how to report research data

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

Make a Gift

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Can Commun Dis Rep
• v.42(9); 2016 Sep 1

Scientific Writing

A reporting guide for qualitative studies.

Qualitative studies provide insight into complex phenomena. Unlike measurement-based studies which typically quantify what happens under experimental conditions, qualitative studies often help explain behaviors or perceptions under actual circumstances. Qualitative studies in the field of communicable diseases can be used to provide insight into why people choose high-risk behaviours and to identify the factors that influence their decisions. For example, a qualitative study may address why healthcare practitioners do not practice adequate hand hygiene and whether patients might help by reminding them to do so. The results can be surprising. For example, a recent study identified that inpatients in one hospital who were most dissatisfied with the care they received were also the least likely to ask healthcare professionals if they had washed their hands ( 1 ). Furthermore, the study identified that the decision not to pose this question was linked to patient awareness that staff satisfaction was low.

Qualitative research analyzes data from direct field observations, in-depth, open-ended interviews and written documents. Inductive analyses yield patterns and themes that generate hypotheses and offer a basis for future research. Although qualitative studies do not create generalizable evidence, well-reported studies provide enough information for readers to assess the applicability or transferability of findings to their own context ( 2 ).

There are a variety of checklists about how to report qualitative studies ( 3 - 6 ). The Canada Communicable Disease Report (CCDR) has developed a 24-item checklist that synthesizes these including the COREQ checklist noted on the EQUATOR Network ( 6 ). The CCDR checklist identifies the importance of describing how data was gathered and summarized, what trends were determined, exploring corroborative findings, offering alternative explanations and identifying possible next steps or further areas of inquiry ( Table 1 ).

Abbreviation: No., Number

Reports of qualitative studies are usually around 2,500 words in length—excluding the abstract, tables and references. As with all submissions, check CCDR’s Information for authors , published at the beginning of each volume in January of each year for general manuscript preparation and submission requirements ( 7 ).

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case NPS+ Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research

Research Reports: Definition and How to Write Them

Reports are usually spread across a vast horizon of topics but are focused on communicating information about a particular topic and a niche target market. The primary motive of research reports is to convey integral details about a study for marketers to consider while designing new strategies.

Certain events, facts, and other information based on incidents need to be relayed to the people in charge, and creating research reports is the most effective communication tool. Ideal research reports are extremely accurate in the offered information with a clear objective and conclusion. These reports should have a clean and structured format to relay information effectively.

What are Research Reports?

Research reports are recorded data prepared by researchers or statisticians after analyzing the information gathered by conducting organized research, typically in the form of surveys or qualitative methods .

A research report is a reliable source to recount details about a conducted research. It is most often considered to be a true testimony of all the work done to garner specificities of research.

The various sections of a research report are:

• Background/Introduction
• Implemented Methods
• Results based on Analysis
• Deliberation

Learn more: Quantitative Research

Components of Research Reports

Research is imperative for launching a new product/service or a new feature. The markets today are extremely volatile and competitive due to new entrants every day who may or may not provide effective products. An organization needs to make the right decisions at the right time to be relevant in such a market with updated products that suffice customer demands.

The details of a research report may change with the purpose of research but the main components of a report will remain constant. The research approach of the market researcher also influences the style of writing reports. Here are seven main components of a productive research report:

• Research Report Summary: The entire objective along with the overview of research are to be included in a summary which is a couple of paragraphs in length. All the multiple components of the research are explained in brief under the report summary.  It should be interesting enough to capture all the key elements of the report.
• Research Introduction: There always is a primary goal that the researcher is trying to achieve through a report. In the introduction section, he/she can cover answers related to this goal and establish a thesis which will be included to strive and answer it in detail.  This section should answer an integral question: “What is the current situation of the goal?”.  After the research design was conducted, did the organization conclude the goal successfully or they are still a work in progress –  provide such details in the introduction part of the research report.
• Research Methodology: This is the most important section of the report where all the important information lies. The readers can gain data for the topic along with analyzing the quality of provided content and the research can also be approved by other market researchers . Thus, this section needs to be highly informative with each aspect of research discussed in detail.  Information needs to be expressed in chronological order according to its priority and importance. Researchers should include references in case they gained information from existing techniques.
• Research Results: A short description of the results along with calculations conducted to achieve the goal will form this section of results. Usually, the exposition after data analysis is carried out in the discussion part of the report.

Learn more: Quantitative Data

• Research Discussion: The results are discussed in extreme detail in this section along with a comparative analysis of reports that could probably exist in the same domain. Any abnormality uncovered during research will be deliberated in the discussion section.  While writing research reports, the researcher will have to connect the dots on how the results will be applicable in the real world.
• Research References and Conclusion: Conclude all the research findings along with mentioning each and every author, article or any content piece from where references were taken.

Learn more: Qualitative Observation

15 Tips for Writing Research Reports

Writing research reports in the manner can lead to all the efforts going down the drain. Here are 15 tips for writing impactful research reports:

• Prepare the context before starting to write and start from the basics:  This was always taught to us in school – be well-prepared before taking a plunge into new topics. The order of survey questions might not be the ideal or most effective order for writing research reports. The idea is to start with a broader topic and work towards a more specific one and focus on a conclusion or support, which a research should support with the facts.  The most difficult thing to do in reporting, without a doubt is to start. Start with the title, the introduction, then document the first discoveries and continue from that. Once the marketers have the information well documented, they can write a general conclusion.
• Keep the target audience in mind while selecting a format that is clear, logical and obvious to them:  Will the research reports be presented to decision makers or other researchers? What are the general perceptions around that topic? This requires more care and diligence. A researcher will need a significant amount of information to start writing the research report. Be consistent with the wording, the numbering of the annexes and so on. Follow the approved format of the company for the delivery of research reports and demonstrate the integrity of the project with the objectives of the company.
• Have a clear research objective: A researcher should read the entire proposal again, and make sure that the data they provide contributes to the objectives that were raised from the beginning. Remember that speculations are for conversations, not for research reports, if a researcher speculates, they directly question their own research.
• Establish a working model:  Each study must have an internal logic, which will have to be established in the report and in the evidence. The researchers’ worst nightmare is to be required to write research reports and realize that key questions were not included.

Learn more: Quantitative Observation

• Gather all the information about the research topic. Who are the competitors of our customers? Talk to other researchers who have studied the subject of research, know the language of the industry. Misuse of the terms can discourage the readers of research reports from reading further.
• Read aloud while writing. While reading the report, if the researcher hears something inappropriate, for example, if they stumble over the words when reading them, surely the reader will too. If the researcher can’t put an idea in a single sentence, then it is very long and they must change it so that the idea is clear to everyone.
• Check grammar and spelling. Without a doubt, good practices help to understand the report. Use verbs in the present tense. Consider using the present tense, which makes the results sound more immediate. Find new words and other ways of saying things. Have fun with the language whenever possible.
• Discuss only the discoveries that are significant. If some data are not really significant, do not mention them. Remember that not everything is truly important or essential within research reports.

Learn more: Qualitative Data

• Try and stick to the survey questions. For example, do not say that the people surveyed “were worried” about an research issue , when there are different degrees of concern.
• The graphs must be clear enough so that they understand themselves. Do not let graphs lead the reader to make mistakes: give them a title, include the indications, the size of the sample, and the correct wording of the question.
• Be clear with messages. A researcher should always write every section of the report with an accuracy of details and language.
• Be creative with titles – Particularly in segmentation studies choose names “that give life to research”. Such names can survive for a long time after the initial investigation.
• Create an effective conclusion: The conclusion in the research reports is the most difficult to write, but it is an incredible opportunity to excel. Make a precise summary. Sometimes it helps to start the conclusion with something specific, then it describes the most important part of the study, and finally, it provides the implications of the conclusions.
• Get a couple more pair of eyes to read the report. Writers have trouble detecting their own mistakes. But they are responsible for what is presented. Ensure it has been approved by colleagues or friends before sending the find draft out.

Learn more: Market Research and Analysis

MORE LIKE THIS

Data Information vs Insight: Essential differences

May 14, 2024

Pricing Analytics Software: Optimize Your Pricing Strategy

May 13, 2024

Relationship Marketing: What It Is, Examples & Top 7 Benefits

May 8, 2024

The Best Email Survey Tool to Boost Your Feedback Game

May 7, 2024

Other categories

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Uncategorized
• Video Learning Series
• What’s Coming Up
• Workforce Intelligence

• Research Report: Definition, Types + [Writing Guide]

One of the reasons for carrying out research is to add to the existing body of knowledge. Therefore, when conducting research, you need to document your processes and findings in a research report. 

With a research report, it is easy to outline the findings of your systematic investigation and any gaps needing further inquiry. Knowing how to create a detailed research report will prove useful when you need to conduct research.  

What is a Research Report?

A research report is a well-crafted document that outlines the processes, data, and findings of a systematic investigation. It is an important document that serves as a first-hand account of the research process, and it is typically considered an objective and accurate source of information.

In many ways, a research report can be considered as a summary of the research process that clearly highlights findings, recommendations, and other important details. Reading a well-written research report should provide you with all the information you need about the core areas of the research process.

Features of a Research Report 

So how do you recognize a research report when you see one? Here are some of the basic features that define a research report. 

• It is a detailed presentation of research processes and findings, and it usually includes tables and graphs. 
• It is written in a formal language.
• A research report is usually written in the third person.
• It is informative and based on first-hand verifiable information.
• It is formally structured with headings, sections, and bullet points.
• It always includes recommendations for future actions. 

Types of Research Report 

The research report is classified based on two things; nature of research and target audience.

Nature of Research

• Qualitative Research Report

This is the type of report written for qualitative research . It outlines the methods, processes, and findings of a qualitative method of systematic investigation. In educational research, a qualitative research report provides an opportunity for one to apply his or her knowledge and develop skills in planning and executing qualitative research projects.

A qualitative research report is usually descriptive in nature. Hence, in addition to presenting details of the research process, you must also create a descriptive narrative of the information.

• Quantitative Research Report

A quantitative research report is a type of research report that is written for quantitative research. Quantitative research is a type of systematic investigation that pays attention to numerical or statistical values in a bid to find answers to research questions. 

In this type of research report, the researcher presents quantitative data to support the research process and findings. Unlike a qualitative research report that is mainly descriptive, a quantitative research report works with numbers; that is, it is numerical in nature. 

Target Audience

Also, a research report can be said to be technical or popular based on the target audience. If you’re dealing with a general audience, you would need to present a popular research report, and if you’re dealing with a specialized audience, you would submit a technical report. 

• Technical Research Report

A technical research report is a detailed document that you present after carrying out industry-based research. This report is highly specialized because it provides information for a technical audience; that is, individuals with above-average knowledge in the field of study. 

In a technical research report, the researcher is expected to provide specific information about the research process, including statistical analyses and sampling methods. Also, the use of language is highly specialized and filled with jargon. 

Examples of technical research reports include legal and medical research reports. 

• Popular Research Report

A popular research report is one for a general audience; that is, for individuals who do not necessarily have any knowledge in the field of study. A popular research report aims to make information accessible to everyone. 

It is written in very simple language, which makes it easy to understand the findings and recommendations. Examples of popular research reports are the information contained in newspapers and magazines. 

Importance of a Research Report 

• Knowledge Transfer: As already stated above, one of the reasons for carrying out research is to contribute to the existing body of knowledge, and this is made possible with a research report. A research report serves as a means to effectively communicate the findings of a systematic investigation to all and sundry.  
• Identification of Knowledge Gaps: With a research report, you’d be able to identify knowledge gaps for further inquiry. A research report shows what has been done while hinting at other areas needing systematic investigation. 
• In market research, a research report would help you understand the market needs and peculiarities at a glance. 
• A research report allows you to present information in a precise and concise manner. 
• It is time-efficient and practical because, in a research report, you do not have to spend time detailing the findings of your research work in person. You can easily send out the report via email and have stakeholders look at it. 

Guide to Writing a Research Report

A lot of detail goes into writing a research report, and getting familiar with the different requirements would help you create the ideal research report. A research report is usually broken down into multiple sections, which allows for a concise presentation of information.

Structure and Example of a Research Report

This is the title of your systematic investigation. Your title should be concise and point to the aims, objectives, and findings of a research report. 

• Table of Contents

This is like a compass that makes it easier for readers to navigate the research report.

An abstract is an overview that highlights all important aspects of the research including the research method, data collection process, and research findings. Think of an abstract as a summary of your research report that presents pertinent information in a concise manner. 

An abstract is always brief; typically 100-150 words and goes straight to the point. The focus of your research abstract should be the 5Ws and 1H format – What, Where, Why, When, Who and How. 

• Introduction

Here, the researcher highlights the aims and objectives of the systematic investigation as well as the problem which the systematic investigation sets out to solve. When writing the report introduction, it is also essential to indicate whether the purposes of the research were achieved or would require more work.

In the introduction section, the researcher specifies the research problem and also outlines the significance of the systematic investigation. Also, the researcher is expected to outline any jargons and terminologies that are contained in the research.  

• Literature Review

A literature review is a written survey of existing knowledge in the field of study. In other words, it is the section where you provide an overview and analysis of different research works that are relevant to your systematic investigation. 

It highlights existing research knowledge and areas needing further investigation, which your research has sought to fill. At this stage, you can also hint at your research hypothesis and its possible implications for the existing body of knowledge in your field of study. 

• An Account of Investigation

This is a detailed account of the research process, including the methodology, sample, and research subjects. Here, you are expected to provide in-depth information on the research process including the data collection and analysis procedures. 

In a quantitative research report, you’d need to provide information surveys, questionnaires and other quantitative data collection methods used in your research. In a qualitative research report, you are expected to describe the qualitative data collection methods used in your research including interviews and focus groups. 

In this section, you are expected to present the results of the systematic investigation. 

This section further explains the findings of the research, earlier outlined. Here, you are expected to present a justification for each outcome and show whether the results are in line with your hypotheses or if other research studies have come up with similar results.

• Conclusions

This is a summary of all the information in the report. It also outlines the significance of the entire study. 

• References and Appendices

This section contains a list of all the primary and secondary research sources. 

Tips for Writing a Research Report

• Define the Context for the Report

As is obtainable when writing an essay, defining the context for your research report would help you create a detailed yet concise document. This is why you need to create an outline before writing so that you do not miss out on anything. 

• Define your Audience

Writing with your audience in mind is essential as it determines the tone of the report. If you’re writing for a general audience, you would want to present the information in a simple and relatable manner. For a specialized audience, you would need to make use of technical and field-specific terms. 

• Include Significant Findings

The idea of a research report is to present some sort of abridged version of your systematic investigation. In your report, you should exclude irrelevant information while highlighting only important data and findings. 

• Include Illustrations

Your research report should include illustrations and other visual representations of your data. Graphs, pie charts, and relevant images lend additional credibility to your systematic investigation.

• Choose the Right Title

A good research report title is brief, precise, and contains keywords from your research. It should provide a clear idea of your systematic investigation so that readers can grasp the entire focus of your research from the title. 

• Proofread the Report

Before publishing the document, ensure that you give it a second look to authenticate the information. If you can, get someone else to go through the report, too, and you can also run it through proofreading and editing software. 

How to Gather Research Data for Your Report  

• Understand the Problem

Every research aims at solving a specific problem or set of problems, and this should be at the back of your mind when writing your research report. Understanding the problem would help you to filter the information you have and include only important data in your report. 

• Know what your report seeks to achieve

This is somewhat similar to the point above because, in some way, the aim of your research report is intertwined with the objectives of your systematic investigation. Identifying the primary purpose of writing a research report would help you to identify and present the required information accordingly. 

• Identify your audience

Knowing your target audience plays a crucial role in data collection for a research report. If your research report is specifically for an organization, you would want to present industry-specific information or show how the research findings are relevant to the work that the company does. 

• Create Surveys/Questionnaires

A survey is a research method that is used to gather data from a specific group of people through a set of questions. It can be either quantitative or qualitative. 

A survey is usually made up of structured questions, and it can be administered online or offline. However, an online survey is a more effective method of research data collection because it helps you save time and gather data with ease. 

You can seamlessly create an online questionnaire for your research on Formplus . With the multiple sharing options available in the builder, you would be able to administer your survey to respondents in little or no time. 

Formplus also has a report summary too l that you can use to create custom visual reports for your research.

Step-by-step guide on how to create an online questionnaire using Formplus  

• Sign into Formplus

In the Formplus builder, you can easily create different online questionnaires for your research by dragging and dropping preferred fields into your form. To access the Formplus builder, you will need to create an account on Formplus. 

Once you do this, sign in to your account and click on Create new form to begin. 

• Edit Form Title : Click on the field provided to input your form title, for example, “Research Questionnaire.”
• Edit Form : Click on the edit icon to edit the form.
• Add Fields : Drag and drop preferred form fields into your form in the Formplus builder inputs column. There are several field input options for questionnaires in the Formplus builder. 
• Edit fields
• Click on “Save”
• Form Customization: With the form customization options in the form builder, you can easily change the outlook of your form and make it more unique and personalized. Formplus allows you to change your form theme, add background images, and even change the font according to your needs. 
• Multiple Sharing Options: Formplus offers various form-sharing options, which enables you to share your questionnaire with respondents easily. You can use the direct social media sharing buttons to share your form link to your organization’s social media pages.  You can also send out your survey form as email invitations to your research subjects too. If you wish, you can share your form’s QR code or embed it on your organization’s website for easy access. 

Conclusion  

Always remember that a research report is just as important as the actual systematic investigation because it plays a vital role in communicating research findings to everyone else. This is why you must take care to create a concise document summarizing the process of conducting any research. 

In this article, we’ve outlined essential tips to help you create a research report. When writing your report, you should always have the audience at the back of your mind, as this would set the tone for the document. 

Connect to Formplus, Get Started Now - It's Free!

• ethnographic research survey
• research report
• research report survey
• busayo.longe

You may also like:

Ethnographic Research: Types, Methods + [Question Examples]

Simple guide on ethnographic research, it types, methods, examples and advantages. Also highlights how to conduct an ethnographic...

How to Write a Problem Statement for your Research

Learn how to write problem statements before commencing any research effort. Learn about its structure and explore examples

Assessment Tools: Types, Examples & Importance

In this article, you’ll learn about different assessment tools to help you evaluate performance in various contexts

21 Chrome Extensions for Academic Researchers in 2022

In this article, we will discuss a number of chrome extensions you can use to make your research process even seamless

Formplus - For Seamless Data Collection

Collect data the right way with a versatile data collection tool. try formplus and transform your work productivity today..

Research report guide: Definition, types, and tips

Last updated

5 March 2024

Reviewed by

From successful product launches or software releases to planning major business decisions, research reports serve many vital functions. They can summarize evidence and deliver insights and recommendations to save companies time and resources. They can reveal the most value-adding actions a company should take.

However, poorly constructed reports can have the opposite effect! Taking the time to learn established research-reporting rules and approaches will equip you with in-demand skills. You’ll be able to capture and communicate information applicable to numerous situations and industries, adding another string to your resume bow.

• What are research reports?

A research report is a collection of contextual data, gathered through organized research, that provides new insights into a particular challenge (which, for this article, is business-related). Research reports are a time-tested method for distilling large amounts of data into a narrow band of focus.

Their effectiveness often hinges on whether the report provides:

Strong, well-researched evidence

Comprehensive analysis

Well-considered conclusions and recommendations

Though the topic possibilities are endless, an effective research report keeps a laser-like focus on the specific questions or objectives the researcher believes are key to achieving success. Many research reports begin as research proposals, which usually include the need for a report to capture the findings of the study and recommend a course of action.

A description of the research method used, e.g., qualitative, quantitative, or other

Statistical analysis

Causal (or explanatory) research (i.e., research identifying relationships between two variables)

Inductive research, also known as ‘theory-building’

Deductive research, such as that used to test theories

Action research, where the research is actively used to drive change

• Importance of a research report

Research reports can unify and direct a company's focus toward the most appropriate strategic action. Of course, spending resources on a report takes up some of the company's human and financial resources. Choosing when a report is called for is a matter of judgment and experience.

Some development models used heavily in the engineering world, such as Waterfall development, are notorious for over-relying on research reports. With Waterfall development, there is a linear progression through each step of a project, and each stage is precisely documented and reported on before moving to the next.

The pace of the business world is faster than the speed at which your authors can produce and disseminate reports. So how do companies strike the right balance between creating and acting on research reports?

The answer lies, again, in the report's defined objectives. By paring down your most pressing interests and those of your stakeholders, your research and reporting skills will be the lenses that keep your company's priorities in constant focus.

Honing your company's primary objectives can save significant amounts of time and align research and reporting efforts with ever-greater precision.

Some examples of well-designed research objectives are:

Proving whether or not a product or service meets customer expectations

Demonstrating the value of a service, product, or business process to your stakeholders and investors

Improving business decision-making when faced with a lack of time or other constraints

Clarifying the relationship between a critical cause and effect for problematic business processes

Prioritizing the development of a backlog of products or product features

Comparing business or production strategies

Evaluating past decisions and predicting future outcomes

• Features of a research report

Research reports generally require a research design phase, where the report author(s) determine the most important elements the report must contain.

Just as there are various kinds of research, there are many types of reports.

Here are the standard elements of almost any research-reporting format:

Report summary. A broad but comprehensive overview of what readers will learn in the full report. Summaries are usually no more than one or two paragraphs and address all key elements of the report. Think of the key takeaways your primary stakeholders will want to know if they don’t have time to read the full document.

Introduction. Include a brief background of the topic, the type of research, and the research sample. Consider the primary goal of the report, who is most affected, and how far along the company is in meeting its objectives.

Methods. A description of how the researcher carried out data collection, analysis, and final interpretations of the data. Include the reasons for choosing a particular method. The methods section should strike a balance between clearly presenting the approach taken to gather data and discussing how it is designed to achieve the report's objectives.

Data analysis. This section contains interpretations that lead readers through the results relevant to the report's thesis. If there were unexpected results, include here a discussion on why that might be. Charts, calculations, statistics, and other supporting information also belong here (or, if lengthy, as an appendix). This should be the most detailed section of the research report, with references for further study. Present the information in a logical order, whether chronologically or in order of importance to the report's objectives.

Conclusion. This should be written with sound reasoning, often containing useful recommendations. The conclusion must be backed by a continuous thread of logic throughout the report.

• How to write a research paper

With a clear outline and robust pool of research, a research paper can start to write itself, but what's a good way to start a research report?

Research report examples are often the quickest way to gain inspiration for your report. Look for the types of research reports most relevant to your industry and consider which makes the most sense for your data and goals.

The research report outline will help you organize the elements of your report. One of the most time-tested report outlines is the IMRaD structure:

Introduction

...and Discussion

Pay close attention to the most well-established research reporting format in your industry, and consider your tone and language from your audience's perspective. Learn the key terms inside and out; incorrect jargon could easily harm the perceived authority of your research paper.

Along with a foundation in high-quality research and razor-sharp analysis, the most effective research reports will also demonstrate well-developed:

Internal logic

Narrative flow

Conclusions and recommendations

Readability, striking a balance between simple phrasing and technical insight

How to gather research data for your report

The validity of research data is critical. Because the research phase usually occurs well before the writing phase, you normally have plenty of time to vet your data.

However, research reports could involve ongoing research, where report authors (sometimes the researchers themselves) write portions of the report alongside ongoing research.

One such research-report example would be an R&D department that knows its primary stakeholders are eager to learn about a lengthy work in progress and any potentially important outcomes.

However you choose to manage the research and reporting, your data must meet robust quality standards before you can rely on it. Vet any research with the following questions in mind:

Does it use statistically valid analysis methods?

Do the researchers clearly explain their research, analysis, and sampling methods?

Did the researchers provide any caveats or advice on how to interpret their data?

Have you gathered the data yourself or were you in close contact with those who did?

Is the source biased?

Usually, flawed research methods become more apparent the further you get through a research report.

It's perfectly natural for good research to raise new questions, but the reader should have no uncertainty about what the data represents. There should be no doubt about matters such as:

Whether the sampling or analysis methods were based on sound and consistent logic

What the research samples are and where they came from

The accuracy of any statistical functions or equations

Validation of testing and measuring processes

When does a report require design validation?

A robust design validation process is often a gold standard in highly technical research reports. Design validation ensures the objects of a study are measured accurately, which lends more weight to your report and makes it valuable to more specialized industries.

Product development and engineering projects are the most common research-report examples that typically involve a design validation process. Depending on the scope and complexity of your research, you might face additional steps to validate your data and research procedures.

If you’re including design validation in the report (or report proposal), explain and justify your data-collection processes. Good design validation builds greater trust in a research report and lends more weight to its conclusions.

Choosing the right analysis method

Just as the quality of your report depends on properly validated research, a useful conclusion requires the most contextually relevant analysis method. This means comparing different statistical methods and choosing the one that makes the most sense for your research.

Most broadly, research analysis comes down to quantitative or qualitative methods (respectively: measurable by a number vs subjectively qualified values). There are also mixed research methods, which bridge the need for merging hard data with qualified assessments and still reach a cohesive set of conclusions.

Some of the most common analysis methods in research reports include:

Significance testing (aka hypothesis analysis), which compares test and control groups to determine how likely the data was the result of random chance.

Regression analysis , to establish relationships between variables, control for extraneous variables , and support correlation analysis.

Correlation analysis (aka bivariate testing), a method to identify and determine the strength of linear relationships between variables. It’s effective for detecting patterns from complex data, but care must be exercised to not confuse correlation with causation.

With any analysis method, it's important to justify which method you chose in the report. You should also provide estimates of the statistical accuracy (e.g., the p-value or confidence level of quantifiable data) of any data analysis.

This requires a commitment to the report's primary aim. For instance, this may be achieving a certain level of customer satisfaction by analyzing the cause and effect of changes to how service is delivered. Even better, use statistical analysis to calculate which change is most positively correlated with improved levels of customer satisfaction.

• Tips for writing research reports

There's endless good advice for writing effective research reports, and it almost all depends on the subjective aims of the people behind the report. Due to the wide variety of research reports, the best tips will be unique to each author's purpose.

Consider the following research report tips in any order, and take note of the ones most relevant to you:

No matter how in depth or detailed your report might be, provide a well-considered, succinct summary. At the very least, give your readers a quick and effective way to get up to speed.

Pare down your target audience (e.g., other researchers, employees, laypersons, etc.), and adjust your voice for their background knowledge and interest levels

For all but the most open-ended research, clarify your objectives, both for yourself and within the report.

Leverage your team members’ talents to fill in any knowledge gaps you might have. Your team is only as good as the sum of its parts.

Justify why your research proposal’s topic will endure long enough to derive value from the finished report.

Consolidate all research and analysis functions onto a single user-friendly platform. There's no reason to settle for less than developer-grade tools suitable for non-developers.

What's the format of a research report?

The research-reporting format is how the report is structured—a framework the authors use to organize their data, conclusions, arguments, and recommendations. The format heavily determines how the report's outline develops, because the format dictates the overall structure and order of information (based on the report's goals and research objectives).

What's the purpose of a research-report outline?

A good report outline gives form and substance to the report's objectives, presenting the results in a readable, engaging way. For any research-report format, the outline should create momentum along a chain of logic that builds up to a conclusion or interpretation.

What's the difference between a research essay and a research report?

There are several key differences between research reports and essays:

Research report:

Ordered into separate sections

More commercial in nature

Often includes infographics

Heavily descriptive

More self-referential

Usually provides recommendations

Research essay

Does not rely on research report formatting

More academically minded

Normally text-only

Less detailed

Omits discussion of methods

Usually non-prescriptive 

Should you be using a customer insights hub?

Do you want to discover previous research faster?

Do you share your research findings with others?

Do you analyze research data?

Start for free today, add your research, and get to key insights faster

Editor’s picks

Last updated: 11 January 2024

Last updated: 15 January 2024

Last updated: 17 January 2024

Last updated: 12 May 2023

Last updated: 30 April 2024

Last updated: 18 May 2023

Last updated: 25 November 2023

Last updated: 13 May 2024

Latest articles

Related topics, .css-je19u9{-webkit-align-items:flex-end;-webkit-box-align:flex-end;-ms-flex-align:flex-end;align-items:flex-end;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-direction:row;-ms-flex-direction:row;flex-direction:row;-webkit-box-flex-wrap:wrap;-webkit-flex-wrap:wrap;-ms-flex-wrap:wrap;flex-wrap:wrap;-webkit-box-pack:center;-ms-flex-pack:center;-webkit-justify-content:center;justify-content:center;row-gap:0;text-align:center;max-width:671px;}@media (max-width: 1079px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}}@media (max-width: 799px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}} decide what to .css-1kiodld{max-height:56px;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}@media (max-width: 1079px){.css-1kiodld{display:none;}} build next, decide what to build next.

Users report unexpectedly high data usage, especially during streaming sessions.

Users find it hard to navigate from the home page to relevant playlists in the app.

It would be great to have a sleep timer feature, especially for bedtime listening.

I need better filters to find the songs or artists I’m looking for.

Log in or sign up

Get started for free

How to Write Data Analysis Reports in 9 Easy Steps

Table of contents

Enjoy reading this blog post written by our experts or partners.

If you want to see what Databox can do for you, click here .

Imagine a bunch of bricks. They don’t have a purpose until you put them together into a house, do they?

In business intelligence, data is your building material, and a quality data analysis report is what you want to see as the result.

But if you’ve ever tried to use the collected data and assemble it into an insightful report, you know it’s not an easy job to do. Data is supposed to tell a story about your performance, but there’s a long way from unprocessed, raw data to a meaningful narrative that you can use to create an actionable plan for making steady progress towards your goals.

This article will help you improve the quality of your data analysis reports and build them effortlessly and fast. Let’s jump right in.

What Is a Data Analysis Report?

Why is data analysis reporting important, how to write a data analysis report 9 simple steps, data analysis report examples.

A data analysis report is a type of business report in which you present quantitative and qualitative data to evaluate your strategies and performance. Based on this data, you give recommendations for further steps and business decisions while using the data as evidence that backs up your evaluation.

Today, data analysis is one of the most important elements of business intelligence strategies as companies have realized the potential of having data-driven insights at hand to help them make data-driven decisions.

Just like you’ll look at your car’s dashboard if something’s wrong, you’ll pull your data to see what’s causing drops in website traffic, conversions, or sales – or any other business metric you may be following. This unprocessed data still doesn’t give you a diagnosis – it’s the first step towards a quality analysis. Once you’ve extracted and organized your data, it’s important to use graphs and charts to visualize it and make it easier to draw conclusions.

Once you add meaning to your data and create suggestions based on it, you have a data analysis report.

A vital detail everyone should know about data analysis reports is their accessibility for everyone in your team, and the ability to innovate. Your analysis report will contain your vital KPIs, so you can see where you’re reaching your targets and achieving goals, and where you need to speed up your activities or optimize your strategy. If you can uncover trends or patterns in your data, you can use it to innovate and stand out by offering even more valuable content, services, or products to your audience.

Data analysis is vital for companies for several reasons.

A reliable source of information

Trusting your intuition is fine, but relying on data is safer. When you can base your action plan on data that clearly shows that something is working or failing, you won’t only justify your decisions in front of the management, clients, or investors, but you’ll also be sure that you’ve taken appropriate steps to fix an issue or seize an important opportunity.

A better understanding of your business

According to Databox’s State of Business Reporting , most companies stated that regular monitoring and reporting improved progress monitoring, increased team effectiveness, allowed them to identify trends more easily, and improved financial performance. Data analysis makes it easier to understand your business as a whole, and each aspect individually. You can see how different departments analyze their workflow and how each step impacts their results in the end, by following their KPIs over time. Then, you can easily conclude what your business needs to grow – to boost your sales strategy, optimize your finances, or up your SEO game, for example.

An additional way to understand your business better is to compare your most important metrics and KPIs against companies that are just like yours. With Databox Benchmarks , you will need only one spot to see how all of your teams stack up against your peers and competitors.

Instantly and Anonymously Benchmark Your Company’s Performance Against Others Just Like You

If you ever asked yourself:

• How does our marketing stack up against our competitors?
• Are our salespeople as productive as reps from similar companies?
• Are our profit margins as high as our peers?

Databox Benchmark Groups can finally help you answer these questions and discover how your company measures up against similar companies based on your KPIs.

When you join Benchmark Groups, you will:

• Get instant, up-to-date data on how your company stacks up against similar companies based on the metrics most important to you. Explore benchmarks for dozens of metrics, built on anonymized data from thousands of companies and get a full 360° view of your company’s KPIs across sales, marketing, finance, and more.
• Understand where your business excels and where you may be falling behind so you can shift to what will make the biggest impact. Leverage industry insights to set more effective, competitive business strategies. Explore where exactly you have room for growth within your business based on objective market data.
• Keep your clients happy by using data to back up your expertise. Show your clients where you’re helping them overperform against similar companies. Use the data to show prospects where they really are… and the potential of where they could be.
• Get a valuable asset for improving yearly and quarterly planning . Get valuable insights into areas that need more work. Gain more context for strategic planning.

The best part?

• Benchmark Groups are free to access.
• The data is 100% anonymized. No other company will be able to see your performance, and you won’t be able to see the performance of individual companies either.

When it comes to showing you how your performance compares to others, here is what it might look like for the metric Average Session Duration:

And here is an example of an open group you could join:

And this is just a fraction of what you’ll get. With Databox Benchmarks, you will need only one spot to see how all of your teams stack up — marketing, sales, customer service, product development, finance, and more. 

• Choose criteria so that the Benchmark is calculated using only companies like yours
• Narrow the benchmark sample using criteria that describe your company
• Display benchmarks right on your Databox dashboards

Sounds like something you want to try out? Join a Databox Benchmark Group today!

It makes data accessible to everyone

Data doesn’t represent a magical creature reserved for data scientists only anymore. Now that you have streamlined and easy-to-follow data visualizations and tools that automatically show the latest figures, you can include everyone in the decision-making process as they’ll understand what means what in the charts and tables. The data may be complex, but it becomes easy to read when combined with proper illustrations. And when your teams gain such useful and accessible insight, they will feel motivated to act on it immediately.

Better collaboration

Data analysis reports help teams collaborate better, as well. You can apply the SMART technique to your KPIs and goals, because your KPIs become assignable. When they’re easy to interpret for your whole team, you can assign each person with one or multiple KPIs that they’ll be in charge of. That means taking a lot off a team leader’s plate so they can focus more on making other improvements in the business. At the same time, removing inaccurate data from your day-to-day operations will improve friction between different departments, like marketing and sales, for instance.

More productivity

You can also expect increased productivity, since you’ll be saving time you’d otherwise spend on waiting for specialists to translate data for other departments, etc. This means your internal procedures will also be on a top level.

Want to give value with your data analysis report? It’s critical to master the skill of writing a quality data analytics report. Want to know how to report on data efficiently? We’ll share our secret in the following section.

• Start with an Outline
• Make a Selection of Vital KPIs
• Pick the Right Charts for Appealing Design
• Use a Narrative
• Organize the Information
• Include a Summary
• Careful with Your Recommendations
• Double-Check Everything
• Use Interactive Dashboards

1. Start with an Outline

If you start writing without having a clear idea of what your data analysis report is going to include, it may get messy. Important insights may slip through your fingers, and you may stray away too far from the main topic. To avoid this, start the report by writing an outline first. Plan the structure and contents of each section first to make sure you’ve covered everything, and only then start crafting the report.

2. Make a Selection of Vital KPIs

Don’t overwhelm the audience by including every single metric there is. You can discuss your whole dashboard in a meeting with your team, but if you’re creating data analytics reports or marketing reports for other departments or the executives, it’s best to focus on the most relevant KPIs that demonstrate the data important for the overall business performance.

PRO TIP: How Well Are Your Marketing KPIs Performing?

Like most marketers and marketing managers, you want to know how well your efforts are translating into results each month. How much traffic and new contact conversions do you get? How many new contacts do you get from organic sessions? How are your email campaigns performing? How well are your landing pages converting? You might have to scramble to put all of this together in a single report, but now you can have it all at your fingertips in a single Databox dashboard.

Our Marketing Overview Dashboard includes data from Google Analytics 4 and HubSpot Marketing with key performance metrics like:

• Sessions . The number of sessions can tell you how many times people are returning to your website. Obviously, the higher the better.
• New Contacts from Sessions . How well is your campaign driving new contacts and customers?
• Marketing Performance KPIs . Tracking the number of MQLs, SQLs, New Contacts and similar will help you identify how your marketing efforts contribute to sales.
• Email Performance . Measure the success of your email campaigns from HubSpot. Keep an eye on your most important email marketing metrics such as number of sent emails, number of opened emails, open rate, email click-through rate, and more.
• Blog Posts and Landing Pages . How many people have viewed your blog recently? How well are your landing pages performing?

Now you can benefit from the experience of our Google Analytics and HubSpot Marketing experts, who have put together a plug-and-play Databox template that contains all the essential metrics for monitoring your leads. It’s simple to implement and start using as a standalone dashboard or in marketing reports, and best of all, it’s free!

You can easily set it up in just a few clicks – no coding required.

To set up the dashboard, follow these 3 simple steps:

Step 1: Get the template 

Step 2: Connect your HubSpot and Google Analytics 4 accounts with Databox. 

Step 3: Watch your dashboard populate in seconds.

3. Pick the Right Charts for Appealing Design

If you’re showing historical data – for instance, how you’ve performed now compared to last month – it’s best to use timelines or graphs. For other data, pie charts or tables may be more suitable. Make sure you use the right data visualization to display your data accurately and in an easy-to-understand manner.

4. Use a Narrative

Do you work on analytics and reporting ? Just exporting your data into a spreadsheet doesn’t qualify as either of them. The fact that you’re dealing with data may sound too technical, but actually, your report should tell a story about your performance. What happened on a specific day? Did your organic traffic increase or suddenly drop? Why? And more. There are a lot of questions to answer and you can put all the responses together in a coherent, understandable narrative.

5. Organize the Information

Before you start writing or building your dashboard, choose how you’re going to organize your data. Are you going to talk about the most relevant and general ones first? It may be the best way to start the report – the best practices typically involve starting with more general information and then diving into details if necessary.

6. Include a Summary

Some people in your audience won’t have the time to read the whole report, but they’ll want to know about your findings. Besides, a summary at the beginning of your data analytics report will help the reader get familiar with the topic and the goal of the report. And a quick note: although the summary should be placed at the beginning, you usually write it when you’re done with the report. When you have the whole picture, it’s easier to extract the key points that you’ll include in the summary.

7. Careful with Your Recommendations

Your communication skills may be critical in data analytics reports. Know that some of the results probably won’t be satisfactory, which means that someone’s strategy failed. Make sure you’re objective in your recommendations and that you’re not looking for someone to blame. Don’t criticize, but give suggestions on how things can be improved. Being solution-oriented is much more important and helpful for the business.

8. Double-Check Everything

The whole point of using data analytics tools and data, in general, is to achieve as much accuracy as possible. Avoid manual mistakes by proofreading your report when you finish, and if possible, give it to another person so they can confirm everything’s in place.

9. Use Interactive Dashboards

Using the right tools is just as important as the contents of your data analysis. The way you present it can make or break a good report, regardless of how valuable the data is. That said, choose a great reporting tool that can automatically update your data and display it in a visually appealing manner. Make sure it offers streamlined interactive dashboards that you can also customize depending on the purpose of the report.

To wrap up the guide, we decided to share nine excellent examples of what awesome data analysis reports can look like. You’ll learn what metrics you should include and how to organize them in logical sections to make your report beautiful and effective.

• Marketing Data Analysis Report Example

SEO Data Analysis Report Example

Sales data analysis report example.

• Customer Support Data Analysis Report Example

Help Desk Data Analysis Report Example

Ecommerce data analysis report example, project management data analysis report example, social media data analysis report example, financial kpi data analysis report example, marketing data report example.

If you need an intuitive dashboard that allows you to track your website performance effortlessly and monitor all the relevant metrics such as website sessions, pageviews, or CTA engagement, you’ll love this free HubSpot Marketing Website Overview dashboard template .

Tracking the performance of your SEO efforts is important. You can easily monitor relevant SEO KPIs like clicks by page, engaged sessions, or views by session medium by downloading this Google Organic SEO Dashboard .

How successful is your sales team? It’s easy to analyze their performance and predict future growth if you choose this HubSpot CRM Sales Analytics Overview dashboard template and track metrics such as average time to close the deal, new deals amount, or average revenue per new client.

Customer Support Analysis Data Report Example

Customer support is one of the essential factors that impact your business growth. You can use this streamlined, customizable Customer Success dashboard template . In a single dashboard, you can monitor metrics such as customer satisfaction score, new MRR, or time to first response time.

Other than being free and intuitive, this HelpScout for Customer Support dashboard template is also customizable and enables you to track the most vital metrics that indicate your customer support agents’ performance: handle time, happiness score, interactions per resolution, and more.

Is your online store improving or failing? You can easily collect relevant data about your store and monitor the most important metrics like total sales, orders placed, and new customers by downloading this WooCommerce Shop Overview dashboard template .

Does your IT department need feedback on their project management performance? Download this Jira dashboard template to track vital metrics such as issues created or resolved, issues by status, etc. Jira enables you to gain valuable insights into your teams’ productivity.

Need to know if your social media strategy is successful? You can find that out by using this easy-to-understand Social Media Awareness & Engagement dashboard template . Here you can monitor and analyze metrics like sessions by social source, track the number of likes and followers, and measure the traffic from each source.

Tracking your finances is critical for keeping your business profitable. If you want to monitor metrics such as the number of open invoices, open deals amount by stage by pipeline, or closed-won deals, use this free QuickBooks + HubSpot CRM Financial Performance dashboard template .

Rely on Accurate Data with Databox

“I don’t have time to build custom reports from scratch.”

“It takes too long and becomes daunting very soon.”

“I’m not sure how to organize the data to make it effective and prove the value of my work.”

Does this sound like you?

Well, it’s something we all said at some point – creating data analytics reports can be time-consuming and tiring. And you’re still not sure if the report is compelling and understandable enough when you’re done.

That’s why we decided to create Databox dashboards – a world-class solution for saving your money and time. We build streamlined and easy-to-follow dashboards that include all the metrics that you may need and allow you to create custom ones if necessary. That way, you can use templates and adjust them to any new project or client without having to build a report from scratch.

You can skip the setup and get your first dashboard for free in just 24 hours, with our fantastic customer support team on the line to assist you with the metrics you should track and the structure you should use.

Enjoy crafting brilliant data analysis reports that will improve your business – it’s never been faster and more effortless. Sign up today and get your free dashboard in no time.

Do you want an All-in-One Analytics Platform?

Hey, we’re Databox. Our mission is to help businesses save time and grow faster. Click here to see our platform in action. 

• Databox Benchmarks
• Future Value Calculator
• ROI Calculator
• Return On Ads Calculator
• Percentage Growth Rate Calculator
• Report Automation
• Client Reporting
• What is a KPI?
• Google Sheets KPIs
• Sales Analysis Report
• Shopify Reports
• Data Analysis Report
• Google Sheets Dashboard
• Best Dashboard Examples
• Analysing Data
• Marketing Agency KPIs
• Automate Agency Google Ads Report
• Marketing Research Report
• Social Media Dashboard Examples
• Ecom Dashboard Examples

Does Your Performance Stack Up?

Are you maximizing your business potential? Stop guessing and start comparing with companies like yours.

A Message From Our CEO

At Databox, we’re obsessed with helping companies more easily monitor, analyze, and report their results. Whether it’s the resources we put into building and maintaining integrations with 100+ popular marketing tools, enabling customizability of charts, dashboards, and reports, or building functionality to make analysis, benchmarking, and forecasting easier, we’re constantly trying to find ways to help our customers save time and deliver better results.

Stefana Zarić is a freelance writer & content marketer. Other than writing for SaaS and fintech clients, she educates future writers who want to build a career in marketing. When not working, Stefana loves to read books, play with her kid, travel, and dance.

Get practical strategies that drive consistent growth

12 Tips for Developing a Successful Data Analytics Strategy

What Is Data Reporting and How to Create Data Reports for Your Business

What is kpi reporting kpi report examples, tips, and best practices.

Build your first dashboard in 5 minutes or less

Latest from our blog

• BTB: Mastering Data-Driven Legal Marketing Success (w/ Guy Alvarez, Good2BSocial) May 15, 2024
• The State of B2B Content Creation: Navigating the Future of In-House Marketing Innovation May 9, 2024
• Metrics & KPIs
• vs. Tableau
• vs. Looker Studio
• vs. Klipfolio
• vs. Power BI
• vs. Whatagraph
• vs. AgencyAnalytics
• Product & Engineering
• Inside Databox
• Terms of Service
• Privacy Policy
• Talent Resources
• We're Hiring!
• Help Center
• API Documentation

Loading metrics

Open Access

Eleven quick tips for finding research data

Contributed equally to this work with: Kathleen Gregory, Siri Jodha Khalsa, William K. Michener, Fotis E. Psomopoulos, Anita de Waard, Mingfang Wu

Affiliation Data Archiving and Networked Services, Royal Netherlands Academy of Arts and Sciences, The Hague, Netherlands

Affiliation National Snow and Ice Data Centre, Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado, United States of America

Affiliation College of University Libraries & Learning Sciences, The University of New Mexico, Albuquerque, New Mexico, United States of America

Affiliation Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece

Affiliation Research Data Management Solutions, Elsevier, Jericho, Vermont, United States of America

* E-mail: [email protected]

Affiliation Australia National Data Service, Melbourne, Australia

• Kathleen Gregory, 
• Siri Jodha Khalsa, 
• William K. Michener, 
• Fotis E. Psomopoulos, 
• Anita de Waard, 
• Mingfang Wu

Published: April 12, 2018

• https://doi.org/10.1371/journal.pcbi.1006038
• Reader Comments

Citation: Gregory K, Khalsa SJ, Michener WK, Psomopoulos FE, de Waard A, Wu M (2018) Eleven quick tips for finding research data. PLoS Comput Biol 14(4): e1006038. https://doi.org/10.1371/journal.pcbi.1006038

Editor: Francis Ouellette, Genome Quebec, CANADA

Copyright: © 2018 Gregory et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: William K. Michener was supported by NSF (#IIA-1301346 and #ACI-1430508). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

This is a PLOS Computational Biology Education paper.

Introduction

Over the past decades, science has experienced rapid growth in the volume of data available for research—from a relative paucity of data in many areas to what has been recently described as a data deluge [‎ 1 ]. Data volumes have increased exponentially across all fields of science and human endeavour, including data from sky, earth, and ocean observatories; social media such as Facebook and Twitter; wearable health-monitoring devices; gene sequences and protein structures; and climate simulations [‎ 2 ]. This brings opportunities to enable more research, especially cross-disciplinary research that could not be done before. However, it also introduces challenges in managing, describing, and making data findable, accessible, interoperable, and reusable by researchers [‎ 3 ].

When this vast amount and variety of data is made available, finding relevant data to meet a research need is increasingly a challenge. In the past, when data were relatively sparse, researchers discovered existing data by searching literature, attending conferences, and asking colleagues. In today’s data-rich environment, with accompanying advances in computational and networking technologies, researchers increasingly conduct web searches to find research data. The success of such searches varies greatly and depends to a large degree on the expertise of the person looking for data, the tools used, and, partially, on luck. This article offers the following 11 quick tips that researchers can follow to more effectively and precisely discover data that meet their specific needs.

• Tip 1: Think about the data you need and why you need them.
• Tip 2: Select the most appropriate resource.
• Tip 3: Construct your query strategically.
• Tip 4: Make the repository work for you.
• Tip 5: Refine your search.
• Tip 6: Assess data relevance and fitness -for -use.
• Tip 7: Save your search and data- source details.
• Tip 8: Look for data services, not just data.
• Tip 9: Monitor the latest data.
• Tip 10: Treat sensitive data responsibly.
• Tip 11: Give back (cite and share data).

Tip 1: Think about the data you need and why you need them

Before embarking on a search for data, consider how you will use the desired data in the context of your overall research question. Are you seeking data for comparison or validation, as the basis for a new study, or for another reason? List the characteristics that the data must have in order to fulfil your identified purpose(s), including requirements such as data format, spatial or temporal coverage, availability, and author or research group. In many cases, your initial data requirements and the identified constraints will change as you progress with the search. Pausing to first analyse what you need and why you need it can lead to a more analytic search, save searching time and facilitating the actions described in Tips 2–6.

Tip 2: Select the most appropriate resource

Directories of research-data repositories, such as re3data.org ( http://www.re3data.org ) and FAIRsharing ( https://fairsharing.org ), web search engines, and colleagues can be consulted to discover domain-specific portals in your discipline. Subject domain is but one criterion to consider when selecting an appropriate data repository. Various certification processes have also been implemented to help develop trustworthiness in repositories and to make their data-governing policies more transparent. For example, repositories earning the CoreTrustSeal ( https://www.coretrustseal.org/about ) Trustworthy Data Repository certification must meet 16 requirements measuring the accessibility, usability, reliability, and long-term stability of their data. Knowing what standards and criteria a repository applies to data and metadata provides more confidence in understanding and reusing the data from that repository.

Domain-specific portals provide ways to quickly narrow your search, offering interfaces and filters tailored to match the data and needs of specific disciplinary domains. Map interfaces for data collected from specific locations (see the National Water Information System, https://maps.waterdata.usgs.gov/mapper/index.html ) and specific search fields and tools (see the National Centre for Biotechnology Information’s complement of databases, ( https://www.ncbi.nlm.nih.gov/guide/all/ ) facilitate discovering disciplinary data. Other domain-focused repositories, such as the National Snow and Ice Data Centre (NSIDC, http://nsidc.org/data/search/ ), collect and apply knowledge about user requirements and incorporate domain semantics into their search engines to help data seekers quickly find appropriate data. Data aggregators, including DataONE ( https://www.dataone.org ) for environmental and earth observation data, VertNet ( http://vertnet.org ) and Global Biodiversity Information Facility (GBIF, https://www.gbif.org ) for museum specimen and biodiversity data, or DataMed ( https://datamed.org ) for biomedical datasets, enable searching multiple data repositories or collections through a single search interface. Some portals may not provide data-search functionality but instead provide a catalogue of data resources. A notable example is the AgBioData ( https://www.agbiodata.org/databases ) portal, which lists links to 12 agricultural biological databases dedicated to specific species (e.g., cotton, grain, or hardwood), where you can directly search for data.

The accessibility of data resources is another important consideration. University librarians can provide advice about particular subscription-based resources available at your institution. Research papers in your field can also point to available data repositories. In domains such as astronomy and genomics, for example, citations of datasets within journal articles are commonplace. These references usually include dataset access information that can be used to locate datasets of interest or to point toward data repositories favoured within a discipline.

Tip 3: Construct your query strategically

Describing your desired data effectively is key to communicating with the search system. Your description will determine if relevant data are retrieved and may inform the order of the hits in the results list. Help pages provide tips on how to construct basic and advanced searches within particular repositories (see for example Research Data Australia https://researchdata.ands.org.au —click on Advanced Search → Help). Note that not all repositories operate in the same manner. Some portals, such as DataONE ( https://www.dataone.org ), use semantic technologies to automatically expand the keywords entered in the search box to include synonyms. If a portal does not use automatic expansion, you may need to manually add various synonyms to your search query (e.g., in addition to ‘demography’ as a search term, one might also add ‘population density’, ‘population growth’, ‘census’, or ‘anthropology’).

• sea level (site:.edu)

Tip 4: Make the repository work for you

Repository developers invest significant time and energy organizing data in ways to make them more discoverable; use their work to your advantage. Familiarize yourself with the controlled vocabularies, subject categories, and search fields used in particular repositories. Searching for and successfully locating data is dependent on the information about the data, termed metadata, that are contained in these fields; this is particularly true for numeric or nontextual data. Browsing subject categories can also help to gauge the appropriateness of a resource, home in on an area of interest, or find related data that have been classified in the same category.

Researchers can also register or create profiles with many data repositories. By registering, you may be able to indicate your general research data interests which can be utilized in subsequent searches or receive alerts about datasets that you have previously downloaded (see also Tip 7).

Tip 5: Refine your search

In many cases, your initial search may not retrieve relevant data or all of the data that you need. Based on the retrieved results, you may need to broaden or narrow your approach. Apart from rephrasing your search query and using search operators, as discussed in Tip 3, facets or filters specific to individual repositories can be used to narrow the scope of your results. Refinements such as data format, types of analysis, and data availability allow users to quickly find usable data.

Examining results that look interesting (for example, by clicking on links for ‘more information’) can be a signal of the type of information that you find relevant. These results can then be linked to related ones (e.g., from the data provider, from different time series), and in subsequent searches, other results algorithmically determined to be related will be brought to the top of the results list.

Tip 6: Assess data relevance and fitness for use

Conduct a preliminary assessment of the retrieved data prior to investing time in subsequent data download, integration, and analytic and visualization efforts. A quick perusal of the metadata (text and/or images) can often enable you to verify that the data satisfy the initial requirements and constraints set forth in Tip 1 (e.g., spatial, temporal, and thematic coverage and data-sharing restrictions). Ideally, the metadata will also contain documentation sufficient to comprehensively assess the relevance and fitness for use of the data, including information about how the data were collected and quality assured, how the data have been previously used, etc. Some data repositories such as the National Science Foundation’s Arctic Data Centre ( https://arcticdata.io ) enable the data seeker to generate and download a metadata quality report that assesses how well the metadata adhere to community best practices for discovery and reusability. Clearly, if none of your criteria for data are met, you may not wish to download and use the associated data.

Attention should also be paid to quality parameters or flags within the data files. Make use of a visualization tool or statistics analysis tool, if provided, to examine quality or fitness of data for intended use before downloading data, especially if the data volume is large and the dataset includes many files.

Tip 7: Save your search and data-source details

Record the data source and data version if you access or download a data product. This may be accomplished by noting the persistent identifier, such as a digital object identifier (DOI) or another Global Unique Identifier (GUID) assigned to the data. Recording the URL from which you obtained the data can be a quick way of returning to it but should not be trusted in the long term for providing access to the data, as URLs can change. It is also a good practice to save a copy of any original data products that you downloaded [‎ 5 ]. You may, for example, need to go back to original data sources and check if there have been any changes or corrections to data. Registering with the data portal (as described in Tip 3) or registering as a user of a specific data product allows the repository to contact you when necessary. Such information may be needed when you publish a paper that builds on the data you accessed. If there are any errors found in the original data, registering with the data service allows them to contact you to see if there is an impact on any research conclusions that you have drawn from this data.

If you have registered with a portal, it may also be possible to save your searches, allowing you to resume your data search at a later time with all previously defined search criteria. Some portals use RESTful search interfaces, which means you can bookmark a results set or dataset and return to it later simply by going to the bookmark.

Tip 8: Look for data services, not just data

The data you seek may be available only via an application programming interface (API) or as linked data [‎ 6 ]. That is, instead of a file residing on a server, the data that best suits your purposes is provided as a service through an API. Examples of such services include the climate change projection data available through the NSW Climate Data Portal ( http://climatechange.environment.nsw.gov.au/Climate-projections-for-NSW/Download-datasets ), in which data are dynamically generated from a simulation model; Google Earth Engine ( https://earthengine.google.com ); or Amazon Web Services (AWS) public datasets ( https://aws.amazon.com/public-datasets/ ). Data made available from these services may not be searchable from general web search engines, but data services may be registered to data catalogues or federations such as Research Data Australia, DataONE, and other resources listed in re3data.org and FAIRsharing. Many repositories that host extremely large volumes of data such as sequencing, environmental observatory, and remotely sensed data provide access to tools, workflows, and computing resources that allow one to access, visualize, process, and download manageable subsets of the data. Often, the processing workflows that one might use to process and download a dataset can also be downloaded, saved, and used again in subsequent searches.

Tip 9: Monitor the latest data

One of the most effective ways to identify new data submissions is to monitor the latest literature, as many journals such as Nature , PLOS , Science , and others require that the data underlying a publication also be published in a public (e.g., Dataverse https://dataverse.org , Dryad http://datadryad.org , or Zenodo https://zenodo.org ) or discipline-based repository (e.g., EASY from Data Archiving and Networked Services [DANS] https://easy.dans.knaw.nl/ , GenBank https://www.ncbi.nlm.nih.gov/genbank/ , or PubChem https://pubchem.ncbi.nlm.nih.gov ).

In addition, many domain-based repositories, such as environmental observatories and sequencing databases, are constantly accepting similar types of data submissions. Publishers and some digital repositories also offer alerting services when new publications or data products are submitted. Depending on the resource, it may be possible to set up a recurring search API or a Rich Site Summary (RSS) feed to automatically monitor specific resources. For example, the NSIDC offers a subscription service where new data meeting a list of user-generated specifications are automatically pushed to a location specified by the user.

Tip 10: Treat sensitive data responsibly

In most cases, after you have located relevant data, you can download them straight away. However, there are cases, such as for medical and health data, endangered and threatened species, and sacred objects and archaeological finds, where you can only see a data description (the metadata) and are not able to download the data directly due to access restrictions imposed to protect the privacy of individuals represented in the data or to safeguard locations and species from harm or unwanted attention. Guidance with respect to sensitive data is available through the 2003 Fort Lauderdale Agreement ( https://www.genome.gov/pages/research/wellcomereport0303.pdf ), the 2009 Toronto Agreement ( https://www.nature.com/articles/461168a ) [ 7 ], the Australian National Data Service ( http://www.ands.org.au/working-with-data/sensitive-data ), and individual institutional and society research ethics committees.

Sensitive data are often discoverable and accessible if identity and location information are anonymized. In other cases, an established data-access agreement specifies the technical requirements as well as the ethical and scientific obligations that accessing and using the data entail. Technical requirements may include aspects such as auditing data access at the local system, defining read-only access rights, and/or ensuring constraints for nonprivileged network access. You can still contact the data owner to explain your intended use and to discuss the conditions and legal restrictions associated with using sensitive data. Such contact may even lead to collaborative research between you and the data owner. Should you be granted access to the data, it is important to use the data ethically and responsibly [ 8 ] to ensure that no harm is done to individuals, species, and culture heritages.

Tip 11: Give back (cite and share data)

There are three ways to give back to the community once you have sought, discovered, and used an existing data product. First, it is essential that you give proper attribution to the data creators (in some cases, the data owners) if you use others’ data for research, education, decision making, or other purposes [ 9 ]. Proper attribution benefits both data creators/providers and data seekers/users. Data creators/providers receive credit for their work, and their practice of sharing data is thus further encouraged. Data seekers/users make their own work more transparent and, potentially, reproducible by uniquely identifying and citing data used in their research.

Many data creators and institutions adopt standard licenses from organizations, such as Creative Commons, that govern how their data products may be shared and used. Creative Commons recommends that a proper attribution should include title, author, source, and license [ 10 ].

Second, provide feedback to the data creators or the data repository about any issues associated with data accessibility, data quality, or metadata completeness and interpretability. Data creators and repositories benefit from knowing that their data products are understandable and usable by others, as well as knowing how the data were used. Future users of the data will also benefit from your feedback.

Third, virtually all data seekers and data users also generate data. The ultimate ‘give-back’ is to also share your data with the broader community.

This paper highlights 11 quick tips that, if followed, should make it easier for a data seeker to discover data that meet a particular need. Regardless of whether you are acting as a data seeker or a data creator, remember that ‘data discovery and reuse are most easily accomplished when: (1) data are logically and clearly organized; (2) data quality is assured; (3) data are preserved and discoverable via an open data repository; (4) data are accompanied by comprehensive metadata; (5) algorithms and code used to create data products are readily available; (6) data products can be uniquely identified and associated with specific data originator(s); and (7) the data originator(s) or data repository have provided recommendations for citation of the data product(s)’ [ 11 ].

Acknowledgments

This work was developed as part of the Research Data Alliance (RDA) ‘WG/IG’ entitled ‘Data Discovery Paradigms’, and we acknowledge the support provided by the RDA community and structures. We would like to thank members of the group for their support, especially Andrea Perego, Mustapha Mokrane, Susanna-Assunta Sansone, Peter McQuilton, and Michel Dumontier who read this paper and provided constructive suggestions.

• 1. Gray J. Jim Gray on eScience: A transformed scientific method. In: Hey T, Tansley S, Tolle K, editors. The Fourth Paradigm: Data-Intensive Scientific Discovery. Richmond, WA: Microsoft Research; 2009. p.xvii–xxxi. Available from: https://www.microsoft.com/en-us/research/publication/fourth-paradigm-data-intensive-scientific-discovery/ .
• 2. Fox G, Hey T, Trefethen A. Where does all the data come from? In: Kleese van Dam K, editor. Data-Intensive Science. Chapman and Hall/CRC; Boca Raton: Taylor and Francis, May 2013. p. 15–51.
• View Article
• PubMed/NCBI
• Google Scholar
• 4. Warner, R. Google Advanced Search: A Comprehensive List of Google Search Operators [Internet]. 2015. Available from: https://bynd.com/news-ideas/google-advanced-search-comprehensive-list-google-search-operators/ . [cited 2017 Oct 26]
• 6. Heath T, Bizer C. Linked Data: Evolving the Web into a global data space. In: Hendler J, van Harmelen F, editors. Synthesis Lectures on the Semantic Web: Theory and Technology. Morgan & Claypool; 2011. p. 1–136.
• 8. Clark K, et al. Guidelines for the Ethical Use of Digital Data in Human Research. www.carltonconnect.com.au: The University of Melbourne; 2015. Available from: https://www.carltonconnect.com.au/wp-content/uploads/2015/06/Ethical-Use-of-Digital-Data.pdf . [cited 2018 Feb. 1].
• 9. Martone M, editor. Data Citation Synthesis Group: Joint Declaration of Data Citation Principles. FORCE11. San Diego, CA; 2014. [cited 2018 Feb 1]. Available from: https://www.force11.org/group/joint-declaration-data-citation-principles-final .
• 10. Creative Commons. Best practices for attribution [Internet]. 2014 [cited 2017 Sep 10]. Available from: https://wiki.creativecommons.org/wiki/Best_practices_for_attribution .
• 11. Michener W. K. Data discovery. In: Recknagel F, Michener WK, editors. Ecological informatics: Data management and knowledge discovery. Springer International Publishing, Cham, Switzerland; 2017.

How To Present Your Market Research Results And Reports In An Efficient Way

Table of Contents

1) What Is A Market Research Report?

2) Market Research Reports Examples

3) Why Do You Need Market Research Reports

4) How To Make A Market Research Report?

5) Types Of Market Research Reports

6) Challenges & Mistakes Market Research Reports

Market research analyses are the go-to solution for many professionals, and for good reason: they save time, offer fresh insights, and provide clarity on your business. In turn, market research reports will help you to refine and polish your strategy. Plus, a well-crafted report will give your work more credibility while adding weight to any marketing recommendations you offer a client or executive.

But, while this is the case, today’s business world still lacks a way to present market-based research results efficiently. The static, antiquated nature of PowerPoint makes it a bad choice for presenting research discoveries, yet it is still widely used to present results. 

Fortunately, things are moving in the right direction. There are online data visualization tools that make it easy and fast to build powerful market research dashboards. They come in handy to manage the outcomes, but also the most important aspect of any analysis: the presentation of said outcomes, without which it becomes hard to make accurate, sound decisions. 

Here, we consider the benefits of conducting research analyses while looking at how to write and present market research reports, exploring their value, and, ultimately, getting the very most from your research results by using professional market research software .

Let’s get started.

What Is a Market Research Report?

A market research report is an online reporting tool used to analyze the public perception or viability of a company, product, or service. These reports contain valuable and digestible information like customer survey responses and social, economic, and geographical insights.

On a typical market research results example, you can interact with valuable trends and gain insight into consumer behavior and visualizations that will empower you to conduct effective competitor analysis. Rather than adding streams of tenuous data to a static spreadsheet, a full market research report template brings the outcomes of market-driven research to life, giving users a data analysis tool to create actionable strategies from a range of consumer-driven insights.

With digital market analysis reports, you can make your business more intelligent more efficient, and, ultimately, meet the needs of your target audience head-on. This, in turn, will accelerate your commercial success significantly.

Your Chance: Want to test a market research reporting software? Explore our 14-day free trial & benefit from interactive research reports!

How To Present Your Results: 4 Essential Market Research Report Templates

When it comes to sharing rafts of invaluable information, research dashboards are invaluable.

Any market analysis report example worth its salt will allow everyone to get a firm grip on their results and discoveries on a single page with ease. These dynamic online dashboards also boast interactive features that empower the user to drill down deep into specific pockets of information while changing demographic parameters, including gender, age, and region, filtering the results swiftly to focus on the most relevant insights for the task at hand.

These four market research report examples are different but equally essential and cover key elements required for market survey report success. You can also modify each and use it as a client dashboard .

While there are numerous types of dashboards that you can choose from to adjust and optimize your results, we have selected the top 3 that will tell you more about the story behind them. Let’s take a closer look.

1. Market Research Report: Brand Analysis

Our first example shares the results of a brand study. To do so, a survey has been performed on a sample of 1333 people, information that we can see in detail on the left side of the board, summarizing the gender, age groups, and geolocation.

**click to enlarge**

At the dashboard's center, we can see the market-driven research discoveries concerning first brand awareness with and without help, as well as themes and celebrity suggestions, to know which image the audience associates with the brand.

Such dashboards are extremely convenient to share the most important information in a snapshot. Besides being interactive (but it cannot be seen on an image), it is even easier to filter the results according to certain criteria without producing dozens of PowerPoint slides. For instance, I could easily filter the report by choosing only the female answers, only the people aged between 25 and 34, or only the 25-34 males if that is my target audience.

Primary KPIs:

a) Unaided Brand Awareness

The first market research KPI in this most powerful report example comes in the form of unaided brand awareness. Presented in a logical line-style chart, this particular market study report sample KPI is invaluable, as it will give you a clear-cut insight into how people affiliate your brand within their niche.

As you can see from our example, based on a specific survey question, you can see how your brand stacks up against your competitors regarding awareness. Based on these outcomes, you can formulate strategies to help you stand out more in your sector and, ultimately, expand your audience.

b) Aided Brand Awareness

This market survey report sample KPI focuses on aided brand awareness. A visualization that offers a great deal of insight into which brands come to mind in certain niches or categories, here, you will find out which campaigns and messaging your target consumers are paying attention to and engaging with.

By gaining access to this level of insight, you can conduct effective competitor research and gain valuable inspiration for your products, promotional campaigns, and marketing messages.

c) Brand image

When it comes to research reporting, understanding how others perceive your brand is one of the most golden pieces of information you could acquire. If you know how people feel about your brand image, you can take informed and very specific actions that will enhance the way people view and interact with your business.

By asking a focused question, this visual of KPIs will give you a definitive idea of whether respondents agree, disagree, or are undecided on particular descriptions or perceptions related to your brand image. If you’re looking to present yourself and your message in a certain way (reliable, charming, spirited, etc.), you can see how you stack up against the competition and find out if you need to tweak your imagery or tone of voice - invaluable information for any modern business.

d) Celebrity analysis

This indicator is a powerful part of our research KPI dashboard on top, as it will give you a direct insight into the celebrities, influencers, or public figures that your most valued consumers consider when thinking about (or interacting with) your brand.

Displayed in a digestible bar chart-style format, this useful metric will not only give you a solid idea of how your brand messaging is perceived by consumers (depending on the type of celebrity they associate with your brand) but also guide you on which celebrities or influencers you should contact.

By working with the right influencers in your niche, you will boost the impact and reach of your marketing campaigns significantly, improving your commercial awareness in the process. And this is the KPI that will make it happen.

2. Market Research Results On Customer Satisfaction

Here, we have some of the most important data a company should care about: their already-existing customers and their perception of their relationship with the brand. It is crucial when we know that it is five times more expensive to acquire a new consumer than to retain one.

This is why tracking metrics like the customer effort score or the net promoter score (how likely consumers are to recommend your products and services) is essential, especially over time. You need to improve these scores to have happy customers who will always have a much bigger impact on their friends and relatives than any of your amazing ad campaigns. Looking at other satisfaction indicators like the quality, pricing, and design, or the service they received is also a best practice: you want a global view of your performance regarding customer satisfaction metrics .

Such research results reports are a great tool for managers who do not have much time and hence need to use them effectively. Thanks to these dashboards, they can control data for long-running projects anytime.

Primary KPIs :

a) Net Promoter Score (NPS)

Another pivotal part of any informative research presentation is your NPS score, which will tell you how likely a customer is to recommend your brand to their peers.

Centered on overall customer satisfaction, your NPS Score can cover the functions and output of many departments, including marketing, sales, and customer service, but also serve as a building block for a call center dashboard . When you’re considering how to present your research effectively, this balanced KPI offers a masterclass. It’s logical, it has a cohesive color scheme, and it offers access to vital information at a swift glance. With an NPS Score, customers are split into three categories: promoters (those scoring your service 9 or 10), passives (those scoring your service 7 or 8), and detractors (those scoring your service 0 to 6). The aim of the game is to gain more promoters. By gaining an accurate snapshot of your NPS Score, you can create intelligent strategies that will boost your results over time.

b) Customer Satisfaction Score (CSAT)

The next in our examples of market research reports KPIs comes in the form of the CSAT. The vast majority of consumers that have a bad experience will not return. Honing in on your CSAT is essential if you want to keep your audience happy and encourage long-term consumer loyalty.

This magnificent, full report KPI will show how satisfied customers are with specific elements of your products or services. Getting to grips with these scores will allow you to pinpoint very specific issues while capitalizing on your existing strengths. As a result, you can take measures to improve your CSAT score while sharing positive testimonials on your social media platforms and website to build trust.

c) Customer Effort Score (CES)

When it comes to presenting research findings, keeping track of your CES Score is essential. The CES Score KPI will give you instant access to information on how easy or difficult your audience can interact with or discover your company based on a simple scale of one to ten.

By getting a clear-cut gauge of how your customers find engagement with your brand, you can iron out any weaknesses in your user experience (UX) offerings while spotting any friction, bottlenecks, or misleading messaging. In doing so, you can boost your CES score, satisfy your audience, and boost your bottom line.

3. Market Research Results On Product Innovation

This final market-driven research example report focuses on the product itself and its innovation. It is a useful report for future product development and market potential, as well as pricing decisions.

Using the same sample of surveyed people as for the first market-focused analytical report , they answer questions about their potential usage and purchase of the said product. It is good primary feedback on how the market would receive the new product you would launch. Then comes the willingness to pay, which helps set a price range that will not be too cheap to be trusted nor too expensive for what it is. That will be the main information for your pricing strategy.

a) Usage Intention

The first of our product innovation KPI-based examples comes in the form of usage intention. When you’re considering how to write a market research report, including metrics centered on consumer intent is critical.

This simple yet effective visualization will allow you to understand not only how users see your product but also whether they prefer previous models or competitor versions . While you shouldn’t base all of your product-based research on this KPI, it is very valuable, and you should use it to your advantage frequently.

b) Purchase Intention

Another aspect to consider when looking at how to present market research data is your audience’s willingness or motivation to purchase your product. Offering percentage-based information, this effective KPI provides a wealth of at-a-glance information to help you make accurate forecasts centered on your product and service offerings.

Analyzing this information regularly will give you the confidence and direction to develop strategies that will steer you to a more prosperous future, meeting the ever-changing needs of your audience on an ongoing basis.

c) Willingness To Pay (WPS)

Our final market research example KPI is based on how willing customers are to pay for a particular service or product based on a specific set of parameters. This dynamic visualization, represented in an easy-to-follow pie chart, will allow you to realign the value of your product (USPs, functions, etc.) while setting price points that are most likely to result in conversions. This is a market research presentation template that every modern organization should use to its advantage.

4. Market Research Report On Customer Demographics 

This particular example of market research report, generated with a modern dashboard creator , is a powerful tool, as it displays a cohesive mix of key demographic information in one intuitive space.

By breaking down these deep pockets of consumer-centric information, you can gain the power to develop more impactful customer communications while personalizing every aspect of your target audience’s journey across every channel or touchpoint. As a result, you can transform theoretical insights into actionable strategies that will result in significant commercial growth. 

Every section of this responsive marketing research report works in unison to build a profile of your core audience in a way that will guide your company’s consumer-facing strategies with confidence. With in-depth visuals based on gender, education level, and tech adoption, you have everything you need to speak directly to your audience at your fingertips.

Let’s look at the key performance indicators (KPIs) of this invaluable market research report example in more detail.

a) Customer By Gender

This KPI is highly visual and offers a clear-cut representation of your company’s gender share over time. By gaining access to this vital information, you can deliver a more personalized experience to specific audience segments while ensuring your messaging is fair, engaging, and inclusive.

b) Customers by education level

The next market analysis report template is a KPI that provides a logical breakdown of your customers’ level of education. By using this as a demographic marker, you can refine your products to suit the needs of your audience while crafting your content in a way that truly resonates with different customer groups.

c) Customers by technology adoption

Particularly valuable if you’re a company that sells tech goods or services, this linear KPI will show you where your customers are in terms of technological know-how or usage. By getting to grips with this information over time, you can develop your products or services in a way that offers direct value to your consumers while making your launches or promotions as successful as possible.

d) Customer age groups

By understanding your customers’ age distribution in detail, you can gain a deep understanding of their preferences. And that’s exactly what this market research report sample KPI does. Presented in a bar chart format, this KPI will give you a full breakdown of your customers’ age ranges, allowing you to build detailed buyer personas and segment your audience effectively.

Why Do You Need Market Research Reports?

As the adage goes, “Look before you leap“ – which is exactly what a research report is here for. As the headlights of a car, they will show you the pitfalls and fast lanes on your road to success: likes and dislikes of a specific market segment in a certain geographical area, their expectations, and readiness. Among other things, a research report will let you:

• Get a holistic view of the market : learn more about the target market and understand the various factors involved in the buying decisions. A broader view of the market lets you benchmark other companies you do not focus on. This, in turn, will empower you to gather the industry data that counts most. This brings us to our next point.
• Curate industry information with momentum: Whether you’re looking to rebrand, improve on an existing service, or launch a new product, time is of the essence. By working with the best market research reports created with modern BI reporting tools , you can visualize your discoveries and data, formatting them in a way that not only unearths hidden insights but also tells a story - a narrative that will gain a deeper level of understanding into your niche or industry. The features and functionality of a market analysis report will help you grasp the information that is most valuable to your organization, pushing you ahead of the pack in the process.
• Validate internal research: Doing the internal analysis is one thing, but double-checking with a third party also greatly helps avoid getting blinded by your own data.
• Use actionable data and make informed decisions: Once you understand consumer behavior as well as the market, your competitors, and the issues that will affect the industry in the future, you are better armed to position your brand. Combining all of it with the quantitative data collected will allow you to more successful product development. To learn more about different methods, we suggest you read our guide on data analysis techniques .
• Strategic planning: When you want to map out big-picture organizational goals, launch a new product development, plan a geographic market expansion, or even a merger and acquisition – all of this strategic thinking needs solid foundations to fulfill the variety of challenges that come along.
• Consistency across the board: Collecting, presenting, and analyzing your results in a way that’s smarter, more interactive, and more cohesive will ensure your customer communications, marketing campaigns, user journey, and offerings meet your audience’s needs consistently across the board. The result? Faster growth, increased customer loyalty, and more profit.
• Better communication: The right market research analysis template (or templates) will empower everyone in the company with access to valuable information - the kind that is relevant and comprehensible. When everyone is moving to the beat of the same drum, they will collaborate more effectively and, ultimately, push the venture forward thanks to powerful online data analysis techniques.
• Centralization: Building on the last point, using a powerful market research report template in the form of a business intelligence dashboard will make presenting your findings to external stakeholders and clients far more effective, as you can showcase a wealth of metrics, information, insights, and invaluable feedback from one centralized, highly visual interactive screen. 
• Brand reputation: In the digital age, brand reputation is everything. By making vital improvements in all of the key areas above, you will meet your customers’ needs head-on with consistency while finding innovative ways to stand out from your competitors. These are the key ingredients of long-term success.

How To Present Market Research Analysis Results?

Here we look at how you should present your research reports, considering the steps it takes to connect with the outcomes you need to succeed:

• Collect your data 

As with any reporting process, you first and foremost need to collect the data you’ll use to conduct your studies. Businesses conduct research studies to analyze their brand awareness, identity, and influence in the market. For product development and pricing decisions, among many others. That said, there are many ways to collect information for a market research report. Among some of the most popular ones, we find: 

• Surveys: Probably the most common way to collect research data, surveys can come in the form of open or closed questions that can be answered anonymously. They are the cheapest and fastest way to collect insights about your customers and business. 
• Interviews : These are face-to-face discussions that allow the researcher to analyze responses as well as the body language of the interviewees. This method is often used to define buyer personas by analyzing the subject's budget, job title, lifestyle, wants, and needs, among other things. 
• Focus groups : This method involves a group of people discussing a topic with a mediator. It is often used to evaluate a new product or new feature or to answer a specific question that the researcher might have. 
• Observation-based research : In this type of research, the researcher or business sits back and watches customers interact with the product without any instructions or help. It allows us to identify pain points as well as strong features. 
• Market segmentation : This study allows you to identify and analyze potential market segments to target. Businesses use it to expand into new markets and audiences. 

These are just a few of the many ways in which you can gather your information. The important point is to keep the research objective as straightforward as possible. Supporting yourself with professional BI solutions to clean, manage, and present your insights is probably the smartest choice.

2. Hone in on your research:

When looking at how to source consumer research in a presentation, you should focus on two areas: primary and secondary research. Primary research comes from your internal data, monitoring existing organizational practices, the effectiveness of sales, and the tools used for communication, for instance. Primary research also assesses market competition by evaluating the company plans of the competitors. Secondary research focuses on existing data collected by a third party, information used to perform benchmarking and market analysis. Such metrics help in deciding which market segments are the ones the company should focus its efforts on or where the brand is standing in the minds of consumers. Before you start the reporting process, you should set your goals, segmenting your research into primary and secondary segments to get to grips with the kind of information you need to work with to achieve effective results.

3. Segment your customers:

To give your market research efforts more context, you should segment your customers into different groups according to the preferences outlined in the survey or feedback results or by examining behavioral or demographic data.

If you segment your customers, you can tailor your market research and analysis reports to display only the information, charts, or graphics that will provide actionable insights into their wants, needs, or industry-based pain points. 

• Identify your stakeholders:

Once you’ve drilled down into your results and segmented your consumer groups, it’s important to consider the key stakeholders within the organization that will benefit from your information the most. 

By looking at both internal and external stakeholders, you will give your results a path to effective presentation, gaining the tools to understand which areas of feedback or data are most valuable, as well as most redundant. As a consequence, you will ensure your results are concise and meet the exact information needs of every stakeholder involved in the process.

• Set your KPIs:

First, remember that your reports should be concise and accurate - straight to the point without omitting any essential information. Work to ensure your insights are clean and organized, with participants grouped into relevant categories (demographics, profession, industry, education, etc.). Once you’ve organized your research, set your goals, and cleaned your data, you should set your KPIs to ensure your report is populated with the right visualizations to get the job done. Explore our full library of interactive KPI examples for inspiration.

• Include competitor’s analysis 

Whether you are doing product innovation research, customer demographics, pricing, or any other, including some level of insights about competitors in your reports is always recommended as it can help your business or client better understand where they stand in the market. That being said, competitor analysis is not as easy as picking a list of companies in the same industry and listing them. Your main competitor can be just a company's division in an entirely different industry. For example, Apple Music competes with Spotify even though Apple is a technology company. Therefore, it is important to carefully analyze competitors from a general but detailed level. 

Providing this kind of information in your reports can also help you find areas that competitors are not exploiting or that are weaker and use them to your advantage to become a market leader. 

• Produce your summary:

To complement your previous efforts, writing an executive summary of one or two pages that will explain the general idea of the report is advisable. Then come the usual body parts:

• An introduction providing background information, target audience, and objectives;
• The qualitative research describes the participants in the research and why they are relevant to the business;
• The survey research outlines the questions asked and answered;
• A summary of the insights and metrics used to draw the conclusions, the research methods chosen, and why;
• A presentation of the findings based on your research and an in-depth explanation of these conclusions.
• Use a mix of visualizations:

When presenting your results and discoveries, you should aim to use a balanced mix of text, graphs, charts, and interactive visualizations.

Using your summary as a guide, you should decide which type of visualization will present each specific piece of market research data most effectively (often, the easier to understand and more accessible, the better).

Doing so will allow you to create a story that will put your research information into a living, breathing context, providing a level of insight you need to transform industry, competitor, or consumer info or feedback into actionable strategies and initiatives.

• Be careful not to mislead 

Expanding on the point above, using a mix of visuals can prove highly valuable in presenting your results in an engaging and understandable way. That being said, when not used correctly, graphs and charts can also become misleading. This is a popular practice in the media, news, and politics, where designers tweak the visuals to manipulate the masses into believing a certain conclusion. This is a very unethical practice that can also happen by mistake when you don’t pick the right chart or are not using it in the correct way. Therefore, it is important to outline the message you are trying to convey and pick the chart type that will best suit those needs. 

Additionally, you should also be careful with the data you choose to display, as it can also become misleading. This can happen if you, for example, cherry-pick data, which means only showing insights that prove a conclusion instead of the bigger picture. Or confusing correlation with causation, which means assuming that because two events happened simultaneously, one caused the other. 

Being aware of these practices is of utmost importance as objectivity is crucial when it comes to dealing with data analytics, especially if you are presenting results to clients. Our guides on misleading statistics and misleading data visualizations can help you learn more about this important topic. 

• Use professional dashboards:

To optimize your market research discoveries, you must work with a dynamic business dashboard . Not only are modern dashboards presentable and customizable, but they will offer you past, predictive, and real-time insights that are accurate, interactive, and yield long-lasting results.

All market research reports companies or businesses gathering industry or consumer-based information will benefit from professional dashboards, as they offer a highly powerful means of presenting your data in a way everyone can understand. And when that happens, everyone wins.

Did you know? The interactive nature of modern dashboards like datapine also offers the ability to quickly filter specific pockets of information with ease, offering swift access to invaluable insights.

• Prioritize interactivity 

The times when reports were static are long gone. Today, to extract the maximum value out of your research data, you need to be able to explore the information and answer any critical questions that arise during the presentation of results. To do so, modern reporting tools provide multiple interactivity features to help you bring your research results to life. 

For instance, a drill-down filter lets you go into lower levels of hierarchical data without generating another graph. For example, imagine you surveyed customers from 10 different countries. In your report, you have a chart displaying the number of customers by country, but you want to analyze a specific country in detail. A drill down filter would enable you to click on a specific country and display data by city on that same chart. Even better, a global filter would allow you to filter the entire report to show only results for that specific country. 

Through the use of interactive filters, such as the one we just mentioned, you’ll not only make the presentation of results more efficient and profound, but you’ll also avoid generating pages-long reports to display static results. All your information will be displayed in a single interactive page that can be filtered and explored upon need.  

• Customize the reports 

This is a tip that is valuable for any kind of research report, especially when it comes to agencies that are reporting to external clients. Customizing the report to match your client’s colors, logo, font, and overall branding will help them grasp the data better, thanks to a familiar environment. This is an invaluable tip as often your audience will not feel comfortable dealing with data and might find it hard to understand or intimidating. Therefore, providing a familiar look that is also interactive and easier to understand will keep them engaged and collaborative throughout the process. 

Plus, customizing the overall appearance of the report will also make your agency look more professional, adding extra value to your service. 

• Know your design essentials 

When you’re presenting your market research reports sample to internal or external stakeholders, having a firm grasp on fundamental design principles will make your metrics and insights far more persuasive and compelling.

By arranging your metrics in a balanced and logical format, you can guide users toward key pockets of information exactly when needed. In turn, this will improve decision-making and navigation, making your reports as impactful as possible.

For essential tips, read our 23 dashboard design principles & best practices to enhance your analytics process.

• Think of security and privacy 

Cyberattacks are increasing at a concerning pace, making security a huge priority for organizations of all sizes today. The costs of having your sensitive information leaked are not only financial but also reputational, as customers might not trust you again if their data ends up in the wrong hands. Given that market research analysis is often performed by agencies that handle data from clients, security and privacy should be a top priority.  

To ensure the required security and privacy, it is necessary to invest in the right tools to present your research results. For instance, tools such as datapine offer enterprise-level security protocols that ensure your information is encrypted and protected at all times. Plus, the tool also offers additional security features, such as being able to share your reports through a password-protected URL or to set viewer rights to ensure only the right people can access and manipulate the data. 

• Keep on improving & evolving

Each time you gather or gain new marketing research reports or market research analysis report intel, you should aim to refine your existing dashboards to reflect the ever-changing landscape around you.

If you update your reports and dashboards according to the new research you conduct and new insights you connect with, you will squeeze maximum value from your metrics, enjoying consistent development in the process.

Types of Market Research Reports: Primary & Secondary Research

With so many market research examples and such little time, knowing how to best present your insights under pressure can prove tricky.

To squeeze every last drop of value from your market research efforts and empower everyone with access to the right information, you should arrange your information into two main groups: primary research and secondary research.

A. Primary research

Primary research is based on acquiring direct or first-hand information related to your industry or sector and the customers linked to it.

Exploratory primary research is an initial form of information collection where your team might set out to identify potential issues, opportunities, and pain points related to your business or industry. This type of research is usually carried out in the form of general surveys or open-ended consumer Q&As, which nowadays are often performed online rather than offline . 

Specific primary research is definitive, with information gathered based on the issues, information, opportunities, or pain points your business has already uncovered. When doing this kind of research, you can drill down into a specific segment of your customers and seek answers to the opportunities, issues, or pain points in question.

When you’re conducting primary research to feed into your market research reporting efforts, it’s important to find reliable information sources. The most effective primary research sources include:

• Consumer-based statistical data
• Social media content
• Polls and Q&A
• Trend-based insights
• Competitor research
• First-hand interviews

B. Secondary research

Secondary research refers to every strand of relevant data or public records you have to gain a deeper insight into your market and target consumers. These sources include trend reports, market stats, industry-centric content, and sales insights you have at your disposal.  Secondary research is an effective way of gathering valuable intelligence about your competitors. 

You can gather very precise, insightful secondary market research insights from:

• Public records and resources like Census data, governmental reports, or labor stats
• Commercial resources like Gartner, Statista, or Forrester
• Articles, documentaries, and interview transcripts

Another essential branch of both primary and secondary research is internal intelligence. When it comes to efficient market research reporting examples that will benefit your organization, looking inward is a powerful move. 

Existing sales, demographic, or marketing performance insights will lead you to valuable conclusions. Curating internal information will ensure your market research discoveries are well-rounded while helping you connect with the information that will ultimately give you a panoramic view of your target market. 

By understanding both types of research and how they can offer value to your business, you can carefully choose the right informational sources, gather a wide range of intelligence related to your specific niche, and, ultimately, choose the right market research report sample for your specific needs.

If you tailor your market research report format to the type of research you conduct, you will present your visualizations in a way that provides the right people with the right insights, rather than throwing bundles of facts and figures on the wall, hoping that some of them stick.

Taking ample time to explore a range of primary and secondary sources will give your discoveries genuine context. By doing so, you will have a wealth of actionable consumer and competitor insights at your disposal at every stage of your organization’s development (a priceless weapon in an increasingly competitive digital age). 

Dynamic market research is the cornerstone of business development, and a dashboard builder is the vessel that brings these all-important insights to life. Once you get into that mindset, you will ensure that your research results always deliver maximum value.

Common Challenges & Mistakes Of Market Research Reporting & Analysis

We’ve explored different types of market research analysis examples and considered how to conduct effective research. Now, it’s time to look at the key mistakes of market research reporting.  Let’s start with the mistakes.

The mistakes

One of the biggest mistakes that stunt the success of a company’s market research efforts is strategy. Without taking the time to gather an adequate mix of insights from various sources and define your key aims or goals, your processes will become disjointed. You will also suffer from a severe lack of organizational vision.

For your market research-centric strategy to work, everyone within the company must be on the same page. Your core aims and objectives must align throughout the business, and everyone must be clear on their specific role. If you try to craft a collaborative strategy and decide on your informational sources from the very start of your journey, your strategy will deliver true growth and intelligence.

• Measurement

Another classic market research mistake is measurement – or, more accurately, a lack of precise measurement. When embarking on market intelligence gathering processes, many companies fail to select the right KPIs and set the correct benchmarks for the task at hand. Without clearly defined goals, many organizations end up with a market analysis report format that offers little or no value in terms of decision-making or market insights.

To drive growth with your market research efforts, you must set clearly defined KPIs that align with your specific goals, aims, and desired outcomes.

• Competition

A common mistake among many new or scaling companies is failing to explore and examine the competition. This will leave you with gaping informational blindspots. To truly benefit from market research, you must gather valuable nuggets of information from every key source available. Rather than solely looking at your consumers and the wider market (which is incredibly important), you should take the time to see what approach your direct competitors have adopted while getting to grips with the content and communications.

One of the most effective ways of doing so (and avoiding such a monumental market research mistake) is by signing up for your competitors’ mailing lists, downloading their apps, and examining their social media content. This will give you inspiration for your own efforts while allowing you to exploit any gaps in the market that your competitors are failing to fill.

The challenges

• Informational quality

We may have an almost infinite wealth of informational insights at our fingertips, but when it comes to market research, knowing which information to trust can prove an uphill struggle.

When working with metrics, many companies risk connecting with inaccurate insights or leading to a fruitless informational rabbit hole, wasting valuable time and resources in the process. To avoid such a mishap, working with a trusted modern market research and analysis sample is the only way forward.

• Senior buy-in

Another pressing market research challenge that stunts organizational growth is the simple case of senior buy-in. While almost every senior decision-maker knows that market research is an essential component of a successful commercial strategy, many are reluctant to invest an ample amount of time or money in the pursuit.

The best way to overcome such a challenge is by building a case that defines exactly how your market research strategies will offer a healthy ROI to every key aspect of the organization, from marketing and sales to customer experience (CX) and beyond.

• Response rates

Low interview, focus group, or poll response rates can have a serious impact on the success and value of your market research strategy. Even with adequate senior buy-in, you can’t always guarantee that you will get enough responses from early-round interviews or poll requests. If you don’t, your market research discoveries run the risk of being shallow or offering little in the way of actionable insight.

To overcome this common challenge, you can improve the incentive you offer your market research prospects while networking across various platforms to discover new contact opportunities. Changing the tone of voice of your ads or emails will also help boost your consumer or client response rates.

Bringing Your Reports a Step Further

Even if it is still widespread for market-style research results presentation, using PowerPoint at this stage is a hassle and presents many downsides and complications. When busy managers or short-on-time top executives grab a report, they want a quick overview that gives them an idea of the results and the big picture that addresses the objectives: they need a dashboard. This can be applied to all areas of a business that need fast and interactive data visualizations to support their decision-making.

We all know that a picture conveys more information than simple text or figures, so managing to bring it all together on an actionable dashboard will convey your message more efficiently. Besides, market research dashboards have the incredible advantage of always being up-to-date since they work with real-time insights: the synchronization/updating nightmare of dozens of PowerPoint slides doesn’t exist for you anymore. This is particularly helpful for tracking studies performed over time that recurrently need their data to be updated with more recent ones.

In today’s fast-paced business environment, companies must identify and grab new opportunities as they arise while staying away from threats and adapting quickly. In order to always be a step further and make the right decisions, it is critical to perform market research studies to get the information needed and make important decisions with confidence.

We’ve asked the question, “What is a market research report?”, and examined the dynamics of a modern market research report example, and one thing’s for sure: a visual market research report is the best way to understand your customer and thus increase their satisfaction by meeting their expectations head-on. 

From looking at a sample of a market research report, it’s also clear that modern dashboards help you see what is influencing your business with clarity, understand where your brand is situated in the market, and gauge the temperature of your niche or industry before a product or service launch. Once all the studies are done, you must present them efficiently to ensure everyone in the business can make the right decisions that result in real progress. Market research reports are your key allies in the matter.

To start presenting your results with efficient, interactive, dynamic research reports and win on tomorrow’s commercial battlefield, try our dashboard reporting software and test every feature with our 14-day free trial !

Analyze Report: How to Write the Best Analytical Report (+ 6 Examples!)

Varun Saharawat is a seasoned professional in the fields of SEO and content writing. With a profound knowledge of the intricate aspects of these disciplines, Varun has established himself as a valuable asset in the world of digital marketing and online content creation.

Organizations analyze reports to improve performance by identifying areas of strength and weakness, understanding customer needs and preferences, optimizing business processes, and making data-driven decisions!

Analyze Report: Picture a heap of bricks scattered on the ground. Individually, they lack purpose until meticulously assembled into a cohesive structure—a house, perhaps?

In the realm of business intelligence , data serves as the fundamental building material, with a well-crafted data analysis report serving as the ultimate desired outcome.

However, if you’ve ever attempted to harness collected data and transform it into an insightful report, you understand the inherent challenges. Bridging the gap between raw, unprocessed data and a coherent narrative capable of informing actionable strategies is no simple feat.

Table of Contents

What is an Analyze Report?

An analytical report serves as a crucial tool for stakeholders to make informed decisions and determine the most effective course of action. For instance, a Chief Marketing Officer (CMO) might refer to a business executive analytical report to identify specific issues caused by the pandemic before adapting an existing marketing strategy.

Marketers often utilize business intelligence tools to generate these informative reports. They vary in layout, ranging from text-heavy documents (such as those created in Google Docs with screenshots or Excel spreadsheets) to visually engaging presentations.

A quick search on Google reveals that many marketers opt for text-heavy documents with a formal writing style, often featuring a table of contents on the first page. In some instances, such as the analytical report example provided below, these reports may consist of spreadsheets filled with numbers and screenshots, providing a comprehensive overview of the data.

Also Read: The Best Business Intelligence Software in 2024

How to Write an Analyze Report?

Writing an Analyze Report requires careful planning, data analysis , and clear communication of findings. Here’s a step-by-step guide to help you write an effective analytical report:

Step 1: Define the Purpose:

• Clearly define the objective and purpose of the report. Determine what problem or question the report aims to address.
• Consider the audience for the report and what information they need to make informed decisions.

Step 2: Gather Data:

• Identify relevant sources of data that can provide insights into the topic.
• Collect data from primary sources (e.g., surveys, interviews) and secondary sources (e.g., research studies, industry reports).
• Ensure that the data collected is accurate, reliable, and up-to-date.

Step 3: Analyze the Data:

• Use analytical tools and techniques to analyze the data effectively. This may include statistical analysis, qualitative coding, or data visualization.
• Look for patterns, trends, correlations, and outliers in the data that may provide insights into the topic.
• Consider the context in which the data was collected and any limitations that may affect the analysis.

Step 4: Organize the Information:

• Structure the report in a logical and coherent manner. Divide the report into sections, such as an introduction, methodology, findings, analysis, and conclusion.
• Ensure that each section flows logically into the next and that there is a clear progression of ideas throughout the report.

Step 5: Write the Introduction:

• Start with an introduction that provides background information on the topic and outlines the scope of the report.
• Clearly state the purpose and objectives of the analysis.
• Provide context for the analysis and explain why it is relevant and important.

Step 6: Present the Methodology:

• Describe the methods and techniques used to gather and analyze the data.
• Explain any assumptions made and the rationale behind your approach.
• Provide sufficient detail so that the reader can understand how the analysis was conducted.

Step 7: Present the Findings:

• Present the findings of your analysis in a clear and concise manner.
• Use charts, graphs, tables, and other visual aids to illustrate key points and make the data easier to understand.
• Provide context for the findings and explain their significance.

Step 8: Analyze the Data:

• Interpret the findings and analyze their implications.
• Discuss any patterns, trends, or insights uncovered by the analysis and explain their significance.
• Consider alternative explanations or interpretations of the data.

Step 9: Draw Conclusions:

• Draw conclusions based on the analysis and findings.
• Summarize the main points and insights of the report.
• Reiterate the key takeaways and their implications for decision-making.

Step 10: Make Recommendations:

• Finally, make recommendations based on your conclusions.
• Suggest actionable steps that can be taken to address any issues identified or capitalize on any opportunities uncovered by the analysis.
• Provide specific, practical recommendations that are feasible and aligned with the objectives of the report.

Step 11: Proofread and Revise:

• Review the report for accuracy, clarity, and coherence.
• Ensure that the writing is clear, concise, and free of errors.
• Make any necessary revisions before finalizing the report.

Step 12: Write the Executive Summary:

• Write a brief executive summary that provides an overview of the report’s key findings, conclusions, and recommendations.
• This summary should be concise and easy to understand for busy stakeholders who may not have time to read the entire report.
• Include only the most important information and avoid unnecessary details.

By following these steps, you can write an analytical report that effectively communicates your findings and insights to your audience.

Also Read: Analytics For BI: What is Business Intelligence and Analytics?

Analyze Report Examples

Analyze Report play a crucial role in providing valuable insights to businesses, enabling informed decision-making and strategic planning. Here are some examples of analytical reports along with detailed descriptions:

1) Executive Report Template:

An executive report serves as a comprehensive overview of a company’s performance, specifically tailored for C-suite executives. This report typically includes key metrics and KPIs that provide insights into the organization’s financial health and operational efficiency. For example, the Highlights tab may showcase total revenue for a specific period, along with the breakdown of transactions and associated costs. 

Additionally, the report may feature visualizations such as cost vs. revenue comparison charts, allowing executives to quickly identify trends and make data-driven decisions. With easy-to-understand graphs and charts, executives can expedite decision-making processes and adapt business strategies for effective cost containment and revenue growth.

2) Digital Marketing Report Template:

In today’s digital age, businesses rely heavily on digital marketing channels to reach their target audience and drive engagement. A digital marketing report provides insights into the performance of various marketing channels and campaigns, helping businesses optimize their marketing strategies for maximum impact. 

This report typically includes key metrics such as website traffic, conversion rates, and ROI for each marketing channel. By analyzing these KPIs, businesses can identify their best-performing channels and allocate resources accordingly. For example, the report may reveal that certain channels, such as social media or email marketing, yield higher response rates than others. Armed with this information, businesses can refine their digital marketing efforts to enhance the user experience, attract more customers, and ultimately drive growth.

3) Sales Performance Report:

A sales performance report provides a detailed analysis of sales activities, including revenue generated, sales volume, customer acquisition, and sales team performance. This report typically includes visualizations such as sales trend charts, pipeline analysis, and territory-wise sales comparisons. By analyzing these metrics, sales managers can identify top-performing products or services, track sales targets, and identify areas for improvement.

4) Customer Satisfaction Report:

A customer satisfaction report evaluates customer feedback and sentiment to measure overall satisfaction levels with products or services. This report may include metrics such as Net Promoter Score (NPS), customer survey results, and customer support ticket data. By analyzing these metrics, businesses can identify areas where they excel and areas where they need to improve to enhance the overall customer experience.

5) Financial Performance Report:

A financial performance report provides an in-depth analysis of an organization’s financial health, including revenue, expenses, profitability, and cash flow. This report may include financial ratios, trend analysis, and variance reports to assess performance against budgeted targets or industry benchmarks. By analyzing these metrics, financial managers can identify areas of strength and weakness and make strategic decisions to improve financial performance .

6) Inventory Management Report:

An inventory management report tracks inventory levels, turnover rates, stockouts, and inventory costs to optimize inventory management processes. This report may include metrics such as inventory turnover ratio, carrying costs, and stock-to-sales ratios. By analyzing these metrics, inventory managers can ensure optimal inventory levels, minimize stockouts, and reduce carrying costs to improve overall operational efficiency.

7) Employee Performance Report:

An employee performance report evaluates individual and team performance based on key performance indicators (KPIs) such as sales targets, customer satisfaction scores, productivity metrics, and attendance records. This report may include visualizations such as performance scorecards, heatmaps, and trend analysis charts to identify top performers, areas for improvement, and training needs.

Also Check: Analytics & Insights: The Difference Between Data, Analytics, and Insights

Why are Analyze Report Important?

Analyze Report are important for several reasons:

• Informed Decision Making: Analytical reports provide valuable insights and data-driven analysis that enable businesses to make informed decisions. By presenting relevant information in a structured format, these reports help stakeholders understand trends, identify patterns, and evaluate potential courses of action.
• Problem Solving: Analytical reports help organizations identify and address challenges or issues within their operations. Whether it’s identifying inefficiencies in processes, addressing customer complaints, or mitigating risks, these reports provide a framework for problem-solving and decision-making.
• Business Opportunities: Analytical reports can uncover new business opportunities by analyzing market trends, customer behavior, and competitor activities. By identifying emerging trends or unmet customer needs, businesses can capitalize on opportunities for growth and innovation.
• Performance Evaluation: Analytical reports are instrumental in evaluating the performance of various aspects of a business, such as sales, marketing campaigns, and financial metrics. By tracking key performance indicators (KPIs) and metrics, organizations can assess their progress towards goals and objectives.
• Accountability and Transparency: Analytical reports promote accountability and transparency within an organization by providing objective data and analysis. By sharing insights and findings with stakeholders, businesses can foster trust and confidence in their decision-making processes.

Overall, analytical reports serve as valuable tools for businesses to gain insights, solve problems, identify opportunities, evaluate performance, and enhance decision-making processes.

Types of Analyze Report

• Financial Analyze Report: These reports analyze the financial performance of an organization, including revenue, expenses, profitability, and cash flow. They help stakeholders understand the financial health of the business and make informed decisions about investments, budgeting, and strategic planning.
• Market Research Reports: Market research reports analyze market trends, consumer behavior, competitive landscape, and other factors affecting a particular industry or market segment. They provide valuable insights for businesses looking to launch new products, enter new markets, or refine their marketing strategies .
• Performance Analysis Reports: These reports evaluate the performance of various aspects of an organization, such as sales performance, operational efficiency, employee productivity, and customer satisfaction. They help identify areas of improvement and inform decision-making to enhance overall performance.
• Risk Assessment Reports: Risk assessment reports analyze potential risks and vulnerabilities within an organization, such as financial risks, operational risks, cybersecurity risks, and regulatory compliance risks. They help stakeholders understand and mitigate risks to protect the organization’s assets and reputation.
• SWOT Analysis Reports: SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis reports assess the internal strengths and weaknesses of an organization, as well as external opportunities and threats in the business environment. They provide a comprehensive overview of the organization’s strategic position and guide decision-making.
• Customer Analysis Reports: Customer analysis reports examine customer demographics, purchasing behavior, satisfaction levels, and preferences. They help businesses understand their target audience better, tailor products and services to meet customer needs, and improve customer retention and loyalty.
• Operational Efficiency Reports: These reports evaluate the efficiency and effectiveness of operational processes within an organization, such as production, logistics, and supply chain management. They identify bottlenecks, inefficiencies, and areas for improvement to optimize operations and reduce costs.
• Compliance and Regulatory Reports: Compliance and regulatory reports assess an organization’s adherence to industry regulations, legal requirements, and internal policies. They ensure that the organization operates ethically and legally, mitigating the risk of fines, penalties, and reputational damage.

For Latest Tech Related Information, Join Our Official Free Telegram Group : PW Skills Telegram Group

Analyze Report FAQs

What is an analytical report.

An analytical report is a document that presents data, analysis, and insights on a specific topic or problem. It provides a detailed examination of information to support decision-making and problem-solving within an organization.

Why are analytical reports important?

Analytical reports are important because they help organizations make informed decisions, solve problems, and identify opportunities for improvement. By analyzing data and providing insights, these reports enable stakeholders to understand trends, patterns, and relationships within their business operations.

What types of data are typically included in analytical reports?

Analytical reports may include various types of data, such as financial data, sales data, customer feedback, market research, and operational metrics. The specific data included depends on the purpose of the report and the information needed to address the topic or problem being analyzed.

How are analytical reports different from other types of reports?

Analytical reports differ from other types of reports, such as descriptive reports or summary reports, in that they go beyond presenting raw data or summarizing information. Instead, analytical reports analyze data in-depth, draw conclusions, and provide recommendations based on the analysis.

What are the key components of an analytical report?

Key components of an analytical report typically include an introduction, methodology, findings, analysis, conclusions, and recommendations. The introduction provides background information on the topic, the methodology outlines the approach used to analyze the data, the findings present the results of the analysis, the analysis interprets the findings, and the conclusions and recommendations offer insights and actionable steps.

What Is A Trusted Analytics Platform?

A trusted analytics platform refers to a software infrastructure or system that helps organizations to securely and effectively analyze large…

10 Best Companies For Data Analysis Internships 2024

This article will help you provide the top 10 best companies for a Data Analysis Internship which will not only…

What Is Business Analytics Business Intelligence?

Want to learn what Business analytics business intelligence is? Reading this article will help you to understand all topics clearly,…

• Collections
• Research Help
• Teaching & Learning
• Library Home

APA 7th Edition Citation Style Guide

• Basics & Help
• Journal Articles
• Web Sources
• Magazine & News Articles
• Audiovisual Media

Technical & Research Reports

Tests, scales, & inventories.

• Legal Documents
• Dissertations & Theses
• References Page
• In-text Citations
• Author Variations (more than 1, or group)
• Citing Business Resources

General Rule:

Author. (Year). Title of report  (Report No. if given). Publisher. DOI or URL

• If the author and the publishing agency are the same omit the publisher from the citation. 

  Federal Interagency Forum on Child and Family Statistics. (2013). America’s children: key national indicators of well-being. http://childstats.gov/americaschildren/index2.asp.

Author or name of group. (Year). Title of data set [description of form]. Publisher Name or Source of

unpublished data. Retrieved month day, year, from DOI or URL

• Include a retrieval date only if the data set is designed to change over time. 
• If a version number and/or database number is available include it with the data set title. 
• No need to include a publisher name if it is the same as the author.
• If the data is unpublished provide the source (e.g. university) if known. 
• If the dataset is untitled, give a description of the data and publication status in square brackets.

Pew Internet & American Life Project. (2012). November 2012- library services [Data file and code book]. http://www.pewinternet.org/Shared-Content/Data-Sets/2012/November-2012--Library-Services.aspx

Jeffri, J., Schriel, A., & Throsby, D. (2003) The aDvANCE Project: A study of career transition for professional dancers (ICPSR 35598; Version V1)  [Data set].  IPCSR.  https://doi.org/10.3886/ICPSR35598.v1  

Whenever possible, give a citation for the measurements' supporting literature (e.g. manual, book, or journal article ). If the supporting literature is unavailable, cite the the test itself or database record using the following rule.

Author name. (year).  Title of the test. URL

Author name. (year).  Title of the test database record [Database record] . Test Database Name. URL

Hofstede , G &  Hofstede , G. J. (2013). Values Survey Module 2013 .  https://geerthofstede.com/research-and-vsm/vsm-2013/

Castellanos, I., Kronenberger, W.G., & Pisoni, D.B.   ( 2018 ). Learning, Executive, and Attention Function Scale

(LEAF) [Database record]. PsycTESTS.  https://doi.org/10.1037/t66008-000

• << Previous: Audiovisual Media
• Next: Legal Documents >>
• Last Updated: Feb 29, 2024 4:13 PM
• URL: https://libguides.wvu.edu/apa

• AI & NLP
• Churn & Loyalty
• Customer Experience
• Customer Journeys
• Customer Metrics
• Feedback Analysis
• Product Experience
• Product Updates
• Sentiment Analysis
• Surveys & Feedback Collection
• Try Thematic

Welcome to the community

Qualitative Data Analysis: Step-by-Step Guide (Manual vs. Automatic)

When we conduct qualitative methods of research, need to explain changes in metrics or understand people's opinions, we always turn to qualitative data. Qualitative data is typically generated through:

• Interview transcripts
• Surveys with open-ended questions
• Contact center transcripts
• Texts and documents
• Audio and video recordings
• Observational notes

Compared to quantitative data, which captures structured information, qualitative data is unstructured and has more depth. It can answer our questions, can help formulate hypotheses and build understanding.

It's important to understand the differences between quantitative data & qualitative data . But unfortunately, analyzing qualitative data is difficult. While tools like Excel, Tableau and PowerBI crunch and visualize quantitative data with ease, there are a limited number of mainstream tools for analyzing qualitative data . The majority of qualitative data analysis still happens manually.

That said, there are two new trends that are changing this. First, there are advances in natural language processing (NLP) which is focused on understanding human language. Second, there is an explosion of user-friendly software designed for both researchers and businesses. Both help automate the qualitative data analysis process.

In this post we want to teach you how to conduct a successful qualitative data analysis. There are two primary qualitative data analysis methods; manual & automatic. We will teach you how to conduct the analysis manually, and also, automatically using software solutions powered by NLP. We’ll guide you through the steps to conduct a manual analysis, and look at what is involved and the role technology can play in automating this process.

More businesses are switching to fully-automated analysis of qualitative customer data because it is cheaper, faster, and just as accurate. Primarily, businesses purchase subscriptions to feedback analytics platforms so that they can understand customer pain points and sentiment.

We’ll take you through 5 steps to conduct a successful qualitative data analysis. Within each step we will highlight the key difference between the manual, and automated approach of qualitative researchers. Here's an overview of the steps:

The 5 steps to doing qualitative data analysis

• Gathering and collecting your qualitative data
• Organizing and connecting into your qualitative data
• Coding your qualitative data
• Analyzing the qualitative data for insights
• Reporting on the insights derived from your analysis

What is Qualitative Data Analysis?

Qualitative data analysis is a process of gathering, structuring and interpreting qualitative data to understand what it represents.

Qualitative data is non-numerical and unstructured. Qualitative data generally refers to text, such as open-ended responses to survey questions or user interviews, but also includes audio, photos and video.

Businesses often perform qualitative data analysis on customer feedback. And within this context, qualitative data generally refers to verbatim text data collected from sources such as reviews, complaints, chat messages, support centre interactions, customer interviews, case notes or social media comments.

How is qualitative data analysis different from quantitative data analysis?

Understanding the differences between quantitative & qualitative data is important. When it comes to analyzing data, Qualitative Data Analysis serves a very different role to Quantitative Data Analysis. But what sets them apart?

Qualitative Data Analysis dives into the stories hidden in non-numerical data such as interviews, open-ended survey answers, or notes from observations. It uncovers the ‘whys’ and ‘hows’ giving a deep understanding of people’s experiences and emotions.

Quantitative Data Analysis on the other hand deals with numerical data, using statistics to measure differences, identify preferred options, and pinpoint root causes of issues.  It steps back to address questions like "how many" or "what percentage" to offer broad insights we can apply to larger groups.

In short, Qualitative Data Analysis is like a microscope,  helping us understand specific detail. Quantitative Data Analysis is like the telescope, giving us a broader perspective. Both are important, working together to decode data for different objectives.

Qualitative Data Analysis methods

Once all the data has been captured, there are a variety of analysis techniques available and the choice is determined by your specific research objectives and the kind of data you’ve gathered.  Common qualitative data analysis methods include:

Content Analysis

This is a popular approach to qualitative data analysis. Other qualitative analysis techniques may fit within the broad scope of content analysis. Thematic analysis is a part of the content analysis.  Content analysis is used to identify the patterns that emerge from text, by grouping content into words, concepts, and themes. Content analysis is useful to quantify the relationship between all of the grouped content. The Columbia School of Public Health has a detailed breakdown of content analysis .

Narrative Analysis

Narrative analysis focuses on the stories people tell and the language they use to make sense of them.  It is particularly useful in qualitative research methods where customer stories are used to get a deep understanding of customers’ perspectives on a specific issue. A narrative analysis might enable us to summarize the outcomes of a focused case study.

Discourse Analysis

Discourse analysis is used to get a thorough understanding of the political, cultural and power dynamics that exist in specific situations.  The focus of discourse analysis here is on the way people express themselves in different social contexts. Discourse analysis is commonly used by brand strategists who hope to understand why a group of people feel the way they do about a brand or product.

Thematic Analysis

Thematic analysis is used to deduce the meaning behind the words people use. This is accomplished by discovering repeating themes in text. These meaningful themes reveal key insights into data and can be quantified, particularly when paired with sentiment analysis . Often, the outcome of thematic analysis is a code frame that captures themes in terms of codes, also called categories. So the process of thematic analysis is also referred to as “coding”. A common use-case for thematic analysis in companies is analysis of customer feedback.

Grounded Theory

Grounded theory is a useful approach when little is known about a subject. Grounded theory starts by formulating a theory around a single data case. This means that the theory is “grounded”. Grounded theory analysis is based on actual data, and not entirely speculative. Then additional cases can be examined to see if they are relevant and can add to the original grounded theory.

Challenges of Qualitative Data Analysis

While Qualitative Data Analysis offers rich insights, it comes with its challenges. Each unique QDA method has its unique hurdles. Let’s take a look at the challenges researchers and analysts might face, depending on the chosen method.

• Time and Effort (Narrative Analysis): Narrative analysis, which focuses on personal stories, demands patience. Sifting through lengthy narratives to find meaningful insights can be time-consuming, requires dedicated effort.
• Being Objective (Grounded Theory): Grounded theory, building theories from data, faces the challenges of personal biases. Staying objective while interpreting data is crucial, ensuring conclusions are rooted in the data itself.
• Complexity (Thematic Analysis): Thematic analysis involves identifying themes within data, a process that can be intricate. Categorizing and understanding themes can be complex, especially when each piece of data varies in context and structure. Thematic Analysis software can simplify this process.
• Generalizing Findings (Narrative Analysis): Narrative analysis, dealing with individual stories, makes drawing broad challenging. Extending findings from a single narrative to a broader context requires careful consideration.
• Managing Data (Thematic Analysis): Thematic analysis involves organizing and managing vast amounts of unstructured data, like interview transcripts. Managing this can be a hefty task, requiring effective data management strategies.
• Skill Level (Grounded Theory): Grounded theory demands specific skills to build theories from the ground up. Finding or training analysts with these skills poses a challenge, requiring investment in building expertise.

Benefits of qualitative data analysis

Qualitative Data Analysis (QDA) is like a versatile toolkit, offering a tailored approach to understanding your data. The benefits it offers are as diverse as the methods. Let’s explore why choosing the right method matters.

• Tailored Methods for Specific Needs: QDA isn't one-size-fits-all. Depending on your research objectives and the type of data at hand, different methods offer unique benefits. If you want emotive customer stories, narrative analysis paints a strong picture. When you want to explain a score, thematic analysis reveals insightful patterns
• Flexibility with Thematic Analysis: thematic analysis is like a chameleon in the toolkit of QDA. It adapts well to different types of data and research objectives, making it a top choice for any qualitative analysis.
• Deeper Understanding, Better Products: QDA helps you dive into people's thoughts and feelings. This deep understanding helps you build products and services that truly matches what people want, ensuring satisfied customers
• Finding the Unexpected: Qualitative data often reveals surprises that we miss in quantitative data. QDA offers us new ideas and perspectives, for insights we might otherwise miss.
• Building Effective Strategies: Insights from QDA are like strategic guides. They help businesses in crafting plans that match people’s desires.
• Creating Genuine Connections: Understanding people’s experiences lets businesses connect on a real level. This genuine connection helps build trust and loyalty, priceless for any business.

How to do Qualitative Data Analysis: 5 steps

Now we are going to show how you can do your own qualitative data analysis. We will guide you through this process step by step. As mentioned earlier, you will learn how to do qualitative data analysis manually , and also automatically using modern qualitative data and thematic analysis software.

To get best value from the analysis process and research process, it’s important to be super clear about the nature and scope of the question that’s being researched. This will help you select the research collection channels that are most likely to help you answer your question.

Depending on if you are a business looking to understand customer sentiment, or an academic surveying a school, your approach to qualitative data analysis will be unique.

Once you’re clear, there’s a sequence to follow. And, though there are differences in the manual and automatic approaches, the process steps are mostly the same.

The use case for our step-by-step guide is a company looking to collect data (customer feedback data), and analyze the customer feedback - in order to improve customer experience. By analyzing the customer feedback the company derives insights about their business and their customers. You can follow these same steps regardless of the nature of your research. Let’s get started.

Step 1: Gather your qualitative data and conduct research (Conduct qualitative research)

The first step of qualitative research is to do data collection. Put simply, data collection is gathering all of your data for analysis. A common situation is when qualitative data is spread across various sources.

Classic methods of gathering qualitative data

Most companies use traditional methods for gathering qualitative data: conducting interviews with research participants, running surveys, and running focus groups. This data is typically stored in documents, CRMs, databases and knowledge bases. It’s important to examine which data is available and needs to be included in your research project, based on its scope.

Using your existing qualitative feedback

As it becomes easier for customers to engage across a range of different channels, companies are gathering increasingly large amounts of both solicited and unsolicited qualitative feedback.

Most organizations have now invested in Voice of Customer programs , support ticketing systems, chatbot and support conversations, emails and even customer Slack chats.

These new channels provide companies with new ways of getting feedback, and also allow the collection of unstructured feedback data at scale.

The great thing about this data is that it contains a wealth of valubale insights and that it’s already there! When you have a new question about user behavior or your customers, you don’t need to create a new research study or set up a focus group. You can find most answers in the data you already have.

Typically, this data is stored in third-party solutions or a central database, but there are ways to export it or connect to a feedback analysis solution through integrations or an API.

Utilize untapped qualitative data channels

There are many online qualitative data sources you may not have considered. For example, you can find useful qualitative data in social media channels like Twitter or Facebook. Online forums, review sites, and online communities such as Discourse or Reddit also contain valuable data about your customers, or research questions.

If you are considering performing a qualitative benchmark analysis against competitors - the internet is your best friend. Gathering feedback in competitor reviews on sites like Trustpilot, G2, Capterra, Better Business Bureau or on app stores is a great way to perform a competitor benchmark analysis.

Customer feedback analysis software often has integrations into social media and review sites, or you could use a solution like DataMiner to scrape the reviews.

Step 2: Connect & organize all your qualitative data

Now you all have this qualitative data but there’s a problem, the data is unstructured. Before feedback can be analyzed and assigned any value, it needs to be organized in a single place. Why is this important? Consistency!

If all data is easily accessible in one place and analyzed in a consistent manner, you will have an easier time summarizing and making decisions based on this data.

The manual approach to organizing your data

The classic method of structuring qualitative data is to plot all the raw data you’ve gathered into a spreadsheet.

Typically, research and support teams would share large Excel sheets and different business units would make sense of the qualitative feedback data on their own. Each team collects and organizes the data in a way that best suits them, which means the feedback tends to be kept in separate silos.

An alternative and a more robust solution is to store feedback in a central database, like Snowflake or Amazon Redshift .

Keep in mind that when you organize your data in this way, you are often preparing it to be imported into another software. If you go the route of a database, you would need to use an API to push the feedback into a third-party software.

Computer-assisted qualitative data analysis software (CAQDAS)

Traditionally within the manual analysis approach (but not always), qualitative data is imported into CAQDAS software for coding.

In the early 2000s, CAQDAS software was popularised by developers such as ATLAS.ti, NVivo and MAXQDA and eagerly adopted by researchers to assist with the organizing and coding of data.  

The benefits of using computer-assisted qualitative data analysis software:

• Assists in the organizing of your data
• Opens you up to exploring different interpretations of your data analysis
• Allows you to share your dataset easier and allows group collaboration (allows for secondary analysis)

However you still need to code the data, uncover the themes and do the analysis yourself. Therefore it is still a manual approach.

Organizing your qualitative data in a feedback repository

Another solution to organizing your qualitative data is to upload it into a feedback repository where it can be unified with your other data , and easily searchable and taggable. There are a number of software solutions that act as a central repository for your qualitative research data. Here are a couple solutions that you could investigate:  

• Dovetail: Dovetail is a research repository with a focus on video and audio transcriptions. You can tag your transcriptions within the platform for theme analysis. You can also upload your other qualitative data such as research reports, survey responses, support conversations, and customer interviews. Dovetail acts as a single, searchable repository. And makes it easier to collaborate with other people around your qualitative research.
• EnjoyHQ: EnjoyHQ is another research repository with similar functionality to Dovetail. It boasts a more sophisticated search engine, but it has a higher starting subscription cost.

Organizing your qualitative data in a feedback analytics platform

If you have a lot of qualitative customer or employee feedback, from the likes of customer surveys or employee surveys, you will benefit from a feedback analytics platform. A feedback analytics platform is a software that automates the process of both sentiment analysis and thematic analysis . Companies use the integrations offered by these platforms to directly tap into their qualitative data sources (review sites, social media, survey responses, etc.). The data collected is then organized and analyzed consistently within the platform.

If you have data prepared in a spreadsheet, it can also be imported into feedback analytics platforms.

Once all this rich data has been organized within the feedback analytics platform, it is ready to be coded and themed, within the same platform. Thematic is a feedback analytics platform that offers one of the largest libraries of integrations with qualitative data sources.

Step 3: Coding your qualitative data

Your feedback data is now organized in one place. Either within your spreadsheet, CAQDAS, feedback repository or within your feedback analytics platform. The next step is to code your feedback data so we can extract meaningful insights in the next step.

Coding is the process of labelling and organizing your data in such a way that you can then identify themes in the data, and the relationships between these themes.

To simplify the coding process, you will take small samples of your customer feedback data, come up with a set of codes, or categories capturing themes, and label each piece of feedback, systematically, for patterns and meaning. Then you will take a larger sample of data, revising and refining the codes for greater accuracy and consistency as you go.

If you choose to use a feedback analytics platform, much of this process will be automated and accomplished for you.

The terms to describe different categories of meaning (‘theme’, ‘code’, ‘tag’, ‘category’ etc) can be confusing as they are often used interchangeably.  For clarity, this article will use the term ‘code’.

To code means to identify key words or phrases and assign them to a category of meaning. “I really hate the customer service of this computer software company” would be coded as “poor customer service”.

How to manually code your qualitative data

• Decide whether you will use deductive or inductive coding. Deductive coding is when you create a list of predefined codes, and then assign them to the qualitative data. Inductive coding is the opposite of this, you create codes based on the data itself. Codes arise directly from the data and you label them as you go. You need to weigh up the pros and cons of each coding method and select the most appropriate.
• Read through the feedback data to get a broad sense of what it reveals. Now it’s time to start assigning your first set of codes to statements and sections of text.
• Keep repeating step 2, adding new codes and revising the code description as often as necessary.  Once it has all been coded, go through everything again, to be sure there are no inconsistencies and that nothing has been overlooked.
• Create a code frame to group your codes. The coding frame is the organizational structure of all your codes. And there are two commonly used types of coding frames, flat, or hierarchical. A hierarchical code frame will make it easier for you to derive insights from your analysis.
• Based on the number of times a particular code occurs, you can now see the common themes in your feedback data. This is insightful! If ‘bad customer service’ is a common code, it’s time to take action.

We have a detailed guide dedicated to manually coding your qualitative data .

Using software to speed up manual coding of qualitative data

An Excel spreadsheet is still a popular method for coding. But various software solutions can help speed up this process. Here are some examples.

• CAQDAS / NVivo - CAQDAS software has built-in functionality that allows you to code text within their software. You may find the interface the software offers easier for managing codes than a spreadsheet.
• Dovetail/EnjoyHQ - You can tag transcripts and other textual data within these solutions. As they are also repositories you may find it simpler to keep the coding in one platform.
• IBM SPSS - SPSS is a statistical analysis software that may make coding easier than in a spreadsheet.
• Ascribe - Ascribe’s ‘Coder’ is a coding management system. Its user interface will make it easier for you to manage your codes.

Automating the qualitative coding process using thematic analysis software

In solutions which speed up the manual coding process, you still have to come up with valid codes and often apply codes manually to pieces of feedback. But there are also solutions that automate both the discovery and the application of codes.

Advances in machine learning have now made it possible to read, code and structure qualitative data automatically. This type of automated coding is offered by thematic analysis software .

Automation makes it far simpler and faster to code the feedback and group it into themes. By incorporating natural language processing (NLP) into the software, the AI looks across sentences and phrases to identify common themes meaningful statements. Some automated solutions detect repeating patterns and assign codes to them, others make you train the AI by providing examples. You could say that the AI learns the meaning of the feedback on its own.

Thematic automates the coding of qualitative feedback regardless of source. There’s no need to set up themes or categories in advance. Simply upload your data and wait a few minutes. You can also manually edit the codes to further refine their accuracy.  Experiments conducted indicate that Thematic’s automated coding is just as accurate as manual coding .

Paired with sentiment analysis and advanced text analytics - these automated solutions become powerful for deriving quality business or research insights.

You could also build your own , if you have the resources!

The key benefits of using an automated coding solution

Automated analysis can often be set up fast and there’s the potential to uncover things that would never have been revealed if you had given the software a prescribed list of themes to look for.

Because the model applies a consistent rule to the data, it captures phrases or statements that a human eye might have missed.

Complete and consistent analysis of customer feedback enables more meaningful findings. Leading us into step 4.

Step 4: Analyze your data: Find meaningful insights

Now we are going to analyze our data to find insights. This is where we start to answer our research questions. Keep in mind that step 4 and step 5 (tell the story) have some overlap . This is because creating visualizations is both part of analysis process and reporting.

The task of uncovering insights is to scour through the codes that emerge from the data and draw meaningful correlations from them. It is also about making sure each insight is distinct and has enough data to support it.

Part of the analysis is to establish how much each code relates to different demographics and customer profiles, and identify whether there’s any relationship between these data points.

Manually create sub-codes to improve the quality of insights

If your code frame only has one level, you may find that your codes are too broad to be able to extract meaningful insights. This is where it is valuable to create sub-codes to your primary codes. This process is sometimes referred to as meta coding.

Note: If you take an inductive coding approach, you can create sub-codes as you are reading through your feedback data and coding it.

While time-consuming, this exercise will improve the quality of your analysis. Here is an example of what sub-codes could look like.

You need to carefully read your qualitative data to create quality sub-codes. But as you can see, the depth of analysis is greatly improved. By calculating the frequency of these sub-codes you can get insight into which  customer service problems you can immediately address.

Correlate the frequency of codes to customer segments

Many businesses use customer segmentation . And you may have your own respondent segments that you can apply to your qualitative analysis. Segmentation is the practise of dividing customers or research respondents into subgroups.

Segments can be based on:

• Demographic
• And any other data type that you care to segment by

It is particularly useful to see the occurrence of codes within your segments. If one of your customer segments is considered unimportant to your business, but they are the cause of nearly all customer service complaints, it may be in your best interest to focus attention elsewhere. This is a useful insight!

Manually visualizing coded qualitative data

There are formulas you can use to visualize key insights in your data. The formulas we will suggest are imperative if you are measuring a score alongside your feedback.

If you are collecting a metric alongside your qualitative data this is a key visualization. Impact answers the question: “What’s the impact of a code on my overall score?”. Using Net Promoter Score (NPS) as an example, first you need to:

• Calculate overall NPS
• Calculate NPS in the subset of responses that do not contain that theme
• Subtract B from A

Then you can use this simple formula to calculate code impact on NPS .

You can then visualize this data using a bar chart.

You can download our CX toolkit - it includes a template to recreate this.

Trends over time

This analysis can help you answer questions like: “Which codes are linked to decreases or increases in my score over time?”

We need to compare two sequences of numbers: NPS over time and code frequency over time . Using Excel, calculate the correlation between the two sequences, which can be either positive (the more codes the higher the NPS, see picture below), or negative (the more codes the lower the NPS).

Now you need to plot code frequency against the absolute value of code correlation with NPS. Here is the formula:

The visualization could look like this:

These are two examples, but there are more. For a third manual formula, and to learn why word clouds are not an insightful form of analysis, read our visualizations article .

Using a text analytics solution to automate analysis

Automated text analytics solutions enable codes and sub-codes to be pulled out of the data automatically. This makes it far faster and easier to identify what’s driving negative or positive results. And to pick up emerging trends and find all manner of rich insights in the data.

Another benefit of AI-driven text analytics software is its built-in capability for sentiment analysis, which provides the emotive context behind your feedback and other qualitative textual data therein.

Thematic provides text analytics that goes further by allowing users to apply their expertise on business context to edit or augment the AI-generated outputs.

Since the move away from manual research is generally about reducing the human element, adding human input to the technology might sound counter-intuitive. However, this is mostly to make sure important business nuances in the feedback aren’t missed during coding. The result is a higher accuracy of analysis. This is sometimes referred to as augmented intelligence .

Step 5: Report on your data: Tell the story

The last step of analyzing your qualitative data is to report on it, to tell the story. At this point, the codes are fully developed and the focus is on communicating the narrative to the audience.

A coherent outline of the qualitative research, the findings and the insights is vital for stakeholders to discuss and debate before they can devise a meaningful course of action.

Creating graphs and reporting in Powerpoint

Typically, qualitative researchers take the tried and tested approach of distilling their report into a series of charts, tables and other visuals which are woven into a narrative for presentation in Powerpoint.

Using visualization software for reporting

With data transformation and APIs, the analyzed data can be shared with data visualisation software, such as Power BI or Tableau , Google Studio or Looker. Power BI and Tableau are among the most preferred options.

Visualizing your insights inside a feedback analytics platform

Feedback analytics platforms, like Thematic, incorporate visualisation tools that intuitively turn key data and insights into graphs.  This removes the time consuming work of constructing charts to visually identify patterns and creates more time to focus on building a compelling narrative that highlights the insights, in bite-size chunks, for executive teams to review.

Using a feedback analytics platform with visualization tools means you don’t have to use a separate product for visualizations. You can export graphs into Powerpoints straight from the platforms.

Conclusion - Manual or Automated?

There are those who remain deeply invested in the manual approach - because it’s familiar, because they’re reluctant to spend money and time learning new software, or because they’ve been burned by the overpromises of AI.  

For projects that involve small datasets, manual analysis makes sense. For example, if the objective is simply to quantify a simple question like “Do customers prefer X concepts to Y?”. If the findings are being extracted from a small set of focus groups and interviews, sometimes it’s easier to just read them

However, as new generations come into the workplace, it’s technology-driven solutions that feel more comfortable and practical. And the merits are undeniable.  Especially if the objective is to go deeper and understand the ‘why’ behind customers’ preference for X or Y. And even more especially if time and money are considerations.

The ability to collect a free flow of qualitative feedback data at the same time as the metric means AI can cost-effectively scan, crunch, score and analyze a ton of feedback from one system in one go. And time-intensive processes like focus groups, or coding, that used to take weeks, can now be completed in a matter of hours or days.

But aside from the ever-present business case to speed things up and keep costs down, there are also powerful research imperatives for automated analysis of qualitative data: namely, accuracy and consistency.

Finding insights hidden in feedback requires consistency, especially in coding.  Not to mention catching all the ‘unknown unknowns’ that can skew research findings and steering clear of cognitive bias.

Some say without manual data analysis researchers won’t get an accurate “feel” for the insights. However, the larger data sets are, the harder it is to sort through the feedback and organize feedback that has been pulled from different places.  And, the more difficult it is to stay on course, the greater the risk of drawing incorrect, or incomplete, conclusions grows.

Though the process steps for qualitative data analysis have remained pretty much unchanged since psychologist Paul Felix Lazarsfeld paved the path a hundred years ago, the impact digital technology has had on types of qualitative feedback data and the approach to the analysis are profound.  

If you want to try an automated feedback analysis solution on your own qualitative data, you can get started with Thematic .

Community & Marketing

Tyler manages our community of CX, insights & analytics professionals. Tyler's goal is to help unite insights professionals around common challenges.

We make it easy to discover the customer and product issues that matter.

Unlock the value of feedback at scale, in one platform. Try it for free now!

• Questions to ask your Feedback Analytics vendor
• How to end customer churn for good
• Scalable analysis of NPS verbatims
• 5 Text analytics approaches
• How to calculate the ROI of CX

Our experts will show you how Thematic works, how to discover pain points and track the ROI of decisions. To access your free trial, book a personal demo today.

Recent posts

Watercare is New Zealand's largest water and wastewater service provider. They are responsible for bringing clean water to 1.7 million people in Tamaki Makaurau (Auckland) and safeguarding the wastewater network to minimize impact on the environment. Water is a sector that often gets taken for granted, with drainage and

Become a qualitative theming pro! Creating a perfect code frame is hard, but thematic analysis software makes the process much easier.

Qualtrics is one of the most well-known and powerful Customer Feedback Management platforms. But even so, it has limitations. We recently hosted a live panel where data analysts from two well-known brands shared their experiences with Qualtrics, and how they extended this platform’s capabilities. Below, we’ll share the

Along with Stanford news and stories, show me:

• Student information
• Faculty/Staff information

We want to provide announcements, events, leadership messages and resources that are relevant to you. Your selection is stored in a browser cookie which you can remove at any time using “Clear all personalization” below.

Image credit: Claire Scully

New advances in technology are upending education, from the recent debut of new artificial intelligence (AI) chatbots like ChatGPT to the growing accessibility of virtual-reality tools that expand the boundaries of the classroom. For educators, at the heart of it all is the hope that every learner gets an equal chance to develop the skills they need to succeed. But that promise is not without its pitfalls.

“Technology is a game-changer for education – it offers the prospect of universal access to high-quality learning experiences, and it creates fundamentally new ways of teaching,” said Dan Schwartz, dean of Stanford Graduate School of Education (GSE), who is also a professor of educational technology at the GSE and faculty director of the Stanford Accelerator for Learning . “But there are a lot of ways we teach that aren’t great, and a big fear with AI in particular is that we just get more efficient at teaching badly. This is a moment to pay attention, to do things differently.”

For K-12 schools, this year also marks the end of the Elementary and Secondary School Emergency Relief (ESSER) funding program, which has provided pandemic recovery funds that many districts used to invest in educational software and systems. With these funds running out in September 2024, schools are trying to determine their best use of technology as they face the prospect of diminishing resources.

Here, Schwartz and other Stanford education scholars weigh in on some of the technology trends taking center stage in the classroom this year.

AI in the classroom

In 2023, the big story in technology and education was generative AI, following the introduction of ChatGPT and other chatbots that produce text seemingly written by a human in response to a question or prompt. Educators immediately worried that students would use the chatbot to cheat by trying to pass its writing off as their own. As schools move to adopt policies around students’ use of the tool, many are also beginning to explore potential opportunities – for example, to generate reading assignments or coach students during the writing process.

AI can also help automate tasks like grading and lesson planning, freeing teachers to do the human work that drew them into the profession in the first place, said Victor Lee, an associate professor at the GSE and faculty lead for the AI + Education initiative at the Stanford Accelerator for Learning. “I’m heartened to see some movement toward creating AI tools that make teachers’ lives better – not to replace them, but to give them the time to do the work that only teachers are able to do,” he said. “I hope to see more on that front.”

He also emphasized the need to teach students now to begin questioning and critiquing the development and use of AI. “AI is not going away,” said Lee, who is also director of CRAFT (Classroom-Ready Resources about AI for Teaching), which provides free resources to help teach AI literacy to high school students across subject areas. “We need to teach students how to understand and think critically about this technology.”

Immersive environments

The use of immersive technologies like augmented reality, virtual reality, and mixed reality is also expected to surge in the classroom, especially as new high-profile devices integrating these realities hit the marketplace in 2024.

The educational possibilities now go beyond putting on a headset and experiencing life in a distant location. With new technologies, students can create their own local interactive 360-degree scenarios, using just a cell phone or inexpensive camera and simple online tools.

“This is an area that’s really going to explode over the next couple of years,” said Kristen Pilner Blair, director of research for the Digital Learning initiative at the Stanford Accelerator for Learning, which runs a program exploring the use of virtual field trips to promote learning. “Students can learn about the effects of climate change, say, by virtually experiencing the impact on a particular environment. But they can also become creators, documenting and sharing immersive media that shows the effects where they live.”

Integrating AI into virtual simulations could also soon take the experience to another level, Schwartz said. “If your VR experience brings me to a redwood tree, you could have a window pop up that allows me to ask questions about the tree, and AI can deliver the answers.”

Gamification

Another trend expected to intensify this year is the gamification of learning activities, often featuring dynamic videos with interactive elements to engage and hold students’ attention.

“Gamification is a good motivator, because one key aspect is reward, which is very powerful,” said Schwartz. The downside? Rewards are specific to the activity at hand, which may not extend to learning more generally. “If I get rewarded for doing math in a space-age video game, it doesn’t mean I’m going to be motivated to do math anywhere else.”

Gamification sometimes tries to make “chocolate-covered broccoli,” Schwartz said, by adding art and rewards to make speeded response tasks involving single-answer, factual questions more fun. He hopes to see more creative play patterns that give students points for rethinking an approach or adapting their strategy, rather than only rewarding them for quickly producing a correct response.

Data-gathering and analysis

The growing use of technology in schools is producing massive amounts of data on students’ activities in the classroom and online. “We’re now able to capture moment-to-moment data, every keystroke a kid makes,” said Schwartz – data that can reveal areas of struggle and different learning opportunities, from solving a math problem to approaching a writing assignment.

But outside of research settings, he said, that type of granular data – now owned by tech companies – is more likely used to refine the design of the software than to provide teachers with actionable information.

The promise of personalized learning is being able to generate content aligned with students’ interests and skill levels, and making lessons more accessible for multilingual learners and students with disabilities. Realizing that promise requires that educators can make sense of the data that’s being collected, said Schwartz – and while advances in AI are making it easier to identify patterns and findings, the data also needs to be in a system and form educators can access and analyze for decision-making. Developing a usable infrastructure for that data, Schwartz said, is an important next step.

With the accumulation of student data comes privacy concerns: How is the data being collected? Are there regulations or guidelines around its use in decision-making? What steps are being taken to prevent unauthorized access? In 2023 K-12 schools experienced a rise in cyberattacks, underscoring the need to implement strong systems to safeguard student data.

Technology is “requiring people to check their assumptions about education,” said Schwartz, noting that AI in particular is very efficient at replicating biases and automating the way things have been done in the past, including poor models of instruction. “But it’s also opening up new possibilities for students producing material, and for being able to identify children who are not average so we can customize toward them. It’s an opportunity to think of entirely new ways of teaching – this is the path I hope to see.”

• About the New York Fed
• Bank Leadership
• Diversity and Inclusion
• Communities We Serve
• Board of Directors
• Disclosures
• Ethics and Conflicts of Interest
• Annual Financial Statements
• News & Events
• Advisory Groups
• Vendor Information
• Holiday Schedule

At the New York Fed, our mission is to make the U.S. economy stronger and the financial system more stable for all segments of society. We do this by executing monetary policy, providing financial services, supervising banks and conducting research and providing expertise on issues that impact the nation and communities we serve.

The New York Innovation Center bridges the worlds of finance, technology, and innovation and generates insights into high-value central bank-related opportunities.

Do you have a request for information and records? Learn how to submit it.

Learn about the history of the New York Fed and central banking in the United States through articles, speeches, photos and video.

• Markets & Policy Implementation
• Reference Rates
• Effective Federal Funds Rate
• Overnight Bank Funding Rate
• Secured Overnight Financing Rate
• SOFR Averages & Index
• Broad General Collateral Rate
• Tri-Party General Collateral Rate
• Desk Operations
• Treasury Securities
• Agency Mortgage-Backed Securities
• Reverse Repos
• Securities Lending
• Central Bank Liquidity Swaps
• System Open Market Account Holdings
• Primary Dealer Statistics
• Historical Transaction Data
• Monetary Policy Implementation
• Agency Commercial Mortgage-Backed Securities
• Agency Debt Securities
• Repos & Reverse Repos
• Discount Window
• Treasury Debt Auctions & Buybacks as Fiscal Agent
• INTERNATIONAL MARKET OPERATIONS
• Foreign Exchange
• Foreign Reserves Management
• Central Bank Swap Arrangements
• Statements & Operating Policies
• Survey of Primary Dealers
• Survey of Market Participants
• Annual Reports
• Primary Dealers
• Standing Repo Facility Counterparties
• Reverse Repo Counterparties
• Foreign Exchange Counterparties
• Foreign Reserves Management Counterparties
• Operational Readiness
• Central Bank & International Account Services
• Programs Archive
• Economic Research
• Consumer Expectations & Behavior
• Survey of Consumer Expectations
• Household Debt & Credit Report
• Home Price Changes
• Growth & Inflation
• Equitable Growth Indicators
• Multivariate Core Trend Inflation
• New York Fed DSGE Model
• New York Fed Staff Nowcast
• R-star: Natural Rate of Interest
• Labor Market
• Labor Market for Recent College Graduates
• Financial Stability
• Corporate Bond Market Distress Index
• Outlook-at-Risk
• Treasury Term Premia
• Yield Curve as a Leading Indicator
• Banking Research Data Sets
• Quarterly Trends for Consolidated U.S. Banking Organizations
• Empire State Manufacturing Survey
• Business Leaders Survey
• Supplemental Survey Report
• Regional Employment Trends
• Early Benchmarked Employment Data
• INTERNATIONAL ECONOMY
• Global Economic Indicators
• Global Supply Chain Pressure Index
• Staff Economists
• Visiting Scholars
• Resident Scholars
• PUBLICATIONS
• Liberty Street Economics
• Staff Reports
• Economic Policy Review
• RESEARCH CENTERS
• Applied Macroeconomics & Econometrics Center (AMEC)
• Center for Microeconomic Data (CMD)
• Economic Indicators Calendar
• Financial Institution Supervision
• Regulations
• Reporting Forms
• Correspondence
• Bank Applications
• Community Reinvestment Act Exams
• Frauds and Scams

As part of our core mission, we supervise and regulate financial institutions in the Second District. Our primary objective is to maintain a safe and competitive U.S. and global banking system.

The Governance & Culture Reform hub is designed to foster discussion about corporate governance and the reform of culture and behavior in the financial services industry.

Need to file a report with the New York Fed? Here are all of the forms, instructions and other information related to regulatory and statistical reporting in one spot.

The New York Fed works to protect consumers as well as provides information and resources on how to avoid and report specific scams.

• Financial Services & Infrastructure
• Services For Financial Institutions
• Payment Services
• Payment System Oversight
• International Services, Seminars & Training
• Tri-Party Repo Infrastructure Reform
• Managing Foreign Exchange
• Money Market Funds
• Over-The-Counter Derivatives

The Federal Reserve Bank of New York works to promote sound and well-functioning financial systems and markets through its provision of industry and payment services, advancement of infrastructure reform in key markets and training and educational support to international institutions.

The New York Fed offers the Central Banking Seminar and several specialized courses for central bankers and financial supervisors.

The New York Fed has been working with tri-party repo market participants to make changes to improve the resiliency of the market to financial stress.

• Community Development & Education
• Household Financial Well-being
• Fed Communities
• Fed Listens
• Fed Small Business
• Workforce Development
• Other Community Development Work
• High School Fed Challenge
• College Fed Challenge
• Teacher Professional Development
• Classroom Visits
• Museum & Learning Center Visits
• Educational Comic Books
• Economist Spotlight Series
• Lesson Plans and Resources
• Economic Education Calendar

We are connecting emerging solutions with funding in three areas—health, household financial stability, and climate—to improve life for underserved communities. Learn more by reading our strategy.

The Economic Inequality & Equitable Growth hub is a collection of research, analysis and convenings to help better understand economic inequality.

Household Debt Rose by $184 Billion in Q1 2024; Delinquency Transition Rates Increased Across All Debt Types

NEW YORK — The Federal Reserve Bank of New York’s Center for Microeconomic Data today issued its Quarterly Report on Household Debt and Credit . The report shows total household debt increased by $184 billion (1.1%) in the first quarter of 2024, to $17.69 trillion. The report is based on data from the New York Fed’s nationally representative Consumer Credit Panel .

The New York Fed also issued an accompanying Liberty Street Economics blog post examining credit card utilization and its relationship with delinquency. The Quarterly Report also includes a one-page summary of key takeaways and their supporting data points.

“In the first quarter of 2024, credit card and auto loan transition rates into serious delinquency continued to rise across all age groups,” said Joelle Scally, Regional Economic Principal within the Household and Public Policy Research Division at the New York Fed. “An increasing number of borrowers missed credit card payments, revealing worsening financial distress among some households.”

Mortgage balances rose by $190 billion from the previous quarter and was $12.44 trillion at the end of March. Balances on home equity lines of credit (HELOC) increased by $16 billion, representing the eighth consecutive quarterly increase since Q1 2022, and now stand at $376 billion. Credit card balances decreased by $14 billion to $1.12 trillion. Other balances, which include retail cards and consumer loans, also decreased by $11 billion. Auto loan balances increased by $9 billion, continuing the upward trajectory seen since 2020, and now stand at $1.62 trillion.

Mortgage originations continued increasing at the same pace seen in the previous three quarters, and now stand at $403 billion. Aggregate limits on credit card accounts increased modestly by $63 billion, representing a 1.3% increase from the previous quarter. Limits on HELOC grew by $30 billion and have grown by 14% over the past two years, after 10 years of observed declines.

Aggregate delinquency rates increased in Q1 2024, with 3.2% of outstanding debt in some stage of delinquency at the end of March. Delinquency transition rates increased for all debt types. Annualized, approximately 8.9% of credit card balances and 7.9% of auto loans transitioned into delinquency. Delinquency transition rates for mortgages increased by 0.3 percentage points yet remain low by historic standards.

Household Debt and Credit Developments as of Q1 2024

*Change from Q4 2023 to Q1 2024 ** Change from Q1 2023 to Q1 2024

Flow into Serious Delinquency (90 days or more delinquent)

About the Report

The Federal Reserve Bank of New York’s Household Debt and Credit Report provides unique data and insight into the credit conditions and activity of U.S. consumers. Based on data from the New York Fed’s Consumer Credit Panel , a nationally representative sample drawn from anonymized Equifax credit data, the report provides a quarterly snapshot of household trends in borrowing and indebtedness, including data about mortgages, student loans, credit cards, auto loans and delinquencies. The report aims to help community groups, small businesses, state and local governments and the public to better understand, monitor, and respond to trends in borrowing and indebtedness at the household level. Sections of the report are presented as interactive graphs on the New York Fed’s  Household Debt and Credit Report web page  and the full report is available for download.

• Request a Speaker
• International Seminars & Training
• Governance & Culture Reform
• Data Visualization
• Economic Research Tracker
• Markets Data APIs
• Terms of Use

A .gov website belongs to an official government organization in the United States.

A lock ( ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

• Legionnaires' disease Investigations
• Public Health Strategy
• Data Methodology
• Publications
• Investigating Healthcare-associated Cases and Outbreaks
• Investigation Resources
• Legionella (Legionnaires' Disease and Pontiac Fever)
• Controlling Legionella

About the Data: Case Report Forms and Instructions

• Legionellosis, which includes Legionnaires' disease, is a nationally notifiable disease.
• Report cases to the Supplemental Legionnaires' Disease Surveillance System (SLDSS) using any of the following forms.

Data reporting

After choosing the form you want to use, complete as much of it as possible. Travel-related reports should include

• Travel destination(s)
• Accommodation or facility name[s] and address[es]
• Dates of travel
• Notable water exposures during travel

If you have questions, email  [email protected] .

Extended case report form

A form with additional variables (beyond those on the Core Form) for jurisdictions able to collect expanded exposure or comorbidity information. Additional variables are consistent with the Legionellosis Message Mapping Guide . This form can also be used for hypothesis generation during case investigations.

• Extended Form last updated 02/02/2023
• Spanish Version last updated 02/02/2023

Core case report form

A shorter form with SLDSS core variables.

• Core Form last updated 01/23/2020

Cruise exposure reporting

For cases with cruise travel during the exposure period, complete the cruise ship questionnaire template . Submit the completed questionnaire when reporting travel-associated cases.

Submitting forms to CDC

Mail : Centers for Disease Control & Prevention ATTN: Respiratory Diseases Branch Mailstop H24-8 1600 Clifton Rd NE Atlanta, GA 30329-4027

Fax: (404) 315-4680

Email: [email protected]

SLDSS reporting timelines

Any case : Ideally within 30 days of receiving a case report

Travel-associated cases : Within 7 days of receiving a case report

CDC also accepts bulk case data in Excel or CSV files using a standardized format. This is the preferred submission method, although travel reports should be submitted within 7 days via email or fax. Email us for more information about bulk data extract submissions.

Instructions for completing the forms

Extended form.

The new Extended Form includes all the variables found in the Core Form. It also includes variables to assist in the hypothesis-generating process. Two new sections obtain information on the case patient's expanded exposures and underlying conditions. Download the Extended Case Report Form .

Instructions are currently under development for the Extended Form.

The Core Form contains the core variables necessary to report legionellosis cases to SLDSS. Jurisdictions should continue to complete the Core Form if resources don't allow for the completion of the Extended Form. Download the Core Case Report Form .

Use the instructions below in conjunction with the CDC Legionellosis Case Report Core Form.

Section 1. Patient Information and Demographics

State health dept. case no.:  Enter the unique identifier assigned by the state health department.

Reporting state:  Use the 2 letter postal abbreviation (e.g., GA) of the state health department reporting to CDC.

County of residence:  Indicate the patient’s county of residence.

State of residence:  Use the 2 letter postal abbreviation (e.g., GA) of patient’s state of residence.

Occupation:  Indicate occupation of patient at time of symptom onset. Please consider occupational settings that may expose the patient to aerosolized water, such as maintenance (e.g., water service provider, water appliances, cooling towers), construction, leisure (e.g., hotel, cruise ship, water park), industrial/manufacturing plants with water spray systems, and long-haul/commercial truck drivers.

Date of birth:  Enter date of birth of patient in this format (mm/dd/yyyy).

Age:  Enter age of patient at time of symptom onset; indicate age unit as days, months, or years.

Sex:  Indicate sex of patient.

Ethnicity:  Indicate ethnicity of patient.

Race:  Indicate race of patient, using multiple boxes if needed. Do not make assumptions based on name or native language. If race is unknown, please check “Unknown.”

Section 2. Clinical Information and Outcome

Diagnosis:  Indicate legionellosis type:

• Legionnaires’ disease
• Pontiac fever
• Although extremely rare,  Legionella  can also cause extrapulmonary infections, such as endocarditis or wound infections.
• If you select Extrapulmonary legionellosis please specify anatomic location of infection.
• Extrapulmonary legionellosis is not intended to reflect “unknown.” Please make your best attempt to categorize cases in one of these three discrete categories.

Refer to CDC’s Clinical Features page for key clinical differences between Legionnaires’ disease and Pontiac fever. Learn more about the CSTE case definition.  Additional criteria for defining Legionnaires’ disease and Pontiac fever are subject to local health department jurisdictions. CDC is available for  consultation and assistance  for questions.

Date of symptom onset:  Enter the date (mm/dd/yyyy) when patient-described signs and symptoms of legionellosis first occurred. If the patient had existing respiratory symptoms at baseline (e.g., chronic cough), use the date when symptoms got worse. If exact date is unavailable, enter the best guess based on medical records and patient/proxy interviews. Use the Comments field for any discrepancies (e.g., between patient/proxy interviews and medical records), or if date is unsure.

Date of first report to public health at any level:  Enter date (mm/dd/yyyy) when this case was first reported to public health at any level of jurisdiction.

Hospitalization:  Indicate whether the patient was hospitalized during treatment for legionellosis. If so, enter the admission date (mm/dd/yyyy), full name of hospital (without acronyms or abbreviations), and the city and state of the hospital. If the patient was admitted to a hospital before the date of legionellosis symptom onset and/or admitted due to non-legionellosis reasons, please enter the hospitalization information in the healthcare exposure section (Q18 and Q19).

Outcome:  Indicate if the patient survived, died, or is still ill from this illness at time of reporting. If unknown, indicate “Unknown.”

Section 3. Travel, Healthcare, and Other Exposure Information

Travel:  Indicate whether the patient spent any nights away from home, whether in-state, out-of-state, or out of the country, in the 14 days before onset excluding healthcare settings and senior/assisted living facilities. If travel was to a healthcare setting or a senior/assisted living facility, please enter this information in Q18, Q19 and Q20, respectively. For this section, please include:

• Complete name of hotel or travel accommodation
• Full address, city, state, zip code, and country
• Dates of arrival and departure
• The room number (or floor level), if known

As travel can occur in various settings, refer to the table below for examples on the types of travel information captured by our surveillance system:

Additionally, patients may stay overnight at congregate living facilities, such as shelters or correctional facilities. Although CDC does not consider this type of setting to be travel-associated according to the surveillance definition, it is still important to systematically capture these settings as part of the patient’s exposure history. The best place to enter this information would be in this section. You can enter additional details in the Comments field as needed.

Hot tub exposure: Indicate whether the patient spent time near or inside a hot tub with water jets (e.g., Jacuzzi ® , whirlpool spa, hydrotherapy tub) in the 14 days before onset. Hot tub exposure can occur in a variety of settings, such as healthcare facilities, fitness centers/gyms, hotels/resorts, fairs, home and garden shows with hot tub displays, cruises, vacation rental units/homes, and community complexes. If so, indicate the location name and dates of exposure.

Respiratory therapy equipment usage:  Indicate whether the patient has used a nebulizer, CPAP, BiPAP, or any other respiratory therapy equipment (for treatment of sleep apnea, COPD, asthma, or for any other reason) in the 14 days before symptom onset. If yes, indicate if the device uses a humidifier. If the device uses a humidifier, indicate the type of water that it uses (check all that apply).

Healthcare setting:  Indicate whether the patient spent any time in a healthcare setting in the 14 days before onset. CDC defines a healthcare setting/facility as a hospital, long-term care facility, or clinic. Refer to the table below for healthcare facility categories and examples of facilities that fall within them.

1 Examples of specialty clinics include outpatient cancer treatment centers, outpatient infusion centers, dental offices, or subspecialist offices that provide clinical care not affiliated with a hospital. Other healthcare facilities not listed here include associated sites such as pharmacies and outpatient laboratories. 2 If a clinic visit or same-day surgery occurs within a hospital, the setting for that exposure is hospital, not clinic.

Type of healthcare exposure : Indicate the type of healthcare exposure, which is defined as inpatient, outpatient, visitor/volunteer, or employee. For surveillance purposes, indicate “inpatient” if the patient spent any nights in the facility.

Healthcare facility details:  Enter the complete name of the facility and building (without acronyms or abbreviations), if applicable, including city and state. Indicate whether this facility is also a transplant center. Include the reason and dates of visit. If the patient visits a facility X number of times throughout a time period, please indicate this information either in this section or in the Comments section.

Healthcare exposure determination:  Further classify the healthcare exposure based on the duration of healthcare exposure:

• Presumptive healthcare-associated Legionnaires’ disease: A case with ≥10 days of continuous stay at a healthcare facility during the 14 days before onset of symptoms.
• Possible healthcare-associated Legionnaires’ disease: A case that spent a portion of the 14 days before date of symptom onset in one or more healthcare facilities, but does not meet the criteria for presumptive healthcare-associated Legionnaires’ disease.

Note the following:

• Visitor and employee visits to healthcare facilities during their exposure period are considered possible healthcare-associated.
• A patient who spent ≥10 days of continuous stay in multiple healthcare facilities would be considered a presumptive healthcare-associated case for surveillance purposes.
• If there are pertinent healthcare exposures outside of the 14 day exposure period, these can be noted in the Comments field even though this would not be considered a healthcare-associated case for surveillance purposes.

Refer to the table below for example scenarios and corresponding responses to Q18 and Q19:

Assisted and senior living facility:  Indicate the type of exposure, which is defined as resident, visitor/volunteer, or employee. Enter the complete name (without acronyms or abbreviations), city, and state of the facility, as well as dates of stay. Assisted living facilities provide custodial care and assistance with activities of daily living, such as bathing and dressing. Senior living facilities (including retirement homes without skilled nursing or personal care) provide independent living for the elderly. CDC does not consider assisted or senior living facilities to be healthcare facilities. Community-based residential facilities and other residential facilities that do not provide skilled nursing care are included in this section.

Outbreak:  Indicate if this case is associated with a known outbreak or possible cluster. If yes, specify the associated establishment (if applicable), city, and state of the outbreak or cluster.

Section 4. Laboratory Data

Learn more about the CSTE case definition. Learn more about the CSTE case definition.

Confirmed Case

Urinary antigen positive:  If the clinician performed a urinary antigen test and it was positive, enter the sample collection date.

Culture positive: If there was a positive culture of any  Legionella  organism from respiratory secretions, lung tissue, pleural fluid, or extrapulmonary site, enter the sample collection date. If known, specify species and serogroup.

Antibody titer:  If there was a fourfold or greater rise in specific serum antibody titer to  Legionella pneumophila  serogroup 1, enter the sample collection dates for the initial (acute) and convalescent titer. Both acute and convalescent titers are necessary to fulfill this criterion. Must have paired sera collected at acute onset to 2 weeks after symptoms and 3 to 6 weeks later.*

Nucleic acid assay (e.g., polymerase chain reaction or PCR):  If the laboratory detects  Legionella  species by a validated nucleic acid assay, such as PCR, enter the sample collection date and indicate sample site. If known, specify species and serogroup.

Suspect Case

Antibody titer:  If there was a fourfold or greater rise in antibody titer to specific species or serogroups of  Legionella  other than  Legionella pneumophila  serogroup 1 or to multiple species or serogroups of  Legionella  using pooled antigen, enter the sample collection dates for the initial (acute) and convalescent titer. If known, specify species and serogroup. Both acute and convalescent titers are necessary to fulfill this criterion. Must have paired sera collected at acute onset to 2 weeks after symptoms and 3 to 6 weeks later.*

Direct fluorescent antibody (DFA) or immunohistochemistry (IHC):  If the laboratory detects specific  Legionella  antigen or staining of the organism in respiratory secretions, lung tissue, or pleural fluid by DFA or IHC, enter the sample collection date and indicate sample site. If known, specify species and serogroup.

Probable Case

Check this box if the case has clinically compatible symptoms, an epidemiologic link  in the 14 days before disease onset, but no  Legionella  laboratory testing. Indicate the nature of the epidemiologic link the Comments field.

Comments:  Use this space to provide additional information or expand on areas in the form where there was not enough room. *The CSTE case definition does not specify timing for acute and convalescent sera collection. The timing of sera collection is in accordance with McDade et. al. 1977. 1

• McDade JE, Shepard CC, Fraser DW, Tsai TR, Redus MA, Dowdle WR. Legionnaires' disease: isolation of a bacterium and demonstration of its role in other respiratory disease . N Engl J Med . 1977;297(22):1197–1203.

LD Investigations

Epidemiology, environmental health, laboratory science, and health communication all play critical roles in Legionnaires' disease investigations.

• NIH Grants & Funding
• Blog Policies

NIH Extramural Nexus

Reporting Data Management and Sharing (DMS) Plan Activities in the Research Performance Progress Report (RPPR) 

NIH will be updating the Research Performance Progress Report (RPPR) instructions to address the NIH Data Management and Sharing Policy. NIH plans to implement new questions about updates on the status of data sharing and repositories and unique identifiers for data that have been shared for RPPRs submitted on or after October 1, 2024.  For more details, see the full Guide Notice .  

RELATED NEWS

Before submitting your comment, please review our blog comment policies.

Your email address will not be published. Required fields are marked *

Numbers, Facts and Trends Shaping Your World

Read our research on:

Full Topic List

Regions & Countries

• Publications
• Our Methods
• Short Reads
• Tools & Resources

Read Our Research On:

Broad Public Support for Legal Abortion Persists 2 Years After Dobbs

By more than 2 to 1, americans say medication abortion should be legal, table of contents.

• Other abortion attitudes
• Overall attitudes about abortion
• Americans’ views on medication abortion in their states
• How statements about abortion resonate with Americans
• Acknowledgments
• The American Trends Panel survey methodology

Pew Research Center conducted this study to understand Americans’ views on the legality of abortion, as well as their perceptions of abortion access. For this analysis, we surveyed 8,709 adults from April 8 to 14, 2024. Everyone who took part in this survey is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

Here are the questions used for the report and its methodology .

Nearly two years after the Supreme Court overturned the 1973 Roe v. Wade decision guaranteeing a national right to abortion, a majority of Americans continue to express support for abortion access.

About six-in-ten (63%) say abortion should be legal in all or most cases. This share has grown 4 percentage points since 2021 – the year prior to the 2022 decision in Dobbs v. Jackson Women’s Health Organization that overturned Roe.

The new Pew Research Center survey, conducted April 8-14, 2024, among 8,709 adults, surfaces ongoing – and often partisan – divides over abortion attitudes:

• Democrats and Democratic-leaning independents (85%) overwhelmingly say abortion should be legal in all or most cases, with near unanimous support among liberal Democrats.
• By comparison, Republicans and Republican leaners (41%) are far less likely to say abortion should be legal in all or most cases. However, two-thirds of moderate and liberal Republicans still say it should be.

Since before Roe was overturned, both parties have seen a modest uptick in the share who say abortion should be legal.

As in the past, relatively few Americans (25%) say abortion should be legal in all cases, while even fewer (8%) say it should be illegal in all cases. About two-thirds of Americans do not take an absolutist view: 38% say it should be legal in most cases, and 28% say it should be illegal in most cases.

Related: Americans overwhelmingly say access to IVF is a good thing

Women’s abortion decisions

A narrow majority of Americans (54%) say the statement “the decision about whether to have an abortion should belong solely to the pregnant woman” describes their views extremely or very well. Another 19% say it describes their views somewhat well, and 26% say it does not describe their views well.

Views on an embryo’s rights

About a third of Americans (35%) say the statement “human life begins at conception, so an embryo is a person with rights” describes their views extremely or very well, while 45% say it does not describe their views well.

But many Americans are cross-pressured in their views: 32% of Americans say both statements about women’s decisions and embryos’ rights describe their views at least somewhat well.

Abortion access

About six-in-ten Americans in both parties say getting an abortion in the area where they live would be at least somewhat easy, compared with four-in-ten or fewer who say it would be difficult.

However, U.S. adults are divided over whether getting an abortion should be easier or harder:

• 31% say it should be easier for someone to get an abortion in their area, while 25% say it should be harder. Four-in-ten say the ease of access should be about what it is now.
• 48% of Democrats say that obtaining an abortion should be easier than it is now, while just 15% of Republicans say this. Instead, 40% of Republicans say it should be harder (just 11% of Democrats say this).

As was the case last year, views about abortion access vary widely between those who live in states where abortion is legal and those who live in states where it is not allowed.

For instance, 20% of adults in states where abortion is legal say it would be difficult to get an abortion where they live, but this share rises to 71% among adults in states where abortion is prohibited.

Medication abortion

Americans say medication abortion should be legal rather than illegal by a margin of more than two-to-one (54% vs. 20%). A quarter say they are not sure.

Like opinions on the legality of abortion overall, partisans differ greatly in their views of medication abortion:

• Republicans are closely split but are slightly more likely to say it should be legal (37%) than illegal (32%). Another 30% aren’t sure.
• Democrats (73%) overwhelmingly say medication abortion should be legal. Just 8% say it should be illegal, while 19% are not sure.

Across most other demographic groups, Americans are generally more supportive than not of medication abortion.

Across demographic groups, support for abortion access has changed little since this time last year.

Today, roughly six-in-ten (63%) say abortion should be legal in all (25%) or most (38%) cases. And 36% say it should be illegal in all (8%) or most (28%) cases.

While differences are only modest by gender, other groups vary more widely in their views.

Race and ethnicity

Support for legal abortion is higher among Black (73%) and Asian (76%) adults compared with White (60%) and Hispanic (59%) adults.

Compared with older Americans, adults under 30 are particularly likely to say abortion should be legal: 76% say this, versus about six-in-ten among other age groups.

Those with higher levels of formal education express greater support for legal abortion than those with lower levels of educational attainment.

About two-thirds of Americans with a bachelor’s degree or more education (68%) say abortion should be legal in all or most cases, compared with six-in-ten among those without a degree.

White evangelical Protestants are about three times as likely to say abortion should be illegal (73%) as they are to say it should be legal (25%).

By contrast, majorities of White nonevangelical Protestants (64%), Black Protestants (71%) and Catholics (59%) say abortion should be legal. And religiously unaffiliated Americans are especially likely to say abortion should be legal (86% say this).

Partisanship and ideology

Democrats (85%) are about twice as likely as Republicans (41%) to say abortion should be legal in all or most cases.

But while more conservative Republicans say abortion should be illegal (76%) than legal (27%), the reverse is true for moderate and liberal Republicans (67% say legal, 31% say illegal).

By comparison, a clear majority of conservative and moderate Democrats (76%) say abortion should be legal, with liberal Democrats (96%) overwhelmingly saying this.

Views of abortion access by state

About six-in-ten Americans (58%) say it would be easy for someone to get an abortion in the area where they live, while 39% say it would be difficult.

This marks a slight shift since last year, when 54% said obtaining an abortion would be easy. But Americans are still less likely than before the Dobbs decision to say obtaining an abortion would be easy.

Still, Americans’ views vary widely depending on whether they live in a state that has banned or restricted abortion.

In states that prohibit abortion, Americans are about three times as likely to say it would be difficult to obtain an abortion where they live as they are to say it would be easy (71% vs. 25%). The share saying it would be difficult has risen 19 points since 2019.

In states where abortion is restricted or subject to legal challenges, 51% say it would be difficult to get an abortion where they live. This is similar to the share who said so last year (55%), but higher than the share who said this before the Dobbs decision (38%).

By comparison, just 20% of adults in states where abortion is legal say it would be difficult to get one. This is little changed over the past five years.

Americans’ attitudes about whether it should be easier or harder to get an abortion in the area where they live also varies by geography.

Overall, a decreasing share of Americans say it should be harder to obtain an abortion: 33% said this in 2019, compared with 25% today.

This is particularly true of those in states where abortion is now prohibited or restricted.

In both types of states, the shares of Americans saying it should be easier to obtain an abortion have risen 12 points since before Roe was overturned, as the shares saying it should be harder have gradually declined.

By comparison, changes in views among those living in states where abortion is legal have been more modest.

While Americans overall are more supportive than not of medication abortion (54% say it should be legal, 20% say illegal), there are modest differences in support across groups:

• Younger Americans are somewhat more likely to say medication abortion should be legal than older Americans. While 59% of adults ages 18 to 49 say it should be legal, 48% of those 50 and older say the same.
• Asian adults (66%) are particularly likely to say medication abortion should be legal compared with White (55%), Black (51%) and Hispanic (47%) adults.
• White evangelical Protestants oppose medication abortion by about two-to-one (45% vs. 23%), with White nonevangelicals, Black Protestants, Catholics and religiously unaffiliated adults all being more likely than not to say medication abortion should be legal.
• Republicans are closely divided over medication abortion: 37% say it should be legal while 32% say it should be illegal. But similar to views on abortion access overall, conservative Republicans are more opposed (43% illegal, 27% legal), while moderate and liberals are more supportive (55% legal, 14% illegal).

Just over half of Americans (54%) say “the decision about whether to have an abortion should belong solely to the pregnant woman” describes their views extremely or very well, compared with 19% who say somewhat well and 26% who say not too or not at all well.

Democrats (76%) overwhelmingly say this statement describes their views extremely or very well, with just 8% saying it does not describe their views well.

Republicans are more divided: 44% say it does not describe their views well while 33% say it describes them extremely or very well. Another 22% say it describes them somewhat well.

Fewer Americans (35%) say the statement “human life begins at conception, so an embryo is a person with rights” describes their views extremely or very well. Another 19% say it describes their views somewhat well while 45% say it describes them not too or not at all well.

(The survey asks separately whether “a fetus is a person with rights.” The results are roughly similar: 37% say that statement describes their views extremely or very well.)

Republicans are about three times as likely as Democrats to say “an embryo is a person with rights” describes their views extremely or very well (53% vs. 18%). In turn, Democrats (66%) are far more likely than Republicans (25%) to say it describes their views not too or not at all well.

Some Americans are cross-pressured about abortion

When results on the two statements are combined, 41% of Americans say the statement about a pregnant woman’s right to choose describes their views at least somewhat well , but not the statement about an embryo being a person with rights. About two-in-ten (21%) say the reverse.

But for nearly a third of U.S. adults (32%), both statements describe their views at least somewhat well.

Just 4% of Americans say neither statement describes their views well.

Sign up for our weekly newsletter

Fresh data delivery Saturday mornings

Sign up for The Briefing

Weekly updates on the world of news & information

• Partisanship & Issues

Support for legal abortion is widespread in many places, especially in Europe

Public opinion on abortion, americans overwhelmingly say access to ivf is a good thing, what the data says about abortion in the u.s., nearly a year after roe’s demise, americans’ views of abortion access increasingly vary by where they live, most popular, report materials.

1615 L St. NW, Suite 800 Washington, DC 20036 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 |  Media Inquiries

Research Topics

• Age & Generations
• Coronavirus (COVID-19)
• Economy & Work
• Family & Relationships
• Gender & LGBTQ
• Immigration & Migration
• International Affairs
• Internet & Technology
• Methodological Research
• News Habits & Media
• Non-U.S. Governments
• Other Topics
• Politics & Policy
• Race & Ethnicity
• Email Newsletters

ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

Copyright 2024 Pew Research Center

An official website of the United States government

Here’s how you know

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Take action

• Report an antitrust violation
• File adjudicative documents
• Find banned debt collectors
• View competition guidance
• Competition Matters Blog

New HSR thresholds and filing fees for 2024

View all Competition Matters Blog posts

We work to advance government policies that protect consumers and promote competition.

View Policy

Search or browse the Legal Library

Find legal resources and guidance to understand your business responsibilities and comply with the law.

Browse legal resources

• Find policy statements
• Submit a public comment

Vision and Priorities

Memo from Chair Lina M. Khan to commission staff and commissioners regarding the vision and priorities for the FTC.

Technology Blog

P = np not exactly, but here are some research questions from the office of technology..

View all Technology Blog posts

Advice and Guidance

Learn more about your rights as a consumer and how to spot and avoid scams. Find the resources you need to understand how consumer protection law impacts your business.

• Report fraud
• Report identity theft
• Register for Do Not Call
• Sign up for consumer alerts

Get Business Blog updates

• Get your free credit report
• Find refund cases
• Order bulk publications
• Consumer Advice
• Shopping and Donating
• Credit, Loans, and Debt
• Jobs and Making Money
• Unwanted Calls, Emails, and Texts
• Identity Theft and Online Security
• Business Guidance
• Advertising and Marketing
• Credit and Finance
• Privacy and Security
• By Industry
• For Small Businesses
• Browse Business Guidance Resources
• Business Blog

Servicemembers: Your tool for financial readiness

Visit militaryconsumer.gov

Get consumer protection basics, plain and simple

Visit consumer.gov

Learn how the FTC protects free enterprise and consumers

Visit Competition Counts

Looking for competition guidance?

• Competition Guidance

News and Events

Latest news, ftc announces tentative agenda for may 23 open commission meeting.

View News and Events

Upcoming Event

Scheduling conference before administrative law judge: in re tapestry, inc. and capri holdings limited, docket 9429.

View more Events

Sign up for the latest news

Follow us on social media

-->   -->   -->   -->   -->  

Playing it Safe: Explore the FTC's Top Video Game Cases

Learn about the FTC's notable video game cases and what our agency is doing to keep the public safe.

Latest Data Visualization

FTC Refunds to Consumers

Explore refund statistics including where refunds were sent and the dollar amounts refunded with this visualization.

About the FTC

Our mission is protecting the public from deceptive or unfair business practices and from unfair methods of competition through law enforcement, advocacy, research, and education.

Learn more about the FTC

Meet the Chair

Lina M. Khan was sworn in as Chair of the Federal Trade Commission on June 15, 2021.

Chair Lina M. Khan

Looking for legal documents or records? Search the Legal Library instead.

• Cases and Proceedings
• Premerger Notification Program
• Merger Review
• Anticompetitive Practices
• Competition and Consumer Protection Guidance Documents
• Warning Letters
• Consumer Sentinel Network
• Criminal Liaison Unit
• FTC Refund Programs
• Notices of Penalty Offenses
• Advocacy and Research
• Advisory Opinions
• Cooperation Agreements
• Federal Register Notices
• Public Comments
• Policy Statements
• International
• Office of Technology Blog
• Military Consumer
• Consumer.gov
• Bulk Publications
• Data and Visualizations
• Stay Connected
• Commissioners and Staff
• Bureaus and Offices
• Budget and Strategy
• Office of Inspector General
• Careers at the FTC

Cars & Consumer Data: On Unlawful Collection & Use

Some say the car a person drives can say a lot about them. As cars get “connected,” this turns out to be truer than many people might have realized. While connectivity can let drivers do things like play their favorite internet radio stations or unlock their car with an app, connected cars can also collect a lot of data about people. This data could be sensitive—such as biometric information or location—and its collection, use, and disclosure can threaten consumers’  privacy and financial welfare .

Connected cars have been on the FTC’s radar for years. The FTC highlighted concerns related to connected cars as part of an “Internet of Things”  workshop held in 2013, followed by a  2015 report . In 2018, the FTC hosted a  connected cars workshop highlighting issues ranging from unexpected secondary uses of data to security risks. The agency has also published  guidance to consumers reminding them to wipe the data on their cars before selling them—much as anyone would when trying to resell a computer or smart phone.

Over the years, privacy advocates have raised concerns about the vast amount of data that could be collected from cars, such as  biometric , telematic, geolocation, video, and other personal information. News reports have  also   suggested that data from connected cars could be used to stalk people or affect their insurance rates. Many have noted that when any company collects a large amount of sensitive data, it can pose national security issues if that data is shared with foreign actors.

Car manufacturers—and all businesses—should take note that the FTC will take action to protect consumers against the illegal collection, use, and disclosure of their personal data. Recent enforcement actions illustrate this point:

• Geolocation data is sensitive and subject to enhanced protections under the FTC Act . Cars are much like mobile phones when it comes to revealing consumers’ persistent, precise location. In a series of seminal cases in recent years, the Commission has established that the collection, use, and disclosure of location can be an unfair practice. In X-Mode , the FTC alleged that the data could be used to track people’s visits to sensitive locations like medical or reproductive health clinics, places of worship, or domestic abuse shelters. Similarly, in  InMarket, the Commission alleged that the company’s internal use of sensitive data to group consumers into highly sensitive categories for advertising purposes was unlawful. The orders resolving these matters prohibit these companies from selling sensitive location information.
• Surreptitious disclosure of sensitive information can be an unfair practice. Companies that have legitimate access to consumers’ sensitive information must ensure that the data is used only for the reasons they collected that information. For example, the Commission recently alleged that BetterHelp , which offers online counseling services—including those marketed to specific groups like Christians, teens, and the LGBTQ+ community—revealed consumers’ email addresses and health questionnaire information to third parties for advertising purposes. Similarly, the Commission  took action against mental telehealth provider Cerebral for, among other things, the company’s unfair privacy and security practices. The FTC obtained settlements requiring BetterHelp and Cerebral to pay millions of dollars so that affected consumers could receive partial refunds, and the Cerebral settlement bans the company from using or disclosing consumers’ personal information for advertising purposes.
• Using sensitive data for automated decisions can also be unlawful.  Companies that feed consumer data into algorithms may be liable for harmful automated decisions. The FTC recently took action against Rite Aid, saying in a  complaint that the company enrolled people into a facial recognition program that alerted employees when suspected matches entered their stores. The complaint includes allegations that Rite Aid failed to take reasonable steps to prevent low-quality images from being used with the program, increasing the likelihood of false-positive match alerts. In some cases, false alerts came with recommended actions, such as removing people from the store or calling the police, and employees followed through on those recommendations. As a result of the FTC’s action, Rite Aid agreed to a 5-year ban on the use of facial recognition technology.

These cases underscore the significant potential liability associated with the collection, use, and disclosure of sensitive data, such as biometrics and location data. As the FTC  has stated , firms do not have the free license to monetize people’s information beyond purposes needed to provide their requested product or service, and firms shouldn’t let business model incentives outweigh the need for meaningful privacy safeguards.

The easiest way that companies can avoid harming consumers from the collection, use, and sharing of sensitive information is by simply not collecting it in the first place. When they are motivated to, all businesses—including auto manufacturers—are capable of building products with safeguards that protect consumers. 

Thank you to staff from across the Office of Technology and the Division of Privacy and Identity Protection in the Bureau of Consumer Protection who collaborated on this post.

• Consumer Protection
• Consumer Privacy
• Data Security
• Office of Technology

More from the Technology Blog

Consumer facing applications: a quote book from the tech summit on ai, data and models: a quote book from the tech summit on ai, security principles: addressing vulnerabilities systematically.