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Hi everyone! As a junior in high school, I'm trying to compile a list of potential colleges and universities to apply to. I keep coming across the term 'Research 1' university. Can someone please explain to me what this means and how it might impact my college experience if I attend one? Thanks!

Hi there! I'd be happy to explain the 'Research 1' university term for you. The Carnegie Classification of Institutions of Higher Education categorizes schools based on their research activity levels. A 'Research 1' or 'R1' university is one with the highest level of research activity. This means these institutions are characterized by extensive research efforts and significant funding for research-related initiatives.

The impact of attending a Research 1 university can vary depending on the student and their personal interests. If you're considering a career in research, or a field that values discovery and innovation, attending an R1 institution can offer benefits such as access to high-quality research facilities, engagement with leading scholars, and potential opportunities for research-related internships or on-campus work.

However, it's important to remember that the college experience is different for everyone, and just because a university is an R1 institution doesn't mean it's the best fit for you. It's essential to consider factors beyond research activity, such as size, location, school culture, and academic programs.

Good luck with your college search!

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Unlocking Opportunities: Why Research 1 Universities Matter

March 15, 2024 - Megan Borders

The University of New Mexico is our state’s only institution with this prestigious designation, and we are in your backyard!

This means that our faculty members not only take teaching and community engagement service seriously but also produce research and publish more than most doctoral programs nationwide. Anderson faculty collaborate, research, investigate, study, and then write, edit and rewrite numerous research articles per year to help UNM maintain this designation. More so, Anderson students, at both undergraduate and graduate levels, have access to cutting-edge research projects for real-world, hands-on experience.

This designation also helps attract top faculty members, researchers and students who strengthen Anderson’s academic reputation and the public’s perception of our programs. It highlights our faculty’s advancements in their industries and their capability to add to the national and international discourse on best practices in their areas of specialization.

Additionally, having an R1 university provides numerous advantages to New Mexico. It positively impacts the state’s economic growth and helps our officials tackle the biggest concerns through research and implementation recommendations, such as sustainable space research, child health and transitioning to green energy.

Being rated as an R1 university signifies excellence in research, a high-caliber student experience, and the ability to help lead New Mexico into the future. Understanding the importance of this designation as an active and caring community member is important, so that we can identify ways to partner and help our state in ways meaningful to all of us.

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Old Dominion University Earns Prestigious Research 1 Designation from Carnegie Classification of Institutions of Higher Education

By Amber Kennedy

Old Dominion University earned the Research 1 Classification, indicating "very high research activity," from the Carnegie Classification of Institutions of Higher Education, placing it among the highest level of research institutions in the United States. The Carnegie Classification® has been the leading measure of research activities at U.S. colleges and institutions for more than half a century.

The University joins a group of just 137, or 5%, of four-year research institutions with the R1 classification in the nation. The classification, produced this year by the Center for Postsecondary Research at Indiana University, is published every three years. Previously, ODU was ranked as a Research 2 institution for doctoral institutions with "high research activity."

The Research 1 designation represents a key step in recruiting high-quality faculty and students, obtaining prestigious and larger research grants, and attracting industry and government agency partners.

"This is a significant milestone for our campus community and a historic moment in our research efforts," said President Brian O. Hemphill, Ph.D. "ODU is truly honored to join the elite ranks of the nation's top-producing research institutions, which comprise less than 5% of all institutions. Our faculty are so deserving of this prestigious honor and the limitless opportunities and well-deserved recognition that come with it!"

Governor Ralph Northam, a Norfolk resident, celebrated the University's designation, stating, "I'm so excited to move back home and live next door to a leading research university. Congratulations to the whole ODU team - this is an important achievement!"

The Carnegie Commission on Higher Education developed the Carnegie Classification® in 1970 to support its program of research and policy analysis. The classification is considered the leading framework for describing the diversity of U.S. colleges and universities. The tool informs research, grant-making and funding decisions, and annual rankings, including those by U.S. News & World Report.

Universities with the R1 classification engage in the highest levels of research activity. To be considered R1, universities must meet benchmarks across 10 indicators, including research doctorates awarded, total research expenditures, the aggregate level of research activity and the number of research staff.

"ODU's designation as an R1 institution demonstrates its national standing as a top-tier research university," Virginia Secretary of Education Fran Bradford said. "I am pleased that ODU has become the latest Virginia member of that elite academic club and thankful for all that ODU does for innovation and workforce development in Hampton Roads and the commonwealth."

Over the past decade, the number of scientific publications from ODU faculty has doubled, and over the past five years external funding for applied research projects has tripled, contributing to $69 million annually in overall research expenditures. More than half of ODU faculty have been involved in externally funded research projects, which have grown by 22% in total expenditures in the last decade. In the same period, ODU doubled the size of its nonfaculty Ph.D. research staff.

ODU has nationally known research strengths in coastal resilience, modeling and simulation, bioelectrics, port logistics and maritime engineering, and cybersecurity. ODU is a key academic partner for the Thomas Jefferson National Accelerator Facility (JLab), NASA Langley Research Center, NASA Wallops Flight Facility, the National Oceanic and Atmospheric Administration and the Department of Defense, among other agencies.

"ODU's strategy has been to focus on research that is meaningful to people living and working in Hampton Roads that also is applicable globally, such as in maritime domains," Vice President for Research Morris Foster said. "R1 designation recognizes the national significance of that local strategy."

Provost and Vice President for Academic Affairs Austin Agho noted the classification could be attributed to the hard work and dedication of the ODU community.

"This accomplishment represents the collective efforts of our faculty, scientists, students and staff to further ODU's mission as a leading public doctoral research institution," he said. "Being named a Research 1 institution signals ODU is well-positioned to support research faculty, provide robust opportunities to undergraduates and graduate students, and work with partners to find innovative answers to complex and challenging questions."

The new classification, released Dec. 15, is undergoing a review and comment period that concludes at the end of January, when the classifications become official. The 2021 update will be the final administered by the IU Center for Postsecondary Research, now transferring responsibility to Albion College in Michigan.

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Module 1: Introduction: What is Research?

Learning Objectives

By the end of this module, you will be able to:

• Explain how the scientific method is used to develop new knowledge
• Describe why it is important to follow a research plan

The Scientific Method consists of observing the world around you and creating a  hypothesis  about relationships in the world. A hypothesis is an informed and educated prediction or explanation about something. Part of the research process involves testing the  hypothesis , and then examining the results of these tests as they relate to both the hypothesis and the world around you. When a researcher forms a hypothesis, this acts like a map through the research study. It tells the researcher which factors are important to study and how they might be related to each other or caused by a  manipulation  that the researcher introduces (e.g. a program, treatment or change in the environment). With this map, the researcher can interpret the information he/she collects and can make sound conclusions about the results.

Research can be done with human beings, animals, plants, other organisms and inorganic matter. When research is done with human beings and animals, it must follow specific rules about the treatment of humans and animals that have been created by the U.S. Federal Government. This ensures that humans and animals are treated with dignity and respect, and that the research causes minimal harm.

No matter what topic is being studied, the value of the research depends on how well it is designed and done. Therefore, one of the most important considerations in doing good research is to follow the design or plan that is developed by an experienced researcher who is called the  Principal Investigator  (PI). The PI is in charge of all aspects of the research and creates what is called a  protocol  (the research plan) that all people doing the research must follow. By doing so, the PI and the public can be sure that the results of the research are real and useful to other scientists.

Module 1: Discussion Questions

• How is a hypothesis like a road map?
• Who is ultimately responsible for the design and conduct of a research study?
• How does following the research protocol contribute to informing public health practices?

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At North Dakota State University, research shapes students’ futures, generates new discoveries and builds economic opportunity for the region. With more than $100 million in research expenditures annually, NDSU’s research frequently places it in lists of top 100 research universities. NDSU’s highly qualified and skilled faculty, staff and student researchers conduct basic and applied research across many disciplines to advance scientific knowledge, while collaborating with industry and government on cutting-edge projects.

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What Is Research, and Why Do People Do It?

• Open Access
• First Online: 03 December 2022

Cite this chapter

You have full access to this open access chapter

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

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

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Abstractspiepr Abs1

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

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

Part I. What Is Research?

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

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

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

Exercise 1.1

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

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

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

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

Exercise 1.2

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

Creating an Image of Scientific Inquiry

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

Descriptor 1. Experience Carefully Planned in Advance

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

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

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

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

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

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

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

Exercise 1.3

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

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

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

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

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

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

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

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

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

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

Doing Scientific Inquiry

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

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

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

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

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

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

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

Exercise 1.4

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

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

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

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

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

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

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

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

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

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

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

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

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

Exercise 1.5

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

Exercise 1.6

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

Learning from Doing Scientific Inquiry

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

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

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

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

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

Part II. Why Do Educators Do Research?

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

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

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

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

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

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

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

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

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

Exercise 1.7

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

Part III. Conducting Research as a Practice of Failing Productively

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

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

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

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

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

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

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

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

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

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

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

Exercise 1.8

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

Exercise 1.9

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

Part IV. Preview of Chap. 2

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

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

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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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Basic Classification

The Basic Classification is an update of the traditional classification framework developed by the Carnegie Commission on Higher Education in the early 1970s to support its research program. The Basic Classification was originally published for public use in 1973, and subsequently updated in 1976, 1987, 1994, 2000, 2005, 2010, 2015, 2018, and 2021. The 2021 update included only minor changes. Specifically, the label “Tribal Colleges” has been changed back to “Tribal Colleges and Universities.” In addition, there is a new category “Special Focus Research Institution,” comprised of the special focus institutions that meet the criteria for being considered a “Research University” but confer degrees in a limited range of academic programs. Additionally, the special focus categories of “Engineering Schools” and “Other Technology-Related Schools” have been combined into a single category.

Basic Classification Methodology

View the flowchart illustrating the logic of the six all-inclusive Classifications.

DOCTORAL UNIVERSITIES

Includes institutions that awarded at least 20 research/scholarship doctoral degrees during the update year and also institutions with below 20 research/scholarship doctoral degrees that awarded at least 30 professional practice doctoral degrees in at least 2 programs. Excludes Special Focus Institutions and Tribal Colleges and Universities.

The first two categories include only institutions that awarded at least 20 research/scholarship doctoral degrees and had at least $5 million in total research expenditures (as reported through the National Science Foundation (NSF) Higher Education Research & Development Survey (HERD)).

Institutions were included in these categories if they awarded at least 20 research/scholarship doctorates in 2019-20 or awarded at least 30 professional practice doctorates across at least 2 programs. These categories were limited to institutions that were not identified as Tribal Colleges and Universities or Special Focus Institutions.

Institutions that conferred at least 20 research/scholarship doctorates in 2019-20 and reported at least $5 million in total research expenditures in FY20 were assigned to one of two categories based on a measure of research activity. The research activity index includes the following correlates of research activity: research & development (R&D) expenditures in science and engineering; R&D expenditures in non-S&E fields; S&E research staff (postdoctoral appointees and other non-faculty research staff with doctorates); doctoral conferrals in humanities, social science, STEM (science, technology, engineering, and mathematics) fields, and in other fields (e.g., business, education, public policy, social work). The mapping of doctoral degrees to these four disciplinary clusters is documented in this  Excel file . These data were statistically combined using principal components analysis to create two indices of research activity reflecting the total variation across these measures (based on the first principal component in each analysis).

One index represents the aggregate level of research activity, and the other captures per-capita research activity using the expenditure and staffing measures divided by the number of full-time faculty within the assistant, associate, and full professor ranks. The values on each index were then used to locate each institution on a two-dimensional graph. We calculated each institution's distance from a common reference point (the minima of each scale), and then used the results to assign institutions to one of two groups based on their distance from the reference point. Before conducting the analysis, raw data were converted to rank scores to reduce the influence of outliers and to improve discrimination at the lower end of the distributions where many institutions were clustered. Detailed information about how the research activity index was calculated can be found  here . A more detailed description of the methodology is available  here .

Doctoral degree conferrals by field were based on IPEDS Completions data reporting 2019-20 degree conferrals. Faculty counts were from the IPEDS (HR) Full-time instructional staff by academic rank, faculty and tenure status, Fall 2020. R&D expenditures came from the NSF Higher Education Research and Development (HERD) Survey for fiscal year 2020. Research staff data came from the NSF Survey of Graduate Students and Postdoctorates in Science and Engineering for Fiscal Year 2018-19. These were the most current and complete data available at the time of our analysis, and we judged currency to be more important than temporal alignment of all data sources.

As in prior years, although to a lesser extent, there were some cases in which the NSF data were reported at a higher level of aggregation than is needed for classification purposes (i.e., a university system comprising multiple campuses that are distinct entities for classification purposes, but that are reported together as a single entity in the NSF data). We used the proportion of research/doctoral degrees conferred by campus as a proxy for allocating the expenditures across campuses. For the staffing data, where aggregate reporting was more slightly more common, we used the proportionate distribution of expenditures data to allocate staffing among multiple institutions reported as a single entity on the NSF research staffing survey.

Search by Classification

R1: Doctoral Universities – Very high research activity

R2: Doctoral Universities – High research activity

D/PU: Doctoral/Professional Universities

MASTER’S COLLEGES AND UNIVERSITIES

Generally includes institutions that awarded at least 50 master’s degrees and fewer than 20 doctoral degrees during the update year (with occasional exceptions – see Methodology). Excludes Special Focus Institutions and Tribal Colleges and Universities.

Institutions were included in these categories if they awarded at least 50 master's and/or doctoral degrees in 2019-20, but fewer than 20 research doctorates (as defined above). Some institutions with smaller master's programs were also included (see below). These categories were limited to institutions that were not identified as Tribal Colleges and Universities or Special Focus Institutions.

Master's program size was based on the number of master's and/or doctoral degrees awarded in 2019-20. Those awarding at least 200 degrees were included among larger programs; those awarding 100–199 were included among medium programs; and those awarding 50–99 were included among smaller programs. The smaller programs group also includes institutions that awarded fewer than 50 master's degrees if (a) their Enrollment Profile classification is Exclusively Graduate/Professional or (b) their Enrollment Profile classification is Majority Graduate/Professional and they awarded more graduate/professional degrees than undergraduate degrees.

Some institutions that were initially classified among Master's Colleges and Universities were reclassified or given the option of classification among Baccalaureate Colleges based on their overall profile (see Exception below).

M1: Master’s Colleges and Universities – Larger programs

M2: Master’s Colleges and Universities – Medium programs

M3: Master’s Colleges and Universities – Small programs

BACCALAUREATE COLLEGES

Includes institutions where baccalaureate or higher degrees represent at least 50 percent of all degrees but where fewer than 50 master’s degrees or 20 doctoral degrees were awarded during the update year. (Some institutions above the master’s degree threshold are also included; see Exception.) Excludes Special Focus Institutions and Tribal Colleges and Universities. The formal expression of these classifications is (Classification):(Subset). For example: Baccalaureate Colleges: Diverse Fields.

Institutions were included in these categories if bachelor's degrees accounted for at least 50 % of all degrees awarded and they awarded fewer than 50 master's degrees (2019-20 degree conferrals). In addition, these categories were limited to institutions that were not identified as Tribal Colleges and Universities or as Special Focus Institutions.

Institutions in which at least half of bachelor's degree majors were in arts and sciences fields were included in the "Arts & Sciences" group, while the remaining institutions were included in the "Diverse Fields" group.

The analysis of major field of study is based on degree conferral data (IPEDS Completions). Up to two majors can be reported, and both were considered for this analysis. Thus for an institution with 1,000 bachelor's degree recipients, half of whom completed double majors, the analysis would consider all 1,500 majors. The mapping of fields of study to arts & sciences or professions is documented in this  Ex cel  file.

As in the past, some institutions that had been classified among Master's Colleges and Universities or Doctoral/Professional Universities are given the option of classification among Baccalaureate Colleges based on their overall profile. These institutions met the following criteria:

• FTE enrollment of fewer than 4,000 students
• Highly residential (Size & Setting classification)
• Enrollment Profile classification of Very high undergraduate or High undergraduate, combined with No graduate coexistence or Some graduate coexistence (Undergraduate Instructional Program classification)
• Enrollment Profile classification of Majority undergraduate combined with No graduate coexistence.

We continue to provide this choice and used previous requests for exception to determine which institutions were granted this exception.

Arts & Sciences Focus

Diverse Fields

BACCALAUREATE/ASSOCIATE’S COLLEGES

Includes four-year colleges, by virtue of having at least one baccalaureate degree program, that conferred more than 50 percent of degrees at the associate’s level (but excluding special focus institutions, Tribal Colleges and Universities, and institutions that have sufficient master’s or doctoral degrees to fall into those categories). These institutions are divided into two subcategories: Mixed Baccalaureate/Associate’s Colleges are those that conferred more than 10% of degrees at the baccalaureate level or higher (fewer than 90% associate’s degrees); Associate’s Dominant institutions are those that conferred fewer than 10% of degrees at the baccalaureate level or higher (at least 90% associate’s degrees).

Mixed Baccalaureate/Associate’s Colleges

Associate’s Dominant

ASSOCIATE’S COLLEGES

Institutions at which the highest level of degree awarded is an associate’s degree. The institutions are sorted into nine categories based on the intersection of two factors: disciplinary focus (transfer, career & technical or mixed) and dominant student type (traditional, nontraditional or mixed). Excludes Special Focus Institutions and Tribal Colleges and Universities. The formal expression of these classifications is (Classification):(Subset). For example: Associate’s Colleges: Mixed Transfer/Career & Technical-Mixed Traditional/Nontraditional.

For institutions that conferred associate degrees as the highest degree level offering, we used the methodology introduced with the 2015 update. First, the institutions were separated according to whether their total awards (associate degrees and certificates), were primarily in one or a few disciplinary fields. These fields were identified according to the first two digits of the CIP* code. Institutions that meet the criteria of being a special focus institutions (see section below) were classified into one of four Special Focus groups noted below. Two-year institutions not designated as special focus were classified according to the combination of two factors, each divided into three groups (3x3=9 categories total): program mix and student mix.

Because IPEDS does not capture information regarding type of associate degree conferred (e.g., AA, AS, AAA or AAS), we use the field of study for awarded associate's degrees and longer term certificates (at least 1 but less than 2 years) as a proxy measure to categorize institutions into one of three program mix groups: high transfer, mixed transfer/career-technical, and high career-technical. This designation was derived starting with the arts & sciences and professions distinction used for classifying baccalaureate colleges. The "professional" disciplines are then further distinguished according to the percent of awards nationally, awarded at the associate degree or less than two-year certificate levels. The CIP codes were then further reviewed and additional ones changed from professional to career & technical if they were in a sequence where the majority were career & technical, or if it was obvious that the field of study was associated with employment opportunities that did not require a higher credential. The detailed disciplinary designations are available in an Excel spreadsheet.

Institutions in which 35.7% or less of awards were in career & technical disciplines were designated as having a high transfer program mix. Those with at least 53.8% of programs in such disciplines were considered as having a high career & technical program mix. Institutions between which 35.7% and 53.8% of awards were in career & technical fields were categorized as mixed transfer/career & technical program mix. This categorization is based on the rationale that the career & technical programs are designed to provide the award recipient with a credential for immediate employment within that field. Awards in other fields (professional and arts & sciences) generally require further education to obtain employment requiring a postbaccalaureate or higher credential in the field. We recognize that many associate's colleges offer awards in the specific field, "Liberal Arts & Sciences, General Studies or Humanities" as a "Transfer Degree." However, in this classification, we take a broader view of transfer preparation to include fields in which the terminal associate's degree or more than one-year but less than two-year certificate is not sufficient for employment in positions within the field that require a baccalaureate or higher credential. In creating this classification, we recognize that individual states and individual institutions vary in their policies and practices as to whether such non-career-technical fields (by our definition) prepare students for transfer to a four-year institution.

Student mix, within this classification, is determined by a combination of the proportion of total enrollment accounted for by "degree-seeking" students (as opposed to "non-degree" students), and the ratio of fall headcount to annual unduplicated headcount. Specifically, we multiply these two ratios and designate as "high traditional" student focus those institutions for which the product is greater than 0.628. Institutions for which the product is lower than 0.533 are designated as "high non-traditional," and the remaining institutions are designated as "mixed traditional/nontraditional." The student mix index was created by examining the distribution of students within these institutions across the stated variables as well as percent part-time students and the percent students age 25 or older. The derived factor was selected due the comprehensiveness of data availability and the distributional properties that allowed for identification of cutoff points for groups of three roughly equivalent numbers of institutions.

HIGH TRANSFER

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• Mixed Traditional/Nontraditional
• High Nontraditional

MIXED TRANSFER/CAREER & TECHNICAL

SPECIAL FOCUS INSTITUTIONS

Institutions where a high concentration of degrees is in a single field or set of related fields. Excludes Tribal Colleges and Universities. The formal expression of these classifications is (Classification):(Subset). For example:  Special Focus Two-Year: Technical Professions.

The special-focus designation was based on the concentration of degrees in a single field or set of related fields, at both the undergraduate and graduate levels. Institutions were determined to have a special focus if they met any of the following conditions:

• Conferred at least 75% of degrees in just one field (as determined by the first two digits of the CIP Code) other than "Liberal Arts & Sciences, General Studies or Humanities" (CIP2=24) and did not confer degrees in any more than 6 different CIP2 categories
• Conferred 70-74% in one field and conferred degrees in no more than 2 other CIP2 categories.
• Conferred 60-69% in one field and conferred degrees in no more than 1 other CIP2 category.

We also examined institutions’ past classifications to determine if the special focus designation was still appropriate. Institution web sites also were consulted to determine the nature of the institution’s mission and program mix.

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MTSU Mondays: Geosciences heats up climate research; Daniels Center honors 9/11 survivor grad

Here's the latest news from Middle Tennessee State University .

Middle Tennessee State University geosciences faculty recently landed a National Science Foundation grant to make the critical, yet sometimes overwhelming issue of climate change more concrete and relatable to their undergraduate students.

Alisa Hass, assistant professor, collaborated with Mark Abolins, professor, to win the $260,000 NSF grant and develop the research project involving around 300 students wearing iButton temperature and humidity sensors, known academically as “hygrochrons,” and analyzing the resulting data of their climates.

“Climate change is something that affects all of us, and this student population represents the people who are going to be dealing with it and hopefully helping to solve it,” said Hass, who has an extensive background in researching the effects of heat on different groups. “It can feel like such an intangible, big topic, so we came up with the temperature sensor project to help them see how they’re being directly affected by their personal climates.”

Abolins, whose background is in caves, first learned about the grant and brought it to Hass, sparked by his additional interest in undergraduate education innovation.

“It seemed like a great fit and opportunity to get her (Hass) and her climate research work more support,” Abolins said. “She’s done a lot of this heat impact work with a smaller sample size (on average about 30 participants), and this was an opportunity to not only get a large number of MTSU students involved for their own educational benefit, but it also intersected with furthering the understanding of climate, heat and humidity and how people experience them.”

Additionally, Hass and Abolins tapped colleagues Brittany Price, assistant professor, and Jeremy Aber, professor, to conduct the project with their general education science students as well, plus the grant offered the opportunity to bring on a graduate research assistant. Hass added Sean Sanders, a geoscience master’s student, to their team. 

“This project is helping me gain relevant work experience because my main job doesn’t involve research,” Sanders said. “Now I can say I’ve used industry-specific programs, and I’m part of a project that can help these students see what types of climates they are exposed to and how it can affect their physical health.”

Hass said the Office of Research and Sponsored Programs helped the team with grant submissions and negotiations, and that the team’s project rollout this spring and initial responses from students capturing and observing their “microclimate” data have been going well so far.

Hass and her team will repeat the activity with their students next fall and spring semesters and plan to launch the project in local community colleges next year as well.

Learn more about opportunities at the Department of Geosciences at https://mtsu.edu/geosciences/ . Learn more about the opportunities and support at the Office of Sponsored and Research Programs at https://mtsu.edu/research/ .

9/11 Pentagon survivor, hero earns Daniels Center leadership award

U.S. Air Force veteran Bill Lickman not only survived the 9/11 terrorist attack on the Pentagon in Washington, D.C., his actions led thousands of Pentagon staff members to safety, and he later received a Purple Heart for injuries he received that day.

Nearly 23 years later, the Murfreesboro resident graduated from MTSU with a bachelor’s in video and film production and received the Veteran Leadership Award recently during the spring Graduating Veterans Stole Ceremony at Miller Education Center.

Stole ceremonies have become a tradition for the Charlie and Hazel Daniels Veterans and Military Family Center. Fifty student veterans attended the recent ceremony, where they received special red stoles to wear during May 3-4 commencement ceremonies in Murphy Center.

“It’s appreciated,” Lickman, 45, said of the award given to a graduating student veteran who has demonstrated superior leadership, academic achievement and selfless service to MTSU and the Daniels Center community. “I’ve tried to make a point to not just be a student that goes to class and then goes home. I engage in community. I understand the importance of community and networking.”

Lickman has been totally immersed at MTSU: production manager and highlight camera operator for MTSU’s ESPN+ sports broadcasts, a photographer for Sidelines, the student newspaper, social media manager for MTSU’s student-run television production company and Student Government Association veteran senator.

Retiring after 23 years in the Air Force, Lickman was one of four USAF Joint Staff Military Security Forces members on duty during the 9/11 attack. After helping people evacuate, he returned to the burning building to protect critical facilities and senior Department of Defense leadership including Secretary of Defense Donald Rumsfeld.

MTSU Mondays content is provided by submissions from MTSU News and Media Relations .

This article originally appeared on Murfreesboro Daily News Journal: MTSU Mondays: Geosciences heats up climate research; Daniels Center honors 9/11 survivor grad

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Volume 52, Issue 8, 8 May 2024

Critical steps in the assembly process of the bacterial 50s ribosomal subunit.

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The diversity of splicing modifiers acting on A -1 bulged 5′-splice sites reveals rules for rational drug design

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Discovering DNA shape motifs with multiple DNA shape features: generalization, methods, and validation

Chromatin damage generated by DNA intercalators leads to degradation of RNA Polymerase II

Differential expression of paralog RNA binding proteins establishes a dynamic splicing program required for normal cerebral cortex development

Activation of zinc uptake regulator by zinc binding to three regulatory sites

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The effect of pseudoknot base pairing on cotranscriptional structural switching of the fluoride riboswitch

Identifying human pre-mRNA cleavage and polyadenylation factors by genome-wide CRISPR screens using a dual fluorescence readthrough reporter

Determinants of CRISPR Cas12a nuclease activation by DNA and RNA targets

Molecular basis of A. thaliana KEOPS complex in biosynthesizing tRNA t 6 A

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RNA polymerase SI3 domain modulates global transcriptional pausing and pause-site fluctuations

Temperature-sensitive splicing defects in Arabidopsis mitochondria caused by mutations in the ROOT PRIMORDIUM DEFECTIVE 1 gene

All exons are not created equal—exon vulnerability determines the effect of exonic mutations on splicing

RIP-seq reveals RNAs that interact with RNA polymerase and primary sigma factors in bacteria

Ribosomal collision is not a prerequisite for ZNF598-mediated ribosome ubiquitination and disassembly of ribosomal complexes by ASCC

Landscape of RNA pseudouridylation in archaeon Sulfolobus islandicus

The crystal structure of bacteriophage λ RexA provides novel insights into the DNA binding properties of Rex-like phage exclusion proteins

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Protein G-quadruplex interactions and their effects on phase transitions and protein aggregation

Structure-functional characterization of Lactococcus AbiA phage defense system

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Correction to ‘somamutdb: a database of somatic mutations in normal human tissues’, correction to ‘pot-3 preferentially binds the terminal dna-repeat on the telomeric g-overhang’, live-cell imaging of human apurinic/apyrimidinic endonuclease 1 in the nucleus and nucleolus using a chaperone@dna probe.

txtools: an R package facilitating analysis of RNA modifications, structures, and interactions

ORBIT for E. coli : kilobase-scale oligonucleotide recombineering at high throughput and high efficiency

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A gene long thought to just raise the risk for Alzheimer’s may cause some cases

FILE - A section of a human brain with Alzheimer’s disease is displayed at the Museum of Neuroanatomy at the University at Buffalo, in Buffalo, N.Y., Oct. 7, 2003. A long-feared gene appears to do more than raise people’s risk of Alzheimer’s: Inheriting two copies can cause the mind-robbing disease, according to research published in the journal Nature Medicine on Monday, May 6, 2024. (AP Photo/David Duprey, File)

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WASHINGTON (AP) — For the first time, researchers have identified a genetic form of late-in-life Alzheimer’s disease — in people who inherit two copies of a worrisome gene.

Scientists have long known a gene called APOE4 is one of many things that can increase people’s risk for Alzheimer’s, including simply getting older. The vast majority of Alzheimer’s cases occur after age 65. But research published Monday suggests that for people who carry not one but two copies of the gene, it’s more than a risk factor, it’s an underlying cause of the mind-robbing disease.

The findings mark a distinction with “profound implications,” said Dr. Juan Fortea, who led the study the Sant Pau Research Institute in Barcelona, Spain.

Among them: Symptoms can begin seven to 10 years sooner than in other older adults who develop Alzheimer’s.

An estimated 15% of Alzheimer’s patients carry two copies of APOE4, meaning those cases “can be tracked back to a cause and the cause is in the genes,” Fortea said. Until now, genetic forms of Alzheimer’s were thought to be only types that strike at much younger ages and account for less than 1% of all cases.

Scientists say the research makes it critical to develop treatments that target the APOE4 gene. Some doctors won’t offer the only drug that has been shown to modestly slow the disease, Leqembi, to people with the gene pair because they’re especially prone to a dangerous side effect, said Dr. Reisa Sperling, a study coauthor at Harvard-affiliated Brigham and Women’s Hospital in Boston.

Sperling hunts ways to prevent or at least delay Alzheimer’s and “this data for me says wow, what an important group to be able to go after before they become symptomatic.”

But the news doesn’t mean people should race for a gene test. “It’s important not to scare everyone who has a family history” of Alzheimer’s because this gene duo isn’t behind most cases, she told The Associated Press.

HOW DO GENETICS AFFECT ALZHEIMER’S?

More than 6 million Americans, and millions more worldwide, have Alzheimer’s. A handful of genes are known to cause rare “early-onset” forms, mutations passed through families that trigger symptoms unusually young, by age 50. Some cases also are linked to Down syndrome.

But Alzheimer’s most commonly strikes after 65, especially in the late 70s to 80s, and the APOE gene – which also affects how the body handles fats -- was long known to play some role. There are three main varieties. Most people carry the APOE3 variant that appears to neither increase nor decrease Alzheimer’s risk. Some carry APOE2, which provides some protection against Alzheimer’s.

APOE4 has long been labeled the biggest genetic risk factor for late-in-life Alzheimer’s, with two copies risker than one. About 2% of the global population is estimated to have inherited a copy from each parent.

RESEARCH POINTS TO A CAUSE FOR A SUBSET OF ALZHEIMER’S

To better understand the gene’s role, Fortea’s team used data from 3,297 brains donated for research and from over 10,000 people in U.S. and European Alzheimer’s studies. They examined symptoms and early hallmarks of Alzheimer’s such as sticky amyloid in the brain.

People with two APOE4 copies were accumulating more amyloid at age 55 than those with just one copy or the “neutral” APOE3 gene variety, they reported in the journal Nature Medicine. By age 65, brain scans showed significant plaque buildup in nearly three-quarters of those double carriers – who also were more likely to have initial Alzheimer’s symptoms around that age rather than in the 70s or 80s.

Fortea said the disease’s underlying biology was remarkably similar to young inherited types.

It appears more like “a familial form of Alzheimer’s,” said Dr. Eliezer Masliah of the National Institute on Aging. “It is not just a risk factor.”

Importantly, not everyone with two APOE4 genes develops Alzheimer’s symptoms and researchers need to learn why, Sperling cautioned.

“It’s not quite destiny,” she said.

HOW THE NEW FINDINGS MAY AFFECT ALZHEIMER’S RESEARCH AND TREATMENT

The drug Leqembi works by clearing away some sticky amyloid but Sperling said it’s not clear if carriers of two APOE4 genes benefit because they have such a high risk of a side effect from the drug – dangerous brain swelling and bleeding. One research question is whether they’d do better starting such drugs sooner than other people.

Masliah said other research aims to develop gene therapy or drugs to specifically target APOE4. He said it’s also crucial to understand APOE4’s effects in diverse populations since it’s been studied mostly in white people of European ancestry.

As for gene tests, for now they’re typically used only to evaluate if someone’s a candidate for Leqembi or for people enrolling in Alzheimer’s research – especially studies of possible ways to prevent the disease. Sperling said the people most likely to carry two APOE4 genes had parents who both got Alzheimer’s relatively early, in their 60s rather than 80s.

The Associated Press Health and Science Department receives support from the Howard Hughes Medical Institute’s Science and Educational Media Group. The AP is solely responsible for all content.

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Key takeaways

• J.P. Morgan Research forecasts that the GLP-1 market will exceed $100 bn by 2030, driven equally by diabetes and obesity usage.
• Total GLP-1 users in the U.S. may number 30 mn by 2030 — or around 9% of the overall population.
• The increasing appetite for obesity drugs will have myriad implications, boosting sectors such as biotech and creating headwinds for industries such as food and beverage.

Ozempic. Wegovy. Mounjaro. Zepbound. Originally developed to treat diabetes, these GLP-1 agonists — now also popularly known as obesity drugs — have been making headlines for their weight-loss effects. According to the Centers for Disease Control and Prevention (CDC), the prevalence of obesity in the U.S. has grown from 30.5% over 1999–2000 to 41.9% over 2017–2020.

What’s whetting the consumer appetite for these weight-loss drugs, and what does this mean for sectors ranging from biotech to food? 

“The newest generations of GLP-1s and combos lead to 15–25+% weight loss on average, well above prior generations of products.”

Chris Schott

Senior Analyst covering the U.S. Diversified Biopharma sector, J.P. Morgan

What are GLP-1 agonists?  

Glp-1 agonists are a class of medications used to treat type 2 diabetes (t2d). besides helping to lower blood sugar levels, they also suppress appetite and reduce calorie intake — fueling their growing popularity as obesity drugs. , “glp-1s have been used to treat t2d since 2005, starting with the approval of byetta, with follow-on products continually improving on efficacy. the most recent, ozempic and mounjaro, offer significant advantages over previous products and have accelerated class growth,” said chris schott, a senior analyst covering the u.s. diversified biopharma sector at j.p. morgan. “indeed, the newest generations of glp-1s and combos lead to 15–25+% weight loss on average, well above prior generations of products.” .

What’s driving the increase in appetite for obesity drugs?

Originally developed to treat diabetes, GLP-1 agonists — or obesity drugs — have risen in popularity thanks to their weight-loss effects.

J.P. Morgan Research forecasts the GLP-1 market will exceed $100 bn by 2030, fueled equally by diabetes and obesity usage.

Total GLP-1 users in the U.S. may number 30 mn by 2030 — or around 9% of the overall population.

This could lead to a paradigm shift in health care and also impact other sectors, from biotech to food.

What’s the market for obesity drugs?

J.p. morgan research forecasts the glp-1 category will exceed $100 bn by 2030, driven equally by diabetes and obesity usage. , today, glp-1s are used by around 10-12% of t2d patients in the u.s. “we model glp-1 usage expanding to around 35% of diabetics in the u.s. in 2030 and would not be surprised to see upside to this number, especially as outcomes data continues to emerge,” schott noted. “in addition, we forecast that around 15 mn obese patients will be on glp-1s by the end of the decade.” overall, total glp-1 users in the u.s. may number 30 mn by 2030 — or around 9% of the population. , the glp-1 landscape is currently dominated by two major players: u.s.-based eli lilly and denmark-based novo nordisk. “we expect the obesity market to largely be a duopoly between both companies, with modest share attributed to later entrants,” schott said. “while demand could continue to outstrip supply for the next several years, we do see these issues resolving in the longer term with more plants coming online and more competitive oral options becoming available.” , the u.s. obesity market is expanding rapidly .

The U.S. obesity market is forecast to reach $44 bn in 2030 — up from just $0.5 bn in 2020.

Sector implications

What this means for … health care.

The growing popularity of GLP-1s could transform how obesity is viewed and managed. “We believe this marks the beginning of a paradigm shift in the way that obesity is treated, with physicians moving to a weight-centric treatment of multiple co-morbidities associated with the condition. We expect this to drive substantial uptake of GLP-1s,” said Richard Vosser, Head of European Pharma & Biotech at J.P. Morgan. For instance, GLP-1s may aid in the management of cardiovascular disease and heart failure, which around 9 mn obese patients suffer from. 

Likewise, the rise of GLP-1s will shape diabetes treatment. “In diabetes, we see growth of GLP-1s driven by a shift in medical guidelines, including those proposed by the American Diabetes Association and European Association for the Study of Diabetes, which place weight management and assessment of co-morbidities profile on par with glycaemic control,” Vosser added. 

What this means for … biotech

With the GLP-1 market proving to be highly lucrative, new biotech firms will seek to enter the drug race. “Naturally, with a class so potentially unprecedentedly large, we expect many biotechs across the market cap range to be motivated to participate. Even capturing a small share of such a large market could be very interesting for some companies, and these efforts may also lead to attractive partnering opportunities,” said Jessica Fye, a Senior Analyst covering the Large-Cap Biotechnology sector at J.P. Morgan. 

There is also scope for biotech firms to explore, through clinical trials, how certain medications work in tandem with GLP-1s. “For example, we believe companies investigating drugs targeting certain cardiovascular indications may want to consider planning to generate data that includes patients on GLP-1s,” said Anupam Rama, a Senior Analyst covering the U.S. Biotechnology sector at J.P. Morgan. “All in all, we see GLP-1s as an exciting category, with biotechs angling for a slice of the pie.” 

What this means for … medtech

How will GLP-1s impact other technologies used to treat diabetes and obesity? Despite the recent fall in medtech share prices, J.P. Morgan Research does not see an imminent threat to devices such as insulin pumps and continuous glucose monitors (CGMs). “In fact, we anticipate CGM utilization could increase as they will be vital to track progress and determine if GLP-1s are actually working,” said Robbie Marcus, a Senior Analyst covering the U.S. Medical Supplies & Devices sector at J.P. Morgan. “Plus, the weight loss benefit from CGMs, while not quite comparable to that of GLP-1s, is nevertheless material, especially considering how much more affordable they are.” 

Weight loss surgery will also continue to be in demand due to its superior and more sustainable clinical outcomes. “Even those patients who opt for drugs will most likely still undergo bariatric surgery down the line due to the low adherence rates and high recurrence of weight gain from GLP-1s,” Marcus noted. 

Overall, the outlook is still positive for the medtech industry. “With medtech now trading at a slight discount to the S&P 500 vs. a 15–25% historical premium, we think a reasonable amount of GLP-1 risk is already priced in,” Marcus said. “While GLP-1s will be a huge drug class, medtech volumes can also increase over time. We think both can live side by side and don’t see them as mutually exclusive.” 

What this means for … insurance

In the U.S., many insurance providers are scaling back on coverage of GLP-1s due to the high costs involved. As of November 2023, a month’s supply of Zepbound is priced around $1,060, while a month’s supply of Wegovy is around $1,350.

However, J.P. Morgan Research expects coverage to eventually improve, especially for obesity treatment. “Coverage for obesity currently far lags that for T2D, and this will likely remain the biggest debate in the class for some time,” Schott noted. “We estimate current coverage at only around 40%, but this will likely reach the 80% range by the end of the decade, driven by a series of outcomes studies that we expect will show broad health benefits from losing weight.”

In the life insurance space, companies that cover mortality risk will benefit the most from GLP-1s to the extent that the treatment of diabetes, obesity and related co-morbidities translates to longer life spans for the insured population. “In financial terms, higher life expectancies would allow life insurers to earn more premium income and higher investment income on reserves, as mortality claims are deferred,” said Jimmy Bhullar, Head of the U.S. Insurance research team at J.P. Morgan. 

On the other hand, GLP-1s could have a negative impact on life insurers that cover longevity risk through products such as structured settlements and pension risk transfer (PRT) plans, or lapse-supported policies such as long-term care and universal life with secondary guarantees (ULSG), where insurer economics deteriorate the longer the policy stays in force. “This is because longer life spans would translate to more benefits paid in the future,” Bhullar noted. 

What this means for … food and beverage

GLP-1s could have a significant impact on food and beverage consumption. The advent of GLP-1 use for appetite suppression has been a key factor in the median larger-cap U.S. food producers underperforming the S&P 500 by nearly 40% year to date. “We have seen a number of trends and possible disruptions come and go in consumer staples over the years, but never one quite like GLP-1s,” said Ken Goldman, Lead Equity Research Analyst for the U.S. Food Producers and Food Retailers sectors at J.P. Morgan. 

Using data from alternative data provider Numerator, J.P. Morgan Research has found that current GLP-1 users purchased around 8% less food — including snacks, soft drinks and high=carb products — for at-home consumption over the last 12 months compared with the average consumer. Food intake could decrease by  -3% in North America by 2030E, though the figure could be higher for packaged foods. While European food companies derive up to 30–40% of their sales in North America, many of them have broad category and regional exposures, mitigating potential headwinds.

“Overall, we think that if GLP-1s start to make a meaningful difference in consumption patterns, grocers will be hurt less than packaged food companies,” Goldman said. “This is especially as they sell a lot of higher-margin fresh food, which could offset much of the impact on the center store and snacking in particular.” 

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ChadTough gala raises $1.7 million for pediatric brain cancer research

• Updated: May. 13, 2024, 11:40 a.m. |
• Published: May. 13, 2024, 11:34 a.m.

The ChadTough Defeat DIPG Foundation 2024 gala co-chair Aidan Hutchinson (middle, orange jacket) and his parents Melissa (third from left) and Chris (far right), along with other guests. (Photo: ChadTough Defeat DIPG Foundation) ChadTough Defeat DIPG Foundation

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ANN ARBOR -- Jake Rudock’s lone season as Michigan’s quarterback, in 2015, was the same year Chad Carr died of brain cancer.

The 5-year-old grandson of former Michigan football coach Lloyd Carr stopped by practice a few times that season, with the team rallying around the youngster whose time on Earth was limited.

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Maine’s first research satellite to launch this year

Maine’s first small research satellite, designed to engage middle and high school students in STEM, is expected to launch into orbit within the next couple of months. 

Firefly Aerospace, a company based in Cedar Rapids, Texas, integrated the nearly 1 foot tall satellite, known as MESAT1, into a rocket to be launched from the Vandenberg Space Force Base in Southern California. The integration was conducted inside Firefly’s clean room in Vandenberg by Joseph Patton, a Ph.D. student at the University of Maine Department of Electrical and Computer Engineering, and Ali Abedi, principal investigator for the project and professor of electrical and computer engineering. 

An exact day and time for the launch will be announced at a later date. 

“Integrating the satellite into the dispenser on board the launch vehicle is the most important milestone before launch,” said Abedi, who also serves as associate vice president of research at UMaine. “This signifies culmination of five years of research and development, building, testing, licensing and flight certification.”

Once launched into Earth’s low orbit, MESAT1 will orbit from six months to two years, providing students and teachers in Maine access to space data for educational and research purposes. Developers hope access to satellite data will encourage students to pursue STEM careers. 

“Working on the MESAT1 project has been a challenging and educational opportunity, and it’s exciting to finally get to launch the spacecraft into orbit,” Patton said. 

The satellite is equipped with three cubic-shaped payloads designed by students in Falmouth High School, Fryeburg Academy and Saco Middle School who are conducting individual experiments. Equipped with four cameras, the satellite will offer data for these experiments by periodically taking pictures of Earth and sending them back to the ground station at UMaine, where the satellite will be controlled. 

The payload from Saco Middle School, dubbed ALBEDO, will investigate the impact of albedo — the fraction of solar irradiation reflected back into space — on local temperature. The goal is to compare temperature and albedo across urban and rural areas and determine whether urban heat islands can be mitigated through architectural designs that maximize albedo. 

The third payload from Falmouth High School, HAB, will study harmful algal blooms to see if they increase atmospheric temperature and water vapor levels in the atmosphere above them. Developing the capacity to monitor and identify algal blooms from orbit will provide a simple way to track the development, distribution and dispersion of blooms. 

The projects from Falmouth High School, Fryeburg Academy and Saco Middle School were chosen from 11 proposals submitted during a statewide competition for schools hosted by the Maine Space Grant Consortium in 2019. 

MESAT1 is one of 18 small research satellites selected by NASA to carry auxiliary payloads into space as part of its CubeSat Launch Initiative. The program provides opportunities for nanosatellite science and technology payloads built by universities, schools and nonprofit organizations to rideshare on space launches. 

The satellite is one of several projects from the UMaine Space Initiative, which brings together faculty, administrators, staff and students to advance Maine’s space-based economy and help build a skilled workforce in the field. Additionally, efforts are underway to develop a new Maine SpacePort Complex to develop, manufacture and launch nanosatellites into polar orbit. The complex will include an Innovation Hub with research, development and manufacturing facilities. 

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