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Science, health, and public trust.

September 8, 2021

Explaining How Research Works

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

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

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

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

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

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

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

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

Below are some additional resources:

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

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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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Educational resources and simple solutions for your research journey

What is Research? Definition, Types, Methods, and Examples

Academic research is a methodical way of exploring new ideas or understanding things we already know. It involves gathering and studying information to answer questions or test ideas and requires careful thinking and persistence to reach meaningful conclusions. Let’s try to understand what research is.   

Table of Contents

Why is research important?    

Whether it’s doing experiments, analyzing data, or studying old documents, research helps us learn more about the world. Without it, we rely on guesswork and hearsay, often leading to mistakes and misconceptions. By using systematic methods, research helps us see things clearly, free from biases. (1)   

What is the purpose of research?  

In the real world, academic research is also a key driver of innovation. It brings many benefits, such as creating valuable opportunities and fostering partnerships between academia and industry. By turning research into products and services, science makes meaningful improvements to people’s lives and boosts the economy. (2)(3)  

What are the characteristics of research?    

The research process collects accurate information systematically. Logic is used to analyze the collected data and find insights. Checking the collected data thoroughly ensures accuracy. Research also leads to new questions using existing data.   

Accuracy is key in research, which requires precise data collection and analysis. In scientific research, laboratories ensure accuracy by carefully calibrating instruments and controlling experiments. Every step is checked to maintain integrity, from instruments to final results. Accuracy gives reliable insights, which in turn help advance knowledge.   

Types of research    

The different forms of research serve distinct purposes in expanding knowledge and understanding:    

• Exploratory research ventures into uncharted territories, exploring new questions or problem areas without aiming for conclusive answers. For instance, a study may delve into unexplored market segments to better understand consumer behaviour patterns.   
• Descriptive research delves into current issues by collecting and analyzing data to describe the behaviour of a sample population. For instance, a survey may investigate millennials’ spending habits to gain insights into their purchasing behaviours.   
• Explanatory research, also known as causal research, seeks to understand the impact of specific changes in existing procedures. An example might be a study examining how changes in drug dosage over some time improve patients’ health.   
• Correlational research examines connections between two sets of data to uncover meaningful relationships. For instance, a study may analyze the relationship between advertising spending and sales revenue.   
• Theoretical research deepens existing knowledge without attempting to solve specific problems. For example, a study may explore theoretical frameworks to understand the underlying principles of human behaviour.   
• Applied research focuses on real-world issues and aims to provide practical solutions. An example could be a study investigating the effectiveness of a new teaching method in improving student performance in schools.  (4)

Types of research methods

• Qualitative Method: Qualitative research gathers non-numerical data through interactions with participants. Methods include one-to-one interviews, focus groups, ethnographic studies, text analysis, and case studies. For example, a researcher interviews cancer patients to understand how different treatments impact their lives emotionally.    
• Quantitative Method: Quantitative methods deal with numbers and measurable data to understand relationships between variables. They use systematic methods to investigate events and aim to explain or predict outcomes. For example, Researchers study how exercise affects heart health by measuring variables like heart rate and blood pressure in a large group before and after an exercise program. (5)  

Basic steps involved in the research process    

Here are the basic steps to help you understand the research process:   

• Choose your topic: Decide the specific subject or area that you want to study and investigate. This decision is the foundation of your research journey.   
• Find information: Look for information related to your research topic. You can search in journals, books, online, or ask experts for help.   
• Assess your sources: Make sure the information you find is reliable and trustworthy. Check the author’s credentials and the publication date.   
• Take notes: Write down important information from your sources that you can use in your research.   
• Write your paper: Use your notes to write your research paper. Broadly, start with an introduction, then write the body of your paper, and finish with a conclusion.   
• Cite your sources: Give credit to the sources you used by including citations in your paper.   
• Proofread: Check your paper thoroughly for any errors in spelling, grammar, or punctuation before you submit it. (6)

How to ensure research accuracy?  

Ensuring accuracy in research is a mix of several essential steps:    

• Clarify goals: Start by defining clear objectives for your research. Identify your research question, hypothesis, and variables of interest. This clarity will help guide your data collection and analysis methods, ensuring that your research stays focused and purposeful.   
• Use reliable data: Select trustworthy sources for your information, whether they are primary data collected by you or secondary data obtained from other sources. For example, if you’re studying climate change, use data from reputable scientific organizations with transparent methodologies.   
• Validate data: Validate your data to ensure it meets the standards of your research project. Check for errors, outliers, and inconsistencies at different stages, such as during data collection, entry, cleaning, or analysis.    
• Document processes: Documenting your data collection and analysis processes is essential for transparency and reproducibility. Record details such as data collection methods, cleaning procedures, and analysis techniques used. This documentation not only helps you keep track of your research but also enables others to understand and replicate your work.   
• Review results: Finally, review and verify your research findings to confirm their accuracy and reliability. Double-check your analyses, cross-reference your data, and seek feedback from peers or supervisors. (7) 

Research is crucial for better understanding our world and for social and economic growth. By following ethical guidelines and ensuring accuracy, researchers play a critical role in driving this progress, whether through exploring new topics or deepening existing knowledge.   

References:  

• Why is Research Important – Introductory Psychology – Washington State University  
• The Role Of Scientific Research In Driving Business Innovation – Forbes  
• Innovation – Royal Society  
• Types of Research – Definition & Methods – Bachelor Print  
• What Is Qualitative vs. Quantitative Study? – National University  
• Basic Steps in the Research Process – North Hennepin Community College  
• Best Practices for Ensuring Data Accuracy in Research – LinkedIn  

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Masks Strongly Recommended but Not Required in Maryland, Starting Immediately

Due to the downward trend in respiratory viruses in Maryland, masking is no longer required but remains strongly recommended in Johns Hopkins Medicine clinical locations in Maryland. Read more .

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Understanding Clinical Trials

Clinical research: what is it.

Your doctor may have said that you are eligible for a clinical trial, or you may have seen an ad for a clinical research study. What is clinical research, and is it right for you?

Clinical research is the comprehensive study of the safety and effectiveness of the most promising advances in patient care. Clinical research is different than laboratory research. It involves people who volunteer to help us better understand medicine and health. Lab research generally does not involve people — although it helps us learn which new ideas may help people.

Every drug, device, tool, diagnostic test, technique and technology used in medicine today was once tested in volunteers who took part in clinical research studies.

At Johns Hopkins Medicine, we believe that clinical research is key to improve care for people in our community and around the world. Once you understand more about clinical research, you may appreciate why it’s important to participate — for yourself and the community.

What Are the Types of Clinical Research?

There are two main kinds of clinical research:

Observational Studies

Observational studies are studies that aim to identify and analyze patterns in medical data or in biological samples, such as tissue or blood provided by study participants.

Clinical Trials

Clinical trials, which are also called interventional studies, test the safety and effectiveness of medical interventions — such as medications, procedures and tools — in living people.

Clinical research studies need people of every age, health status, race, gender, ethnicity and cultural background to participate. This will increase the chances that scientists and clinicians will develop treatments and procedures that are likely to be safe and work well in all people. Potential volunteers are carefully screened to ensure that they meet all of the requirements for any study before they begin. Most of the reasons people are not included in studies is because of concerns about safety.

Both healthy people and those with diagnosed medical conditions can take part in clinical research. Participation is always completely voluntary, and participants can leave a study at any time for any reason.

“The only way medical advancements can be made is if people volunteer to participate in clinical research. The research participant is just as necessary as the researcher in this partnership to advance health care.” Liz Martinez, Johns Hopkins Medicine Research Participant Advocate

Types of Research Studies

Within the two main kinds of clinical research, there are many types of studies. They vary based on the study goals, participants and other factors.

Biospecimen studies

Healthy volunteer studies.

Clinical trials study the safety and effectiveness of interventions and procedures on people’s health. Interventions may include medications, radiation, foods or behaviors, such as exercise. Usually, the treatments in clinical trials are studied in a laboratory and sometimes in animals before they are studied in humans. The goal of clinical trials is to find new and better ways of preventing, diagnosing and treating disease. They are used to test:

Drugs or medicines

New types of surgery

Medical devices

New ways of using current treatments

New ways of changing health behaviors

New ways to improve quality of life for sick patients

 Goals of Clinical Trials

Because every clinical trial is designed to answer one or more medical questions, different trials have different goals. Those goals include:

Treatment trials

Prevention trials, screening trials, phases of a clinical trial.

In general, a new drug needs to go through a series of four types of clinical trials. This helps researchers show that the medication is safe and effective. As a study moves through each phase, researchers learn more about a medication, including its risks and benefits.

Is the medication safe and what is the right dose?   Phase one trials involve small numbers of participants, often normal volunteers.

Does the new medication work and what are the side effects?   Phase two trials test the treatment or procedure on a larger number of participants. These participants usually have the condition or disease that the treatment is intended to remedy.

Is the new medication more effective than existing treatments?  Phase three trials have even more people enrolled. Some may get a placebo (a substance that has no medical effect) or an already approved treatment, so that the new medication can be compared to that treatment.

Is the new medication effective and safe over the long term?   Phase four happens after the treatment or procedure has been approved. Information about patients who are receiving the treatment is gathered and studied to see if any new information is seen when given to a large number of patients.

“Johns Hopkins has a comprehensive system overseeing research that is audited by the FDA and the Association for Accreditation of Human Research Protection Programs to make certain all research participants voluntarily agreed to join a study and their safety was maximized.” Gail Daumit, M.D., M.H.S., Vice Dean for Clinical Investigation, Johns Hopkins University School of Medicine

Is It Safe to Participate in Clinical Research?

There are several steps in place to protect volunteers who take part in clinical research studies. Clinical Research is regulated by the federal government. In addition, the institutional review board (IRB) and Human Subjects Research Protection Program at each study location have many safeguards built in to each study to protect the safety and privacy of participants.

Clinical researchers are required by law to follow the safety rules outlined by each study's protocol. A protocol is a detailed plan of what researchers will do in during the study.

In the U.S., every study site's IRB — which is made up of both medical experts and members of the general public — must approve all clinical research. IRB members also review plans for all clinical studies. And, they make sure that research participants are protected from as much risk as possible.

Earning Your Trust

This was not always the case. Many people of color are wary of joining clinical research because of previous poor treatment of underrepresented minorities throughout the U.S. This includes medical research performed on enslaved people without their consent, or not giving treatment to Black men who participated in the Tuskegee Study of Untreated Syphilis in the Negro Male. Since the 1970s, numerous regulations have been in place to protect the rights of study participants.

Many clinical research studies are also supervised by a data and safety monitoring committee. This is a group made up of experts in the area being studied. These biomedical professionals regularly monitor clinical studies as they progress. If they discover or suspect any problems with a study, they immediately stop the trial. In addition, Johns Hopkins Medicine’s Research Participant Advocacy Group focuses on improving the experience of people who participate in clinical research.

Clinical research participants with concerns about anything related to the study they are taking part in should contact Johns Hopkins Medicine’s IRB or our Research Participant Advocacy Group .

Learn More About Clinical Research at Johns Hopkins Medicine

For information about clinical trial opportunities at Johns Hopkins Medicine, visit our trials site.

Video Clinical Research for a Healthier Tomorrow: A Family Shares Their Story

Clinical Research for a Healthier Tomorrow: A Family Shares Their Story

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))

19k Accesses

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 » Research – Types, Methods and Examples

Research – Types, Methods and Examples

Table of Contents

Definition:

Research refers to the process of investigating a particular topic or question in order to discover new information , develop new insights, or confirm or refute existing knowledge. It involves a systematic and rigorous approach to collecting, analyzing, and interpreting data, and requires careful planning and attention to detail.

History of Research

The history of research can be traced back to ancient times when early humans observed and experimented with the natural world around them. Over time, research evolved and became more systematic as people sought to better understand the world and solve problems.

In ancient civilizations such as those in Greece, Egypt, and China, scholars pursued knowledge through observation, experimentation, and the development of theories. They explored various fields, including medicine, astronomy, and mathematics.

During the Middle Ages, research was often conducted by religious scholars who sought to reconcile scientific discoveries with their faith. The Renaissance brought about a renewed interest in science and the scientific method, and the Enlightenment period marked a major shift towards empirical observation and experimentation as the primary means of acquiring knowledge.

The 19th and 20th centuries saw significant advancements in research, with the development of new scientific disciplines and fields such as psychology, sociology, and computer science. Advances in technology and communication also greatly facilitated research efforts.

Today, research is conducted in a wide range of fields and is a critical component of many industries, including healthcare, technology, and academia. The process of research continues to evolve as new methods and technologies emerge, but the fundamental principles of observation, experimentation, and hypothesis testing remain at its core.

Types of Research

Types of Research are as follows:

• Applied Research : This type of research aims to solve practical problems or answer specific questions, often in a real-world context.
• Basic Research : This type of research aims to increase our understanding of a phenomenon or process, often without immediate practical applications.
• Experimental Research : This type of research involves manipulating one or more variables to determine their effects on another variable, while controlling all other variables.
• Descriptive Research : This type of research aims to describe and measure phenomena or characteristics, without attempting to manipulate or control any variables.
• Correlational Research: This type of research examines the relationships between two or more variables, without manipulating any variables.
• Qualitative Research : This type of research focuses on exploring and understanding the meaning and experience of individuals or groups, often through methods such as interviews, focus groups, and observation.
• Quantitative Research : This type of research uses numerical data and statistical analysis to draw conclusions about phenomena or populations.
• Action Research: This type of research is often used in education, healthcare, and other fields, and involves collaborating with practitioners or participants to identify and solve problems in real-world settings.
• Mixed Methods Research : This type of research combines both quantitative and qualitative research methods to gain a more comprehensive understanding of a phenomenon or problem.
• Case Study Research: This type of research involves in-depth examination of a specific individual, group, or situation, often using multiple data sources.
• Longitudinal Research: This type of research follows a group of individuals over an extended period of time, often to study changes in behavior, attitudes, or health outcomes.
• Cross-Sectional Research : This type of research examines a population at a single point in time, often to study differences or similarities among individuals or groups.
• Survey Research: This type of research uses questionnaires or interviews to gather information from a sample of individuals about their attitudes, beliefs, behaviors, or experiences.
• Ethnographic Research : This type of research involves immersion in a cultural group or community to understand their way of life, beliefs, values, and practices.
• Historical Research : This type of research investigates events or phenomena from the past using primary sources, such as archival records, newspapers, and diaries.
• Content Analysis Research : This type of research involves analyzing written, spoken, or visual material to identify patterns, themes, or messages.
• Participatory Research : This type of research involves collaboration between researchers and participants throughout the research process, often to promote empowerment, social justice, or community development.
• Comparative Research: This type of research compares two or more groups or phenomena to identify similarities and differences, often across different countries or cultures.
• Exploratory Research : This type of research is used to gain a preliminary understanding of a topic or phenomenon, often in the absence of prior research or theories.
• Explanatory Research: This type of research aims to identify the causes or reasons behind a particular phenomenon, often through the testing of theories or hypotheses.
• Evaluative Research: This type of research assesses the effectiveness or impact of an intervention, program, or policy, often through the use of outcome measures.
• Simulation Research : This type of research involves creating a model or simulation of a phenomenon or process, often to predict outcomes or test theories.

Data Collection Methods

• Surveys : Surveys are used to collect data from a sample of individuals using questionnaires or interviews. Surveys can be conducted face-to-face, by phone, mail, email, or online.
• Experiments : Experiments involve manipulating one or more variables to measure their effects on another variable, while controlling for other factors. Experiments can be conducted in a laboratory or in a natural setting.
• Case studies : Case studies involve in-depth analysis of a single case, such as an individual, group, organization, or event. Case studies can use a variety of data collection methods, including interviews, observation, and document analysis.
• Observational research : Observational research involves observing and recording the behavior of individuals or groups in a natural setting. Observational research can be conducted covertly or overtly.
• Content analysis : Content analysis involves analyzing written, spoken, or visual material to identify patterns, themes, or messages. Content analysis can be used to study media, social media, or other forms of communication.
• Ethnography : Ethnography involves immersion in a cultural group or community to understand their way of life, beliefs, values, and practices. Ethnographic research can use a range of data collection methods, including observation, interviews, and document analysis.
• Secondary data analysis : Secondary data analysis involves using existing data from sources such as government agencies, research institutions, or commercial organizations. Secondary data can be used to answer research questions, without collecting new data.
• Focus groups: Focus groups involve gathering a small group of people together to discuss a topic or issue. The discussions are usually guided by a moderator who asks questions and encourages discussion.
• Interviews : Interviews involve one-on-one conversations between a researcher and a participant. Interviews can be structured, semi-structured, or unstructured, and can be conducted in person, by phone, or online.
• Document analysis : Document analysis involves collecting and analyzing written documents, such as reports, memos, and emails. Document analysis can be used to study organizational communication, policy documents, and other forms of written material.

Data Analysis Methods

Data Analysis Methods in Research are as follows:

• Descriptive statistics : Descriptive statistics involve summarizing and describing the characteristics of a dataset, such as mean, median, mode, standard deviation, and frequency distributions.
• Inferential statistics: Inferential statistics involve making inferences or predictions about a population based on a sample of data, using methods such as hypothesis testing, confidence intervals, and regression analysis.
• Qualitative analysis: Qualitative analysis involves analyzing non-numerical data, such as text, images, or audio, to identify patterns, themes, or meanings. Qualitative analysis can be used to study subjective experiences, social norms, and cultural practices.
• Content analysis: Content analysis involves analyzing written, spoken, or visual material to identify patterns, themes, or messages. Content analysis can be used to study media, social media, or other forms of communication.
• Grounded theory: Grounded theory involves developing a theory or model based on empirical data, using methods such as constant comparison, memo writing, and theoretical sampling.
• Discourse analysis : Discourse analysis involves analyzing language use, including the structure, function, and meaning of words and phrases, to understand how language reflects and shapes social relationships and power dynamics.
• Network analysis: Network analysis involves analyzing the structure and dynamics of social networks, including the relationships between individuals and groups, to understand social processes and outcomes.

Research Methodology

Research methodology refers to the overall approach and strategy used to conduct a research study. It involves the systematic planning, design, and execution of research to answer specific research questions or test hypotheses. The main components of research methodology include:

• Research design : Research design refers to the overall plan and structure of the study, including the type of study (e.g., observational, experimental), the sampling strategy, and the data collection and analysis methods.
• Sampling strategy: Sampling strategy refers to the method used to select a representative sample of participants or units from the population of interest. The choice of sampling strategy will depend on the research question and the nature of the population being studied.
• Data collection methods : Data collection methods refer to the techniques used to collect data from study participants or sources, such as surveys, interviews, observations, or secondary data sources.
• Data analysis methods: Data analysis methods refer to the techniques used to analyze and interpret the data collected in the study, such as descriptive statistics, inferential statistics, qualitative analysis, or content analysis.
• Ethical considerations: Ethical considerations refer to the principles and guidelines that govern the treatment of human participants or the use of sensitive data in the research study.
• Validity and reliability : Validity and reliability refer to the extent to which the study measures what it is intended to measure and the degree to which the study produces consistent and accurate results.

Applications of Research

Research has a wide range of applications across various fields and industries. Some of the key applications of research include:

• Advancing scientific knowledge : Research plays a critical role in advancing our understanding of the world around us. Through research, scientists are able to discover new knowledge, uncover patterns and relationships, and develop new theories and models.
• Improving healthcare: Research is instrumental in advancing medical knowledge and developing new treatments and therapies. Clinical trials and studies help to identify the effectiveness and safety of new drugs and medical devices, while basic research helps to uncover the underlying causes of diseases and conditions.
• Enhancing education: Research helps to improve the quality of education by identifying effective teaching methods, developing new educational tools and technologies, and assessing the impact of various educational interventions.
• Driving innovation: Research is a key driver of innovation, helping to develop new products, services, and technologies. By conducting research, businesses and organizations can identify new market opportunities, gain a competitive advantage, and improve their operations.
• Informing public policy : Research plays an important role in informing public policy decisions. Policy makers rely on research to develop evidence-based policies that address societal challenges, such as healthcare, education, and environmental issues.
• Understanding human behavior : Research helps us to better understand human behavior, including social, cognitive, and emotional processes. This understanding can be applied in a variety of settings, such as marketing, organizational management, and public policy.

Importance of Research

Research plays a crucial role in advancing human knowledge and understanding in various fields of study. It is the foundation upon which new discoveries, innovations, and technologies are built. Here are some of the key reasons why research is essential:

• Advancing knowledge: Research helps to expand our understanding of the world around us, including the natural world, social structures, and human behavior.
• Problem-solving: Research can help to identify problems, develop solutions, and assess the effectiveness of interventions in various fields, including medicine, engineering, and social sciences.
• Innovation : Research is the driving force behind the development of new technologies, products, and processes. It helps to identify new possibilities and opportunities for improvement.
• Evidence-based decision making: Research provides the evidence needed to make informed decisions in various fields, including policy making, business, and healthcare.
• Education and training : Research provides the foundation for education and training in various fields, helping to prepare individuals for careers and advancing their knowledge.
• Economic growth: Research can drive economic growth by facilitating the development of new technologies and innovations, creating new markets and job opportunities.

When to use Research

Research is typically used when seeking to answer questions or solve problems that require a systematic approach to gathering and analyzing information. Here are some examples of when research may be appropriate:

• To explore a new area of knowledge : Research can be used to investigate a new area of knowledge and gain a better understanding of a topic.
• To identify problems and find solutions: Research can be used to identify problems and develop solutions to address them.
• To evaluate the effectiveness of programs or interventions : Research can be used to evaluate the effectiveness of programs or interventions in various fields, such as healthcare, education, and social services.
• To inform policy decisions: Research can be used to provide evidence to inform policy decisions in areas such as economics, politics, and environmental issues.
• To develop new products or technologies : Research can be used to develop new products or technologies and improve existing ones.
• To understand human behavior : Research can be used to better understand human behavior and social structures, such as in psychology, sociology, and anthropology.

Characteristics of Research

The following are some of the characteristics of research:

• Purpose : Research is conducted to address a specific problem or question and to generate new knowledge or insights.
• Systematic : Research is conducted in a systematic and organized manner, following a set of procedures and guidelines.
• Empirical : Research is based on evidence and data, rather than personal opinion or intuition.
• Objective: Research is conducted with an objective and impartial perspective, avoiding biases and personal beliefs.
• Rigorous : Research involves a rigorous and critical examination of the evidence and data, using reliable and valid methods of data collection and analysis.
• Logical : Research is based on logical and rational thinking, following a well-defined and logical structure.
• Generalizable : Research findings are often generalized to broader populations or contexts, based on a representative sample of the population.
• Replicable : Research is conducted in a way that allows others to replicate the study and obtain similar results.
• Ethical : Research is conducted in an ethical manner, following established ethical guidelines and principles, to ensure the protection of participants’ rights and well-being.
• Cumulative : Research builds on previous studies and contributes to the overall body of knowledge in a particular field.

Advantages of Research

Research has several advantages, including:

• Generates new knowledge: Research is conducted to generate new knowledge and understanding of a particular topic or phenomenon, which can be used to inform policy, practice, and decision-making.
• Provides evidence-based solutions : Research provides evidence-based solutions to problems and issues, which can be used to develop effective interventions and strategies.
• Improves quality : Research can improve the quality of products, services, and programs by identifying areas for improvement and developing solutions to address them.
• Enhances credibility : Research enhances the credibility of an organization or individual by providing evidence to support claims and assertions.
• Enables innovation: Research can lead to innovation by identifying new ideas, approaches, and technologies.
• Informs decision-making : Research provides information that can inform decision-making, helping individuals and organizations make more informed and effective choices.
• Facilitates progress: Research can facilitate progress by identifying challenges and opportunities and developing solutions to address them.
• Enhances understanding: Research can enhance understanding of complex issues and phenomena, helping individuals and organizations navigate challenges and opportunities more effectively.
• Promotes accountability : Research promotes accountability by providing a basis for evaluating the effectiveness of policies, programs, and interventions.
• Fosters collaboration: Research can foster collaboration by bringing together individuals and organizations with diverse perspectives and expertise to address complex issues and problems.

Limitations of Research

Some Limitations of Research are as follows:

• Cost : Research can be expensive, particularly when large-scale studies are required. This can limit the number of studies that can be conducted and the amount of data that can be collected.
• Time : Research can be time-consuming, particularly when longitudinal studies are required. This can limit the speed at which research findings can be generated and disseminated.
• Sample size: The size of the sample used in research can limit the generalizability of the findings to larger populations.
• Bias : Research can be affected by bias, both in the design and implementation of the study, as well as in the analysis and interpretation of the data.
• Ethics : Research can present ethical challenges, particularly when human or animal subjects are involved. This can limit the types of research that can be conducted and the methods that can be used.
• Data quality: The quality of the data collected in research can be affected by a range of factors, including the reliability and validity of the measures used, as well as the accuracy of the data entry and analysis.
• Subjectivity : Research can be subjective, particularly when qualitative methods are used. This can limit the objectivity and reliability of the findings.
• Accessibility : Research findings may not be accessible to all stakeholders, particularly those who are not part of the academic or research community.
• Interpretation : Research findings can be open to interpretation, particularly when the data is complex or contradictory. This can limit the ability of researchers to draw firm conclusions.
• Unforeseen events : Unexpected events, such as changes in the environment or the emergence of new technologies, can limit the relevance and applicability of research findings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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What Makes a Good Research Study?

Find out what separates a solid research study from a so-so one..

Posted March 31, 2018

One day you read online that drinking coffee reduces the chances of having age-related memory decline . You start drinking coffee. The next month you read that drinking coffee increases your chances of having age-related memory decline. What gives? In this article, you will learn how to "decipher" research studies to figure out what a research study is really saying - and what it doesn't say. You'll also discover how to tell if the reporting on a particular study was accurate or not. Ask yourself the following six questions when looking at a research study. Keep in mind these are just six of the many factors that make up a "clean" study.

1. Did the study use a placebo , and were the staff blinded to treatment?

The brain is very susceptible to placebos. There is evidence that even when you tell study subjects (participants) that they are getting a placebo, they improve (Carvalho, et al., 2016). In pharmaceutical studies, the US Food and Drug Administration (FDA) requires pharmaceutical companies to do double-blind placebo-controlled studies. This means that the study subjects, the physicians dispensing the drug, and the clinicians rating the subjects' behavior don't know what subjects are getting - drug or placebo. This eliminates a lot of bias , and it helps show whether the drug actually works.

2. Was there a bogus/sham treatment?

A bogus/sham treatment is one in which subjects are given a treatment that looks very much like the real treatment, except for one major difference. The bogus/sham treatment doesn't actually provide the therapeutic part of the treatment. For example, some acupuncture studies use a sham/bogus treatment, such as a 2017 study by Ugurlu, et al. regarding acupuncture treatment for fibromyalgia .

Bogus/sham treatments, when compared to active treatments, help researchers discover whether the active treatment is what works, or the fact that people think they are getting the active treatment.

3. How many people were there in the study (N)?

Logic says the more people you have in a study, or the study's "N", the better chance you have of your study representing of the general population (the "generalizability" of a study). Let's say you're studying the effects of apple juice on ADHD symptoms, and you have a total N of ten people. By chance, seven of those ten people have severe ADHD, two have moderate ADHD, and one has mild ADHD. You now could throw off the results of your study because you have so many people with severe ADHD in the study. When you have more subjects, or a larger N, in a study, there is more of a chance that you would have people that have mild, moderate, and severe ADHD.

4. Were the study groups randomized?

A good study randomizes their subjects into the active treatment and placebo groups. This means that the subjects are in those particular groups by chance. This provides extra "backup" that the effects from a treatment were actually from that treatment, not from study staff bias.

5. Who conducted the research, and who is paying for it?

If the people that created a treatment are also testing a treatment, this is a concern. When you have a horse in the race, so to speak, it is more difficult to be unbiased. Another concern is if an entity with a vested interest in a particular study outcome is paying for that study. For example, if there is a study on the effectiveness of widgets, and the sole source of funding is Widgets are Wonderful, Inc., and the researchers are employees of Widgets are Wonderful, that study better have some seriously good methodology to help eliminate bias. Even better, an independent research group is funded by an organization without ties to the study outcome.

6. Was the article published in a refereed (peer-reviewed/scholarly) journal?

In a refereed journal, a manuscript is reviewed by other experts in the field before it is published as an article. The authors of the manuscript are not disclosed to the reviewers, in order to reduce possible bias. When we review manuscripts for a journal, there are three main categories: reject, meaning the article goes no further; accept, with revisions, meaning the authors must edit their article before resubmitting it for publication; and accept as written, which is rare, but once in a while there is a manuscript with such good research methodology and writing that no additional editing is needed.

When a journal is not refereed, the standard of inclusion into that journal is not as high. This means the quality of the research may not be up to the same standards. Look up the journal online to find out if it is a peer-reviewed journal.

If you don't have university access, you can at least access the abstracts of journal articles at Google Scholar . The abstract lets you know the study's methodology, the number of study subjects, the outcomes, and the author's conclusions.

You may also see the term "open-access" used to describe a journal. An open-access journal is one that users can freely access, without a subscription or fees. Some open-access journals are peer-reviewed, some are not.

Copyright 2018 Sarkis Media

Carvalho, C., Caetano, J. M., Cunha, L., Rebouta, P., Kaptchuk, T. J., & Kirsch, I. (2016). Open-label Placebo Treatment in Chronic Low Back Pain: A Randomized Controlled Trial. Pain, 157(12), 2766–2772. http://doi.org/10.1097/j.pain.0000000000000700

Uğurlu, F. G., Sezer, N., Aktekin, L., Fidan, F., Tok, F., & Akkuş, S. (2017). The effects of acupuncture versus sham acupuncture in the treatment of fibromyalgia: a randomized controlled clinical trial. Acta reumatologica portuguesa, (1).

Stephanie Moulton Sarkis, Ph.D., N.C.C., D.C.M.H.S., L.M.H.C ., is the author of Gaslighting: Recognize Manipulative and Emotionally Abusive People — and Break Free .

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What Are Cancer Research Studies?

What is cancer research and why is it important.

Research is the key to progress against cancer and is a complex process involving professionals from many fields. It is also thanks to the participation of people with cancer, cancer survivors, and healthy volunteers that any breakthroughs go on to improve treatment and care for those who need it.

Cancer research studies may lead to discoveries such as new drugs to treat cancer, new therapies to make symptoms less severe, or lifestyle changes to reduce the chances of getting cancer.

Cancer research may also address big picture questions like why cancer is more prevalent in certain populations or how doctors can make existing cancer detection tools more effective in health care settings.

These discoveries can help people with cancer and their caregivers live fuller lives.

Who should join cancer research studies?

When you choose to participate in a research study, you become a partner in scientific discovery. Your generous contribution can make a world of difference for people like you.

As scientists continue to conduct cancer research, anyone can consider joining a research study. The best research includes everyone, and everyone includes you.

Your unique experience with cancer is incredibly valuable and may help current and future generations lead healthier lives.

When more people of all different races, ethnicities, ages, genders, abilities, and backgrounds participate, more people benefit.

It is important for scientists to capture the full genetic diversity of human populations so that the lessons learned are applicable to everyone.

What are the types of cancer research studies?

See below for definitions on the four major types of research and their subtypes:

• basic research
• quality of life/supportive care
• natural history
• longitudinal
• population-based
• epidemiological research
• translational research

Basic Research

Basic cancer research studies explore the very laws of nature. Scientists learn how cancer cells grow and divide, for example, by growing and testing bacteria , viruses , fungi , animal cells, and human cells in a lab. Scientists also study, for example, the genes that make up tumors in mice and rats in the lab. These experiments help build the foundation for further discovery.

Why Participate in a Clinical Trial?

Get information on how to evaluate a clinical trial and what questions to ask.

Clinical Research

Clinical research involves the study of cancer in people. These cancer research studies are further broken down into two types: clinical trials and observational studies .

• Treatment trials test how safe and useful a new treatment or way of using existing treatments is for people with cancer. Test treatments may include drugs, approaches to surgery or radiation therapy , or combinations of treatments.
• Prevention trials are for people who do not have cancer but are at a high risk for developing cancer or for cancer coming back. Prevention clinical trials target lifestyle changes (doing something) or focus on certain nutrients or medicines (adding something).
• Screening trials test how effective screening tests are for healthy people. The goal of these trials is to discover screening tools or methods that reduce deaths from cancer by finding it earlier.
• Quality-of-life/supportive care tests aim to help people with cancer, as well as their family and loved ones, cope with side effects like pain, nutrition problems, nausea and vomiting , sleeping problems, and depression . These trials may involve drugs or activities like therapy and exercising.  

Find Observation Studies >

View a studies that are looking for people now.

• Natural history studies look at certain conditions in people with cancer or people who are at a high risk of developing cancer. Researchers often collect information about a person and their family medical history , as well as blood, saliva, and tumor samples. For example, a biomarker test may be used to get a genetic profile of a person’s cancer tissue. This may reveal how certain tumors change over the course of treatment .
• Longitudinal studies gather data on people or groups of people over time, often to see the result of a habit, treatment, or change. For example, two groups of people may be identified as those who smoke and those who do not. These two groups are compared over time to see whether one group is more likely to develop cancer than the other group.
• Population-based studies explore the causes of cancer, cancer trends, and factors that affect cancer care in specific populations. For example, a population-based study may explore the causes of a high cancer rate in a regional Native American population.

Epidemiological Research

Epidemiological research is the study of the patterns, causes, and effects of cancer in a group of people of a certain background. This research encompasses both observational population-based studies but also includes clinical epidemiological studies where the relationship between a population’s risk factors and treatments are tested.

Translational Research

Translational research is when cancer research moves across research disciplines, from basic lab research into clinical settings, and from clinical settings into everyday care. In turn, findings from clinical studies and population-based studies can inform basic cancer research. For example, data from the genetic profile of a tumor during an observational study may help scientists develop a clinical trial to test which drugs to prescribe to cancer patients with specific tumor genes.

Monica Bertagnolli, Director, NIH; former director, NCI; cancer survivor

Participation in Cancer Research Matters

I am so happy to have the opportunity to acknowledge the courage and generosity of an estimated 494,018 women who agreed to participate in randomized clinical trials with results reported between 1971 and 2018.

Their contributions showed that mammography can detect cancer at an early stage, that mastectomies and axillary lymph node dissections are not always necessary, that chemotherapy can benefit some people with early estrogen receptor–positive, progesterone receptor–positive, HER2-negative breast cancer but is not needed for all, and that hormonal therapy can prevent disease recurrence.

For just the key studies that produced these results, it took the strength and commitment of almost 500,000 women. I am the direct beneficiary of their contributions, and I am profoundly grateful.

The true number of brave souls contributing to this reduction in breast cancer mortality over the past 30 years? Many millions. These are our heroes.

— From NCI Director’s Remarks by then-NCI Director Monica M. Bertagnolli, M.D., at the American Society of Clinical Oncology Annual Meeting, June 3, 2023

Study vs. Research — What's the Difference?

Difference Between Study and Research

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UK researcher studies how cerebellum may influence thirst

LEXINGTON, Ky. (July 23, 2024) —  Water is crucial to human survival, composing about 60% of the body. It plays a vital role in cellular function, internal temperature regulation and organ health.

Without sufficient water, the body’s processes rapidly fail, leading to death within just a few days. Thirst is the brain’s warning sign of dehydration. But being consistently thirsty (or not thirsty at all) could be signs of other health problems.

A researcher at the University of Kentucky is among a group of scientists who are some of the first to find a new role for the cerebellum — a brain region traditionally associated with movement and balance — in regulating thirst.

Ila Mishra, Ph.D., an assistant professor in the Division of Endocrinology, Diabetes and Metabolism in the College of Medicine’s Department of Internal Medicine, was one of the lead authors on the study recently published in Nature Neuroscience .

This study was led by Atul Chopra, M.D., Ph.D., and his lab at Harrington Discovery Institute at University Hospitals in Cleveland, Ohio, where Mishra previously conducted this research before joining UK in 2023.

“The cerebellum has a unique structure and complex cells," said Mishra. "It’s fascinated researchers for centuries because it’s one of the most ancient brain regions in evolutionary terms.”

Recent studies have expanded understanding of the cerebellum, linking it to cognition, sensation, emotion and autonomic functions. This research highlights the cerebellum’s involvement in thirst regulation. Previously, this role was attributed to other brain regions responsible for sensing and regulating internal water balance.

“Our study shows that mice drink more water when the cerebellar neurons called Purkinje neurons, one of the very first neuronal types to be recognized and amongst the largest neurons of the brain, are activated by the hormone asprosin,” Mishra explained.

This study reports that activation of these neurons by asprosin resulted in immediate drinking behavior in mice and deletion of the asprosin receptor from these cells reduced water intake.

“Asprosin is a protein hormone that was discovered in 2016. It has been shown to activate the hypothalamic ‘hunger’ neurons called AgRP neurons,” said Mishra. “In 2022, we identified Ptprd as the neural receptor through which asprosin acts to stimulate appetite. It was interesting to see that this receptor, Ptprd is highly expressed in the cerebellum, but we just didn’t know what it does there.

“It is fascinating how asprosin affects both appetite and thirst, but via different brain pathways,” Mishra said. “While asprosin stimulates appetite via the AgRP neurons, its action on cerebellar Purkinje neurons triggers increased water intake.”

Researchers said targeting the Purkinje neuronal asprosin signaling pathway could become a potential therapeutic approach for treating thirst disorders like polydipsia (feeling extremely thirsty) and hypodipsia (not feeling thirst). However, the team said further study is needed to better understand what’s going on in the brain during this process.

“Our findings highlight a powerful and clinically relevant neural circuit for the modulation of thirst,” said Mishra.

The research team included scientists from the University of Kentucky, Case Western Reserve University, University Hospitals Cleveland Medical Center, Louisiana State University, Baylor College of Medicine, Texas Children’s Hospital and the University of Dayton.

As the state’s flagship, land-grant institution, the University of Kentucky exists to advance the Commonwealth. We do that by preparing the next generation of leaders — placing students at the heart of everything we do — and transforming the lives of Kentuckians through education, research and creative work, service and health care. We pride ourselves on being a catalyst for breakthroughs and a force for healing, a place where ingenuity unfolds. It's all made possible by our people — visionaries, disruptors and pioneers — who make up 200 academic programs, a $476.5 million research and development enterprise and a world-class medical center, all on one campus.   

In 2022, UK was ranked by Forbes as one of the “Best Employers for New Grads” and named a “Diversity Champion” by INSIGHT into Diversity, a testament to our commitment to advance Kentucky and create a community of belonging for everyone. While our mission looks different in many ways than it did in 1865, the vision of service to our Commonwealth and the world remains the same. We are the University for Kentucky.   

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How to Thrive as You Age

Rapamycin may slow aging. here's one way the drug will be tested.

Allison Aubrey

Anti-aging drug Rapamycin to prevent gum disease 

A generic drug that's used to treat transplant patients has been shown to extend the life span of some animals. Guido Mieth/Getty Images hide caption

A few years back, Matt Kaeberlein was diagnosed with a frozen shoulder. “It was really bad,” he recalls. He wasn’t sleeping well and couldn’t throw a ball due to the pain. His doctor recommended physical therapy, and told him that it may take a year to get better.

Feeling frustrated, he decided to try rapamycin. In recent years, some high-profile longevity scientists have started taking the drug in hopes of fending off age-related health problems. So far, it’s untested in people taking it for anti-aging, but rapamycin has been shown to extend the lifespan of mice .

“I decided to try it,” Kaeberlein says. It was his "first foray into biohacking,” and he was very pleased with what happened next. “Within two weeks, 50% of the pain was gone,” he says. And by the end of 10 weeks, he had regained range of motion and the pain was completely gone.

“And it hasn’t come back,” he says.

Kaeberlein is no stranger to rapamycin. He’s a biologist and co-founded the Dog Aging Project to study how rapamycin influences dogs’ healthspans. He’s also the former director of the Healthy Aging and Longevity Research Institute at the University of Washington.

Rapamycin was first approved by the FDA for use in transplant patients in the late 1990s. At high doses it suppresses the immune system. At low doses, Kaeberlein says it seems to help tamp down inflammation. It works by inhibiting a signaling pathway in the body called mTOR — which seems to be a key regulator of lifespan and aging.

Shots - Health News

Scientists can tell how fast you're aging. now, the trick is to slow it down.

The drug is not approved for pain or anti-aging, but some physicians prescribe rapamycin off-label with the aim of fending off age-related conditions. Kaeberlein and his colleagues surveyed about 300 of these patients, who take low doses, and many report benefits.

But anecdotes are no replacement for science. To figure out the risks and benefits of a drug, research is needed. And that's where a dentist comes in.

Dr. Jonathan An , at the University of Washington, has been granted FDA approval to test rapamycin in patients with gum disease — a common condition that tends to accelerate with age. When he treats patients with gum disease, he says there’s not much he can do beyond cleaning and removing the plaque — a buildup of bacteria. “All we’re doing is putting a bandage on,” he says. His goal is to find and treat the underlying cause of the disease.

There’s already some evidence from transplant patients that rapamycin may help improve oral health. And as part of the study, An and his collaborators will also measure changes in participants’ microbiomes and their biological clocks.

The study will enroll participants over the age of 50 who have gum disease. They will take the drug, at various doses, intermittently for 8 weeks. Then, An will be able to determine if the drug is safe and effective.

If rapamycin has a beneficial effect he says, it will help demonstrate that it’s possible to target the root cause of the disease. “It really comes down to targeting the biology of aging,” he says.

Dr. An thinks gum disease may be a kind of canary in the coalmine of age-related diseases. For instance, gum disease is linked to a higher risk of heart disease, and maybe dementia, too . Scientists say it’s possible that bacteria in the mouth linked to periodontal disease causes inflammation, which may cause a “cascade” of damage to blood vessels, leading to problems in the heart or brain.

“If we can target that underlying biology, we predict that it might address a lot of these other underlying conditions,” An says.

Rapamycin is a generic drug, so pharmaceutical companies have little incentive to fund new research. An and his collaborators have received a grant to conduct the trial, which could open the door to further studies to determine whether rapamycin can help prevent or slow down other age-related diseases.

Eric Verdin , a physician who heads the Buck Institute for Research on Aging, says his group is fundraising for more research on rapamycin. He says there are a lot of unanswered questions, for example “what is the effect of different concentrations in a single dose?” And he wants to look for a “molecular signature” in people taking rapamycin. He wants to know more about doses and intervals, since many doctors prescribing it off-label recommend cycling on and off the drug.

Researchers are also working on other drugs that may work in similar ways, and there’s a push for new drugs — or other interventions that target biological aging. There’s a new $100 million XPRIZE Healthspan competition , aimed at accelerating the research in the field supported by Hevolution and other funders.

For now, XPRIZE founder Peter Diamandis, a physician who writes about longevity, says he takes rapamycin. “I do six milligrams every Sunday night, so once a week," for three months, he explains. Then he takes a month off. "I believe that rapamycin — in the way I'm utilizing it — is safe and has more upside potential than downside,” he says.

Diamandis constantly monitors his body with many health metrics, and he acknowledges it’s hard to determine the effect of rapamycin given all the other things he does to stay healthy, including eating well, eliminating sugar, working out every day and prioritizing sleep.

His plan is to continue with healthy lifestyle habits while supporting research into interventions and strategies that can help people add more healthy years to their life.

A cheap drug may slow down aging. A study will determine if it works

Find Allison Aubrey on Instagram at  @allison.aubrey  and on X  @AubreyNPR .

This story was edited by Jane Greenhalgh

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Types of Study in Medical Research

Bernd röhrig.

1 MDK Rheinland-Pfalz, Referat Rehabilitation/Biometrie, Alzey

Jean-Baptist du Prel

2 Zentrum für Präventive Pädiatrie, Zentrum für Kinder- und Jugendmedizin, Mainz

Daniel Wachtlin

3 Interdisziplinäres Zentrum Klinische Studien (IZKS), Fachbereich Medizin der Universität Mainz

Maria Blettner

4 Institut für Medizinische Biometrie, Epidemiologie und Informatik (IMBEI), Johannes Gutenberg Universität Mainz

The choice of study type is an important aspect of the design of medical studies. The study design and consequent study type are major determinants of a study’s scientific quality and clinical value.

This article describes the structured classification of studies into two types, primary and secondary, as well as a further subclassification of studies of primary type. This is done on the basis of a selective literature search concerning study types in medical research, in addition to the authors’ own experience.

Three main areas of medical research can be distinguished by study type: basic (experimental), clinical, and epidemiological research. Furthermore, clinical and epidemiological studies can be further subclassified as either interventional or noninterventional.

Conclusions

The study type that can best answer the particular research question at hand must be determined not only on a purely scientific basis, but also in view of the available financial resources, staffing, and practical feasibility (organization, medical prerequisites, number of patients, etc.).

The quality, reliability and possibility of publishing a study are decisively influenced by the selection of a proper study design. The study type is a component of the study design (see the article "Study Design in Medical Research") and must be specified before the study starts. The study type is determined by the question to be answered and decides how useful a scientific study is and how well it can be interpreted. If the wrong study type has been selected, this cannot be rectified once the study has started.

After an earlier publication dealing with aspects of study design, the present article deals with study types in primary and secondary research. The article focuses on study types in primary research. A special article will be devoted to study types in secondary research, such as meta-analyses and reviews. This article covers the classification of individual study types. The conception, implementation, advantages, disadvantages and possibilities of using the different study types are illustrated by examples. The article is based on a selective literature research on study types in medical research, as well as the authors’ own experience.

Classification of study types

In principle, medical research is classified into primary and secondary research. While secondary research summarizes available studies in the form of reviews and meta-analyses, the actual studies are performed in primary research. Three main areas are distinguished: basic medical research, clinical research, and epidemiological research. In individual cases, it may be difficult to classify individual studies to one of these three main categories or to the subcategories. In the interests of clarity and to avoid excessive length, the authors will dispense with discussing special areas of research, such as health services research, quality assurance, or clinical epidemiology. Figure 1 gives an overview of the different study types in medical research.

Classification of different study types

*1 , sometimes known as experimental research; *2 , analogous term: interventional; *3 , analogous term: noninterventional or nonexperimental

This scheme is intended to classify the study types as clearly as possible. In the interests of clarity, we have excluded clinical epidemiology — a subject which borders on both clinical and epidemiological research ( 3 ). The study types in this area can be found under clinical research and epidemiology.

Basic research

Basic medical research (otherwise known as experimental research) includes animal experiments, cell studies, biochemical, genetic and physiological investigations, and studies on the properties of drugs and materials. In almost all experiments, at least one independent variable is varied and the effects on the dependent variable are investigated. The procedure and the experimental design can be precisely specified and implemented ( 1 ). For example, the population, number of groups, case numbers, treatments and dosages can be exactly specified. It is also important that confounding factors should be specifically controlled or reduced. In experiments, specific hypotheses are investigated and causal statements are made. High internal validity (= unambiguity) is achieved by setting up standardized experimental conditions, with low variability in the units of observation (for example, cells, animals or materials). External validity is a more difficult issue. Laboratory conditions cannot always be directly transferred to normal clinical practice and processes in isolated cells or in animals are not equivalent to those in man (= generalizability) ( 2 ).

Basic research also includes the development and improvement of analytical procedures—such as analytical determination of enzymes, markers or genes—, imaging procedures—such as computed tomography or magnetic resonance imaging—, and gene sequencing—such as the link between eye color and specific gene sequences. The development of biometric procedures—such as statistical test procedures, modeling and statistical evaluation strategies—also belongs here.

Clinical studies

Clinical studies include both interventional (or experimental) studies and noninterventional (or observational) studies. A clinical drug study is an interventional clinical study, defined according to §4 Paragraph 23 of the Medicines Act [Arzneimittelgesetz; AMG] as "any study performed on man with the purpose of studying or demonstrating the clinical or pharmacological effects of drugs, to establish side effects, or to investigate absorption, distribution, metabolism or elimination, with the aim of providing clear evidence of the efficacy or safety of the drug."

Interventional studies also include studies on medical devices and studies in which surgical, physical or psychotherapeutic procedures are examined. In contrast to clinical studies, §4 Paragraph 23 of the AMG describes noninterventional studies as follows: "A noninterventional study is a study in the context of which knowledge from the treatment of persons with drugs in accordance with the instructions for use specified in their registration is analyzed using epidemiological methods. The diagnosis, treatment and monitoring are not performed according to a previously specified study protocol, but exclusively according to medical practice."

The aim of an interventional clinical study is to compare treatment procedures within a patient population, which should exhibit as few as possible internal differences, apart from the treatment ( 4 , e1 ). This is to be achieved by appropriate measures, particularly by random allocation of the patients to the groups, thus avoiding bias in the result. Possible therapies include a drug, an operation, the therapeutic use of a medical device such as a stent, or physiotherapy, acupuncture, psychosocial intervention, rehabilitation measures, training or diet. Vaccine studies also count as interventional studies in Germany and are performed as clinical studies according to the AMG.

Interventional clinical studies are subject to a variety of legal and ethical requirements, including the Medicines Act and the Law on Medical Devices. Studies with medical devices must be registered by the responsible authorities, who must also approve studies with drugs. Drug studies also require a favorable ruling from the responsible ethics committee. A study must be performed in accordance with the binding rules of Good Clinical Practice (GCP) ( 5 , e2 – e4 ). For clinical studies on persons capable of giving consent, it is absolutely essential that the patient should sign a declaration of consent (informed consent) ( e2 ). A control group is included in most clinical studies. This group receives another treatment regimen and/or placebo—a therapy without substantial efficacy. The selection of the control group must not only be ethically defensible, but also be suitable for answering the most important questions in the study ( e5 ).

Clinical studies should ideally include randomization, in which the patients are allocated by chance to the therapy arms. This procedure is performed with random numbers or computer algorithms ( 6 – 8 ). Randomization ensures that the patients will be allocated to the different groups in a balanced manner and that possible confounding factors—such as risk factors, comorbidities and genetic variabilities—will be distributed by chance between the groups (structural equivalence) ( 9 , 10 ). Randomization is intended to maximize homogeneity between the groups and prevent, for example, a specific therapy being reserved for patients with a particularly favorable prognosis (such as young patients in good physical condition) ( 11 ).

Blinding is another suitable method to avoid bias. A distinction is made between single and double blinding. With single blinding, the patient is unaware which treatment he is receiving, while, with double blinding, neither the patient nor the investigator knows which treatment is planned. Blinding the patient and investigator excludes possible subjective (even subconscious) influences on the evaluation of a specific therapy (e.g. drug administration versus placebo). Thus, double blinding ensures that the patient or therapy groups are both handled and observed in the same manner. The highest possible degree of blinding should always be selected. The study statistician should also remain blinded until the details of the evaluation have finally been specified.

A well designed clinical study must also include case number planning. This ensures that the assumed therapeutic effect can be recognized as such, with a previously specified statistical probability (statistical power) ( 4 , 6 , 12 ).

It is important for the performance of a clinical trial that it should be carefully planned and that the exact clinical details and methods should be specified in the study protocol ( 13 ). It is, however, also important that the implementation of the study according to the protocol, as well as data collection, must be monitored. For a first class study, data quality must be ensured by double data entry, programming plausibility tests, and evaluation by a biometrician. International recommendations for the reporting of randomized clinical studies can be found in the CONSORT statement (Consolidated Standards of Reporting Trials, www.consort-statement.org ) ( 14 ). Many journals make this an essential condition for publication.

For all the methodological reasons mentioned above and for ethical reasons, the randomized controlled and blinded clinical trial with case number planning is accepted as the gold standard for testing the efficacy and safety of therapies or drugs ( 4 , e1 , 15 ).

In contrast, noninterventional clinical studies (NIS) are patient-related observational studies, in which patients are given an individually specified therapy. The responsible physician specifies the therapy on the basis of the medical diagnosis and the patient’s wishes. NIS include noninterventional therapeutic studies, prognostic studies, observational drug studies, secondary data analyses, case series and single case analyses ( 13 , 16 ). Similarly to clinical studies, noninterventional therapy studies include comparison between therapies; however, the treatment is exclusively according to the physician’s discretion. The evaluation is often retrospective. Prognostic studies examine the influence of prognostic factors (such as tumor stage, functional state, or body mass index) on the further course of a disease. Diagnostic studies are another class of observational studies, in which either the quality of a diagnostic method is compared to an established method (ideally a gold standard), or an investigator is compared with one or several other investigators (inter-rater comparison) or with himself at different time points (intra-rater comparison) ( e1 ). If an event is very rare (such as a rare disease or an individual course of treatment), a single-case study, or a case series, are possibilities. A case series is a study on a larger patient group with a specific disease. For example, after the discovery of the AIDS virus, the Center for Disease Control (CDC) in the USA collected a case series of 1000 patients, in order to study frequent complications of this infection. The lack of a control group is a disadvantage of case series. For this reason, case series are primarily used for descriptive purposes ( 3 ).

Epidemiological studies

The main point of interest in epidemiological studies is to investigate the distribution and historical changes in the frequency of diseases and the causes for these. Analogously to clinical studies, a distinction is made between experimental and observational epidemiological studies ( 16 , 17 ).

Interventional studies are experimental in character and are further subdivided into field studies (sample from an area, such as a large region or a country) and group studies (sample from a specific group, such as a specific social or ethnic group). One example was the investigation of the iodine supplementation of cooking salt to prevent cretinism in a region with iodine deficiency. On the other hand, many interventions are unsuitable for randomized intervention studies, for ethical, social or political reasons, as the exposure may be harmful to the subjects ( 17 ).

Observational epidemiological studies can be further subdivided into cohort studies (follow-up studies), case control studies, cross-sectional studies (prevalence studies), and ecological studies (correlation studies or studies with aggregated data).

In contrast, studies with only descriptive evaluation are restricted to a simple depiction of the frequency (incidence and prevalence) and distribution of a disease within a population. The objective of the description may also be the regular recording of information (monitoring, surveillance). Registry data are also suited for the description of prevalence and incidence; for example, they are used for national health reports in Germany.

In the simplest case, cohort studies involve the observation of two healthy groups of subjects over time. One group is exposed to a specific substance (for example, workers in a chemical factory) and the other is not exposed. It is recorded prospectively (into the future) how often a specific disease (such as lung cancer) occurs in the two groups ( figure 2a ). The incidence for the occurrence of the disease can be determined for both groups. Moreover, the relative risk (quotient of the incidence rates) is a very important statistical parameter which can be calculated in cohort studies. For rare types of exposure, the general population can be used as controls ( e6 ). All evaluations naturally consider the age and gender distributions in the corresponding cohorts. The objective of cohort studies is to record detailed information on the exposure and on confounding factors, such as the duration of employment, the maximum and the cumulated exposure. One well known cohort study is the British Doctors Study, which prospectively examined the effect of smoking on mortality among British doctors over a period of decades ( e7 ). Cohort studies are well suited for detecting causal connections between exposure and the development of disease. On the other hand, cohort studies often demand a great deal of time, organization, and money. So-called historical cohort studies represent a special case. In this case, all data on exposure and effect (illness) are already available at the start of the study and are analyzed retrospectively. For example, studies of this sort are used to investigate occupational forms of cancer. They are usually cheaper ( 16 ).

Graphical depiction of a prospective cohort study (simplest case [2a]) and a retrospective case control study (2b)

In case control studies, cases are compared with controls. Cases are persons who fall ill from the disease in question. Controls are persons who are not ill, but are otherwise comparable to the cases. A retrospective analysis is performed to establish to what extent persons in the case and control groups were exposed ( figure 2b ). Possible exposure factors include smoking, nutrition and pollutant load. Care should be taken that the intensity and duration of the exposure is analyzed as carefully and in as detailed a manner as possible. If it is observed that ill people are more often exposed than healthy people, it may be concluded that there is a link between the illness and the risk factor. In case control studies, the most important statistical parameter is the odds ratio. Case control studies usually require less time and fewer resources than cohort studies ( 16 ). The disadvantage of case control studies is that the incidence rate (rate of new cases) cannot be calculated. There is also a great risk of bias from the selection of the study population ("selection bias") and from faulty recall ("recall bias") (see too the article "Avoiding Bias in Observational Studies"). Table 1 presents an overview of possible types of epidemiological study ( e8 ). Table 2 summarizes the advantages and disadvantages of observational studies ( 16 ).

1 = slight; 2 = moderate; 3 = high; N/A, not applicable.

*Individual cases may deviate from this pattern.

Selecting the correct study type is an important aspect of study design (see "Study Design in Medical Research" in volume 11/2009). However, the scientific questions can only be correctly answered if the study is planned and performed at a qualitatively high level ( e9 ). It is very important to consider or even eliminate possible interfering factors (or confounders), as otherwise the result cannot be adequately interpreted. Confounders are characteristics which influence the target parameters. Although this influence is not of primary interest, it can interfere with the connection between the target parameter and the factors that are of interest. The influence of confounders can be minimized or eliminated by standardizing the procedure, stratification ( 18 ), or adjustment ( 19 ).

The decision as to which study type is suitable to answer a specific primary research question must be based not only on scientific considerations, but also on issues related to resources (personnel and finances), hospital capacity, and practicability. Many epidemiological studies can only be implemented if there is access to registry data. The demands for planning, implementation, and statistical evaluation for observational studies should be just as high for observational studies as for experimental studies. There are particularly strict requirements, with legally based regulations (such as the Medicines Act and Good Clinical Practice), for the planning, implementation, and evaluation of clinical studies. A study protocol must be prepared for both interventional and noninterventional studies ( 6 , 13 ). The study protocol must contain information on the conditions, question to be answered (objective), the methods of measurement, the implementation, organization, study population, data management, case number planning, the biometric evaluation, and the clinical relevance of the question to be answered ( 13 ).

Important and justified ethical considerations may restrict studies with optimal scientific and statistical features. A randomized intervention study under strictly controlled conditions of the effect of exposure to harmful factors (such as smoking, radiation, or a fatty diet) is not possible and not permissible for ethical reasons. Observational studies are a possible alternative to interventional studies, even though observational studies are less reliable and less easy to control ( 17 ).

A medical study should always be published in a peer reviewed journal. Depending on the study type, there are recommendations and checklists for presenting the results. For example, these may include a description of the population, the procedure for missing values and confounders, and information on statistical parameters. Recommendations and guidelines are available for clinical studies ( 14 , 20 , e10 , e11 ), for diagnostic studies ( 21 , 22 , e12 ), and for epidemiological studies ( 23 , e13 ). Since 2004, the WHO has demanded that studies should be registered in a public registry, such as www.controlled-trials.com or www.clinicaltrials.gov . This demand is supported by the International Committee of Medical Journal Editors (ICMJE) ( 24 ), which specifies that the registration of the study before inclusion of the first subject is an essential condition for the publication of the study results ( e14 ).

When specifying the study type and study design for medical studies, it is essential to collaborate with an experienced biometrician. The quality and reliability of the study can be decisively improved if all important details are planned together ( 12 , 25 ).

Acknowledgments

Translated from the original German by Rodney A. Yeates, M.A., Ph.D.

Conflict of interest statement

The authors declare that there is no conflict of interest in the sense of the International Committee of Medical Journal Editors.

Watch CBS News

Here's what a Sam Altman-backed basic income experiment found

By Megan Cerullo

Edited By Anne Marie Lee

Updated on: July 23, 2024 / 10:33 AM EDT / CBS News

A recent study on basic income, backed by OpenAI founder Sam Altman, shows that giving low-income people guaranteed paydays with no strings attached can lead to their working slightly less, affording them more leisure time. 

The study, which is one of the largest and most comprehensive of its kind, examined the impact of guaranteed income on recipients' health, spending, employment, ability to relocate and other facets of their lives.

Altman first announced his desire to fund the study in a 2016 blog post on startup accelerator Y Combinator's site.

Some of the questions he set out to answer about how people behave when they're given free cash included, "Do people sit around and play video games, or do they create new things? Are people happy and fulfilled?" according to the post. Altman, whose OpenAI is behind generative text tool ChatGPT, which threatens to take away some jobs, said in the blog post that he thinks technology's elimination of "traditional jobs"  could make universal basic income necessary in the future. 

How much cash did participants get?

For OpenResearch's Unconditional Cash Study , 3,000 participants in Illinois and Texas received $1,000 monthly for three years beginning in 2020. The cash transfers represented a 40% boost in recipients' incomes. The cash recipients were within 300% of the federal poverty level, with average incomes of less than $29,000. A control group of 2,000 participants received $50 a month for their contributions.

Basic income recipients spent more money, the study found, with their extra dollars going toward essentials like rent, transportation and food.

Researchers also studied the free money's effect on how much recipients worked, and in what types of jobs. They found that recipients of the cash transfers worked 1.3 to 1.4 hours less each week compared with the control group. Instead of working during those hours, recipients used them for leisure time. 

"We observed moderate decreases in labor supply," Eva Vivalt, assistant professor of economics at the University of Toronto and one of the study's principal investigators, told CBS MoneyWatch. "From an economist's point of view, it's a moderate effect." 

More autonomy, better health

Vivalt doesn't view the dip in hours spent working as a negative outcome of the experiment, either. On the contrary, according to Vivalt. "People are doing more stuff, and if the results say people value having more leisure time — that this is what increases their well-being — that's positive." 

In other words, the cash transfers gave recipients more autonomy over how they spent their time, according to Vivalt. 

"It gives people the choice to make their own decisions about what they want to do. In that sense, it necessarily improves their well-being," she said. 

Researchers expected that participants would ultimately earn higher wages by taking on better-paid work, but that scenario didn't pan out. "They thought that if you can search longer for work because you have more of a cushion, you can afford to wait for better jobs, or maybe you quit bad jobs," Vivalt said. "But we don't find any effects on the quality of employment whatsoever."

Uptick in hospitalizations

At a time when even Americans with insurance say they have trouble staying healthy because they struggle to afford care , the study results show that basic-income recipients actually increased their spending on health care services. 

Cash transfer recipients experienced a 26% increase in the number of hospitalizations in the last year, compared with the average control recipient. The average recipient also experienced a 10% increase in the probability of having visited an emergency department in the last year.

Researchers say they will continue to study outcomes of the experiment, as other cities across the U.S. conduct their own tests of the concept.

Megan Cerullo is a New York-based reporter for CBS MoneyWatch covering small business, workplace, health care, consumer spending and personal finance topics. She regularly appears on CBS News 24/7 to discuss her reporting.

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Which artificial sweetener is the safest choice?

There’s mounting evidence that artificial sweeteners may be linked to heart disease and other possible health risks. Scientists say the findings are far from definitive, however, with some leading researchers calling for better-designed clinical trials investigating the long-term health effects of sugar substitutes.

That’s why, in separate trials, researchers are actively working to get a clearer understanding of how artificial sweeteners affect blood glucose levels, gut microbiome health and the cardiovascular system. Some studies are beginning to compare the alternatives against each other, while others hope to learn how they affect the body compared to sugar.

As it is, it’s difficult for consumers to determine which sugar alternative carries the fewest health risks. Most of the research is observational , meaning it doesn’t prove cause and effect. In some cases, researchers looked at people who ate nonsugar sweeteners, analyzed their incidence of certain health risks like heart attacks or diabetes, then noted associations between the two.

All the widely consumed alternatives such as saccharin, aspartame, sucralose, stevia, xylitol and erythritol are approved by the Food and Drug Administration. They’re found in countless products including sports drinks, energy bars, yogurts, cereals, beverages, candies, baked goods and syrups.

Even with FDA approval, Dr. Dariush Mozaffarian, a cardiologist and professor of nutrition science and policy at Tufts University, said “they’re all potentially worrisome and all understudied.”

In recent research, cardiologist Dr. Stanley Hazen at the Cleveland Clinic found that the high concentrations of the sugar alcohol sweeteners xylitol and erythritol may cause the platelets in the blood to become more sticky and prone to clotting, in turn raising the risk of heart attack and stroke. The phenomenon is similar to what happens with high cholesterol, Hazen said. If they get big enough, the clots can block blood flow through crucial veins and arteries.

Some experts say that instead of trying to pinpoint the safest nonsugar sweetener, better studies need to determine whether there’s a benefit to swapping out sugar in the first place.

After publishing research finding a connection between erythritol and increased risk of heart attack and stroke , Hazen and his colleagues conducted the first head-to-head human trial comparing the effects of consuming erythritol versus sugar on the blood platelets that control clotting. The results of that study are pending publication.

Vasanti Malik, an assistant professor of nutritional sciences at the University of Toronto, meanwhile, is conducting a study of more than 500 people directly comparing the health effects of drinking sugar-sweetened beverages, noncaloric sweeteners or water. Malik and her colleagues plan to measure obesity and heart health over time.

At Virginia Tech, registered dietitian Valisa Hedrick is working with the National Institutes of Health on another study comparing the effects of four different artificial sweeteners versus sugar on blood glucose levels and gut microbiome health. The study, which focuses on people with prediabetes, is a controlled feeding trial, meaning participants only eat the meals that NIH provides them, and nothing more.

This is important, Hedrick said, because one of the growing concerns with nonsugar sweeteners is that the products trick the brain in such a way that they increase sugar cravings. People may then end up eating more sugar throughout the day, spiking their blood glucose.

With a controlled study, the researchers can answer whether the sweeteners themselves raise blood glucose directly — not the sugar people could otherwise eat later.

The limits of sweetener studies

A research bias called reverse causation can make it difficult to draw decisive conclusions from prior studies, Malik said.

People often change their diets after they start developing diabetes or putting on weight, Malik said. These people, generally, are most likely to switch from sugar to nonsugar sweeteners. This is where the reverse causation comes into play.

“You get a spurious association between the intake of nonsugar sweeteners and the risk for diabetes,” she said. That is, the data ends up suggesting that these sweeteners are causing health problems that already existed.

Many studies also rely on people to report whether they’ve consumed nonsugar substitutes, which can be unreliable. Names like xylitol can be buried in a long list of ingredients.

Other studies, meanwhile — like Hazen’s erythritol and xylitol studies — may focus directly on what happens in the body after someone consumes one of these sweeteners, but they tend to enroll small numbers of people and track them only for a short time.

“A lot of these studies are really hard to interpret,” said Dr. Michelle Pearlman, a gastroenterologist and the CEO and co-founder of the Prime Institute in Miami. “And the problem is that there’s no head-to-head trials of people eating candy bars versus xylitol, so I can’t make any blanket statements recommending one or the other.”

Both Hedrick and Malik hope to share results from their respective studies in the next several years.

“We need experimental science alongside more rigorous observational research,” Malik said. “There are trials underway, and I think in the next five years we’ll have more clarity on the topic. We’re just not quite there.”

In a statement, the Calorie Control Council, an industry trade group representing more than two dozen sweetener manufacturers, said studies linking alternative sweeteners to health risks are based on flawed research and that the products are safe.

“It is irresponsible to amplify faulty research to those who look to alternative sweeteners to reduce overall sugar intake as well as the millions who use it as a tool to manage their health conditions, including obesity and diabetes,” Carla Saunders, the trade group’s president, said in the statement.

Why it’s important to know

Most low-calorie and sugar-free foods contain at least one sugar substitute, and many contain several. These products are growing more popular, especially in the U.S. By 2033, market research suggests sugar substitutes could be worth more than $28.57 billion.

“They’re ubiquitous,” Mozafarrian said. “And they’re proliferating because people have become so obsessed with avoiding sugar.”

Mozaffarian said these sweeteners soared in popularity following changes to U.S. nutrition labeling requirements in 2016 .

The change required manufacturers to list added sugars on a separate line beneath total sugars. The idea was to help consumers differentiate between foods with naturally occurring sugars, like fruit and plain Greek yogurt, and foods that had sugars mixed in.

“Now, the food industry has a big incentive to make that ‘added sugars’ number as small as possible,” he said. “So you’re seeing these compounds in everything, and we still don’t have enough information on them.”

Some products are labeled as “artificial sweeteners” or “natural sweeteners” based on whether they’re derived from natural sources or chemically engineered.

Even natural sweeteners go through heavy chemical processing, said Dr. Maria Carolina Delgado-Lelievre, a cardiologist at the University of Miami.

For example, stevia comes from processed stevia plant extract, monk fruit sweetener comes from processing a chemical in a gourdlike fruit grown in China, and sucralose is a chemically altered version of sugar about 600 times sweeter, according to the FDA .

Aspartame and saccharin are from human-made fusions of amino acids and chemicals.

Many of these sweeteners are so potent in tiny quantities that they’re mixed with xylitol or erythritol to bulk them up and fill a packet, said the Cleveland Clinic’s Hazen.

Given this label confusion, Hedrick said researchers are increasingly using the term “nonsugar sweeteners.”

Health risks of added sugars

Sugar, of course, is one of the country’s most pressing public health problems. Especially in soda and juice, excess sugar fuels the ongoing obesity epidemic , contributing to heart disease, liver disease, cancer and diabetes .

However, there’s a big difference between processed, concentrated sugars like high-fructose corn syrup and the natural sugars found in fruits, Pearlman, the Miami gastroenterologist, said. Processed sugars are highly addictive.

“Anything with high-fructose corn syrup stimulates the same reward centers in our brain as cocaine and heroin,” she said. “Natural sugars from fruit act differently in the body.”

Sugar’s bad rap has much more to do with the quantity people consume than any intrinsically bad property, experts agree.

“Added sugar is nuanced,” Mozaffarian said. “When you try to take that very real nuance and turn it into a simple message, you get the industry misleading consumers that foods are ‘not good.’”

A little bit of added sugar in otherwise healthy foods, he said — such as lightly sweetened whole-grain cereals — is usually OK.

“The harms of these different nonsugar sweeteners have been greatly underemphasized and the harms of small amounts of added sugar have been overemphasized,” he argued.

Sugar substitutes for children?

The U.S. government’s Dietary Guidelines for Americans recommend that anyone over the age of 2 consume less than 10% of their daily calories from added sugar, or the equivalent of roughly 12 teaspoons of added sugar. In reality, as of 2018, people in the U.S., including children, were consuming about 17 teaspoons of added sugar per day, on average.

Recently, the U.S. Department of Agriculture implemented a new rule limiting added sugars in public school lunches . Michael Goran, a professor of pediatrics at the University of Southern California’s Keck School of Medicine, said he worries that schools will replace sugary foods with artificially sweetened foods to comply with the new rules.

“There’s this general perception that these sweeteners are safe alternatives, but if they’re broadly applied to children, I unfortunately think that’s very risky,” he said.

Mozaffarian said that at their current levels of added sugar, most yogurts would no longer be allowed in school lunches once the new rule goes into effect.

“They’re just above the new limit, so it’s likely these yogurts are now going to be made with a series of sweeteners with uncertain health effects,” Mozaffarian said.

In the meantime, Pearlman said, it’s easy to see they haven’t helped the population become healthier on the whole.

“We have more chronic disease, more diabetes today than we’ve ever had before,” she said. “That shows that despite the diet industry being worth billions of dollars, we’re clearly missing the ball.”

A confusing body of limited research, coupled with the lack of clarity on food labels, puts consumers in a tough position when it comes to selecting the healthiest choices, the experts concluded.

All agreed on the best solution:

• Eat as many whole, unprocessed foods as you can.
• The less processed a food, the less likely it is to be loaded with added sugars or nonsugar sweeteners.

“If I had the choice of eating a store-bought cookie with a lot of sweeteners in it, a store-bought cookie with monk fruit, or a homemade cookie with sugar, I would choose the homemade cookie,” Goran said. “You can still enjoy the cookie, but maybe put a little less sugar in there.”

NBC News contributor Caroline Hopkins is a health and science journalist who covers cancer treatment for Precision Oncology News. She is a graduate of the Columbia University Graduate School of Journalism.  

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A Sam Altman-Backed Group Studied Universal Basic Income For 3 Years. Here’s What They Found.

Findings from a sam altman-backed study on ubi give insight into the method's impact on spending and employment..

For the past three years, participants across Illinois and Texas have received monthly cash payments of $1,000 to take part in a project funded by Sam Altman , the CEO of OpenAI and one of the most prominent proponents of basic income. Newly released findings from the study provide early insights into whether a universal basic income (UBI) is a sustainable model for a future driven by A.I.

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The study measured the spending habits of 1,000 participants against a control group of 2,000 that was given $50 each month. Recipients of the $1,000 cash payments reported a modest decrease in employment, an increase in setting and achieving goals and an emphasis on spending that benefitted basic needs and supported others, according to the study’s results.

The initiative was conducted by OpenResearch, a research organization that has received some $14 million from Altman and around $10 million from OpenAI. Altman, who has pushed for UBI as a solution to concerns that A.I. will replace employment opportunities, first backed the project in 2016 while he was still leading the startup accelerator Y Combinator . “ I’ve been intrigued by the idea for a while,” wrote Altman in a blog post at the time. “I’m fairly confident that at some point in the future, as technology continues to eliminate traditional jobs and massive new wealth gets created, we’re going to see some version of this at a national scale,” he said.

What did Sam Altman’s study discover?

The study’s participants, which were selected from rural, suburban and urban areas and had an average household income of around $29,000, reported different employment trends depending on whether they received payments of $1,000 or $50. The target group on average worked 1.3 fewer hours per week and were 2 percent less likely to be employed, although they were also 10 percent more likely to be actively searching for a job. “Recipients were more likely to select interesting or meaningful work as an essential condition for any job,” according to the study, led by Elizabeth Rhodes , research director at OpenResearch.

This concept of agency was a common theme for the basic income recipients, 14 percent of whom were more likely to have pursued education or job training during the final year of the program. They also displayed a greater interest in moving, reporting an 11 percent bump in their ability to move neighborhoods and a 5 percent increase in their likelihood to pay for housing instead of seeking other living arrangements.

Healthcare was also impacted by the experiment, with the UBI group increasing their spending on medical care by around $20 per month and showcasing a 10 percent rise in the probability of receiving dental care in the past year. “Although we find no significant effects on measures of physical health, the increased utilization of medical care may lead to long-term health benefits,” wrote the study’s authors. Health wasn’t the only area that benefited from additional spending—recipients overall spent $310 more each month. The largest increases went to food, rent and transportation at spending increases of $67, $52 and $50, respectively, while spending on support to others rose by $22 per month.

“ Beth and the OpenResearch team have done critical research to shed light on questions around” UBI, said Altman in a statement to Bloomberg. The OpenAI head has also recently floated a new take on UBI that he calls “ universal basic compute, ” which would see members of society each receive a portion of large language models instead of funds. While other A.I. figures like Elon Musk and academic Geoffrey Hinton have similarly advocated for a form of basic income, not all tech leaders are convinced. Dario Amodei , the head of OpenAI rival Anthropic , has described UBI proposals as “ kind of dystopian ” and urged for alternative methods to offset inequalities caused by A.I.

Expect more findings from Altman’s basic income study in the coming months and years. OpenResearch is set to release results regarding politics, relationships, household composition and effects on children later in 2024, followed by additional findings on well-being, material hardship, crime and children’s education in 2025.

• SEE ALSO : What Melinda French Gates’s Philanthropy Could Look Like Post-Gates Foundation

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• Knowledge Base

Methodology

• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
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Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

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Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

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

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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• Introduction
• Article Information

Data are from Epic Systems Corporation peer benchmarking. Center horizontal lines represent medians; lower and upper bounds of the boxes, 25th and 75th percentiles; vertical lines, 5th to 95th percentile; and dashed horizontal line, 4-hour standard set by The Joint Commission.

Data are from Epic Systems Corporation peer benchmarking. A, Hospital occupancy is the percentage of staffed beds; ED visits are from January 2020.

eTable. Sample Site Characteristics from the Epic Peer Benchmarking Service

• Monthly Rates of Patients Who Left Before Accessing Care in US Emergency Departments JAMA Network Open Research Letter September 30, 2022 This cross-sectional study investigates rates of patients who left emergency departments without being seen from 2017 to 2021. Alexander T. Janke, MD; Edward R. Melnick, MD, MHS; Arjun K. Venkatesh, MD, MBA, MHS

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Janke AT , Melnick ER , Venkatesh AK. Hospital Occupancy and Emergency Department Boarding During the COVID-19 Pandemic. JAMA Netw Open. 2022;5(9):e2233964. doi:10.1001/jamanetworkopen.2022.33964

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Hospital Occupancy and Emergency Department Boarding During the COVID-19 Pandemic

• 1 Department of Emergency Medicine, Yale University School of Medicine, New Haven, Connecticut
• 2 VA Ann Arbor, University of Michigan, National Clinician Scholars Program, Ann Arbor
• 3 Center for Outcomes Research and Evaluation, Yale University, New Haven, Connecticut
• Research Letter Monthly Rates of Patients Who Left Before Accessing Care in US Emergency Departments Alexander T. Janke, MD; Edward R. Melnick, MD, MHS; Arjun K. Venkatesh, MD, MBA, MHS JAMA Network Open

Emergency department (ED) boarding refers to holding admitted patients in the ED, often in hallways, while awaiting an inpatient bed. The Joint Commission identified boarding as a patient safety risk that should not exceed 4 hours. 1 Downstream harms include increased medical errors, compromises to patient privacy, and increased mortality. 2 Boarding is a key indicator of overwhelmed resources and may be more likely to occur when hospital occupancy exceeds 85% to 90%. 3

Hospital resource constraints have become more salient during the COVID-19 pandemic and have been associated with excess mortality. 4 Existing federal data fail to capture a comprehensive view of resource limitations inclusive of ED strain. 5 We used a national benchmarking database to examine hospital occupancy and ED boarding during the COVID-19 pandemic.

This cross-sectional study used aggregated hospital measures available through a voluntary peer benchmarking service offered by Epic Systems Corporation, an electronic health record vendor. Measures were collected monthly from January 2020 to December 2021. Annual ED visit volumes and total hospital beds for participating sites were included (eTable in the Supplement ). We reported median and 5th to 95th percentile for hospital occupancy (percentage of staffed inpatient beds occupied, calculated hourly and averaged over the month), ED boarding time (median time from admission order to ED departure to an inpatient bed), and ED visit count. The study was classified as exempt by the institutional review board at Yale University because the study did not use patient data. This study followed the STROBE reporting guideline.

Distribution of ED boarding time was examined across hospital occupancy levels, with a threshold of 85% or greater based on Kelen et al. 3 We plotted all 3 measures with new national daily COVID-19 cases. 6 The difference in median ED boarding time between high-occupancy and low-occupancy hospital-months was evaluated using the Wilcoxon rank sum test .Analyses were performed using R, version 4.0.2.

Hospitals reporting benchmarking data increased from 1289 in January 2020 to 1769 in December 2021. Occupancy rates and boarding time had a threshold association: when occupancy exceeded 85%, boarding exceeded The Joint Commission 4-hour standard for 88.9% of hospital-months ( Figure 1 ). In those hospital-months, median ED boarding time was 6.58 hours compared with 2.42 hours in other hospital-months ( P  < .001). Across all hospitals, the median ED boarding time was 2.00 hours (5th-95th percentile, 0.93-7.88 hours) in January 2020, 1.58 hours (5th-95th percentile, 0.90-3.51 hours) in April 2020, and 3.42 hours in December 2021 (5th-95th percentile, 1.27-9.14 hours). Median hospital occupancy was highest in January 2020 (69.6%; 5th-95th percentile, 44.3%-69.6%), 48.7% (5th-95th percentile, 28.7%-69.9% hours) in April 2020, and 65.8% (5th-95th percentile, 42.7%-84.8%) in December 2021 ( Figure 2 ).

We found that hospital occupancy greater than 85% was associated with increased ED boarding beyond the 4-hour standard. Throughout 2020 and 2021, ED boarding increased even when hospital occupancy did not increase above January 2020 levels. The harms associated with ED boarding and crowding, long-standing before the pandemic, may have been further entrenched. Study limitations were the inability to differentiate occupancy for specific services, median measures of boarding likely underestimated actual burden, and the sample was anchored to specific data fields within the Epic peer benchmarking service. Future research should explore more complex measures like staffing variability and local outbreak burden. Policy makers should address acute care system strain in future pandemic waves and other disasters to avoid further hospital system capacity strain and unsafe patient care conditions.

Accepted for Publication: June 30, 2022.

Published: September 30, 2022. doi:10.1001/jamanetworkopen.2022.33964

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2022 Janke AT et al. JAMA Network Open .

Corresponding Author: Alexander T. Janke, MD, VA Ann Arbor, University of Michigan, National Clinician Scholars Program, NCRC Building 14, G100, 2800 Plymouth Rd, Ann Arbor MI 48109 ( [email protected] ).

Author Contributions: Dr Janke had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: All authors.

Acquisition, analysis, or interpretation of data: Janke, Melnick.

Drafting of the manuscript: Janke, Melnick.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Janke.

Obtained funding: Janke.

Administrative, technical, or material support: Melnick, Venkatesh.

Supervision: Melnick, Venkatesh.

Conflict of Interest Disclosures: Dr Janke reported receiving support from the Veterans Affairs (VA) Office of Academic Affiliations through the VA/National Clinician Scholars Program and the University of Michigan and funding from an Emerging Infectious Diseases and Preparedness grant from the Society for Academic Emergency Medicine Foundation. Dr Melnick reported receiving grants from the National Institute on Drug Abuse, the American Medical Association, and the Agency for Healthcare Research & Quality outside the submitted work. Dr Venkatesh reported receiving grants from the Centers for Medicare & Medicaid Services and the American College of Emergency Physicians outside the submitted work; receiving funding from an Emerging Infectious Diseases and Preparedness grant from the Society for Academic Emergency Medicine Foundation; and having committee leadership roles with the American College of Emergency Physicians and the Society for Academic Emergency Medicine. No other disclosures were reported.

Funding/Support: Dr Venkatesh was supported by the American Board of Emergency Medicine–National Academy of Medicine Fellowship.

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The contents of this article do not represent the views of the US Department of Veterans Affairs or the US Government.

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