what is the research study

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What is Research: Definition, Methods, Types & Examples

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

Content Index

What is Research?

What are the characteristics of research.

• Comparative analysis chart

Qualitative methods

Quantitative methods, 8 tips for conducting accurate research.

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

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

Research is conducted with a purpose to:

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

What is the purpose of research?

There are three main purposes:

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

LEARN ABOUT: Descriptive Analysis

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

LEARN ABOUT: Best Data Collection Tools

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

Here is a comparative analysis chart for a better understanding:

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

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

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

Types of research methods and Examples

Research methods are broadly classified as Qualitative and Quantitative .

Both methods have distinctive properties and data collection methods .

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

Types of qualitative methods include:

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

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

Types of quantitative methods include:

• Survey research
• Descriptive research
• Correlational research

LEARN MORE: Descriptive Research vs Correlational Research

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

It is essential to ensure that your data is:

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

Gather insights

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

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

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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.

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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.

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

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

About Research Methods

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

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

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

Suggestions for changes and additions to this guide are welcome! 

START HERE: SAGE Research Methods

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

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

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

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

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

Library GIS Services

Other Data Services at Berkeley

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

General Research Methods Resources

Here are some general resources for assistance:

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

Consultants

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

Related Resourcex

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

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What is Research? – Purpose of Research

• By DiscoverPhDs
• September 10, 2020

The purpose of research is to enhance society by advancing knowledge through the development of scientific theories, concepts and ideas. A research purpose is met through forming hypotheses, collecting data, analysing results, forming conclusions, implementing findings into real-life applications and forming new research questions.

What is Research

Simply put, research is the process of discovering new knowledge. This knowledge can be either the development of new concepts or the advancement of existing knowledge and theories, leading to a new understanding that was not previously known.

As a more formal definition of research, the following has been extracted from the Code of Federal Regulations :

While research can be carried out by anyone and in any field, most research is usually done to broaden knowledge in the physical, biological, and social worlds. This can range from learning why certain materials behave the way they do, to asking why certain people are more resilient than others when faced with the same challenges.

The use of ‘systematic investigation’ in the formal definition represents how research is normally conducted – a hypothesis is formed, appropriate research methods are designed, data is collected and analysed, and research results are summarised into one or more ‘research conclusions’. These research conclusions are then shared with the rest of the scientific community to add to the existing knowledge and serve as evidence to form additional questions that can be investigated. It is this cyclical process that enables scientific research to make continuous progress over the years; the true purpose of research.

What is the Purpose of Research

From weather forecasts to the discovery of antibiotics, researchers are constantly trying to find new ways to understand the world and how things work – with the ultimate goal of improving our lives.

The purpose of research is therefore to find out what is known, what is not and what we can develop further. In this way, scientists can develop new theories, ideas and products that shape our society and our everyday lives.

Although research can take many forms, there are three main purposes of research:

• Exploratory: Exploratory research is the first research to be conducted around a problem that has not yet been clearly defined. Exploration research therefore aims to gain a better understanding of the exact nature of the problem and not to provide a conclusive answer to the problem itself. This enables us to conduct more in-depth research later on.
• Descriptive: Descriptive research expands knowledge of a research problem or phenomenon by describing it according to its characteristics and population. Descriptive research focuses on the ‘how’ and ‘what’, but not on the ‘why’.
• Explanatory: Explanatory research, also referred to as casual research, is conducted to determine how variables interact, i.e. to identify cause-and-effect relationships. Explanatory research deals with the ‘why’ of research questions and is therefore often based on experiments.

Characteristics of Research

There are 8 core characteristics that all research projects should have. These are:

• Empirical  – based on proven scientific methods derived from real-life observations and experiments.
• Logical  – follows sequential procedures based on valid principles.
• Cyclic  – research begins with a question and ends with a question, i.e. research should lead to a new line of questioning.
• Controlled  – vigorous measures put into place to keep all variables constant, except those under investigation.
• Hypothesis-based  – the research design generates data that sufficiently meets the research objectives and can prove or disprove the hypothesis. It makes the research study repeatable and gives credibility to the results.
• Analytical  – data is generated, recorded and analysed using proven techniques to ensure high accuracy and repeatability while minimising potential errors and anomalies.
• Objective  – sound judgement is used by the researcher to ensure that the research findings are valid.
• Statistical treatment  – statistical treatment is used to transform the available data into something more meaningful from which knowledge can be gained.

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Types of Research

Research can be divided into two main types: basic research (also known as pure research) and applied research.

Basic Research

Basic research, also known as pure research, is an original investigation into the reasons behind a process, phenomenon or particular event. It focuses on generating knowledge around existing basic principles.

Basic research is generally considered ‘non-commercial research’ because it does not focus on solving practical problems, and has no immediate benefit or ways it can be applied.

While basic research may not have direct applications, it usually provides new insights that can later be used in applied research.

Applied Research

Applied research investigates well-known theories and principles in order to enhance knowledge around a practical aim. Because of this, applied research focuses on solving real-life problems by deriving knowledge which has an immediate application.

Methods of Research

Research methods for data collection fall into one of two categories: inductive methods or deductive methods.

Inductive research methods focus on the analysis of an observation and are usually associated with qualitative research. Deductive research methods focus on the verification of an observation and are typically associated with quantitative research.

Qualitative Research

Qualitative research is a method that enables non-numerical data collection through open-ended methods such as interviews, case studies and focus groups .

It enables researchers to collect data on personal experiences, feelings or behaviours, as well as the reasons behind them. Because of this, qualitative research is often used in fields such as social science, psychology and philosophy and other areas where it is useful to know the connection between what has occurred and why it has occurred.

Quantitative Research

Quantitative research is a method that collects and analyses numerical data through statistical analysis.

It allows us to quantify variables, uncover relationships, and make generalisations across a larger population. As a result, quantitative research is often used in the natural and physical sciences such as engineering, biology, chemistry, physics, computer science, finance, and medical research, etc.

What does Research Involve?

Research often follows a systematic approach known as a Scientific Method, which is carried out using an hourglass model.

A research project first starts with a problem statement, or rather, the research purpose for engaging in the study. This can take the form of the ‘ scope of the study ’ or ‘ aims and objectives ’ of your research topic.

Subsequently, a literature review is carried out and a hypothesis is formed. The researcher then creates a research methodology and collects the data.

The data is then analysed using various statistical methods and the null hypothesis is either accepted or rejected.

In both cases, the study and its conclusion are officially written up as a report or research paper, and the researcher may also recommend lines of further questioning. The report or research paper is then shared with the wider research community, and the cycle begins all over again.

Although these steps outline the overall research process, keep in mind that research projects are highly dynamic and are therefore considered an iterative process with continued refinements and not a series of fixed stages.

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Module 1: Introduction: What is Research?

Learning Objectives

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

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

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

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

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

Module 1: Discussion Questions

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

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What Is Research, and Why Do People Do It?

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• First Online: 03 December 2022

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

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

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Abstractspiepr Abs1

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

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Part I. What Is Research?

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

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

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

Exercise 1.1

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

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

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

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

Exercise 1.2

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

Creating an Image of Scientific Inquiry

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

Descriptor 1. Experience Carefully Planned in Advance

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

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

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

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

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

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

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

Exercise 1.3

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

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

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

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

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

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

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

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

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

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

Doing Scientific Inquiry

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

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

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

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

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

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

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

Exercise 1.4

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

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

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

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

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

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

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

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

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

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

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

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

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

Exercise 1.5

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

Exercise 1.6

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

Learning from Doing Scientific Inquiry

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

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

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

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

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

Part II. Why Do Educators Do Research?

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

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

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

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

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

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

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

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

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

Exercise 1.7

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

Part III. Conducting Research as a Practice of Failing Productively

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

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

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

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

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

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

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

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

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

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

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

Exercise 1.8

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

Exercise 1.9

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

Part IV. Preview of Chap. 2

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

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Home » Research – Types, Methods and Examples

Research – Types, Methods and Examples

Table of Contents

Definition:

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

History of Research

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

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

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

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

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

Types of Research

Types of Research are as follows:

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

Data Collection Methods

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

Data Analysis Methods

Data Analysis Methods in Research are as follows:

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

Research Methodology

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

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

Applications of Research

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

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

Importance of Research

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

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

When to use Research

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

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

Characteristics of Research

The following are some of the characteristics of research:

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

Advantages of Research

Research has several advantages, including:

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

Limitations of Research

Some Limitations of Research are as follows:

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

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

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

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

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

Research Methodology 101

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

What is research methodology?

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

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

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

So, it’s the same as research design?

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

Need a helping hand?

What are qualitative, quantitative and mixed-methods?

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

Let’s take a closer look.

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

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

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

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

What is sampling strategy?

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

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

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

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

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

What are data collection methods?

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

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

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

What are data analysis methods?

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

Popular data analysis methods in qualitative research include:

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

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

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

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

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

How do I choose a research methodology?

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

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

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

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

Example of a research methodology chapter

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

Psst... there’s more!

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

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199 Comments

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

Thanks for your comment.

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

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Great to hear that, Hyacinth. Best of luck with your research!

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Thanks for the feedback, Matobela. Good luck with your research methodology.

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Thanks for the kind words, Edward. Good luck with your research!

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Great to hear that, Ngwisa. Good luck with your research methodology!

Thank you for keeping your presentation simples and short and covering key information for research methodology. My key takeaway: Start with defining your research objective the other will depend on the aims of your research question.

My name is Zanele I would like to be assisted with my research , and the topic is shortage of nursing staff globally want are the causes , effects on health, patients and community and also globally

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Thank you Dr

I was given an assignment to research 2 publications and describe their research methodology? I don’t know how to start this task can someone help me?

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

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I am a bit confused about research design and methodology. Are they the same? If not, what are the differences and how are they related?

Thanks in advance.

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Am a new researcher trying to learn how best to write a research proposal. I find your article spot on and want to download the free template but finding difficulties. Can u kindly send it to my email, the free download entitled, “Free Download: Research Proposal Template (with Examples)”.

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how do i reference this?

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

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

Your explanation is easily understood. Thank you

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hello sir/ma’am, i didn’t find yet that what type of research methodology i am using. because i am writing my report on CSR and collect all my data from websites and articles so which type of methodology i should write in dissertation report. please help me. i am from India.

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• Yale Directories

Institution for Social and Policy Studies

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

New study offers hope for a rare and devastating eye cancer

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

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

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

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

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

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

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

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

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

A tool for predicting success

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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• v.37(16); 2022 Apr 25

A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

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 inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

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New Study Bolsters Idea of Athletic Differences Between Men and Trans Women

Research financed by the International Olympic Committee introduced new data to the unsettled and fractious debate about bans on transgender athletes.

By Jeré Longman

A new study financed by the International Olympic Committee found that transgender female athletes showed greater handgrip strength — an indicator of overall muscle strength — but lower jumping ability, lung function and relative cardiovascular fitness compared with women whose gender was assigned female at birth.

That data, which also compared trans women with men, contradicted a broad claim often made by proponents of rules that bar transgender women from competing in women’s sports. It also led the study’s authors to caution against a rush to expand such policies, which already bar transgender athletes from a handful of Olympic sports.

The study’s most important finding, according to one of its authors, Yannis Pitsiladis, a member of the I.O.C.’s medical and scientific commission, was that, given physiological differences, “Trans women are not biological men.”

Alternately praised and criticized, the study added an intriguing data set to an unsettled and often politicized debate that may only grow louder with the Paris Olympics and a U.S. presidential election approaching.

The authors cautioned against the presumption of immutable and disproportionate advantages for transgender female athletes who compete in women’s sports, and they advised against “precautionary bans and sport eligibility exclusions” that were not based on sport-specific research.

Outright bans, though, continue to proliferate. Twenty-five U.S. states now have laws or regulations barring transgender athletes from competing in girls and women’s sports, according to the Movement Advancement Project , a nonprofit that focuses on gay, lesbian, bisexual and transgender parity. And the National Association of Intercollegiate Athletics , the governing body for smaller colleges, this month barred transgender athletes from competing in women’s sports unless their sex was assigned female at birth and they had not undergone hormone therapy.

Two of the most visible sports at this summer’s Paris Games — swimming and track and field — along with cycling have effectively barred transgender female athletes who went through puberty as males. Rugby has instituted a total ban on trans female athletes, citing safety concerns, and those permitted to participate in other sports often face stricter requirements in suppressing their levels of testosterone.

The International Olympic Committee has left eligibility rules for transgender female athletes up to the global federations that govern individual sports. And while the Olympic committee provided financing for the study — as it does on a variety of topics through a research fund — Olympic officials had no input or influence on the results, Dr. Pitsiladis said.

In general, the argument for the bans has been that profound advantages gained from testosterone-fueled male puberty — broader shoulders, bigger hands, longer torsos, and greater muscle mass, strength, bone density and heart and lung capacity — give transgender female athletes an inequitable and largely irreversible competitive edge.

The new laboratory-based, peer-reviewed and I.O.C.-funded study at the University of Brighton, published this month in the British Journal of Sports Medicine , tested 19 cisgender men (those whose gender identity matches the sex they were assigned at birth) and 12 trans men, along with 23 trans women and 21 cisgender women.

All of the participants played competitive sports or underwent physical training at least three times a week. And all of the trans female athletes had undergone at least a year of treatment suppressing their testosterone levels and taking estrogen supplementation, the researchers said. None of the participants were athletes competing at the national or international level.

The study found that transgender female participants showed greater handgrip strength than cisgender female participants but lower lung function and relative VO2 max, the amount of oxygen used when exercising. Transgender female athletes also scored below cisgender women and men on a jumping test that measured lower-body power.

The study acknowledged some limitations, including its small sample size and the fact that the athletes were not followed over the long term as they transitioned. And, as previous research has indicated, it found that transgender female athletes did retain at least one advantage over cisgender female athletes — a measurement of handgrip strength .

But it is a combination of factors, not a single parameter, that determines athletic performance, said Dr. Pitsiladis, a professor of sport and exercise science.

Athletes who grow taller and heavier after going through puberty as males must “carry this big skeleton with a smaller engine” after transitioning, he said. He cited volleyball as an example, saying that, for transgender female athletes, “the jumping and blocking will not be to the same height as they were doing before. And they may find that, overall, their performance is less good.”

But Michael J. Joyner, a doctor at the Mayo Clinic who studies the physiology of male and female athletes, said that, based on his research and the research of others, science supports the bans in elite sports, where events can be decided by the smallest of margins.

“We know testosterone is performance enhancing,” Dr. Joyner said. “And we know testosterone has residual effects.” Additionally, he added, declines in performance by trans women after taking drugs to suppress their testosterone levels do not fully reduce the typical differences in athletic performance between men and women.

Supporters of transgender athletes, and some scientists who disagree with bans, have accused governing bodies and lawmakers of enacting solutions for a problem that doesn’t exist. There are few elite trans female athletes, they have noted. And there has been limited scientific study of presumed unalterable advantages in strength, power and aerobic capacity gained by experiencing puberty as a male.

For those who have competed in the Olympics, results have varied widely. At the 2021 Tokyo Games, Quinn , a soccer player who is trans nonbinary and was assigned female at birth, helped Canada’s team win a gold medal. But Laurel Hubbard , a transgender weight lifter from New Zealand, failed to complete a lift in her event.

“The idea that trans women are going to take over women’s sports is ludicrous,” said Joanna Harper, a leading researcher of trans athletes and a postdoctoral scholar at Oregon Health & Science University.

Dr. Harper, who is transgender, said it was important for sports to consider physiological differences between transgender women and cisgender women and that she supported certain restrictions, such as requiring the suppression of testosterone levels. But she called blanket bans “unnecessary and unjustified” and said she welcomed the I.O.C.-funded study.

“This fear that trans women aren’t really women, that they’re men who are invading women’s sports, and that trans women will carry all of their male athleticism, their athletic capabilities, into women’s sports — neither of those things are true,” Dr. Harper said.

Sebastian Coe, the president of World Athletics, which governs global track and field, acknowledged that the science remains unresolved. But the organization decided to bar transgender female athletes from international track and field, he said, because “I’m not going to take a risk on this.”

“We think this is in the best interest of preserving the female category,” Mr. Coe said.

In at least two prominent cases, the fight over transgender bans has moved to the courts. The former University of Pennsylvania swimmer Lia Thomas is challenging a ban imposed by World Aquatics, swimming’s global governing body, after she won the 500-yard freestyle race at the 2022 N.C.A.A. championships. That victory made Thomas, who had been among the best men’s swimmers in the Ivy League, the first known trans athlete to win a women’s championship event in college sports’ top division.

Thomas did not dominate all of her races, though, finishing tied for fifth in a second race and eighth in a third. Her winning time in the 500 was more than nine seconds slower than the N.C.A.A. record. Her case, filed at the Swiss-based Court of Arbitration for Sport, is not expected to be resolved before the Paris Olympics begin in July.

Meanwhile, more than a dozen current and former U.S. college athletes, including at least one who competed against Thomas, sued the N.C.A.A. last month . They claimed that, by letting Thomas participate in the national championships, the organization had violated their rights under Title IX, the law that prohibits sex discrimination at institutions that receive federal funding. (Title IX has also been relied upon to argue in favor of transgender female athletes.)

Outsports , a website that reports on L.G.B.T.Q. issues, hailed the I.O.C.-funded study as a “landmark” that concluded that “blanket sports bans are a mistake.” But some scientists and athletes called the study deeply flawed in an article in The Telegraph , which labeled the suggestion that transgender women are at a disadvantage in sports a “new low” for the I.O.C.

So heated is the debate that Dr. Pitsiladis said he and his research team have received threats. That, he warned, could lead other scientists to shy away from pursuing research on the topic.

“Why would any scientist do this if you’re going to get totally slammed and character-assassinated?” he said. “This is no longer a science matter. Unfortunately, it’s become a political matter.”

Jeré Longman covers international sports, focusing on competitive, social, cultural and political issues around the world. More about Jeré Longman

Can Neosporin Protect You From Getting COVID-19?

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

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

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

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

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

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

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

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

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

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

Read More : COVID-Cautious Americans Feel Abandoned

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

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

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

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

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

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

Yuki Noguchi

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

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

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

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

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

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

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

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

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

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

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

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

Popular myths about sleep, debunked

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

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

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

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

Shots - Health News

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

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

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

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

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

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What It Means To Be Asian in America

The lived experiences and perspectives of asian americans in their own words.

Asians are the fastest growing racial and ethnic group in the United States. More than 24 million Americans in the U.S. trace their roots to more than 20 countries in East and Southeast Asia and the Indian subcontinent.

The majority of Asian Americans are immigrants, coming to understand what they left behind and building their lives in the United States. At the same time, there is a fast growing, U.S.-born generation of Asian Americans who are navigating their own connections to familial heritage and their own experiences growing up in the U.S.

In a new Pew Research Center analysis based on dozens of focus groups, Asian American participants described the challenges of navigating their own identity in a nation where the label “Asian” brings expectations about their origins, behavior and physical self. Read on to see, in their own words, what it means to be Asian in America.

• Introduction

Table of Contents

This is how i view my identity, this is how others see and treat me, this is what it means to be home in america, about this project, methodological note, acknowledgments.

No single experience defines what it means to be Asian in the United States today. Instead, Asian Americans’ lived experiences are in part shaped by where they were born, how connected they are to their family’s ethnic origins, and how others – both Asians and non-Asians – see and engage with them in their daily lives. Yet despite diverse experiences, backgrounds and origins, shared experiences and common themes emerged when we asked: “What does it mean to be Asian in America?”

In the fall of 2021, Pew Research Center undertook the largest focus group study it had ever conducted – 66 focus groups with 264 total participants – to hear Asian Americans talk about their lived experiences in America. The focus groups were organized into 18 distinct Asian ethnic origin groups, fielded in 18 languages and moderated by members of their own ethnic groups. Because of the pandemic, the focus groups were conducted virtually, allowing us to recruit participants from all parts of the United States. This approach allowed us to hear a diverse set of voices – especially from less populous Asian ethnic groups whose views, attitudes and opinions are seldom presented in traditional polling. The approach also allowed us to explore the reasons behind people’s opinions and choices about what it means to belong in America, beyond the preset response options of a traditional survey.

The terms “Asian,” “Asians living in the United States” and “Asian American” are used interchangeably throughout this essay to refer to U.S. adults who self-identify as Asian, either alone or in combination with other races or Hispanic identity.

“The United States” and “the U.S.” are used interchangeably with “America” for variations in the writing.

Multiracial participants are those who indicate they are of two or more racial backgrounds (one of which is Asian). Multiethnic participants are those who indicate they are of two or more ethnicities, including those identified as Asian with Hispanic background.

U.S. born refers to people born in the 50 U.S. states or the District of Columbia, Puerto Rico, or other U.S. territories.

Immigrant refers to people who were not U.S. citizens at birth – in other words, those born outside the U.S., Puerto Rico or other U.S. territories to parents who were not U.S. citizens. The terms “immigrant,” “first generation” and “foreign born” are used interchangeably in this report.  

Second generation refers to people born in the 50 states or the District of Columbia with at least one first-generation, or immigrant, parent.

The pan-ethnic term “Asian American” describes the population of about 22 million people living in the United States who trace their roots to more than 20 countries in East and Southeast Asia and the Indian subcontinent. The term was popularized by U.S. student activists in the 1960s and was eventually adopted by the U.S. Census Bureau. However, the “Asian” label masks the diverse demographics and wide economic disparities across the largest national origin groups (such as Chinese, Indian, Filipino) and the less populous ones (such as Bhutanese, Hmong and Nepalese) living in America. It also hides the varied circumstances of groups immigrated to the U.S. and how they started their lives there. The population’s diversity often presents challenges . Conventional survey methods typically reflect the voices of larger groups without fully capturing the broad range of views, attitudes, life starting points and perspectives experienced by Asian Americans. They can also limit understanding of the shared experiences across this diverse population.

Across all focus groups, some common findings emerged. Participants highlighted how the pan-ethnic “Asian” label used in the U.S. represented only one part of how they think of themselves. For example, recently arrived Asian immigrant participants told us they are drawn more to their ethnic identity than to the more general, U.S.-created pan-ethnic Asian American identity. Meanwhile, U.S.-born Asian participants shared how they identified, at times, as Asian but also, at other times, by their ethnic origin and as Americans.

Another common finding among focus group participants is the disconnect they noted between how they see themselves and how others view them. Sometimes this led to maltreatment of them or their families, especially at heightened moments in American history such as during Japanese incarceration during World War II, the aftermath of 9/11 and, more recently, the COVID-19 pandemic. Beyond these specific moments, many in the focus groups offered their own experiences that had revealed other people’s assumptions or misconceptions about their identity.

Another shared finding is the multiple ways in which participants take and express pride in their cultural and ethnic backgrounds while also feeling at home in America, celebrating and blending their unique cultural traditions and practices with those of other Americans.

This focus group project is part of a broader research agenda about Asians living in the United States. The findings presented here offer a small glimpse of what participants told us, in their own words, about how they identify themselves, how others see and treat them, and more generally, what it means to be Asian in America.

Illustrations by Jing Li

Publications from the Being Asian in America project

• Read the data essay: What It Means to Be Asian in America
• Watch the documentary: Being Asian in America
• Explore the interactive: In Their Own Words: The Diverse Perspectives of Being Asian in America
• View expanded interviews: Extended Interviews: Being Asian in America
• About this research project: More on the Being Asian in America project
• Q&A: Why and how Pew Research Center conducted 66 focus groups with Asian Americans

One of the topics covered in each focus group was how participants viewed their own racial or ethnic identity. Moderators asked them how they viewed themselves, and what experiences informed their views about their identity. These discussions not only highlighted differences in how participants thought about their own racial or ethnic background, but they also revealed how different settings can influence how they would choose to identify themselves. Across all focus groups, the general theme emerged that being Asian was only one part of how participants viewed themselves.

The pan-ethnic label ‘Asian’ is often used more in formal settings

“I think when I think of the Asian Americans, I think that we’re all unique and different. We come from different cultures and backgrounds. We come from unique stories, not just as a group, but just as individual humans.” Mali , documentary participant

Many participants described a complicated relationship with the pan-ethnic labels “Asian” or “Asian American.” For some, using the term was less of an active choice and more of an imposed one, with participants discussing the disconnect between how they would like to identify themselves and the available choices often found in formal settings. For example, an immigrant Pakistani woman remarked how she typically sees “Asian American” on forms, but not more specific options. Similarly, an immigrant Burmese woman described her experience of applying for jobs and having to identify as “Asian,” as opposed to identifying by her ethnic background, because no other options were available. These experiences highlight the challenges organizations like government agencies and employers have in developing surveys or forms that ask respondents about their identity. A common sentiment is one like this:

“I guess … I feel like I just kind of check off ‘Asian’ [for] an application or the test forms. That’s the only time I would identify as Asian. But Asian is too broad. Asia is a big continent. Yeah, I feel like it’s just too broad. To specify things, you’re Taiwanese American, that’s exactly where you came from.”

–U.S.-born woman of Taiwanese origin in early 20s

Smaller ethnic groups default to ‘Asian’ since their groups are less recognizable

Other participants shared how their experiences in explaining the geographic location and culture of their origin country led them to prefer “Asian” when talking about themselves with others. This theme was especially prominent among those belonging to smaller origin groups such as Bangladeshis and Bhutanese. A Lao participant remarked she would initially say “Asian American” because people might not be familiar with “Lao.”

“​​[When I fill out] forms, I select ‘Asian American,’ and that’s why I consider myself as an Asian American. [It is difficult to identify as] Nepali American [since] there are no such options in forms. That’s why, Asian American is fine to me.”

–Immigrant woman of Nepalese origin in late 20s

“Coming to a big country like [the United States], when people ask where we are from … there are some people who have no idea about Bhutan, so we end up introducing ourselves as being Asian.”

–Immigrant woman of Bhutanese origin in late 40s

But for many, ‘Asian’ as a label or identity just doesn’t fit

Many participants felt that neither “Asian” nor “Asian American” truly captures how they view themselves and their identity. They argue that these labels are too broad or too ambiguous, as there are so many different groups included within these labels. For example, a U.S.-born Pakistani man remarked on how “Asian” lumps many groups together – that the term is not limited to South Asian groups such as Indian and Pakistani, but also includes East Asian groups. Similarly, an immigrant Nepalese man described how “Asian” often means Chinese for many Americans. A Filipino woman summed it up this way:

“Now I consider myself to be both Filipino and Asian American, but growing up in [Southern California] … I didn’t start to identify as Asian American until college because in [the Los Angeles suburb where I lived], it’s a big mix of everything – Black, Latino, Pacific Islander and Asian … when I would go into spaces where there were a lot of other Asians, especially East Asians, I didn’t feel like I belonged. … In media, right, like people still associate Asian with being East Asian.”

–U.S.-born woman of Filipino origin in mid-20s

Participants also noted they have encountered confusion or the tendency for others to view Asian Americans as people from mostly East Asian countries, such as China, Japan and Korea. For some, this confusion even extends to interactions with other Asian American groups. A Pakistani man remarked on how he rarely finds Pakistani or Indian brands when he visits Asian stores. Instead, he recalled mostly finding Vietnamese, Korean and Chinese items.

Among participants of South Asian descent, some identified with the label “South Asian” more than just “Asian.” There were other nuances, too, when it comes to the labels people choose. Some Indian participants, for example, said people sometimes group them with Native Americans who are also referred to as Indians in the United States. This Indian woman shared her experience at school:

“I love South Asian or ‘Desi’ only because up until recently … it’s fairly new to say South Asian. I’ve always said ‘Desi’ because growing up … I’ve had to say I’m the red dot Indian, not the feather Indian. So annoying, you know? … Always a distinction that I’ve had to make.”

–U.S.-born woman of Indian origin in late 20s

Participants with multiethnic or multiracial backgrounds described their own unique experiences with their identity. Rather than choosing one racial or ethnic group over the other, some participants described identifying with both groups, since this more accurately describes how they see themselves. In some cases, this choice reflected the history of the Asian diaspora. For example, an immigrant Cambodian man described being both Khmer/Cambodian and Chinese, since his grandparents came from China. Some other participants recalled going through an “identity crisis” as they navigated between multiple identities. As one woman explained:

“I would say I went through an identity crisis. … It’s because of being multicultural. … There’s also French in the mix within my family, too. Because I don’t identify, speak or understand the language, I really can’t connect to the French roots … I’m in between like Cambodian and Thai, and then Chinese and then French … I finally lumped it up. I’m just an Asian American and proud of all my roots.”

–U.S.-born woman of Cambodian origin in mid-30s

In other cases, the choice reflected U.S. patterns of intermarriage. Asian newlyweds have the highest intermarriage rate of any racial or ethnic group in the country. One Japanese-origin man with Hispanic roots noted:

“So I would like to see myself as a Hispanic Asian American. I want to say Hispanic first because I have more of my mom’s culture in me than my dad’s culture. In fact, I actually have more American culture than my dad’s culture for what I do normally. So I guess, Hispanic American Asian.”

–U.S.-born man of Hispanic and Japanese origin in early 40s

Other identities beyond race or ethnicity are also important

Focus group participants also talked about their identity beyond the racial or ethnic dimension. For example, one Chinese woman noted that the best term to describe her would be “immigrant.” Faith and religious ties were also important to some. One immigrant participant talked about his love of Pakistani values and how religion is intermingled into Pakistani culture. Another woman explained:

“[Japanese language and culture] are very important to me and ingrained in me because they were always part of my life, and I felt them when I was growing up. Even the word itadakimasu reflects Japanese culture or the tradition. Shinto religion is a part of the culture. They are part of my identity, and they are very important to me.”

–Immigrant woman of Japanese origin in mid-30s

For some, gender is another important aspect of identity. One Korean participant emphasized that being a woman is an important part of her identity. For others, sexual orientation is an essential part of their overall identity. One U.S.-born Filipino participant described herself as “queer Asian American.” Another participant put it this way:

“I belong to the [LGBTQ] community … before, what we only know is gay and lesbian. We don’t know about being queer, nonbinary. [Here], my horizon of knowing what genders and gender roles is also expanded … in the Philippines, if you’ll be with same sex, you’re considered gay or lesbian. But here … what’s happening is so broad, on how you identify yourself.”

–Immigrant woman of Filipino origin in early 20s

Immigrant identity is tied to their ethnic heritage

Participants born outside the United States tended to link their identity with their ethnic heritage. Some felt strongly connected with their ethnic ties due to their citizenship status. For others, the lack of permanent residency or citizenship meant they have stronger ties to their ethnicity and birthplace. And in some cases, participants said they held on to their ethnic identity even after they became U.S. citizens. One woman emphasized that she will always be Taiwanese because she was born there, despite now living in the U.S.

For other participants, family origin played a central role in their identity, regardless of their status in the U.S. According to some of them, this attitude was heavily influenced by their memories and experiences in early childhood when they were still living in their countries of origin. These influences are so profound that even after decades of living in the U.S., some still feel the strong connection to their ethnic roots. And those with U.S.-born children talked about sending their kids to special educational programs in the U.S. to learn about their ethnic heritage.

“Yes, as for me, I hold that I am Khmer because our nationality cannot be deleted, our identity is Khmer as I hold that I am Khmer … so I try, even [with] my children today, I try to learn Khmer through Zoom through the so-called Khmer Parent Association.”

–Immigrant man of Cambodian origin in late 50s

Navigating life in America is an adjustment

Many participants pointed to cultural differences they have noticed between their ethnic culture and U.S. culture. One of the most distinct differences is in food. For some participants, their strong attachment to the unique dishes of their families and their countries of origin helps them maintain strong ties to their ethnic identity. One Sri Lankan participant shared that her roots are still in Sri Lanka, since she still follows Sri Lankan traditions in the U.S. such as preparing kiribath (rice with coconut milk) and celebrating Ramadan.

For other participants, interactions in social settings with those outside their own ethnic group circles highlighted cultural differences. One Bangladeshi woman talked about how Bengalis share personal stories and challenges with each other, while others in the U.S. like to have “small talk” about TV series or clothes.

Many immigrants in the focus groups have found it is easier to socialize when they are around others belonging to their ethnicity. When interacting with others who don’t share the same ethnicity, participants noted they must be more self-aware about cultural differences to avoid making mistakes in social interactions. Here, participants described the importance of learning to “fit in,” to avoid feeling left out or excluded. One Korean woman said:

“Every time I go to a party, I feel unwelcome. … In Korea, when I invite guests to my house and one person sits without talking, I come over and talk and treat them as a host. But in the United States, I have to go and mingle. I hate mingling so much. I have to talk and keep going through unimportant stories. In Korea, I am assigned to a dinner or gathering. I have a party with a sense of security. In America, I have nowhere to sit, and I don’t know where to go and who to talk to.”

–Immigrant woman of Korean origin in mid-40s

And a Bhutanese immigrant explained:

“In my case, I am not an American. I consider myself a Bhutanese. … I am a Bhutanese because I do not know American culture to consider myself as an American. It is very difficult to understand the sense of humor in America. So, we are pure Bhutanese in America.”

–Immigrant man of Bhutanese origin in early 40s

Language was also a key aspect of identity for the participants. Many immigrants in the focus groups said they speak a language other than English at home and in their daily lives. One Vietnamese man considered himself Vietnamese since his Vietnamese is better than his English. Others emphasized their English skills. A Bangladeshi participant felt that she was more accepted in the workplace when she does more “American” things and speaks fluent English, rather than sharing things from Bangladeshi culture. She felt that others in her workplace correlate her English fluency with her ability to do her job. For others born in the U.S., the language they speak at home influences their connection to their ethnic roots.

“Now if I go to my work and do show my Bengali culture and Asian culture, they are not going to take anything out of it. So, basically, I have to show something that they are interested in. I have to show that I am American, [that] I can speak English fluently. I can do whatever you give me as a responsibility. So, in those cases I can’t show anything about my culture.”

–Immigrant woman of Bangladeshi origin in late 20s

“Being bi-ethnic and tri-cultural creates so many unique dynamics, and … one of the dynamics has to do with … what it is to be Americanized. … One of the things that played a role into how I associate the identity is language. Now, my father never spoke Spanish to me … because he wanted me to develop a fluency in English, because for him, he struggled with English. What happened was three out of the four people that raised me were Khmer … they spoke to me in Khmer. We’d eat breakfast, lunch and dinner speaking Khmer. We’d go to the temple in Khmer with the language and we’d also watch videos and movies in Khmer. … Looking into why I strongly identify with the heritage, one of the reasons is [that] speaking that language connects to the home I used to have [as my families have passed away].”

–U.S.-born man of Cambodian origin in early 30s

Balancing between individualistic and collective thinking

For some immigrant participants, the main differences between themselves and others who are seen as “truly American” were less about cultural differences, or how people behave, and more about differences in “mindset,” or how people think . Those who identified strongly with their ethnicity discussed how their way of thinking is different from a “typical American.” To some, the “American mentality” is more individualistic, with less judgment on what one should do or how they should act . One immigrant Japanese man, for example, talked about how other Japanese-origin co-workers in the U.S. would work without taking breaks because it’s culturally inconsiderate to take a break while others continued working. However, he would speak up for himself and other workers when they are not taking any work breaks. He attributed this to his “American” way of thinking, which encourages people to stand up for themselves.

Some U.S.-born participants who grew up in an immigrant family described the cultural clashes that happened between themselves and their immigrant parents. Participants talked about how the second generation (children of immigrant parents) struggles to pursue their own dreams while still living up to the traditional expectations of their immigrant parents.

“I feel like one of the biggest things I’ve seen, just like [my] Asian American friends overall, is the kind of family-individualistic clash … like wanting to do your own thing is like, is kind of instilled in you as an American, like go and … follow your dream. But then you just grow up with such a sense of like also wanting to be there for your family and to live up to those expectations, and I feel like that’s something that’s very pronounced in Asian cultures.”

–U.S.-born man of Indian origin in mid-20s

Discussions also highlighted differences about gender roles between growing up in America compared with elsewhere.

“As a woman or being a girl, because of your gender, you have to keep your mouth shut [and] wait so that they call on you for you to speak up. … I do respect our elders and I do respect hearing their guidance but I also want them to learn to hear from the younger person … because we have things to share that they might not know and that [are] important … so I like to challenge gender roles or traditional roles because it is something that [because] I was born and raised here [in America], I learn that we all have the equal rights to be able to speak and share our thoughts and ideas.”

U.S. born have mixed ties to their family’s heritage

“I think being Hmong is somewhat of being free, but being free of others’ perceptions of you or of others’ attempts to assimilate you or attempts to put pressure on you. I feel like being Hmong is to resist, really.” Pa Houa , documentary participant

How U.S.-born participants identify themselves depends on their familiarity with their own heritage, whom they are talking with, where they are when asked about their identity and what the answer is used for. Some mentioned that they have stronger ethnic ties because they are very familiar with their family’s ethnic heritage. Others talked about how their eating habits and preferred dishes made them feel closer to their ethnic identity. For example, one Korean participant shared his journey of getting closer to his Korean heritage because of Korean food and customs. When some participants shared their reasons for feeling closer to their ethnic identity, they also expressed a strong sense of pride with their unique cultural and ethnic heritage.

“I definitely consider myself Japanese American. I mean I’m Japanese and American. Really, ever since I’ve grown up, I’ve really admired Japanese culture. I grew up watching a lot of anime and Japanese black and white films. Just learning about [it], I would hear about Japanese stuff from my grandparents … myself, and my family having blended Japanese culture and American culture together.”

–U.S.-born man of Japanese origin in late 20s

Meanwhile, participants who were not familiar with their family’s heritage showed less connection with their ethnic ties. One U.S.-born woman said she has a hard time calling herself Cambodian, as she is “not close to the Cambodian community.” Participants with stronger ethnic ties talked about relating to their specific ethnic group more than the broader Asian group. Another woman noted that being Vietnamese is “more specific and unique than just being Asian” and said that she didn’t feel she belonged with other Asians. Some participants also disliked being seen as or called “Asian,” in part because they want to distinguish themselves from other Asian groups. For example, one Taiwanese woman introduces herself as Taiwanese when she can, because she had frequently been seen as Chinese.

Some in the focus groups described how their views of their own identities shifted as they grew older. For example, some U.S.-born and immigrant participants who came to the U.S. at younger ages described how their experiences in high school and the need to “fit in” were important in shaping their own identities. A Chinese woman put it this way:

“So basically, all I know is that I was born in the United States. Again, when I came back, I didn’t feel any barrier with my other friends who are White or Black. … Then I got a little confused in high school when I had trouble self-identifying if I am Asian, Chinese American, like who am I. … Should I completely immerse myself in the American culture? Should I also keep my Chinese identity and stuff like that? So yeah, that was like the middle of that mist. Now, I’m pretty clear about myself. I think I am Chinese American, Asian American, whatever people want.”

–U.S.-born woman of Chinese origin in early 20s

Identity is influenced by birthplace

“I identified myself first and foremost as American. Even on the forms that you fill out that says, you know, ‘Asian’ or ‘Chinese’ or ‘other,’ I would check the ‘other’ box, and I would put ‘American Chinese’ instead of ‘Chinese American.’” Brent , documentary participant

When talking about what it means to be “American,” participants offered their own definitions. For some, “American” is associated with acquiring a distinct identity alongside their ethnic or racial backgrounds, rather than replacing them. One Indian participant put it this way:

“I would also say [that I am] Indian American just because I find myself always bouncing between the two … it’s not even like dual identity, it just is one whole identity for me, like there’s not this separation. … I’m doing [both] Indian things [and] American things. … They use that term like ABCD … ‘American Born Confused Desi’ … I don’t feel that way anymore, although there are those moments … but I would say [that I am] Indian American for sure.”

–U.S.-born woman of Indian origin in early 30s

Meanwhile, some U.S.-born participants view being American as central to their identity while also valuing the culture of their family’s heritage.

Many immigrant participants associated the term “American” with immigration status or citizenship. One Taiwanese woman said she can’t call herself American since she doesn’t have a U.S. passport. Notably, U.S. citizenship is an important milestone for many immigrant participants, giving them a stronger sense of belonging and ultimately calling themselves American. A Bangladeshi participant shared that she hasn’t received U.S. citizenship yet, and she would call herself American after she receives her U.S. passport.

Other participants gave an even narrower definition, saying only those born and raised in the United States are truly American. One Taiwanese woman mentioned that her son would be American since he was born, raised and educated in the U.S. She added that while she has U.S. citizenship, she didn’t consider herself American since she didn’t grow up in the U.S. This narrower definition has implications for belonging. Some immigrants in the groups said they could never become truly American since the way they express themselves is so different from those who were born and raised in the U.S. A Japanese woman pointed out that Japanese people “are still very intimidated by authorities,” while those born and raised in America give their opinions without hesitation.

“As soon as I arrived, I called myself a Burmese immigrant. I had a green card, but I still wasn’t an American citizen. … Now I have become a U.S. citizen, so now I am a Burmese American.”

–Immigrant man of Burmese origin in mid-30s

“Since I was born … and raised here, I kind of always view myself as American first who just happened to be Asian or Chinese. So I actually don’t like the term Chinese American or Asian American. I’m American Asian or American Chinese. I view myself as American first.”

–U.S.-born man of Chinese origin in early 60s

“[I used to think of myself as] Filipino, but recently I started saying ‘Filipino American’ because I got [U.S.] citizenship. And it just sounds weird to say Filipino American, but I’m trying to … I want to accept it. I feel like it’s now marry-able to my identity.”

–Immigrant woman of Filipino origin in early 30s

For others, American identity is about the process of ‘becoming’ culturally American

Immigrant participants also emphasized how their experiences and time living in America inform their views of being an “American.” As a result, some started to see themselves as Americans after spending more than a decade in the U.S. One Taiwanese man considered himself an American since he knows more about the U.S. than Taiwan after living in the U.S. for over 52 years.

But for other immigrant participants, the process of “becoming” American is not about how long they have lived in the U.S., but rather how familiar they are with American culture and their ability to speak English with little to no accent. This is especially true for those whose first language is not English, as learning and speaking it without an accent can be a big challenge for some. One Bangladeshi participant shared that his pronunciation of “hot water” was very different from American English, resulting in confusions in communication. By contrast, those who were more confident in their English skills felt they can better understand American culture and values as a result, leading them to a stronger connection with an American identity.

“[My friends and family tease me for being Americanized when I go back to Japan.] I think I seem a little different to people who live in Japan. I don’t think they mean anything bad, and they [were] just joking, because I already know that I seem a little different to people who live in Japan.”

–Immigrant man of Japanese origin in mid-40s

“I value my Hmong culture, and language, and ethnicity, but I also do acknowledge, again, that I was born here in America and I’m grateful that I was born here, and I was given opportunities that my parents weren’t given opportunities for.”

–U.S.-born woman of Hmong origin in early 30s

During the focus group discussions about identity, a recurring theme emerged about the difference between how participants saw themselves and how others see them. When asked to elaborate on their experiences and their points of view, some participants shared experiences they had with people misidentifying their race or ethnicity. Others talked about their frustration with being labeled the “model minority.” In all these discussions, participants shed light on the negative impacts that mistaken assumptions and labels had on their lives.

All people see is ‘Asian’

For many, interactions with others (non-Asians and Asians alike) often required explaining their backgrounds, reacting to stereotypes, and for those from smaller origin groups in particular, correcting the misconception that being “Asian” means you come from one of the larger Asian ethnic groups. Several participants remarked that in their own experiences, when others think about Asians, they tend to think of someone who is Chinese. As one immigrant Filipino woman put it, “Interacting with [non-Asians in the U.S.], it’s hard. … Well, first, I look Spanish. I mean, I don’t look Asian, so would you guess – it’s like they have a vision of what an Asian [should] look like.” Similarly, an immigrant Indonesian man remarked how Americans tended to see Asians primarily through their physical features, which not all Asian groups share.

Several participants also described how the tendency to view Asians as a monolithic group can be even more common in the wake of the COVID-19 pandemic.

“The first [thing people think of me as] is just Chinese. ‘You guys are just Chinese.’ I’m not the only one who felt [this] after the COVID-19 outbreak. ‘Whether you’re Japanese, Korean, or Southeast Asian, you’re just Chinese [to Americans]. I should avoid you.’ I’ve felt this way before, but I think I’ve felt it a bit more after the COVID-19 outbreak.”

–Immigrant woman of Korean origin in early 30s

At the same time, other participants described their own experiences trying to convince others that they are Asian or Asian American. This was a common experience among Southeast Asian participants.

“I have to convince people I’m Asian, not Middle Eastern. … If you type in Asian or you say Asian, most people associate it with Chinese food, Japanese food, karate, and like all these things but then they don’t associate it with you.”

–U.S.-born man of Pakistani origin in early 30s

The model minority myth and its impact

“I’ve never really done the best academically, compared to all my other Asian peers too. I never really excelled. I wasn’t in honors. … Those stereotypes, I think really [have] taken a toll on my self-esteem.” Diane , documentary participant

Across focus groups, immigrant and U.S.-born participants described the challenges of the seemingly positive stereotypes of Asians as intelligent, gifted in technical roles and hardworking. Participants often referred to this as the “model minority myth.”

The label “model minority” was coined in the 1960s and has been used to characterize Asian Americans as financially and educationally successful and hardworking when compared with other groups. However, for many Asians living in the United States, these characterizations do not align with their lived experiences or reflect their socioeconomic backgrounds. Indeed, among Asian origin groups in the U.S., there are wide differences in economic and social experiences. 

Academic research on the model minority myth has pointed to its impact beyond Asian Americans and towards other racial and ethnic groups, especially Black Americans, in the U.S. Some argue that the model minority myth has been used to justify policies that overlook the historical circumstances and impacts of colonialism, slavery, discrimination and segregation on other non-White racial and ethnic groups.

Many participants noted ways in which the model minority myth has been harmful. For some, expectations based on the myth didn’t match their own experiences of coming from impoverished communities. Some also recalled experiences at school when they struggled to meet their teachers’ expectations in math and science.

“As an Asian person, I feel like there’s that stereotype that Asian students are high achievers academically. They’re good at math and science. … I was a pretty mediocre student, and math and science were actually my weakest subjects, so I feel like it’s either way you lose. Teachers expect you to fit a certain stereotype and if you’re not, then you’re a disappointment, but at the same time, even if you are good at math and science, that just means that you’re fitting a stereotype. It’s [actually] your own achievement, but your teachers might think, ‘Oh, it’s because they’re Asian,’ and that diminishes your achievement.”

–U.S.-born woman of Korean origin in late 20s

Some participants felt that even when being Asian worked in their favor in the job market, they encountered stereotypes that “Asians can do quality work with less compensation” or that “Asians would not complain about anything at work.”

“There is a joke from foreigners and even Asian Americans that says, ‘No matter what you do, Asians always do the best.’ You need to get A, not just B-plus. Otherwise, you’ll be a disgrace to the family. … Even Silicon Valley hires Asian because [an] Asian’s wage is cheaper but [they] can work better. When [work] visa overflow happens, they hire Asians like Chinese and Indian to work in IT fields because we are good at this and do not complain about anything.”

–Immigrant man of Thai origin in early 40s

Others expressed frustration that people were placing them in the model minority box. One Indian woman put it this way:

“Indian people and Asian people, like … our parents or grandparents are the ones who immigrated here … against all odds. … A lot of Indian and Asian people have succeeded and have done really well for themselves because they’ve worked themselves to the bone. So now the expectations [of] the newer generations who were born here are incredibly unrealistic and high. And you get that not only from your family and the Indian community, but you’re also getting it from all of the American people around you, expecting you to be … insanely good at math, play an instrument, you know how to do this, you know how to do that, but it’s not true. And it’s just living with those expectations, it’s difficult.”

–U.S.-born woman of Indian origin in early 20s

Whether U.S. born or immigrants, Asians are often seen by others as foreigners

“Being only not quite 10 years old, it was kind of exciting to ride on a bus to go someplace. But when we went to Pomona, the assembly center, we were stuck in one of the stalls they used for the animals.” Tokiko , documentary participant

Across all focus groups, participants highlighted a common question they are asked in America when meeting people for the first time: “Where are you really from?” For participants, this question implied that people think they are “foreigners,” even though they may be longtime residents or citizens of the United States or were born in the country. One man of Vietnamese origin shared his experience with strangers who assumed that he and his friends are North Korean. Perhaps even more hurtful, participants mentioned that this meant people had a preconceived notion of what an “American” is supposed to look like, sound like or act like. One Chinese woman said that White Americans treated people like herself as outsiders based on her skin color and appearance, even though she was raised in the U.S.

Many focus group participants also acknowledged the common stereotype of treating Asians as “forever foreigners.” Some immigrant participants said they felt exhausted from constantly being asked this question by people even when they speak perfect English with no accent. During the discussion, a Korean immigrant man recalled that someone had said to him, “You speak English well, but where are you from?” One Filipino participant shared her experience during the first six months in the U.S.:

“You know, I spoke English fine. But there were certain things that, you know, people constantly questioning you like, oh, where are you from? When did you come here? You know, just asking about your experience to the point where … you become fed up with it after a while.”

–Immigrant woman of Filipino origin in mid-30s

U.S.-born participants also talked about experiences when others asked where they are from. Many shared that they would not talk about their ethnic origin right away when answering such a question because it often led to misunderstandings and assumptions that they are immigrants.

“I always get that question of, you know, ‘Where are you from?’ and I’m like, ‘I’m from America.’ And then they’re like, ‘No. Where are you from-from ?’ and I’m like, ‘Yeah, my family is from Pakistan,’ so it’s like I always had like that dual identity even though it’s never attached to me because I am like, of Pakistani descent.”

–U.S.-born man of Pakistani origin in early 20s

One Korean woman born in the U.S. said that once people know she is Korean, they ask even more offensive questions such as “Are you from North or South Korea?” or “Do you still eat dogs?”

In a similar situation, this U.S.-born Indian woman shared her responses:

“I find that there’s a, ‘So but where are you from?’ Like even in professional settings when they feel comfortable enough to ask you. ‘So – so where are you from?’ ‘Oh, I was born in [names city], Colorado. Like at [the hospital], down the street.’ ‘No, but like where are you from?’ ‘My mother’s womb?’”

–U.S.-born woman of Indian origin in early 40s

Ignorance and misinformation about Asian identity can lead to contentious encounters

“I have dealt with kids who just gave up on their Sikh identity, cut their hair and groomed their beard and everything. They just wanted to fit in and not have to deal with it, especially [those] who are victim or bullied in any incident.” Surinder , documentary participant

In some cases, ignorance and misinformation about Asians in the U.S. lead to inappropriate comments or questions and uncomfortable or dangerous situations. Participants shared their frustration when others asked about their country of origin, and they then had to explain their identity or correct misunderstandings or stereotypes about their background. At other times, some participants faced ignorant comments about their ethnicity, which sometimes led to more contentious encounters. For example, some Indian or Pakistani participants talked about the attacks or verbal abuse they experienced from others blaming them for the 9/11 terrorist attacks. Others discussed the racial slurs directed toward them since the COVID-19 pandemic in 2020. Some Japanese participants recalled their families losing everything and being incarcerated during World War II and the long-term effect it had on their lives.

“I think like right now with the coronavirus, I think we’re just Chinese, Chinese American, well, just Asian American or Asians in general, you’re just going through the same struggles right now. Like everyone is just blaming whoever looks Asian about the virus. You don’t feel safe.”

–U.S.-born man of Chinese origin in early 30s

“At the beginning of the pandemic, a friend and I went to celebrate her birthday at a club and like these guys just kept calling us COVID.”

–U.S.-born woman of Korean origin in early 20s

“There [were] a lot of instances after 9/11. One day, somebody put a poster about 9/11 [in front of] my business. He was wearing a gun. … On the poster, it was written ‘you Arabs, go back to your country.’ And then someone came inside. He pointed his gun at me and said ‘Go back to your country.’”

–Immigrant man of Pakistani origin in mid-60s

“[My parents went through the] internment camps during World War II. And my dad, he was in high school, so he was – they were building the camps and then he was put into the Santa Anita horse track place, the stables there. And then they were sent – all the Japanese Americans were sent to different camps, right, during World War II and – in California. Yeah, and they lost everything, yeah.”

–U.S.-born woman of Japanese origin in mid-60s

As focus group participants contemplated their identity during the discussions, many talked about their sense of belonging in America. Although some felt frustrated with people misunderstanding their ethnic heritage, they didn’t take a negative view of life in America. Instead, many participants – both immigrant and U.S. born – took pride in their unique cultural and ethnic backgrounds. In these discussions, people gave their own definitions of America as a place with a diverse set of cultures, with their ethnic heritage being a part of it.

Taking pride in their unique cultures

“Being a Pakistani American, I’m proud. … Because I work hard, and I make true my dreams from here.” Shahid , documentary participant

Despite the challenges of adapting to life in America for immigrant participants or of navigating their dual cultural identity for U.S.-born ones, focus group participants called America their home. And while participants talked about their identities in different ways – ethnic identity, racial (Asian) identity, and being American – they take pride in their unique cultures. Many also expressed a strong sense of responsibility to give back or support their community, sharing their cultural heritage with others on their own terms.

“Right now it has been a little difficult. I think it has been for all Asians because of the COVID issue … but I’m glad that we’re all here [in America]. I think we should be proud to be here. I’m glad that our families have traveled here, and we can help make life better for communities, our families and ourselves. I think that’s really a wonderful thing. We can be those role models for a lot of the future, the younger folks. I hope that something I did in the last years will have impacted either my family, friends or students that I taught in other community things that I’ve done. So you hope that it helps someplace along the line.”

“I am very proud of my culture. … There is not a single Bengali at my workplace, but people know the name of my country. Maybe many years [later] – educated people know all about the country. So, I don’t have to explain that there is a small country next to India and Nepal. It’s beyond saying. People after all know Bangladesh. And there are so many Bengali present here as well. So, I am very proud to be a Bangladeshi.”

Where home is

When asked about the definition of home, some immigrant participants said home is where their families are located. Immigrants in the focus groups came to the United States by various paths, whether through work opportunities, reuniting with family or seeking a safe haven as refugees. Along their journey, some received support from family members, their local community or other individuals, while others overcame challenges by themselves. Either way, they take pride in establishing their home in America and can feel hurt when someone tells them to “go back to your country.” In response, one Laotian woman in her mid-40s said, “This is my home. My country. Go away.”

“If you ask me personally, I view my home as my house … then I would say my house is with my family because wherever I go, I cannot marry if I do not have my family so that is how I would answer.”

–Immigrant man of Hmong origin in late 30s

“[If somebody yelled at me ‘go back to your country’] I’d feel angry because this is my country! I live here. America is my country. I grew up here and worked here … I’d say, ‘This is my country! You go back to your country! … I will not go anywhere. This is my home. I will live here.’ That’s what I’d say.”

–Immigrant woman of Laotian origin in early 50s

‘American’ means to blend their unique cultural and ethnic heritage with that in the U.S.

“I want to teach my children two traditions – one American and one Vietnamese – so they can compare and choose for themselves the best route in life.” Helen , documentary participant (translated from Vietnamese)

Both U.S.-born and immigrant participants in the focus groups shared their experiences of navigating a dual cultural environment between their ethnic heritage and American culture. A common thread that emerged was that being Asian in America is a process of blending two or more identities as one.

“Yeah, I want to say that’s how I feel – because like thinking about it, I would call my dad Lao but I would call myself Laotian American because I think I’m a little more integrated in the American society and I’ve also been a little more Americanized, compared to my dad. So that’s how I would see it.”

–U.S.-born man of Laotian origin in late 20s

“I mean, Bangladeshi Americans who are here, we are carrying Bangladeshi culture, religion, food. I am also trying to be Americanized like the Americans. Regarding language, eating habits.”

–Immigrant man of Bangladeshi origin in mid-50s

“Just like there is Chinese American, Mexican American, Japanese American, Italian American, so there is Indian American. I don’t want to give up Indianness. I am American by nationality, but I am Indian by birth. So whenever I talk, I try to show both the flags as well, both Indian and American flags. Just because you make new relatives but don’t forget the old relatives.”

–Immigrant man of Indian origin in late 40s

Pew Research Center designed these focus groups to better understand how members of an ethnically diverse Asian population think about their place in America and life here. By including participants of different languages, immigration or refugee experiences, educational backgrounds, and income levels, this focus group study aimed to capture in people’s own words what it means to be Asian in America. The discussions in these groups may or may not resonate with all Asians living in the United States. Browse excerpts from our focus groups with the interactive quote sorter below, view a video documentary focused on the topics discussed in the focus groups, or tell us your story of belonging in America via social media. The focus group project is part of a broader research project studying the diverse experiences of Asians living in the U.S.

Read sortable quotes from our focus groups

Browse excerpts in the interactive quote sorter from focus group participants in response to the question “What does it mean to be [Vietnamese, Thai, Sri Lankan, Hmong, etc.] like yourself in America?” This interactive allows you to sort quotes from focus group participants by ethnic origin, nativity (U.S. born or born in another country), gender and age.

Video documentary

Videos throughout the data essay illustrate what focus group participants discussed. Those recorded in these videos did not participate in the focus groups but were sampled to have similar demographic characteristics and thematically relevant stories.

Watch the full video documentary and watch additional shorter video clips related to the themes of this data essay.

Share the story of your family and your identity

Did the voices in this data essay resonate? Share your story of what it means to be Asian in America with @pewresearch. Tell us your story by using the hashtag #BeingAsianInAmerica and @pewidentity on Twitter, as well as #BeingAsianInAmerica and @pewresearch on Instagram.

This cross-ethnic, comparative qualitative research project explores the identity, economic mobility, representation, and experiences of immigration and discrimination among the Asian population in the United States. The analysis is based on 66 focus groups we conducted virtually in the fall of 2021 and included 264 participants from across the U.S. More information about the groups and analysis can be found in this appendix .

Pew Research Center is a subsidiary of The Pew Charitable Trusts, its primary funder. This data essay was funded by The Pew Charitable Trusts, with generous support from the Chan Zuckerberg Initiative DAF, an advised fund of the Silicon Valley Community Foundation; the Robert Wood Johnson Foundation; the Henry Luce Foundation; The Wallace H. Coulter Foundation; The Dirk and Charlene Kabcenell Foundation; The Long Family Foundation; Lu-Hebert Fund; Gee Family Foundation; Joseph Cotchett; the Julian Abdey and Sabrina Moyle Charitable Fund; and Nanci Nishimura.

The accompanying video clips and video documentary were made possible by The Pew Charitable Trusts, with generous support from The Sobrato Family Foundation and The Long Family Foundation.

We would also like to thank the Leaders Forum for its thought leadership and valuable assistance in helping make this study possible. This is a collaborative effort based on the input and analysis of a number of individuals and experts at Pew Research Center and outside experts.

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