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Princeton Correspondents on Undergraduate Research

How to Make a Successful Research Presentation

Turning a research paper into a visual presentation is difficult; there are pitfalls, and navigating the path to a brief, informative presentation takes time and practice. As a TA for  GEO/WRI 201: Methods in Data Analysis & Scientific Writing this past fall, I saw how this process works from an instructor’s standpoint. I’ve presented my own research before, but helping others present theirs taught me a bit more about the process. Here are some tips I learned that may help you with your next research presentation:

More is more

In general, your presentation will always benefit from more practice, more feedback, and more revision. By practicing in front of friends, you can get comfortable with presenting your work while receiving feedback. It is hard to know how to revise your presentation if you never practice. If you are presenting to a general audience, getting feedback from someone outside of your discipline is crucial. Terms and ideas that seem intuitive to you may be completely foreign to someone else, and your well-crafted presentation could fall flat.

Less is more

Limit the scope of your presentation, the number of slides, and the text on each slide. In my experience, text works well for organizing slides, orienting the audience to key terms, and annotating important figures–not for explaining complex ideas. Having fewer slides is usually better as well. In general, about one slide per minute of presentation is an appropriate budget. Too many slides is usually a sign that your topic is too broad.

Limit the scope of your presentation

Don’t present your paper. Presentations are usually around 10 min long. You will not have time to explain all of the research you did in a semester (or a year!) in such a short span of time. Instead, focus on the highlight(s). Identify a single compelling research question which your work addressed, and craft a succinct but complete narrative around it.

You will not have time to explain all of the research you did. Instead, focus on the highlights. Identify a single compelling research question which your work addressed, and craft a succinct but complete narrative around it.

Craft a compelling research narrative

After identifying the focused research question, walk your audience through your research as if it were a story. Presentations with strong narrative arcs are clear, captivating, and compelling.

• Introduction (exposition — rising action)

Orient the audience and draw them in by demonstrating the relevance and importance of your research story with strong global motive. Provide them with the necessary vocabulary and background knowledge to understand the plot of your story. Introduce the key studies (characters) relevant in your story and build tension and conflict with scholarly and data motive. By the end of your introduction, your audience should clearly understand your research question and be dying to know how you resolve the tension built through motive.

• Methods (rising action)

The methods section should transition smoothly and logically from the introduction. Beware of presenting your methods in a boring, arc-killing, ‘this is what I did.’ Focus on the details that set your story apart from the stories other people have already told. Keep the audience interested by clearly motivating your decisions based on your original research question or the tension built in your introduction.

• Results (climax)

Less is usually more here. Only present results which are clearly related to the focused research question you are presenting. Make sure you explain the results clearly so that your audience understands what your research found. This is the peak of tension in your narrative arc, so don’t undercut it by quickly clicking through to your discussion.

• Discussion (falling action)

By now your audience should be dying for a satisfying resolution. Here is where you contextualize your results and begin resolving the tension between past research. Be thorough. If you have too many conflicts left unresolved, or you don’t have enough time to present all of the resolutions, you probably need to further narrow the scope of your presentation.

• Conclusion (denouement)

Return back to your initial research question and motive, resolving any final conflicts and tying up loose ends. Leave the audience with a clear resolution of your focus research question, and use unresolved tension to set up potential sequels (i.e. further research).

Use your medium to enhance the narrative

Visual presentations should be dominated by clear, intentional graphics. Subtle animation in key moments (usually during the results or discussion) can add drama to the narrative arc and make conflict resolutions more satisfying. You are narrating a story written in images, videos, cartoons, and graphs. While your paper is mostly text, with graphics to highlight crucial points, your slides should be the opposite. Adapting to the new medium may require you to create or acquire far more graphics than you included in your paper, but it is necessary to create an engaging presentation.

The most important thing you can do for your presentation is to practice and revise. Bother your friends, your roommates, TAs–anybody who will sit down and listen to your work. Beyond that, think about presentations you have found compelling and try to incorporate some of those elements into your own. Remember you want your work to be comprehensible; you aren’t creating experts in 10 minutes. Above all, try to stay passionate about what you did and why. You put the time in, so show your audience that it’s worth it.

For more insight into research presentations, check out these past PCUR posts written by Emma and Ellie .

— Alec Getraer, Natural Sciences Correspondent

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Definition of research

 (Entry 1 of 2)

Definition of research  (Entry 2 of 2)

transitive verb

intransitive verb

• disquisition
• examination
• exploration
• inquisition
• investigation
• delve (into)
• inquire (into)
• investigate
• look (into)

Examples of research in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'research.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

Middle French recerche , from recercher to go about seeking, from Old French recerchier , from re- + cerchier, sercher to search — more at search

1577, in the meaning defined at sense 3

1588, in the meaning defined at transitive sense 1

Phrases Containing research

• operations research
• marketing research
• research park

research and development

• market research
• translational research
• oppo research

Dictionary Entries Near research

Cite this entry.

“Research.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/research. Accessed 6 Apr. 2024.

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Kids Definition of research  (Entry 2 of 2)

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What Is Research, and Why Do People Do It?

• Open Access
• First Online: 03 December 2022

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• James Hiebert 6 ,
• Jinfa Cai 7 ,
• Stephen Hwang 7 ,
• Anne K Morris 6 &
• Charles Hohensee 6  

Part of the book series: Research in Mathematics Education ((RME))

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Abstractspiepr Abs1

Every day people do research as they gather information to learn about something of interest. In the scientific world, however, research means something different than simply gathering information. Scientific research is characterized by its careful planning and observing, by its relentless efforts to understand and explain, and by its commitment to learn from everyone else seriously engaged in research. We call this kind of research scientific inquiry and define it as “formulating, testing, and revising hypotheses.” By “hypotheses” we do not mean the hypotheses you encounter in statistics courses. We mean predictions about what you expect to find and rationales for why you made these predictions. Throughout this and the remaining chapters we make clear that the process of scientific inquiry applies to all kinds of research studies and data, both qualitative and quantitative.

You have full access to this open access chapter,  Download chapter PDF

Part I. What Is Research?

Have you ever studied something carefully because you wanted to know more about it? Maybe you wanted to know more about your grandmother’s life when she was younger so you asked her to tell you stories from her childhood, or maybe you wanted to know more about a fertilizer you were about to use in your garden so you read the ingredients on the package and looked them up online. According to the dictionary definition, you were doing research.

Recall your high school assignments asking you to “research” a topic. The assignment likely included consulting a variety of sources that discussed the topic, perhaps including some “original” sources. Often, the teacher referred to your product as a “research paper.”

Were you conducting research when you interviewed your grandmother or wrote high school papers reviewing a particular topic? Our view is that you were engaged in part of the research process, but only a small part. In this book, we reserve the word “research” for what it means in the scientific world, that is, for scientific research or, more pointedly, for scientific inquiry .

Exercise 1.1

Before you read any further, write a definition of what you think scientific inquiry is. Keep it short—Two to three sentences. You will periodically update this definition as you read this chapter and the remainder of the book.

This book is about scientific inquiry—what it is and how to do it. For starters, scientific inquiry is a process, a particular way of finding out about something that involves a number of phases. Each phase of the process constitutes one aspect of scientific inquiry. You are doing scientific inquiry as you engage in each phase, but you have not done scientific inquiry until you complete the full process. Each phase is necessary but not sufficient.

In this chapter, we set the stage by defining scientific inquiry—describing what it is and what it is not—and by discussing what it is good for and why people do it. The remaining chapters build directly on the ideas presented in this chapter.

A first thing to know is that scientific inquiry is not all or nothing. “Scientificness” is a continuum. Inquiries can be more scientific or less scientific. What makes an inquiry more scientific? You might be surprised there is no universally agreed upon answer to this question. None of the descriptors we know of are sufficient by themselves to define scientific inquiry. But all of them give you a way of thinking about some aspects of the process of scientific inquiry. Each one gives you different insights.

Exercise 1.2

As you read about each descriptor below, think about what would make an inquiry more or less scientific. If you think a descriptor is important, use it to revise your definition of scientific inquiry.

Creating an Image of Scientific Inquiry

We will present three descriptors of scientific inquiry. Each provides a different perspective and emphasizes a different aspect of scientific inquiry. We will draw on all three descriptors to compose our definition of scientific inquiry.

Descriptor 1. Experience Carefully Planned in Advance

Sir Ronald Fisher, often called the father of modern statistical design, once referred to research as “experience carefully planned in advance” (1935, p. 8). He said that humans are always learning from experience, from interacting with the world around them. Usually, this learning is haphazard rather than the result of a deliberate process carried out over an extended period of time. Research, Fisher said, was learning from experience, but experience carefully planned in advance.

This phrase can be fully appreciated by looking at each word. The fact that scientific inquiry is based on experience means that it is based on interacting with the world. These interactions could be thought of as the stuff of scientific inquiry. In addition, it is not just any experience that counts. The experience must be carefully planned . The interactions with the world must be conducted with an explicit, describable purpose, and steps must be taken to make the intended learning as likely as possible. This planning is an integral part of scientific inquiry; it is not just a preparation phase. It is one of the things that distinguishes scientific inquiry from many everyday learning experiences. Finally, these steps must be taken beforehand and the purpose of the inquiry must be articulated in advance of the experience. Clearly, scientific inquiry does not happen by accident, by just stumbling into something. Stumbling into something unexpected and interesting can happen while engaged in scientific inquiry, but learning does not depend on it and serendipity does not make the inquiry scientific.

Descriptor 2. Observing Something and Trying to Explain Why It Is the Way It Is

When we were writing this chapter and googled “scientific inquiry,” the first entry was: “Scientific inquiry refers to the diverse ways in which scientists study the natural world and propose explanations based on the evidence derived from their work.” The emphasis is on studying, or observing, and then explaining . This descriptor takes the image of scientific inquiry beyond carefully planned experience and includes explaining what was experienced.

According to the Merriam-Webster dictionary, “explain” means “(a) to make known, (b) to make plain or understandable, (c) to give the reason or cause of, and (d) to show the logical development or relations of” (Merriam-Webster, n.d. ). We will use all these definitions. Taken together, they suggest that to explain an observation means to understand it by finding reasons (or causes) for why it is as it is. In this sense of scientific inquiry, the following are synonyms: explaining why, understanding why, and reasoning about causes and effects. Our image of scientific inquiry now includes planning, observing, and explaining why.

We need to add a final note about this descriptor. We have phrased it in a way that suggests “observing something” means you are observing something in real time—observing the way things are or the way things are changing. This is often true. But, observing could mean observing data that already have been collected, maybe by someone else making the original observations (e.g., secondary analysis of NAEP data or analysis of existing video recordings of classroom instruction). We will address secondary analyses more fully in Chap. 4 . For now, what is important is that the process requires explaining why the data look like they do.

We must note that for us, the term “data” is not limited to numerical or quantitative data such as test scores. Data can also take many nonquantitative forms, including written survey responses, interview transcripts, journal entries, video recordings of students, teachers, and classrooms, text messages, and so forth.

Exercise 1.3

What are the implications of the statement that just “observing” is not enough to count as scientific inquiry? Does this mean that a detailed description of a phenomenon is not scientific inquiry?

Find sources that define research in education that differ with our position, that say description alone, without explanation, counts as scientific research. Identify the precise points where the opinions differ. What are the best arguments for each of the positions? Which do you prefer? Why?

Descriptor 3. Updating Everyone’s Thinking in Response to More and Better Information

This descriptor focuses on a third aspect of scientific inquiry: updating and advancing the field’s understanding of phenomena that are investigated. This descriptor foregrounds a powerful characteristic of scientific inquiry: the reliability (or trustworthiness) of what is learned and the ultimate inevitability of this learning to advance human understanding of phenomena. Humans might choose not to learn from scientific inquiry, but history suggests that scientific inquiry always has the potential to advance understanding and that, eventually, humans take advantage of these new understandings.

Before exploring these bold claims a bit further, note that this descriptor uses “information” in the same way the previous two descriptors used “experience” and “observations.” These are the stuff of scientific inquiry and we will use them often, sometimes interchangeably. Frequently, we will use the term “data” to stand for all these terms.

An overriding goal of scientific inquiry is for everyone to learn from what one scientist does. Much of this book is about the methods you need to use so others have faith in what you report and can learn the same things you learned. This aspect of scientific inquiry has many implications.

One implication is that scientific inquiry is not a private practice. It is a public practice available for others to see and learn from. Notice how different this is from everyday learning. When you happen to learn something from your everyday experience, often only you gain from the experience. The fact that research is a public practice means it is also a social one. It is best conducted by interacting with others along the way: soliciting feedback at each phase, taking opportunities to present work-in-progress, and benefitting from the advice of others.

A second implication is that you, as the researcher, must be committed to sharing what you are doing and what you are learning in an open and transparent way. This allows all phases of your work to be scrutinized and critiqued. This is what gives your work credibility. The reliability or trustworthiness of your findings depends on your colleagues recognizing that you have used all appropriate methods to maximize the chances that your claims are justified by the data.

A third implication of viewing scientific inquiry as a collective enterprise is the reverse of the second—you must be committed to receiving comments from others. You must treat your colleagues as fair and honest critics even though it might sometimes feel otherwise. You must appreciate their job, which is to remain skeptical while scrutinizing what you have done in considerable detail. To provide the best help to you, they must remain skeptical about your conclusions (when, for example, the data are difficult for them to interpret) until you offer a convincing logical argument based on the information you share. A rather harsh but good-to-remember statement of the role of your friendly critics was voiced by Karl Popper, a well-known twentieth century philosopher of science: “. . . if you are interested in the problem which I tried to solve by my tentative assertion, you may help me by criticizing it as severely as you can” (Popper, 1968, p. 27).

A final implication of this third descriptor is that, as someone engaged in scientific inquiry, you have no choice but to update your thinking when the data support a different conclusion. This applies to your own data as well as to those of others. When data clearly point to a specific claim, even one that is quite different than you expected, you must reconsider your position. If the outcome is replicated multiple times, you need to adjust your thinking accordingly. Scientific inquiry does not let you pick and choose which data to believe; it mandates that everyone update their thinking when the data warrant an update.

Doing Scientific Inquiry

We define scientific inquiry in an operational sense—what does it mean to do scientific inquiry? What kind of process would satisfy all three descriptors: carefully planning an experience in advance; observing and trying to explain what you see; and, contributing to updating everyone’s thinking about an important phenomenon?

We define scientific inquiry as formulating , testing , and revising hypotheses about phenomena of interest.

Of course, we are not the only ones who define it in this way. The definition for the scientific method posted by the editors of Britannica is: “a researcher develops a hypothesis, tests it through various means, and then modifies the hypothesis on the basis of the outcome of the tests and experiments” (Britannica, n.d. ).

Notice how defining scientific inquiry this way satisfies each of the descriptors. “Carefully planning an experience in advance” is exactly what happens when formulating a hypothesis about a phenomenon of interest and thinking about how to test it. “ Observing a phenomenon” occurs when testing a hypothesis, and “ explaining ” what is found is required when revising a hypothesis based on the data. Finally, “updating everyone’s thinking” comes from comparing publicly the original with the revised hypothesis.

Doing scientific inquiry, as we have defined it, underscores the value of accumulating knowledge rather than generating random bits of knowledge. Formulating, testing, and revising hypotheses is an ongoing process, with each revised hypothesis begging for another test, whether by the same researcher or by new researchers. The editors of Britannica signaled this cyclic process by adding the following phrase to their definition of the scientific method: “The modified hypothesis is then retested, further modified, and tested again.” Scientific inquiry creates a process that encourages each study to build on the studies that have gone before. Through collective engagement in this process of building study on top of study, the scientific community works together to update its thinking.

Before exploring more fully the meaning of “formulating, testing, and revising hypotheses,” we need to acknowledge that this is not the only way researchers define research. Some researchers prefer a less formal definition, one that includes more serendipity, less planning, less explanation. You might have come across more open definitions such as “research is finding out about something.” We prefer the tighter hypothesis formulation, testing, and revision definition because we believe it provides a single, coherent map for conducting research that addresses many of the thorny problems educational researchers encounter. We believe it is the most useful orientation toward research and the most helpful to learn as a beginning researcher.

A final clarification of our definition is that it applies equally to qualitative and quantitative research. This is a familiar distinction in education that has generated much discussion. You might think our definition favors quantitative methods over qualitative methods because the language of hypothesis formulation and testing is often associated with quantitative methods. In fact, we do not favor one method over another. In Chap. 4 , we will illustrate how our definition fits research using a range of quantitative and qualitative methods.

Exercise 1.4

Look for ways to extend what the field knows in an area that has already received attention by other researchers. Specifically, you can search for a program of research carried out by more experienced researchers that has some revised hypotheses that remain untested. Identify a revised hypothesis that you might like to test.

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

To get a full sense of the definition of scientific inquiry we will use throughout this book, it is helpful to spend a little time with each of the key terms.

We first want to make clear that we use the term “hypothesis” as it is defined in most dictionaries and as it used in many scientific fields rather than as it is usually defined in educational statistics courses. By “hypothesis,” we do not mean a null hypothesis that is accepted or rejected by statistical analysis. Rather, we use “hypothesis” in the sense conveyed by the following definitions: “An idea or explanation for something that is based on known facts but has not yet been proved” (Cambridge University Press, n.d. ), and “An unproved theory, proposition, or supposition, tentatively accepted to explain certain facts and to provide a basis for further investigation or argument” (Agnes & Guralnik, 2008 ).

We distinguish two parts to “hypotheses.” Hypotheses consist of predictions and rationales . Predictions are statements about what you expect to find when you inquire about something. Rationales are explanations for why you made the predictions you did, why you believe your predictions are correct. So, for us “formulating hypotheses” means making explicit predictions and developing rationales for the predictions.

“Testing hypotheses” means making observations that allow you to assess in what ways your predictions were correct and in what ways they were incorrect. In education research, it is rarely useful to think of your predictions as either right or wrong. Because of the complexity of most issues you will investigate, most predictions will be right in some ways and wrong in others.

By studying the observations you make (data you collect) to test your hypotheses, you can revise your hypotheses to better align with the observations. This means revising your predictions plus revising your rationales to justify your adjusted predictions. Even though you might not run another test, formulating revised hypotheses is an essential part of conducting a research study. Comparing your original and revised hypotheses informs everyone of what you learned by conducting your study. In addition, a revised hypothesis sets the stage for you or someone else to extend your study and accumulate more knowledge of the phenomenon.

We should note that not everyone makes a clear distinction between predictions and rationales as two aspects of hypotheses. In fact, common, non-scientific uses of the word “hypothesis” may limit it to only a prediction or only an explanation (or rationale). We choose to explicitly include both prediction and rationale in our definition of hypothesis, not because we assert this should be the universal definition, but because we want to foreground the importance of both parts acting in concert. Using “hypothesis” to represent both prediction and rationale could hide the two aspects, but we make them explicit because they provide different kinds of information. It is usually easier to make predictions than develop rationales because predictions can be guesses, hunches, or gut feelings about which you have little confidence. Developing a compelling rationale requires careful thought plus reading what other researchers have found plus talking with your colleagues. Often, while you are developing your rationale you will find good reasons to change your predictions. Developing good rationales is the engine that drives scientific inquiry. Rationales are essentially descriptions of how much you know about the phenomenon you are studying. Throughout this guide, we will elaborate on how developing good rationales drives scientific inquiry. For now, we simply note that it can sharpen your predictions and help you to interpret your data as you test your hypotheses.

Hypotheses in education research take a variety of forms or types. This is because there are a variety of phenomena that can be investigated. Investigating educational phenomena is sometimes best done using qualitative methods, sometimes using quantitative methods, and most often using mixed methods (e.g., Hay, 2016 ; Weis et al. 2019a ; Weisner, 2005 ). This means that, given our definition, hypotheses are equally applicable to qualitative and quantitative investigations.

Hypotheses take different forms when they are used to investigate different kinds of phenomena. Two very different activities in education could be labeled conducting experiments and descriptions. In an experiment, a hypothesis makes a prediction about anticipated changes, say the changes that occur when a treatment or intervention is applied. You might investigate how students’ thinking changes during a particular kind of instruction.

A second type of hypothesis, relevant for descriptive research, makes a prediction about what you will find when you investigate and describe the nature of a situation. The goal is to understand a situation as it exists rather than to understand a change from one situation to another. In this case, your prediction is what you expect to observe. Your rationale is the set of reasons for making this prediction; it is your current explanation for why the situation will look like it does.

You will probably read, if you have not already, that some researchers say you do not need a prediction to conduct a descriptive study. We will discuss this point of view in Chap. 2 . For now, we simply claim that scientific inquiry, as we have defined it, applies to all kinds of research studies. Descriptive studies, like others, not only benefit from formulating, testing, and revising hypotheses, but also need hypothesis formulating, testing, and revising.

One reason we define research as formulating, testing, and revising hypotheses is that if you think of research in this way you are less likely to go wrong. It is a useful guide for the entire process, as we will describe in detail in the chapters ahead. For example, as you build the rationale for your predictions, you are constructing the theoretical framework for your study (Chap. 3 ). As you work out the methods you will use to test your hypothesis, every decision you make will be based on asking, “Will this help me formulate or test or revise my hypothesis?” (Chap. 4 ). As you interpret the results of testing your predictions, you will compare them to what you predicted and examine the differences, focusing on how you must revise your hypotheses (Chap. 5 ). By anchoring the process to formulating, testing, and revising hypotheses, you will make smart decisions that yield a coherent and well-designed study.

Exercise 1.5

Compare the concept of formulating, testing, and revising hypotheses with the descriptions of scientific inquiry contained in Scientific Research in Education (NRC, 2002 ). How are they similar or different?

Exercise 1.6

Provide an example to illustrate and emphasize the differences between everyday learning/thinking and scientific inquiry.

Learning from Doing Scientific Inquiry

We noted earlier that a measure of what you have learned by conducting a research study is found in the differences between your original hypothesis and your revised hypothesis based on the data you collected to test your hypothesis. We will elaborate this statement in later chapters, but we preview our argument here.

Even before collecting data, scientific inquiry requires cycles of making a prediction, developing a rationale, refining your predictions, reading and studying more to strengthen your rationale, refining your predictions again, and so forth. And, even if you have run through several such cycles, you still will likely find that when you test your prediction you will be partly right and partly wrong. The results will support some parts of your predictions but not others, or the results will “kind of” support your predictions. A critical part of scientific inquiry is making sense of your results by interpreting them against your predictions. Carefully describing what aspects of your data supported your predictions, what aspects did not, and what data fell outside of any predictions is not an easy task, but you cannot learn from your study without doing this analysis.

Analyzing the matches and mismatches between your predictions and your data allows you to formulate different rationales that would have accounted for more of the data. The best revised rationale is the one that accounts for the most data. Once you have revised your rationales, you can think about the predictions they best justify or explain. It is by comparing your original rationales to your new rationales that you can sort out what you learned from your study.

Suppose your study was an experiment. Maybe you were investigating the effects of a new instructional intervention on students’ learning. Your original rationale was your explanation for why the intervention would change the learning outcomes in a particular way. Your revised rationale explained why the changes that you observed occurred like they did and why your revised predictions are better. Maybe your original rationale focused on the potential of the activities if they were implemented in ideal ways and your revised rationale included the factors that are likely to affect how teachers implement them. By comparing the before and after rationales, you are describing what you learned—what you can explain now that you could not before. Another way of saying this is that you are describing how much more you understand now than before you conducted your study.

Revised predictions based on carefully planned and collected data usually exhibit some of the following features compared with the originals: more precision, more completeness, and broader scope. Revised rationales have more explanatory power and become more complete, more aligned with the new predictions, sharper, and overall more convincing.

Part II. Why Do Educators Do Research?

Doing scientific inquiry is a lot of work. Each phase of the process takes time, and you will often cycle back to improve earlier phases as you engage in later phases. Because of the significant effort required, you should make sure your study is worth it. So, from the beginning, you should think about the purpose of your study. Why do you want to do it? And, because research is a social practice, you should also think about whether the results of your study are likely to be important and significant to the education community.

If you are doing research in the way we have described—as scientific inquiry—then one purpose of your study is to understand , not just to describe or evaluate or report. As we noted earlier, when you formulate hypotheses, you are developing rationales that explain why things might be like they are. In our view, trying to understand and explain is what separates research from other kinds of activities, like evaluating or describing.

One reason understanding is so important is that it allows researchers to see how or why something works like it does. When you see how something works, you are better able to predict how it might work in other contexts, under other conditions. And, because conditions, or contextual factors, matter a lot in education, gaining insights into applying your findings to other contexts increases the contributions of your work and its importance to the broader education community.

Consequently, the purposes of research studies in education often include the more specific aim of identifying and understanding the conditions under which the phenomena being studied work like the observations suggest. A classic example of this kind of study in mathematics education was reported by William Brownell and Harold Moser in 1949 . They were trying to establish which method of subtracting whole numbers could be taught most effectively—the regrouping method or the equal additions method. However, they realized that effectiveness might depend on the conditions under which the methods were taught—“meaningfully” versus “mechanically.” So, they designed a study that crossed the two instructional approaches with the two different methods (regrouping and equal additions). Among other results, they found that these conditions did matter. The regrouping method was more effective under the meaningful condition than the mechanical condition, but the same was not true for the equal additions algorithm.

What do education researchers want to understand? In our view, the ultimate goal of education is to offer all students the best possible learning opportunities. So, we believe the ultimate purpose of scientific inquiry in education is to develop understanding that supports the improvement of learning opportunities for all students. We say “ultimate” because there are lots of issues that must be understood to improve learning opportunities for all students. Hypotheses about many aspects of education are connected, ultimately, to students’ learning. For example, formulating and testing a hypothesis that preservice teachers need to engage in particular kinds of activities in their coursework in order to teach particular topics well is, ultimately, connected to improving students’ learning opportunities. So is hypothesizing that school districts often devote relatively few resources to instructional leadership training or hypothesizing that positioning mathematics as a tool students can use to combat social injustice can help students see the relevance of mathematics to their lives.

We do not exclude the importance of research on educational issues more removed from improving students’ learning opportunities, but we do think the argument for their importance will be more difficult to make. If there is no way to imagine a connection between your hypothesis and improving learning opportunities for students, even a distant connection, we recommend you reconsider whether it is an important hypothesis within the education community.

Notice that we said the ultimate goal of education is to offer all students the best possible learning opportunities. For too long, educators have been satisfied with a goal of offering rich learning opportunities for lots of students, sometimes even for just the majority of students, but not necessarily for all students. Evaluations of success often are based on outcomes that show high averages. In other words, if many students have learned something, or even a smaller number have learned a lot, educators may have been satisfied. The problem is that there is usually a pattern in the groups of students who receive lower quality opportunities—students of color and students who live in poor areas, urban and rural. This is not acceptable. Consequently, we emphasize the premise that the purpose of education research is to offer rich learning opportunities to all students.

One way to make sure you will be able to convince others of the importance of your study is to consider investigating some aspect of teachers’ shared instructional problems. Historically, researchers in education have set their own research agendas, regardless of the problems teachers are facing in schools. It is increasingly recognized that teachers have had trouble applying to their own classrooms what researchers find. To address this problem, a researcher could partner with a teacher—better yet, a small group of teachers—and talk with them about instructional problems they all share. These discussions can create a rich pool of problems researchers can consider. If researchers pursued one of these problems (preferably alongside teachers), the connection to improving learning opportunities for all students could be direct and immediate. “Grounding a research question in instructional problems that are experienced across multiple teachers’ classrooms helps to ensure that the answer to the question will be of sufficient scope to be relevant and significant beyond the local context” (Cai et al., 2019b , p. 115).

As a beginning researcher, determining the relevance and importance of a research problem is especially challenging. We recommend talking with advisors, other experienced researchers, and peers to test the educational importance of possible research problems and topics of study. You will also learn much more about the issue of research importance when you read Chap. 5 .

Exercise 1.7

Identify a problem in education that is closely connected to improving learning opportunities and a problem that has a less close connection. For each problem, write a brief argument (like a logical sequence of if-then statements) that connects the problem to all students’ learning opportunities.

Part III. Conducting Research as a Practice of Failing Productively

Scientific inquiry involves formulating hypotheses about phenomena that are not fully understood—by you or anyone else. Even if you are able to inform your hypotheses with lots of knowledge that has already been accumulated, you are likely to find that your prediction is not entirely accurate. This is normal. Remember, scientific inquiry is a process of constantly updating your thinking. More and better information means revising your thinking, again, and again, and again. Because you never fully understand a complicated phenomenon and your hypotheses never produce completely accurate predictions, it is easy to believe you are somehow failing.

The trick is to fail upward, to fail to predict accurately in ways that inform your next hypothesis so you can make a better prediction. Some of the best-known researchers in education have been open and honest about the many times their predictions were wrong and, based on the results of their studies and those of others, they continuously updated their thinking and changed their hypotheses.

A striking example of publicly revising (actually reversing) hypotheses due to incorrect predictions is found in the work of Lee J. Cronbach, one of the most distinguished educational psychologists of the twentieth century. In 1955, Cronbach delivered his presidential address to the American Psychological Association. Titling it “Two Disciplines of Scientific Psychology,” Cronbach proposed a rapprochement between two research approaches—correlational studies that focused on individual differences and experimental studies that focused on instructional treatments controlling for individual differences. (We will examine different research approaches in Chap. 4 ). If these approaches could be brought together, reasoned Cronbach ( 1957 ), researchers could find interactions between individual characteristics and treatments (aptitude-treatment interactions or ATIs), fitting the best treatments to different individuals.

In 1975, after years of research by many researchers looking for ATIs, Cronbach acknowledged the evidence for simple, useful ATIs had not been found. Even when trying to find interactions between a few variables that could provide instructional guidance, the analysis, said Cronbach, creates “a hall of mirrors that extends to infinity, tormenting even the boldest investigators and defeating even ambitious designs” (Cronbach, 1975 , p. 119).

As he was reflecting back on his work, Cronbach ( 1986 ) recommended moving away from documenting instructional effects through statistical inference (an approach he had championed for much of his career) and toward approaches that probe the reasons for these effects, approaches that provide a “full account of events in a time, place, and context” (Cronbach, 1986 , p. 104). This is a remarkable change in hypotheses, a change based on data and made fully transparent. Cronbach understood the value of failing productively.

Closer to home, in a less dramatic example, one of us began a line of scientific inquiry into how to prepare elementary preservice teachers to teach early algebra. Teaching early algebra meant engaging elementary students in early forms of algebraic reasoning. Such reasoning should help them transition from arithmetic to algebra. To begin this line of inquiry, a set of activities for preservice teachers were developed. Even though the activities were based on well-supported hypotheses, they largely failed to engage preservice teachers as predicted because of unanticipated challenges the preservice teachers faced. To capitalize on this failure, follow-up studies were conducted, first to better understand elementary preservice teachers’ challenges with preparing to teach early algebra, and then to better support preservice teachers in navigating these challenges. In this example, the initial failure was a necessary step in the researchers’ scientific inquiry and furthered the researchers’ understanding of this issue.

We present another example of failing productively in Chap. 2 . That example emerges from recounting the history of a well-known research program in mathematics education.

Making mistakes is an inherent part of doing scientific research. Conducting a study is rarely a smooth path from beginning to end. We recommend that you keep the following things in mind as you begin a career of conducting research in education.

First, do not get discouraged when you make mistakes; do not fall into the trap of feeling like you are not capable of doing research because you make too many errors.

Second, learn from your mistakes. Do not ignore your mistakes or treat them as errors that you simply need to forget and move past. Mistakes are rich sites for learning—in research just as in other fields of study.

Third, by reflecting on your mistakes, you can learn to make better mistakes, mistakes that inform you about a productive next step. You will not be able to eliminate your mistakes, but you can set a goal of making better and better mistakes.

Exercise 1.8

How does scientific inquiry differ from everyday learning in giving you the tools to fail upward? You may find helpful perspectives on this question in other resources on science and scientific inquiry (e.g., Failure: Why Science is So Successful by Firestein, 2015).

Exercise 1.9

Use what you have learned in this chapter to write a new definition of scientific inquiry. Compare this definition with the one you wrote before reading this chapter. If you are reading this book as part of a course, compare your definition with your colleagues’ definitions. Develop a consensus definition with everyone in the course.

Part IV. Preview of Chap. 2

Now that you have a good idea of what research is, at least of what we believe research is, the next step is to think about how to actually begin doing research. This means how to begin formulating, testing, and revising hypotheses. As for all phases of scientific inquiry, there are lots of things to think about. Because it is critical to start well, we devote Chap. 2 to getting started with formulating hypotheses.

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Hiebert, J., Cai, J., Hwang, S., Morris, A.K., Hohensee, C. (2023). What Is Research, and Why Do People Do It?. In: Doing Research: A New Researcher’s Guide. Research in Mathematics Education. Springer, Cham. https://doi.org/10.1007/978-3-031-19078-0_1

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How To Present Research Data?

Tong seng fah.

MMed (FamMed UKM), Department of Family Medicine, Universiti Kebangsaan Malaysia

Aznida Firzah Abdul Aziz

Introduction.

The result section of an original research paper provides answer to this question “What was found?” The amount of findings generated in a typical research project is often much more than what medical journal can accommodate in one article. So, the first thing the author needs to do is to make a selection of what is worth presenting. Having decided that, he/she will need to convey the message effectively using a mixture of text, tables and graphics. The level of details required depends a great deal on the target audience of the paper. Hence it is important to check the requirement of journal we intend to send the paper to (e.g. the Uniform Requirements for Manuscripts Submitted to Medical Journals 1 ). This article condenses some common general rules on the presentation of research data that we find useful.

SOME GENERAL RULES

• Keep it simple. This golden rule seems obvious but authors who have immersed in their data sometime fail to realise that readers are lost in the mass of data they are a little too keen to present. Present too much information tends to cloud the most pertinent facts that we wish to convey.
• First general, then specific. Start with response rate and description of research participants (these information give the readers an idea of the representativeness of the research data), then the key findings and relevant statistical analyses.
• Data should answer the research questions identified earlier.
• Leave the process of data collection to the methods section. Do not include any discussion. These errors are surprising quite common.
• Always use past tense in describing results.
• Text, tables or graphics? These complement each other in providing clear reporting of research findings. Do not repeat the same information in more than one format. Select the best method to convey the message.

Consider these two lines:

• Mean baseline HbA 1c of 73 diabetic patients before intervention was 8.9% and mean HbA 1c after intervention was 7.8%.
• Mean HbA 1c of 73 of diabetic patients decreased from 8.9% to 7.8% after an intervention.

In line 1, the author presents only the data (i.e. what exactly was found in a study) but the reader is forced to analyse and draw their own conclusion (“mean HbA 1c decreased”) thus making the result more difficult to read. In line 2, the preferred way of writing, the data was presented together with its interpretation.

• Data, which often are numbers and figures, are better presented in tables and graphics, while the interpretation are better stated in text. By doing so, we do not need to repeat the values of HbA 1c in the text (which will be illustrated in tables or graphics), and we can interpret the data for the readers. However, if there are too few variables, the data can be easily described in a simple sentence including its interpretation. For example, the majority of diabetic patients enrolled in the study were male (80%) compare to female (20%).
• Using qualitative words to attract the readers’ attention is not helpful. Such words like “remarkably” decreased, “extremely” different and “obviously” higher are redundant. The exact values in the data will show just how remarkable, how extreme and how obvious the findings are.

“It is clearly evident from Figure 1B that there was significant different (p=0.001) in HbA 1c level at 6, 12 and 18 months after diabetic self-management program between 96 patients in intervention group and 101 patients in control group, but no difference seen from 24 months onwards.” [Too wordy]

Changes of HbA 1c level after diabetic self-management program.

The above can be rewritten as:

“Statistical significant difference was only observed at 6, 12 and 18 months after diabetic self-management program between intervention and control group (Fig 1B)”. [The p values and numbers of patients are already presented in Figure 1B and need not be repeated.]

• Avoid redundant words and information. Do not repeat the result within the text, tables and figures. Well-constructed tables and graphics should be self-explanatory, thus detailed explanation in the text is not required. Only important points and results need to be highlighted in the text.

Tables are useful to highlight precise numerical values; proportions or trends are better illustrated with charts or graphics. Tables summarise large amounts of related data clearly and allow comparison to be made among groups of variables. Generally, well-constructed tables should be self explanatory with four main parts: title, columns, rows and footnotes.

• Title. Keep it brief and relate clearly the content of the table. Words in the title should represent and summarise variables used in the columns and rows rather than repeating the columns and rows’ titles. For example, “Comparing full blood count results among different races” is clearer and simpler than “Comparing haemoglobin, platelet count, and total white cell count among Malays, Chinese and Indians”.

*WC, waist circumference (in cm)

†SBP, systolic blood pressure (in mmHg)

‡DBP, diastolic blood pressure (in mmHg)

£LDL-cholesterol (in mmol/L)

*Odds ratio (95% confidence interval)

†p=0.04

‡p=0.01

• Footnotes. These add clarity to the data presented. They are listed at the bottom of tables. Their use is to define unconventional abbreviation, symbols, statistical analysis and acknowledgement (if the table is adapted from a published table). Generally the font size is smaller in the footnotes and follows a sequence of foot note signs (*, †, ‡, §, ‖, ¶, **, ††, # ). 1 These symbols and abbreviation should be standardised in all tables to avoid confusion and unnecessary long list of footnotes. Proper use of footnotes will reduce the need for multiple columns (e.g. replacing a list of p values) and the width of columns (abbreviating waist circumference to WC as in table 1B )
• Consistent use of units and its decimal places. The data on systolic blood pressure in Table 1B is neater than the similar data in Table 1A .
• Arrange date and timing from left to the right.
• Round off the numbers to fewest decimal places possible to convey meaningful precision. Mean systolic blood pressure of 165.1mmHg (as in Table 1B ) does not add much precision compared to 165mmHg. Furthermore, 0.1mmHg does not add any clinical importance. Hence blood pressure is best to round off to nearest 1mmHg.
• Avoid listing numerous zeros, which made comparison incomprehensible. For example total white cell count is best represented with 11.3 ×10 6 /L rather than 11,300,000/L. This way, we only need to write 11.3 in the cell of the table.
• Avoid too many lines in a table. Often it is sufficient to just have three horizontal lines in a table; one below the title; one dividing the column titles and data; one dividing the data and footnotes. Vertical lines are not necessary. It will only make a table more difficult to read (compare Tables 1A and ​ and1B 1B ).
• Standard deviation can be added to show precision of the data in our table. Placement of standard deviation can be difficult to decide. If we place the standard deviation at the side of our data, it allows clear comparison when we read down ( Table 1B ). On the other hand, if we place the standard deviation below our data, it makes comparison across columns easier. Hence, we should decide what we want the readers to compare.
• It is neater and space-saving if we highlight statistically significant finding with an asterisk (*) or other symbols instead of listing down all the p values ( Table 2 ). It is not necessary to add an extra column to report the detail of student-t test or chi-square values.

Graphics are particularly good for demonstrating a trend in the data that would not be apparent in tables. It provides visual emphasis and avoids lengthy text description. However, presenting numerical data in the form of graphs will lose details of its precise values which tables are able to provide. The authors have to decide the best format of getting the intended message across. Is it for data precision or emphasis on a particular trend and pattern? Likewise, if the data is easily described in text, than text will be the preferred method, as it is more costly to print graphics than text. For example, having a nicely drawn age histogram is take up lots of space but carries little extra information. It is better to summarise it as mean ±SD or median depends on whether the age is normally distributed or skewed. Since graphics should be self-explanatory, all information provided has to be clear. Briefly, a well-constructed graphic should have a title, figure legend and footnotes along with the figure. As with the tables, titles should contain words that describe the data succinctly. Define symbols and lines used in legends clearly.

Some general guides to graphic presentation are:

• Bar charts, either horizontal or column bars, are used to display categorical data. Strictly speaking, bar charts with continuous data should be drawn as histograms or line graphs. Usually, data presented in bar charts are better illustrated in tables unless there are important pattern or trends need to be emphasised.

• Line graphs are most appropriate in tracking changing values between variables over a period of time or when the changing values are continuous data. Independent variables (e.g. time) are usually on the X-axis and dependant variables (for example, HbA 1c ) are usually on the Y-axis. The trend of HbA 1c changes is much more apparent with Figure 1B than Figure 1A , and HbA 1c level at any time after intervention can be accurately read in Figure 1B .
• Pie charts should not be used often as any data in a pie chart is better represented in bar charts (if there are specific data trend to be emphasised) or simple text description (if there are only a few variables). A common error is presenting sex distribution of study subjects in a pie chart. It is simpler by just stating % of male or female in text form.
• Patients’ identity in all illustrations, for example pictures of the patients, x-ray films, and investigation results should remain confidential. Use patient’s initials instead of their real names. Cover or blackout the eyes whenever possible. Obtain consent if pictures are used. Highlight and label areas in the illustration, which need emphasis. Do not let the readers search for details in the illustration, which may result in misinterpretation. Remember, we write to avoid misunderstanding whilst maintaining clarity of data.

Papers are often rejected because wrong statistical tests are used or interpreted incorrectly. A simple approach is to consult the statistician early. Bearing in mind that most readers are not statisticians, the reporting of any statistical tests should aim to be understandable by the average audience but sufficiently rigorous to withstand the critique of experts.

• Simple statistic such as mean and standard deviation, median, normality testing is better reported in text. For example, age of group A subjects was normally distributed with mean of 45.4 years old kg (SD=5.6). More complicated statistical tests involving many variables are better illustrated in tables or graphs with their interpretation by text. (See section on Tables).
• We should quote and interpret p value correctly. It is preferable to quote the exact p value, since it is now easily obtained from standard statistical software. This is more so if the p value is statistically not significant, rather just quoting p>0.05 or p=ns. It is not necessary to report the exact p value that is smaller than 0.001 (quoting p<0.001 is sufficient); it is incorrect to report p=0.0000 (as some software apt to report for very small p value).
• We should refrain from reporting such statement: “mean systolic blood pressure for group A (135mmHg, SD=12.5) was higher than group B (130mmHg, SD= 9.8) but did not reach statistical significance (t=4.5, p=0.56).” When p did not show statistical significance (it might be >0.01 or >0.05, depending on which level you would take), it simply means no difference among groups.
• Confidence intervals. It is now preferable to report the 95% confidence intervals (95%CI) together with p value, especially if a hypothesis testing has been performed.

The main core of the result section consists of text, tables and graphics. As a general rule, text provides narration and interpretation of the data presented. Simple data with few categories is better presented in text form. Tables are useful in summarising large amounts of data systemically and graphics should be used to highlight evidence and trends in the data presented. The content of the data presented must match the research questions and objectives of the study in order to give meaning to the data presented. Keep the data and its statistical analyses as simple as possible to give the readers maximal clarity.

Contributor Information

Tong Seng Fah, MMed (FamMed UKM), Department of Family Medicine, Universiti Kebangsaan Malaysia.

Aznida Firzah Abdul Aziz, MMed (FamMed UKM), Department of Family Medicine, Universiti Kebangsaan Malaysia.

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• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

A research design is a strategy for answering your   research question  using empirical data. Creating a research design means making decisions about:

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

• Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
• Descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

• Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

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As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarize your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

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

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

 Statistics

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

Research bias

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

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

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

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

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Research Guide

Chapter 7 presenting your findings.

Now that you have worked so hard in your project, how to ensure that you can communicate your findings in an effective and efficient way? In this section, I will introduce a few tips that could help you prepare your slides and preparing for your final presentation.

7.1 Sections of the Presentation

When preparing your slides, you need to ensure that you have a clear roadmap. You have a limited time to explain the context of your study, your results, and the main takeaways. Thus, you need to be organized and efficient when deciding what material will be included in the slides.

You need to ensure that your presentation contains the following sections:

• Motivation : Why did you choose your topic? What is the bigger question?
• Research question : Needs to be clear and concise. Include secondary questions, if available, but be clear about what is your research question.
• Literature Review : How does your paper fit in the overall literature? What are your contributions?
• Context : Give an overview of the issue and the population/countries that you analyzed
• Study Characteristics : This section is key, as it needs to include your model, identification strategy, and introduce your data (sources, summary statistics, etc.).
• Results : In this section, you need to answer your research question(s). Include tables that are readable.
• Additional analysis : Here, include any additional information that your public needs to know. For instance, did you try different specifications? did you find an obstacle (i.e. your data is very noisy, the sample is very small, something else) that may bias your results or create some issues in your analysis? Tell your audience! No research project is perfect, but you need to be clear about the imperfections of your project.
• Conclusion : Be repetitive! What was your research question? How did you answer it? What did you find? What is next in this topic?

7.2 How to prepare your slides

When preparing your slides, remember that humans have a limited capacity to pay attention. If you want to convey your convey your message in an effective way, you need to ensure that the message is simple and that you keep your audience attention. Here are some strategies that you may want to follow:

• Have a clear roadmap at the beginning of the presentation. Tell your audience what to expect.
• Number your slides. This will help you and your audience to know where you are in your analysis.
• Ensure that each slide has a purpose
• Ensure that each slide is connected to your key point.
• Make just one argument per slide
• State the objective of each slide in the headline
• Use bullet points. Do not include more than one sentence per bullet point.
• Choose a simple background.
• If you want to direct your audience attention to a specific point, make it more attractive (using a different font color)
• Each slide needs to have a similar structure (going from the general to the particular detauls).
• Use images/graphs when possible. Ensure that the axes for the graphs are clear.
• Use a large font for your tables. Keep them as simple as possible.
• If you can say it with an image, choose it over a table.
• Have an Appendix with slides that address potential questions.

7.3 How to prepare your presentation

One of the main constraints of having simple presentations is that you cannot rely on them and read them. Instead, you need to have extra notes and memorize them to explain things beyond what is on your slides. The following are some suggestions on how to ensure you communicate effectively during your presentation.

• Practice, practice, practice!
• Keep the right volume (practice will help you with that)
• Be journalistic about your presentation. Indicate what you want to say, then say it.
• Ensure that your audience knows where you are going
• Avoid passive voice.
• Be consistent with the terms you are using. You do not want to confuse your audience, even if using synonyms.
• Face your audience and keep an eye contact.
• Do not try reading your slides
• Ensure that your audience is focused on what you are presenting and there are no other distractions that you can control.
• Do not rush your presentation. Speak calmly and controlled.
• Be comprehensive when answering questions. Avoid yes/no answers. Instead, rephrase question (to ensure you are answering the right question), then give a short answer, then develop.
• If you lose track, do not panick. Go back a little bit or ask your audience for assistance.
• Again, practice is the secret.

You have worked so hard in your final project, and the presentation is your opportunity to share that work with the rest of the world. Use this opportunity to shine and enjoy it.

Since this is the first iteration of the Guide, I expect that there are going to be multiple typos and structure issues. Please feel free to let me know, and I will correct accordingly. ↩︎

Note that you would still need to refine some of the good questions even more. ↩︎

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Home » Research Methodology – Types, Examples and writing Guide

Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

• Provide an overview of the research problem and the need for a research methodology section
• Outline the main research questions and objectives

II. Research Design

• Explain the research design chosen and why it is appropriate for the research question(s) and objectives
• Discuss any alternative research designs considered and why they were not chosen
• Describe the research setting and participants (if applicable)

III. Data Collection Methods

• Describe the methods used to collect data (e.g., surveys, interviews, observations)
• Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

• Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
• Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or software used for data analysis

V. Ethical Considerations

• Discuss any ethical issues that may arise from the research and how they were addressed
• Explain how informed consent was obtained (if applicable)
• Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

• Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

• Summarize the key aspects of the research methodology section
• Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

• Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
• Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
• Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
• Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
• Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
• Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
• Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
• Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

• To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
• To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
• To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
• To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
• To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
• To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
• To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
• To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
• To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

• To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
• To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
• To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
• To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
• To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

• Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
• Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
• Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
• Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
• Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
• Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
• Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
• Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

About the author.

Muhammad Hassan

Researcher, Academic Writer, Web developer

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What Is Desk Research? Meaning, Methodology, Examples

Apr 4, 2024

10 min. read

Research in the digital age takes many shapes and forms. There are traditional methods that collect first-hand data via testing, focus groups, interviews, and proprietary data. And then there are ways to tap into the time and effort others have put into research, playing “armchair detective” by conducting desk research .

Desk research gives you a shortcut to insights by pulling data from other resources, which is crucial for understanding the customer journey . It takes less time and is more cost-effective compared to conducting primary market research . Most importantly, it can give you the consumer insights you need to make important business decisions.

Let’s explore the official desk research definition along with types of desk research, methodologies, examples, and how to do desk research effectively.

Desk Research Meaning: What is Desk Research?

Advantages and limitations of desk research, desk research methodology and methods, how to conduct desk research effectively, best practices for desk research, applications of desk research, how to conduct desk research with meltwater.

Desk Research definition: Desk research, also known as secondary research, involves gathering information and data from existing sources, such as books, journals, articles, websites, reports, and other published materials. Users analyze and synthesize information from already available information.

Companies use desk research at the onset of a project to gain a better understanding of a topic, identify knowledge gaps, and inform the next stages of research. It can also supplement original findings and provide context and background information.

Desk research gives marketers attractive advantages over traditional primary research, but it’s not without its shortcomings. Let’s explore these in more detail.

Desk research advantages

• Quick insights. Conducting interviews, focus groups, panels, and tests can take weeks or even months, along with additional time to analyze your findings. With desk research, you can pull from existing information to gain similar results in less time.
• Cost-effectiveness. Desk market research is usually less expensive than primary research because it requires less time and fewer resources. You don’t have to recruit participants or administer surveys, for example.
• Accessibility. There’s a world of data out there ready for you to leverage, including online databases, research studies, libraries, and archives.
• Diverse sources. Desk market research doesn’t limit you to one information source. You can use a combination of sources to gain a comprehensive overview of a topic.

Want to see how Meltwater can supercharge your market research efforts? Simply fill out the form at the bottom of this post and we'll be in touch.

Desk research limitations 

• Data quality. Marketers don’t know how reliable or valid the data is, which is why it’s important to choose your sources carefully. Only use data from credible sources, ideally ones that do not have a financial interest in the data’s findings.
• Less control. Users are at the mercy of the data that’s available and cannot tailor it to their needs. There’s no opportunity to ask follow-up questions or address specific research needs.
• Potential bias. Some sources may include biased findings and/or outdated information, which can lead to inaccurate conclusions. Users can mitigate the risk of bias by relying only on credible sources or corroborating evidence with multiple sources.

Desk research typically involves multiple sources and processes to gain a comprehensive understanding of an idea. There are two main desk methodologies: qualitative research and quantitative research .

• Qualitative research refers to analyzing existing data (e.g., interviews, surveys, observations) to gain insights into people's behaviors, motivations, and opinions. This method delves deeper into the context and meaning behind the data.
• Quantitative research refers to analyzing and interpreting numerical data to draw conclusions and make predictions. This involves quantifying patterns and trends to find relationships between variables.

Both desk research methodologies use a variety of methods to find and analyze data and make decisions.

Examples of desk research methods include but are not limited to:

• Literature review. Analyze findings from various types of literature, including medical journals, studies, academic papers, books, articles, online publications, and government agencies.
• Competitor analysis . Learn more about the products, services, and strategies of your competitors, including identifying their strengths and weaknesses, market gaps, and overall sentiment.
• Social listening . Discover trending topics and sentiments on social media channels to learn more about your target audience and brand health.
• Consumer intelligence . Understand your audience based on digital behaviors, triggers, web usage patterns, and interests.
• Market research . Analyze market reports, industry trends, demographics, and consumer buying patterns to identify market opportunities and strengthen your positioning.

Now let’s look at how to use these methods to their full potential.

While desk research techniques can vary, they all follow a similar formula. Here’s how you can conduct desk research effectively, even if it’s your first time.

1. Define your objective

Desk research starts with a specific question you want to answer. 

In marketing , your objective might be to:

• Learn about Gen Z buying behaviors for home goods
• Gauge the effectiveness of influencer marketing for food brands
• Understand the pain points of your competitor’s customers

These questions can help you find credible sources that can provide answers.

2. Choose reliable data sources

Based on your objectives, start collecting secondary data sources that have done the heavy lifting for you. Examples include:

• Market reports (often available as gated assets from research companies)
• Trade publications
• Academic journals
• Company websites
• Government publications and data
• Online databases and resources, such as Google Scholar 
• Secondary market research tools like Meltwater and Linkfluence
• Online blogs, articles, case studies, and white papers from credible sources

In many cases, you’ll use a combination of these source types to gain a thorough answer to your question.

3. Start gathering evidence

Go through your source materials to start answering your question. This is usually the most time-intensive part of desk research; you’ll need to extract insights and do some fact-checking to trust those insights.

One of your top priorities in this step is to use reliable sources. Here are some ways you can evaluate sources to use in your desk research:

• Consider the authority and reputation of the source (e.g., do they have expertise in your subject)
• Check whether the content is sponsored, which could indicate bias
• Assess whether the data is current
• Evaluate the publisher’s peer review processes , if applicable
• Review the content’s citations and references
• Seek consensus among multiple sources
• Use sources with built-in credibility, such as .gov or .edu sites or well-known medical and academic journals

If your source materials have supporting elements, such as infographics, charts, or graphs, include those with your desk research.

4. Cross-reference your findings with other sources

For desk research to be effective, you need to be able to trust the data you find. One way to build trust is to cross-reference your findings with other sources. 

For instance, you might see who else is citing the same sources you are in their research. If there are reputable companies using those same sources, you might feel they’re more credible compared to a random internet fact that lacks supporting evidence. 

5. Draw your conclusions & document the results

Organize and synthesize your findings in a way that makes sense for your objectives. Consider your stakeholders and why the information is important.

For example, the way you share your research with an internal team might have a different structure and tone compared to a client-facing document.

Bonus tip: Include a list of sources with your documentation to build credibility in your findings. 

When conducting desk research, follow these best practices to ensure a reliable and helpful outcome.

Organize and manage your research data

It’s helpful to have a system to organize your research data. This way, you can easily go back to review sources or share information with others. Spreadsheets, databases, and platforms like Meltwater for market research are great options to keep your desk research in one place.

Create actionable recommendations

It’s not enough to state your findings; make sure others know why the data matters. Share the data along with your conclusions and recommendations for what to do next.

Remember, desk research is about decision-making, not the data itself.

Document your sources

Whether you choose to share your sources or not, it’s best practice to document your sources for your own records. This makes it easier to provide evidence if someone asks for it or to look back at your research if you have additional questions.

Now for the big question: How can marketers apply desk research to their day-to-day tasks?

Try these desk research examples to power your marketing efforts.

Use desk research for market intelligence

Markets, preferences, and buying habits change over time, and marketers need to stay up to date on their industries. Desk research can provide market intelligence insights, including new competitors, trends, and audience segments that may impact your business.

Apply desk research in competitive analysis

Desk research can help you identify your true competitors and provide more context about their strengths and weaknesses. Marketers can use this intel to improve their positioning and messaging. For instance, a competitor’s weak spot might be something your company does well, and you can emphasize this area in your messaging.

Include desk research in content strategy and audience analysis

Desk research can support consumer intelligence by helping you define various audience segments and how to market to them. These insights can help you develop content and creative assets on the right topics and in the right formats, as well as share them in the best channels to reach your audience.

Emerging technologies like Meltwater's integrated suite of solutions have a strong impact on desk research, helping you streamline how you find and vet data to support your desired topics.

Using a combination of data science, AI, and market research expertise, Meltwater offers the largest media database of its kind to help marketers learn more about their audience and how to connect with them. Millions of real-time data points cover all niches, topics, and industries, giving you the on-demand insights you need.

Our clients use Meltwater for desk research to measure audience sentiment and identify audience segments as well as to conduct competitor analysis , social listening , and brand monitoring , all of which benefit from real-time data. 

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OU students, alumni and faculty present research at professional psychology conference

Earlier this month, 13 current Oglethorpe students and three alumni accompanied five faculty to the annual meeting of the Southeastern Psychological Association (SEPA) in Orlando, FL. This continued a longstanding tradition of the Oglethorpe psychology department preparing students to submit their own research to peer-reviewed professional conferences.

Isabel Berlin ’24 presents research at SEPA

This year’s student participants were Emily Moore ’24, Jose Valtierra ’24, Sarah Farmer ’24, Alex Nukpi ’24, Porter Deal ’25, Mekela Iorio ’25, Samantha Leblanc ’24, Anabel Dimova ’24, Isabel Berlin ’24, Piper Lashley ’24, Alex Swanson ’24, Nia Kherani ’25, Sarah Clayton ’24 and Brit Rosser ’24. All students attending this year’s conference were awarded Oglethorpe research funding to assist with cost.

Oglethorpe alumni that attended this year’s conference were Kharynton Beggs ’23, Alexa Tringali ’23 and Caidyn Ellis ’23 .

Each Petrel presented their original research with the support and encouragement of faculty mentors Drs. Leah Zinner , Justin Wise , Lisa Hayes, Brooke Bays and Emily Bailey.

A range of research subjects were explored in the poster and paper presentations:

• The Influence of Social and Academic Pressure on Student Morality and Cheating Behavior – Emily Moore, Lisa Hayes
• The Effects of Notetaking Styles and Distractions on Memory – Jose Valtierra, Lisa Hayes
• The Effects of Player Gender and Game Genre on Perceptions of Competence and Aggression in Video Gameplay – Sarah Farmer, Lisa Hayes
• Impact of Face Masks and Pathogen Disgust Sensitivity on First Impressions – Alex Nukpi, Porter Deal, Mekela Iorio, Samantha Leblanc, Anabel Dimova, Lisa Hayes, Brooke Bays, Justin Wise
• Investigating the Effect of Two Mood Induction Procedures on Arousal and False Memory – Alexa Tringali, Justin Wise,
• How the Profile of a School Shooter Affects Blame Attribution – Isabel Berlin, Justin Wise
• On-line vs In-Person Samples in a Personnel Evaluation Study – Kharynton Beggs, Leah Zinner
• The Role of Procrastination, Perfectionism, Social Well-Being, and Cognitive Flexibility in Predicting Anxiety Among College Students – Piper Lashley, Alex Swanson, Nia Kherani, Brooke Bays, Lisa Hayes
• How Adults View Rough Sex and Non-Conforming Gender Roles – Sarah Clayton, Justin Wise
• The Effect of Retention Intervals and Crime Severity on Memory – Brit Rosser, Lisa Hayes
• Stigma and Autism: The Impact of Gender and Diagnosis Label – Caidyn Ellis, Lisa Hayes

“Attending this conference allows students showcase their research at a large professional meeting and interact with experts in the field,” says Dr. Wise.

OU alum Sarah Clayton ’23 presents at SEPA

The projects were completed as part of PSY 320, PSY 321 and PSY 322 (Psychological Statistics and Research Methods I and II, and Advanced Experimental Psychology). One students, Sarah Farmer, conducted her research as part of her Honors thesis — a faculty-guided, independent project allowing students to further explore areas of interest at a high academic level.

“Further, attendance at this meeting affords exposure to the most current research methods and findings that are related to students’ personal research and academic interest areas,” Dr. Wise continued. “Finally, students gain invaluable public speaking experience and intellectual interaction.”

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Educational Possibilities: UH Researchers to Present Findings at AERA 2024 Conference

Posted April 4, 2024 — More than three dozen researchers from the University of Houston College of Education will share their scholarship on teacher development, STEM education, college culture and other initiatives to support marginalized students at the 2024 American Educational Research Association Annual Meeting.

The high-profile conference, taking place April 11-14 in Philadelphia,  will bring together thousands of scholars around the theme of “Dismantling Racial Injustice and Constructing Educational Possibilities: A Call to Action.”

The UH presenters at AERA include faculty, staff, students and alumni.

Several presentations from faculty in the Department of Curriculum & Instruction will focus on supporting multilingual students. For example, Assistant Professor Xin Li will share findings to promote socio-emotional development among language-minority youth. Assistant Professor Tairan Qiu, who organized a symposium on transnational literacies, will discuss approaches for humanizing qualitative research methodologies. And Clinical Assistant Professor Marédil León will present an analysis of Colombian textbooks to teach English.

To improve the preparation of teachers, Clinical Assistant Professor Bernadette Castillo and faculty from Minnesota State University will discuss insights gleaned from student-teachers.

“In this presentation,” she said, “you will learn about the tensions that emerged from teacher candidates’ perceptions of their experiences in a teacher education program with a vision and goal that embeds racial consciousness and social justice.”

Honing in on the college experience, Assistant Professor Allison Master and Paul Turcotte, a Ph.D. student in the Measurement, Quantitative Methods and Learning Sciences program, will present survey results assessing engagement throughout the COVID-19 pandemic.

“First-year students were particularly affected in 2021 but showed consistent signs of recent improvement in engagement and belonging in 2023,” they explained. “Overall, students’ engagement practices show promising recent signs of improving to pre-pandemic levels. However, all students showed significant disruptions to their educational belonging and engagement.”

Also presenting on higher education, Clinical Professor and Associate Dean Tiffany J. Davis will highlight “promising practices that institutions can use to help reduce equity gaps for Black first-generation college women.”

“Specifically,” she added, “we share the unique barriers our participants faced that impacted their collegiate experience due to their multiple minoritized identities, discuss the role of financial resources and mental health support in mitigating challenges, and articulate the significant difference the role of institutional interventions, such as the McNair Scholars Program, play in Black college women’s lives.”

Similarly, Teranda Donatto, a Ph.D. student in the higher education program, will share research on how culture and family characteristics shape African American students’ college choice.

“The findings highlight the influence of family members and school staff as well as the importance of the centrality of community, spirituality, the value of education, and agency to students’ decision-making,” said Donatto, a program director for the UH Advancing Community Engagement and Service Institute. “With these findings, educators can create pre-college programming that draws on African American family and culture as a strength in helping these students in overcoming barriers to college choice.”

Explore the list of all AERA presentations and meetings featuring UH College of Education members.

Wednesday, April 10, 2024 (Pre-conference Session)

• Division A Early Career Mentoring Pre-Conference Session 1 – 5 p.m., Pennsylvania Convention Center, Floor: Level 100, Room 116 Chair: April Peters-Hawkins

Thursday, April 11, 2024

• The Importance of Role Models Who Show Enthusiasm about STEM 9 – 10:30 a.m., Pennsylvania Convention Center, Floor: Level 100, Room 112B Authors: Kahyun Lee , Jennifer Thompson  and Allison Master
• Cultivating Antibias Antiracism Early Childhood Teachers Through Practice 9 – 10:30 a.m., Pennsylvania Convention Center, Floor: Level 100, Room 106B Author: Xin Li
• College Student Engagement and Belonging Across the COVID-19 Pandemic 10:50 a.m. – 12:20 p.m., Philadelphia Marriott Downtown, Floor: Level 3, Room 305 Authors: Paul Turcotte  and  Allison Master
• AERA Council Meeting 11 a.m. – 1:30 p.m., Pennsylvania Convention Center, Floor: Level 100, Room 103C Participant: Virginia Snodgrass Rangel
• Racialized Funding: Federal and Institutional Mechanisms for Addressing Educational Equity 12:40 – 2:10 p.m., Pennsylvania Convention Center, Floor: Level 100, Room 108B Chair: Binh Chi Bui
• Walking a Raciolinguistics Path Con Mi Abuela 12:40 – 2:10 p.m., Philadelphia Marriott Downtown, Floor: Level 4, Franklin 13 Author: Jeannette Alarcón
• Making the Invisible Visible: Latina Art Teachers Navigating Issues of Oppression in the Workplace 12:40 – 2:10 p.m., Pennsylvania Convention Center, Floor: Level 100, Room 109A Author: Andrea Allen
• Addressing Work-Life Balance Challenges of Asian American Female Teachers in the Post-Pandemic Era 2:30 – 4 p.m., Pennsylvania Convention Center, Floor: Second Floor, Exhibit Hall B Authors: Yi Zhang , Miao Li and Xincui Zhang
• Dehumanization as Policy: A Frame Analysis of Immigration and Detention Portrayals in the Media 4:20 – 5:50 p.m., Pennsylvania Convention Center, Floor: Second Floor, Exhibit Hall B Author: Mikel Cole

Friday, April 12, 2024

• Exploring Incongruities Between Local Maps, Data, and Students’ Sense of Place 7:45 - 9:15 a.m., Pennsylvania Convention Center, Floor: Second Floor, Exhibit Hall B Author: Travis Weiland
• STEM Teacher Candidates Perceptions of a Culturally Responsive Classroom Management Course: A Qualitative Analysis 7:45 - 9:15 a.m., Philadelphia Marriott Downtown, Floor: Level 4, Room 401 Authors: Karen McIntush ,  Paige Evans  and Ramona Mateer
• Identity, Agency and Social Justice in Mathematics Education 7:45 - 9:15 a.m., Pennsylvania Convention Center, Floor: Second Floor, Exhibit Hall B Chair: Melissa Gallagher
• A Critical Statistical Literacy Framework for Reading Data Visualizations 9:35 - 11:05 a.m., Pennsylvania Convention Center, Floor: Level 100, Room 112A Authors: Travis Weiland and Laura Shelton
• Great Start Readiness Program and Children’s Academic Achievement: A Longitudinal Study 9:35 - 11:05 a.m., Pennsylvania Convention Center, Floor: Second Floor, Exhibit Hall B Author: Xin Li
• The Insidiousness of Invisibility: Exploring the Mitigating Impact of a Mentoring Program on Marginalized Students 9:35 - 11:05 a.m., Pennsylvania Convention Center, Floor: Second Floor, Exhibit Hall B Authors: Amy Murdock and Yali Zou
• A Morpho-Phonemic Intervention for Elementary Bilingual Students With Reading Difficulties: Evidence From a Single-Subject Design 11:25 a.m. - 12:55 p.m., Pennsylvania Convention Center, Floor: Level 100, Room 103A Authors: Lana Kharabi-Yamato  and Jie Zhang
• Working in, with, and for Our Communities: Humanizing Method(ologie)s in Transnational Literacies Research Research as Teaching: Fostering Translingual Transnational Literacies with a China-U.S. Transnational Youth 11:25 a.m. - 12:55 p.m., Philadelphia Marriott Downtown, Floor: Level 4, Franklin 4 Chair/Author: Tairan Qiu
• Multimodal Representations of the Ideal Neoliberal Citizen in Colombian ELT Textbooks 11:25 a.m. - 12:55 p.m., Pennsylvania Convention Center, Floor: Second Floor, Exhibit Hall B Author: Marédil León
• Looking at Mentoring from a PK-12 School District Perspective 11:25 a.m. - 12:55 p.m., Philadelphia Marriott Downtown, Floor: Level 4, Room 402 Chair: April Peters-Hawkins
• Translanguaging in Global K-12 Science Classrooms: Accounting for the Institutional, Ideological, and Linguistic Barriers 3:05 - 4:30 p.m., Philadelphia Marriott Downtown, Floor: Level 4, Franklin 6 Authors: Hien Thi Tran , Zhenjie Hou , Jie Zhang , Raju Ahmmed , Haemin Kim  and Ada Clerigo
• 10th Annual Language and Social Processes Mentoring Session 4:55 - 6:25 p.m., Philadelphia Marriott Downtown, Floor: Level 4, Franklin 2 Chair: Tairan Qiu
• Linguistic Complexity of Science Standardized Assessments: Implications for Assessment and Instructions for English Learners 4:55 - 6:25 p.m., Pennsylvania Convention Center, Floor: Second Floor, Exhibit Hall B Author: Haemin Kim
• Fostering Inclusive Engagement: Insights from Student Experience 4:55 - 6:25 p.m., Pennsylvania Convention Center, Floor: Second Floor, Exhibit Hall B Chair: Binh Chi Bui
• Leadership for Social Justice SIG Business Meeting 6:45 - 8:15 p.m., Pennsylvania Convention Center, Floor: Level 100, Room 107B Participants: Detra Johnson , April Peters-Hawkins  and Virginia Snodgrass Rangel
• Division H Movers and Shakers Reception 6:45 - 8:15 p.m., Philadelphia Marriott Downtown, Floor: Level 4, Franklin 4 Chair:  Virginia Snodgrass Rangel

Saturday, April 13, 2024

• “Teach Me to Navigate the System”: How McNair Programs Help Reimagine Belonging for Black First-generation Women 7:45 - 9:15 a.m., Pennsylvania Convention Center, Floor: Second Floor, Exhibit Hall B Author: Tiffany J. Davis
• Out of these waters: The Little Mermaid and casting backlashes as racial projects for history classrooms 7:45 - 9:15 a.m., Pennsylvania Convention Center, Floor: Second Floor, Exhibit Hall B Author: Joanna Batt
• Morphological Problem-solving with Morphemic Structure and Context Support: Explore Impact of Learner and Word Attributes 9:35 - 11:05 a.m., Philadelphia Marriott Downtown, Floor: Level 4, Franklin 8 Authors:  Zhenjie Hou  and  Jie Zhang
• Cultivating Global Awareness Through Online Dialogic Teaching: A Case Study 9:35 - 11:05 a.m., Pennsylvania Convention Center, Floor: Level 200, Exhibit Hall B Author: Yali Zou
• When am I Going to Learn How to Teach?: Tensions with Anti-Racist Theory and Practice 9:35 - 11:05 a.m., Pennsylvania Convention Center, Floor: Level 100, Room 104A Author: Bernadette Castillo
• A Situated Expectancy-Value Approach to Understanding Indian and American Adolescents’ Science Attitudes and Beliefs 9:35 - 11:05 a.m., Pennsylvania Convention Center, Floor: Second Floor, Exhibit Hall A Author: Allison Master
• AERA Open: Closed Editorial Board Meeting 11:25 a.m. - 12:55 p.m, Pennsylvania Convention Center, Floor: Level 100, Room 105B Participant: Cathy Horn
• A Systematic Review of Conceptual Frameworks on Moderators in Online Discussions 11:25 a.m. - 12:55 p.m., Philadelphia Marriott Downtown, Floor: Level 4, Franklin 11 Authors: Michael Ahlf  and  Sara McNeil
• Exploring the Dynamics of Daily Experiences, Emotions, and Depression Among Asian American Students 3:05 - 4:30 p.m., Pennsylvania Convention Center, Floor: Second Floor, Exhibit Hall A Authors: Kahyun Lee and Allison Master
• The Ecological Environmental Supports for Language-Minority Students’ Socioemotional Development 1:15 - 2:45 p.m., Pennsylvania Convention Center, Floor: Second Floor, Exhibit Hall B Author: Xin Li
• Teaching of Educational Psychology Business Meeting 3:05 - 4:35 p.m., Philadelphia Marriott Downtown, Floor: Level 4, Room 402 Participant: Allison Master
• Caribbean and African Studies in Education Business Meeting 6:45 - 8:15 p.m., Philadelphia Marriott Downtown, Floor: Level 4, Room 401 Chair: Dave Louis
• Research on Women and Education SIG Business Meeting 6:45 - 8:15 p.m., Philadelphia Marriott Downtown, Floor: Level 5, Salon I Chair: Detra Johnson
• Division H - Research, Evaluation and Assessment in Schools Business Meeting and Reception 6:45 - 8:15 p.m., Philadelphia Marriott Downtown, Floor: Level 4, Franklin 12 Officer: Virginia Snodgrass Rangel
• Division D and Division H Joint Social 8:15 - 9:45 p.m., Philadelphia Marriott Downtown, Floor: Level 5, Salon A&B Chair: Virginia Snodgrass Rangel

Sunday, April 14, 2024

• The Infrastructures of Inclusion: Comparing Booker T. Washington and Syed Ahmad Khan's Social Reform Strategies 7:45 - 9:15. a.m., Pennsylvania Convention Center, Floor: Level 100, Room 113C Authors: Muhammad Arif Khan and Yali Zou
• AERA Executive Director and Division Vice Presidents: Closed Meeting 7:45 - 9:15. a.m., Pennsylvania Convention Center, Floor: Level 100, Room 106A Participant: Virginia Snodgrass Rangel
• Hidden in Plain Sight: Situating the Identities and Experiences of AAPI Students at an AANAPISI (Asian American and Native American Pacific Islander–Serving Institution) 7:45 - 9:15. a.m., Pennsylvania Convention Center, Floor: Second Floor, Exhibit Hall B Author: Trang Phan
• Language and Discourse in Mathematics Education 7:45 - 9:15. a.m., Pennsylvania Convention Center, Floor: Second Floor, Exhibit Hall B Chair: Melissa Gallagher
• Examining Student Involvement as a Process Variable 9:35 - 11:05. a.m., Philadelphia Marriott Downtown, Floor: Level 4, Franklin 7 Author: Binh Chi Bui
• Contracting for Success? An Analysis of Performance Contracts Under Senate Bill 1882 in Texas 9:35 - 11:05. a.m., Pennsylvania Convention Center, Floor: Second Floor, Exhibit Hall B Author: Fiza Mairaj
• Partnering in Equity Work: Tensions that Accompany Collaborative Efforts to Advance Equity in Schools 11:25 a.m. - 12:55 p.m., Pennsylvania Convention Center, Floor: Level 100, Room 113C Author: April Peters-Hawkins
• Hear our voices: Black Feminist Articulations of Systemic Oppression and Organizational Failure at HWIs 11:25 a.m. - 12:55. p.m., Pennsylvania Convention Center, Floor: Level 100, Room 103A Discussant: Tiffany J. Davis
• “African Root and American Fruit”: African American Family and Culture in College Choice 11:25 a.m. - 12:55. p.m., Pennsylvania Convention Center, Floor: Level 300, Room 305 Author: Teranda Donatto
• Leaders of Color and Perceptions of Organizational Culture: Measuring Racial Stress 1:15 - 2:45 p.m., Pennsylvania Convention Center, Floor: Second Floor, Exhibit Hall B Author: April Peters-Hawkins
• Educational Equity and the Logics of COVID-19: Informing School Leadership Practices in a New Period of Democratic Education 3:05 - 4:35 p.m., Philadelphia Marriott Downtown, Floor: Level 4, Room 412 Author: April Peters-Hawkins
• AERA Research Advisory Committee Closed Meeting 3:05 - 4:35 p.m., Pennsylvania Convention Center, Floor: Level 100, Room 106A Participant: Virginia Snodgrass Rangel  
• Entering the Hip-Hop Curriculum for 21st-Century Homeless Youth to Increase Racial Consciousness 3:05 - 4:35 p.m., Pennsylvania Convention Center, Floor: Second Floor, Exhibit Hall B Author: Miao Li  

— By Ericka Mellon and Kathy Patnaude

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SOE Research Assistants Sonia Leone and Catherine Mardosa Present Research at the Maine Sustainability and Water Conference

SOE Research Assistants Sonia Leone and Catherine Mardosa recently presented research at the 2024 Maine Sustainability and Water Conference. Leone and Mardosa presented research done in collaboration with Drs. Sharon Klein and Caroline Noblet as well as other colleagues. The poster, Engaging With Low-Income and Disadvantaged Communities for State Climate Action Planning , presented initial findings from research led by Drs. Klein and Noblet and funded by the State of Maine’s Governor’s Office of Planning and Innovation. Their team is doing research in collaboration with the Maine Climate Council. Congratulations Sonia Leone and Catherine Mardosa!

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From coast to coast: first-generation college students present research at California conference

Students, alumni share value of experiential learning for STEM career exploration

By GRACE HOGGARTH '22 on April 4, 2024

Three TU students shared a unique experience when they presented their research to a national audience for the first time at the Western Society of Naturalists conference in Monterey, California in November. 

Senior Jayden Steward ’24, and alumni Troy Stern ’23 and Yuridia Gonzales ’23 research began in professor Will Ryan’s lab researching the evolutionary ecology of marine invertebrates. While in the field, they studied the roles of the environment and landscape on the lifecycles and population structure of sea anemones and hydrozoans in the ocean.

They collected samples of invasive sea anemones in Delaware and then hermit crabs, which play host to a specific kind of colonial hydrozoan, in Virginia.

Their goal was to understand how complex habitats like salt marshes shape connections between populations of these invertebrates, which can only move as far as their hermit crab hosts can walk. Their research also aimed to determine how competition between the colonies living on the same shell influenced the traits of those that survived.

During the conference, TU students presented their research during an afternoon poster session, sat in on panel discussions, listened to individual talks from guest speakers and took part in inclusive discussions for first-generation college students.

Steward and his peers also ventured to the Monterey Bay aquarium and the Pacific Grove monarch butterfly sanctuary and conducted tide pool observations at Asilomar Beach.

For Steward in particular, this professional opportunity offered several firsts: flying in an airplane and exploring the U.S. beyond the East Coast. The biology major found that this was also an opportunity for him to broaden his horizons and visualize what it could be like to work in environments outside of Maryland and explore new career paths.

“It’s really enhanced my academic experience at TU. It’s given me new outlets and even has me considering grad school,” Steward says. “It’s overwhelmingly had a positive impact on my life. Not many college students can say they went to California to present research. I’m really grateful for it.”

Experiential learning and networking opportunities are invaluable for young researchers, particularly first-generation college students, like Steward, Stern and Gonzales.

Alexei Kolesnikov, director of the Office of Undergraduate Research and Creative Inquiry (OURCI), attests, “There is proven value in engaging in research and attending conferences for students. After a conference, students return with a deeper sense of professional identity in their field, a broader view of career options and a more-focused understanding of how their research can lead to further opportunities. Networking during conferences helps students connect with professionals and peers, enhancing their academic and career prospects."

This was true for Ryan when he got involved with the Western Society of Naturalists during his undergraduate career. Years down the line, he is still actively involved and spoke at this year’s conference to share insight into the importance of inclusive, safe and accessible fieldwork for the transgender and broader LGBTQ+ communities.

Ryan wanted to use this opportunity and his research lab to pay it forward to his students and guide them through the hidden curriculum that most first-generation college students are unaware of when they begin their academic journey.

“What I really wanted for them was what this conference had done for me as an undergraduate. I was a first-generation student and didn’t really understand college, let alone academia or how to be a scientist,” says Ryan. “Taking that idealized experimental design and trying to implement it into the world gives you a much greater appreciation for how hard won all the knowledge we have is. Providing students with these opportunities to actually do the work   and see   the way their life would be if they chose this path is the most important thing.” 

The research Steward and his peers participated in was funded by a grant from the Fisher College of Science and Mathematics (FCSM) Endowment. Travel to the conference was jointly funded by the Office of Undergraduate Research & Creative Inquiry (OURCI), the FCSM and the Department of Biology. Students can explore additional learning opportunities through programs like experiential and advanced learning and TU Research Enhancement Program (TUREP) courses .

MassURC Announces UMass Dartmouth Bioengineering Professor, Undergraduate Research Director Lamya Karim as Keynote Speaker

Organizers for the 2024 Massachusetts Undergraduate Research Conference (MassURC) have announced that Lamya Karim, associate professor of bioengineering and director of the Office of Undergraduate Research at UMass Dartmouth, will be the keynote speaker at the 30th annual conference to be held on the UMass Amherst campus April 19.

Karim’s keynote, “Turning Your ‘Failures’ Into Successes: A Scholar’s Journey Through Research and Perseverance,” will highlight her journey as an undergraduate student and how her early involvement in research and learning helped shape her career trajectory and continues to influence her current day-to-day work.

Karim’s research interests focus on skeletal mechanics, particularly as it pertains to osteoporosis, aging, diabetes and other health conditions. She holds a B.E. from Stony Brook University (2007) and a doctorate from Rensselaer Polytechnic Institute (2011), both in biomedical engineering.

The MassURC is a one-day conference where undergraduate students from the public colleges and universities within the Commonwealth present research, share knowledge and learn from fellow students. For the past 30 years, the conference serves to broaden understanding of research, showcasing investigations in over 60 subject areas, including biochemistry, history, architecture, marketing, fine art and sociology.

“We are so excited to welcome Dr. Karim as our keynote speaker at the conference this year,” says Mari Castañeda, dean of Commonwealth Honors College, which organizes and administers the conference on behalf of the state of Massachusetts. “Her profound insight into student research – and the story of her own path in research – will be an inspiration for presenters.”

The 2024 conference will be held in-person at the Campus Center auditorium. Karim’s keynote will be presented in the Student Union Building, Cape Cod Lounge from 9:15 - 10:15 a.m. on the conference day, with options for the public to view it through the MassURC Hub .

The MassURC is supported by Commonwealth Honors College, the Massachusetts Department of Higher Education, the Massachusetts State Universities Council of Presidents and the Massachusetts Association of Community Colleges. The keynote presentation is supported by the Williamson Lecture Funds.

For more information and registration, visit  https://www.umass.edu/honors/MassURC , and for questions about the conference contact  [email protected] .