difference of study and research project

Research vs. Study

What's the difference.

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

Further Detail

Introduction.

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

Definition and Purpose

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

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

Approach and Methodology

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

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

Scope and Depth

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

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

Application and Output

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

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

Limitations and Challenges

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

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

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

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Home » Education » What is the Difference Between Research and Project

What is the Difference Between Research and Project

The main difference between research and project is that research is the systematic investigation and study of materials and sources to establish facts and reach new conclusions, while a project is a specific and finite activity that gives a measurable and observable result under preset requirements.

Both research and projects use a systematic approach. We also sometimes use the term research project to refer to research studies.

Key Areas Covered

1.  What is Research       – Definition, Features 2. What is a Project      – Definition, Features 3.  Difference Between Research and Project      – Comparison of Key Differences

Research, Project

What is Research

Research is a careful study a researcher conducts using a systematic approach and scientific methods. A research study typically involves several components: abstract, introduction ,  literature review ,  research design, and method , results and analysis, conclusion, bibliography. Researchers usually begin a formal research study with a hypothesis; then, they test this hypothesis rigorously. They also explore and analyze the literature already available on their research subject. This allows them to study the research subject from multiple perspectives, acknowledging different problems that need to be solved.

There are different types of research, the main two categories being quantitative research and qualitative research. Depending on their research method and design, we can also categorize research as descriptive research, exploratory research, longitudinal research, cross-sectional research, etc.

Furthermore, research should always be objective or unbiased. Moreover, if the research involves participants, for example, in surveys or interviews, the researcher should always make sure to obtain their written consent first.

What is a Project

A project is a collaborative or individual enterprise that is carefully planned to achieve a particular aim. We can also describe it as a specific and finite activity that gives a measurable and observable result under preset requirements. This result can be tangible or intangible; for example, product, service, competitive advantage, etc. A project generally involves a series of connected tasks planned for execution over a fixed period of time and within certain limitations like quality and cost. The Project Management Body of Knowledge (PMBOK) defines a project as a “temporary endeavor with a beginning and an end, and it must be used to create a unique product, service or result.”

Difference Between Research and Project

Research is a careful study conducted using a systematic approach and scientific methods, whereas a project is a collaborative or individual enterprise that is carefully planned to achieve a particular aim.

Research studies are mainly carried out in academia, while projects can be seen in a variety of contexts, including businesses.

The main aim of the research is to seek or revise facts, theories, or principles, while the main aim of a project is to achieve a tangible or intangible result; for example, product, service, competitive advantage, etc.

The main difference between research and project is that research is the systematic investigation and study of materials and sources to establish facts and reach new conclusions, while the project is a specific and finite activity that gives a measurable and observable result under preset requirements.

1. “ What Is a Project? – Definition, Lifecycle and Key Characteristics .” Your Guide to Project Management Best Practices .

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Research Design | Step-by-Step Guide with Examples

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

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

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

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

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

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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Dissertation vs Thesis vs Capstone Project What’s the difference?

By: Derek Jansen (MBA) | Expert Reviewed By: Dr. Eunice Rautenbach | October 2020

At Grad Coach, we receive questions about dissertation and thesis writing on a daily basis – everything from how to find a good research topic to which research methods to use  and how to analyse the data.

One of the most common questions we receive is “what’s the difference between a dissertation and thesis?” . If you look around online, you’ll find a lot of confusing and often contrasting answers. In this post we’ll clear it up, once and for all…

Need a helping hand?

Dissertation vs Thesis: Showdown Time

Before comparing dissertations to theses, it’s useful to first understand what both of these are and what they have in common .

Dissertations and theses are both formal academic research projects . In other words, they’re academic projects that involve you undertaking research in a structured, systematic way. The research process typically involves the following steps :

• Asking a well-articulated and meaningful research question (or questions).
• Assessing what other researchers have said in relation to that question (this is usually called a literature review – you can learn more about that up here).
• Undertaking your own research using a clearly justified methodology – this often involves some sort of fieldwork such as interviews or surveys – and lastly,
• Deriving an answer to your research question based on your analysis.

In other words, theses and dissertations are both formal, structured research projects that involve using a clearly articulated methodology to draw out insights and answers to your research questions . So, in this respect, they are, for the most part, the same thing.

But, how are they different then?

Well, the key difference between a dissertation and a thesis is, for the most part, the level of study – in other words, undergrad, master or PhD. By extension, this also means that the complexity and rigorousness of the research differs between dissertations and theses.

So, which is which?

This is where it gets a bit confusing. The meaning of dissertation or thesis varies depending on the country or region of study. For example, in the UK, a dissertation is generally a research project that’s completed at the end of a Masters-level degree, whereas a thesis is completed for a Doctoral-level degree.

Conversely, the terminology is flipped around in the US (and some other countries). In other words, a thesis is completed for a Masters-level degree, while a dissertation is completed for PhD (or any other doctoral-level degree).

Simply put, a dissertation and a thesis are essentially the same thing, but at different levels of study . The exact terminology varies from country to country, and sometimes it even varies between universities in the same country. Some universities will also refer to this type of project as a capstone project . In addition, some universities will also require an oral exam or viva voce , especially for doctoral-level projects. 

Given that there are more than 25,000 universities scattered across the globe, all of this terminological complexity can cause some confusion. To be safe, make sure that you thoroughly read the brief provided by your university for your dissertation or thesis, and if possible, visit the university library to have a look at past students’ projects . This will help you get a feel for your institution’s norms and spot any nuances in terms of their specific requirements so that you can give them exactly what they want.

Let’s recap

Dissertations and theses are both formal academic research projects . The main difference is the level of study – undergrad, Masters or PhD. Terminology tends to vary from country to country, and even within countries.

Need help with your research project?

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

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What'S The Difference Between A Project And A Research Project?

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However, the main difference is that while an academic research proposal is for a specific line of research, a project proposal is for approval of a relatively smaller enterprise or scientific scheme; most often, project proposals are written with the intent of obtaining support in the form of budget penalties and permission to devote time and effort to the chosen project. Here it must be remembered that the forms, procedures and principles of academic research proposals are much more rigorous than for project proposals; it goes without saying that even the standard is much more demanding than in the project proposals. 

While format, length, and content may vary, the overall goal of academic research proposals and project proposals remains the same: approval by supervisors, academic committees, or reviews . This article will discuss the complexities of academic research proposals and project proposals, thereby helping readers understand the differences between the two. The following steps describe a simple and effective research paper writing strategy.  You will most likely start your research with a working, preliminary, or preliminary thesis, which you will refine until you are sure where the evidence leads. The thesis says what you believe and what you are going to prove. Good thesis statement distinguishes a thoughtful research project from a mere review of the facts. A good experimental thesis will help you focus your search for information. 

Before embarking on serious research, do some preliminary research to determine if there is enough information for your needs and to set the context for your research. Now that the direction of your research is clear to you, you can start searching for material on your topic. Choose a topic on which you can find an acceptable amount of information.  People wishing to publish the results of a quality assurance project should read this guide. Worksheets for assessing whether a quality assurance activity is also exploratory The following are two worksheets to help researchers determine whether to consult with the IRB before starting a quality assurance project. 

The main similarity between a thesis and a research project is that both can be inserted as academic papers. To understand the difference between a thesis and a research project, it is necessary to understand the similarities between the two terms. A dissertation is much more thorough than a research project; is a collection of various studies carried out in the field of study, which includes a critical analysis of their results. It aims to present and justify the necessity and importance of conducting research, as well as to present practical ways of conducting research. In addition, he should discuss the main issues and questions that the researcher will raise during the course of the study. Take on a topic that can be adequately covered in the given project format. A strong thesis is provocative; takes a stand and justifies the discussion you present. 

It contains the introduction, problematic hypothesis, objectives, hypothesis, methodology, rationale, and implications of the research project. The information collected during the study culminates in an application document such as policy recommendations, curriculum development, or program evaluation. The purpose of a design study is to collect information that will help solve an identifiable problem in a specific context. The purpose of design research is not to add to our understanding of research on a topic. The key difference between design research and a dissertation is that design research does not start from a research problem. The main difference between a terminating project and a thesis is that a terminating project addresses a specific problem, problem, or problem in your field of study, while a dissertation attempts to create new knowledge. The final project focuses on a narrow and specific topic, while the dissertation addresses a broader and more general issue. 

The main difference between projects and programs is usually that projects are designed to produce results while programs are designed to achieve business results. Obviously, there are some similarities between projects and programs, namely that they are both interested in change, i.e., in creating something new, and both require the use of a team to achieve a goal. To make the difference between project and programme more concrete, let's look at a practical example of the difference between project and programme. But to understand the difference, you need to start by understanding the definitions of projects and programmes. In a project portfolio, each project is responsible for managing multiple projects. The figure also highlights the differences between the project management level and the program and portfolio. 

Program Managers Project Managers Program Managers create the overall plans that are used to manage projects. Project management has a defined timeline with a defined deliverable that determines the end date. The program manager defines the vision, which is especially important when he oversees several projects at the same time. Program managers need to think strategically, especially as they often have to negotiate between different organizations and sometimes between multiple projects interacting over a program. Indeed, some of these projects can be so large and complex that they are programs in their own right. Thus, our software projects will only be one of the projects controlled by the program. Project Report Research Report Mainly focuses on achieving the desired outcome of the project. The focus is on providing information derived from data and problem analysis. A project report, as the name suggests, is simply a report that provides useful and important information to make better business decisions and also helps in project management. 

Conversely, a research report defines what is being sought, sources of data collection, how data is collected (for example, a research report focuses on the results of a completed research work. The research proposal has been submitted, evaluated, taking into account a number of factors, such as the associated costs , potential impact, soundness of the project implementation plan This is usually a request for research funding on the subject of study.  Instead, the research report is prepared after the project is completed. The research proposal is written in the future, the time used in the research report is past because it is written in the third person. Research proposals are approximately 4-10 pages in length. On the other hand, research consists of proving the main thesis backed up by evidence and data. Originality and personal research are important components of a dissertation. This dissertation engages the student in stimulating or provocative research and shows a level of thinking that opens up new horizons. Researching and writing an article will be more enjoyable if you are writing about something interesting. 

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Research vs. study

The confusion about these words is that they can both be either nouns or verbs. If you ask someone, "Does 'studies' mean the same as 'researches'?" you may hear "Yes," but it is only true if they are used as verbs. As nouns, they have subtly different meanings.

"This team has done a lot of good research. I just read their latest study, which they wrote about calcium in germinating soybeans. It described several interesting experiments."

research 1. to perform a systematic investigation

1. "What kind of scientist is he? He's a botanist. He researches plants."

study 1. to perform a systematic investigation; 2. to actively learn or memorize academic material

1. "What kind of scientist is he? He's a botanist. He studies plants."

2. "Mindy studies every day. That is why she gets such excellent grades. She wants to go to college to study math."

Some authors say "research" when they mean "study." "Research," as a verb, means "to perform a study or studies," but "research" as a noun refers to the sum of many studies. "Chemical research" means the sum of all chemical studies. If you find yourself writing "a research" or "in this research," change it to "a study" or "in this study."

research The act of performing research. Also, the results of research. Note that "research" is a mass noun. It is already plural in meaning but grammatically singular. If you want to indicate more than one type, say "bodies of research" or "pieces of research," not "researches."

"Dr. Lee was a prolific scientist. She performed a great deal of research over her long career."

study A single research project or paper.

"Dr. Lee was a prolific scientist. She performed a great many studies over her long career."

The noun "study" refers to a single paper or project. You can replace "paper" with "study" in almost all cases (but not always the other way around), to the point where you can say "I wrote a study." The noun "research" means more like a whole body of research including many individual studies: The research of a field. The lifetime achievements of a scientist or research team.

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• v.37(16); 2022 Apr 25

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

Edward barroga.

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

Glafera Janet Matanguihan

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

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

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

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

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

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

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

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

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

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

Research questions in quantitative research

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

Hypotheses in quantitative research

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

Research questions in qualitative research

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

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

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

Hypotheses in qualitative research

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

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

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

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

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

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

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

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

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

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

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

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

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

Author Contributions:

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

• Aims and Objectives – A Guide for Academic Writing
• Doing a PhD

One of the most important aspects of a thesis, dissertation or research paper is the correct formulation of the aims and objectives. This is because your aims and objectives will establish the scope, depth and direction that your research will ultimately take. An effective set of aims and objectives will give your research focus and your reader clarity, with your aims indicating what is to be achieved, and your objectives indicating how it will be achieved.

Introduction

There is no getting away from the importance of the aims and objectives in determining the success of your research project. Unfortunately, however, it is an aspect that many students struggle with, and ultimately end up doing poorly. Given their importance, if you suspect that there is even the smallest possibility that you belong to this group of students, we strongly recommend you read this page in full.

This page describes what research aims and objectives are, how they differ from each other, how to write them correctly, and the common mistakes students make and how to avoid them. An example of a good aim and objectives from a past thesis has also been deconstructed to help your understanding.

What Are Aims and Objectives?

Research aims.

A research aim describes the main goal or the overarching purpose of your research project.

In doing so, it acts as a focal point for your research and provides your readers with clarity as to what your study is all about. Because of this, research aims are almost always located within its own subsection under the introduction section of a research document, regardless of whether it’s a thesis , a dissertation, or a research paper .

A research aim is usually formulated as a broad statement of the main goal of the research and can range in length from a single sentence to a short paragraph. Although the exact format may vary according to preference, they should all describe why your research is needed (i.e. the context), what it sets out to accomplish (the actual aim) and, briefly, how it intends to accomplish it (overview of your objectives).

To give an example, we have extracted the following research aim from a real PhD thesis:

Example of a Research Aim

The role of diametrical cup deformation as a factor to unsatisfactory implant performance has not been widely reported. The aim of this thesis was to gain an understanding of the diametrical deformation behaviour of acetabular cups and shells following impaction into the reamed acetabulum. The influence of a range of factors on deformation was investigated to ascertain if cup and shell deformation may be high enough to potentially contribute to early failure and high wear rates in metal-on-metal implants.

Note: Extracted with permission from thesis titled “T he Impact And Deformation Of Press-Fit Metal Acetabular Components ” produced by Dr H Hothi of previously Queen Mary University of London.

Research Objectives

Where a research aim specifies what your study will answer, research objectives specify how your study will answer it.

They divide your research aim into several smaller parts, each of which represents a key section of your research project. As a result, almost all research objectives take the form of a numbered list, with each item usually receiving its own chapter in a dissertation or thesis.

Following the example of the research aim shared above, here are it’s real research objectives as an example:

Example of a Research Objective

• Develop finite element models using explicit dynamics to mimic mallet blows during cup/shell insertion, initially using simplified experimentally validated foam models to represent the acetabulum.
• Investigate the number, velocity and position of impacts needed to insert a cup.
• Determine the relationship between the size of interference between the cup and cavity and deformation for different cup types.
• Investigate the influence of non-uniform cup support and varying the orientation of the component in the cavity on deformation.
• Examine the influence of errors during reaming of the acetabulum which introduce ovality to the cavity.
• Determine the relationship between changes in the geometry of the component and deformation for different cup designs.
• Develop three dimensional pelvis models with non-uniform bone material properties from a range of patients with varying bone quality.
• Use the key parameters that influence deformation, as identified in the foam models to determine the range of deformations that may occur clinically using the anatomic models and if these deformations are clinically significant.

It’s worth noting that researchers sometimes use research questions instead of research objectives, or in other cases both. From a high-level perspective, research questions and research objectives make the same statements, but just in different formats.

Taking the first three research objectives as an example, they can be restructured into research questions as follows:

Restructuring Research Objectives as Research Questions

• Can finite element models using simplified experimentally validated foam models to represent the acetabulum together with explicit dynamics be used to mimic mallet blows during cup/shell insertion?
• What is the number, velocity and position of impacts needed to insert a cup?
• What is the relationship between the size of interference between the cup and cavity and deformation for different cup types?

Difference Between Aims and Objectives

Hopefully the above explanations make clear the differences between aims and objectives, but to clarify:

• The research aim focus on what the research project is intended to achieve; research objectives focus on how the aim will be achieved.
• Research aims are relatively broad; research objectives are specific.
• Research aims focus on a project’s long-term outcomes; research objectives focus on its immediate, short-term outcomes.
• A research aim can be written in a single sentence or short paragraph; research objectives should be written as a numbered list.

How to Write Aims and Objectives

Before we discuss how to write a clear set of research aims and objectives, we should make it clear that there is no single way they must be written. Each researcher will approach their aims and objectives slightly differently, and often your supervisor will influence the formulation of yours on the basis of their own preferences.

Regardless, there are some basic principles that you should observe for good practice; these principles are described below.

Your aim should be made up of three parts that answer the below questions:

• Why is this research required?
• What is this research about?
• How are you going to do it?

The easiest way to achieve this would be to address each question in its own sentence, although it does not matter whether you combine them or write multiple sentences for each, the key is to address each one.

The first question, why , provides context to your research project, the second question, what , describes the aim of your research, and the last question, how , acts as an introduction to your objectives which will immediately follow.

Scroll through the image set below to see the ‘why, what and how’ associated with our research aim example.

Note: Your research aims need not be limited to one. Some individuals per to define one broad ‘overarching aim’ of a project and then adopt two or three specific research aims for their thesis or dissertation. Remember, however, that in order for your assessors to consider your research project complete, you will need to prove you have fulfilled all of the aims you set out to achieve. Therefore, while having more than one research aim is not necessarily disadvantageous, consider whether a single overarching one will do.

Research Objectives

Each of your research objectives should be SMART :

• Specific – is there any ambiguity in the action you are going to undertake, or is it focused and well-defined?
• Measurable – how will you measure progress and determine when you have achieved the action?
• Achievable – do you have the support, resources and facilities required to carry out the action?
• Relevant – is the action essential to the achievement of your research aim?
• Timebound – can you realistically complete the action in the available time alongside your other research tasks?

In addition to being SMART, your research objectives should start with a verb that helps communicate your intent. Common research verbs include:

Table of Research Verbs to Use in Aims and Objectives

Last, format your objectives into a numbered list. This is because when you write your thesis or dissertation, you will at times need to make reference to a specific research objective; structuring your research objectives in a numbered list will provide a clear way of doing this.

To bring all this together, let’s compare the first research objective in the previous example with the above guidance:

Checking Research Objective Example Against Recommended Approach

Research Objective:

1. Develop finite element models using explicit dynamics to mimic mallet blows during cup/shell insertion, initially using simplified experimentally validated foam models to represent the acetabulum.

Checking Against Recommended Approach:

Q: Is it specific? A: Yes, it is clear what the student intends to do (produce a finite element model), why they intend to do it (mimic cup/shell blows) and their parameters have been well-defined ( using simplified experimentally validated foam models to represent the acetabulum ).

Q: Is it measurable? A: Yes, it is clear that the research objective will be achieved once the finite element model is complete.

Q: Is it achievable? A: Yes, provided the student has access to a computer lab, modelling software and laboratory data.

Q: Is it relevant? A: Yes, mimicking impacts to a cup/shell is fundamental to the overall aim of understanding how they deform when impacted upon.

Q: Is it timebound? A: Yes, it is possible to create a limited-scope finite element model in a relatively short time, especially if you already have experience in modelling.

Q: Does it start with a verb? A: Yes, it starts with ‘develop’, which makes the intent of the objective immediately clear.

Q: Is it a numbered list? A: Yes, it is the first research objective in a list of eight.

Mistakes in Writing Research Aims and Objectives

1. making your research aim too broad.

Having a research aim too broad becomes very difficult to achieve. Normally, this occurs when a student develops their research aim before they have a good understanding of what they want to research. Remember that at the end of your project and during your viva defence , you will have to prove that you have achieved your research aims; if they are too broad, this will be an almost impossible task. In the early stages of your research project, your priority should be to narrow your study to a specific area. A good way to do this is to take the time to study existing literature, question their current approaches, findings and limitations, and consider whether there are any recurring gaps that could be investigated .

Note: Achieving a set of aims does not necessarily mean proving or disproving a theory or hypothesis, even if your research aim was to, but having done enough work to provide a useful and original insight into the principles that underlie your research aim.

2. Making Your Research Objectives Too Ambitious

Be realistic about what you can achieve in the time you have available. It is natural to want to set ambitious research objectives that require sophisticated data collection and analysis, but only completing this with six months before the end of your PhD registration period is not a worthwhile trade-off.

3. Formulating Repetitive Research Objectives

Each research objective should have its own purpose and distinct measurable outcome. To this effect, a common mistake is to form research objectives which have large amounts of overlap. This makes it difficult to determine when an objective is truly complete, and also presents challenges in estimating the duration of objectives when creating your project timeline. It also makes it difficult to structure your thesis into unique chapters, making it more challenging for you to write and for your audience to read.

Fortunately, this oversight can be easily avoided by using SMART objectives.

Hopefully, you now have a good idea of how to create an effective set of aims and objectives for your research project, whether it be a thesis, dissertation or research paper. While it may be tempting to dive directly into your research, spending time on getting your aims and objectives right will give your research clear direction. This won’t only reduce the likelihood of problems arising later down the line, but will also lead to a more thorough and coherent research project.

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Error bars indicate 95% CIs for percentages. P value shows significance of test for difference in percentages between all groups using the Pearson χ 2 test. CORS indicates Coworker Concern Observation Reporting System.

Adjusted odds ratios (ORs) of at least 1 coworker concern report of any type, with 95% CI. Effect estimates derived using logistic regression adjusting for clinician specialty, pediatric focus status, region, and academic practice setting status.

Error bars represent 95% CIs for percentages. P value shows significance of test for difference in percentages between all groups using Pearson χ 2 test.

eTable 1. Examples of Coworker Observation Reporting System Reports by Type and Specialty

eTable 2. Physician Specialty Grouping

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Cooper WO , Hickson GB , Dmochowski RR, et al. Physician Specialty Differences in Unprofessional Behaviors Observed and Reported by Coworkers. JAMA Netw Open. 2024;7(6):e2415331. doi:10.1001/jamanetworkopen.2024.15331

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Physician Specialty Differences in Unprofessional Behaviors Observed and Reported by Coworkers

• 1 Departments of Pediatrics and Health Policy, Center for Patient and Professional Advocacy, Vanderbilt University Medical Center, Nashville, Tennessee
• 2 Department of Pediatrics, Center for Patient and Professional Advocacy, Vanderbilt University Medical Center, Nashville, Tennessee
• 3 Department of Urologic Surgery, Center for Patient and Professional Advocacy, Vanderbilt University Medical Center, Nashville, Tennessee
• 4 Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
• 5 Arkansas Children’s Hospital, Little Rock
• 6 Department of Orthopaedic Surgery, Wake Forest University School of Medicine, Winston-Salem, North Carolina
• 7 Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
• 8 Department of Surgery, Stanford University School of Medicine, Palo Alto, California
• 9 Departments of Emergency Medicine and Pediatrics, University of Michigan Medical School, Ann Arbor
• 10 Department of Medicine, Center for Patient and Professional Advocacy, Vanderbilt University Medical Center, Nashville, Tennessee
• 11 Department of Pediatrics, Stanford University School of Medicine, California and Stanford Medicine Children’s Health, Palo Alto, California
• 12 Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee

Question   Are there differences by specialty in the proportion of physicians who are identified in safety event reports submitted by coworkers describing unprofessional behaviors?

Findings   In this cohort study of 35 120 physicians, 9.1% had at least 1 report from a coworker describing unprofessional behavior. Surgeons were most likely to receive a coworker report, and physicians with a pediatric focus were the least likely to receive a report of unprofessional behavior.

Meaning   Understanding more about the distribution and patterns of unprofessional behaviors in health care that interfere with individual and team performance can support coworker well-being and the ability to deliver safe high-quality care.

Importance   Because unprofessional behaviors are associated with patient complications, malpractice claims, and well-being concerns, monitoring concerns requiring investigation and individuals identified in multiple reports may provide important opportunities for health care leaders to support all team members.

Objective   To examine the distribution of physicians by specialty who demonstrate unprofessional behaviors measured through safety reports submitted by coworkers.

Design, Setting, and Participants   This retrospective cohort study was conducted among physicians who practiced at the 193 hospitals in the Coworker Concern Observation Reporting System (CORS), administered by the Vanderbilt Center for Patient and Professional Advocacy. Data were collected from January 2018 to December 2022.

Exposure   Submitted reports concerning communication, professional responsibility, medical care, and professional integrity.

Main Outcomes and Measures   Physicians’ total number and categories of CORS reports. The proportion of physicians in each specialty (nonsurgeon nonproceduralists, emergency medicine physicians, nonsurgeon proceduralists, and surgeons) who received at least 1 report and who qualified for intervention were calculated; logistic regression was used to calculate the odds of any CORS report.

Results   The cohort included 35 120 physicians: 18 288 (52.1%) nonsurgeon nonproceduralists, 1876 (5.3%) emergency medicine physicians, 6743 (19.2%) nonsurgeon proceduralists, and 8213 (23.4%) surgeons. There were 3179 physicians (9.1%) with at least 1 CORS report. Nonsurgeon nonproceduralists had the lowest percentage of physicians with at least 1 report (1032 [5.6%]), followed by emergency medicine (204 [10.9%]), nonsurgeon proceduralists (809 [12.0%]), and surgeons (1134 [13.8%]). Nonsurgeon nonproceduralists were less likely to be named in a CORS report than other specialties (5.6% vs 12.8% for other specialties combined; difference in percentages, −7.1 percentage points; 95% CI, −7.7 to −6.5 percentage points; P  < .001). Pediatric-focused nonsurgeon nonproceduralists (2897 physicians) were significantly less likely to be associated with a CORS report than nonpediatric nonsurgeon nonproceduralists (15 391 physicians) (105 [3.6%] vs 927 [6.0%]; difference in percentages, −2.4 percentage points, 95% CI, −3.2 to −1.6 percentage points; P  < .001). Pediatric-focused emergency medicine physicians, nonsurgeon proceduralists, and surgeons had no significant differences in reporting compared with nonpediatric-focused physicians.

Conclusions and Relevance   In this cohort study, less than 10% of physicians ever received a coworker report with a concern about unprofessional behavior. Monitoring reports of unprofessional behaviors provides important opportunities for health care organizations to identify and intervene as needed to support team members.

High-functioning teams in health care, which include physicians, clinical staff, patients, and families, are essential to promote safe health outcomes. Most health care professionals consistently model professionalism toward coworkers, defined as clear communication; respect for patients, colleagues, and established safety practices; commitment to excellent technical care; and integrity. 1 However, a small number of clinicians account for a disproportionate share of reports of unprofessional behaviors. 2 - 4 Unprofessional behaviors threaten individual and team function 5 , 6 and increase avoidable patient complications 7 ; individuals who model unprofessional behaviors are associated with well-being concerns 8 , 9 and increased malpractice claims. 10

The Coworker Concern Obversation Reporting System (CORS) program is a national collaborative directed by the Vanderbilt Health Center for Patient and Professional Advocacy (CPPA) at Vanderbilt University Medical Center. A total of 193 participating hospitals and practice sites send electronic safety event reports describing concerns about unprofessional behaviors to CPPA, which uses qualitative coding 11 to identify individual reports of unprofessional behavior as well as individual clinicians with repeated concerns. 2 , 4 Individual reports and patterns of repeated concerns are used to identify clinicians who then receive feedback as a part of tiered interventions designed to promote self-regulation and to reduce reports of unprofessional behavior. 2

The CORS program addresses behaviors by physicians, advanced practice clinicians, and nursing professionals. For the purposes of this study, we were interested in focusing on differences among physicians defined by their specialty because of the varying needs of teams that deliver care in different settings defined by physician specialty. Studies describing unprofessional behaviors among physicians toward coworkers have typically included all specialties and have used simulations or other models to identify the impact of unprofessional behavior. 2 , 3 , 5 , 6 Limited data are available to understand the distribution and types of unprofessional behaviors among physicians by specialty and the proportion of physicians with repeated professionalism concerns. Understanding more about the distribution and patterns of repeated unprofessional behaviors can offer insight to leaders and organizations in supporting individual and team well-being and delivering safe care.

This study was designed to address the following study questions: what is the distribution of physicians by specialty who have received with reports of perceived unprofessional behaviors, measured through electronic safety reports in CORS? What are the types of concerns described in coworker reports about unprofessional behavior? Are there differences by specialty among physicians who develop apparent patterns of unprofessional behavior as described in CORS reports?

This retrospective cohort study included credentialed physicians (ie, no residents or fellows) in the national CORS collaborative who had at least 1 day of active practice at a CORS site during the study period (January 1, 2018, to December 31, 2022). All physicians were eligible to be identified in a report. Physicians entered the cohort either on the first day of the study period or the physician’s first date of employment at an active CORS site, whichever came later. Physicians exited the cohort on the last day of the study period or the physician’s departure from the site. If a physician moved to another CORS site, only the first site’s data were used.

Research datasets were produced from existing data collected in support of the CORS process at each site. The dataset was deidentified without physician identifiers by a computer analyst at Vanderbilt University Medical Center who was not involved in the conduct of the research. Because the analysis was conducted on datasets that could not be linked to an individual, the Vanderbilt University Medical Center institutional review board determined that the study did not qualify as human participant research or require informed consent per CFR §46.102(e)(1). The study followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline for conducting cohort studies. 12

CORS data for each site are held at Vanderbilt under the terms of business associate agreements in place between each site and Vanderbilt University Medical Center. Each site provides names and practice specialties for credentialed physicians. At each site, reports of coworker concerns about a professional colleague’s behavior are entered into the health system’s electronic safety event reporting systems by any individual with access to the electronic safety reporting system.

Data are sent to Vanderbilt, and all patient, staff, and professionals’ identifiers are removed. Independent coders review CORS reports and reliably assign reports to various categories defined in previous studies. 2 , 11 Coders assign unique concerns to categories, including clear and respectful communication, professional responsibility, competent medical care, and integrity. 11 Coders are tested for reliability every 6 months and are found to have consistent coding 88% to 92% of the time. Reports with statements such as “Dr XX was about to start the bronchoscopy, but I reminded him we needed to do the time out. He mumbled, ‘You’re a bossy cow’” would be identified as disrespectful communication (eTable 1 in Supplement 1 ). “The patient was confused and had no idea where he was. I asked Dr YY if we should wait for the patient’s spouse to arrive. Dr YY said, ‘We’ll be here all night if we do that. If you won’t sign as a witness, I’ll get someone else who will’” includes statements that would be coded to professional responsibility. Reports about medical care might include reports such as “Dr XX removed the patient’s foley catheter at the end of the case without wearing gloves… had visible urine on his hands and did not wash them… proceeded to touch things in the operating room and left the room.” Reports about integrity would include reports such as, “Dr ZZ billed the visit as a level 5 visit, but I know they only spent 4 minutes with the patient.”

The outcomes of interest were physicians’ total number and categories of CORS reports during the study period. 11 Patterns were defined using a proprietary algorithm, which weights CORS reports based on recency and severity, with more recent and more severe reports contributing more than older and less severe reports. 2 Physicians who are associated with CORS reports are supported by a tiered intervention model, including peer-delivered messages for single reports and awareness interventions for those with apparent patterns of reports, in which their status relative to local and national benchmarks is provided to them in an effort to guide self-regulation and improved performance. 2

Study variables included physician specialty identified from credentialing file categories, which were grouped as nonsurgeon nonproceduralists, emergency medicine physicians, nonsurgeon proceduralists, and surgeons, similar to previous studies (eTable 2 in Supplement 1 ). 13 - 16 To assess whether previous findings showing that pediatric-focused clinicians generated fewer professionalism concerns reported by patients might also apply to coworker concerns, 16 physicians whose credentialing files included a pediatric specialty or subspecialty were identified as pediatric focused. Site characteristics included the region of the country and whether the physician’s site of practice was academic or community (ie, regional health system or community-based multispecialty group).

We calculated the proportion of physicians in each specialty who received at least 1 report and who qualified for an awareness intervention based on having a pattern of repeated professionalism concerns. Proportions of physicians in each specialty having single reports and those qualifying for awareness interventions were reported with a 95% CI and compared using the Pearson χ 2 test. In addition, the proportion of clinicians receiving at least 1 report of each type was reported by specialty. Logistic regression was used to calculate odds of any CORS report, adjusting for specialty, region, academic practice status, and pediatric specialty status. Adjusted odds ratios (ORs) with 95% CIs were reported by specialty and by pediatric focus. Two-sided P values of less than .05 were considered statistically significant. Sensitivity analyses restricted to physicians with at least 2 years of follow-up (99% of the original cohort) were not materially different than primary models, so analyses reported here include the full cohort. All statistical analyses were performed with R version 4.2.3 (R Project for Statistical Computing) from April 14 to September 8, 2023.

The cohort included 35 120 physicians, with 18 288 (52.1%) nonsurgeon nonproceduralists, 1876 (5.3%) emergency medicine physicians, 6743 (19.2%) nonsurgeon proceduralists, and 8213 (23.4%) surgeons ( Table 1 ). The largest proportion of physicians practiced in the Midwest region of the United States, and the second largest proportion practiced in the Northeast region, with the exception of emergency medicine physicians, for whom the second largest proportion practiced in the West. The largest proportion of physicians practiced in academic settings, reflecting the distribution of CPPA sites. 7 There were 4705 physicians (13.4%) who practiced in pediatric settings. Surgeons had the smallest proportion of physicians with a pediatric-focused practice (451 [5.5%]).

There were 3179 physicians (9.1%) who were associated with at least 1 CORS report. The proportion of individual physicians named in at least 1 CORS report differed by specialty ( Figure 1 ). Nonsurgeon nonproceduralists had the lowest percentage of physicians associated with at least 1 report (1032 physicians [5.6%]), followed by emergency medicine (204 [10.9%]), nonsurgeon proceduralists (809 [12.0%]), and surgeons (1134 [13.8%]). Nonsurgeon nonproceduralists were significantly less likely to be named in a CORS report than the other specialties (1032 [5.6%] vs 2147 [12.8%] for other specialties combined; difference in percentages, −7.1 percentage points; 95% CI, −7.7 to −6.5 percentage points; P  < .001). Overall, physicians who were pediatric focused (4705 physicians) were significantly less likely to have at least 1 CORS report than physicians who were not pediatric focused (30 415 physicians) (319 [6.8%] vs 2860 [9.4%]; difference in percentages,−2.6 percentage points; 95% CI, −3.4 to −1.8 percentage points; P  < .001). Pediatric-focused nonsurgeon nonproceduralists (2897 physicians) were significantly less likely to be associated with a CORS report than nonpediatric-focused nonsurgeon nonproceduralists (15 391 physicians) (105 [3.6%] vs 927 [6.0%]; difference in percentages, −2.4 percentage points; 95% CI, −3.2 to −1.6 percentage points; P  < .001) ( Figure 1 A). For other specialty focus areas, there was no statistically significant difference in the proportion of physicians associated with having at least 1 CORS report between those with a pediatric focus and those with a nonpediatric focus.

In a multivariable logistic regression model controlling for physician and practice site characteristics ( Figure 2 ), all specialty types had significantly higher odds of at least 1 coworker concern report when compared with nonsurgeon nonproceduralists, including emergency medicine physicians (adjusted OR, 1.91; 95% CI, 1.63-2.24), nonsurgeon proceduralists (adjusted OR, 2.34; 95% CI, 2.12-2.57), and surgeons (adjusted OR, 2.75; 95% CI, 2.51-3.01) ( P  < .001). Pediatric-focused physicians were significantly less likely to have a coworker concern report than those with a nonpediatric focus (adjusted OR, 0.69; 95% CI, 0.61-0.78; P  < .001).

The most common types of CORS reports for all physician specialties involved clear and respectful communication, followed by professional responsibility ( Table 2 ). The least common type of CORS report involved professional integrity. There was a statistically significant difference between specialties in the proportion having reports for each type of CORS report with nonsurgeon nonproceduralists demonstrating the lowest rate of reports of each type.

There were 338 physicians (1.0%) who had repeated CORS reports that represented a pattern. The proportion of physicians who had a pattern of CORS reports ranged from 0.5% (881 of 8288 nonsurgeon nonproceduralists) to 1.9% (155 of 8213 surgeons) ( Figure 3 ). Emergency medicine physicians (14 of 1876 [0.7%]) and nonsurgeon proceduralists (81 of 6743 [1.2%]) had incrementally higher proportions of individuals with a pattern of repeated CORS reports than nonsurgeon nonproceduralists ( P  = .001).

In this cohort study of 35 120 physicians, 9.1% were associated with at least 1 report about unprofessional behavior by coworkers, meaning that most physicians never received any reports. The proportion receiving at least 1 CORS report ranged from 5.6% to 13.8% with observed differences by specialty focus. In a multivariate model, physicians who practiced in nonsurgical, nonprocedural–focused specialties were significantly less likely to have CORS reports than the other specialties. Physicians who had a pediatric focus were significantly less likely to be identified in a CORS report than physicians who did not have a pediatric focus. There was an even smaller proportion (<1%) of physicians with a pattern of repeated CORS reports. The proportion of individuals associated with a pattern in each specialty differed significantly, with surgeons being the group most likely to be associated with a pattern of repeated CORS reports.

Unprofessional behavior observed and described by patients and coworkers has been shown to result in increased risk for patient complications 7 , 17 and malpractice claims. 17 Physicians with repeated professionalism concerns are also likely to have a deleterious impact on culture and team performance. 5 , 6 , 17 What are ways in which unprofessional behaviors might increase the risk for complications? Exposure to dismissive behaviors has been shown to reduce individual clinician and team performance, often mediated through reduced vigilance, help-seeking, and communication. 5 , 6 Individuals and teams who are more focused on the unprofessional colleague may not pay as much attention to the task at hand or may not speak up if they have concerns about clinical care. In this study, concerns about unprofessional communication were the most common reports and concerns about professional integrity were the least common. The types and frequencies of unprofessional behavior types in this study parallel the distribution seen in previous studies. 3 , 7 , 11

Surgeons were more likely to have at least 1 CORS report of unprofessional behavior and to have patterns of CORS reports than other specialties. Previous studies have shown higher risk among surgeons for being named in unsolicited patient complaints and malpractice claims. 10 , 18 - 20 It is possible that surgeons practice in more stressful environments than the other specialties, resulting in interactions during high-stakes events that increased the likelihood of a coworker reporting a concern. In addition, surgeons serve on teams that require interdependence, potentially increasing the frustration for the surgeon or for other members of the team. It is also possible that personality characteristics of surgeons, nurses, and other clinicians who deliver care in perioperative settings differ from clinicians who provide care in other settings. These dynamics could affect both the frequency of unprofessional behaviors and the likelihood that an individual would report the behavior.

Nonsurgeon proceduralists are likely to practice in both procedural areas (ie, endoscopy suites) and inpatient and ambulatory settings, so individual team member’s perceptions of unprofessional behavior could be governed by the degree of complexity and stress in those environments. In addition, team-based care for nonsurgeon procedural specialties could introduce additional stresses and tension points that might affect team performance. Emergency medicine physicians were less likely than surgeons and nonsurgeon proceduralists to be associated with a coworker concern. The structure of teams in emergency medicine and the interaction of emergency medicine physicians with multiple professions and specialties introduces unique stresses on team function in that setting. In addition, care in emergency departments can be stressful and overwhelming at times. Interestingly, physicians who practiced with pediatric-focused privileges were significantly less likely to receive a CORS report even when controlling for specialty and practice setting. There may be a difference in physicians and nurses who choose to practice in pediatrics, possibly reflecting a different temperament or practice style. Overall, however, regardless of specialty, the majority of physicians in this cohort (91% overall) never received any reports of unprofessional behavior during the study period.

The findings of this study have implications for both individual physicians and health care leaders. Understanding more about how different team members in complex health care teams perceive behaviors provides insight into culture, psychological safety, and trust in a variety of settings. Previous studies have demonstrated that physicians with well-being concerns are more likely to be associated with reports of unprofessional behavior. 8 , 9 , 21 Physicians who develop patterns of unprofessional behavior reports may need an assessment of their well-being. In a similar fashion, if entire groups of physicians begin to receive reports more than other groups, systems issues, culture, or leadership factors should be considered. 22 It is worth noting that 86% to 90% of physicians by specialty did not have any CORS reports during the study period. Health care leaders can use these data to underscore the importance of professional accountability in creating optimal patient outcomes and individual and team function. 1 , 2 , 17

This study has important limitations. It is possible that some physicians modeled unprofessional behaviors that went unreported because of individual’s fear of retaliation or lack of psychological safety. 23 Other environmental variables related to the physician’s practice that are unmeasured here could contribute to either unprofessional behaviors or to differential reporting when behaviors occur. Reports were not investigated to determine whether the reporter’s perception was true or not. The assignment of physicians to specialty was based on credentialing files that may not reflect the physician’s actual practice. For example, some cardiologists might perform procedures, while others may not, which would bias the findings to the null, since nonproceduralists were identified in fewer reports as a group. It is possible that physicians identified with a pediatric focus may have a range of practice exposure in pediatric settings. For example, individuals who practice in a large center with highly specialized care (ie, a children’s hospital) may have a primary focus on pediatric care, while individuals in other settings may deliver care to children and adults and thus may share attributes more in common with nonpediatric clinicians. The bias of such misclassification of pediatric focus would bias the findings toward the null. Even though the study cohort included more than 35 000 physicians, it was not possible to do extensive analyses among specific specialties and subspecialties. In addition, study records did not include gender; in previous studies, women were less likely to receive CORS reports. 3 , 7 The study included a greater proportion of academic than community-based centers, so the cohort may not be representative of the general population of physicians.

In this study, less than 10% of physicians received a coworker report for unprofessional behavior during the study period. Physicians who practiced in pediatric settings were the least likely to receive a report about unprofessional behavior. Surgeons and proceduralists were more likely to receive a CORS report than nonsurgeon nonproceduralists and emergency medicine physicians. Because unprofessional behaviors are associated with patient complications, malpractice claims, and well-being concerns, monitoring concerning behavior and especially those physicians with repeated reports provides important opportunities for physicians and leaders to support professionalism, which increases the chance of health care organizations meeting their clinical, cultural, and other performance goals.

Accepted for Publication: April 4, 2024.

Published: June 6, 2024. doi:10.1001/jamanetworkopen.2024.15331

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Cooper WO et al. JAMA Network Open .

Corresponding Author: William O. Cooper, MD, MPH, Center for Patient and Professional Advocacy, Vanderbilt University Medical Center, 2135 Blakemore Ave, Nashville, TN 37212 ( [email protected] ).

Author Contributions: Mr Domenico and Dr Cooper had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Cooper, Hickson, Dmochowski, Barr, Emory, Webber.

Acquisition, analysis, or interpretation of data: Dmochowski, Domenico, Barr, Emory, Gilbert, Hartman, Lozon, Martinez, Noland.

Drafting of the manuscript: Cooper, Hickson, Dmochowski, Domenico, Barr, Gilbert.

Critical review of the manuscript for important intellectual content: Cooper, Dmochowski, Domenico, Barr, Emory, Gilbert, Hartman, Lozon, Martinez, Noland, Webber.

Statistical analysis: Domenico.

Administrative, technical, or material support: Barr, Emory, Webber.

Supervision: Cooper, Dmochowski, Barr, Hartman, Webber.

Conflict of Interest Disclosures: Dr Hickson reported receiving honorarium from the Medtronic Speaker’ Bureau outside the submitted work as well as serving on the board of the directors of USC Keck, serving as chair of the IHI board of directors, and serving on the International Regulatory Expert Advisory Group to the Australian Health Practitioner Regulation Agency. Dr Gilbert reported receiving personal fees from MJH Life Sciences Fellows outside the submitted work. No other disclosures were reported.

Data Sharing Statement: See Supplement 2 .

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• Published: 05 June 2024

Post-January 6th deplatforming reduced the reach of misinformation on Twitter

• Stefan D. McCabe   ORCID: orcid.org/0000-0002-7180-145X 1   na1 ,
• Diogo Ferrari   ORCID: orcid.org/0000-0003-2454-0776 2   na1 ,
• Jon Green 3 ,
• David M. J. Lazer   ORCID: orcid.org/0000-0002-7991-9110 4 , 5 &
• Kevin M. Esterling   ORCID: orcid.org/0000-0002-5529-6422 2 , 6  

Nature volume  630 ,  pages 132–140 ( 2024 ) Cite this article

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The social media platforms of the twenty-first century have an enormous role in regulating speech in the USA and worldwide 1 . However, there has been little research on platform-wide interventions on speech 2 , 3 . Here we evaluate the effect of the decision by Twitter to suddenly deplatform 70,000 misinformation traffickers in response to the violence at the US Capitol on 6 January 2021 (a series of events commonly known as and referred to here as ‘January 6th’). Using a panel of more than 500,000 active Twitter users 4 , 5 and natural experimental designs 6 , 7 , we evaluate the effects of this intervention on the circulation of misinformation on Twitter. We show that the intervention reduced circulation of misinformation by the deplatformed users as well as by those who followed the deplatformed users, though we cannot identify the magnitude of the causal estimates owing to the co-occurrence of the deplatforming intervention with the events surrounding January 6th. We also find that many of the misinformation traffickers who were not deplatformed left Twitter following the intervention. The results inform the historical record surrounding the insurrection, a momentous event in US history, and indicate the capacity of social media platforms to control the circulation of misinformation, and more generally to regulate public discourse.

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Data availability.

Aggregate data used in the analysis are publicly available at the OSF project website ( https://doi.org/10.17605/OSF.IO/KU8Z4 ) to any researcher for purposes of reproducing or extending the analysis. The tweet-level data and specific user demographics cannot be publicly shared owing to privacy concerns arising from matching data to administrative records, data use agreements and platforms’ terms of service. Our replication materials include the code used to produce the aggregate data from the tweet-level data, and the tweet-level data can be accessed after signing a data-use agreement. For access requests, please contact D.M.J.L.

Code availability

All code necessary for reproduction of the results is available at the OSF project site https://doi.org/10.17605/OSF.IO/KU8Z4 .

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Acknowledgements

The authors thank N. Grinberg, L. Friedland and K. Joseph for earlier technical work on the development of the Twitter dataset. Earlier versions of this paper were presented at the Social Media Analysis Workshop, UC Riverside, 26 August 2022; at the Annual Meeting of the American Political Science Association, 17 September 2022; and at the Center for Social Media and Politics, NYU, 23 April 2021. Special thanks go to A. Guess for suggesting the DID analysis. D.M.J.L. acknowledges support from the William & Flora Hewlett Foundation and the Volkswagen Foundation. S.D.M. was supported by the John S. and James L. Knight Foundation through a grant to the Institute for Data, Democracy & Politics at the George Washington University.

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Institute for Data, Democracy & Politics, George Washington University, Washington, DC, USA

Stefan D. McCabe

Department of Political Science, University of California, Riverside, Riverside, CA, USA

Diogo Ferrari & Kevin M. Esterling

Department of Political Science, Duke University, Durham, NC, USA

Network Science Institute, Northeastern University, Boston, MA, USA

David M. J. Lazer

Institute for Quantitative Social Science, Harvard University, Cambridge, MA, USA

School of Public Policy, University of California, Riverside, Riverside, CA, USA

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The order of author listed here does not indicate level of contribution. Conceptualization of theory and research design: S.D.M., D.M.J.L., D.F., K.M.E. and J.G. Data curation: S.D.M. and J.G. Methodology: D.F. Visualization: D.F. Funding acquisition: D.M.J.L. Project administration: K.M.E., S.D.M. and D.M.J.L. Writing, original draft: K.M.E. and D.M.J.L. Writing, review and editing: K.M.E., D.F., S.D.M., D.M.J.L. and J.G.

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Extended data figures and tables

Extended data fig. 1 replication of the did results varying the number of deplatformed accounts..

DID estimates where the intervention depends on the number of deplatformed users that were followed by the not-deplatformed misinformation sharers. Results are two-way fixed effect point estimates (dots) and 95% confidence intervals (bars) of the difference-in-differences for all activity levels combined. Estimates use ordinary least squares with clustered standard errors at user-level. The Figure shows results including and excluding Trump followers (color code). The x-axis shows the minimum number of deplatformed accounts the user followed from at least one (1+) to at least ten (10+). Total sample sizes for each dosage level: Follow Trump (No): 1: 625,865; 2: 538,460; 3: 495,723; 4: 470,380; 5: 451,468; 6: 437,574; 7: 426,772; 8: 417,200; 9: 408,672; 10: 401,467; Follow Trump (Yes): 1: 688,174; 2: 570,637; 3: 514,352; 4: 481,684; 5: 460,676; 6: 444,656; 7: 432,659; 8: 421,924; 9: 413,241; 10: 405,766.

Extended Data Fig. 2 SRD results for total (bottom row) and average (top row) misinformation tweets and retweets, for deplatformed and not-deplatformed users.

Sample size includes 546 observations (days) on average across groups (x-axis), 404 before and 136 after. The effective number of observations is 64.31 days before and after on average. The estimation excludes data between Jan 6 (cutoff point) and 12 (included). January 6th is the score value 0, and January 12th the score value 1. Optimal bandwidth of 32.6 days with triangular kernel and order-one polynomial. Bars indicate 95% robust bias-corrected confidence intervals.

Extended Data Fig. 3 Time series of the daily mean of non-misinformation URL sharing.

Degree five polynomial regression (fitted line) before and after the deplatforming intervention, separated by subgroup (panel rows), for liberal-slant news (right column), and conservative-slant news (left column) sharing activity. Shaded area around the fitted line is the 95% confidence interval of the fitted values. As a placebo test we evaluate the effect of the intervention on sharing non-fake news for each of our subgroups. Since sharing non-misinformation does not violate Twitter’s Civic Integrity policy – irrespective of the ideological slant of the news – we do not expect the intervention to have an impact on this form of Twitter engagement; see SI for how we identify liberal and conservative slant of these domains from ref. 52 . Among the subgroups, users typically did not change their sharing of liberal or conservative non-fake news. Taking these results alongside those in Fig. 2 implies that these subgroups of users did not substitute non-misinformation conservative news sharing during and after the insurrection in place of misinformation.

Extended Data Fig. 4 Time series of misinformation tweets and retweets (panel columns), separately for high, medium and low activity users (panel rows).

Fitted straight lines describe a linear regression fitted using ordinary least squares of daily total misinformation retweeted standardized (y-axis) on days (x-axis) before January 6th and after January 12th. Shaded areas around the fitted line are 95% confidence intervals.

Extended Data Fig. 5 Replicates Fig. 5 but with adjustment covariates.

Corresponding regression tables are Supplementary Information Tables 1 to 3 . Two-way fixed effect point estimates (dots) and 95% confidence intervals (bars) of the difference-in-differences for high, moderate, and low activity users, as well as all these levels combined (x-axis). P-values (stars) are from two-sided t-tests based on ordinary least squares estimates with clustered standard errors at user-level. Estimates compare followers (treated group) and not-followers (reference group) of deplatformed users after January 12th (post-treatment period) and before January 6th (pre-treatment period). No multiple test correction was used. See Supplementary Information Tables 1 – 3 for exact values with all activity level users combined. Total sample sizes of not-followers (reference) and Trump-only followers: combined: 306,089, high: 53,962, moderate: 219,375, low: 32,003; Followers: combined: 662,216, high: 156,941, moderate: 449,560, low: 53,442; Followers (4+): combined: 463,176, high: 115,264, moderate: 302,907, low: 43,218.

Extended Data Fig. 6 Placebo test of SRD results for total (bottom row) and average (top row) shopping and sports tweets and retweets at the deplatforming intervention, among those not deplatformed.

Sample size includes 545 observations (days), 404 before the intervention and 141 after. Optimal bandwidth of 843.6 days with triangular kernel and order-one polynomial. Cutoff points on January 6th (score 0) and January 12th (score 1). Bars indicate 95% robust bias-corrected confidence intervals. These are placebo tests since tweets about sports and shoppings should not be affected by the insurrection or deplatforming.

Extended Data Fig. 7 Placebo test of SRD results for total (bottom row) and average (top row) misinformation tweets and retweets using December 20th as an arbitrary cutoff point.

Sample size includes 551 observations (days), 387 before the intervention and 164 after. Optimal bandwidth of 37.2 days with triangular kernel and order-one polynomial. Bars indicate 95% robust bias-corrected confidence intervals about the SRD coefficients. This is a placebo test of the intervention period.

Extended Data Fig. 8 Placebo test of SRD results for total (bottom row) and average (top row) misinformation tweets and retweets using January 18th as a cutoff point.

The parameters are very similar to Extended Data Fig. 7 .

Supplementary information

Supplementary information.

Supplementary Figs. 1–5 provide descriptive information about our subgroups, a replication of the panel data using the Decahose, and robustness analyses for the SRD. Supplementary Tables 1–5 show full parameter estimates for the DID models, summary statistics for follower type and activity level, and P values for the DID analyses under different multiple comparisons corrections.

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McCabe, S.D., Ferrari, D., Green, J. et al. Post-January 6th deplatforming reduced the reach of misinformation on Twitter. Nature 630 , 132–140 (2024). https://doi.org/10.1038/s41586-024-07524-8

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Project 2025 partner claims Trump's conviction was the result of witchcraft

Senior reporter and editor at the Family Research Council’s blog cites “ancient Egypt” and “Babylon” to claim “dark arts” are aligning against Trump

Written by Sophie Lawton

Published 06/05/24 9:13 AM EDT

The Family Research Council’s news outlet, The Washington Stand, posted a June 3 commentary claiming “the Left” used witchcraft and ancient spiritual warfare tactics to secure 34 guilty verdicts in former President Donald Trump’s New York hush money trial last week. FRC, which has been designated a hate group by the Southern Poverty Law Center for its anti-LGBTQ activism, is a partner of The Heritage Foundation’s Project 2025 , a large-scale effort to provide staff and policy proposals for the next GOP presidential administration. 

The blog titled “ The Spiritual Warfare behind the Trump Conviction ” was written by senior reporter and editor Ben Johnson, who opens by claiming he is a “prophet” for predicting in 2021 that “the Left” would go after Trump with lawfare in the Southern District of New York. 

Johnson explains his view on how witchcraft factored into the prosecution and conviction of Trump in his New York City trial, claiming Manhattan District Attorney Alvin Bragg “pulled off an act of voodoo jurisprudence”:

Just as Christians’ spiritual warfare usually takes place without dramatic manifestations and apparitions of angels and demons, so too can witchcraft manifest itself without cauldrons and hexes — possibly even without TikTok and Teen Vogue. In a secular age that trusts government as its “god,” modern witchcraft means using the State to harass and control, to loose and to bind, and to impose a dark will on a subdued populace.

Johnson also describes the ways that “spiritual warfare of our age manifests itself.” The first is apparently related to the “dark arts” of “ancient Egypt, where Pharaoh imposed the dictates of his hardened heart on God’s people with the help of his official advisors, the ‘wise men and sorcerers’ and magicians.” Johnson concluded this “gaudy occultism” has returned, as seen by pro-abortion and pro-LGBTQ protesters yelling “Hail Satan” and using other satanic references. 

Johnson also refers to a period when “self-described witches attempted to place a ‘hex’” on Trump, adding: “Wiccan practitioners on TikTok — which they have aptly dubbed ‘WitchTok’ — also exerted their spiritual energies against Supreme Court Justice Brett Kavanaugh and presidential hopeful Vivek Ramaswamy.” Johnson later claims that “Christians consistently living out their faith have little to fear from demonic attacks” but infers that American Christians are likely at risk due to their acceptance of sex outside of marriage, linking to a Pew Research Center study on religious attitudes toward casual sex. 

Johnson also cites ancient Babylon to support his argument, recounting a story about an alleged spiritual weakness during the reign of King Nebuchadnezzar that left the public vulnerable to a “battle against God’s people through the instrumentality of government.” 

Echoing Christian nationalist rhetoric used by Trump allies and other Project 2025 partners, Johnson concludes that Christians are in danger from the left's spiritual warfare and should “consecrate their political action” around removing such dangers from society, telling readers:

The next era of Christian politics must expel the army of career bureaucrats and politicized prosecutors who bend the law to their master’s will. In its place, we must reestablish one binding rule of law for all people (Numbers 15:16). And if those who abused the legal process broke any applicable laws or statutes in the process, perhaps they should learn the truth of the Scriptures: “Whatever a man sows, that he will also reap” (Galatians 6:7).

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Research: Smaller, More Precise Discounts Could Increase Your Sales

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Why bigger discounts don’t necessarily attract more customers.

Retailers might think that bigger discounts attract more customers. But new research suggests that’s not always true. Sometimes, a smaller discount that looks more precise — say 6.8% as compared to 7% — can make people think the deal won’t last long, and they’ll buy more. In a series of nine experimental studies involving around 2,000 individuals considering online or retail purchases of a variety of products, the authors found precise discount depths — the difference between the original and sale price — can increase purchase intentions by up to 21%.

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• DG Dinesh Gauri is a professor and Walmart chair in the department of marketing at the Sam M. Walton College of Business at the University of Arkansas. He is also the executive director of retail information at the Walton College. His research and teaching interests include retailing, pricing, marketing analytics, retail media, e-commerce and social media marketing. He advises for various companies in these areas and is a recognized leader in marketing.
• AG Abhijit Guha is an associate professor in the department of marketing at the Darla Moore School of Business at the University of South Carolina. His research and teaching interests include retailing, pricing, and artificial intelligence.
• AB Abhijit Biswas is the Kmart endowed chair and professor of marketing, chair of the department of marketing, and distinguished faculty fellow at the Mike Ilitch School of Business, Wayne State University. His research and teaching interests include retailing, pricing and advertising. He has published over a hundred articles, majority of which are in academic journals including the Journal of Marketing , Journal of Marketing Research , etc.
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Lifeway Research

Enlightening today’s church with relevant research and insights

Pastoral Support for Same-Sex Marriage Stalls

Pastor Views | Lifeway Research | Jun 4, 2024

Most Protestant pastors remain opposed to same-sex marriage, and the supporting percentage isn’t growing any larger.

By Aaron Earls

Almost a decade after the Supreme Court legalized same-sex marriage across the country, most pastors remain opposed, and the supporting percentage isn’t growing any larger.

One in 5 U.S. Protestant pastors (21%) say they see nothing wrong with two people of the same gender getting married, according to a Lifeway Research study. Three in 4 (75%) are opposed, including 69% who strongly disagree with same-sex marriage. Another 4% say they aren’t sure.

Previous Lifeway Research studies found growing support among pastors. In 2010 , 15% of U.S. Protestant pastors had no moral issues with the practice. The percentage in favor grew to 24% in 2019 . Today, support is statistically unchanged at 21%.

“Debates continue within denominations at national and judicatory levels on the morality of same-sex marriage, yet the overall number of Protestant pastors who support same-sex marriage is not growing,” said Scott McConnell, executive director of Lifeway Research. “The previous growth was seen most clearly among mainline pastors, and that level did not rise in our latest survey.”

Pastors are slightly more supportive of legal civil unions between two people of the same gender, but most still disagree. Currently, 28% back such arrangements, statistically unchanged from the 32% in 2019 and 28% in 2018.

For most pastors, this remains a somewhat theoretical issue. Almost 9 in 10 say they’ve never been asked to perform a same-sex ceremony, according to a 2022 Lifeway Research study .

Mainline vs. evangelical

The previous growth in clergy support of same-sex marriages was driven by U.S. mainline Protestant pastors. In 2010, a third (32%) were in favor. By 2019, almost half (47%) saw nothing wrong. Current support among self-identified mainline pastors remains at similar levels (46%).

Evangelical pastors have been consistently opposed to same-sex marriage. Fewer than 1 in 10 have expressed support for the practice since 2010. Today, 7% of self-identified U.S. evangelical Protestant pastors say they see nothing wrong with two people of the same gender getting married.

A similar divide exists regarding civil unions between two people of the same gender. Most mainline pastors (54%) are supportive, while only 14% of evangelical pastors agree.

Methodists (53%), Presbyterian/Reformed (36%) and Lutherans (34%) are more likely to be supportive of same-sex marriage than Restorationist Movement (8%), non-denominational (5%), Baptist (4%) or Pentecostal (1%) pastors.

Additionally, female pastors (42%), who are more common among mainline denominations , are far more likely than their male counterparts (16%) to back same-sex marriage.

Other demographic groups also have varying degrees of support, though none as drastic as the denominational differences.

Other differences

Younger pastors are more likely to be supportive than the oldest pastors. Protestant pastors 18 to 44 (27%) and 55 to 64 (22%) are more likely than pastors 65 and older (15%) to see nothing wrong with same-sex marriage.

“The moral and doctrinal beliefs of individuals do not tend to move very often or very far, so we wouldn’t expect pastors’ positions to change much,” said McConnell. “However, the differences we see by age make it noteworthy that the higher numbers of young pastors seeing nothing wrong with same-sex marriage is not yet having much of an impact on overall numbers.”

Those with more education are more supportive. Pastors with a master’s (30%) or doctoral degree (26%) are more likely than those with no college degree (9%) or a bachelor’s degree (7%) to say they’re OK with same-sex marriage.

Pastors in the Northeast (27%), where same-sex marriage was first legalized in the U.S., and the Midwest (25%), are more likely than those in the South (18%) to be supportive.

Those leading smaller churches are more likely to see nothing wrong with two people of the same gender getting married. Pastors at churches with fewer than 50 in attendance (27%) and those at congregations of 50 to 99 (25%) are more likely than those at churches with attendance between 100 and 249 (11%) and 250 or more (8%) to be in favor of same-sex marriage.

“Because fewer pastors in mid- and large-size churches are open to same-sex marriage morally, an even larger majority of Protestant churchgoers are in churches in which their pastor does not support same-sex marriages or civil unions,” said McConnell.

Many of the differences between various types of pastors exist for civil unions as well. Younger pastors are more likely to be supportive than older pastors. Pastors with more formal education are more likely to back civil unions. Those in the Northeast and Midwest tend to be more in favor than those in the South. Pastors at the smallest churches are more likely to see nothing wrong with civil unions between two people of the same gender than those at larger churches.

Lifeway Research studies can be used and referenced in news articles freely. This news release can also be republished in its entirety on other websites and in other publications without obtaining permission.

Aaron Earls

@WardrobeDoor

Aaron is the senior writer at Lifeway Research.

For more information, view the complete report .

Methodology  

The phone survey of 1,004 Protestant pastors was conducted Aug. 29, 2023 – Sept. 20, 2023. The calling list was a stratified random sample, drawn from a list of all Protestant churches. Quotas were used for church size. Each interview was conducted with the senior pastor, minister or priest at the church. Responses were weighted by region and church size to reflect the population more accurately. The completed sample is 1,004 surveys. The sample provides 95% confidence that the sampling error does not exceed plus or minus 3.2%. This margin of error accounts for the effect of weighting. Margins of error are higher in sub-groups.

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