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Research methods--quantitative, qualitative, and more: overview.

• Quantitative Research
• Qualitative Research
• Data Science Methods (Machine Learning, AI, Big Data)
• Text Mining and Computational Text Analysis
• Evidence Synthesis/Systematic Reviews
• Get Data, Get Help!

About Research Methods

This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley. 

As Patten and Newhart note in the book Understanding Research Methods , "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge. The accumulation of knowledge through research is by its nature a collective endeavor. Each well-designed study provides evidence that may support, amend, refute, or deepen the understanding of existing knowledge...Decisions are important throughout the practice of research and are designed to help researchers collect evidence that includes the full spectrum of the phenomenon under study, to maintain logical rules, and to mitigate or account for possible sources of bias. In many ways, learning research methods is learning how to see and make these decisions."

The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more.  This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will answer your question. 

Suggestions for changes and additions to this guide are welcome! 

START HERE: SAGE Research Methods

Without question, the most comprehensive resource available from the library is SAGE Research Methods.  HERE IS THE ONLINE GUIDE  to this one-stop shopping collection, and some helpful links are below:

• SAGE Research Methods
• Little Green Books  (Quantitative Methods)
• Little Blue Books  (Qualitative Methods)
• Dictionaries and Encyclopedias  
• Case studies of real research projects
• Sample datasets for hands-on practice
• Streaming video--see methods come to life
• Methodspace- -a community for researchers
• SAGE Research Methods Course Mapping

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

The LDSP offers a variety of services and tools !  From this link, check out pages for each of the following topics:  discovering data, managing data, collecting data, GIS data, text data mining, publishing data, digital scholarship, open science, and the Research Data Management Program.

Be sure also to check out the visual guide to where to seek assistance on campus with any research question you may have!

Library GIS Services

Other Data Services at Berkeley

D-Lab Supports Berkeley faculty, staff, and graduate students with research in data intensive social science, including a wide range of training and workshop offerings Dryad Dryad is a simple self-service tool for researchers to use in publishing their datasets. It provides tools for the effective publication of and access to research data. Geospatial Innovation Facility (GIF) Provides leadership and training across a broad array of integrated mapping technologies on campu Research Data Management A UC Berkeley guide and consulting service for research data management issues

General Research Methods Resources

Here are some general resources for assistance:

• Assistance from ICPSR (must create an account to access): Getting Help with Data , and Resources for Students
• Wiley Stats Ref for background information on statistics topics
• Survey Documentation and Analysis (SDA) .  Program for easy web-based analysis of survey data.

Consultants

• D-Lab/Data Science Discovery Consultants Request help with your research project from peer consultants.
• Research data (RDM) consulting Meet with RDM consultants before designing the data security, storage, and sharing aspects of your qualitative project.
• Statistics Department Consulting Services A service in which advanced graduate students, under faculty supervision, are available to consult during specified hours in the Fall and Spring semesters.

Related Resourcex

• IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
• OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
• Sponsored Projects Sponsored projects works with researchers applying for major external grants.
• Next: Quantitative Research >>
• Last Updated: Apr 25, 2024 11:09 AM
• URL: https://guides.lib.berkeley.edu/researchmethods

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Nih clinical research trials and you, finding a clinical trial, around the nation and worldwide.

NIH conducts clinical research trials for many diseases and conditions, including cancer , Alzheimer’s disease , allergy and infectious diseases , and neurological disorders . To search for other diseases and conditions, you can visit ClinicalTrials.gov.

ClinicalTrials.gov [ How to Use Search ] This is a searchable registry and results database of federally and privately supported clinical trials conducted in the United States and around the world. ClinicalTrials.gov gives you information about a trial's purpose, who may participate, locations, and phone numbers for more details. This information should be used in conjunction with advice from health care professionals.

Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read the disclaimer on ClinicalTrials.gov for details.

Before participating in a study, talk to your health care provider and learn about the risks and potential benefits.

At the NIH Clinical Center in Bethesda, Maryland

Search NIH Clinical Research Studies The NIH maintains an online database of clinical research studies taking place at its Clinical Center, which is located on the NIH campus in Bethesda, Maryland. Studies are conducted by most of the institutes and centers across the NIH. The Clinical Center hosts a wide range of studies from rare diseases to chronic health conditions, as well as studies for healthy volunteers. Visitors can search by diagnosis, sign, symptom or other key words.

Join a National Registry of Research Volunteers

ResearchMatch This is an NIH-funded initiative to connect 1) people who are trying to find research studies, and 2) researchers seeking people to participate in their studies. It is a free, secure registry to make it easier for the public to volunteer and to become involved in clinical research studies that contribute to improved health in the future.

This page last reviewed on November 6, 2018

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What Is Research Methodology? A Plain-Language Explanation & Definition (With Examples)

By Derek Jansen (MBA)  and Kerryn Warren (PhD) | June 2020 (Last updated April 2023)

If you’re new to formal academic research, it’s quite likely that you’re feeling a little overwhelmed by all the technical lingo that gets thrown around. And who could blame you – “research methodology”, “research methods”, “sampling strategies”… it all seems never-ending!

In this post, we’ll demystify the landscape with plain-language explanations and loads of examples (including easy-to-follow videos), so that you can approach your dissertation, thesis or research project with confidence. Let’s get started.

Research Methodology 101

• What exactly research methodology means
• What qualitative , quantitative and mixed methods are
• What sampling strategy is
• What data collection methods are
• What data analysis methods are
• How to choose your research methodology
• Example of a research methodology

What is research methodology?

Research methodology simply refers to the practical “how” of a research study. More specifically, it’s about how  a researcher  systematically designs a study  to ensure valid and reliable results that address the research aims, objectives and research questions . Specifically, how the researcher went about deciding:

• What type of data to collect (e.g., qualitative or quantitative data )
• Who  to collect it from (i.e., the sampling strategy )
• How to  collect  it (i.e., the data collection method )
• How to  analyse  it (i.e., the data analysis methods )

Within any formal piece of academic research (be it a dissertation, thesis or journal article), you’ll find a research methodology chapter or section which covers the aspects mentioned above. Importantly, a good methodology chapter explains not just   what methodological choices were made, but also explains  why they were made. In other words, the methodology chapter should justify  the design choices, by showing that the chosen methods and techniques are the best fit for the research aims, objectives and research questions. 

So, it’s the same as research design?

Not quite. As we mentioned, research methodology refers to the collection of practical decisions regarding what data you’ll collect, from who, how you’ll collect it and how you’ll analyse it. Research design, on the other hand, is more about the overall strategy you’ll adopt in your study. For example, whether you’ll use an experimental design in which you manipulate one variable while controlling others. You can learn more about research design and the various design types here .

Need a helping hand?

What are qualitative, quantitative and mixed-methods?

Qualitative, quantitative and mixed-methods are different types of methodological approaches, distinguished by their focus on words , numbers or both . This is a bit of an oversimplification, but its a good starting point for understanding.

Let’s take a closer look.

Qualitative research refers to research which focuses on collecting and analysing words (written or spoken) and textual or visual data, whereas quantitative research focuses on measurement and testing using numerical data . Qualitative analysis can also focus on other “softer” data points, such as body language or visual elements.

It’s quite common for a qualitative methodology to be used when the research aims and research questions are exploratory  in nature. For example, a qualitative methodology might be used to understand peoples’ perceptions about an event that took place, or a political candidate running for president. 

Contrasted to this, a quantitative methodology is typically used when the research aims and research questions are confirmatory  in nature. For example, a quantitative methodology might be used to measure the relationship between two variables (e.g. personality type and likelihood to commit a crime) or to test a set of hypotheses .

As you’ve probably guessed, the mixed-method methodology attempts to combine the best of both qualitative and quantitative methodologies to integrate perspectives and create a rich picture. If you’d like to learn more about these three methodological approaches, be sure to watch our explainer video below.

What is sampling strategy?

Simply put, sampling is about deciding who (or where) you’re going to collect your data from . Why does this matter? Well, generally it’s not possible to collect data from every single person in your group of interest (this is called the “population”), so you’ll need to engage a smaller portion of that group that’s accessible and manageable (this is called the “sample”).

How you go about selecting the sample (i.e., your sampling strategy) will have a major impact on your study.  There are many different sampling methods  you can choose from, but the two overarching categories are probability   sampling and  non-probability   sampling .

Probability sampling  involves using a completely random sample from the group of people you’re interested in. This is comparable to throwing the names all potential participants into a hat, shaking it up, and picking out the “winners”. By using a completely random sample, you’ll minimise the risk of selection bias and the results of your study will be more generalisable  to the entire population. 

Non-probability sampling , on the other hand,  doesn’t use a random sample . For example, it might involve using a convenience sample, which means you’d only interview or survey people that you have access to (perhaps your friends, family or work colleagues), rather than a truly random sample. With non-probability sampling, the results are typically not generalisable .

To learn more about sampling methods, be sure to check out the video below.

What are data collection methods?

As the name suggests, data collection methods simply refers to the way in which you go about collecting the data for your study. Some of the most common data collection methods include:

• Interviews (which can be unstructured, semi-structured or structured)
• Focus groups and group interviews
• Surveys (online or physical surveys)
• Observations (watching and recording activities)
• Biophysical measurements (e.g., blood pressure, heart rate, etc.)
• Documents and records (e.g., financial reports, court records, etc.)

The choice of which data collection method to use depends on your overall research aims and research questions , as well as practicalities and resource constraints. For example, if your research is exploratory in nature, qualitative methods such as interviews and focus groups would likely be a good fit. Conversely, if your research aims to measure specific variables or test hypotheses, large-scale surveys that produce large volumes of numerical data would likely be a better fit.

What are data analysis methods?

Data analysis methods refer to the methods and techniques that you’ll use to make sense of your data. These can be grouped according to whether the research is qualitative  (words-based) or quantitative (numbers-based).

Popular data analysis methods in qualitative research include:

• Qualitative content analysis
• Thematic analysis
• Discourse analysis
• Narrative analysis
• Interpretative phenomenological analysis (IPA)
• Visual analysis (of photographs, videos, art, etc.)

Qualitative data analysis all begins with data coding , after which an analysis method is applied. In some cases, more than one analysis method is used, depending on the research aims and research questions . In the video below, we explore some  common qualitative analysis methods, along with practical examples.  

Moving on to the quantitative side of things, popular data analysis methods in this type of research include:

• Descriptive statistics (e.g. means, medians, modes )
• Inferential statistics (e.g. correlation, regression, structural equation modelling)

Again, the choice of which data collection method to use depends on your overall research aims and objectives , as well as practicalities and resource constraints. In the video below, we explain some core concepts central to quantitative analysis.

How do I choose a research methodology?

As you’ve probably picked up by now, your research aims and objectives have a major influence on the research methodology . So, the starting point for developing your research methodology is to take a step back and look at the big picture of your research, before you make methodology decisions. The first question you need to ask yourself is whether your research is exploratory or confirmatory in nature.

If your research aims and objectives are primarily exploratory in nature, your research will likely be qualitative and therefore you might consider qualitative data collection methods (e.g. interviews) and analysis methods (e.g. qualitative content analysis). 

Conversely, if your research aims and objective are looking to measure or test something (i.e. they’re confirmatory), then your research will quite likely be quantitative in nature, and you might consider quantitative data collection methods (e.g. surveys) and analyses (e.g. statistical analysis).

Designing your research and working out your methodology is a large topic, which we cover extensively on the blog . For now, however, the key takeaway is that you should always start with your research aims, objectives and research questions (the golden thread). Every methodological choice you make needs align with those three components. 

Example of a research methodology chapter

In the video below, we provide a detailed walkthrough of a research methodology from an actual dissertation, as well as an overview of our free methodology template .

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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199 Comments

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I am writing a APA Format paper . I using questionnaire with 120 STDs teacher for my participant. Can you write me mthology for this research. Send it through email sent. Just need a sample as an example please. My topic is ” impacts of overcrowding on students learning

Thanks for your comment.

We can’t write your methodology for you. If you’re looking for samples, you should be able to find some sample methodologies on Google. Alternatively, you can download some previous dissertations from a dissertation directory and have a look at the methodology chapters therein.

All the best with your research.

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Sure. You’re welcome to book an initial consultation with one of our Research Coaches to discuss how we can assist – https://gradcoach.com/book/new/ .

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how do i reference this?

MLA Jansen, Derek, and Kerryn Warren. “What (Exactly) Is Research Methodology?” Grad Coach, June 2021, gradcoach.com/what-is-research-methodology/.

APA Jansen, D., & Warren, K. (2021, June). What (Exactly) Is Research Methodology? Grad Coach. https://gradcoach.com/what-is-research-methodology/

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• Free Download: Research Proposal Template (With Examples) - Grad Coach - […] Research design (methodology) […]
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Module 1: Introduction: What is Research?

Learning Objectives

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

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

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

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

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

Module 1: Discussion Questions

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

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What Is Research, and Why Do People Do It?

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• First Online: 03 December 2022

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

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

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Abstractspiepr Abs1

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

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Part I. What Is Research?

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

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

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

Exercise 1.1

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

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

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

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

Exercise 1.2

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

Creating an Image of Scientific Inquiry

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

Descriptor 1. Experience Carefully Planned in Advance

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

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

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

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

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

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

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

Exercise 1.3

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

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

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

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

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

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

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

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

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

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

Doing Scientific Inquiry

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

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

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

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

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

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

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

Exercise 1.4

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

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

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

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

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

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

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

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

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

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

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

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

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

Exercise 1.5

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

Exercise 1.6

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

Learning from Doing Scientific Inquiry

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

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

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

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

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

Part II. Why Do Educators Do Research?

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

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

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

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

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

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

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

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

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

Exercise 1.7

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

Part III. Conducting Research as a Practice of Failing Productively

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

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

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

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

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

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

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

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

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

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

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

Exercise 1.8

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

Exercise 1.9

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

Part IV. Preview of Chap. 2

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

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

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Home Market Research

What is Research: Definition, Methods, Types & Examples

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

Content Index

What is Research?

What are the characteristics of research.

• Comparative analysis chart

Qualitative methods

Quantitative methods, 8 tips for conducting accurate research.

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

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

Research is conducted with a purpose to:

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

What is the purpose of research?

There are three main purposes:

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

LEARN ABOUT: Descriptive Analysis

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

LEARN ABOUT: Best Data Collection Tools

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

Here is a comparative analysis chart for a better understanding:

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

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

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

Types of research methods and Examples

Research methods are broadly classified as Qualitative and Quantitative .

Both methods have distinctive properties and data collection methods .

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

Types of qualitative methods include:

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

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

Types of quantitative methods include:

• Survey research
• Descriptive research
• Correlational research

LEARN MORE: Descriptive Research vs Correlational Research

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

It is essential to ensure that your data is:

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

Gather insights

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

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

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Research vs. Study

What's the difference.

Research and study are two essential components of the learning process, but they differ in their approach and purpose. Research involves a systematic investigation of a particular topic or issue, aiming to discover new knowledge or validate existing theories. It often involves collecting and analyzing data, conducting experiments, and drawing conclusions. On the other hand, study refers to the process of acquiring knowledge or understanding through reading, memorizing, and reviewing information. It is typically focused on a specific subject or discipline and aims to deepen one's understanding or mastery of that subject. While research is more exploratory and investigative, study is more focused on acquiring and retaining information. Both research and study are crucial for intellectual growth and expanding our knowledge base.

Further Detail

Introduction.

Research and study are two fundamental activities that play a crucial role in acquiring knowledge and understanding. While they share similarities, they also have distinct attributes that set them apart. In this article, we will explore the characteristics of research and study, highlighting their differences and similarities.

Definition and Purpose

Research is a systematic investigation aimed at discovering new knowledge, expanding existing knowledge, or solving specific problems. It involves gathering and analyzing data, formulating hypotheses, and drawing conclusions based on evidence. Research is often conducted in a structured and scientific manner, employing various methodologies and techniques.

On the other hand, study refers to the process of acquiring knowledge through reading, memorizing, and understanding information. It involves examining and learning from existing materials, such as textbooks, articles, or lectures. The purpose of study is to gain a comprehensive understanding of a particular subject or topic.

Approach and Methodology

Research typically follows a systematic approach, involving the formulation of research questions or hypotheses, designing experiments or surveys, collecting and analyzing data, and drawing conclusions. It often requires a rigorous methodology, including literature review, data collection, statistical analysis, and peer review. Research can be qualitative or quantitative, depending on the nature of the investigation.

Study, on the other hand, does not necessarily follow a specific methodology. It can be more flexible and personalized, allowing individuals to choose their own approach to learning. Study often involves reading and analyzing existing materials, taking notes, summarizing information, and engaging in discussions or self-reflection. While study can be structured, it is generally less formalized compared to research.

Scope and Depth

Research tends to have a broader scope and aims to contribute to the overall body of knowledge in a particular field. It often involves exploring new areas, pushing boundaries, and generating original insights. Research can be interdisciplinary, involving multiple disciplines and perspectives. The depth of research is often extensive, requiring in-depth analysis, critical thinking, and the ability to synthesize complex information.

Study, on the other hand, is usually more focused and specific. It aims to gain a comprehensive understanding of a particular subject or topic within an existing body of knowledge. Study can be deep and detailed, but it is often limited to the available resources and materials. While study may not contribute directly to the advancement of knowledge, it plays a crucial role in building a solid foundation of understanding.

Application and Output

Research is often driven by the desire to solve real-world problems or contribute to practical applications. The output of research can take various forms, including scientific papers, patents, policy recommendations, or technological advancements. Research findings are typically shared with the academic community and the public, aiming to advance knowledge and improve society.

Study, on the other hand, focuses more on personal development and learning. The application of study is often seen in academic settings, where individuals acquire knowledge to excel in their studies or careers. The output of study is usually reflected in improved understanding, enhanced critical thinking skills, and the ability to apply knowledge in practical situations.

Limitations and Challenges

Research faces several challenges, including limited resources, time constraints, ethical considerations, and the potential for bias. Conducting research requires careful planning, data collection, and analysis, which can be time-consuming and costly. Researchers must also navigate ethical guidelines and ensure the validity and reliability of their findings.

Study, on the other hand, may face challenges such as information overload, lack of motivation, or difficulty in finding reliable sources. It requires self-discipline, time management, and the ability to filter and prioritize information. Without proper guidance or structure, study can sometimes lead to superficial understanding or misconceptions.

In conclusion, research and study are both essential activities in the pursuit of knowledge and understanding. While research focuses on generating new knowledge and solving problems through a systematic approach, study aims to acquire and comprehend existing information. Research tends to be more formalized, rigorous, and contributes to the advancement of knowledge, while study is often more flexible, personalized, and focused on individual learning. Both research and study have their unique attributes and challenges, but together they form the foundation for intellectual growth and development.

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Can Neosporin Protect You From Getting COVID-19?

F or years, researchers have been working on vaccines that aim to prevent viral infections by strengthening immune defenses at viruses’ doorway to the body: the nose.

A small study recently published in PNAS presents a similar, if lower-tech, idea. Coating the inside of the nose with the over-the-counter antibiotic ointment Neosporin seems to trigger an immune response that may help the body repel respiratory viruses like those that cause COVID-19 and the flu, the study suggests.

The research raises the idea that Neosporin could serve as an “extra layer” of protection against respiratory illnesses, on top of existing tools like vaccines and masks, says study co-author Akiko Iwasaki, an immunobiologist at the Yale School of Medicine and one of the U.S.’ leading nasal vaccine researchers .

The study builds upon some of Iwasaki’s prior research —which has shown that similar antibiotics can trigger potentially protective immune changes in the body—but it’s still preliminary, she cautions. For the new study, her team had 12 people apply Neosporin inside their nostrils twice a day for a week, while another seven people used Vaseline for comparison. At several points during the study, the researchers swabbed the participants’ noses and ran PCR tests to see what was going on inside.

Read More : What to Do About Your Bunions

They found that Neosporin—and specifically one of its active ingredients, the antibiotic neomycin sulfate—seems to stimulate receptors in the nose that “are fooled into thinking there’s a viral infection” and in turn create “a barrier that’s put up against any virus,” Iwasaki explains. In theory, she says, that means it could protect against a range of different infections.

Right now, though, that’s just a theory. For this study, Iwasaki’s team didn’t take the next step of testing whether that immune response actually prevents people from getting infected when they’re exposed to viruses—in part because it’s ethically questionable to intentionally expose people to pathogens for research. (They did, however, demonstrate that rodents whose noses were coated with neomycin were protected from the virus that causes COVID-19.)

On its website, the maker of Neosporin says that the product has not "been tested or formulated to prevent against COVID-19 or any other virus," and also note that they do not advise putting the product inside the eyes, nose, or mouth.

Dr. James Crowe, who directs the Vanderbilt Vaccine Center and was not involved in the research, says the study is “intriguing,” but he’d need to see more human data before he gets excited. “I’m skeptical it would be strongly effective in people,” Crowe says. “If you have a modest effect on the virus, is that enough to really benefit you clinically?”

It is somewhat counterintuitive to think that an antibiotic, which kills bacteria, could do anything to protect people from viruses. It’s not that the antibiotic has a direct effect against viruses, Iwasaki explains. Instead, it seems that neomycin, when applied topically, provoke changes in the body that help it fight off viruses—essentially, triggering a natural antiviral effect.

So should you smear Neosporin in your nose next time a COVID-19 wave hits ? Not so fast, says Dr. Benjamin Bleier, who specializes in nasal disorders at Massachusetts Eye and Ear and has studied nasal immunity .

Read More : COVID-Cautious Americans Feel Abandoned

Bleier, who was not involved in the new study, calls the research “very well done,” but says there are questions that need to be answered before it hits “clinical prime time.” First, could the body develop tolerance or resistance to neomycin if the antibiotic were regularly used in this way? (Antibiotic resistance is a growing concern, and overusing or inappropriately prescribing antibiotics is a contributor to the problem.) Second, could the average person apply neomycin deeply and thoroughly enough for meaningful protection? And finally, could this approach damage the delicate inner nose or have other side effects over time? (Even in the small study, one of the people who used intranasal Neosporin dropped out due to minor side effects, apparently related to a drug allergy.)

“It’s great science, but there’s still a long way to go before we should put it in our noses,” agrees Dr. Sean Liu, an infectious disease physician at New York’s Mount Sinai health system who was also not involved in the study.

Iwasaki agrees that more research is necessary. She says the next step is testing higher doses of neomycin, since Neosporin contains a fairly small amount that may not be enough to provide robust protection for humans. To gather more data, she says, researchers could track people going about their normal lives—except that some apply neomycin to their noses and some apply Vaseline—and see if one group gets sick less often than the other, though that would require a lot of time and people.

Despite the difficulties, Liu says there’s good reason for further study. Finding new uses for affordable, widely accessible medications is good for public health, and any progress toward neutralizing viruses is welcome. If the approach is proven to work, it could also be useful to have a tool that's effective against a broad range of viruses and could potentially be paired with other drugs to strengthen its efficacy, Crowe adds.

Plus, Iwasaki says, additional disease-prevention tools could help people who are especially vulnerable to respiratory diseases—such as those who are immunocompromised —and need additional protection to feel safe. If further research proves promising, Iwasaki says, she could imagine neomycin serving as an additional disease-fighting tool when people are in particularly germy places, like a crowded party or an airport.

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Qualitative study.

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Last Update: September 18, 2022 .

• Introduction

Qualitative research is a type of research that explores and provides deeper insights into real-world problems. [1] Instead of collecting numerical data points or intervene or introduce treatments just like in quantitative research, qualitative research helps generate hypotheses as well as further investigate and understand quantitative data. Qualitative research gathers participants' experiences, perceptions, and behavior. It answers the hows and whys instead of how many or how much. It could be structured as a stand-alone study, purely relying on qualitative data or it could be part of mixed-methods research that combines qualitative and quantitative data. This review introduces the readers to some basic concepts, definitions, terminology, and application of qualitative research.

Qualitative research at its core, ask open-ended questions whose answers are not easily put into numbers such as ‘how’ and ‘why’. [2] Due to the open-ended nature of the research questions at hand, qualitative research design is often not linear in the same way quantitative design is. [2] One of the strengths of qualitative research is its ability to explain processes and patterns of human behavior that can be difficult to quantify. [3] Phenomena such as experiences, attitudes, and behaviors can be difficult to accurately capture quantitatively, whereas a qualitative approach allows participants themselves to explain how, why, or what they were thinking, feeling, and experiencing at a certain time or during an event of interest. Quantifying qualitative data certainly is possible, but at its core, qualitative data is looking for themes and patterns that can be difficult to quantify and it is important to ensure that the context and narrative of qualitative work are not lost by trying to quantify something that is not meant to be quantified.

However, while qualitative research is sometimes placed in opposition to quantitative research, where they are necessarily opposites and therefore ‘compete’ against each other and the philosophical paradigms associated with each, qualitative and quantitative work are not necessarily opposites nor are they incompatible. [4] While qualitative and quantitative approaches are different, they are not necessarily opposites, and they are certainly not mutually exclusive. For instance, qualitative research can help expand and deepen understanding of data or results obtained from quantitative analysis. For example, say a quantitative analysis has determined that there is a correlation between length of stay and level of patient satisfaction, but why does this correlation exist? This dual-focus scenario shows one way in which qualitative and quantitative research could be integrated together.

Examples of Qualitative Research Approaches

Ethnography

Ethnography as a research design has its origins in social and cultural anthropology, and involves the researcher being directly immersed in the participant’s environment. [2] Through this immersion, the ethnographer can use a variety of data collection techniques with the aim of being able to produce a comprehensive account of the social phenomena that occurred during the research period. [2] That is to say, the researcher’s aim with ethnography is to immerse themselves into the research population and come out of it with accounts of actions, behaviors, events, etc. through the eyes of someone involved in the population. Direct involvement of the researcher with the target population is one benefit of ethnographic research because it can then be possible to find data that is otherwise very difficult to extract and record.

Grounded Theory

Grounded Theory is the “generation of a theoretical model through the experience of observing a study population and developing a comparative analysis of their speech and behavior.” [5] As opposed to quantitative research which is deductive and tests or verifies an existing theory, grounded theory research is inductive and therefore lends itself to research that is aiming to study social interactions or experiences. [3] [2] In essence, Grounded Theory’s goal is to explain for example how and why an event occurs or how and why people might behave a certain way. Through observing the population, a researcher using the Grounded Theory approach can then develop a theory to explain the phenomena of interest.

Phenomenology

Phenomenology is defined as the “study of the meaning of phenomena or the study of the particular”. [5] At first glance, it might seem that Grounded Theory and Phenomenology are quite similar, but upon careful examination, the differences can be seen. At its core, phenomenology looks to investigate experiences from the perspective of the individual. [2] Phenomenology is essentially looking into the ‘lived experiences’ of the participants and aims to examine how and why participants behaved a certain way, from their perspective . Herein lies one of the main differences between Grounded Theory and Phenomenology. Grounded Theory aims to develop a theory for social phenomena through an examination of various data sources whereas Phenomenology focuses on describing and explaining an event or phenomena from the perspective of those who have experienced it.

Narrative Research

One of qualitative research’s strengths lies in its ability to tell a story, often from the perspective of those directly involved in it. Reporting on qualitative research involves including details and descriptions of the setting involved and quotes from participants. This detail is called ‘thick’ or ‘rich’ description and is a strength of qualitative research. Narrative research is rife with the possibilities of ‘thick’ description as this approach weaves together a sequence of events, usually from just one or two individuals, in the hopes of creating a cohesive story, or narrative. [2] While it might seem like a waste of time to focus on such a specific, individual level, understanding one or two people’s narratives for an event or phenomenon can help to inform researchers about the influences that helped shape that narrative. The tension or conflict of differing narratives can be “opportunities for innovation”. [2]

Research Paradigm

Research paradigms are the assumptions, norms, and standards that underpin different approaches to research. Essentially, research paradigms are the ‘worldview’ that inform research. [4] It is valuable for researchers, both qualitative and quantitative, to understand what paradigm they are working within because understanding the theoretical basis of research paradigms allows researchers to understand the strengths and weaknesses of the approach being used and adjust accordingly. Different paradigms have different ontology and epistemologies . Ontology is defined as the "assumptions about the nature of reality” whereas epistemology is defined as the “assumptions about the nature of knowledge” that inform the work researchers do. [2] It is important to understand the ontological and epistemological foundations of the research paradigm researchers are working within to allow for a full understanding of the approach being used and the assumptions that underpin the approach as a whole. Further, it is crucial that researchers understand their own ontological and epistemological assumptions about the world in general because their assumptions about the world will necessarily impact how they interact with research. A discussion of the research paradigm is not complete without describing positivist, postpositivist, and constructivist philosophies.

Positivist vs Postpositivist

To further understand qualitative research, we need to discuss positivist and postpositivist frameworks. Positivism is a philosophy that the scientific method can and should be applied to social as well as natural sciences. [4] Essentially, positivist thinking insists that the social sciences should use natural science methods in its research which stems from positivist ontology that there is an objective reality that exists that is fully independent of our perception of the world as individuals. Quantitative research is rooted in positivist philosophy, which can be seen in the value it places on concepts such as causality, generalizability, and replicability.

Conversely, postpositivists argue that social reality can never be one hundred percent explained but it could be approximated. [4] Indeed, qualitative researchers have been insisting that there are “fundamental limits to the extent to which the methods and procedures of the natural sciences could be applied to the social world” and therefore postpositivist philosophy is often associated with qualitative research. [4] An example of positivist versus postpositivist values in research might be that positivist philosophies value hypothesis-testing, whereas postpositivist philosophies value the ability to formulate a substantive theory.

Constructivist

Constructivism is a subcategory of postpositivism. Most researchers invested in postpositivist research are constructivist as well, meaning they think there is no objective external reality that exists but rather that reality is constructed. Constructivism is a theoretical lens that emphasizes the dynamic nature of our world. “Constructivism contends that individuals’ views are directly influenced by their experiences, and it is these individual experiences and views that shape their perspective of reality”. [6] Essentially, Constructivist thought focuses on how ‘reality’ is not a fixed certainty and experiences, interactions, and backgrounds give people a unique view of the world. Constructivism contends, unlike in positivist views, that there is not necessarily an ‘objective’ reality we all experience. This is the ‘relativist’ ontological view that reality and the world we live in are dynamic and socially constructed. Therefore, qualitative scientific knowledge can be inductive as well as deductive.” [4]

So why is it important to understand the differences in assumptions that different philosophies and approaches to research have? Fundamentally, the assumptions underpinning the research tools a researcher selects provide an overall base for the assumptions the rest of the research will have and can even change the role of the researcher themselves. [2] For example, is the researcher an ‘objective’ observer such as in positivist quantitative work? Or is the researcher an active participant in the research itself, as in postpositivist qualitative work? Understanding the philosophical base of the research undertaken allows researchers to fully understand the implications of their work and their role within the research, as well as reflect on their own positionality and bias as it pertains to the research they are conducting.

Data Sampling 

The better the sample represents the intended study population, the more likely the researcher is to encompass the varying factors at play. The following are examples of participant sampling and selection: [7]

• Purposive sampling- selection based on the researcher’s rationale in terms of being the most informative.
• Criterion sampling-selection based on pre-identified factors.
• Convenience sampling- selection based on availability.
• Snowball sampling- the selection is by referral from other participants or people who know potential participants.
• Extreme case sampling- targeted selection of rare cases.
• Typical case sampling-selection based on regular or average participants. 

Data Collection and Analysis

Qualitative research uses several techniques including interviews, focus groups, and observation. [1] [2] [3] Interviews may be unstructured, with open-ended questions on a topic and the interviewer adapts to the responses. Structured interviews have a predetermined number of questions that every participant is asked. It is usually one on one and is appropriate for sensitive topics or topics needing an in-depth exploration. Focus groups are often held with 8-12 target participants and are used when group dynamics and collective views on a topic are desired. Researchers can be a participant-observer to share the experiences of the subject or a non-participant or detached observer.

While quantitative research design prescribes a controlled environment for data collection, qualitative data collection may be in a central location or in the environment of the participants, depending on the study goals and design. Qualitative research could amount to a large amount of data. Data is transcribed which may then be coded manually or with the use of Computer Assisted Qualitative Data Analysis Software or CAQDAS such as ATLAS.ti or NVivo. [8] [9] [10]

After the coding process, qualitative research results could be in various formats. It could be a synthesis and interpretation presented with excerpts from the data. [11] Results also could be in the form of themes and theory or model development.

Dissemination

To standardize and facilitate the dissemination of qualitative research outcomes, the healthcare team can use two reporting standards. The Consolidated Criteria for Reporting Qualitative Research or COREQ is a 32-item checklist for interviews and focus groups. [12] The Standards for Reporting Qualitative Research (SRQR) is a checklist covering a wider range of qualitative research. [13]

Examples of Application

Many times a research question will start with qualitative research. The qualitative research will help generate the research hypothesis which can be tested with quantitative methods. After the data is collected and analyzed with quantitative methods, a set of qualitative methods can be used to dive deeper into the data for a better understanding of what the numbers truly mean and their implications. The qualitative methods can then help clarify the quantitative data and also help refine the hypothesis for future research. Furthermore, with qualitative research researchers can explore subjects that are poorly studied with quantitative methods. These include opinions, individual's actions, and social science research.

A good qualitative study design starts with a goal or objective. This should be clearly defined or stated. The target population needs to be specified. A method for obtaining information from the study population must be carefully detailed to ensure there are no omissions of part of the target population. A proper collection method should be selected which will help obtain the desired information without overly limiting the collected data because many times, the information sought is not well compartmentalized or obtained. Finally, the design should ensure adequate methods for analyzing the data. An example may help better clarify some of the various aspects of qualitative research.

A researcher wants to decrease the number of teenagers who smoke in their community. The researcher could begin by asking current teen smokers why they started smoking through structured or unstructured interviews (qualitative research). The researcher can also get together a group of current teenage smokers and conduct a focus group to help brainstorm factors that may have prevented them from starting to smoke (qualitative research).

In this example, the researcher has used qualitative research methods (interviews and focus groups) to generate a list of ideas of both why teens start to smoke as well as factors that may have prevented them from starting to smoke. Next, the researcher compiles this data. The research found that, hypothetically, peer pressure, health issues, cost, being considered “cool,” and rebellious behavior all might increase or decrease the likelihood of teens starting to smoke.

The researcher creates a survey asking teen participants to rank how important each of the above factors is in either starting smoking (for current smokers) or not smoking (for current non-smokers). This survey provides specific numbers (ranked importance of each factor) and is thus a quantitative research tool.

The researcher can use the results of the survey to focus efforts on the one or two highest-ranked factors. Let us say the researcher found that health was the major factor that keeps teens from starting to smoke, and peer pressure was the major factor that contributed to teens to start smoking. The researcher can go back to qualitative research methods to dive deeper into each of these for more information. The researcher wants to focus on how to keep teens from starting to smoke, so they focus on the peer pressure aspect.

The researcher can conduct interviews and/or focus groups (qualitative research) about what types and forms of peer pressure are commonly encountered, where the peer pressure comes from, and where smoking first starts. The researcher hypothetically finds that peer pressure often occurs after school at the local teen hangouts, mostly the local park. The researcher also hypothetically finds that peer pressure comes from older, current smokers who provide the cigarettes.

The researcher could further explore this observation made at the local teen hangouts (qualitative research) and take notes regarding who is smoking, who is not, and what observable factors are at play for peer pressure of smoking. The researcher finds a local park where many local teenagers hang out and see that a shady, overgrown area of the park is where the smokers tend to hang out. The researcher notes the smoking teenagers buy their cigarettes from a local convenience store adjacent to the park where the clerk does not check identification before selling cigarettes. These observations fall under qualitative research.

If the researcher returns to the park and counts how many individuals smoke in each region of the park, this numerical data would be quantitative research. Based on the researcher's efforts thus far, they conclude that local teen smoking and teenagers who start to smoke may decrease if there are fewer overgrown areas of the park and the local convenience store does not sell cigarettes to underage individuals.

The researcher could try to have the parks department reassess the shady areas to make them less conducive to the smokers or identify how to limit the sales of cigarettes to underage individuals by the convenience store. The researcher would then cycle back to qualitative methods of asking at-risk population their perceptions of the changes, what factors are still at play, as well as quantitative research that includes teen smoking rates in the community, the incidence of new teen smokers, among others. [14] [15]

Qualitative research functions as a standalone research design or in combination with quantitative research to enhance our understanding of the world. Qualitative research uses techniques including structured and unstructured interviews, focus groups, and participant observation to not only help generate hypotheses which can be more rigorously tested with quantitative research but also to help researchers delve deeper into the quantitative research numbers, understand what they mean, and understand what the implications are.  Qualitative research provides researchers with a way to understand what is going on, especially when things are not easily categorized. [16]

• Issues of Concern

As discussed in the sections above, quantitative and qualitative work differ in many different ways, including the criteria for evaluating them. There are four well-established criteria for evaluating quantitative data: internal validity, external validity, reliability, and objectivity. The correlating concepts in qualitative research are credibility, transferability, dependability, and confirmability. [4] [11] The corresponding quantitative and qualitative concepts can be seen below, with the quantitative concept is on the left, and the qualitative concept is on the right:

• Internal validity--- Credibility
• External validity---Transferability
• Reliability---Dependability
• Objectivity---Confirmability

In conducting qualitative research, ensuring these concepts are satisfied and well thought out can mitigate potential issues from arising. For example, just as a researcher will ensure that their quantitative study is internally valid so should qualitative researchers ensure that their work has credibility.  

Indicators such as triangulation and peer examination can help evaluate the credibility of qualitative work.

• Triangulation: Triangulation involves using multiple methods of data collection to increase the likelihood of getting a reliable and accurate result. In our above magic example, the result would be more reliable by also interviewing the magician, back-stage hand, and the person who "vanished." In qualitative research, triangulation can include using telephone surveys, in-person surveys, focus groups, and interviews as well as surveying an adequate cross-section of the target demographic.
• Peer examination: Results can be reviewed by a peer to ensure the data is consistent with the findings.

‘Thick’ or ‘rich’ description can be used to evaluate the transferability of qualitative research whereas using an indicator such as an audit trail might help with evaluating the dependability and confirmability.

• Thick or rich description is a detailed and thorough description of details, the setting, and quotes from participants in the research. [5] Thick descriptions will include a detailed explanation of how the study was carried out. Thick descriptions are detailed enough to allow readers to draw conclusions and interpret the data themselves, which can help with transferability and replicability.
• Audit trail: An audit trail provides a documented set of steps of how the participants were selected and the data was collected. The original records of information should also be kept (e.g., surveys, notes, recordings).

One issue of concern that qualitative researchers should take into consideration is observation bias. Here are a few examples:

• Hawthorne effect: The Hawthorne effect is the change in participant behavior when they know they are being observed. If a researcher was wanting to identify factors that contribute to employee theft and tells the employees they are going to watch them to see what factors affect employee theft, one would suspect employee behavior would change when they know they are being watched.
• Observer-expectancy effect: Some participants change their behavior or responses to satisfy the researcher's desired effect. This happens in an unconscious manner for the participant so it is important to eliminate or limit transmitting the researcher's views.
• Artificial scenario effect: Some qualitative research occurs in artificial scenarios and/or with preset goals. In such situations, the information may not be accurate because of the artificial nature of the scenario. The preset goals may limit the qualitative information obtained.
• Clinical Significance

Qualitative research by itself or combined with quantitative research helps healthcare providers understand patients and the impact and challenges of the care they deliver. Qualitative research provides an opportunity to generate and refine hypotheses and delve deeper into the data generated by quantitative research. Qualitative research does not exist as an island apart from quantitative research, but as an integral part of research methods to be used for the understanding of the world around us. [17]

• Enhancing Healthcare Team Outcomes

Qualitative research is important for all members of the health care team as all are affected by qualitative research. Qualitative research may help develop a theory or a model for health research that can be further explored by quantitative research.  Much of the qualitative research data acquisition is completed by numerous team members including social works, scientists, nurses, etc.  Within each area of the medical field, there is copious ongoing qualitative research including physician-patient interactions, nursing-patient interactions, patient-environment interactions, health care team function, patient information delivery, etc. 

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Disclosure: Steven Tenny declares no relevant financial relationships with ineligible companies.

Disclosure: Janelle Brannan declares no relevant financial relationships with ineligible companies.

Disclosure: Grace Brannan declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page Tenny S, Brannan JM, Brannan GD. Qualitative Study. [Updated 2022 Sep 18]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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• Study from Phoenix Children’s Research Institute Reveals New Way to Prevent Lung Cancer from Spreading

News Release

PHOENIX [April 25, 2024] – Research conducted at the Phoenix Children’s Research Institute at the University of Arizona College of Medicine — Phoenix shows normalizing cancer tumor vessels and alleviating low oxygen levels in the tumor microenvironment can improve the effectiveness of chemotherapy treatment in lung cancer, according to a paper published in EMBO Molecular Medicine .

This study, authored by Tanya Kalin, MD, PhD, vice chair of translational research for the Phoenix Children’s Center for Cancer and Blood Disorders and professor of Child Health at the University of Arizona College of Medicine — Phoenix, ”FOXF1 Promotes Vessel Normalization and Prevents Lung Cancer Progression Through FZD4, is a complete pivot from how researchers have historically studied cancer treatment by providing a solution that strengthens blood vessels feeding cancer tumors.

“Cancer research is often focused on ways to deplete cancer tumors of blood vessels and oxygen by basically starving the cancer cells to prevent tumor growth and metastasis, but it was shown this approach can make the tumor cells become even more aggressive and metastatic,” said Dr. Kalin. “Our study takes a completely different approach and instead of obliterating the tumor-associated blood vessels, we normalize and repair the tumor vessels by increasing the FOXF1 protein in endothelial cells, which, in turn, prevents lung cancer progression.”

Dr. Kalin, a renowned scientist focused on developing effective treatments for pediatric cancers, has spent the last few decades researching how to create a non-toxic small molecule inhibitor compound to kill cancer cells and developing tumor-cell-specific nanoparticles that deliver the inhibitor compound directly to cancer cells, blocking specific targeted proteins. This lung cancer study builds upon her years of research to develop a more targeted approach to cancer treatment. The insertion of the FOXF1 protein increases tumor vessel stability and stimulates nanoparticle delivery into the cancer-causing cells which destroys them from the inside with minimal chemotherapy.

“This is a promising study for future therapies in non-small cell lung cancer and other types of cancer and will hopefully change how we look at cancer treatment moving forward,” said Dr. Kalin.

Lung cancer is the leading cause of cancer-related mortality worldwide. Current treatment strategies include chemotherapy and/or radiotherapy, and surgery in the case of patients diagnosed with early-stage lung cancer. The 5-year survival rate of patients with advanced Non-Small Cell Lung Cancer (NSCLC) remains less than 20 percent, emphasizing a need to develop better treatment strategies. Interactions between the tumor-microenvironment and tumor cells play a crucial role in tumor progression and to this point traditional therapies have failed to improve overall survival of lung cancer patients, suggesting a deeper understanding of tumor-associated vascular biology is required.

“Every breakthrough in pediatric cancer research is cause for celebration, especially studies like Dr. Kalin’s that shed light on tumor microenvironments and stopping the spread of cancer,” said Stewart Goldman, MD, senior vice president of research for Phoenix Children’s and Sybil B. Harrington endowed chair and professor of Child Health at University of Arizona College of Medicine — Phoenix. “Dr. Kalin’s remarkable findings will have implications for non-small cell lung cancer, as well as many childhood cancers, hopefully changing cancer therapies in the near term and I believe this research will be one of the most cited cancer research studies for years to come.”

In March, Phoenix Children’s released another study, “CRISPR/Cas9 Genome Editing Allows Generation of the Mouse Lung in a Rat,” that focuses on finding an innovative solution for babies born with chronic lung diseases caused by either prematurity or severe genetic conditions.

The Phoenix Children's Research Institute at the University of Arizona College of Medicine — Phoenix launched in May 2023, formalizing a longstanding research collaboration between the health system and the University of Arizona College of Medicine — Phoenix. The Research Institute includes more than 700 active studies, 640 research investigators and 90 research staff members including research scientists, associates, biostatisticians, pharmacists, nurses and coordinators. Scientists engage in research across multiple clinical disciplines including cancer , neurology , cardiology , pulmonology and more.

Phoenix Children’s Names New Chief of Neurosurgery April 24, 2024

Phoenix Children’s Unveils “Wonder And Wander,” a 407-Foot Mural to Advance Hope and Healing for Patient Families April 18, 2024

Dr. Ashish S. Patel Named Physician-in-Chief at Phoenix Children’s April 15, 2024

Advanced cell atlas opens new doors in biomedical research

Researchers at Karolinska Institutet have developed a web-based platform that offers an unprecedented view of the human body at the cellular level. The aim is to create an invaluable resource for researchers worldwide to increase knowledge about human health and disease. The study is published in Genome Biology.

Simultaneous measurement of numerous biomolecular variables, known as multi-omics, enables deep and comprehensive profiling of human biology. The new Single Cell Atlas (SCA) is based on analyses of thousands of human tissue samples from 125 different adult and fetal tissues. The researchers combined eight cutting-edge omics technologies, including single-cell RNA sequencing, whole-genome sequencing, and spatial transcriptomics to map and localise genes expressed in the tissue.

The platform provides unique insights into individual cell properties and their interactions within tissues. The extensive collection of data is freely accessible through the platform's website.

"The Single Cell Atlas not only saves time and resources but also fosters a collaborative environment for scientists from diverse fields, paving the way for new discoveries and innovations," says the study's first author Lu Pan, researcher at the Institute of Environmental Medicine, Karolinska Institutet, Sweden.

Looking ahead, the team plans to refine the SCA by introducing more detailed analyses and annual updates. These enhancements will fill gaps in tissue representation and expand the sample size, allowing for more precise research.

"The creation of the SCA marks a significant step forward in biomedical research," says the study's last author Xuexin Li, researcher at the Department of Physiology and Pharmacology (previously at the Department of Medical Biochemistry and Biophysics), Karolinska Institutet. "Our goal is to continually enrich the atlas, making it an invaluable resource for understanding human health and disease."

The research was done in collaboration with China Medical University and several other international collaboration partners in The Single Cell Atlas Consortium. The study was financed by Karolinska Institutet and the KI Network Medicine Global Alliance (KI NMA). Coauthor Volker Lauschke is CEO and shareholder of HepaPredict AB, co-founder and shareholder of PersoMedix AB, and discloses consultancy work for Enginzyme AB. The other authors declare that they have no competing interests.

• Human Biology
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• Personalized Medicine
• Biotechnology and Bioengineering
• Biochemistry Research
• Developmental Biology
• Health science
• Stem cell treatments
• Scientific method
• Public health
• Veterinary medicine
• Thyroid hormone
• Electron microscope

Story Source:

Materials provided by Karolinska Institutet . Note: Content may be edited for style and length.

Journal Reference :

• Lu Pan, Paolo Parini, Roman Tremmel, Joseph Loscalzo, Volker M. Lauschke, Bradley A. Maron, Paola Paci, Ingemar Ernberg, Nguan Soon Tan, Zehuan Liao, Weiyao Yin, Sundararaman Rengarajan, Xuexin Li. Single Cell Atlas: a single-cell multi-omics human cell encyclopedia . Genome Biology , 2024; 25 (1) DOI: 10.1186/s13059-024-03246-2

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Advancing research • shaping policy • developing leaders, from tech tools to human values: isps conference explores the impact of ai in government.

Artificial Intelligence (AI) is not the future of government. In many ways, it’s happening now.

Government officials increasingly use AI and data-driven algorithms to influence critical choices, ranging from determining the distribution of food assistance and parole decisions to selecting targets for tax audits and planning the routes for police patrols.

“As AI algorithms become more powerful and impactful, so does the realization that we are facing a major change that touches on the very core of what makes us a democracy, namely the way that we make public decisions,” said Shir Raviv , a postdoctoral research fellow at Columbia University and a nonresident fellow with Yale’s Institution for Social and Policy Studies’ Democratic Innovations program. “It raises some urgent and timely questions about how to unlock the potential value of using AI to improve government decisions and processing while maintaining democratic values and human rights.”

Raviv organized a one-day conference at ISPS last week to explore the latest research on how government uses technology to guide decision making and what might be done to ensure it is used responsibly.

The conference included presentations from Kirk Bansak of the University of California, Berkeley on refugee integration; Virginia Eubanks of the University at Albany, State University of New York on automating caregiving; and Kaylyn Jackson Schiff of Purdue University on citizen perceptions of AI in policing.

Further discussions focused on AI in the criminal justice system, introducing new frameworks to assess the risks and benefits of AI, including presentations by Melody Huang of Harvard University on whether AI helps humans make better decisions; Dasha Pruss of Harvard University on judicial resistance to a recidivism risk assessment instrument; and Eddie Yang of the University of California, San Diego on ethnic discrimination in AI-assisted criminal sentencing in China.

Other presentations discussed the politics involved in using AI in government. Baobao Zhang of Syracuse University shared survey evidence from machine learning and AI researchers on the ethics and governance of artificial intelligence. Daniel Schiff of Purdue University focused on the viewpoints of policymakers and legislators. And Raviv examined the public’s reaction to AI.

ISPS Director Alan Gerber , Sterling Professor of Political Science, moderated an interdisciplinary roundtable discussion on the promise and challenges of ensuring responsible AI in government, featuring ISPS faculty fellow and Democratic Innovations co-coordinator Hélène Landemore , a political scientist specializing in non-electoral forms of government representation who is co-leading a three-year project on the ethics of AI; Yale political science Ph.D. candidate Eliza Oak , who researches innovations in technology and democracy; Savannah Thais of Columbia University, a physicist who develops responsible and trustworthy machine learning; and Suresh Venkatasubramanian of Brown University, a professor of computer science and data science who was the co-author of the Blueprint for an AI Bill of Rights — one of the first significant actions taken by the Biden-Harris administration to regulate AI.

“We are thrilled to have Shir as an active member of our community at ISPS,” Gerber said. “Her forward-thinking research and success in gathering such an impressive group of scholars to explore the political implications of new technologies demonstrate the guiding principles of our Democratic Innovations program.”

Democratic Innovations aims to identify and test new ideas for improving the quality of democratic representation and governance.

“Though these systems promised to lower administrative barriers to programs to allow people to claim benefits from their cell phones or from the comfort of their own homes, in reality, the systems tend to work best for those people who are least vulnerable.”

And because new automated decisions reduce the need for frontline caseworkers, fewer people receive the support they are seeking, Eubanks said.

“These systems end up working really badly for folks who are particularly vulnerable,” she said. “There is less hands-on help. These are the very people who public benefits programs are supposed to be helping.”

Pruss, a fellow at the Berkman Klein Center for Internet & Society and an Embedded EthiCS postdoctoral fellow at Harvard, presented her research showing criminal judges in Pennsylvania ignoring a new tool intended to help sentencing decisions through evidence-based risk assessment. She argued that policymakers should be wary when presented with a new instrument advertised as evidence based.

“In evidence-based sentencing, the term ‘evidence based’ carries a lot of political authority, but that label gets used in a fairly misleading way because sentencing decisions are being grounded in past arrest or conviction data, which are inherently biased,” Pruss said. “It’s called evidence based, but there is no evidence about what actually happens in the future when the tools get implemented on the ground.”

But Pruss did not dismiss the utility of AI or algorithms to help build a more just world. She said policymakers should frame the intention of a technological tool in criminal justice based on the human values they seek to uphold.

“What outcomes are considered important to predict?” she said. “Somebody’s risk of reconviction? Or is it more important to predict, say, which judges are going to be making the most discriminatory decisions? Should we use data to incarcerate more people who are at higher risk of committing more crimes or use evidence to allocate extra resources to people who really need it?”

In concluding her presentation, Eubanks echoed other participants at the conference on what she considered the central question facing a society drifting quickly into automation.

“We need to center human dignity,” she said. “We need to make the labor of love visible.”

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• How Hispanic Americans Get Their News

U.S.-born Latinos overwhelmingly prefer to get their news in English; about half of immigrant Latinos prefer it in Spanish

Table of contents.

• Where do Hispanic Americans get news?
• Major differences between U.S.-born Latinos and immigrants
• Bilingual Hispanics much more likely to get news in English than Spanish
• U.S.-born Hispanics less likely to engage with Hispanic news outlets
• Latinos with lower levels of income more likely to get news from Hispanic outlets and about origin countries
• Who gets news from Hispanic outlets and about origin countries?
• Acknowledgments
• The American Trends Panel survey methodology
• How family income tiers are calculated

Pew Research Center conducted this study to understand Hispanic Americans’ habits around news and information, including the languages in which they consume news and their engagement with Hispanic media outlets.

Most of the questions in this report are from Pew Research Center’s 2023 National Survey of Latinos, a survey of 5,078 U.S. Hispanic adults conducted Nov. 6-19, 2023. This includes 1,524 Hispanic adults on the Center’s  American Trends Panel  (ATP) and 3,554 Hispanic adults on Ipsos’ KnowledgePanel . Respondents on both panels are recruited through national, random sampling of residential addresses. Recruiting panelists by phone or mail ensures that nearly all U.S. adults have a chance of selection. This gives us confidence that any sample can represent the whole population, or in this case the whole U.S. Hispanic population. (For more information, watch our  Methods 101 explainer  on random sampling.)

To further ensure the survey reflects a balanced cross-section of the nation’s Hispanic adults, the data is weighted to match the U.S. Hispanic adult population by age, gender, education, nativity, Hispanic origin group and other categories. Read more about the  ATP’s methodology . Refer to the topline for the questions used for our National Survey of Latinos , along with responses, and to methodology for more details.

The questions about how often people get news from various platforms, which platforms they prefer for getting news, and which social media sites people get news from are from an ATP survey of 8,842 U.S. adults, including 1,193 Hispanic adults, conducted Sept. 25-Oct. 1, 2023. Refer to the topline for t he questions used for this survey , along with responses, and to the methodology for more details.

Pew Research Center is a subsidiary of The Pew Charitable Trusts, its primary funder. This is the latest report in Pew Research Center’s ongoing investigation of the state of news, information and journalism in the digital age, a research program funded by The Pew Charitable Trusts, with generous support from the John S. and James L. Knight Foundation.

The terms  Hispanic  and  Latino  are used interchangeably in this report.

Hispanic/Latino Americans, Hispanic/Latino adults , and Hispanics/Latinos are used interchangeably in this report to refer to survey respondents who self-identify as Hispanic or Latino in the United States. They include those who say their race is White, Black, Asian or some other race and those who identify as multiracial. Hispanic/Latino Americans live in the U.S. but are not necessarily U.S. citizens.

U.S. born  refers to people born in the 50 states or the District of Columbia.

Immigrant  refers to people born outside the 50 states or D.C. For the purposes of this report, immigrants include those born in Puerto Rico or another U.S. territory. Although individuals born in Puerto Rico are U.S. citizens by birth, they are grouped with immigrant respondents because they were born into a Spanish-dominant culture and because on many points their attitudes, views and beliefs more closely resemble those of Hispanics born outside the U.S. than Hispanics born in the 50 states or D.C., and even U.S.-born Hispanics who identify as being of Puerto Rican origin.

Second generation  refers to people born in the 50 states or D.C. who have at least one parent born in a different country, Puerto Rico or another U.S. territory.

Third generation   or higher refers to people born in the 50 states or D.C. who have two parents born in the 50 states or D.C.

Language dominance  is a composite measure based on self-described assessments of speaking and reading abilities.  Spanish-dominant  people are more proficient in Spanish than in English (i.e., they speak and read Spanish “very well” or “pretty well” but rate their English ability lower).  Bilingual  refers to people who are proficient in both English and Spanish.  English-dominant  people are more proficient in English than in Spanish.

“Middle income” is defined here as two-thirds to double the median annual family income for panelists on the American Trends Panel. “Lower income” falls below that range; “upper income” falls above it. Refer to the methodology for more details.

Hispanic news outlets are those outlets that focus on providing news and information specifically to Hispanic audiences. These can include newspapers, radio or TV stations, podcasts, or social media accounts created for and by Hispanic people. Their content could be in Spanish, English, both languages or another language.

Country of origin refers to the country that survey respondents, their parents or their Hispanic ancestors came from.

Just over half of U.S. Hispanic adults (54%) get their news mostly in English – far higher than the share who get their news mostly in Spanish (21%). About a quarter of Hispanic Americans (23%) say they consume news in both languages about equally.

There is an almost identical pattern on the question of preferred language for news: 51% prefer to get their news in English, 24% prefer Spanish and 23% say they do not have a preference.

But a new Pew Research Center survey of adults who identify as Hispanic or Latino finds major differences in news consumption habits between U.S.-born Hispanics and those who immigrated from other countries .

While U.S.-born Latinos overwhelmingly get their news in English, and prefer it in English, those born outside the United States have much more varied habits: 41% get their news mostly in Spanish, 26% get it primarily in English and 31% do both about equally. Similarly, 47% of Latino immigrants prefer to get their news in Spanish, while 22% prefer English and 31% do not express a preference.

Among Latino immigrants, those who have spent more years in the U.S. are less inclined than more recent arrivals to get news in Spanish, and more inclined to get it in English. There is little difference in the shares who get news in both languages about equally.

Jump to more information on the languages in which U.S. Latinos consume news.

We asked these questions to better understand how a group that makes up nearly one-in-five Americans stays informed, especially as its demographics and use of Spanish continue to change. Immigrants are declining as a share of all U.S. Hispanics , and the share of Hispanics who speak Spanish at home has also dropped – even though the number of Hispanics who speak Spanish at home has increased due to overall growth in the Hispanic population.

Other key findings about Hispanics’ news consumption include:

Most Latino adults prefer digital devices for news

Latinos get their news from a variety of sources, but most say they prefer to use digital devices over other platforms. Nearly nine-in-ten (87%) say they get news from digital devices at least sometimes, and 65% say they prefer this form of news over TV, radio or print. Digital devices have become an increasingly common source for news among Latinos – and among Americans overall – in recent decades, a shift driven by the rise of the internet .

Latinos are more likely than White Americans (55%) and Black Americans (50%) to prefer getting news from digital devices. Latinos also are more likely than White and Black adults to get news from social media, at least in part because Latino adults tend to be younger than other groups, and young adults are more inclined to use social media for news.

Nearly three-quarters of Latino adults under 50 (73%) prefer to get their news on digital devices, including 27% who prefer social media specifically.

Jump to more information on the platforms where U.S. Latinos get news.

Attention to news is declining among U.S. Latinos

About one-in-five Latino adults (22%) say they follow the news all or most of the time, while an additional 36% follow the news some of the time. The share of Latinos who follow the news all or most of the time has fluctuated in recent years but has dropped by 9 percentage points between 2020 (31%) and 2023 (22%), similar to a pattern seen across the general U.S. public .

In recent years, Hispanic Americans have followed the news less closely than Black and White Americans. Again, the high share of young adults within the Hispanic population plays a role, because young people are less likely to follow the news closely. Among Hispanic adults ages 18 to 29, just 10% say they follow the news all or most of the time – far below the share of Hispanics ages 65 and older who do so (44%).

Jump to more information on U.S. Hispanics’ news consumption habits.

Half of Hispanic adults get news from Hispanic news outlets

Half of U.S. Hispanic adults say they at least sometimes get news from Hispanic news outlets – those that specifically cater to Hispanic audiences. This includes 21% who say they do this extremely or very often. Just over half of Hispanics (54%) get news about their or their family’s country of origin at least sometimes, including 24% who do this often. 

Hispanic immigrants are much more likely than U.S.-born Hispanics to get news from Hispanic outlets and about their origin country. In both cases, about seven-in-ten immigrants say they at least sometimes get these types of news: 69% get news from Hispanic outlets and 72% get news about their country of origin. Among Hispanic adults who were born in the U.S., 33% at least sometimes get news from Hispanic outlets, and 38% get news about their family’s country of origin.

There are further differences among U.S.-born Hispanics: Those whose parents were also born in the U.S. are even less likely than those with one or more immigrant parent to get these types of news.

Jump to more information on Hispanic news outlets and news about Hispanic Americans’ origin countries.

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New study offers hope for a rare and devastating eye cancer

After more than a decade studying a rare eye cancer that produces some of the hardest-to-fight tumors, researchers from University of Pittsburgh Medical Center have found a treatment that works on some patients and, more importantly, a tool that can predict when it is likely to succeed.

The work, published in Nature Communications, is being validated in a clinical trial involving at least 30 patients. It could pave the way for similar methods designed to overcome one of the enduring frustrations of cancer care.

Because tumors differ, not only between patients but even inside the same patient, a treatment that works on one mass may fail on another, even when both are of the same cancer type.

The researchers in Pittsburgh tackled this problem in uveal melanoma, an eye cancer that afflicts only 5 people in a million, but that half the time spreads to other parts of the body, often the liver. The median survival once uveal melanoma has spread has been less than seven months, according to a 2018 study in the journal JAMA Ophthalmology.

“We chose this because it was one of the only cancers that 10 years ago when we started, there was nothing approved for it,” said Udai Kammula, who led the study and directs the Solid Tumor Cell Therapy Program at UPMC Hillman Cancer Center in Pittsburgh.

Scientists had long speculated that the reason uveal melanoma is so tough to fight is that something helps the tumor keep out T cells, a key part of the body’s immune system that develops in bone marrow. However, previous studies by Kammula and his colleagues showed that uveal melanoma tumors actually have T cells inside, and they are turned on.

The problem? The cells lie dormant instead of multiplying and reaching numbers large enough to overwhelm the tumor.

The culprit appears to reside somewhere inside the tumor’s ecosystem of cells, molecules and blood vessels, known formally as the tumor’s “microenvironment.” Kammula compares this ecosystem to the infrastructure that supports a city. Something in that infrastructure helps protect uveal melanoma tumors by preventing the critical T cells from multiplying.

“Ultimately, if we’re going to get rid of cancer, we have to get rid of this infrastructure,” Kammula said.

A tool for predicting success

He and his colleagues have had some success using a treatment known as adoptive cell therapy, which was developed in the 1980s by Steven Rosenberg at the National Institutes of Health.

The treatment involves removing the T cells from the tumor, where they have been unable to proliferate. Scientists then take those T cells and grow them outside the body in a lab dish. They treat patients with chemotherapy to kill off the last of their old immune systems. Finally, they reinfuse the lab-grown T cells into the patient’s blood stream and the cells, now in much greater numbers, go on to attack the tumor.

In this treatment, the T cells are often referred to as tumor-infiltrating leukocytes, or TILs.

Kammula said his team has found that tumors shrink partially or completely in about 35 percent of patients who receive the treatment. But they wanted to know why it doesn’t work in the majority of cases, and whether there might be some way to predict beforehand when it will succeed.

To find out, the researchers analyzed samples from 100 different uveal melanoma tumors that had spread to different parts of the body in 84 patients, seeking to examine all of the tumors’ genetic material.

“We basically put the tumor biopsy in a blender that had the stroma [supportive tissue], the blood vessels, the immune cells, the tumor cells. It had everything,” Kammula said, explaining that they then analyzed all of the tumor’s genetic material.

They found 2,394 genes that could have helped make the tumor susceptible to treatment, some of them genes that experts would regard as “the usual suspects” and others that were unexpected. Using this long list of genes, the scientists searched for characteristics that they shared.

The genes were predominantly involved in helping the body defend itself against viruses, bacteria and other foreign invaders by removing the invaders and helping tissue heal. Kammula and the study’s lead author, Shravan Leonard-Murali, a postdoctoral fellow in the lab, used the different activity levels of these genes to develop a clinical tool.

The tool, known as a biomarker, assigns a score to a uveal melanoma tumor based on the likelihood that it will respond well to the treatment ― removing T cells, growing them outside the body, then reinfusing them.

So far, Kammula said, the biomarker has been “extremely good,” in predicting when the treatment will be effective, though he added, “these findings will need confirmation in the current ongoing clinical trial.”

“I thought it was somewhat of a tour de force, honestly,” said Eric Tran, an associate member of the Earle A. Chiles Research Institute, a division of Providence Cancer Institute in Portland, Ore. Tran did not participate in the study.

He said that while it will be important to validate these results, “I was certainly encouraged by their studies. And from my perspective, I wonder if that sort of strategy can be deployed in other cancers.”

Ryan J. Sullivan, an oncologist at Massachusetts General Hospital and associate professor at Harvard Medical School who was not involved in the study, called the team’s work “timely” and said “it is even more significant that they appear to have a [tool] that appears to predict which patients will benefit.”

The team at UPMC is already investigating possible wider application of both the treatment and the biomarker in a second clinical trial that involves a dozen different cancers.

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Helping women get better sleep by calming the relentless 'to-do lists' in their heads

Yuki Noguchi

Katie Krimitsos is among the majority of American women who have trouble getting healthy sleep, according to a new Gallup survey. Krimitsos launched a podcast called Sleep Meditation for Women to offer some help. Natalie Champa Jennings/Natalie Jennings, courtesy of Katie Krimitsos hide caption

Katie Krimitsos is among the majority of American women who have trouble getting healthy sleep, according to a new Gallup survey. Krimitsos launched a podcast called Sleep Meditation for Women to offer some help.

When Katie Krimitsos lies awake watching sleepless hours tick by, it's almost always because her mind is wrestling with a mental checklist of things she has to do. In high school, that was made up of homework, tests or a big upcoming sports game.

"I would be wide awake, just my brain completely spinning in chaos until two in the morning," says Krimitsos.

There were periods in adulthood, too, when sleep wouldn't come easily, like when she started a podcasting company in Tampa, or nursed her first daughter eight years ago. "I was already very used to the grainy eyes," she says.

Now 43, Krimitsos says in recent years she found that mounting worries brought those sleepless spells more often. Her mind would spin through "a million, gazillion" details of running a company and a family: paying the electric bill, making dinner and dentist appointments, monitoring the pets' food supply or her parents' health checkups. This checklist never, ever shrank, despite her best efforts, and perpetually chased away her sleep.

"So we feel like there are these enormous boulders that we are carrying on our shoulders that we walk into the bedroom with," she says. "And that's what we're laying down with."

By "we," Krimitsos means herself and the many other women she talks to or works with who complain of fatigue.

Women are one of the most sleep-troubled demographics, according to a recent Gallup survey that found sleep patterns of Americans deteriorating rapidly over the past decade.

"When you look in particular at adult women under the age of 50, that's the group where we're seeing the most steep movement in terms of their rate of sleeping less or feeling less satisfied with their sleep and also their rate of stress," says Gallup senior researcher Sarah Fioroni.

Overall, Americans' sleep is at an all time low, in terms of both quantity and quality.

A majority – 57% – now say they could use more sleep, which is a big jump from a decade ago. It's an acceleration of an ongoing trend, according to the survey. In 1942, 59% of Americans said that they slept 8 hours or more; today, that applies to only 26% of Americans. One in five people, also an all-time high, now sleep fewer than 5 hours a day.

Popular myths about sleep, debunked

"If you have poor sleep, then it's all things bad," says Gina Marie Mathew, a post-doctoral sleep researcher at Stony Brook Medicine in New York. The Gallup survey did not cite reasons for the rapid decline, but Mathew says her research shows that smartphones keep us — and especially teenagers — up later.

She says sleep, as well as diet and exercise, is considered one of the three pillars of health. Yet American culture devalues rest.

"In terms of structural and policy change, we need to recognize that a lot of these systems that are in place are not conducive to women in particular getting enough sleep or getting the sleep that they need," she says, arguing things like paid family leave and flexible work hours might help women sleep more, and better.

No one person can change a culture that discourages sleep. But when faced with her own sleeplessness, Tampa mom Katie Krimitsos started a podcast called Sleep Meditation for Women , a soothing series of episodes in which she acknowledges and tries to calm the stresses typical of many women.

Shots - Health News

Many grouchy, error-prone workers just need more sleep.

That podcast alone averages about a million unique listeners a month, and is one of 20 podcasts produced by Krimitsos's firm, Women's Meditation Network.

"Seven of those 20 podcasts are dedicated to sleep in some way, and they make up for 50% of my listenership," Krimitsos notes. "So yeah, it's the biggest pain point."

Krimitsos says she thinks women bear the burdens of a pace of life that keeps accelerating. "Our interpretation of how fast life should be and what we should 'accomplish' or have or do has exponentially increased," she says.

She only started sleeping better, she says, when she deliberately cut back on activities and commitments, both for herself and her two kids. "I feel more satisfied at the end of the day. I feel more fulfilled and I feel more willing to allow things that are not complete to let go."