover a diverse range of topics, the analysis indicates that the results can be broadly classified into three themes, namely, the "development of or increase in certain aspects" (f:68), "design of STEM Clubs" (f:17), and "identification of various aspects" (f:16). Based on the analysis, the findings in the studies are associated with the development of certain aspects such as skills or the increase in specific outcomes like academic achievement. Furthermore, the studies explore the design of STEM Clubs through the description of specific cases, such as sample implementations and challenges. Additionally, the studies focus on the identification of various aspects, such as factors and perceptions.

It is evident from the findings that the studies predominantly yield results related to the development of or increase in certain aspects (f:68). Within this theme, the most commonly observed result is the development of STEM or academic achievement or STEM competency (f:11). This is followed by an increase in STEM major choice or career aspiration (f:9), an increase in engagement or participation in STEM clubs (f:5), the development of identity including STEM, science, engineering, under-representative groups (f:5), the development of interest in STEM (f:4), an increase in enjoyment (f:4), and the development of collaboration, leadership, or communication skills (f:4). Furthermore, it can be observed that there are some results, such as the development of critical thinking, perseverance and the teachers’ profession, that were yielded less frequently (f:1). The results of 16 studies were found with a frequency of 1.

Within the design of STEM Clubs, the sample implementation or design model for different purposes such as the usage of robotic program or students with disabilities (f:7), design principles or ideas for STEM clubs, activities or curriculum (f:4), challenges or factors effecting STEM Clubs success and sustainability (f:3) were presented as a result. Additionally, the comparison was made between in-school and out-of-school learning environments (f:3), highlighting the contradictions of STEM clubs and science classes, as well as the differences in STEM activities and continues-discontinues learning experiences in mathematics. Within the identification of various aspects, the most commonly gathered result was the identification of factors affecting participation or motivation to STEM clubs (f:5). This was followed by the identification of barriers to participation (f:2). The identification of other aspects, such as parents' roles and perspectives on STEM, was comparatively less frequent.

Considering the wide variety of STEM Clubs found in different regions around the world, this study aimed to investigate the current state of research on STEM Clubs. It is not surprising to observe an increase in the number of studies conducted on STEM Clubs over the years. This can be attributed to the overall growth in research on STEM education (Zhan et al., 2022 ), as STEM education often includes activities and after-school programs as integral components (Blanchard et al., 2017 ). Identifying relevant keywords and incorporating them into a search strategy is crucial for conducting a comprehensive and rigorous systematic review (Corrin et al., 2022 ). To gain a broader understanding of keyword usage in the context of STEM Clubs, a word cloud analysis was performed (McNaught & Lam, 2010 ). Additionally, based on the content analysis method, six different categories for keywords were immerged: disciplines, technological concepts, academic community, learning experiences, core elements of education, and psychosocial factors (variables). The analysis revealed that the keyword "STEM" was used most frequently in the studies examined. This may be because authors want their studies to be easily found and widely searchable by others, so they use "STEM" as a general term for their studies (Corrin et al., 2022 ). Similarly, the frequent use of keywords like "education" and "learning" from the "core elements of education" category could be attributed to authors' desire to use broad, searchable terms to make their studies more discoverable (Corrin et al., 2022 ). Additionally, it was observed that from the STEM components, only "science" and "engineering" were used as keywords, while "mathematics" and "technology" were not present. This finding aligns with claims in the literature that mathematics is often underemphasized in STEM integration (Fitzallen, 2015 ; Maass et al., 2019 ; Stohlmann, 2018 ). Although the specific term "technology" did not appear in the word cloud, technology-related keywords such as "arduino," "robots," "coding," and "innovative" were present. Furthermore, the analysis revealed that authors preferred to use keywords related to their sample populations, such as "middle (school students)," "elementary (students)," "high school students," or "teachers." Additionally, keywords describing learning experiences, such as "extracurricular," "informal," "afterschool," "out-of-school," "social," "clubs," and "practice" were commonly used. This preference may stem from the fact that STEM clubs are often part of informal learning environments, out-of-school programs, or afterschool activities, and these concepts are closely related to each other (Baran et al., 2016 ; Cooper, 2011 ; Kalkan & Eroglu, 2017 ; Schweingruber et al., 2014 ). Moreover, the analysis showed that keywords related to psychosocial factors (variables), such as "disabilities," "skills," "interest," "attainment," "enactment," "expectancy-value," "self-efficacy," "engagement," "motivation," "career," "gender," "cognitive," and "identity" were also prevalent. This suggests that the articles investigated the effects of STEM club practices on these psychosocial variables. To sum up, by using these keywords, researchers can gain valuable insights and effectively search for relevant articles related to STEM clubs, enabling them to locate appropriate resources for their research (Corrin et al., 2022 ).

The popularity of case studies as a research design, based on the analysis, can be attributed to the fact that studies on STEM Clubs were conducted in diverse learning environments, highlighting sample implementation designs (Adams et al., 2014 ; Bell et al., 2009 ; Robelen, 2011 ). At this point, case studies offer the opportunity to present practical applications and real-world examples (Hamilton & Corbett-Whittier, 2012 ), which is highly valuable in the context of STEM Clubs. Additionally, the observation that quantitative methods were not as commonly utilized as qualitative methods in studies related to STEM Clubs contrasts with the predominant reliance on quantitative methods in STEM education research (Aslam et al., 2022 ; Irwanto et al., 2022 ; Lin et al., 2019 ). This suggests a lack of quantitative studies specifically focused on STEM Clubs, indicating a need for more research in this area employing quantitative approaches. Therefore, it is important to prioritize and conduct additional quantitative studies to further enhance our understanding of STEM Clubs and their impact. In studies on STEM Club, there is a higher frequency of research involving K-12 students, particularly middle school students, parallel to some studies on literature (Aslam et al., 2022 ), compared to other groups such as pre-service teachers, undergraduate students, teachers, and parents. This can be attributed to the fact that STEM Clubs are designed for K-12 students, and middle school is a crucial period for introducing them to STEM concepts and careers. Middle school students are developmentally ready for hands-on and inquiry-based learning, commonly used in STEM education. Additionally, time constraints, especially for high school students preparing for university, may limit their involvement in extensive STEM activities. Furthermore, STEM Clubs were primarily employed with sample groups ranging from 11–15, 16–20, and 201–250 participants. The preference for 11–20 participants, rather than less than 10, may be attributed to the collaborative nature of STEM activities, which often require a larger team for effective teamwork and group dynamics (Magaji et al., 2022 ). Utilizing small groups as samples can result in the case study research design being the most frequently employed approach due to its compatibility with smaller sample sizes. On the other hand, the inclusion of larger groups (201–250) is suitable for survey studies, as this number can represent the total student population attending STEM Clubs throughout a semester with multiple sessions (Boys & Girls Club of America, 2019 ).

According to studies on STEM Clubs, surveys or questionnaires and observations were predominantly used as data collection methods. This preference can be attributed to the fact that surveys or questionnaires allow researchers to gather data on diverse aspects, including students' attitudes, perceptions, and experiences related to STEM Clubs, facilitating generalization and comparison (McLafferty, 2016 ). Furthermore, observations were frequently employed because they can offer a deeper understanding of the lived experiences and actual practices within STEM Clubs (Baker, 2006 ). Along with data collection tools, descriptive analysis was predominantly utilized in studies on STEM Clubs, with quantitative methods including descriptive statistics and inferential statistics being used to a similar extent. The preference for descriptive analysis may arise from its effectiveness in describing activities, experiences, and practices within STEM Clubs. Given the predominance of case study research in the analysed studies, it is not surprising to observe a high frequency of descriptive statistics in the findings. On the other hand, the extensive use of quantitative analysing methods can be attributed to the need for statistical analysis of surveys and questionnaires (Young, 2015 ). Consequently, future studies on STEM Clubs could benefit from considering the use of tests and field notes as additional data collection tools, along with surveys, observations and interviews. Additionally, the development of tests specifically designed to assess aspects related to STEM could provide valuable insights (Capraro & Corlu, 2013 ; Grangeat et al., 2021 ). Moreover, increasing the utilization of content analysis and constant comparative analysis methods could further enhance the depth and richness of data analysis in STEM Club research (White & Marsh, 2006 ). In the studies on STEM Clubs, the duration and scheduling of the clubs varied considerably. While there was no common period of time for STEM Clubs, they were implemented for different numbers of weeks and sessions, with session durations ranging from several minutes to 60 to 120 min. However, it was observed that STEM Clubs were predominantly conducted over the course of three semesters, including the academic year and summer, or for durations of 2 to 16 weeks. This scheduling pattern can be attributed to the fact that STEM Clubs were often implemented as after-school programs, and they were designed to align with the academic semesters and summer school periods to effectively reach students. Additionally, the number of weeks in these studies may have been arranged according to the duration of academic semesters, although some studies were conducted for less than a semester (Gutierrez, 2016 ). The most common use of multiple sessions with a time range of 60 to 120 min can be attributed to the nature of the activities involved in STEM Clubs. These activities often require more time than regular class hours, and splitting them into separate sessions allows students to effectively concentrate on their work and engage in more in-depth learning experiences (Vennix et al., 2017 ).

The purposes of the studies on STEM Clubs were mostly related to effects of participation in STEM Clubs on various aspects such as attitudes towards STEM disciplines or career paths, STEM major choice/career aspiration, achievement etc., evolution of a sample program for STEM Clubs and its implementation including the development of program/activity, identification of program's challenges and limitations, and implementation of it, followed by the examination of certain aspects such as the experiences and perceptions of students and the factors influencing specific subjects, identification of such as the types of attitudinal effects and non-academic skills, and comparison of in-school and out-school STEM experiences. Therefore, the results of the studies parallel to the purposes were mostly related to development of or increase in certain aspects such as STEM or academic achievement or STEM competency STEM major choice or career aspiration engagement or participation in STEM Clubs, identity, interest in STEM, enjoyment, collaboration, communication skills, critical thinking, the design of STEM Clubs including the sample implementation or design model for different purposes such as the usage of robotic program or students with disabilities, design principles or ideas for STEM clubs or activities, challenges or factors effecting STEM Clubs success and sustainability, and the comparison between in-school and out-of-school learning environments. Also, they are related to the identification of various aspects such as factors affecting participation or motivation to STEM clubs, barriers to participation. At this point, it is evident that these identified categories align with the findings of studies in the literature. These studies claim that after-school programs, such as STEM Clubs, have positive impacts on students' achievement levels (NRC, 2015 ; Kazu & Kurtoglu Yalcin, 2021 ; Shernoff & Vandell, 2007 ), communication, and innovative problem-solving abilities (Mahoney et al., 2007 ), leadership skills (Lipscomb et al., 2017 ), career decision-making (Bybee, 2001 ; Dabney et al., 2012 ; Sahin et al., 2018 ; Tai et al., 2006 ), creativity (Wan et al., 2023 ), 21st-century skills (Hirsch, 2011 ; Zeng et al., 2018 ), interest in STEM professions (Blanchard et al., 2017 ; Chittum et al., 2017 ; Wang et al., 2011 ), and knowledge in STEM fields (Adams et al., 2014 ; Bell et al., 2009 ). Furthermore, it can be inferred that the studies on STEM Clubs paid significant attention to the design descriptions of programs or activities (Nation et al., 2019 ). This may be because there is a need for studies that focus on designing program models for different cases (Calabrese Barton & Tan, 2018 ; Estrada et al., 2016 ). These studies can serve as examples and provide guidance for the development of STEM clubs in various settings. By creating sample models, researchers can contribute to the improvement and expansion of STEM clubs across different environments (Cakir & Guven, 2019 ; Estrada et al., 2016 ).

In conclusion, as the studies on the trends in STEM education (Bozkurt et al., 2019 ; Chomphuphra et al., 2019 ; Irwanto et al., 2022 ; Li et al., 2020 ; Lin et al., 2019 ; Martín-Páez et al., 2019 ; Noris et al., 2023 ), the analysis of prevailing research trends specifically in STEM Clubs, which are implemented in diverse environments with varying methods and purposes, can provide a comprehensive understanding of these clubs as a whole.

It can also serve as a valuable resource for guiding future investigations in this field. By identifying common approaches and identifying gaps in methods and results, a holistic perspective on STEM Clubs can be achieved, leading to a more informed and targeted direction for future research endeavours.

Recommendations

Future research on STEM Clubs should consider the trends identified in the study and address methodological gaps. For instance, there is a lack of research in this area that employs quantitative approaches. Therefore, it is important for future studies to incorporate quantitative methods to enhance the understanding of STEM Clubs and their impact. This includes exploring underrepresented populations, investigating the long-term impacts of STEM Clubs, and examining the effectiveness of specific pedagogical approaches or interventions within these clubs. Researchers should conduct an analysis to identify common approaches used in STEM Clubs across different settings. This analysis can help uncover effective strategies, best practices, and successful models that can be replicated or adapted in various contexts. By undertaking these efforts, researchers can contribute to a more comprehensive understanding of STEM Clubs, leading to advancements in the field of STEM education.

Limitations

It is important to consider the limitations of the study when interpreting its findings. The study's findings are based on the literature selected from two databases, which may introduce biases and limitations. Additionally, the study's findings are constrained by the timeframe of the literature review, and new studies may have emerged since the cut-off date, potentially impacting the representation and generalizability of the research trends identified. Another limitation lies in the construction of categories during the coding process. The coding scheme used may not have fully captured or represented all relevant terms or concepts. Some relevant terms may have been inadequately represented or identified using different words or phrases, potentially introducing limitations to the analysis. While efforts were made to ensure validity and reliability, there is still a possibility of unintended biases or inconsistencies in the categorization process.

Data Availability

The datasets (documents, excel analysis) utilized in this article are available upon request from the corresponding author.

Adams, J. D., Gupta, P., & Cotumaccio, A. (2014). A museum program enhances girls’ STEM interest, motivation and persistence. Afterschool Matters, 12 , 14–20.

Google Scholar  

Afterschool Alliance (2015).  Full STEM ahead: Afterschool programs step up as key partners in STEM education . Retrieved November 2023 from http://www.afterschoolalliance.org/AA3PM/

Aslam, S., Saleem, A., Kennedy, T. J., Kumar, T., Parveen, K., Akram, H., & Zhang, B. (2022). Identifying the research and trends in STEM education in Pakistan: A systematic literature review. SAGE Open, 12 (3), 21582440221118544.

Article   Google Scholar  

Ayers, K. A., Wade-Jaimes, K., Wang, L., Pennella, R. A., & Pounds, S. B. (2020). The St. Jude STEM clubs: An after-school STEM club for upper elementary school students in Memphis, TN. Journal of STEM Outreach, 3 (1), 1–26. https://doi.org/10.15695/jstem/v3i1.13

Bae, S. H. (2018). Concepts, models, and research of extended education. International Journal for Research on Extended Education, 6 (2), 153–165.

Baker, L. (2006). Observation: A complex research method. Library Trends, 55 (1), 171–189.

Baran, E., Bilici, S. C., Mesutoglu, C., & Ocak, C. (2016). Moving STEM beyond schools: Students’ perceptions about an out-of-school STEM education program. International Journal of Education in Mathematics, Science and Technology, 4 (1), 9–19. https://doi.org/10.18404/ijemst.71338

Bell, P., Lewenstein, B., Shouse, A. W., & Feder, M. A. (2009). Learning science in informal environments: People, places and pursuits . National Research Council of the National Academies.

Blanchard, M. R., Hoyle, K. S., & Gutierrez, K. S. (2017). How to start a STEM club. Science Scope, 41 (3), 88–94.

Boys and Girls Club of America (2019). Annual report . Retrieved November 2023 from https://www.bgca.org/about-us/annual-report

Bozkurt, A., Ucar, H., Durak, G., & Idin, S. (2019). The current state of the art in STEM research: A systematic review study. Cypriot Journal of Educational Science,  14 (3), 374–383. https://doi.org/10.18844/cjes.v14i3.3447

Bybee, R. W. (2001). Achieving scientific literacy: Strategies for ensuring that free choice science education complements national formal science education efforts. In J. H. Falk (Ed.), Free choice education: How we learn science outside of school (pp. 44–63). Teachers College Press.

Cakir, N. K., & Guven, G. (2019). Arduino-assisted robotic and coding applications in science teaching: Pulsimeter activity in compliance with the 5E learning model. Science Activities, 56 (2), 42–51.

Calabrese Barton, A., & Tan, E. (2018). A longitudinal study of equity-oriented STEM-rich making among youth from historically marginalized communities. American Educational Research Journal, 55 (4), 761–800.

Capraro, R. M., & Corlu, M. S. (2013). Changing views on assessment for STEM project-based learning. In R. M. Capraro, M. M. Capraro, & J. R. Morgan (Eds.),  STEM project-based learning (pp. 109–118). Brill.

Chapter   Google Scholar  

Casing, P. I., & Casing, L. M. R. (2024). Fostering students’ mathematics achievement through after-school program in the 21st century. Online Submission, 12 (3), 118–122.

Chittum, J. R., Jones, B. D., Akalin, S., & Schram, A. B. (2017). The effects of an afterschool STEM program on students’ motivation and engagement. International Journal of STEM Education, 4 , 1–16.

Chomphuphra, P., Chaipidech, P., & Yuenyong, C. (2019). Trends and research issues of STEM education: A review of academic publications from 2007 to 2017. Journal of Physics: Conference Series, 1340 (1), 012069.

Civil, M. (2007). Building on community knowledge: An avenue to equity in mathematics education. In N. S. Nasir & P. Cobb (Eds.), Improving access to mathematics: Diversity and equity in the classroom (pp. 105–117). Teachers College.

Cooper, S. (2011). An exploration of the potential for mathematical experiences in informal learning environments. Visitor Studies, 14 (1), 48–65. https://doi.org/10.1080/10645578.2011.557628

Corrin, L., Thompson, K., Hwang, G. J., & Lodge, J. M. (2022). The importance of choosing the right keywords for educational technology publications. Australasian Journal of Educational Technology, 38 (2), 1–8.

Dabney, K. P., Tai, R. H., Almarode, J. T., Miller-Friedmann, J. L., Sonnert, G., Sadler, P. M., & Hazari, Z. (2012). Out-of-school time science activities and their association with a career interest in STEM. International Journal of Science Education, Part B, 2 (1), 63–79. https://doi.org/10.1080/21548455.2011.629455

DePaolo, C. A., & Wilkinson, K. (2014). Get your head into the clouds: Using word clouds for analyzing qualitative assessment data. TechTrends, 58 , 38–44. https://doi.org/10.1007/s11528-014-0750-9

Donnelly, M., ažetić, P., Sandoval-Hernandez, A., Kumar, K., & Whewall, S. (2019). An unequal playing field-extra-curricular activities, soft skills and social mobility . Social Mobility Commission.

Durlak, J. A., & Weissberg, R. P. (2007). The impact of after-school programs that promote personal and social skills. Collaborative for Academic, Social, and Emotional Learning (CASEL). Retrieved from www.casel.org

Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing, 62 (1), 107–115.

Estrada, M., Burnett, M., Campbell, A. G., Campbell, P. B., Denetclaw, W. F., Gutiérrez, C. G., Hurtado, S., John, G. H., Matsui, J., McGee, R., Okpodu, C. M, Robinson, T. J., Summers, M. F., Werner-Washburne, M., & Zavala, M. (2016). Improving underrepresented minority student persistence in STEM. CBE—Life Sciences Education , 15 (3), es5.

Fitzallen, N. (2015). STEM Education: What does mathematics have to offer? In M. Marshman, V. Geiger, & A. Bennison (Eds.), Mathematics education in the margins. Proceedings of The 38th Annual Conference of the Mathematics Education Research Group of Australasia (pp. 237–244). MERGA.

Fraenkel, J., Wallen, N., & Hyun, H. (2012). How to design and evaluate research in education (10th ed.). McGraw-Hill Education.

Gay, L. R., Mills, G. E., & Airasian, P. W. (2012). Educational research: competencies for analysis and applications (10th ed.). Pearson.

Grangeat, M., Harrison, C., & Dolin, J. (2021). Exploring assessment in STEM inquiry learning classrooms. International Journal of Science Education, 43 (3), 345–361.

Gutierrez, K. S. (2016). Investigating the climate change beliefs, knowledge, behaviors, and cultural worldviews of rural middle school students and their families during an out-of-school intervention: A mixed-methods study (Publication No. 11320) [Doctoral dissertation, North Carolina State University]. NC State University Libraries.

Hamilton, L., & Corbett-Whittier, C. (2012). Using case study in education research . Sage.

Hein, G. (2009). Learning science in informal environments: People, places, and pursuits. Museums & Social Issues, 4 (1), 113–124.

Hirsch, B. (2011). Learning and development in after-school programs. Phi Delta Kappan, 92 (5), 66–69. https://doi.org/10.1177/2F003172171109200516

Irwanto, I., Saputro, A. D., Widiyanti, W., Ramadhan, M. F., & Lukman, I. R. (2022). Research trends in STEM education from 2011 to 2020: A systematic review of publications in selected journals. International Journal of Interactive Mobile Technologies (iJIM), 16 (5), 19–32.

Kalkan, C., & Eroglu, S. (2017). Designing sample activities based on STEM materials for gifted/talented students in support education rooms. Journal of Gifted Education and Creativity , 4 (2), 36–46. Retrieved November 2023 from  https://dergipark.org.tr/tr/pub/jgedc/issue/38702/449432

Kazu, I. Y., & Kurtoglu Yalcin, C. (2021). The effect of STEM education on academic performance: A meta-analysis study. Turkish Online Journal of Educational Technology-TOJET, 20 (4), 101–116.

Lauer, P. A., Akiba, M., Wilkerson, S. B., Apthorp, H. S., Snow, D., & Martin-Glenn, M. L. (2006). Out-of-school-time programs: A meta-analysis of effects for at-risk students. Review of Educa- Tional Research, 76 (2), 275–313.

Li, Y., Wang, K., Xiao, Y., & Froyd, J. E. (2020). Research and trends in STEM education: A systematic review of journal publications. International Journal of STEM Education, 7 (1), 1–16.

Lin, T. C., Lin, T. J., & Tsai, C. C. (2014). Research trends in science education from 2008 to 2012: A systematic content analysis of publications in selected journals. International Journal of Science Education, 36 (8), 1346–1372.

Lin, T. J., Lin, T. C., Potvin, P., & Tsai, C. C. (2019). Research trends in science education from 2013 to 2017: A systematic content analysis of publications in selected journals. International Journal of Science Education, 41 (3), 367–387.

Lipscomb, S., Haimson, J., Liu, A. Y., Burghardt, J., Johnson, D. R., & Thurlow, M. L. (2017). Preparing for life after high school: The characteristics and experiences of youth in special education. Findings from the National Longitudinal Transition Study 2012. Volume 2: Comparisons across disability groups: Full report (Report No. NCEE 2017–4018). U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance.

Little, P., Wimer, C., & Weiss, H. B. (2008). After school programs in the 21st century: Their poten- tial and what it takes to achieve it. Issues and Opportunities in out-of-School Time Evaluation, 10 , 1–12.

Maass, K., Geiger, V., Ariza, M. R., & Goos, M. (2019). The role of mathematics in interdisciplinary STEM education. ZDM, 51 , 869–884. https://doi.org/10.1007/s11858-019-01100-5

Magaji, A., Ade-Ojo, G., & Bijlhout, D. (2022). The impact of after school science club on the learning progress and attainment of students. International Journal of Instruction, 15 (3), 171–190.

Mahoney, J. L., Parente, M. E., & Lord, H. (2007). After-school program engagement: Links to child competence and program quality and content. The Elementary School Journal, 107 (4), 385–404.

Martín-Páez, T., Aguilera, D., Perales-Palacios, F. J., & Vílchez-González, J. M. (2019). What are we talking about when we talk about STEM education? A Review of Literature. Science Education, 103 (4), 799–822.

McLafferty, S. (2016). Conducting questionnaire surveys. Key Methods in Geography, 3 , 129–142.

McNaught, C., & Lam, P. (2010). Using Wordle as a supplementary research tool. Qualitative Report, 15 (3), 630–643.

Merrill, C., & Daugherty, J. (2010). STEM education and leadership: A mathematics and science partnership approach. Journal of Technology Education, 21 (2), 21–34.

Nation, J. M., Harlow, D., Arya, D. J., & Longtin, M. (2019). Being and becoming scientists: Design-based STEM programming for girls. Afterschool Matters, 29 , 36–44.

National Research Council, Division of Behavioral, Board on Science Education, & Committee on Successful Out-of-School STEM Learning (2015). Identifying and supporting productive STEM programs in out-of-school settings . National Academies Press.

Noris, M., Saputro, S., & Ulimaz, A. (2023). STEM research trends from 2013 to 2022: A systematic literature review. International Journal of Technology in Education (IJTE), 6 (2), 224–237. https://doi.org/10.46328/ijte.390

Pastchal-Temple, A. S. (2012). The effect of regular participation in an after-school program on student achievement, attendance, and behavior (Publication No. 4368) [Doctoral dissertation, Mississippi State University]. Mississippi State University Libraries.

Resnick, L. B. (1987). Education and learning to think . National Academy Press.

Robelen, E. (2011). New STEM schools target underrepresented groups. Education Week, 31 (1), 18–19.

Sahin, A., Ekmekci, A., & Waxman, H. C. (2018). Collective effects of individual, behavioral, and contextual factors on high school students’ future STEM career plans. International Journal of Science and Mathematics Education, 16 , 69–89.

Schweingruber, H., Pearson, G., & Honey, M. (Eds.). (2014). STEM integration in K-12 education: Status, prospects, and an agenda for research . National Academies Press.

Shernoff, D. J., & Vandell, D. L. (2007). Engagement in after school program activities: Quality of experience from the perspective of participants. Journal of Youth Adolescence, 36 , 891–903.

Stemler, S. (2000). An overview of content analysis. Practical Assessment, Research & Evaluation, 7 (17), 1–6. https://doi.org/10.7275/z6fm-2e34

Stohlmann, M. (2018). A vision for future work to focus on the “m” in integrated STEM. School Science and Mathematics, 118 (7), 310–319. https://doi.org/10.1111/ssm.12301

Sozbilir, M., Kutu, H., & Yasar, M. D. (2012). Science education research in Turkey: A content analysis of selected features of papers published. In J. Dillon & D. Jorde (Eds.), The world of science education: Handbook of research in Europe (pp. 1–35). Sense publishers.

Suri, H., & Clarke, D. (2009). Advancements in research systhesis methods: From a methodologically inclusive perspective. Review of Educational Research, 79 (1), 395–430.

Tai, R. H., Qi Liu, C., Maltese, A. V., & Fan, X. (2006). Planning early for careers in science. Science, 312 (5777), 1143–1144.

Vennix, J., Den Brok, P., & Taconis, R. (2017). Perceptions of STEM-based outreach learning activities in secondary education. Learning Environments Research, 20 , 21–46.

Wan, Z. H., So, W. M. W., & Zhan, Y. (2023). Investigating the effects of design-based STEM learning on primary students’ STEM creativity and epistemic beliefs. International Journal of Science and Mathematics Education, 21 (Suppl. 1), 87–108.

Wang, H. H., Moore, T. J., Roehrig, G. H., & Park, M. S. (2011). STEM integration: Teacher perceptions and practice. Journal of Pre-College Engineering Education Research, 1 (2), 1–13.

White, M. D., & Marsh, E. E. (2006). Content analysis: A flexible methodology. Library Trends, 55 (1), 22–45.

Young, T. J. (2015). Questionnaires and surveys. In Z. Hua (Ed.), Research methods in intercultural communication: A practical guide (pp. 163–180). John Wiley & Sons. https://doi.org/10.1002/9781119166283.ch11

Zeng, Z., Yao, J., Gu, H., & Przybylski, R. (2018). A meta-analysis on the effects of STEM education on students’ abilities. Science Insights Education Frontiers, 1 (1), 3–16.

Zhan, Z., Shen, W., Xu, Z., Niu, S., & You, G. (2022). A bibliometric analysis of the global landscape on STEM education (2004–2021): Towards global distribution, subject integration, and research trends. Asia Pacific Journal of Innovation and Entrepreneurship, 16 (2), 171–203.

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