Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

  • Knowledge Base
  • Methodology

Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

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

  • Your overall aims and approach
  • The type of research design you’ll use
  • Your sampling methods or criteria for selecting subjects
  • Your data collection methods
  • The procedures you’ll follow to collect data
  • Your data analysis methods

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

Table of contents

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

  • Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

Prevent plagiarism, run a free check.

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

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalisation

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

The research methods you use depend on the type of data you need to answer your research question .

  • If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
  • If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
  • If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2023, March 20). Research Design | Step-by-Step Guide with Examples. Scribbr. Retrieved 14 May 2024, from https://www.scribbr.co.uk/research-methods/research-design/

Is this article helpful?

Shona McCombes

Shona McCombes

What Is Research, and Why Do People Do It?

  • Open Access
  • First Online: 03 December 2022

Cite this chapter

You have full access to this open access chapter

research study and

  • 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))

17k Accesses

Abstractspiepr Abs1

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

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

Part I. What Is Research?

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

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

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

Exercise 1.1

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

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

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

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

An image of the book's description with the words like research, science, and inquiry and what the word research meant in the scientific world.

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.

An image represents the observation required in the scientific inquiry including planning and explaining.

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.

An image represents the data explanation as it is not limited and takes numerous non-quantitative forms including an interview, journal entries, etc.

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. ).

An image represents the scientific inquiry definition given by the editors of Britannica and also defines the hypothesis on the basis of the experiments.

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.

An image represents the rationale and the prediction for the scientific inquiry and different types of information provided by the terms.

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.

An image represents the cycle of events that take place before making predictions, developing the rationale, and studying the prediction and rationale multiple times.

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

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

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

Part II. Why Do Educators Do Research?

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

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

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

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

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

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

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

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

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

Exercise 1.7

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

Part III. Conducting Research as a Practice of Failing Productively

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

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

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

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

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

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

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

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

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

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

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

Exercise 1.8

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

Exercise 1.9

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

Part IV. Preview of Chap. 2

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

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

Google Scholar  

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

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

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

Article   Google Scholar  

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

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

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

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

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

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

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

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

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

Download references

Author information

Authors and affiliations.

School of Education, University of Delaware, Newark, DE, USA

James Hiebert, Anne K Morris & Charles Hohensee

Department of Mathematical Sciences, University of Delaware, Newark, DE, USA

Jinfa Cai & Stephen Hwang

You can also search for this author in PubMed   Google Scholar

Rights and permissions

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Reprints and permissions

Copyright information

© 2023 The Author(s)

About this chapter

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

Download citation

DOI : https://doi.org/10.1007/978-3-031-19078-0_1

Published : 03 December 2022

Publisher Name : Springer, Cham

Print ISBN : 978-3-031-19077-3

Online ISBN : 978-3-031-19078-0

eBook Packages : Education Education (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Publish with us

Policies and ethics

  • Find a journal
  • Track your research
  • Skip to main content
  • Skip to primary sidebar
  • Skip to footer
  • QuestionPro

survey software icon

  • Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case NPS+ Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
  • Resources Blog eBooks Survey Templates Case Studies Training Help center

research study and

Home Market Research

What is Research: Definition, Methods, Types & Examples

What is Research

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

what is research

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

What is a research - tips

  • 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.

LEARN MORE ABOUT OUR SOFTWARE         FREE TRIAL

MORE LIKE THIS

email survey tool

The Best Email Survey Tool to Boost Your Feedback Game

May 7, 2024

Employee Engagement Survey Tools

Top 10 Employee Engagement Survey Tools

employee engagement software

Top 20 Employee Engagement Software Solutions

May 3, 2024

customer experience software

15 Best Customer Experience Software of 2024

May 2, 2024

Other categories

  • Academic Research
  • Artificial Intelligence
  • Assessments
  • Brand Awareness
  • Case Studies
  • Communities
  • Consumer Insights
  • Customer effort score
  • Customer Engagement
  • Customer Experience
  • Customer Loyalty
  • Customer Research
  • Customer Satisfaction
  • Employee Benefits
  • Employee Engagement
  • Employee Retention
  • Friday Five
  • General Data Protection Regulation
  • Insights Hub
  • Life@QuestionPro
  • Market Research
  • Mobile diaries
  • Mobile Surveys
  • New Features
  • Online Communities
  • Question Types
  • Questionnaire
  • QuestionPro Products
  • Release Notes
  • Research Tools and Apps
  • Revenue at Risk
  • Survey Templates
  • Training Tips
  • Uncategorized
  • Video Learning Series
  • What’s Coming Up
  • Workforce Intelligence

Regions & Countries

  • Publications

Our Methods

  • Short Reads
  • Tools & Resources

Read Our Research On:

Broad Public Support for Legal Abortion Persists 2 Years After Dobbs

Teens and video games today, a majority of latinas feel pressure to support their families or to succeed at work.

More than half of Latinas say they often feel pressure to provide for their loved ones at home or succeed in their jobs. Latinas feel cross-pressured in other ways too, as they juggle cultural expectations around gender roles rooted in Latin America  and  those rooted in the U.S.

Americans’ Changing Relationship With Local News

Growing partisan divisions over nato and ukraine, sign up for our weekly newsletter.

Fresh data delivered Saturday mornings

Latest Publications

Many juggle cultural expectations and gender roles from both Latin America and the U.S., like doing housework and succeeding at work.

Public Opinion on Abortion

Abortion has long been a contentious issue in the United States, and it is one that sharply divides Americans along partisan, ideological and religious lines.

Americans overwhelmingly say access to IVF is a good thing

Seven-in-ten Americans say in vitro fertilization access is a good thing. Just 8% say it is a bad thing, and 22% are unsure.

Views are split by political party, but support for legal abortion has risen modestly in both groups since before the 2022 Dobbs decision.

85% of U.S. teens say they play video games. They see both positive and negative sides, from making friends to harassment and sleep loss.

All publications >

Most Popular

Sign up for the briefing.

Weekly updates on the world of news & information

  • Politics & Policy

In Tight Presidential Race, Voters Are Broadly Critical of Both Biden and Trump

Voters are evenly split in their support for Trump (49%) and Biden (48%), but overall lack confidence in both on a range of traits.

As Biden and Trump seek reelection, who are the oldest – and youngest – current world leaders?

More than 80% of americans believe elected officials don’t care what people like them think, a growing share of americans have little or no confidence in netanyahu, what the data says about crime in the u.s..

All Politics and Policy research >

The Hardships and Dreams of Asian Americans Living in Poverty

What public k-12 teachers want americans to know about teaching, how people in 24 countries think democracy can improve, religious restrictions around the world.

All Features >

  • International Affairs

Americans are less likely than others around the world to feel close to people in their country or community

A median of 83% across 24 nations surveyed say they feel close to other people in their country, while 66% of Americans hold this view.

58% of Americans see NATO favorably, down 4 points since 2023. Democrats and Republicans are increasingly divided on the alliance and on Ukraine aid.

Americans Remain Critical of China

About eight-in-ten Americans report an unfavorable view of China, and Chinese President Xi Jinping receives similarly negative ratings.

Younger Americans stand out in their views of the Israel-Hamas war

33% of adults under 30 say their sympathies lie either entirely or mostly with the Palestinian people, while 14% say their sympathies lie with the Israeli people.

All INTERNATIONAL AFFAIRS RESEARCH >

  • Internet & Technology

Americans’ Views of Technology Companies

Most Americans are wary of social media’s role in politics and its overall impact on the country, and these concerns are ticking up among Democrats. Still, Republicans stand out on several measures, with a majority believing major technology companies are biased toward liberals.

6 facts about Americans and TikTok

62% of U.S. adults under 30 say they use TikTok, compared with 39% of those ages 30 to 49, 24% of those 50 to 64, and 10% of those 65 and older.

Many Americans think generative AI programs should credit the sources they rely on

22% of Americans say they interact with artificial intelligence almost constantly or several times a day. 27% say they do this about once a day or several times a week.

All INTERNET & TECHNOLOGY RESEARCH >

  • Race & Ethnicity

How Hispanic Americans Get Their News

U.S.-born Latinos mostly get their news in English and prefer it in English, while immigrant Latinos have much more varied habits.

Key facts about Asian Americans living in poverty

Burmese (19%) and Hmong Americans (17%) were among the Asian origin groups with the highest poverty rates in 2022.

Latinos’ Views on the Migrant Situation at the U.S.-Mexico Border

U.S. Hispanics are less likely than other Americans to say increasing deportations or a larger wall along the border will help the situation.

Black Americans’ Views on Success in the U.S.

While Black adults define personal and financial success in different ways, most see these measures of success as major sources of pressure in their lives.

5 facts about Black Americans and health care 

More Black Americans say health outcomes for Black people in the United States have improved over the past 20 years than say outcomes have worsened.

All Race & Ethnicity RESEARCH >

research study and

U.S. Surveys

Pew Research Center has deep roots in U.S. public opinion research. Launched as a project focused primarily on U.S. policy and politics in the early 1990s, the Center has grown over time to study a wide range of topics vital to explaining America to itself and to the world.

research study and

International Surveys

Pew Research Center regularly conducts public opinion surveys in countries outside the United States as part of its ongoing exploration of attitudes, values and behaviors around the globe.

research study and

Data Science

Pew Research Center’s Data Labs uses computational methods to complement and expand on the Center’s existing research agenda.

research study and

Demographic Research

Pew Research Center tracks social, demographic and economic trends, both domestically and internationally.

research study and

All Methods research >

Our Experts

“A record 23 million Asian Americans trace their roots to more than 20 countries … and the U.S. Asian population is projected to reach 46 million by 2060.”

A headshot of Neil Ruiz, head of new research initiatives and associate director of race and ethnicity research.

Neil G. Ruiz , Head of New Research Initiatives

Key facts about asian americans >

Methods 101 Videos

Methods 101: random sampling.

The first video in Pew Research Center’s Methods 101 series helps explain random sampling – a concept that lies at the heart of all probability-based survey research – and why it’s important.

Methods 101: Survey Question Wording

Methods 101: mode effects, methods 101: what are nonprobability surveys.

All Methods 101 Videos >

Add Pew Research Center to your Alexa

Say “Alexa, enable the Pew Research Center flash briefing”

Signature Reports

Race and lgbtq issues in k-12 schools, representative democracy remains a popular ideal, but people around the world are critical of how it’s working, americans’ dismal views of the nation’s politics, measuring religion in china, diverse cultures and shared experiences shape asian american identities, parenting in america today, editor’s pick, religious ‘nones’ in america: who they are and what they believe, among young adults without children, men are more likely than women to say they want to be parents someday, fewer young men are in college, especially at 4-year schools, about 1 in 5 u.s. teens who’ve heard of chatgpt have used it for schoolwork, women and political leadership ahead of the 2024 election, #blacklivesmatter turns 10.

  • Immigration & Migration

Migrant encounters at the U.S.-Mexico border hit a record high at the end of 2023

How americans view the situation at the u.s.-mexico border, its causes and consequences, what we know about unauthorized immigrants living in the u.s., latinos’ views of and experiences with the spanish language, social media, how teens and parents approach screen time, 5 facts about how americans use facebook, two decades after its launch, a declining share of adults, and few teens, support a u.s. tiktok ban, 81% of u.s. adults – versus 46% of teens – favor parental consent for minors to use social media, how americans view data privacy.

1615 L St. NW, Suite 800 Washington, DC 20036 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 |  Media Inquiries

Research Topics

  • Age & Generations
  • Coronavirus (COVID-19)
  • Economy & Work
  • Family & Relationships
  • Gender & LGBTQ
  • Methodological Research
  • News Habits & Media
  • Non-U.S. Governments
  • Other Topics
  • Email Newsletters

ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

Copyright 2024 Pew Research Center

  • International edition
  • Australia edition
  • Europe edition

Semaglutide, the active ingredient in brands including Wegovy and Ozempic, has been prescribed for weight loss on the NHS since 2023.

Weight loss drug could reduce heart attack risk by 20%, study finds

Researchers say semaglutide, the active ingredient in Wegovy and Ozempic, could be biggest medical breakthrough since statins

A weight loss injection could reduce the risk of heart attacks and benefit the cardiovascular health of millions of adults across the UK, in what could be the largest medical breakthrough since statins, according to a study.

It found that participants taking the medication semaglutide, the active ingredient in brands including Wegovy and Ozempic, had a 20% lower risk of heart attack, stroke, or death due to cardiovascular disease.

The study , presented at the European Congress of Obesity (ECO) and led by researchers at University College London, also found that semaglutide brought about cardiovascular benefits for its participants, regardless of their starting weight or the amount of weight that they had lost. It suggests that those with mild obesity or who have lost only a small amount of weight could have an improved cardiovascular outcome.

Prof John Deanfield, the director of the National Institute for Cardiovascular Outcomes Research and the lead author of the study, said the findings showed that the medication should be routinely prescribed to treat cardiovascular illnesses, and that millions of people across the UK could be taking the medication in the next few years.

“This fantastic drug really is a gamechanger. This [study] suggests that here are potentially alternative mechanisms for that improved cardiovascular outcome with semaglutide beyond weight loss … Quite clearly, something else is going on that benefits the cardiovascular system,” Deanfield said.

The study involved 17,604 adults aged 45 and over with a body mass index of over 27 from across 41 countries. The participants, who had also previously experienced a cardiovascular event such as a heart attack, were prescribed either a 2.5mg weekly dose of semaglutide or a placebo for an average period of 40 months.

Of the 8,803 patients in the semaglutide group, 569 (6.5%) experienced a primary cardiovascular end-point event, such as a heart attack, compared with 701 (8%) of the 8,801 patients in the placebo group.

Semaglutide under the brand name Wegovy has been prescribed for weight loss on the NHS since 2023 .

Deanfield said that in the 1990s, statins – drugs that lower cholesterol – were considered a medical breakthrough and revolutionary in treating cardiology practice, and he said semaglutide could be seen as similarly groundbreaking in regarding to improving cardiovascular health. “We now have a class of drugs that could equally transform many chronic diseases of ageing,” he said.

Prof Jason Halford, president of the European Association for the Study of Obesity , said that as the medication could be seen to improve cardiovascular health, it could be economically beneficial for it to be prescribed widely.

“I think in the next 10 years we’ll see a radical change in the approach to healthcare,” he said. “Once the costs come down then the cost savings to the NHS will be significant. There are already people in the Treasury thinking about the savings to the economy because of the opportunity to boost productivity. You need to get your workforce as fit as possible.”

About 7.6 million people in the UK are living with heart or circulatory disease, according to the British Heart Foundation.

Another study based on the same clinical trial found that participants who were prescribed semaglutide lost an average of 10.2% of their body weight and 7.7cm from their waist over a four-year period, while the placebo group lost 1.5% of body weight and 1.3cm from the waist.

A separate study looking at a new slimming jab has found that it could be much more effective than those already on the market. Retatrutide, a weekly injection, works by suppressing appetite and also by helping the body burn more fat, according to its phase 2 clinical trial.

The trial of 338 participants living with obesity showed that participants lost 24% of their body weight over a 48-week period. Researchers say it is more effective for weight loss than Ozempic or Wegovy, which only work by suppressing appetite.

Prof Naveed Sattar, of the University of Glasgow, who has worked on trials of other weight loss treatments, said: “Five or 10 years ago, we could never have imagined drugs that would cause this kind of weight loss. The trial suggests retatrutide still hadn’t plateaued, so it’s probably going to see more weight loss. If we give this drug even longer, I think it could reach nearly 30% of someone’s body weight.”

  • Medical research
  • Heart attack
  • Heart disease

Most viewed

U.S. flag

An official website of the United States government

The .gov means it's official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you're on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

  • Publications
  • Account settings
  • Browse Titles

NCBI Bookshelf. A service of the National Library of Medicine, National Institutes of Health.

InformedHealth.org [Internet]. Cologne, Germany: Institute for Quality and Efficiency in Health Care (IQWiG); 2006-.

Cover of InformedHealth.org

InformedHealth.org [Internet].

In brief: what types of studies are there.

Last Update: September 8, 2016 ; Next update: 2024.

There are various types of scientific studies such as experiments and comparative analyses, observational studies, surveys, or interviews. The choice of study type will mainly depend on the research question being asked.

When making decisions, patients and doctors need reliable answers to a number of questions. Depending on the medical condition and patient's personal situation, the following questions may be asked:

  • What is the cause of the condition?
  • What is the natural course of the disease if left untreated?
  • What will change because of the treatment?
  • How many other people have the same condition?
  • How do other people cope with it?

Each of these questions can best be answered by a different type of study.

In order to get reliable results, a study has to be carefully planned right from the start. One thing that is especially important to consider is which type of study is best suited to the research question. A study protocol should be written and complete documentation of the study's process should also be done. This is vital in order for other scientists to be able to reproduce and check the results afterwards.

The main types of studies are randomized controlled trials (RCTs), cohort studies, case-control studies and qualitative studies.

  • Randomized controlled trials

If you want to know how effective a treatment or diagnostic test is, randomized trials provide the most reliable answers. Because the effect of the treatment is often compared with "no treatment" (or a different treatment), they can also show what happens if you opt to not have the treatment or diagnostic test.

When planning this type of study, a research question is stipulated first. This involves deciding what exactly should be tested and in what group of people. In order to be able to reliably assess how effective the treatment is, the following things also need to be determined before the study is started:

  • How long the study should last
  • How many participants are needed
  • How the effect of the treatment should be measured

For instance, a medication used to treat menopause symptoms needs to be tested on a different group of people than a flu medicine. And a study on treatment for a stuffy nose may be much shorter than a study on a drug taken to prevent strokes .

“Randomized” means divided into groups by chance. In RCTs participants are randomly assigned to one of two or more groups. Then one group receives the new drug A, for example, while the other group receives the conventional drug B or a placebo (dummy drug). Things like the appearance and taste of the drug and the placebo should be as similar as possible. Ideally, the assignment to the various groups is done "double blinded," meaning that neither the participants nor their doctors know who is in which group.

The assignment to groups has to be random in order to make sure that only the effects of the medications are compared, and no other factors influence the results. If doctors decided themselves which patients should receive which treatment, they might – for instance – give the more promising drug to patients who have better chances of recovery. This would distort the results. Random allocation ensures that differences between the results of the two groups at the end of the study are actually due to the treatment and not something else.

Randomized controlled trials provide the best results when trying to find out if there is a cause-and-effect relationship. RCTs can answer questions such as these:

  • Is the new drug A better than the standard treatment for medical condition X?
  • Does regular physical activity speed up recovery after a slipped disk when compared to passive waiting?
  • Cohort studies

A cohort is a group of people who are observed frequently over a period of many years – for instance, to determine how often a certain disease occurs. In a cohort study, two (or more) groups that are exposed to different things are compared with each other: For example, one group might smoke while the other doesn't. Or one group may be exposed to a hazardous substance at work, while the comparison group isn't. The researchers then observe how the health of the people in both groups develops over the course of several years, whether they become ill, and how many of them pass away. Cohort studies often include people who are healthy at the start of the study. Cohort studies can have a prospective (forward-looking) design or a retrospective (backward-looking) design. In a prospective study, the result that the researchers are interested in (such as a specific illness) has not yet occurred by the time the study starts. But the outcomes that they want to measure and other possible influential factors can be precisely defined beforehand. In a retrospective study, the result (the illness) has already occurred before the study starts, and the researchers look at the patient's history to find risk factors.

Cohort studies are especially useful if you want to find out how common a medical condition is and which factors increase the risk of developing it. They can answer questions such as:

  • How does high blood pressure affect heart health?
  • Does smoking increase your risk of lung cancer?

For example, one famous long-term cohort study observed a group of 40,000 British doctors, many of whom smoked. It tracked how many doctors died over the years, and what they died of. The study showed that smoking caused a lot of deaths, and that people who smoked more were more likely to get ill and die.

  • Case-control studies

Case-control studies compare people who have a certain medical condition with people who do not have the medical condition, but who are otherwise as similar as possible, for example in terms of their sex and age. Then the two groups are interviewed, or their medical files are analyzed, to find anything that might be risk factors for the disease. So case-control studies are generally retrospective.

Case-control studies are one way to gain knowledge about rare diseases. They are also not as expensive or time-consuming as RCTs or cohort studies. But it is often difficult to tell which people are the most similar to each other and should therefore be compared with each other. Because the researchers usually ask about past events, they are dependent on the participants’ memories. But the people they interview might no longer remember whether they were, for instance, exposed to certain risk factors in the past.

Still, case-control studies can help to investigate the causes of a specific disease, and answer questions like these:

  • Do HPV infections increase the risk of cervical cancer ?
  • Is the risk of sudden infant death syndrome (“cot death”) increased by parents smoking at home?

Cohort studies and case-control studies are types of "observational studies."

  • Cross-sectional studies

Many people will be familiar with this kind of study. The classic type of cross-sectional study is the survey: A representative group of people – usually a random sample – are interviewed or examined in order to find out their opinions or facts. Because this data is collected only once, cross-sectional studies are relatively quick and inexpensive. They can provide information on things like the prevalence of a particular disease (how common it is). But they can't tell us anything about the cause of a disease or what the best treatment might be.

Cross-sectional studies can answer questions such as these:

  • How tall are German men and women at age 20?
  • How many people have cancer screening?
  • Qualitative studies

This type of study helps us understand, for instance, what it is like for people to live with a certain disease. Unlike other kinds of research, qualitative research does not rely on numbers and data. Instead, it is based on information collected by talking to people who have a particular medical condition and people close to them. Written documents and observations are used too. The information that is obtained is then analyzed and interpreted using a number of methods.

Qualitative studies can answer questions such as these:

  • How do women experience a Cesarean section?
  • What aspects of treatment are especially important to men who have prostate cancer ?
  • How reliable are the different types of studies?

Each type of study has its advantages and disadvantages. It is always important to find out the following: Did the researchers select a study type that will actually allow them to find the answers they are looking for? You can’t use a survey to find out what is causing a particular disease, for instance.

It is really only possible to draw reliable conclusions about cause and effect by using randomized controlled trials. Other types of studies usually only allow us to establish correlations (relationships where it isn’t clear whether one thing is causing the other). For instance, data from a cohort study may show that people who eat more red meat develop bowel cancer more often than people who don't. This might suggest that eating red meat can increase your risk of getting bowel cancer. But people who eat a lot of red meat might also smoke more, drink more alcohol, or tend to be overweight. The influence of these and other possible risk factors can only be determined by comparing two equal-sized groups made up of randomly assigned participants.

That is why randomized controlled trials are usually the only suitable way to find out how effective a treatment is. Systematic reviews, which summarize multiple RCTs , are even better. In order to be good-quality, though, all studies and systematic reviews need to be designed properly and eliminate as many potential sources of error as possible.

  • German Network for Evidence-based Medicine. Glossar: Qualitative Forschung.  Berlin: DNEbM; 2011. 
  • Greenhalgh T. Einführung in die Evidence-based Medicine: kritische Beurteilung klinischer Studien als Basis einer rationalen Medizin. Bern: Huber; 2003. 
  • Institute for Quality and Efficiency in Health Care (IQWiG, Germany). General methods . Version 5.0. Cologne: IQWiG; 2017.
  • Klug SJ, Bender R, Blettner M, Lange S. Wichtige epidemiologische Studientypen. Dtsch Med Wochenschr 2007; 132:e45-e47. [ PubMed : 17530597 ]
  • Schäfer T. Kritische Bewertung von Studien zur Ätiologie. In: Kunz R, Ollenschläger G, Raspe H, Jonitz G, Donner-Banzhoff N (eds.). Lehrbuch evidenzbasierte Medizin in Klinik und Praxis. Cologne: Deutscher Ärzte-Verlag; 2007.

IQWiG health information is written with the aim of helping people understand the advantages and disadvantages of the main treatment options and health care services.

Because IQWiG is a German institute, some of the information provided here is specific to the German health care system. The suitability of any of the described options in an individual case can be determined by talking to a doctor. informedhealth.org can provide support for talks with doctors and other medical professionals, but cannot replace them. We do not offer individual consultations.

Our information is based on the results of good-quality studies. It is written by a team of health care professionals, scientists and editors, and reviewed by external experts. You can find a detailed description of how our health information is produced and updated in our methods.

  • Cite this Page InformedHealth.org [Internet]. Cologne, Germany: Institute for Quality and Efficiency in Health Care (IQWiG); 2006-. In brief: What types of studies are there? [Updated 2016 Sep 8].

In this Page

Informed health links, related information.

  • PubMed Links to PubMed

Recent Activity

  • In brief: What types of studies are there? - InformedHealth.org In brief: What types of studies are there? - InformedHealth.org

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

Connect with NLM

National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894

Web Policies FOIA HHS Vulnerability Disclosure

Help Accessibility Careers

statistics

ScienceDaily

Prostate cancer study: More health benefits from plant-based diet

Men with prostate cancer could significantly reduce the chances of the disease worsening by eating more fruits, vegetables, nuts, and olive oil, according to new research by UC San Francisco.

A study of more than 2,000 men with localized prostate cancer found that eating a primarily plant-based diet was associated with a 47% lower risk that their cancer would progress, compared with those who consumed the most animal products.

This amounted to eating just one or two more servings per day of healthy foods, particularly vegetables, fruits, and whole grains, while eating fewer animal products, like dairy and meat. The study followed the men, whose median age was 65 years old, over time to see how dietary factors affected the progression of their cancer.

Plant-based diets include fruits, vegetables, whole grains, nuts, legumes, vegetable oils, tea and coffee. The researchers measured consumption using a plant-based index and compared the men who scored in the highest 20% to those who scored in the lowest 20%.

"These results could guide people to make better, more healthful choices across their whole diet, rather than adding or removing select foods," said Vivian N. Liu, formerly lead clinical research coordinator at the UCSF Osher Center for Integrative Health and first author of the study, which appears in JAMA Network Open .

"Progressing to advanced disease is one of many pivotal concerns among patients with prostate cancer, their family, caregivers and physicians," she said. "This adds to numerous other health benefits associated with consuming a primarily plant-based diet, such as a reduction in diabetes, cardiovascular disease and overall mortality."

Antioxidants and anti-inflammatory compounds

Plant-based diets are becoming increasingly popular in the United States, and evidence is accumulating that they can be beneficial to patients with prostate cancer, the most common cancer among men in the country after non-melanoma skin cancer.

Fruits and vegetables contain antioxidants, as well as anti-inflammatory compounds that have been shown to protect against prostate cancer, and prior research has consistently demonstrated the importance of dietary factors to overall health and well-being.

"Making small changes in one's diet each day is beneficial," said senior author Stacey A. Kenfield, ScD, a UCSF professor of urology and the Helen Diller Family Chair in Population Science for Urologic Cancer. "Greater consumption of plant-based food after a prostate cancer diagnosis has also recently been associated with better quality of life, including sexual function, urinary function and vitality, so it's a win-win on both levels."

Coauthors: From UCSF, other authors are Erin L. Van Blarigan, ScD; Li Zhang, PhD; Rebecca E. Graff, ScD; Crystal S. Langlais, PhD; Janet E. Cowan, MA; Peter R. Carroll, MD, MPH; and June M. Chan, ScD.

  • Men's Health
  • Prostate Cancer
  • Diseases and Conditions
  • Agriculture and Food
  • Food and Agriculture
  • Prostate cancer
  • Stomach cancer
  • Mediterranean diet

Story Source:

Materials provided by University of California - San Francisco . Original written by Elizabeth Fernandez. Note: Content may be edited for style and length.

Journal Reference :

  • Vivian N. Liu, Erin L. Van Blarigan, Li Zhang, Rebecca E. Graff, Stacy Loeb, Crystal S. Langlais, Janet E. Cowan, Peter R. Carroll, June M. Chan, Stacey A. Kenfield. Plant-Based Diets and Disease Progression in Men With Prostate Cancer . JAMA Network Open , 2024; 7 (5): e249053 DOI: 10.1001/jamanetworkopen.2024.9053

Cite This Page :

Explore More

  • Nature's 3D Printer: Bristle Worms
  • Giant ' Cotton Candy' Planet
  • A Young Whale's Journey
  • No Inner Voice Linked to Poorer Verbal Memory
  • Bird Flu A(H5N1) Transmitted from Cow to Human
  • Universe's Oldest Stars in Our Galactic Backyard
  • Polygenic Embryo Screening for IVF: Opinions
  • VR With Cinematoghraphics More Engaging
  • 2023 Was the Hottest Summer in 2000 Years
  • Fastest Rate of CO2 Rise Over Last 50,000 Years

Trending Topics

Strange & offbeat.

We've detected unusual activity from your computer network

To continue, please click the box below to let us know you're not a robot.

Why did this happen?

Please make sure your browser supports JavaScript and cookies and that you are not blocking them from loading. For more information you can review our Terms of Service and Cookie Policy .

For inquiries related to this message please contact our support team and provide the reference ID below.

Are Markups Driving the Ups and Downs of Inflation?

Sylvain Leduc

Download PDF (158 KB)

FRBSF Economic Letter 2024-12 | May 13, 2024

How much impact have price markups for goods and services had on the recent surge and the subsequent decline of inflation? Since 2021, markups have risen substantially in a few industries such as motor vehicles and petroleum. However, aggregate markups—which are more relevant for overall inflation—have generally remained flat, in line with previous economic recoveries over the past three decades. These patterns suggest that markup fluctuations have not been a main driver of the ups and downs of inflation during the post-pandemic recovery.

In the recovery from the pandemic, U.S. inflation surged to a peak of over 7% in June 2022 and has since declined to 2.7% in March 2024, as measured by the 12-month change in the personal consumption expenditures (PCE) price index. What factors have been driving the ups and downs of inflation? Production costs are traditionally considered a main contributor, particularly costs stemming from fluctuations in demand for and supply of goods and services. As demand for their products rises, companies need to hire more workers and buy more intermediate goods, pushing up production costs. Supply chain disruptions can also push up the cost of production. Firms may pass on all or part of the cost increases to consumers by raising prices. Thus, an important theoretical linkage runs from cost increases to inflation. Likewise, decreases in costs should lead to disinflation.

Labor costs are an important factor of production costs and are often useful for gauging inflationary pressures. However, during the post-pandemic surge in inflation, nominal wages rose more slowly than prices, such that real labor costs were falling until early 2023. By contrast, disruptions to global supply chains pushed up intermediate goods costs, contributing to the surge in inflation (see, for example, Liu and Nguyen 2023). However, supply chains have more direct impacts on goods inflation than on services inflation, which also rose substantially.

In this Economic Letter , we consider another factor that might drive inflation fluctuations: changes in firms’ pricing power and markups. An increase in pricing power would be reflected in price-cost markups, leading to higher inflation; likewise, a decline in pricing power and markups could alleviate inflation pressures. We use industry-level measures of markups to trace their evolving impact on inflation during the current expansion. We find that markups rose substantially in some sectors, such as the motor vehicles industry. However, the aggregate markup across all sectors of the economy, which is more relevant for inflation, has stayed essentially flat during the post-pandemic recovery. This is broadly in line with patterns during previous business cycle recoveries. Overall, our analysis suggests that fluctuations in markups were not a main driver of the post-pandemic surge in inflation, nor of the recent disinflation that started in mid-2022.

Potential drivers of inflation: Production costs and markups

To support households and businesses during the pandemic, the Federal Reserve lowered the federal funds rate target to essentially zero, and the federal government provided large fiscal transfers and increased unemployment benefits. These policies boosted demand for goods and services, especially as the economy recovered from the depth of the pandemic.

The increase in overall demand, combined with supply shortages, boosted the costs of production, contributing to the surge in inflation during the post-pandemic recovery. Although labor costs account for a large part of firms’ total production costs, real labor costs were falling between early 2021 and mid-2022 such that the increases in prices outpaced those in nominal wages. This makes it unlikely that labor costs were driving the surge in inflation.

Instead, we focus on another potential alternative driver of inflation that resulted from firms’ ability to adjust prices, known as pricing power. As demand for goods surged early in the post-pandemic recovery, companies may have had a greater ability to raise their prices above their production costs, a gap known as markups. Following a sharp drop in spending at the height of the pandemic, people may have become eager to resume normal spending patterns and hence more tolerant to price increases than in the past. In fact, growth of nonfinancial corporate profits accelerated in the early part of the recovery (see Figure 1), suggesting that companies had increased pricing power. Some studies have pointed to the strong growth in nonfinancial corporate profits in 2021 as evidence that increased markups have contributed to inflation (see, for example, Weber and Wasmer 2023). However, the figure also shows that growth in corporate profits is typically volatile. Corporate profits tend to rise in the early stages of economic recoveries. Data for the current recovery show that the increase in corporate profits is not particularly pronounced compared with previous recoveries.

Figure 1 Profit growth for nonfinancial businesses

research study and

More importantly, corporate profits are an imperfect measure of a firm’s pricing power because several other factors can drive changes in profitability. For instance, much of the recent rise in corporate profits can be attributed to lower business taxes and higher subsidies from pandemic-related government support, as well as lower net interest payments due to monetary policy accommodation (Pallazzo 2023).

Instead of relying on profits as a measure of pricing power, we construct direct measures of markups based on standard economic models. Theory suggests that companies set prices as a markup over variable production costs, and that markup can be inferred from the share of a firm’s revenue spent on a given variable production factor, such as labor or intermediate goods. Over the period of data we use, we assume that the specific proportion of a company’s production costs going toward inputs does not change. If the share of a firm’s revenue used for inputs falls, it would imply a rise in the firm’s price-cost margin or markup. In our main analysis, we use industry-level data from the Bureau of Economic Analysis (BEA) to compute markups based on the share of revenue spent on intermediate inputs. Our results are similar if we instead use the share of revenue going toward labor costs.

We compare the evolution of markups to that of prices, as measured by the PCE price index, since the recovery from the pandemic. In constructing this price index, the BEA takes into account changes in product characteristics (for instance, size) that could otherwise bias the inflation measure by comparing the prices of inherently different products over time. Similarly, based upon standard economic theory, our markup measure implicitly captures changes in those characteristics (see, for example, Aghion et al. 2023).

The post-pandemic evolution of markups

We examine the evolution of markups in each industry since the third quarter of 2020, the start of the post-pandemic recovery. Figure 2 shows that some sectors, such as the motor vehicles and petroleum industries, experienced large cumulative increases in markups during the recovery. Markups also rose substantially in general merchandise, such as department stores, and for other services, such as repair and maintenance, personal care, and laundry services. Since the start of the expansion, markups in those industries rose by over 10%—comparable in size to the cumulative increases over the same period in the core PCE price index, which excludes volatile food and energy components. However, the surge in inflation through June 2022 was broad based, with prices also rising substantially outside of these sectors. Thus, understanding the importance of markups for driving inflation requires a macroeconomic perspective that examines the evolution of aggregate markups across all sectors of the economy.

Figure 2 Cumulative changes in markups for salient industries

research study and

The role of aggregate markups in the economy

To assess how much markup changes contribute to movements in inflation more broadly, we use our industry-level measurements to calculate an aggregate markup at the macroeconomic level. We aggregate the cumulative changes in industry markups, applying two different weighting methods, as displayed in Figure 3. In the first method (green line), we match our industry categories to the spending categories in the core PCE price index for ease of comparison; we then use the PCE weights for each category to compute the aggregate markup. Alternatively, we use each industry’s cost weights to compute the aggregate markup (blue line). Regardless of the weighting method, Figure 3 shows that aggregate markups have stayed essentially flat since the start of the recovery, while the core PCE price index (gray line) rose by more than 10%. Thus, changes in markups are not likely to be the main driver of inflation during the recovery, which aligns with results from Glover, Mustre-del-Río, and von Ende-Becker (2023) and Hornstein (2023) using different methodologies or data. Markups also have not played much of a role in the slowing of inflation since the summer of 2022.

Figure 3 Cumulative changes in aggregate markups and prices

research study and

Moreover, the path of aggregate markups over the past three years is not unusual compared with previous recoveries. Figure 4 shows the cumulative changes in aggregate markups since the start of the current recovery (dark blue line), alongside aggregate markups following the 1991 (green line), 2001 (yellow line), and 2008 (light blue line) recessions. Aggregate markups have stayed roughly constant throughout all four recoveries.

Figure 4 Cumulative changes of aggregate markups in recoveries

research study and

Firms’ pricing power may change over time, resulting in markup fluctuations. In this Letter , we examine whether increases in markups played an important role during the inflation surge between early 2021 and mid-2022 and if declines in markups have contributed to disinflation since then. Using industry-level data, we show that markups did rise substantially in a few important sectors, such as motor vehicles and petroleum products. However, aggregate markups—the more relevant measure for overall inflation—have stayed essentially flat since the start of the recovery. As such, rising markups have not been a main driver of the recent surge and subsequent decline in inflation during the current recovery.

Aghion, Philippe, Antonin Bergeaud, Timo Boppart, Peter J. Klenow, and Huiyu Li. 2023. “A Theory of Falling Growth and Rising Rents.”  Review of Economic Studies  90(6), pp.2,675-2,702.

Glover, Andrew, José Mustre-del-Río, and Alice von Ende-Becker. 2023. “ How Much Have Record Corporate Profits Contributed to Recent Inflation? ” FRB Kansas City Economic Review 108(1).

Hornstein, Andreas. 2023. “ Profits and Inflation in the Time of Covid .” FRB Richmond Economic Brief 23-38 (November).

Liu, Zheng, and Thuy Lan Nguyen. 2023. “ Global Supply Chain Pressures and U.S. Inflation .” FRBSF Economic Letter 2023-14 (June 20).

Palazzo, Berardino. 2023. “ Corporate Profits in the Aftermath of COVID-19 .” FEDS Notes , Federal Reserve Board of Governors, September 8.

Weber, Isabella M. and Evan Wasner. 2023. “Sellers’ Inflation, Profits and Conflict: Why Can Large Firms Hike Prices in an Emergency?” Review of Keynesian Economics 11(2), pp. 183-213.

Opinions expressed in FRBSF Economic Letter do not necessarily reflect the views of the management of the Federal Reserve Bank of San Francisco or of the Board of Governors of the Federal Reserve System. This publication is edited by Anita Todd and Karen Barnes. Permission to reprint portions of articles or whole articles must be obtained in writing. Please send editorial comments and requests for reprint permission to [email protected]

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • View all journals
  • Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • Published: 14 May 2024

2023 summer warmth unparalleled over the past 2,000 years

  • Jan Esper   ORCID: orcid.org/0000-0003-3919-014X 1 , 2 ,
  • Max Torbenson   ORCID: orcid.org/0000-0003-2720-2238 1 &
  • Ulf Büntgen 2 , 3 , 4  

Nature ( 2024 ) Cite this article

Metrics details

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

  • Climate change
  • Palaeoclimate

Including an exceptionally warm Northern Hemisphere (NH) summer 1 ,2 , 2023 has been reported as the hottest year on record 3-5 . Contextualizing recent anthropogenic warming against past natural variability is nontrivial, however, because the sparse 19 th century meteorological records tend to be too warm 6 . Here, we combine observed and reconstructed June-August (JJA) surface air temperatures to show that 2023 was the warmest NH extra-tropical summer over the past 2000 years exceeding the 95% confidence range of natural climate variability by more than half a degree Celsius. Comparison of the 2023 JJA warming against the coldest reconstructed summer in 536 CE reveals a maximum range of pre-Anthropocene-to-2023 temperatures of 3.93°C. Although 2023 is consistent with a greenhouse gases-induced warming trend 7 that is amplified by an unfolding El Niño event 8 , this extreme emphasizes the urgency to implement international agreements for carbon emission reduction.

This is a preview of subscription content, access via your institution

Access options

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

24,99 € / 30 days

cancel any time

Subscribe to this journal

Receive 51 print issues and online access

185,98 € per year

only 3,65 € per issue

Rent or buy this article

Prices vary by article type

Prices may be subject to local taxes which are calculated during checkout

Similar content being viewed by others

research study and

Large-scale emergence of regional changes in year-to-year temperature variability by the end of the 21st century

research study and

Cooler Arctic surface temperatures simulated by climate models are closer to satellite-based data than the ERA5 reanalysis

research study and

Warming events projected to become more frequent and last longer across Antarctica

Author information, authors and affiliations.

Department of Geography, Johannes Gutenberg University, Mainz, Germany

Jan Esper & Max Torbenson

Global Change Research Institute of the Czech Academy of Sciences, Brno, Czech Republic

Jan Esper & Ulf Büntgen

Department of Geography, University of Cambridge, Cambridge, United Kingdom

Ulf Büntgen

Department of Geography, Masaryk University, Brno, Czech Republic

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Jan Esper .

Rights and permissions

Reprints and permissions

About this article

Cite this article.

Esper, J., Torbenson, M. & Büntgen, U. 2023 summer warmth unparalleled over the past 2,000 years. Nature (2024). https://doi.org/10.1038/s41586-024-07512-y

Download citation

Received : 16 January 2024

Accepted : 02 May 2024

Published : 14 May 2024

DOI : https://doi.org/10.1038/s41586-024-07512-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

  • Explore articles by subject
  • Guide to authors
  • Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

research study and

Low testosterone levels linked to higher risk of dying early, new research suggests

  • A study of 24,109 men found that low testosterone was linked to a higher risk of early death.
  • The findings suggest the hormone is an important indicator of health and longevity for men. 
  • It's not clear whether hormone therapy boosts longevity, but diet and exercise can help. 

Insider Today

Testosterone levels may be an important indicator of longevity for men, new research suggests.

A study of 24,109 men from around the world found that low testosterone was linked to a higher risk of dying early, according to a report published May 13 in the Annals of Internal Medicine .

Researchers from the University of Western Australia worked with a global team of scientists to compare baseline testosterone levels with health outcomes using data from previous studies on older men, ranging in age from late 40s to mid-70s on average.

They found a higher risk of dying early, from any cause, among men with a low baseline testosterone level — defined as below 213 nanograms per deciliter (ng/dL). A urologist previously told Business Insider that healthy testosterone levels could range from 260 to 900 ng/dL, depending on age.

The analysis also found that men with very low testosterone levels (below 153 ng/dL) had a higher risk of dying from cardiovascular disease.

The findings are observational, which means the researchers found a pattern but didn't directly show that low testosterone caused a higher mortality rate.

Related stories

Still, an independent researcher wrote in an editorial published alongside the study that it helped to shed light on mixed research around longevity and the hormone, particularly when it came to boosting low testosterone with hormone therapy.

Testosterone is a big deal in the longevity and biohacking field

The new study focused on endogenous testosterone, the kind naturally occurring in the body, not hormone therapy. But understanding how testosterone may help or hurt longevity could make big waves in the booming hormone-therapy market of products and services advertised to help men reclaim their youth with pills, patches, injections, or gels.

Many of the entrepreneurs touting testosterone as an antiaging panacea are also involved in biohacking , using science to try to optimize health and extend lifespan.

But it's not clear yet whether supplementing testosterone will boost longevity , and there's some evidence to the contrary. The US Food and Drug Administration previously issued a warning that testosterone-boosting treatments may raise the risk of heart attack or stroke, although subsequent research has since found no significant increase in heart problems.

Signs of low testosterone and what to do about it

Testosterone levels tend to drop with age and can also dip in response to health conditions and medications.

Low testosterone can cause symptoms such as fatigue, brain fog, and disruptions in mood and libido.

Lifestyle factors such as eating a nutritious diet, getting enough exercise, and managing stress are linked to healthy testosterone levels and can also play a role in preventing heart disease and other chronic conditions.

Treatments such as hormone-replacement therapy via pill, injection, gel, or patch can help raise testosterone to normal levels. But doctors previously told BI that medical supervision was key to avoiding any unexpected side effects of changing hormone levels.

research study and

  • Main content

IMAGES

  1. Essential Things to Do Before Starting Your Research Study

    research study and

  2. How Do the Different Types of Research Studies Work?

    research study and

  3. Types of Study

    research study and

  4. What is Research?

    research study and

  5. Research Process Steps to Follow for Conducting a Research Study

    research study and

  6. Introduction to Research and 10 Purposes of Research

    research study and

VIDEO

  1. Research Design, Research Method: What's the Difference?

  2. Research, Educational research

  3. Metho1: What Is Research?

  4. Panel Study| Research Method, Business Research Methodology #shortnotes #bba #bcom

  5. HOW TO READ and ANALYZE A RESEARCH STUDY

  6. 3.Three type of main Research in education

COMMENTS

  1. What Is a Research Design

    A research design is a strategy for answering your research question using empirical data. Creating a research design means making decisions about: Your overall research objectives and approach. Whether you'll rely on primary research or secondary research. Your sampling methods or criteria for selecting subjects. Your data collection methods.

  2. Types of studies and research design

    Types of study design. Medical research is classified into primary and secondary research. Clinical/experimental studies are performed in primary research, whereas secondary research consolidates available studies as reviews, systematic reviews and meta-analyses. Three main areas in primary research are basic medical research, clinical research ...

  3. Research Design

    Table of contents. Step 1: Consider your aims and approach. Step 2: Choose a type of research design. Step 3: Identify your population and sampling method. Step 4: Choose your data collection methods. Step 5: Plan your data collection procedures. Step 6: Decide on your data analysis strategies.

  4. Research 101: Understanding Research Studies

    The basis of a scientific research study follows a common pattern: Define the question. Gather information and resources. Form hypotheses. Perform an experiment and collect data. Analyze the data ...

  5. Study designs: Part 1

    Research study design is a framework, or the set of methods and procedures used to collect and analyze data on variables specified in a particular research problem. Research study designs are of many types, each with its advantages and limitations. The type of study design used to answer a particular research question is determined by the ...

  6. Research

    Meta-research is the study of research through the use of research methods. Also known as "research on research", it aims to reduce waste and increase the quality of research in all fields. Meta-research concerns itself with the detection of bias, methodological flaws, and other errors and inefficiencies. ...

  7. A Practical Guide to Writing Quantitative and Qualitative Research

    INTRODUCTION. Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses.1,2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results.3,4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the ...

  8. Planning Qualitative Research: Design and Decision Making for New

    While many books and articles guide various qualitative research methods and analyses, there is currently no concise resource that explains and differentiates among the most common qualitative approaches. We believe novice qualitative researchers, students planning the design of a qualitative study or taking an introductory qualitative research course, and faculty teaching such courses can ...

  9. What is Research? Definition, Types, Methods and Process

    Research is defined as a meticulous and systematic inquiry process designed to explore and unravel specific subjects or issues with precision. This methodical approach encompasses the thorough collection, rigorous analysis, and insightful interpretation of information, aiming to delve deep into the nuances of a chosen field of study.

  10. What Is Research, and Why Do People Do It?

    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 ...

  11. What is 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.".

  12. Google Scholar

    Google Scholar provides a simple way to broadly search for scholarly literature. Search across a wide variety of disciplines and sources: articles, theses, books, abstracts and court opinions.

  13. ScienceDaily: Your source for the latest research news

    Breaking science news and articles on global warming, extrasolar planets, stem cells, bird flu, autism, nanotechnology, dinosaurs, evolution -- the latest discoveries ...

  14. Pew Research Center

    Pew Research Center has deep roots in U.S. public opinion research. Launched as a project focused primarily on U.S. policy and politics in the early 1990s, the Center has grown over time to study a wide range of topics vital to explaining America to itself and to the world.

  15. Study vs Research: When to Opt for One Term Over Another

    If you're talking about learning or acquiring knowledge about a subject, then study is the appropriate term. If you're conducting a formal investigation or inquiry into a topic, then research is the correct word to use. Now that we've established the difference between study and research, let's dive deeper into each one.

  16. ResearchGate

    Access 160+ million publications and connect with 25+ million researchers. Join for free and gain visibility by uploading your research.

  17. A Beginner's Guide to Starting the Research Process

    Step 4: Create a research design. The research design is a practical framework for answering your research questions. It involves making decisions about the type of data you need, the methods you'll use to collect and analyze it, and the location and timescale of your research. There are often many possible paths you can take to answering ...

  18. What is the difference between study and research?

    Research is a synonym of study. As verbs the difference between study and research is that study is to revise materials already learned in order to make sure one does not forget them, usually in preparation for an examination while research is to search or examine with continued care; to seek diligently. As nouns the difference between study and research is that study is a state of mental ...

  19. Weight loss drug could reduce heart attack risk by 20%, study finds

    Another study based on the same clinical trial found that participants who were prescribed semaglutide lost an average of 10.2% of their body weight and 7.7cm from their waist over a four-year ...

  20. In brief: What types of studies are there?

    There are various types of scientific studies such as experiments and comparative analyses, observational studies, surveys, or interviews. The choice of study type will mainly depend on the research question being asked. When making decisions, patients and doctors need reliable answers to a number of questions. Depending on the medical condition and patient's personal situation, the following ...

  21. Why it's essential to study sex and gender, even as tensions rise

    Some scholars are reluctant to research sex and gender out of fear that their studies will be misused. In a series of specially commissioned articles, Nature encourages scientists to engage.

  22. Prostate cancer study: More health benefits from plant-based diet

    A study of more than 2,000 men with localized prostate cancer found that eating a primarily plant-based diet was associated with a 47% lower risk that their cancer would progress, compared with ...

  23. Novo to Examine Alcohol Use in Liver Disease Treatment Study

    The Danish drugmaker is planning a study whose secondary goal is seeing whether its compounds can change daily alcohol consumption, according to a US government clinical trials registry. The main ...

  24. Allen Institute and Seattle Children's to study inflammatory bowel

    The new initiative, called the Seattle Spatial Transcriptomic Research in Inflammatory Bowel Disease Evaluation (STRIDE) study, will be a first-of-its-kind pediatric study. Researchers will use ...

  25. Are Markups Driving the Ups and Downs of Inflation?

    These patterns suggest that markup fluctuations have not been a main driver of the ups and downs of inflation during the post-pandemic recovery. In the recovery from the pandemic, U.S. inflation surged to a peak of over 7% in June 2022 and has since declined to 2.7% in March 2024, as measured by the 12-month change in the personal consumption ...

  26. 2023 summer warmth unparalleled over the past 2,000 years

    Authors and Affiliations. Department of Geography, Johannes Gutenberg University, Mainz, Germany. Jan Esper & Max Torbenson. Global Change Research Institute of the Czech Academy of Sciences, Brno ...

  27. What Is Quantitative Research?

    Quantitative research methods. You can use quantitative research methods for descriptive, correlational or experimental research. In descriptive research, you simply seek an overall summary of your study variables.; In correlational research, you investigate relationships between your study variables.; In experimental research, you systematically examine whether there is a cause-and-effect ...

  28. Low Testosterone Levels Linked to Higher Risk of Early Death: Study

    A study of 24,109 men found that low testosterone was linked to a higher risk of early death. The findings suggest the hormone is an important indicator of health and longevity for men.