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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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• Knowledge Base
• Methodology

Research Design | Step-by-Step Guide with Examples

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

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

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

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

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

Prevent plagiarism, run a free check.

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

Types of quantitative research designs

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

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

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

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalisation

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

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

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

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

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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

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

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McCombes, S. (2023, March 20). Research Design | Step-by-Step Guide with Examples. Scribbr. Retrieved 22 April 2024, from https://www.scribbr.co.uk/research-methods/research-design/

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• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• Reading Research Effectively
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
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• Qualitative Methods
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• Insiderness
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• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
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• USC Libraries Tutorials and Other Guides
• Bibliography

Reading a Scholarly Article or Research Paper

Identifying a research problem to investigate usually requires a preliminary search for and critical review of the literature in order to gain an understanding about how scholars have examined a topic. Scholars rarely structure research studies in a way that can be followed like a story; they are complex and detail-intensive and often written in a descriptive and conclusive narrative form. However, in the social and behavioral sciences, journal articles and stand-alone research reports are generally organized in a consistent format that makes it easier to compare and contrast studies and to interpret their contents.

General Reading Strategies

W hen you first read an article or research paper, focus on asking specific questions about each section. This strategy can help with overall comprehension and with understanding how the content relates [or does not relate] to the problem you want to investigate. As you review more and more studies, the process of understanding and critically evaluating the research will become easier because the content of what you review will begin to coalescence around common themes and patterns of analysis. Below are recommendations on how to read each section of a research paper effectively. Note that the sections to read are out of order from how you will find them organized in a journal article or research paper.

1.  Abstract

The abstract summarizes the background, methods, results, discussion, and conclusions of a scholarly article or research paper. Use the abstract to filter out sources that may have appeared useful when you began searching for information but, in reality, are not relevant. Questions to consider when reading the abstract are:

• Is this study related to my question or area of research?
• What is this study about and why is it being done ?
• What is the working hypothesis or underlying thesis?
• What is the primary finding of the study?
• Are there words or terminology that I can use to either narrow or broaden the parameters of my search for more information?

2.  Introduction

If, after reading the abstract, you believe the paper may be useful, focus on examining the research problem and identifying the questions the author is trying to address. This information is usually located within the first few paragraphs of the introduction or in the concluding paragraph. Look for information about how and in what way this relates to what you are investigating. In addition to the research problem, the introduction should provide the main argument and theoretical framework of the study and, in the last paragraphs of the introduction, describe what the author(s) intend to accomplish. Questions to consider when reading the introduction include:

• What is this study trying to prove or disprove?
• What is the author(s) trying to test or demonstrate?
• What do we already know about this topic and what gaps does this study try to fill or contribute a new understanding to the research problem?
• Why should I care about what is being investigated?
• Will this study tell me anything new related to the research problem I am investigating?

3.  Literature Review

The literature review describes and critically evaluates what is already known about a topic. Read the literature review to obtain a big picture perspective about how the topic has been studied and to begin the process of seeing where your potential study fits within the domain of prior research. Questions to consider when reading the literature review include:

• W hat other research has been conducted about this topic and what are the main themes that have emerged?
• What does prior research reveal about what is already known about the topic and what remains to be discovered?
• What have been the most important past findings about the research problem?
• How has prior research led the author(s) to conduct this particular study?
• Is there any prior research that is unique or groundbreaking?
• Are there any studies I could use as a model for designing and organizing my own study?

4.  Discussion/Conclusion

The discussion and conclusion are usually the last two sections of text in a scholarly article or research report. They reveal how the author(s) interpreted the findings of their research and presented recommendations or courses of action based on those findings. Often in the conclusion, the author(s) highlight recommendations for further research that can be used to develop your own study. Questions to consider when reading the discussion and conclusion sections include:

• What is the overall meaning of the study and why is this important? [i.e., how have the author(s) addressed the " So What? " question].
• What do you find to be the most important ways that the findings have been interpreted?
• What are the weaknesses in their argument?
• Do you believe conclusions about the significance of the study and its findings are valid?
• What limitations of the study do the author(s) describe and how might this help formulate my own research?
• Does the conclusion contain any recommendations for future research?

5.  Methods/Methodology

The methods section describes the materials, techniques, and procedures for gathering information used to examine the research problem. If what you have read so far closely supports your understanding of the topic, then move on to examining how the author(s) gathered information during the research process. Questions to consider when reading the methods section include:

• Did the study use qualitative [based on interviews, observations, content analysis], quantitative [based on statistical analysis], or a mixed-methods approach to examining the research problem?
• What was the type of information or data used?
• Could this method of analysis be repeated and can I adopt the same approach?
• Is enough information available to repeat the study or should new data be found to expand or improve understanding of the research problem?

6.  Results

After reading the above sections, you should have a clear understanding of the general findings of the study. Therefore, read the results section to identify how key findings were discussed in relation to the research problem. If any non-textual elements [e.g., graphs, charts, tables, etc.] are confusing, focus on the explanations about them in the text. Questions to consider when reading the results section include:

• W hat did the author(s) find and how did they find it?
• Does the author(s) highlight any findings as most significant?
• Are the results presented in a factual and unbiased way?
• Does the analysis of results in the discussion section agree with how the results are presented?
• Is all the data present and did the author(s) adequately address gaps?
• What conclusions do you formulate from this data and does it match with the author's conclusions?

7.  References

The references list the sources used by the author(s) to document what prior research and information was used when conducting the study. After reviewing the article or research paper, use the references to identify additional sources of information on the topic and to examine critically how these sources supported the overall research agenda. Questions to consider when reading the references include:

• Do the sources cited by the author(s) reflect a diversity of disciplinary viewpoints, i.e., are the sources all from a particular field of study or do the sources reflect multiple areas of study?
• Are there any unique or interesting sources that could be incorporated into my study?
• What other authors are respected in this field, i.e., who has multiple works cited or is cited most often by others?
• What other research should I review to clarify any remaining issues or that I need more information about?

NOTE :  A final strategy in reviewing research is to copy and paste the title of the source [journal article, book, research report] into Google Scholar . If it appears, look for a "cited by" followed by a hyperlinked number [e.g., Cited by 45]. This number indicates how many times the study has been subsequently cited in other, more recently published works. This strategy, known as citation tracking, can be an effective means of expanding your review of pertinent literature based on a study you have found useful and how scholars have cited it. The same strategies described above can be applied to reading articles you find in the list of cited by references.

Reading Tip

Specific Reading Strategies

Effectively reading scholarly research is an acquired skill that involves attention to detail and an ability to comprehend complex ideas, data, and theoretical concepts in a way that applies logically to the research problem you are investigating. Here are some specific reading strategies to consider.

As You are Reading

• Focus on information that is most relevant to the research problem; skim over the other parts.
• As noted above, read content out of order! This isn't a novel; you want to start with the spoiler to quickly assess the relevance of the study.
• Think critically about what you read and seek to build your own arguments; not everything may be entirely valid, examined effectively, or thoroughly investigated.
• Look up the definitions of unfamiliar words, concepts, or terminology. A good scholarly source is Credo Reference .

Taking notes as you read will save time when you go back to examine your sources. Here are some suggestions:

• Mark or highlight important text as you read [e.g., you can use the highlight text  feature in a PDF document]
• Take notes in the margins [e.g., Adobe Reader offers pop-up sticky notes].
• Highlight important quotations; consider using different colors to differentiate between quotes and other types of important text.
• Summarize key points about the study at the end of the paper. To save time, these can be in the form of a concise bulleted list of statements [e.g., intro has provides historical background; lit review has important sources; good conclusions].

Write down thoughts that come to mind that may help clarify your understanding of the research problem. Here are some examples of questions to ask yourself:

• Do I understand all of the terminology and key concepts?
• Do I understand the parts of this study most relevant to my topic?
• What specific problem does the research address and why is it important?
• Are there any issues or perspectives the author(s) did not consider?
• Do I have any reason to question the validity or reliability of this research?
• How do the findings relate to my research interests and to other works which I have read?

Adapted from text originally created by Holly Burt, Behavioral Sciences Librarian, USC Libraries, April 2018.

Another Reading Tip

When is it Important to Read the Entire Article or Research Paper

Laubepin argues, "Very few articles in a field are so important that every word needs to be read carefully." However, this implies that some studies are worth reading carefully. As painful and time-consuming as it may seem, there are valid reasons for reading a study in its entirety from beginning to end. Here are some examples:

• Studies Published Very Recently .  The author(s) of a recent, well written study will provide a survey of the most important or impactful prior research in the literature review section. This can establish an understanding of how scholars in the past addressed the research problem. In addition, the most recently published sources will highlight what is currently known and what gaps in understanding currently exist about a topic, usually in the form of the need for further research in the conclusion .
• Surveys of the Research Problem .  Some papers provide a comprehensive analytical overview of the research problem. Reading this type of study can help you understand underlying issues and discover why scholars have chosen to investigate the topic. This is particularly important if the study was published very recently because the author(s) should cite all or most of the key prior research on the topic. Note that, if it is a long-standing problem, there may be studies that specifically review the literature to identify gaps that remain. These studies often include the word review in their title [e.g., Hügel, Stephan, and Anna R. Davies. "Public Participation, Engagement, and Climate Change Adaptation: A Review of the Research Literature." Wiley Interdisciplinary Reviews: Climate Change 11 (July-August 2020): https://doi.org/10.1002/ wcc.645].
• Highly Cited .  If you keep coming across the same citation to a study while you are reviewing the literature, this implies it was foundational in establishing an understanding of the research problem or the study had a significant impact within the literature [positive or negative]. Carefully reading a highly cited source can help you understand how the topic emerged and motivated scholars to further investigate the problem. It also could be a study you need to cite as foundational in your own paper to demonstrate to the reader that you understand the roots of the problem.
• Historical Overview .  Knowing the historical background of a research problem may not be the focus of your analysis. Nevertheless, carefully reading a study that provides a thorough description and analysis of the history behind an event, issue, or phenomenon can add important context to understanding the topic and what aspect of the problem you may want to examine further.
• Innovative Methodological Design .  Some studies are significant and worth reading in their entirety because the author(s) designed a unique or innovative approach to researching the problem. This may justify reading the entire study because it can motivate you to think creatively about pursuing an alternative or non-traditional approach to examining your topic of interest. These types of studies are generally easy to identify because they are often cited in others works because of their unique approach to studying the research problem.
• Cross-disciplinary Approach .  R eviewing studies produced outside of your discipline is an essential component of investigating research problems in the social and behavioral sciences. Consider reading a study that was conducted by author(s) based in a different discipline [e.g., an anthropologist studying political cultures; a study of hiring practices in companies published in a sociology journal]. This approach can generate a new understanding or a unique perspective about the topic . If you are not sure how to search for studies published in a discipline outside of your major or of the course you are taking, contact a librarian for assistance.

Laubepin, Frederique. How to Read (and Understand) a Social Science Journal Article . Inter-University Consortium for Political and Social Research (ISPSR), 2013; Shon, Phillip Chong Ho. How to Read Journal Articles in the Social Sciences: A Very Practical Guide for Students . 2nd edition. Thousand Oaks, CA: Sage, 2015; Lockhart, Tara, and Mary Soliday. "The Critical Place of Reading in Writing Transfer (and Beyond): A Report of Student Experiences." Pedagogy 16 (2016): 23-37; Maguire, Moira, Ann Everitt Reynolds, and Brid Delahunt. "Reading to Be: The Role of Academic Reading in Emergent Academic and Professional Student Identities." Journal of University Teaching and Learning Practice 17 (2020): 5-12.

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What Is Research, and Why Do People Do It?

• Open Access
• 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.

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

Part I. What Is Research?

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

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

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

Exercise 1.1

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

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

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

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

Exercise 1.2

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

Creating an Image of Scientific Inquiry

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

Descriptor 1. Experience Carefully Planned in Advance

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

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

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

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

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

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

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

Exercise 1.3

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

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

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

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

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

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

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

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

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

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

Doing Scientific Inquiry

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

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

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

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

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

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

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

Exercise 1.4

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

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

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

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

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

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

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

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

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

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

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

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

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

Exercise 1.5

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

Exercise 1.6

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

Learning from Doing Scientific Inquiry

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

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

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

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

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

Part II. Why Do Educators Do Research?

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

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

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

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

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

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

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

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

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

Exercise 1.7

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

Part III. Conducting Research as a Practice of Failing Productively

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

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

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

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

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

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

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

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

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

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

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

Exercise 1.8

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

Exercise 1.9

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

Part IV. Preview of Chap. 2

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

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Home » Background of The Study – Examples and Writing Guide

Background of The Study – Examples and Writing Guide

Table of Contents

Background of The Study

Definition:

Background of the study refers to the context, circumstances, and history that led to the research problem or topic being studied. It provides the reader with a comprehensive understanding of the subject matter and the significance of the study.

The background of the study usually includes a discussion of the relevant literature, the gap in knowledge or understanding, and the research questions or hypotheses to be addressed. It also highlights the importance of the research topic and its potential contributions to the field. A well-written background of the study sets the stage for the research and helps the reader to appreciate the need for the study and its potential significance.

How to Write Background of The Study

Here are some steps to help you write the background of the study:

Identify the Research Problem

Start by identifying the research problem you are trying to address. This problem should be significant and relevant to your field of study.

Provide Context

Once you have identified the research problem, provide some context. This could include the historical, social, or political context of the problem.

Review Literature

Conduct a thorough review of the existing literature on the topic. This will help you understand what has been studied and what gaps exist in the current research.

Identify Research Gap

Based on your literature review, identify the gap in knowledge or understanding that your research aims to address. This gap will be the focus of your research question or hypothesis.

State Objectives

Clearly state the objectives of your research . These should be specific, measurable, achievable, relevant, and time-bound (SMART).

Discuss Significance

Explain the significance of your research. This could include its potential impact on theory , practice, policy, or society.

Finally, summarize the key points of the background of the study. This will help the reader understand the research problem, its context, and its significance.

How to Write Background of The Study in Proposal

The background of the study is an essential part of any proposal as it sets the stage for the research project and provides the context and justification for why the research is needed. Here are the steps to write a compelling background of the study in your proposal:

• Identify the problem: Clearly state the research problem or gap in the current knowledge that you intend to address through your research.
• Provide context: Provide a brief overview of the research area and highlight its significance in the field.
• Review literature: Summarize the relevant literature related to the research problem and provide a critical evaluation of the current state of knowledge.
• Identify gaps : Identify the gaps or limitations in the existing literature and explain how your research will contribute to filling these gaps.
• Justify the study : Explain why your research is important and what practical or theoretical contributions it can make to the field.
• Highlight objectives: Clearly state the objectives of the study and how they relate to the research problem.
• Discuss methodology: Provide an overview of the methodology you will use to collect and analyze data, and explain why it is appropriate for the research problem.
• Conclude : Summarize the key points of the background of the study and explain how they support your research proposal.

How to Write Background of The Study In Thesis

The background of the study is a critical component of a thesis as it provides context for the research problem, rationale for conducting the study, and the significance of the research. Here are some steps to help you write a strong background of the study:

• Identify the research problem : Start by identifying the research problem that your thesis is addressing. What is the issue that you are trying to solve or explore? Be specific and concise in your problem statement.
• Review the literature: Conduct a thorough review of the relevant literature on the topic. This should include scholarly articles, books, and other sources that are directly related to your research question.
• I dentify gaps in the literature: After reviewing the literature, identify any gaps in the existing research. What questions remain unanswered? What areas have not been explored? This will help you to establish the need for your research.
• Establish the significance of the research: Clearly state the significance of your research. Why is it important to address this research problem? What are the potential implications of your research? How will it contribute to the field?
• Provide an overview of the research design: Provide an overview of the research design and methodology that you will be using in your study. This should include a brief explanation of the research approach, data collection methods, and data analysis techniques.
• State the research objectives and research questions: Clearly state the research objectives and research questions that your study aims to answer. These should be specific, measurable, achievable, relevant, and time-bound.
• Summarize the chapter: Summarize the chapter by highlighting the key points and linking them back to the research problem, significance of the study, and research questions.

How to Write Background of The Study in Research Paper

Here are the steps to write the background of the study in a research paper:

• Identify the research problem: Start by identifying the research problem that your study aims to address. This can be a particular issue, a gap in the literature, or a need for further investigation.
• Conduct a literature review: Conduct a thorough literature review to gather information on the topic, identify existing studies, and understand the current state of research. This will help you identify the gap in the literature that your study aims to fill.
• Explain the significance of the study: Explain why your study is important and why it is necessary. This can include the potential impact on the field, the importance to society, or the need to address a particular issue.
• Provide context: Provide context for the research problem by discussing the broader social, economic, or political context that the study is situated in. This can help the reader understand the relevance of the study and its potential implications.
• State the research questions and objectives: State the research questions and objectives that your study aims to address. This will help the reader understand the scope of the study and its purpose.
• Summarize the methodology : Briefly summarize the methodology you used to conduct the study, including the data collection and analysis methods. This can help the reader understand how the study was conducted and its reliability.

Examples of Background of The Study

Here are some examples of the background of the study:

Problem : The prevalence of obesity among children in the United States has reached alarming levels, with nearly one in five children classified as obese.

Significance : Obesity in childhood is associated with numerous negative health outcomes, including increased risk of type 2 diabetes, cardiovascular disease, and certain cancers.

Gap in knowledge : Despite efforts to address the obesity epidemic, rates continue to rise. There is a need for effective interventions that target the unique needs of children and their families.

Problem : The use of antibiotics in agriculture has contributed to the development of antibiotic-resistant bacteria, which poses a significant threat to human health.

Significance : Antibiotic-resistant infections are responsible for thousands of deaths each year and are a major public health concern.

Gap in knowledge: While there is a growing body of research on the use of antibiotics in agriculture, there is still much to be learned about the mechanisms of resistance and the most effective strategies for reducing antibiotic use.

Edxample 3:

Problem : Many low-income communities lack access to healthy food options, leading to high rates of food insecurity and diet-related diseases.

Significance : Poor nutrition is a major contributor to chronic diseases such as obesity, type 2 diabetes, and cardiovascular disease.

Gap in knowledge : While there have been efforts to address food insecurity, there is a need for more research on the barriers to accessing healthy food in low-income communities and effective strategies for increasing access.

Examples of Background of The Study In Research

Here are some real-life examples of how the background of the study can be written in different fields of study:

Example 1 : “There has been a significant increase in the incidence of diabetes in recent years. This has led to an increased demand for effective diabetes management strategies. The purpose of this study is to evaluate the effectiveness of a new diabetes management program in improving patient outcomes.”

Example 2 : “The use of social media has become increasingly prevalent in modern society. Despite its popularity, little is known about the effects of social media use on mental health. This study aims to investigate the relationship between social media use and mental health in young adults.”

Example 3: “Despite significant advancements in cancer treatment, the survival rate for patients with pancreatic cancer remains low. The purpose of this study is to identify potential biomarkers that can be used to improve early detection and treatment of pancreatic cancer.”

Examples of Background of The Study in Proposal

Here are some real-time examples of the background of the study in a proposal:

Example 1 : The prevalence of mental health issues among university students has been increasing over the past decade. This study aims to investigate the causes and impacts of mental health issues on academic performance and wellbeing.

Example 2 : Climate change is a global issue that has significant implications for agriculture in developing countries. This study aims to examine the adaptive capacity of smallholder farmers to climate change and identify effective strategies to enhance their resilience.

Example 3 : The use of social media in political campaigns has become increasingly common in recent years. This study aims to analyze the effectiveness of social media campaigns in mobilizing young voters and influencing their voting behavior.

Example 4 : Employee turnover is a major challenge for organizations, especially in the service sector. This study aims to identify the key factors that influence employee turnover in the hospitality industry and explore effective strategies for reducing turnover rates.

Examples of Background of The Study in Thesis

Here are some real-time examples of the background of the study in the thesis:

Example 1 : “Women’s participation in the workforce has increased significantly over the past few decades. However, women continue to be underrepresented in leadership positions, particularly in male-dominated industries such as technology. This study aims to examine the factors that contribute to the underrepresentation of women in leadership roles in the technology industry, with a focus on organizational culture and gender bias.”

Example 2 : “Mental health is a critical component of overall health and well-being. Despite increased awareness of the importance of mental health, there are still significant gaps in access to mental health services, particularly in low-income and rural communities. This study aims to evaluate the effectiveness of a community-based mental health intervention in improving mental health outcomes in underserved populations.”

Example 3: “The use of technology in education has become increasingly widespread, with many schools adopting online learning platforms and digital resources. However, there is limited research on the impact of technology on student learning outcomes and engagement. This study aims to explore the relationship between technology use and academic achievement among middle school students, as well as the factors that mediate this relationship.”

Examples of Background of The Study in Research Paper

Here are some examples of how the background of the study can be written in various fields:

Example 1: The prevalence of obesity has been on the rise globally, with the World Health Organization reporting that approximately 650 million adults were obese in 2016. Obesity is a major risk factor for several chronic diseases such as diabetes, cardiovascular diseases, and cancer. In recent years, several interventions have been proposed to address this issue, including lifestyle changes, pharmacotherapy, and bariatric surgery. However, there is a lack of consensus on the most effective intervention for obesity management. This study aims to investigate the efficacy of different interventions for obesity management and identify the most effective one.

Example 2: Antibiotic resistance has become a major public health threat worldwide. Infections caused by antibiotic-resistant bacteria are associated with longer hospital stays, higher healthcare costs, and increased mortality. The inappropriate use of antibiotics is one of the main factors contributing to the development of antibiotic resistance. Despite numerous efforts to promote the rational use of antibiotics, studies have shown that many healthcare providers continue to prescribe antibiotics inappropriately. This study aims to explore the factors influencing healthcare providers’ prescribing behavior and identify strategies to improve antibiotic prescribing practices.

Example 3: Social media has become an integral part of modern communication, with millions of people worldwide using platforms such as Facebook, Twitter, and Instagram. Social media has several advantages, including facilitating communication, connecting people, and disseminating information. However, social media use has also been associated with several negative outcomes, including cyberbullying, addiction, and mental health problems. This study aims to investigate the impact of social media use on mental health and identify the factors that mediate this relationship.

Purpose of Background of The Study

The primary purpose of the background of the study is to help the reader understand the rationale for the research by presenting the historical, theoretical, and empirical background of the problem.

More specifically, the background of the study aims to:

• Provide a clear understanding of the research problem and its context.
• Identify the gap in knowledge that the study intends to fill.
• Establish the significance of the research problem and its potential contribution to the field.
• Highlight the key concepts, theories, and research findings related to the problem.
• Provide a rationale for the research questions or hypotheses and the research design.
• Identify the limitations and scope of the study.

When to Write Background of The Study

The background of the study should be written early on in the research process, ideally before the research design is finalized and data collection begins. This allows the researcher to clearly articulate the rationale for the study and establish a strong foundation for the research.

The background of the study typically comes after the introduction but before the literature review section. It should provide an overview of the research problem and its context, and also introduce the key concepts, theories, and research findings related to the problem.

Writing the background of the study early on in the research process also helps to identify potential gaps in knowledge and areas for further investigation, which can guide the development of the research questions or hypotheses and the research design. By establishing the significance of the research problem and its potential contribution to the field, the background of the study can also help to justify the research and secure funding or support from stakeholders.

Advantage of Background of The Study

The background of the study has several advantages, including:

• Provides context: The background of the study provides context for the research problem by highlighting the historical, theoretical, and empirical background of the problem. This allows the reader to understand the research problem in its broader context and appreciate its significance.
• Identifies gaps in knowledge: By reviewing the existing literature related to the research problem, the background of the study can identify gaps in knowledge that the study intends to fill. This helps to establish the novelty and originality of the research and its potential contribution to the field.
• Justifies the research : The background of the study helps to justify the research by demonstrating its significance and potential impact. This can be useful in securing funding or support for the research.
• Guides the research design: The background of the study can guide the development of the research questions or hypotheses and the research design by identifying key concepts, theories, and research findings related to the problem. This ensures that the research is grounded in existing knowledge and is designed to address the research problem effectively.
• Establishes credibility: By demonstrating the researcher’s knowledge of the field and the research problem, the background of the study can establish the researcher’s credibility and expertise, which can enhance the trustworthiness and validity of the research.

Disadvantages of Background of The Study

Some Disadvantages of Background of The Study are as follows:

• Time-consuming : Writing a comprehensive background of the study can be time-consuming, especially if the research problem is complex and multifaceted. This can delay the research process and impact the timeline for completing the study.
• Repetitive: The background of the study can sometimes be repetitive, as it often involves summarizing existing research and theories related to the research problem. This can be tedious for the reader and may make the section less engaging.
• Limitations of existing research: The background of the study can reveal the limitations of existing research related to the problem. This can create challenges for the researcher in developing research questions or hypotheses that address the gaps in knowledge identified in the background of the study.
• Bias : The researcher’s biases and perspectives can influence the content and tone of the background of the study. This can impact the reader’s perception of the research problem and may influence the validity of the research.
• Accessibility: Accessing and reviewing the literature related to the research problem can be challenging, especially if the researcher does not have access to a comprehensive database or if the literature is not available in the researcher’s language. This can limit the depth and scope of the background of the study.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Research ethics.

Jennifer M. Barrow ; Grace D. Brannan ; Paras B. Khandhar .

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Last Update: September 18, 2022 .

• Introduction

Multiple examples of past unethical research studies conducted in the past throughout the world have cast a significant historical shadow on research involving human subjects. Examples include the Tuskegee Syphilis Study from 1932 to 1972, Nazi medical experimentation in the 1930s and 1940s, and research conducted at the Willowbrook State School in the 1950s and 1960s. [1]  As the aftermath of these practices, wherein uninformed and unaware patients were exposed to disease or subject to other unproven treatments, became known, the need for rules governing the design and implementation of human-subject research protocols became very evident.

The first such ethical code for research was the Nuremberg Code, arising in the aftermath of Nazi research atrocities brought to light in the post-World War II Nuremberg Trials. [1]  This set of international research standards sought to prevent gross research misconduct and abuse of vulnerable and unwitting research subjects by establishing specific human subject protective factors. A direct descendant of this code was drafted in 1978 in the United States, known as the Belmont Report, and this legislation forms the backbone of regulation of clinical research in the USA since its adoption. [2]  The Belmont Report contains three basic ethical principles: (1) respect for persons, (2) beneficence, and (3) justice. Additionally, the Belmont Report details research-based protective applications for informed consent, risk/benefit assessment, and participant selection. [3]

• Issues of Concern

The first protective principle stemming from the 1978 Belmont Report is the principle of Respect for Persons, also known as human dignity. [2]  This dictates researchers must work to protect research participants’ autonomy while also ensuring full disclosure of factors surrounding the study, including potential harms and benefits. According to the Belmont Report, “an autonomous person is an individual capable of deliberation about personal goals and of acting under the direction of such deliberation." [1]

To ensure participants have the autonomous right to self-determination, researchers must ensure that potential participants understand that they have the right to decide whether or not to participate in research studies voluntarily and that declining to participate in any research will not affect in any way their access to current or subsequent care. Also, self-determined participants must have the ability to ask the researcher questions and the ability to comprehend questions asked by the researcher. Researchers must also inform participants that they may stop participating in the study at any time without fear of penalty. [4]  As noted in the Belmont Report definition above, not all individuals have the capacity to be autonomous concerning research participation. Whether because of the individual’s developmental level or because of various illnesses or disabilities, some individuals require special research protections that may involve exclusion from research activities that can cause potential harm, or appointing a third-party guardian to oversee the participation of such vulnerable persons. [5]

Researchers must also ensure that they do not coerce potential participants into agreeing to participate in studies. Coercion refers to threats of penalty, whether implied or explicit, if participants decline to participate or otherwise opt-out of a study. Additionally, giving potential participants extreme rewards for agreeing to participate can also be a form of coercion, because the rewards may provide an enticing-enough incentive that the participant feels they need participate, while if such a reward were not offered they would otherwise have declined. While researchers often use various rewards and incentives in studies, they must review carefully this possibility of coercion. Some incentives may pressure potential participants into joining a study, thereby stripping participants of complete self-determination. [3]

An additional aspect of respecting potential participants’ self-determination is to ensure that researchers have fully disclosed information about the study and explained the voluntary nature of participation (including the right to refuse without repercussion) and possible benefits and risks related to study participation. Without complete information, a potential participant cannot make a truly informed decision. This aspect of the Belmont Report can be troublesome for some researchers based on their study designs and research questions. Noted biases related to reactivity may occur when study participants know the exact guiding research questions and purposes. Some researchers may try to avoid reactivity biases by using covert data collection methods or masking of key study information. Masking frequently occurs in pharmaceutical trials with placebos because knowledge of placebo receipt can affect study outcomes. However, masking and concealed data collection methods may not fully respect participants’ rights to autonomy and the associated informed consent process. Any researcher considering concealed data collection or masking of some research information from participants must present their plans to an Institutional Review Board (IRB) for oversight, as well as explain the potential masking to prospective patients in the consent process (i.e. - explaining to potential participants in a medication trial that they will be randomly assigned either the medication or a placebo). The IRB will make a final determination if studies warrant concealed data collection or masking methods in light of the research design and methods and study-specific protections. [6]

The second Belmont Report principle is the principle of beneficence. Beneficence refers to acting in such a way to benefit others while promoting their welfare and safety. [7]  Although not specifically mentioned by name, the biomedical ethical principle of nonmaleficence (do no harm) also appears within the Belmont Report’s section on beneficence. The beneficence principle includes two specific research aspects: (1) participants’ right to freedom from harm and discomfort and (2) participants’ rights to protection from exploitation. [8]

Before seeking IRB approval and conducting a study, researchers must analyze potential risks and benefits to research participants. Examples of possible participant risks include physical harm, loss of privacy, unforeseen side effects, emotional distress or embarrassment, monetary costs, physical discomfort, and loss of time. Possible benefits include access to a potentially valuable intervention, increased understanding of a medical condition, and satisfaction of helping others with similar issues. [8]  These potential risks and benefits should explicitly appear in the written informed consent document used in the study. Researchers must implement specific protections to minimize all forms of discomfort and harm to align with the principle of beneficence. Under the principle of beneficence, researchers must also protect participants from exploitation. Any information provided by participants through their study involvement must be protected.

The final principle contained in the Belmont Report is the principle of justice, which pertains to participants’ right to fair treatment and right to privacy. The selection of the types of participants desired for a research study should be guided by research questions and requirements so as not to exclude any group, and to be as representative of the overall target population as possible. Researchers and IRBs must scrutinize the selection of research participants to determine whether researchers are systematically selecting some groups (e.g., participants receiving public financial assistance, specific ethnic and racial minorities, or those who are institutionalized) because of their vulnerability or ease of access. The right to fair treatment also relates to researchers treating those who decline to participate in a study fairly without any prejudice. [3]

The right to privacy also falls under the Belmont Report’s principle of justice. Researchers must keep any shared information in their strictest confidence. Upholding the right to privacy often involves procedures for anonymity or confidentiality. For participants’ data to be completely anonymous, the researcher cannot have the ability to connect the participant to their data. If researchers can make participant-data connections, even if they use codes or pseudonyms in place of personal identifiers, the study is no longer anonymous. Instead, researchers are providing participant confidentiality. Various methods can help researchers assure confidentiality, including locking any participant identifying data and substituting code numbers instead of names, with a correlation key available only to a safety or oversight functionary in case of emergency, but not readily available to researchers themselves. [3]

• Clinical Significance

One of the most common safeguards for the ethical conduct of research involves the use of external reviewers known as an Institutional Review Board (IRB). Researchers seeking to begin a study must submit a full research proposal to the IRB, which includes specific data collection instruments, research advertisements, and informed consent documentation. The IRB may perform a complete or expedited review depending on the nature of the study and the risks involved. Until researchers obtain full IRB approval, they cannot contact potential participants or start collecting data. Sometimes, multi-site studies require approvals from several IRBs, all of which may have different forms and review processes. [3]

A significant study aspect of interest to IRB members is the use of any participants from vulnerable groups. Vulnerable groups may include individuals who cannot give fully informed consent or those individuals who may be at elevated risk of unplanned side effects. Examples of vulnerable participants include pregnant women, children younger than the age of consent, terminally ill individuals, institutionalized individuals, and those with mental or emotional disabilities. In the case of minors, assent is also an element that must be addressed per Subpart D of the Code of Federal Regulations, 45 CFR 46.402 which defines assent to mean “a child's affirmative agreement to participate in research; mere failure to object should not, absent affirmative agreement, be construed as assent.” [9] There is paucity in the literature on when minors possess the ability to understand research although current research suggests that the age by which a minor could assent is around 14. [10]  Anytime researchers include vulnerable groups in their studies, they must include extra safeguards to uphold Belmont Report ethical principles, especially the principle of beneficence. [3]

• Enhancing Healthcare Team Outcomes

Research ethics is a foundational principle of modern medical research across all disciplines. The overarching body, the IRB, is intentionally comprised of experts across a range of disciplines that can include ethicists, social workers, physicians, nurses, other scientific researchers, counselors and mental health professionals, and advocates for vulnerable subjects. There is also often a legal expert either on the panel, or available to it, to discuss any questions regarding the legality or ramifications of studies.

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Disclosure: Jennifer Barrow declares no relevant financial relationships with ineligible companies.

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

Disclosure: Paras Khandhar declares no relevant financial relationships with ineligible companies.

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

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• The historical, ethical, and legal background of human-subjects research. [Respir Care. 2008] The historical, ethical, and legal background of human-subjects research. Rice TW. Respir Care. 2008 Oct; 53(10):1325-9.
• The Belmont Report at 40: Reckoning With Time. [Am J Public Health. 2018] The Belmont Report at 40: Reckoning With Time. Adashi EY, Walters LB, Menikoff JA. Am J Public Health. 2018 Oct; 108(10):1345-1348. Epub 2018 Aug 23.
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Advancing research • shaping policy • developing leaders, from tech tools to human values: isps conference explores the impact of ai in government.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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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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

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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.

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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."

Can nasal Neosporin fight COVID? Surprising new research suggests it works

A potential treatment for covid-19 may have been hiding in our medicine cabinets, a new study in pnas has found, by nicole karlis.

Four years ago, when COVID-19 first began to spread globally, it didn't just damage our physical health, but also the health of our information ecosystem. Ever since, the internet has been rife with health misinformation on ways to treat or protect oneself against the coronavirus. First, internet healers falsely suggested that gargling salt water and vinegar could prevent a coronavirus infection. Then, despite multiple studies debunking the effectiveness of ivermectin , an anti-parasitic drug used in horses (and less commonly in humans), Joe Rogan fans continued to cling onto it as a potential treatment .

Health misinformation is a symptom of a lack of certainty. When there is no guaranteed preventative measure or treatment, people are bound to find solutions on their own. Thanks to cognitive biases like confirmation bias , they might even appear to work. But what if a way to reduce exposure to COVID-19, and treat it, was hiding in our medicine cabinets all along — and it wasn’t pseudoscience? 

A new study published in the journal Proceedings of the National Academy of Sciences suggests that neomycin, an ingredient in the first aid ointment Neosporin , may prevent or treat a range of respiratory viral infections such as COVID-19 and influenza when applied to the nose. 

In the study, researchers found that mice who had neomycin in their nostrils exhibited strong antiviral activity against both SARS-CoV- 2 and a highly virulent strain of influenza A virus. It also mitigated contact transmission of SARS-CoV- 2 between hamsters. 

"When we compared the gene expression in the nose, Neosporin stimulated genes whereas those people who had Vaseline did not."

“We decided to see if neomycin applied into the nose can protect animals from infection with COVID as well as the flu,” Dr. Akiko Iwasaki , the lead author of the study and a professor of immunobiology at the Yale University School of Medicine, told Salon in a phone interview. “And what we found is that treatment with neomycin significantly prevented infection and also reduced disease burden in animals.”

Iwasaki described the work as “encouraging” because it shows that neomycin can trigger an antiviral response in animals by creating a localized immune response. “That’s resulting in this protection that we see,” Iwasaki said. 

Want more health and science stories in your inbox? Subscribe to Salon's weekly newsletter Lab Notes .

The results are encouraging for mice and hamsters. But what about humans? The researchers proceeded to recruit healthy volunteers and asked them to apply Neosporin with a cotton swab to their nose, twice a day. The placebo for some was vaseline. The researchers measured their antiviral response and found similar results.

“When we compared the gene expression in the nose, Neosporin stimulated genes whereas those people who had Vaseline did not,” Iwasaki said.  “So this suggests that we might be able to use Neosporin or neomycin in humans to induce this antiviral state that we also saw in animals.”

Does that mean we should all be applying Neosporin to our noses in high-risk situations? Not exactly, but it probably wouldn’t hurt either — as long as someone isn’t allergic to the cream, which is a combination of the antibiotics bacitracin, neomycin and polymyxin B. Notably, details around the dosage remain unclear. 

“We know from the dose response that we did in animals that we probably need to give humans more Neosporin, or neomycin,” she said. “Because Neosporin has very little neomycin compared to what we were able to achieve in the animal model.”

"This could be a potential broad spectrum antiviral treatment and prophylaxis."

Iwasaki added they know that Neosporin can produce a similar effect in humans as it did in animals, but whether or not it can reduce transmission has yet to be determined. 

“For that, we need different kinds of study and a much larger study to determine that,” she said. 

Amesh Adalja, a senior scholar at the Johns Hopkins Center and infectious disease doctor who wasn’t involved in the study, told Salon via email that the research could have broader implications that extend beyond COVID-19. 

“This could be a potential broad spectrum antiviral treatment and prophylaxis,”Adalja said. “The molecules in the topical antibiotic cream induce certain antiviral compounds to be made by cells where the ointment has been applied; these antiviral compounds produce non-specific immunity that impacts various viruses.”

Iwasaki cautioned against the idea that people swabbing their noses with Neosporin will be a cure-all in the future. Instead, she said she sees this as another possible layer of protection . 

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“We know how important it is to layer protection against infections,” Iwasaki said. “Vaccines and masks and other measures are very important, but this type of strategy where we can trigger the host to produce antiviral factors may be another layer that we can add on to the existing ones.”

The more layers a person has, Iwasaki said, the less likely a person is to get infected. 

“And that's really important for preventing diseases like long COVID,” Iwasaki said, referring to a condition in which COVID symptoms last for months or even years . “So I think it's definitely worth kind of moving forward with an approach like this.”

An approach that was right under our noses all this time.

about COVID

• Do COVID-19 vaccines really have worse side effects than other vaccines? Here's what experts say
• Infectious desire: How the pandemic is still negatively impacting our sex lives
• Does your immune system need a workout? The bad science behind "immunity debt," explained

Nicole Karlis is a senior writer at Salon, specializing in health and science. Tweet her @nicolekarlis .

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What the data says about crime in the U.S.

A growing share of Americans say reducing crime should be a top priority for the president and Congress to address this year. Around six-in-ten U.S. adults (58%) hold that view today, up from 47% at the beginning of Joe Biden’s presidency in 2021.

We conducted this analysis to learn more about U.S. crime patterns and how those patterns have changed over time.

The analysis relies on statistics published by the FBI, which we accessed through the Crime Data Explorer , and the Bureau of Justice Statistics (BJS), which we accessed through the  National Crime Victimization Survey data analysis tool .

To measure public attitudes about crime in the U.S., we relied on survey data from Pew Research Center and Gallup.

Additional details about each data source, including survey methodologies, are available by following the links in the text of this analysis.

With the issue likely to come up in this year’s presidential election, here’s what we know about crime in the United States, based on the latest available data from the federal government and other sources.

How much crime is there in the U.S.?

It’s difficult to say for certain. The  two primary sources of government crime statistics  – the Federal Bureau of Investigation (FBI) and the Bureau of Justice Statistics (BJS) – paint an incomplete picture.

The FBI publishes  annual data  on crimes that have been reported to law enforcement, but not crimes that haven’t been reported. Historically, the FBI has also only published statistics about a handful of specific violent and property crimes, but not many other types of crime, such as drug crime. And while the FBI’s data is based on information from thousands of federal, state, county, city and other police departments, not all law enforcement agencies participate every year. In 2022, the most recent full year with available statistics, the FBI received data from 83% of participating agencies .

BJS, for its part, tracks crime by fielding a  large annual survey of Americans ages 12 and older and asking them whether they were the victim of certain types of crime in the past six months. One advantage of this approach is that it captures both reported and unreported crimes. But the BJS survey has limitations of its own. Like the FBI, it focuses mainly on a handful of violent and property crimes. And since the BJS data is based on after-the-fact interviews with crime victims, it cannot provide information about one especially high-profile type of offense: murder.

All those caveats aside, looking at the FBI and BJS statistics side-by-side  does  give researchers a good picture of U.S. violent and property crime rates and how they have changed over time. In addition, the FBI is transitioning to a new data collection system – known as the National Incident-Based Reporting System – that eventually will provide national information on a much larger set of crimes , as well as details such as the time and place they occur and the types of weapons involved, if applicable.

Which kinds of crime are most and least common?

Property crime in the U.S. is much more common than violent crime. In 2022, the FBI reported a total of 1,954.4 property crimes per 100,000 people, compared with 380.7 violent crimes per 100,000 people.  

By far the most common form of property crime in 2022 was larceny/theft, followed by motor vehicle theft and burglary. Among violent crimes, aggravated assault was the most common offense, followed by robbery, rape, and murder/nonnegligent manslaughter.

BJS tracks a slightly different set of offenses from the FBI, but it finds the same overall patterns, with theft the most common form of property crime in 2022 and assault the most common form of violent crime.

How have crime rates in the U.S. changed over time?

Both the FBI and BJS data show dramatic declines in U.S. violent and property crime rates since the early 1990s, when crime spiked across much of the nation.

Using the FBI data, the violent crime rate fell 49% between 1993 and 2022, with large decreases in the rates of robbery (-74%), aggravated assault (-39%) and murder/nonnegligent manslaughter (-34%). It’s not possible to calculate the change in the rape rate during this period because the FBI  revised its definition of the offense in 2013 .

The FBI data also shows a 59% reduction in the U.S. property crime rate between 1993 and 2022, with big declines in the rates of burglary (-75%), larceny/theft (-54%) and motor vehicle theft (-53%).

Using the BJS statistics, the declines in the violent and property crime rates are even steeper than those captured in the FBI data. Per BJS, the U.S. violent and property crime rates each fell 71% between 1993 and 2022.

While crime rates have fallen sharply over the long term, the decline hasn’t always been steady. There have been notable increases in certain kinds of crime in some years, including recently.

In 2020, for example, the U.S. murder rate saw its largest single-year increase on record – and by 2022, it remained considerably higher than before the coronavirus pandemic. Preliminary data for 2023, however, suggests that the murder rate fell substantially last year .

How do Americans perceive crime in their country?

Americans tend to believe crime is up, even when official data shows it is down.

In 23 of 27 Gallup surveys conducted since 1993 , at least 60% of U.S. adults have said there is more crime nationally than there was the year before, despite the downward trend in crime rates during most of that period.

While perceptions of rising crime at the national level are common, fewer Americans believe crime is up in their own communities. In every Gallup crime survey since the 1990s, Americans have been much less likely to say crime is up in their area than to say the same about crime nationally.

Public attitudes about crime differ widely by Americans’ party affiliation, race and ethnicity, and other factors . For example, Republicans and Republican-leaning independents are much more likely than Democrats and Democratic leaners to say reducing crime should be a top priority for the president and Congress this year (68% vs. 47%), according to a recent Pew Research Center survey.

How does crime in the U.S. differ by demographic characteristics?

Some groups of Americans are more likely than others to be victims of crime. In the  2022 BJS survey , for example, younger people and those with lower incomes were far more likely to report being the victim of a violent crime than older and higher-income people.

There were no major differences in violent crime victimization rates between male and female respondents or between those who identified as White, Black or Hispanic. But the victimization rate among Asian Americans (a category that includes Native Hawaiians and other Pacific Islanders) was substantially lower than among other racial and ethnic groups.

The same BJS survey asks victims about the demographic characteristics of the offenders in the incidents they experienced.

In 2022, those who are male, younger people and those who are Black accounted for considerably larger shares of perceived offenders in violent incidents than their respective shares of the U.S. population. Men, for instance, accounted for 79% of perceived offenders in violent incidents, compared with 49% of the nation’s 12-and-older population that year. Black Americans accounted for 25% of perceived offenders in violent incidents, about twice their share of the 12-and-older population (12%).

As with all surveys, however, there are several potential sources of error, including the possibility that crime victims’ perceptions about offenders are incorrect.

How does crime in the U.S. differ geographically?

There are big geographic differences in violent and property crime rates.

For example, in 2022, there were more than 700 violent crimes per 100,000 residents in New Mexico and Alaska. That compares with fewer than 200 per 100,000 people in Rhode Island, Connecticut, New Hampshire and Maine, according to the FBI.

The FBI notes that various factors might influence an area’s crime rate, including its population density and economic conditions.

What percentage of crimes are reported to police? What percentage are solved?

Most violent and property crimes in the U.S. are not reported to police, and most of the crimes that  are  reported are not solved.

In its annual survey, BJS asks crime victims whether they reported their crime to police. It found that in 2022, only 41.5% of violent crimes and 31.8% of household property crimes were reported to authorities. BJS notes that there are many reasons why crime might not be reported, including fear of reprisal or of “getting the offender in trouble,” a feeling that police “would not or could not do anything to help,” or a belief that the crime is “a personal issue or too trivial to report.”

Most of the crimes that are reported to police, meanwhile,  are not solved , at least based on an FBI measure known as the clearance rate . That’s the share of cases each year that are closed, or “cleared,” through the arrest, charging and referral of a suspect for prosecution, or due to “exceptional” circumstances such as the death of a suspect or a victim’s refusal to cooperate with a prosecution. In 2022, police nationwide cleared 36.7% of violent crimes that were reported to them and 12.1% of the property crimes that came to their attention.

Which crimes are most likely to be reported to police? Which are most likely to be solved?

Around eight-in-ten motor vehicle thefts (80.9%) were reported to police in 2022, making them by far the most commonly reported property crime tracked by BJS. Household burglaries and trespassing offenses were reported to police at much lower rates (44.9% and 41.2%, respectively), while personal theft/larceny and other types of theft were only reported around a quarter of the time.

Among violent crimes – excluding homicide, which BJS doesn’t track – robbery was the most likely to be reported to law enforcement in 2022 (64.0%). It was followed by aggravated assault (49.9%), simple assault (36.8%) and rape/sexual assault (21.4%).

The list of crimes  cleared  by police in 2022 looks different from the list of crimes reported. Law enforcement officers were generally much more likely to solve violent crimes than property crimes, according to the FBI.

The most frequently solved violent crime tends to be homicide. Police cleared around half of murders and nonnegligent manslaughters (52.3%) in 2022. The clearance rates were lower for aggravated assault (41.4%), rape (26.1%) and robbery (23.2%).

When it comes to property crime, law enforcement agencies cleared 13.0% of burglaries, 12.4% of larcenies/thefts and 9.3% of motor vehicle thefts in 2022.

Are police solving more or fewer crimes than they used to?

Nationwide clearance rates for both violent and property crime are at their lowest levels since at least 1993, the FBI data shows.

Police cleared a little over a third (36.7%) of the violent crimes that came to their attention in 2022, down from nearly half (48.1%) as recently as 2013. During the same period, there were decreases for each of the four types of violent crime the FBI tracks:

• Police cleared 52.3% of reported murders and nonnegligent homicides in 2022, down from 64.1% in 2013.
• They cleared 41.4% of aggravated assaults, down from 57.7%.
• They cleared 26.1% of rapes, down from 40.6%.
• They cleared 23.2% of robberies, down from 29.4%.

The pattern is less pronounced for property crime. Overall, law enforcement agencies cleared 12.1% of reported property crimes in 2022, down from 19.7% in 2013. The clearance rate for burglary didn’t change much, but it fell for larceny/theft (to 12.4% in 2022 from 22.4% in 2013) and motor vehicle theft (to 9.3% from 14.2%).

Note: This is an update of a post originally published on Nov. 20, 2020.

• Criminal Justice

John Gramlich is an associate director at Pew Research Center

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