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How to Do Research

Last Updated: March 13, 2023 References

This article was co-authored by Matthew Snipp, PhD and by wikiHow staff writer, Jennifer Mueller, JD . C. Matthew Snipp is the Burnet C. and Mildred Finley Wohlford Professor of Humanities and Sciences in the Department of Sociology at Stanford University. He is also the Director for the Institute for Research in the Social Science’s Secure Data Center. He has been a Research Fellow at the U.S. Bureau of the Census and a Fellow at the Center for Advanced Study in the Behavioral Sciences. He has published 3 books and over 70 articles and book chapters on demography, economic development, poverty and unemployment. He is also currently serving on the National Institute of Child Health and Development’s Population Science Subcommittee. He holds a Ph.D. in Sociology from the University of Wisconsin—Madison. This article has been viewed 227,095 times.

The idea of doing research may seem daunting, but as long as you keep yourself organized and focus on the question you want to answer, you'll be fine. If you're curious and interested in the topic, you might even find it fun! We here at wikiHow have gathered answers to all your most common questions about how to do research, from finding a good topic to identifying the best sources and writing your final paper.

How do I find a topic to research?

• For example, if you're researching in the political science field, you might be interested in determining what leads people to believe that the 2020 US presidential election was illegitimate.

How do I get started on my research?

• For example, if you're researching the 2020 election, you might find that "absentee ballots" and "voting by mail" come up frequently. Those are issues you could look into further to figure out how they impacted the final election results.
• You don't necessarily have to use the overview articles you look at as resources in your actual paper. Even Wikipedia articles can be a good way to learn more about a topic and you can check the references for more reputable sources that might work for your paper.

What's the best way to keep track of my sources?

• Research papers typically discuss 2 or 3 separate things that work together to answer the research question. You might also want to make a note on the front of which thing that source relates to. That'll make it easier for you to organize your sources later.
• For example, if you're researching the 2020 election, you might have a section of your paper discussing voting by mail. For the sources that directly address that issue, write "voting by mail" in the corner.

What kind of notes should I be taking as I research?

• If you find something that you think would make a good quote, copy it out exactly with quote marks around it, then add the page number where it appears so you can correctly cite it in your paper without having to go back and hunt for it again.

How do I evaluate the quality of a source?

• Does the article discuss or reference another article? (If so, use that article instead.)
• What expertise or authority does the author have?
• When was the material written? (Is it the most up-to-date reference you could use?)
• Why was the article published? (Is it trying to sell you something or persuade you to adopt a certain viewpoint?)
• Are the research methods used consistent and reliable? (Appropriate research methods depend on what was studied.)

What if I'm having a hard time finding good sources?

• For example, if you're writing about the 2020 election, you might find tons of stories online, but very little that is reputable enough for you to use in your paper. Because the election happened so recently, it might be too soon for there to be a lot of solid academic research on it. Instead, you might focus on the 2016 election.
• You can also ask for help. Your instructor might be able to point you toward good sources. Research librarians are also happy to help you.

How do I organize my research for my paper?

• For example, if you're researching the effect of the COVID-19 pandemic on the 2020 election, you might have sections on social distancing and cleaning at in-person voting locations, the accessibility of mail-in ballots, and early voting.

What's the best way to start writing my paper?

• Include an in-text citation for everything that needs one, even in your initial rough draft. That'll help you make sure that you don't inadvertently misattribute or fail to cite something as you work your way through substantive drafts.
• Write your introduction and conclusion only after you're satisfied that the body of your paper is essentially what you want to turn in. Then, you can polish everything up for the final draft.

How can I make sure I'm not plagiarizing?

• If you have any doubt over whether you should cite something, go ahead and do it. You're better off to err on the side of over-citing than to look like you're taking credit for an idea that isn't yours.
• ↑ https://www.nhcc.edu/student-resources/library/doinglibraryresearch/basic-steps-in-the-research-process
• ↑ Matthew Snipp, PhD. Sociology Professor, Stanford University. Expert Interview. 26 March 2020.
• ↑ https://library.taylor.edu/eng-212/research-paper
• ↑ http://www.butte.edu/departments/cas/tipsheets/research/research_paper.html
• ↑ https://www.potsdam.edu/sites/default/files/documents/support/tutoring/cwc/6-Simple-Steps-for-Writing-a-Research-Paper.pdf
• ↑ https://www.umgc.edu/current-students/learning-resources/writing-center/online-guide-to-writing/tutorial/chapter4/ch4-05.html

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About This Article

If you need to do research on a particular topic, start by searching the internet for any information you can find on the subject. In particular, look for sites that are sourced by universities, scientists, academic journals, and government agencies. Next, visit your local library and use the electric card catalog to research which books, magazines, and journals will have information on your topic. Take notes as you read, and write down all of the information you’ll need to cite your sources in your final project. To learn how interviewing a first-hand source can help you during your research, read on! Did this summary help you? Yes No

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Science, health, and public trust.

September 8, 2021

Explaining How Research Works

We’ve heard “follow the science” a lot during the pandemic. But it seems science has taken us on a long and winding road filled with twists and turns, even changing directions at times. That’s led some people to feel they can’t trust science. But when what we know changes, it often means science is working.

Explaining the scientific process may be one way that science communicators can help maintain public trust in science. Placing research in the bigger context of its field and where it fits into the scientific process can help people better understand and interpret new findings as they emerge. A single study usually uncovers only a piece of a larger puzzle.

Questions about how the world works are often investigated on many different levels. For example, scientists can look at the different atoms in a molecule, cells in a tissue, or how different tissues or systems affect each other. Researchers often must choose one or a finite number of ways to investigate a question. It can take many different studies using different approaches to start piecing the whole picture together.

Sometimes it might seem like research results contradict each other. But often, studies are just looking at different aspects of the same problem. Researchers can also investigate a question using different techniques or timeframes. That may lead them to arrive at different conclusions from the same data.

Using the data available at the time of their study, scientists develop different explanations, or models. New information may mean that a novel model needs to be developed to account for it. The models that prevail are those that can withstand the test of time and incorporate new information. Science is a constantly evolving and self-correcting process.

Scientists gain more confidence about a model through the scientific process. They replicate each other’s work. They present at conferences. And papers undergo peer review, in which experts in the field review the work before it can be published in scientific journals. This helps ensure that the study is up to current scientific standards and maintains a level of integrity. Peer reviewers may find problems with the experiments or think different experiments are needed to justify the conclusions. They might even offer new ways to interpret the data.

It’s important for science communicators to consider which stage a study is at in the scientific process when deciding whether to cover it. Some studies are posted on preprint servers for other scientists to start weighing in on and haven’t yet been fully vetted. Results that haven't yet been subjected to scientific scrutiny should be reported on with care and context to avoid confusion or frustration from readers.

We’ve developed a one-page guide, "How Research Works: Understanding the Process of Science" to help communicators put the process of science into perspective. We hope it can serve as a useful resource to help explain why science changes—and why it’s important to expect that change. Please take a look and share your thoughts with us by sending an email to  [email protected].

Below are some additional resources:

• Discoveries in Basic Science: A Perfectly Imperfect Process
• When Clinical Research Is in the News
• What is Basic Science and Why is it Important?
• ​ What is a Research Organism?
• What Are Clinical Trials and Studies?
• Basic Research – Digital Media Kit
• Decoding Science: How Does Science Know What It Knows? (NAS)
• Can Science Help People Make Decisions ? (NAS)

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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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• CAREER COLUMN
• 15 March 2019

A student’s guide to undergraduate research

• Shiwei Wang 0

Shiwei Wang is a junior undergraduate student studying Integrated Science and Chemistry at Northwestern University in Evanston, Illinois. Twitter: @W_Shiwei

You can also search for this author in PubMed   Google Scholar

I have thoroughly enjoyed my experience working in a materials-chemistry laboratory at Northwestern University in Evanston, Illinois, for the past two years. Being able to mix an undergraduate education with original research in a proper laboratory has been a fantastic opportunity.

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doi: https://doi.org/10.1038/d41586-019-00871-x

This is an article from the Nature Careers Community, a place for Nature readers to share their professional experiences and advice. Guest posts are encouraged. You can get in touch with the editor at [email protected].

Wang, S. et al. Preprint at ChemRxiv https://doi.org/10.26434/chemrxiv.7824707.v2 (2019).

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What Is Research, and Why Do People Do It?

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• First Online: 03 December 2022

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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.

Agnes, M., & Guralnik, D. B. (Eds.). (2008). Hypothesis. In Webster’s new world college dictionary (4th ed.). Wiley.

Google Scholar  

Britannica. (n.d.). Scientific method. In Encyclopaedia Britannica . Retrieved July 15, 2022 from https://www.britannica.com/science/scientific-method

Brownell, W. A., & Moser, H. E. (1949). Meaningful vs. mechanical learning: A study in grade III subtraction . Duke University Press..

Cai, J., Morris, A., Hohensee, C., Hwang, S., Robison, V., Cirillo, M., Kramer, S. L., & Hiebert, J. (2019b). Posing significant research questions. Journal for Research in Mathematics Education, 50 (2), 114–120. https://doi.org/10.5951/jresematheduc.50.2.0114

Article   Google Scholar  

Cambridge University Press. (n.d.). Hypothesis. In Cambridge dictionary . Retrieved July 15, 2022 from https://dictionary.cambridge.org/us/dictionary/english/hypothesis

Cronbach, J. L. (1957). The two disciplines of scientific psychology. American Psychologist, 12 , 671–684.

Cronbach, L. J. (1975). Beyond the two disciplines of scientific psychology. American Psychologist, 30 , 116–127.

Cronbach, L. J. (1986). Social inquiry by and for earthlings. In D. W. Fiske & R. A. Shweder (Eds.), Metatheory in social science: Pluralisms and subjectivities (pp. 83–107). University of Chicago Press.

Hay, C. M. (Ed.). (2016). Methods that matter: Integrating mixed methods for more effective social science research . University of Chicago Press.

Merriam-Webster. (n.d.). Explain. In Merriam-Webster.com dictionary . Retrieved July 15, 2022, from https://www.merriam-webster.com/dictionary/explain

National Research Council. (2002). Scientific research in education . National Academy Press.

Weis, L., Eisenhart, M., Duncan, G. J., Albro, E., Bueschel, A. C., Cobb, P., Eccles, J., Mendenhall, R., Moss, P., Penuel, W., Ream, R. K., Rumbaut, R. G., Sloane, F., Weisner, T. S., & Wilson, J. (2019a). Mixed methods for studies that address broad and enduring issues in education research. Teachers College Record, 121 , 100307.

Weisner, T. S. (Ed.). (2005). Discovering successful pathways in children’s development: Mixed methods in the study of childhood and family life . University of Chicago Press.

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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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Home Market Research

What is Research: Definition, Methods, Types & Examples

The search for knowledge is closely linked to the object of study; that is, to the reconstruction of the facts that will provide an explanation to an observed event and that at first sight can be considered as a problem. It is very human to seek answers and satisfy our curiosity. Let’s talk about research.

Content Index

What is Research?

What are the characteristics of research.

• Comparative analysis chart

Qualitative methods

Quantitative methods, 8 tips for conducting accurate research.

Research is the careful consideration of study regarding a particular concern or research problem using scientific methods. According to the American sociologist Earl Robert Babbie, “research is a systematic inquiry to describe, explain, predict, and control the observed phenomenon. It involves inductive and deductive methods.”

Inductive methods analyze an observed event, while deductive methods verify the observed event. Inductive approaches are associated with qualitative research , and deductive methods are more commonly associated with quantitative analysis .

Research is conducted with a purpose to:

• Identify potential and new customers
• Understand existing customers
• Set pragmatic goals
• Develop productive market strategies
• Address business challenges
• Put together a business expansion plan
• Identify new business opportunities
• Good research follows a systematic approach to capture accurate data. Researchers need to practice ethics and a code of conduct while making observations or drawing conclusions.
• The analysis is based on logical reasoning and involves both inductive and deductive methods.
• Real-time data and knowledge is derived from actual observations in natural settings.
• There is an in-depth analysis of all data collected so that there are no anomalies associated with it.
• It creates a path for generating new questions. Existing data helps create more research opportunities.
• It is analytical and uses all the available data so that there is no ambiguity in inference.
• Accuracy is one of the most critical aspects of research. The information must be accurate and correct. For example, laboratories provide a controlled environment to collect data. Accuracy is measured in the instruments used, the calibrations of instruments or tools, and the experiment’s final result.

What is the purpose of research?

There are three main purposes:

• Exploratory: As the name suggests, researchers conduct exploratory studies to explore a group of questions. The answers and analytics may not offer a conclusion to the perceived problem. It is undertaken to handle new problem areas that haven’t been explored before. This exploratory data analysis process lays the foundation for more conclusive data collection and analysis.

LEARN ABOUT: Descriptive Analysis

• Descriptive: It focuses on expanding knowledge on current issues through a process of data collection. Descriptive research describe the behavior of a sample population. Only one variable is required to conduct the study. The three primary purposes of descriptive studies are describing, explaining, and validating the findings. For example, a study conducted to know if top-level management leaders in the 21st century possess the moral right to receive a considerable sum of money from the company profit.

LEARN ABOUT: Best Data Collection Tools

• Explanatory: Causal research or explanatory research is conducted to understand the impact of specific changes in existing standard procedures. Running experiments is the most popular form. For example, a study that is conducted to understand the effect of rebranding on customer loyalty.

Here is a comparative analysis chart for a better understanding:

It begins by asking the right questions and choosing an appropriate method to investigate the problem. After collecting answers to your questions, you can analyze the findings or observations to draw reasonable conclusions.

When it comes to customers and market studies, the more thorough your questions, the better the analysis. You get essential insights into brand perception and product needs by thoroughly collecting customer data through surveys and questionnaires . You can use this data to make smart decisions about your marketing strategies to position your business effectively.

To make sense of your study and get insights faster, it helps to use a research repository as a single source of truth in your organization and manage your research data in one centralized data repository .

Types of research methods and Examples

Research methods are broadly classified as Qualitative and Quantitative .

Both methods have distinctive properties and data collection methods .

Qualitative research is a method that collects data using conversational methods, usually open-ended questions . The responses collected are essentially non-numerical. This method helps a researcher understand what participants think and why they think in a particular way.

Types of qualitative methods include:

• One-to-one Interview
• Focus Groups
• Ethnographic studies
• Text Analysis

Quantitative methods deal with numbers and measurable forms . It uses a systematic way of investigating events or data. It answers questions to justify relationships with measurable variables to either explain, predict, or control a phenomenon.

Types of quantitative methods include:

• Survey research
• Descriptive research
• Correlational research

LEARN MORE: Descriptive Research vs Correlational Research

Remember, it is only valuable and useful when it is valid, accurate, and reliable. Incorrect results can lead to customer churn and a decrease in sales.

It is essential to ensure that your data is:

• Valid – founded, logical, rigorous, and impartial.
• Accurate – free of errors and including required details.
• Reliable – other people who investigate in the same way can produce similar results.
• Timely – current and collected within an appropriate time frame.
• Complete – includes all the data you need to support your business decisions.

Gather insights

• Identify the main trends and issues, opportunities, and problems you observe. Write a sentence describing each one.
• Keep track of the frequency with which each of the main findings appears.
• Make a list of your findings from the most common to the least common.
• Evaluate a list of the strengths, weaknesses, opportunities, and threats identified in a SWOT analysis .
• Prepare conclusions and recommendations about your study.
• Act on your strategies
• Look for gaps in the information, and consider doing additional inquiry if necessary
• Plan to review the results and consider efficient methods to analyze and interpret results.

Review your goals before making any conclusions about your study. Remember how the process you have completed and the data you have gathered help answer your questions. Ask yourself if what your analysis revealed facilitates the identification of your conclusions and recommendations.

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What’s Included: Research Paper Template

If you’re preparing to write an academic research paper, our free research paper template is the perfect starting point. In the template, we cover every section step by step, with clear, straightforward explanations and examples .

The template’s structure is based on the tried and trusted best-practice format for formal academic research papers. The template structure reflects the overall research process, ensuring your paper will have a smooth, logical flow from chapter to chapter.

The research paper template covers the following core sections:

• The title page/cover page
• Abstract (sometimes also called the executive summary)
• Section 1: Introduction 
• Section 2: Literature review 
• Section 3: Methodology
• Section 4: Findings /results
• Section 5: Discussion
• Section 6: Conclusion
• Reference list

Each section is explained in plain, straightforward language , followed by an overview of the key elements that you need to cover within each section. We’ve also included links to free resources to help you understand how to write each section.

The cleanly formatted Google Doc can be downloaded as a fully editable MS Word Document (DOCX format), so you can use it as-is or convert it to LaTeX.

FAQs: Research Paper Template

What format is the template (doc, pdf, ppt, etc.).

The research paper template is provided as a Google Doc. You can download it in MS Word format or make a copy to your Google Drive. You’re also welcome to convert it to whatever format works best for you, such as LaTeX or PDF.

What types of research papers can this template be used for?

The template follows the standard best-practice structure for formal academic research papers, so it is suitable for the vast majority of degrees, particularly those within the sciences.

Some universities may have some additional requirements, but these are typically minor, with the core structure remaining the same. Therefore, it’s always a good idea to double-check your university’s requirements before you finalise your structure.

Is this template for an undergrad, Masters or PhD-level research paper?

This template can be used for a research paper at any level of study. It may be slight overkill for an undergraduate-level study, but it certainly won’t be missing anything.

How long should my research paper be?

This depends entirely on your university’s specific requirements, so it’s best to check with them. We include generic word count ranges for each section within the template, but these are purely indicative. 

What about the research proposal?

If you’re still working on your research proposal, we’ve got a template for that here .

We’ve also got loads of proposal-related guides and videos over on the Grad Coach blog .

How do I write a literature review?

We have a wealth of free resources on the Grad Coach Blog that unpack how to write a literature review from scratch. You can check out the literature review section of the blog here.

How do I create a research methodology?

We have a wealth of free resources on the Grad Coach Blog that unpack research methodology, both qualitative and quantitative. You can check out the methodology section of the blog here.

Can I share this research paper template with my friends/colleagues?

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How to Become a Research Psychologist

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

Why Become a Research Psychologist

What do research psychologists do, where do research psychologists work, research psychology careers.

What do you do if you love psychology but have no interest in working in mental health? Fortunately, psychology is a very diverse field, and there are plenty of opportunities. One that you might consider is becoming a research psychologist.

Consider the following question from a reader:

" I love psychology, which is why I'm currently working on my bachelor's degree in psychology. I don't want to work in mental health, so my ultimate plan is to become a researcher. While I know that this means I will probably need to go to graduate school, I'm not quite sure exactly where to begin. What type of psychology degree do I need if I want to work in research?"

At a Glance

What do you do if you love psychology but don't want to become a therapist or work in mental health? You might consider becoming a research psychologist. These professionals conduct research and may work in various settings, including universities, research labs, the military, government agencies, and private businesses.

This scenario is not at all uncommon in psychology. Many students love the subject, yet are not at all interested in working in mental health settings. Experimental fields are great options for people who are fascinated by psychology and enjoy performing research.

As a psychology student , you've probably already gotten a taste of just how diverse the field can be. This can be a great thing because it allows for so many different career paths and options, but it can also be confusing for students as they struggle to select an educational path.

Just like many other areas of psychology , becoming a research psychologist is not a "one size fits all" career. There are actually many different degrees that you could potentially pursue.

Start by taking into account what type of research you want to perform and what specific topics interest you the most.

In order to decide if this field is right for you, it is important to first understand exactly what these professionals do:

• Also known as experimental psychologists , research psychologists study a broad range of human and animal behavior.
• They design and conduct experiments exploring how people act, think, behave, interact, learn, feel, and perform under different conditions.
• They also design studies and evaluate research for flaws and bias.
• This can encompass an enormous range of topics, including memory , attention, cognition, decision-making, perception, and just about any psychological topic you can think of!

If you enjoy research and still want to work in mental health, there are also mental health professionals who perform research and conduct studies in clinical settings.

Educational backgrounds and requirements for experimental psychologists can vary depending upon where you want to work.

In most cases, you will start by earning a bachelor's degree in psychology. Some students may then choose to earn a master's, but many will go on to receive a doctorate degree.

Bachelor's Degree

Many students interested in becoming research psychologists begin with a bachelor's in psychology . However, some come from a background in a related area such as social work or even from an entirely unrelated degree area altogether.

Remember, it is possible to switch to psychology for graduate school , even if your undergraduate degree is in an unrelated subject.

Master's Degree

In some cases, students might then choose to pursue a master's degree in experimental psychology. However, it is important to note that job opportunities are generally more limited with a master's degree, which is why many opt to instead go on to earn a doctorate degree in psychology .

Doctorate Degree

While you might think you are limited to earning a PhD in experimental psychology, there are actually many different options that you might opt to pursue.

For example, if you are interested in studying the human brain, you might earn a degree focused on neuropsychology. Have an active interest in social behavior? Then, you might want to consider a doctorate in social psychology .

How to Get Started

While you might not be exactly sure about what specialty you want to pursue, you can now do plenty of things to prepare for your future as a research psychologist . Start by taking as many undergraduate courses in research methods , statistics , and experimental design as possible.

Sign up for research opportunities through your school's psychology department and consider signing up as a research assistant. It's a great way to gain valuable experience while earning college credits.

As you can see, research is something that plays a significant role in virtually every field of psychology . Your goal now is to determine which particular specialty area interests you the most and exactly where you might want to work someday.

Research psychologists are employed in a wide range of sectors, including private research firms, universities, corporations, the military, and government agencies.

So what kind of jobs will you be able to get as a research psychologist? While there are many different options, a few that you might consider include:

College Professor

Many research psychologists work at colleges and universities, teaching undergraduate and graduate students and conducting research.

Research Analyst

A research analyst evaluates data that has been collected. This career involves performing statistical analyses and managing data to ensure it is collected, recorded, and analyzed properly.

Research Scientist

A research scientist conducted grant-funded research. They are often the lead investigators of a study and are responsible for hiring assistants, managing projects, designing experiments, writing journal articles, and sharing the results of their experiments.

If you enjoy research and aren't interested in working in the field of mental health, a career as a research psychologist might be a great choice for you. To enter this field, you should focus on earning an undergraduate degree in psychology before going to graduate school to get your doctorate. Taking coursework in statistics and research methodology can help, but you should also take advantage of any opportunity to participate in research.

Bishop DV. The psychology of experimental psychologists: Overcoming cognitive constraints to improve research: The 47th Sir Frederic Bartlett Lecture .  Q J Exp Psychol (Hove) . 2020;73(1):1-19. doi:10.1177/1747021819886519

Smith KV, Thew GR. Conducting research in clinical psychology practice: Barriers, facilitators, and recommendations .  Br J Clin Psychol . 2017;56(3):347-356. doi:10.1111/bjc.12142

Scholtz SE, de Klerk W, de Beer LT. The use of research methods in psychological research: A systematised review .  Front Res Metr Anal . 2020;5:1. doi:10.3389/frma.2020.00001

American Psychological Association.  Pursuing a Career in Experimental Psychology . Updated March 2014.

The Princeton Review.  Experimental Psychology .

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Physicists create five-lane superhighway for electrons

Work could lead to ultra-efficient electronics and more.

Elizabeth A. Thomson | Materials Research Laboratory

Artist’s rendition of the superhighway for electrons that can occur in rhombohedral graphene, a special kind of graphite (pencil lead).

Credit: Sampson Wilcox, MIT Research Laboratory of Electronics

MIT physicists and colleagues have created a five-lane superhighway for electrons that could allow ultra-efficient electronics and more. The work, reported in the May 9 issue of Science , is one of several important discoveries by the same team over the last year involving a material that is essentially a unique form of pencil lead.

“This discovery has direct implications for low-power electronic devices because no energy is lost during the propagation of electrons, which is not the case in regular materials where the electrons are scattered,” says Long Ju, an assistant professor in the MIT Department of Physics and corresponding author of the Science paper.

The phenomenon is akin to cars traveling down an open turnpike as opposed to those moving through neighborhoods. The neighborhood cars can be stopped or slowed by other drivers making abrupt stops or U-turns that disrupt an otherwise smooth commute.

A New Material

The material behind this work, known as rhombohedral pentalayer graphene, was discovered two years ago by physicists led by Ju. “We found a goldmine, and every scoop is revealing something new,” says Ju, who is also affiliated with MIT’s Materials Research Laboratory.

In a Nature Nanotechnology paper last October Ju and colleagues reported the discovery of three important properties arising from rhombohedral graphene. For example, they showed that it could be topological, or allow the unimpeded movement of electrons around the edge of the material but not through the middle. That resulted in a superhighway, but required the application of a large magnetic field some tens of thousands times stronger than the Earth’s magnetic field.

In the current work, the team reports creating the superhighway without any magnetic field.

Tonghang Han, an MIT graduate student in physics, is a co-first author of the paper. “We are not the first to discover this general phenomenon, but we did so in a very different system. And compared to previous systems, ours is simpler and also supports more electron channels.” Explains Ju, “other materials can only support one lane of traffic on the edge of the material. We suddenly bumped it up to five.”

Additional co-first authors of the paper who contributed equally to the work are Zhengguang Lu and Yuxuan Yao. Lu is a postdoctoral associate in the Materials Research Laboratory. Yao conducted the work as a visiting undergraduate student from Tsinghua University. Other authors are MIT Professor Liang Fu of physics;   Jixiang Yang and Junseok Seo, both graduate students in MIT physics; Chiho Yoon and Fan Zhang of the University of Texas at Dallas; and Kenji Watanabe and Takashi Taniguchi of the National Institute for Materials Science in Japan.

How it Works

Pencil lead, or graphite, is composed of graphene, a single layer of carbon atoms arranged in hexagons resembling a honeycomb structure. Rhombohedral graphene is composed of five layers of graphene stacked in a specific overlapping order.

Ju and colleagues isolated rhombohedral graphene thanks to a novel microscope Ju built at MIT in 2021 that can quickly and relatively inexpensively determine a variety of important characteristics of a material at the nanoscale. Pentalayer rhombohedral stacked graphene is only a few billionths of a meter thick.

In the current work, the team tinkered with the original system, adding a layer of tungsten disulfide (WS 2 ). “The interaction between the WS 2 and the pentalayer rhombohedral graphene resulted in this five-lane superhighway that operates at zero magnetic field,” says Ju.

Comparison to Superconductivity

The phenomenon that the Ju group discovered in rhombohedral graphene that allows electrons to travel with no resistance at zero magnetic field is known as the quantum anomalous Hall effect. Most people are more familiar with superconductivity, a completely different phenomenon that does the same thing but happens in very different materials.

Ju notes that although superconductors were discovered in the 1910s, it took some 100 years of research to coax the system to work at the higher temperatures necessary for applications. “And the world record is still well below room temperature,” he notes.

Similarly, the rhombohedral graphene superhighway currently operates at about 2 Kelvin, or -456 Fahrenheit. “It will take a lot of effort to elevate the temperature, but as physicists , our job is to provide the insight; a different way for realizing this [phenomenon],” Ju says.

Very Exciting

The discoveries involving rhombohedral graphene came as a result of painstaking research that wasn’t guaranteed to work. “We tried many recipes over many months,” says Han, “so it was very exciting when we cooled the system to a very low temperature and [a five-lane superhighway operating at zero magnetic field] just popped out.”

Says Ju, “it’s very exciting to be the first to discover a phenomenon in a new system, especially in a material that we uncovered.”

This work was supported by a Sloan Fellowship; the U.S. National Science Foundation; the U.S. Office of the Under Secretary of Defense for Research and Engineering; the Japan Society for the Promotion of Science KAKENHI; and the World Premier International Research Initiative of Japan.

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Ordered back to the office, top tech talent left instead, study finds

In the months following return-to-office mandates, an increased number of senior employees departed Apple, Microsoft and SpaceX, often to work for competitors.

Return-to-office mandates at some of the most powerful tech companies — Apple, Microsoft and SpaceX — were followed by a spike in departures among the most senior, tough-to-replace talent, according to a case study published last week by researchers at the University of Chicago and the University of Michigan.

Researchers drew on résumé data from People Data Labs to understand the impact that forced returns to offices had on employee tenure and the movement of workers between companies. What they found was a strong correlation between the departures of senior-level employees and the implementation of a mandate, suggesting that these policies “had a negative effect on the tenure and seniority of their respective workforce.” High-ranking employees stayed several months less than they might have without the mandate, the research suggests — and in many cases, they went to work for direct competitors.

At Microsoft, the share of senior employees as a portion of the company’s overall workforce declined more than five percentage points after the return-to-office mandate took effect, the researchers found. At Apple, the decline was four percentage points, while at SpaceX — the only company of the three to require workers to be fully in-person — the share of senior employees dropped 15 percentage points.

“We find experienced employees impacted by these policies at major tech companies seek work elsewhere, taking some of the most valuable human capital investments and tools of productivity with them,” said Austin Wright, an assistant professor of public policy at the University of Chicago and one of the study’s authors. “Business leaders should weigh carefully employee preferences and market opportunities when deciding when, or if, they mandate a return to office.”

Technology is an industry “where the discourse over the return to office was most heated,” said David Van Dijcke, a researcher at the University of Michigan who worked on the study. Microsoft, Apple and SpaceX play an outsize role in the sector — collectively they represent more than 2 percent of the tech workforce and 30 percent of the industry’s revenue, according to the researchers — and their office policy “sets the precedent for the wider debate around the return to office,” the study’s authors wrote.

Those three companies also were among the first Big Tech firms to pursue return-to-office mandates in 2022, allowing researchers to separate the effects of mandates from the widespread tech layoffs that rocked the industry later in the year, Van Dijcke said.

Amy Coleman, corporate vice president of human resources and corporate functions at Microsoft, countered in a statement to The Washington Post on Monday that its internal data “does not align with these findings, especially around attrition.” It was “inaccurate” to frame the company’s hybrid work policy as a return-to-office mandate, she added.

“We have a hybrid workplace that revolves around flexibility and a mix of workstyles across worksite, work location and work hours,” Coleman added.

Apple spokesman Josh Rosenstock criticized the study as work that draws “inaccurate conclusions” and “does not reflect the realities of our business.”

“In fact, attrition is at historically low levels,” Rosenstock said.

SpaceX did not respond to a request for comment from The Post.

Apple, Microsoft and SpaceX differ “markedly” in their corporate cultures and lines of business, the researchers noted, and took different approaches in their return-to-office policies. Yet the similar effects of the RTO mandates found in the study suggest that “the effects are driven by common underlying dynamics,” the authors wrote.

“Our findings suggest that RTO mandates cost the company more than previously thought,” Van Dijcke said. “These attrition rates aren’t just something that can be managed away.”

The tug-of-war over offices has been locked in a stalemate for roughly a year: Office occupancy data tracked by Kastle Systems shows that the national average across the country’s top metro areas — including New York, Washington and San Francisco — has hovered stubbornly around 50 percent of pre-pandemic levels since early 2023.

A spike in departures of senior employees following return-to-office mandates could reflect the “double pinch” they inflict on managers, who have to deal with the policy’s effects on the teams they lead and in their own lives, said Christopher Myers, an associate professor of management and organization health at Johns Hopkins University, who did not work on the study.

He compared it to leading employees amid layoffs or wage stagnation.

“It’s a change to the work structure, sure, but it’s also just a hit to morale,” said Myers, who is also a scholar with the Academy of Management. Maybe managers leave shortly after mandates, he posited, “because it’s easier to manage a team that’s happy.”

Tech executives have extolled the values of in-person work, citing benefits to connectedness and innovation. CEOs such as OpenAI’s Sam Altman, Meta’s Mark Zuckerberg and Tesla’s Elon Musk have criticized remote work’s effects on company culture and productivity. In an April interview with CNBC , Nike CEO John Donahoe attributed a slowdown in innovation at the company to remote work, saying that “it’s really hard to do bold, disruptive innovation, to develop a boldly disruptive shoe on Zoom.”

Executives have not provided much evidence that a return to office actually benefits their workforces, said Robert Ployhart, a professor of business administration and management at the University of South Carolina. For example, there’s nothing pointing to a widespread drop-off in productivity as hybrid work has increased, he said.

“The people sitting at the apex may not like the way they feel the organization is being run, but if they’re not bringing data to that point of view, it’s really hard to argue why people should be coming back to the workplace more frequently,” Ployhart said.

Senior employees, he said, are “the caretakers of a company’s culture,” and having to replace them can have negative effects on team morale and productivity.

“By driving those employees away, they’ve actually enhanced and sped up the very thing they were trying to stop,” Ployhart said.

Although the study focused on three companies, its findings broadly reflect the impact of return-to-office mandates on workforces across the country, according to Ployhart, who is also a scholar with the Academy of Management. Companies are still struggling to adapt to a landscape fundamentally altered by hybrid work.

“We really have a very fragmented world, and these one-size-fits-all policies tend to struggle to be successful when there’s so much nuance in the way we work,” Ployhart said.

RESEARCH ASST I (Student/Work Study) - BIO

The research assistant will work with Dr. Rebecca Tonietto on a greenhouse study.

Responsibilities*

• Assist with mesocosm establishment
• Watering and rotating mesocosms in the greenhouse
• Assist with the simulator experiments

Required Qualifications*

• Must be a current University of Michigan-Flint undergraduate student in good academic standing or recently graduated from a UM-Flint undergraduate program and not currently enrolled in a graduate program
• Must have an interest in outdoor field work and working with plants
• Must have successfully completed Ecology (BIO 327)

Desired Qualifications*

Experience with plant identification preferred.

Work Schedule

The appointment will be for the summer 2024 semester.

Additional Information

University of Michigan-Flint - Plan for Diversity, Equity and Inclusion

The University of Michigan-Flint's DEI plan can be found at: https://www.umflint.edu/dei/?  

The University of Michigan-Flint exhibits its commitment to diversity, equity, and inclusion through enacting fair practices, policies, and procedures particularly in support of the equitable participation of the historically underserved. UM-Flint recognizes the value of diversity in our efforts to provide equitable access and opportunities to all regardless of individual identities in support of a climate where everyone feels a sense of belonging, community, and agency.

Diversity is a core value at University of Michigan-Flint. We are passionate about building and sustaining an inclusive and equitable working and learning environment for all students, staff, and faculty. The University of Michigan-Flint seeks to recruit and retain a diverse workforce as a reflection of our commitment to serve the diverse people of Michigan, to maintain the excellence of the University, and to offer our students richly varied disciplines, perspectives, and ways of knowing and learning for the purpose of becoming global citizens in a connected world.

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The University of Michigan conducts background checks on all job candidates upon acceptance of a contingent offer and may use a third party administrator to conduct background checks.  Background checks are performed in compliance with the Fair Credit Reporting Act.

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Postdoctoral Research Associate - AI/HPC for Distributed Energy Resource Optimization

Date: May 15, 2024

Location: Oak Ridge, TN, US, 37830

Company: Oak Ridge National Laboratory

Requisition Id 13050 

We are seeking a Postdoctoral Research Associate who will support a growing portfolio of research in large language models, large vision models, model vulnerability assessment, privacy preserving federated learning techniques, and knowledge distillation to target resource-constrained training and inference, especially in edge computing scenarios at Oak Ridge National Laboratory (ORNL).  

As part of the Geo AI group, you will support and lead research tasks related to deploying AI advances toward distributed energy resource optimization needs. The GeoAI Group is under the Geospatial Science and Human Security Division (GSHSD) at ORNL. The group performs artificial intelligence, computer vision, and federated learning research initiatives, with emphasis on large scale geospatial data analysis. Under the mentorship of senior research staff, a selected applicant will take roles on multidisciplinary teams supporting ground breaking research and engineering with large-scale distributed geospatial workflows, using GPU-based high-performance computing (HPC) across multiple platforms. 

As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an extraordinary 80-year history of solving the nation’s biggest problems. We have a dedicated and creative staff of over 6,000 people! Our vision for diversity, equity, inclusion, and accessibility (DEIA) is to cultivate an environment and practices that encourage diversity in ideas and in the people across the organization, as well as to ensure ORNL is recognized as a workplace of choice. These elements are critical for enabling the execution of ORNL’s broader mission to accelerate scientific discoveries and their translation into energy, environment, and security solutions for the nation. 

Major Duties and Responsibilities: 

Develop workflows that integrate with existing or new LLMs and LVMs for resource optimization with energy grid data. 

Provide coding support to implement privacy preserving federated learning techniques. 

Support the design of knowledge distillation methods for resource-constrained training and inference for edge computing scenarios 

Publish research results in journal articles, conference papers, and technical manuals. 

Ensure all work is carried out safely, securely, and in compliance with ORNL policies, standards, and procedures. 

Commit to excellence in research, operations, and community engagement, and work collaboratively to useR scientific capabilities across ORNL. 

Collaborate with data scientists, machine learning scientists, remote sensing scientists, HPC engineers, Energy grid subject matter experts, and geographers to deliver prototypes. 

Basic Requirements: 

Requires a Ph.D. in electrical and computer engineering, computer science, applied mathematics or related area, completed within the last 5 years 

Experience in developing AI/ML methods for analyzing large-scale observation based or simulated datasets 

Strong research profile and be able to conduct independent research 

Strong written and oral communication skills. 

The ability to work in a dynamic, team environment. 

Preferred Qualifications: 

Experience working with spatio-temporal datasets, remote sensing imagery, simulations, and time series analysis 

Experience in development and evaluation of energy grid data, natural language processing applications 

Hands-on experience with training machine learning models on high performance computing infrastructures with GPU accelerators 

Experience in the development of project research proposals 

Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks 

You'll report to a Group Leader and work closely with R&D Section Heads to implement the group’s scientific vision; develop group members to enable their career advancement; establish capabilities that enable programs to excel at the forefront of science and technology; perform R&D to advance the field of knowledge and/or technology in one’s respective specialty; sets, implements, and models standards for performance of work consistent with Environment, Safety, Security, Health, and Quality (ESH&Q) requirements and business rules; and ensures a diverse and inclusive work environment where every employee feels safe, heard, and appreciated—a workplace that sets an example for the broader community. 

The National Security Sciences Directorate conducts research and development to confront some of the nation’s most difficult security challenges and adversaries. Our directorate houses S&T leadership in cybersecurity and cyber-physical resiliency, data analytics, geospatial science and technology, nuclear nonproliferation, and high-performance computing for critical national security missions. We draw on the Laboratory’s outstanding facilities and work closely with leading researchers in other areas at the lab such as nuclear and chemical sciences and engineering, applied materials, sophisticated manufacturing, biosecurity, transportation, and computing. Our multi-disciplinary research teams are passionate about discovery and innovation as we build science-based solutions to security threats that put public safety, national defense, energy infrastructure, and the economy at risk. 

Our dedication to diversity:

As we strive to become the world’s premier research institution in the sciences and technologies that underpin critical national security missions, we are committed to creating an inclusive environment that highly values a diverse workforce. We recognize that a breadth of perspectives, insights, and experiences are vital to drive the level of innovation and discovery that is important to national security sciences. Our dedication extends beyond our workforce to the next generation of researchers with STEM education outreach that seeks to engage a diverse range of students. 

This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired.

We accept Word (.doc, .docx), Adobe (unsecured .pdf), Rich Text Format (.rtf), and HTML (.htm, .html) up to 5MB in size. Resumes from third party vendors will not be accepted; these resumes will be deleted and the candidates submitted will not be considered for employment.

If you have trouble applying for a position, please email [email protected].

ORNL is an equal opportunity employer. All qualified applicants, including individuals with disabilities and protected veterans, are encouraged to apply.  UT-Battelle is an E-Verify employer.

Nearest Major Market: Knoxville

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Latinas juggle gender rules and 'dueling cultural pressures,' survey shows

Most Latinas say they feel pressure to succeed at work and provide for their loved ones at home while also juggling cultural expectations surrounding traditional Hispanic gender roles: doing housework, starting families and living up to certain beauty standards, according to a Pew Re s earch Center s urvey published Tuesday.

“These are widespread pressures that women face in the U.S. overall, but one unique thing about Latinas is that many of them have recent immigrant connections and this produces unique cultural pressures that they may feel from family or friends,” Jens Manuel Krogstad, a senior writer and editor at Pew who co-authored the survey, told NBC News.

Growing up as the oldest of 10 siblings in a Catholic, Mexican American home in North Carolina, Flor Herrera said she mostly feels stressed about being financially prepared to take care of her parents "when they're no longer able to take care of themselves."

At 33, Herrera said her family constantly asks when she plans to start her own family. She has even considered freezing her eggs, but she feels conflicted about having "to step back in my career if I want to be a mother," said Herrera, who serves as the executive director of Casa Azul de Wilson, a nonprofit organization she co-founded with one of her sisters to help first-generation Latino college students.

Such tensions have ignited an "inner battle" within Herrera as she figures out how much of her desire to start a family is innate or "because society has told me that I want it," she said. "It does seem like an achievement that I want to accomplish, just not right now. But when the time comes, what if I can't? That's also a big fear."

'Dueling cultural pressures'

According to the Pew survey, about 6 in 10 Latinas (63%) reported often feeling family or work-related pressures, similar to the ones Herrera expressed.

At 22.2 million, Latinas are 17% of the nation's adult women population. From 2010 to 2022, the U.S. Latina population grew 5.6 million, making it the largest increase of any major female racial or ethnic group, Pew noted. Additionally, the vast majority of Latinas (77%) are either immigrants themselves or have at least one immigrant parent.

These trends highlight "the dueling cultural pressures" Latinas navigate that other groups of U.S. women may not have to deal with, Krogstad said, adding that the survey shows "the complexity of what it’s like to live in the U.S. as a Latina today."

Even though Estefany Londoño, a sociology graduate student in Florida, is not big on putting on makeup, she found herself getting dolled up on Mother’s Day mainly to please her mother on her day.

“That stuff is rewarded,” Londoño said. “There’s definitely an expectation, both unspoken and spoken.”

After coming out as queer in college, Londoño said she felt pressure from her mother to present herself “as feminine as possible.” When it comes to expectations around doing housework, Londoño said, “I almost rebelled against it a little bit” after growing up in a Latino household where “doing chores was everything.”

Almost 7 in 10 (68%) Latinas said they face a great deal or fair amount of pressure to cook and clean at home, according to the Pew survey.

Sixty-two percent of Latinas surveyed said Hispanic women face pressure to be beautiful by dressing nicely, wearing makeup, or doing their hair and nails, and 56% reported feeling pressure to get married and have children.

'Don't you want to get married or have children?'

Valerie Rodríguez of Puerto Rico said she always aspired to study as much as she could before turning 30. As she worked to earn degrees in communications and government administration, her relatives peppered her with questions like "You're going to keep studying? ... Don't you want to get married or have children?"

At 32, Rodríguez said she's in no rush to have children. She wants to enjoy her adult life a bit longer and wait until her partner finishes medical school, so they can more equally share parenting tasks.

Though she is clear about what she wants, that doesn't stop relatives and society at large from placing their more traditional expectations on her, Rodríguez said.

Both Irene Godinez, 41, of North Carolina and Beatriz Marquez, 37, of Puerto Rico said they feel a big pressure to not only succeed at work, but “leave a positive impression” as one of a few Latinas in most workplaces.

"I will be seen as a brown Latina first before I’m seen as a southerner, as a woman, as a mother, as a queer person, as a professional or anything else," said Godinez, who leads Poder NC Action, her own civic engagement nonprofit.

As someone who works in the music industry, Marquez said she feels “this big responsibility that I have placed on myself to ensure that our voices are heard, to make sure that I’m not only good at my job, but that I exceed expectations.”

Despite the pressures and the tensions, 86% of Latinas reported being happy with the direction their lives are going, according to the Pew survey.

About half of the Latinas surveyed said the situation of U.S. Hispanic women has improved over the past decade, "and about half say they expect improvement in the next 10 years," Krogstad said.

Herrera agreed.

"We have come a long way in terms of breaking societal norms, but obviously we have a long way to go," she said.

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Nicole Acevedo is a reporter for NBC News Digital. She reports, writes and produces stories for NBC Latino and NBCNews.com.