work cited in research methodology

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MLA Works Cited Page: Basic Format

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MLA (Modern Language Association) style is most commonly used to write papers and cite sources within the liberal arts and humanities. This resource, updated to reflect the MLA Handbook (9 th  ed.), offers examples for the general format of MLA research papers, in-text citations, endnotes/footnotes, and the Works Cited page.

According to MLA style, you must have a Works Cited page at the end of your research paper. All entries in the Works Cited page must correspond to the works cited in your main text.

Basic rules

• Begin your Works Cited page on a separate page at the end of your research paper. It should have the same one-inch margins and last name, page number header as the rest of your paper.
• Only the title should be centered. The citation entries themselves should be aligned with the left margin.
• Double space all citations, but do not skip spaces between entries.
• Indent the second and subsequent lines of citations by 0.5 inches to create a hanging indent.
• List page numbers of sources efficiently, when needed. If you refer to a journal article that appeared on pages 225 through 250, list the page numbers on your Works Cited page as pp. 225-50 (Note: MLA style dictates that you should omit the first sets of repeated digits. In our example, the digit in the hundreds place is repeated between 2 25 and 2 50, so you omit the 2 from 250 in the citation: pp. 225-50). If the excerpt spans multiple pages, use “pp.”   Note that MLA style uses a hyphen in a span of pages.
• If only one page of a print source is used, mark it with the abbreviation “p.” before the page number (e.g., p. 157). If a span of pages is used, mark it with the abbreviation “pp.” before the page number (e.g., pp. 157-68).
• If you're citing an article or a publication that was originally issued in print form but that you retrieved from an online database, you should type the online database name in italics. You do not need to provide subscription information in addition to the database name.
• For online sources, you should include a location to show readers where you found the source. Many scholarly databases use a DOI (digital object identifier). Use a DOI in your citation if you can; otherwise use a URL. Delete “http://” from URLs. The DOI or URL is usually the last element in a citation and should be followed by a period.
• All works cited entries end with a period.

Additional basic rules new to MLA 2021

New to MLA 2021:

• Apps and databases should be cited only when they are containers of the particular works you are citing, such as when they are the platforms of publication of the works in their entirety, and not an intermediary that redirects your access to a source published somewhere else, such as another platform. For example, the Philosophy Books app should be cited as a container when you use one of its many works, since the app contains them in their entirety. However, a PDF article saved to the Dropbox app is published somewhere else, and so the app should not be cited as a container.
• If it is important that your readers know an author’s/person’s pseudonym, stage-name, or various other names,  then you should generally cite the better-known form of author’s/person’s name. For example, since the author of Alice's Adventures in Wonderland is better-known by his pseudonym, cite Lewis Carroll opposed to Charles Dodgson (real name).
• For annotated bibliographies , annotations should be appended at the end of a source/entry with one-inch indentations from where the entry begins. Annotations may be written as concise phrases or complete sentences, generally not exceeding one paragraph in length.

Capitalization and punctuation

• Capitalize each word in the titles of articles, books, etc, but do not capitalize articles (the, an), prepositions, or conjunctions unless one is the first word of the title or subtitle: Gone with the Wind, The Art of War, There Is Nothing Left to Lose .
• Use italics (instead of underlining) for titles of larger works (books, magazines) and quotation marks for titles of shorter works (poems, articles)

Listing author names

Entries are listed alphabetically by the author's last name (or, for entire edited collections, editor names). Author names are written with the last name first, then the first name, and then the middle name or middle initial when needed:

Do not  list titles (Dr., Sir, Saint, etc.) or degrees (PhD, MA, DDS, etc.) with names. A book listing an author named "John Bigbrain, PhD" appears simply as "Bigbrain, John." Do, however, include suffixes like "Jr." or "II." Putting it all together, a work by Dr. Martin Luther King, Jr. would be cited as "King, Martin Luther, Jr." Here the suffix following the first or middle name and a comma.

More than one work by an author

If you have cited more than one work by a particular author, order the entries alphabetically by title, and use three hyphens in place of the author's name for every entry after the first:

Burke, Kenneth. A Grammar of Motives . [...]

---. A Rhetoric of Motives . [...]

When an author or collection editor appears both as the sole author of a text and as the first author of a group, list solo-author entries first:

Heller, Steven, ed. The Education of an E-Designer .

Heller, Steven, and Karen Pomeroy. Design Literacy: Understanding Graphic Design.

Work with no known author

Alphabetize works with no known author by their title; use a shortened version of the title in the parenthetical citations in your paper. In this case, Boring Postcards USA has no known author:

Baudrillard, Jean. Simulacra and Simulations.  [...]

Boring Postcards USA  [...]

Burke, Kenneth. A Rhetoric of Motives . [...] 

Work by an author using a pseudonym or stage-name

New to MLA 9th edition, there are now steps to take for citing works by an author or authors using a pseudonym, stage-name, or different name. 

If the person you wish to cite is well-known, cite the better-known form of the name of the author. For example, since Lewis Carroll is  not only a pseudonym of Charles Dodgson , but also the better-known form of the author’s name, cite the former name opposed to the latter. 

If the real name of the author is less well-known than their pseudonym, cite the author’s pseudonym in square brackets following the citation of their real name: “Christie, Agatha [Mary Westmacott].”

Authors who published various works under many names may be cited under a single form of the author’s name. When the form of the name you wish to cite differs from that which appears on the author’s work, include the latter in square brackets following an italicized published as : “Irving, Washington [ published as Knickerbocker, Diedrich].”.

Another acceptable option, in cases where there are only two forms of the author’s name, is to cite both forms of the author’s names as separate entries along with cross-references in square brackets: “Eliot, George [ see also Evans, Mary Anne].”.

MLA Works Cited — Format, Sources, and Examples

MLA works cited

An MLA works cited page provides a list of works (sources) used in a research-based humanities paper. Identifying the source material avoids plagiarism and provides readers with a list of resources should they want to study the topic further.

Here is an example of a properly formatted source for a MLA work cited page:

Gibaldi, Joseph. MLA Style Manual and Guide to Scholarly Publishing . Modern Language Association of America, 1998.

Each source listed on a works cited page, or reference list, needs at least one in-text citation in the research paper, including paraphrases. If information from a source does not appear directly in the paper, then it does not need an entry on the works cited page.

A works consulted page or annotated bibliography contains all sources reviewed during the research process regardless of whether the information is included in the paper. Works consulted pages are structured and formatted the same as a works cited page.

The instructions below follow the 9th edition of the MLA Handbook .

Works cited MLA format

When constructing MLA citations on a works cited page, the following formatting rules applies:

The works cited page is a continuation of the paper. It is located on its own page at the end of the document.

Double space the text with no extra spaces between entries.

The page should have the same 1-inch margins as the body of the paper.

Use the same font as the body of the paper, typically 12-point Times New Roman.

The writer’s name and page number should continue in a running head from the body of the paper in the header aligned to the right side of the page.

Place the title, Works Cited, at the top of the page, centered, and in title case in the same font and font size as the rest of the paper. Do not bold, italicize, underline, or place the title in quotation marks.

Include all sources used in the body of the paper on the works cited page.

The first line of a source citation should be flush with the left margin. Indent the second and all subsequent lines using a hanging indent.

Alphabetize sources by the author’s last name. Works with no known author should be in alphabetical order by title. If a title begins with an article (a, an, the), use the first word that follows it to determine its placement.

If two or more works have the same author, organize them based on the title of the work. Provide the author’s name in the first entry only. Use three hyphens followed by a period in place of the name for the entries that follow.

MLA sources

When structuring a works cited entry, there are nine core elements to consider:

Identify the author, the person or group responsible for creating or producing the work. With two authors, only the first author is written with the last name first. When there are more than two authors, list the first followed by “et al.” (Latin for “and others”).

If there is an editor but no author, place the editor’s name in the author position followed by “ed.”. Do not repeat the editor’s name under “other contributors.”

Enter the title exactly as it appears in the source. Modify titles so they fit MLA’s capitalization rules.

Quotation Marks: If the source is part of a larger work, place it between quotations marks. Examples include articles, essays, poems, short stories, song titles, and episode titles.

Italics: The titles of larger works that are self-contained (books, newspapers, films, etc.) are italicized.

A container is the larger work in which the source appears. Example containers: newspapers, magazines, websites, online databases, and books containing a collection of essays, poems, short stories, etc.

When an individual contributes to the work in some important way, add their name to the entry. Their role should precede their name (edited by, translated by, illustrated by, directed by, etc.).

Include the version if there is more than one form of the source, such as editions or revised editions.

If the source appears in a work that is part of a numbered sequence, include the volume number (encyclopedias, journals, etc.).

The publisher is the organization responsible for providing the source to the public.

Identify the publication date, when the publisher produced the work.

The location specifies where the information was found within the larger container.

Print: Includes page numbers

Online: Includes the URL of the web page followed by the date of access

Not every core element will apply to each source; if a source is missing one, proceed to the next element.

The following identifies the placement of each core element along with the necessary punctuation:

Author (last name, first name). Title of Book/Source. Title of Container , Other Contributors, Version, Number, Publisher, Publication Date (day month year), Location.

Following the core elements is the easiest way to construct a source entry on the works cited page.

The following citation examples highlight those elements in each main type of source:

Print Sources

One Author: Gibaldi, Joseph. MLA Style Manual and Guide to Scholarly Publishing . Modern Language Association of America, 1998.

Two Authors: Gibaldi, Joseph, and Walter S. Achtert. MLA Style Manual and Guide to Scholarly Publishing . Modern Language Association of America, 1998.

More than Two Authors: Gibaldi, Joseph, et al. MLA Style Manual and Guide to Scholarly Publishing . Modern Language Association of America, 1998.

Anthology or Collection

Boyd, Carrie C. “MLA and Research.” Collection, edited by Diane B. Lipsett, Westminster John Knox Press, 2014, pp. 103-114.

Article in a Reference Book

"Citing Sources.” The Writer’s Encyclopedia , 3rd ed., Dell, 1997, p. 369.

Newspaper Article

Schackner, Bill. “Students at Pennsylvania’s State-Owned Universities Will See a Tuition Freeze for the Fourth Straight Year.” Pittsburgh Post-Gazette, 18 April 2022, A3.

Academic Journal Article

Author: Steffen, Will, et al. “How to Write a Research Paper.” Writing 101 , vol. 2, no. 1, Jan. 2015, pp. 81–98.

No Author: “How to Write a Research Paper.” Writing 101 , vol. 2, no. 1, Jan. 2015, pp. 81–98.

Government Reports

United States, Congress, House, Committee on the Education and Labor. Impact of Research in Education. Department of Education. Government Printing Office, 2015.

Legal Documents

Supreme Court. Brown v. Board of Education . 17 May 1954. Legal Information Institute, Cornell U Law School.

Digital and electronic sources

Online Book

Silva, Paul J. How to Write a Lot: A Practical Guide to Productive Academic Writing , eBook, American Psychological Association, 2007. Digital Library, www.digitallibrary.com. Accessed 18 April 2022.

Online Article

Millard, Avery. “Research Dos and Don’ts.”  Writer’s Digest . 10 Aug. 2013, www.writersdigestquarterly.com. Accessed 18 April 2022.

Online Database

Trier, James. “‘Cool’ Engagements with YouTube: Part 2.” Journal of Adolescent & Adult Literacy, vol. 50, no. 7, April 2007, pp. 598-603. JSTOR, https://doi.org/10.1598/JAAL.50. Accessed 18 April 2022.

Modern Language Association. 1 Jan. 2022, https://www.mla.org/. Accessed 18 April 2022.

The following sample paper illustrates the structure of the page and the placement of the sources:

Works-Cited-List Entries

Works cited: a quick guide, core elements.

Each entry in the list of works cited is composed of facts common to most works—the MLA core elements. They are assembled in a specific order.

The concept of containers is crucial to MLA style. When the source being documented forms part of a larger whole, the larger whole can be thought of as a container that holds the source. For example, a short story may be contained in an anthology. The short story is the source, and the anthology is the container.

Practice Template

Learn how to use the MLA practice template to create entries in the list of works cited.

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Home » Research Methodology – Types, Examples and writing Guide

Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

• Provide an overview of the research problem and the need for a research methodology section
• Outline the main research questions and objectives

II. Research Design

• Explain the research design chosen and why it is appropriate for the research question(s) and objectives
• Discuss any alternative research designs considered and why they were not chosen
• Describe the research setting and participants (if applicable)

III. Data Collection Methods

• Describe the methods used to collect data (e.g., surveys, interviews, observations)
• Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

• Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
• Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or software used for data analysis

V. Ethical Considerations

• Discuss any ethical issues that may arise from the research and how they were addressed
• Explain how informed consent was obtained (if applicable)
• Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

• Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

• Summarize the key aspects of the research methodology section
• Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

• Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
• Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
• Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
• Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
• Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
• Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
• Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
• Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

• To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
• To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
• To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
• To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
• To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
• To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
• To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
• To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
• To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

• To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
• To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
• To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
• To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
• To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

• Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
• Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
• Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
• Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
• Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
• Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
• Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
• Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

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What is Research Methodology? Definition, Types, and Examples

Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of the research. Several aspects must be considered before selecting an appropriate research methodology, such as research limitations and ethical concerns that may affect your research.

The research methodology section in a scientific paper describes the different methodological choices made, such as the data collection and analysis methods, and why these choices were selected. The reasons should explain why the methods chosen are the most appropriate to answer the research question. A good research methodology also helps ensure the reliability and validity of the research findings. There are three types of research methodology—quantitative, qualitative, and mixed-method, which can be chosen based on the research objectives.

What is research methodology ?

A research methodology describes the techniques and procedures used to identify and analyze information regarding a specific research topic. It is a process by which researchers design their study so that they can achieve their objectives using the selected research instruments. It includes all the important aspects of research, including research design, data collection methods, data analysis methods, and the overall framework within which the research is conducted. While these points can help you understand what is research methodology, you also need to know why it is important to pick the right methodology.

Why is research methodology important?

Having a good research methodology in place has the following advantages: 3

• Helps other researchers who may want to replicate your research; the explanations will be of benefit to them.
• You can easily answer any questions about your research if they arise at a later stage.
• A research methodology provides a framework and guidelines for researchers to clearly define research questions, hypotheses, and objectives.
• It helps researchers identify the most appropriate research design, sampling technique, and data collection and analysis methods.
• A sound research methodology helps researchers ensure that their findings are valid and reliable and free from biases and errors.
• It also helps ensure that ethical guidelines are followed while conducting research.
• A good research methodology helps researchers in planning their research efficiently, by ensuring optimum usage of their time and resources.

Writing the methods section of a research paper? Let Paperpal help you achieve perfection

Types of research methodology.

There are three types of research methodology based on the type of research and the data required. 1

• Quantitative research methodology focuses on measuring and testing numerical data. This approach is good for reaching a large number of people in a short amount of time. This type of research helps in testing the causal relationships between variables, making predictions, and generalizing results to wider populations.
• Qualitative research methodology examines the opinions, behaviors, and experiences of people. It collects and analyzes words and textual data. This research methodology requires fewer participants but is still more time consuming because the time spent per participant is quite large. This method is used in exploratory research where the research problem being investigated is not clearly defined.
• Mixed-method research methodology uses the characteristics of both quantitative and qualitative research methodologies in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method.

What are the types of sampling designs in research methodology?

Sampling 4 is an important part of a research methodology and involves selecting a representative sample of the population to conduct the study, making statistical inferences about them, and estimating the characteristics of the whole population based on these inferences. There are two types of sampling designs in research methodology—probability and nonprobability.

• Probability sampling

In this type of sampling design, a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are:

• Systematic —sample members are chosen at regular intervals. It requires selecting a starting point for the sample and sample size determination that can be repeated at regular intervals. This type of sampling method has a predefined range; hence, it is the least time consuming.
• Stratified —researchers divide the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized, and then a sample can be drawn from each group separately.
• Cluster —the population is divided into clusters based on demographic parameters like age, sex, location, etc.
• Convenience —selects participants who are most easily accessible to researchers due to geographical proximity, availability at a particular time, etc.
• Purposive —participants are selected at the researcher’s discretion. Researchers consider the purpose of the study and the understanding of the target audience.
• Snowball —already selected participants use their social networks to refer the researcher to other potential participants.
• Quota —while designing the study, the researchers decide how many people with which characteristics to include as participants. The characteristics help in choosing people most likely to provide insights into the subject.

What are data collection methods?

During research, data are collected using various methods depending on the research methodology being followed and the research methods being undertaken. Both qualitative and quantitative research have different data collection methods, as listed below.

Qualitative research 5

• One-on-one interviews: Helps the interviewers understand a respondent’s subjective opinion and experience pertaining to a specific topic or event
• Document study/literature review/record keeping: Researchers’ review of already existing written materials such as archives, annual reports, research articles, guidelines, policy documents, etc.
• Focus groups: Constructive discussions that usually include a small sample of about 6-10 people and a moderator, to understand the participants’ opinion on a given topic.
• Qualitative observation : Researchers collect data using their five senses (sight, smell, touch, taste, and hearing).

Quantitative research 6

• Sampling: The most common type is probability sampling.
• Interviews: Commonly telephonic or done in-person.
• Observations: Structured observations are most commonly used in quantitative research. In this method, researchers make observations about specific behaviors of individuals in a structured setting.
• Document review: Reviewing existing research or documents to collect evidence for supporting the research.
• Surveys and questionnaires. Surveys can be administered both online and offline depending on the requirement and sample size.

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What are data analysis methods.

The data collected using the various methods for qualitative and quantitative research need to be analyzed to generate meaningful conclusions. These data analysis methods 7 also differ between quantitative and qualitative research.

Quantitative research involves a deductive method for data analysis where hypotheses are developed at the beginning of the research and precise measurement is required. The methods include statistical analysis applications to analyze numerical data and are grouped into two categories—descriptive and inferential.

Descriptive analysis is used to describe the basic features of different types of data to present it in a way that ensures the patterns become meaningful. The different types of descriptive analysis methods are:

• Measures of frequency (count, percent, frequency)
• Measures of central tendency (mean, median, mode)
• Measures of dispersion or variation (range, variance, standard deviation)
• Measure of position (percentile ranks, quartile ranks)

Inferential analysis is used to make predictions about a larger population based on the analysis of the data collected from a smaller population. This analysis is used to study the relationships between different variables. Some commonly used inferential data analysis methods are:

• Correlation: To understand the relationship between two or more variables.
• Cross-tabulation: Analyze the relationship between multiple variables.
• Regression analysis: Study the impact of independent variables on the dependent variable.
• Frequency tables: To understand the frequency of data.
• Analysis of variance: To test the degree to which two or more variables differ in an experiment.

Qualitative research involves an inductive method for data analysis where hypotheses are developed after data collection. The methods include:

• Content analysis: For analyzing documented information from text and images by determining the presence of certain words or concepts in texts.
• Narrative analysis: For analyzing content obtained from sources such as interviews, field observations, and surveys. The stories and opinions shared by people are used to answer research questions.
• Discourse analysis: For analyzing interactions with people considering the social context, that is, the lifestyle and environment, under which the interaction occurs.
• Grounded theory: Involves hypothesis creation by data collection and analysis to explain why a phenomenon occurred.
• Thematic analysis: To identify important themes or patterns in data and use these to address an issue.

How to choose a research methodology?

Here are some important factors to consider when choosing a research methodology: 8

• Research objectives, aims, and questions —these would help structure the research design.
• Review existing literature to identify any gaps in knowledge.
• Check the statistical requirements —if data-driven or statistical results are needed then quantitative research is the best. If the research questions can be answered based on people’s opinions and perceptions, then qualitative research is most suitable.
• Sample size —sample size can often determine the feasibility of a research methodology. For a large sample, less effort- and time-intensive methods are appropriate.
• Constraints —constraints of time, geography, and resources can help define the appropriate methodology.

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How to write a research methodology .

A research methodology should include the following components: 3,9

• Research design —should be selected based on the research question and the data required. Common research designs include experimental, quasi-experimental, correlational, descriptive, and exploratory.
• Research method —this can be quantitative, qualitative, or mixed-method.
• Reason for selecting a specific methodology —explain why this methodology is the most suitable to answer your research problem.
• Research instruments —explain the research instruments you plan to use, mainly referring to the data collection methods such as interviews, surveys, etc. Here as well, a reason should be mentioned for selecting the particular instrument.
• Sampling —this involves selecting a representative subset of the population being studied.
• Data collection —involves gathering data using several data collection methods, such as surveys, interviews, etc.
• Data analysis —describe the data analysis methods you will use once you’ve collected the data.
• Research limitations —mention any limitations you foresee while conducting your research.
• Validity and reliability —validity helps identify the accuracy and truthfulness of the findings; reliability refers to the consistency and stability of the results over time and across different conditions.
• Ethical considerations —research should be conducted ethically. The considerations include obtaining consent from participants, maintaining confidentiality, and addressing conflicts of interest.

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Frequently Asked Questions

Q1. What are the key components of research methodology?

A1. A good research methodology has the following key components:

• Research design
• Data collection procedures
• Data analysis methods
• Ethical considerations

Q2. Why is ethical consideration important in research methodology?

A2. Ethical consideration is important in research methodology to ensure the readers of the reliability and validity of the study. Researchers must clearly mention the ethical norms and standards followed during the conduct of the research and also mention if the research has been cleared by any institutional board. The following 10 points are the important principles related to ethical considerations: 10

• Participants should not be subjected to harm.
• Respect for the dignity of participants should be prioritized.
• Full consent should be obtained from participants before the study.
• Participants’ privacy should be ensured.
• Confidentiality of the research data should be ensured.
• Anonymity of individuals and organizations participating in the research should be maintained.
• The aims and objectives of the research should not be exaggerated.
• Affiliations, sources of funding, and any possible conflicts of interest should be declared.
• Communication in relation to the research should be honest and transparent.
• Misleading information and biased representation of primary data findings should be avoided.

Q3. What is the difference between methodology and method?

A3. Research methodology is different from a research method, although both terms are often confused. Research methods are the tools used to gather data, while the research methodology provides a framework for how research is planned, conducted, and analyzed. The latter guides researchers in making decisions about the most appropriate methods for their research. Research methods refer to the specific techniques, procedures, and tools used by researchers to collect, analyze, and interpret data, for instance surveys, questionnaires, interviews, etc.

Research methodology is, thus, an integral part of a research study. It helps ensure that you stay on track to meet your research objectives and answer your research questions using the most appropriate data collection and analysis tools based on your research design.

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• Research methodologies. Pfeiffer Library website. Accessed August 15, 2023. https://library.tiffin.edu/researchmethodologies/whatareresearchmethodologies
• Types of research methodology. Eduvoice website. Accessed August 16, 2023. https://eduvoice.in/types-research-methodology/
• The basics of research methodology: A key to quality research. Voxco. Accessed August 16, 2023. https://www.voxco.com/blog/what-is-research-methodology/
• Sampling methods: Types with examples. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/types-of-sampling-for-social-research/
• What is qualitative research? Methods, types, approaches, examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-qualitative-research-methods-types-examples/
• What is quantitative research? Definition, methods, types, and examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-quantitative-research-types-and-examples/
• Data analysis in research: Types & methods. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/data-analysis-in-research/#Data_analysis_in_qualitative_research
• Factors to consider while choosing the right research methodology. PhD Monster website. Accessed August 17, 2023. https://www.phdmonster.com/factors-to-consider-while-choosing-the-right-research-methodology/
• What is research methodology? Research and writing guides. Accessed August 14, 2023. https://paperpile.com/g/what-is-research-methodology/
• Ethical considerations. Business research methodology website. Accessed August 17, 2023. https://research-methodology.net/research-methodology/ethical-considerations/

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

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

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

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

Research Methodology 101

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

What is research methodology?

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

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

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

So, it’s the same as research design?

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

Need a helping hand?

What are qualitative, quantitative and mixed-methods?

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

Let’s take a closer look.

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

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

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

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

What is sampling strategy?

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

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

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

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

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

What are data collection methods?

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

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

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

What are data analysis methods?

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

Popular data analysis methods in qualitative research include:

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

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

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

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

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

How do I choose a research methodology?

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

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

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

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

Example of a research methodology chapter

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

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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198 Comments

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Thanks for your comment.

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

All the best with your research.

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

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

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Works cited.

AAPOR. 2003. “ Interviewer Falsification in Survey Research: Current Best Methods for Prevention, Detection and Repair of Its Effects .”

Benford, Frank. 1938. “ The Law of Anomalous Numbers .” Proceedings of the American Philosophical Society.

Bredl, Sebastian, Peter Winker and Kerstin Kötschau. 2012. “ A statistical approach to detect cheating interviewer falsification of survey data .” Survey Methodology.

Bredl, Sebastian, Nina Storfinger and Natalja Menold. 2011. “ A literature review of methods to detect fabricated survey data .” Discussion Papers from Justus Liebig University Giessen, Center for international Development and Environmental Research (ZEU).

Converse, Philip E. 1964. “ The nature of belief systems in mass publics .” In Joseph W. Elder, ed., “Ideology and Discontent.”

Crespi, Leo P. 1945. “ The Cheater Problem in Polling .” Public Opinion Quarterly.

Diekmann, Andreas. 2002. “Diagnose von Fehlerquellen und methodische Qualität in der sozialwissenschaftlichen Forschung.” Institut fuer Technikfolgenabschaetzung (ITA).

Diakité, Souleymane. 2013. “Statistical methods for the detection of falsified data by interviewers and application survey data in Africa.” Sixth International Conference on Agricultural Statistics.

Groves, Robert M., Floyd J. Fowler Jr., Mick P. Couper, James M. Lepkowski, Eleanor Singer and Roger Tourangeau. 2009. “Survey Methodology.”

Hood, Catherine C. and John M. Bushery. 1997. “ Getting More Bang from the Reinterview Buck: Identifying ‘At Risk’ Interviewers .” Proceedings of the American Statistical Association.

Judge, George and Laura Schechter. 2009. “ Detecting Problems in Survey Data Using Benford’s Law .” The Journal of Human Resources.

Koch, Achim. 1995. “ Gefälschte Interviews: Ergebnisse der Interviewerkontrolle beim ALLBUS 1994 .” ZUMA Nachrichten.

Kosyakova, Yuliya, Jan Skopek and Stephanie Eckman. 2015. “ Do Interviewers Manipulate Responses to Filter Questions? Evidence from a Multilevel Approach .” International Journal of Public Opinion Research.

Kuriakose, Noble and Michael Robbins. 2015. “ Falsification in Survey Research: Detecting Near Duplicate Observations .” American Political Science Association Annual Meetings 2015.

Li, Jianzhu, J. Michael Brick, Bac Tran and Phyllis Singer. 2009. “ Using Statistical Models for Sample Design of a Reinterview Program .” Proceedings of the Research Methods Section, American Statistical Association.

Loosveldt, Geert. 2008. “Face-To-Face interviews.” In Edith D. deLeeuw, Joop Hox and Don Dillman, eds., “International Handbook of Survey Methodology.”

Lyberg, Lars and Paul Biemer. 2008. “Quality Assurance and Quality Control in Surveys.” In Edith D. deLeeuw, Joop Hox and Don Dillman, eds., “International Handbook of Survey Methodology.”

Lyberg, Lars and Diana Maria Stukel. 2010. “Quality Assurance and Quality Control in Cross-National Comparative Studies.” In Harkness, Janet A., et al., eds. “Survey Methods in Multinational, Multiregional, and Multicultural Contexts.”

Menold, Natalja and Christoph Kemper. 2014. “ How Do Real and Falsified Data Differ? Psychology of Survey Response as a Source of Falsification Indicators in Face-to-Face Surveys .” Journal of International Public Opinion Research.

Reuband, Karl-Heinz. 1990. “ Interviews, die keine sind – ‘Erfolge’ und ‘Mißerfolge’ beim Fälschen von Interviews .” Kölner Zeitschrift für Soziologie und Sozialpsychologie.

Schnell, Rainer. 1991. “ Der Einfluß gefälschter Interviews auf Survey Ergebnisse .” Zeitschrift für Soziologie.

Schraepler, Joerg-Peter and Gert Wagner. 2005. “ Characteristics and impact of faked interviews in surveys – An analysis of genuine fakes in the raw data of SOEP .” Allgemeines Statistisches Archiv.

Schreiner, Irwin, Karen Pennie, and Jennifer Newbrough. 1988. “ Interviewer Falsification in Census Bureau Surveys .” Proceedings of the Research Methods Section, American Statistical Association.

Singer, Eleanor. 2008. “Ethical Issues in Surveys.” In Edith D. deLeeuw, Joop Hox and Don Dillman, eds., “International Handbook of Survey Methodology.”

Winker, Peter, Natalja Menold, Nina Storfinger, Sabrina Stukowski, Christoph J. Kemper, and Sabrina Stutkowski. 2013. “ A Method for ex-post Identification of Falsifications in Survey Data. ”  NTTS 2013 – Conferences on New Techniques and Technologies for Statistics.

Zaller, John R. 1992. “The Nature and Origins of Mass Opinion.”

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How pew research center has dealt with the challenges of international polling during the pandemic, the coronavirus pandemic’s impact on our polling, the coronavirus pandemic’s impact on pew research center’s global polling, methods 101: how is polling done around the world, how is polling done around the world, most popular.

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

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What is research methodology?

The basics of research methodology

Why do you need a research methodology, what needs to be included, why do you need to document your research method, what are the different types of research instruments, qualitative / quantitative / mixed research methodologies, how do you choose the best research methodology for you, frequently asked questions about research methodology, related articles.

When you’re working on your first piece of academic research, there are many different things to focus on, and it can be overwhelming to stay on top of everything. This is especially true of budding or inexperienced researchers.

If you’ve never put together a research proposal before or find yourself in a position where you need to explain your research methodology decisions, there are a few things you need to be aware of.

Once you understand the ins and outs, handling academic research in the future will be less intimidating. We break down the basics below:

A research methodology encompasses the way in which you intend to carry out your research. This includes how you plan to tackle things like collection methods, statistical analysis, participant observations, and more.

You can think of your research methodology as being a formula. One part will be how you plan on putting your research into practice, and another will be why you feel this is the best way to approach it. Your research methodology is ultimately a methodological and systematic plan to resolve your research problem.

In short, you are explaining how you will take your idea and turn it into a study, which in turn will produce valid and reliable results that are in accordance with the aims and objectives of your research. This is true whether your paper plans to make use of qualitative methods or quantitative methods.

The purpose of a research methodology is to explain the reasoning behind your approach to your research - you'll need to support your collection methods, methods of analysis, and other key points of your work.

Think of it like writing a plan or an outline for you what you intend to do.

When carrying out research, it can be easy to go off-track or depart from your standard methodology.

Tip: Having a methodology keeps you accountable and on track with your original aims and objectives, and gives you a suitable and sound plan to keep your project manageable, smooth, and effective.

With all that said, how do you write out your standard approach to a research methodology?

As a general plan, your methodology should include the following information:

• Your research method.  You need to state whether you plan to use quantitative analysis, qualitative analysis, or mixed-method research methods. This will often be determined by what you hope to achieve with your research.
• Explain your reasoning. Why are you taking this methodological approach? Why is this particular methodology the best way to answer your research problem and achieve your objectives?
• Explain your instruments.  This will mainly be about your collection methods. There are varying instruments to use such as interviews, physical surveys, questionnaires, for example. Your methodology will need to detail your reasoning in choosing a particular instrument for your research.
• What will you do with your results?  How are you going to analyze the data once you have gathered it?
• Advise your reader.  If there is anything in your research methodology that your reader might be unfamiliar with, you should explain it in more detail. For example, you should give any background information to your methods that might be relevant or provide your reasoning if you are conducting your research in a non-standard way.
• How will your sampling process go?  What will your sampling procedure be and why? For example, if you will collect data by carrying out semi-structured or unstructured interviews, how will you choose your interviewees and how will you conduct the interviews themselves?
• Any practical limitations?  You should discuss any limitations you foresee being an issue when you’re carrying out your research.

In any dissertation, thesis, or academic journal, you will always find a chapter dedicated to explaining the research methodology of the person who carried out the study, also referred to as the methodology section of the work.

A good research methodology will explain what you are going to do and why, while a poor methodology will lead to a messy or disorganized approach.

You should also be able to justify in this section your reasoning for why you intend to carry out your research in a particular way, especially if it might be a particularly unique method.

Having a sound methodology in place can also help you with the following:

• When another researcher at a later date wishes to try and replicate your research, they will need your explanations and guidelines.
• In the event that you receive any criticism or questioning on the research you carried out at a later point, you will be able to refer back to it and succinctly explain the how and why of your approach.
• It provides you with a plan to follow throughout your research. When you are drafting your methodology approach, you need to be sure that the method you are using is the right one for your goal. This will help you with both explaining and understanding your method.
• It affords you the opportunity to document from the outset what you intend to achieve with your research, from start to finish.

A research instrument is a tool you will use to help you collect, measure and analyze the data you use as part of your research.

The choice of research instrument will usually be yours to make as the researcher and will be whichever best suits your methodology.

There are many different research instruments you can use in collecting data for your research.

Generally, they can be grouped as follows:

• Interviews (either as a group or one-on-one). You can carry out interviews in many different ways. For example, your interview can be structured, semi-structured, or unstructured. The difference between them is how formal the set of questions is that is asked of the interviewee. In a group interview, you may choose to ask the interviewees to give you their opinions or perceptions on certain topics.
• Surveys (online or in-person). In survey research, you are posing questions in which you ask for a response from the person taking the survey. You may wish to have either free-answer questions such as essay-style questions, or you may wish to use closed questions such as multiple choice. You may even wish to make the survey a mixture of both.
• Focus Groups.  Similar to the group interview above, you may wish to ask a focus group to discuss a particular topic or opinion while you make a note of the answers given.
• Observations.  This is a good research instrument to use if you are looking into human behaviors. Different ways of researching this include studying the spontaneous behavior of participants in their everyday life, or something more structured. A structured observation is research conducted at a set time and place where researchers observe behavior as planned and agreed upon with participants.

These are the most common ways of carrying out research, but it is really dependent on your needs as a researcher and what approach you think is best to take.

It is also possible to combine a number of research instruments if this is necessary and appropriate in answering your research problem.

There are three different types of methodologies, and they are distinguished by whether they focus on words, numbers, or both.

➡️ Want to learn more about the differences between qualitative and quantitative research, and how to use both methods? Check out our guide for that!

If you've done your due diligence, you'll have an idea of which methodology approach is best suited to your research.

It’s likely that you will have carried out considerable reading and homework before you reach this point and you may have taken inspiration from other similar studies that have yielded good results.

Still, it is important to consider different options before setting your research in stone. Exploring different options available will help you to explain why the choice you ultimately make is preferable to other methods.

If proving your research problem requires you to gather large volumes of numerical data to test hypotheses, a quantitative research method is likely to provide you with the most usable results.

If instead you’re looking to try and learn more about people, and their perception of events, your methodology is more exploratory in nature and would therefore probably be better served using a qualitative research methodology.

It helps to always bring things back to the question: what do I want to achieve with my research?

Once you have conducted your research, you need to analyze it. Here are some helpful guides for qualitative data analysis:

➡️  How to do a content analysis

➡️  How to do a thematic analysis

➡️  How to do a rhetorical analysis

Research methodology refers to the techniques used to find and analyze information for a study, ensuring that the results are valid, reliable and that they address the research objective.

Data can typically be organized into four different categories or methods: observational, experimental, simulation, and derived.

Writing a methodology section is a process of introducing your methods and instruments, discussing your analysis, providing more background information, addressing your research limitations, and more.

Your research methodology section will need a clear research question and proposed research approach. You'll need to add a background, introduce your research question, write your methodology and add the works you cited during your data collecting phase.

The research methodology section of your study will indicate how valid your findings are and how well-informed your paper is. It also assists future researchers planning to use the same methodology, who want to cite your study or replicate it.

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• Works Cited Generator

Free Works Cited Generator

Generate a Works Cited page in MLA format automatically, with MyBib!

😕 What is a Works Cited Generator?

A works cited generator is a tool that automatically creates a works cited page in the Modern Language Association (MLA) citation format. The generator will take in information about the sources you have cited in your paper, such as document titles, authors, and URLs, and will output a fully formatted works cited page that can be added to the end of your paper (just as your teacher asked!).

The citations included in a Works Cited page show the sources that you used to construct your argument in the body of your school paper, either directly as references and quotes, or indirectly as ideas.

👩‍🎓 Who uses a Works Cited Generator?

Students in middle school and high school will usually be expected to produce a works cited page to accompany their academic papers. Therefore, they will generally be the users of a works cited generator.

Alongside generating a works cited page, at middle school and high school level it is also important to learn why it's critical to cite sources, not just how to cite them.

🙌 Why should I use a Works Cited Generator?

Formatting works cited pages manually is time consuming, and ensuring accuracy is mind-numbing.

Automating this process with a works cited generator is a quick and easy way to be sure you are doing it correctly (and according to the MLA format!). Our generator also provides a backed-up location to save your citations to as you write each part of your paper -- just keep the MyBib website open in a browser tab while you work and add to your works cited page as you go along!

⚙️ How do I use MyBib's Works Cited Generator?

Using our Works Cited Generator is so easy. Every time you cite a source in your paper, just come back to the generator at the top of this page and enter the source you are citing. Our generator can cite books, journal articles, and webpages automatically, and can cite over 30 other sources if you enter the source details manually.

Save each source to your bibliography, then when you have finished writing your paper just click the 'download' button and the generator will produce a formatted Works Cited page that can be copied and pasted directly to the end of your document.

Daniel is a qualified librarian, former teacher, and citation expert. He has been contributing to MyBib since 2018.

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Advanced Research Methods

Writing the research paper.

• What Is Research?
• Library Research
• Writing a Research Proposal

Before Writing the Paper

Methods, thesis, and hypothesis, clarity, precision, and academic expression, format your paper, typical problems, a few suggestions, avoid plagiarism.

• Presenting the Research Paper
• Try to find a subject that really interests you.
• While you explore the topic, narrow or broaden your target and focus on something that gives the most promising results.
• Don't choose a huge subject if you have to write a 3 page long paper, and broaden your topic sufficiently if you have to submit at least 25 pages.
• Consult your class instructor (and your classmates) about the topic.
• Find primary and secondary sources in the library.
• Read and critically analyse them.
• Take notes.
• Compile surveys, collect data, gather materials for quantitative analysis (if these are good methods to investigate the topic more deeply).
• Come up with new ideas about the topic. Try to formulate your ideas in a few sentences.
• Review your notes and other materials and enrich the outline.
• Try to estimate how long the individual parts will be.
• Do others understand what you want to say?
• Do they accept it as new knowledge or relevant and important for a paper?
• Do they agree that your thoughts will result in a successful paper?
• Qualitative: gives answers on questions (how, why, when, who, what, etc.) by investigating an issue
• Quantitative:requires data and the analysis of data as well
• the essence, the point of the research paper in one or two sentences.
• a statement that can be proved or disproved.
• Be specific.
• Avoid ambiguity.
• Use predominantly the active voice, not the passive.
• Deal with one issue in one paragraph.
• Be accurate.
• Double-check your data, references, citations and statements.

Academic Expression

• Don't use familiar style or colloquial/slang expressions.
• Write in full sentences.
• Check the meaning of the words if you don't know exactly what they mean.
• Avoid metaphors.
• Almost the rough content of every paragraph.
• The order of the various topics in your paper.
• On the basis of the outline, start writing a part by planning the content, and then write it down.
• Put a visible mark (which you will later delete) where you need to quote a source, and write in the citation when you finish writing that part or a bigger part.
• Does the text make sense?
• Could you explain what you wanted?
• Did you write good sentences?
• Is there something missing?
• Check the spelling.
• Complete the citations, bring them in standard format.

Use the guidelines that your instructor requires (MLA, Chicago, APA, Turabian, etc.).

• Adjust margins, spacing, paragraph indentation, place of page numbers, etc.
• Standardize the bibliography or footnotes according to the guidelines.

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(Based on English Composition 2 from Illinois Valley Community College):

• Weak organization
• Poor support and development of ideas
• Weak use of secondary sources
• Excessive errors
• Stylistic weakness

When collecting materials, selecting research topic, and writing the paper:

• Be systematic and organized (e.g. keep your bibliography neat and organized; write your notes in a neat way, so that you can find them later on.
• Use your critical thinking ability when you read.
• Write down your thoughts (so that you can reconstruct them later).
• Stop when you have a really good idea and think about whether you could enlarge it to a whole research paper. If yes, take much longer notes.
• When you write down a quotation or summarize somebody else's thoughts in your notes or in the paper, cite the source (i.e. write down the author, title, publication place, year, page number).
• If you quote or summarize a thought from the internet, cite the internet source.
• Write an outline that is detailed enough to remind you about the content.
• Read your paper for yourself or, preferably, somebody else. 
• When you finish writing, check the spelling;
• Use the citation form (MLA, Chicago, or other) that your instructor requires and use it everywhere.

Plagiarism : somebody else's words or ideas presented without citation by an author

• Cite your source every time when you quote a part of somebody's work.
• Cite your source  every time when you summarize a thought from somebody's work.
• Cite your source  every time when you use a source (quote or summarize) from the Internet.

Consult the Citing Sources research guide for further details.

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Introduction to qualitative research methods – Part I

Shagufta bhangu.

Department of Global Health and Social Medicine, King's College London, London, United Kingdom

Fabien Provost

Carlo caduff.

Qualitative research methods are widely used in the social sciences and the humanities, but they can also complement quantitative approaches used in clinical research. In this article, we discuss the key features and contributions of qualitative research methods.

INTRODUCTION

Qualitative research methods refer to techniques of investigation that rely on nonstatistical and nonnumerical methods of data collection, analysis, and evidence production. Qualitative research techniques provide a lens for learning about nonquantifiable phenomena such as people's experiences, languages, histories, and cultures. In this article, we describe the strengths and role of qualitative research methods and how these can be employed in clinical research.

Although frequently employed in the social sciences and humanities, qualitative research methods can complement clinical research. These techniques can contribute to a better understanding of the social, cultural, political, and economic dimensions of health and illness. Social scientists and scholars in the humanities rely on a wide range of methods, including interviews, surveys, participant observation, focus groups, oral history, and archival research to examine both structural conditions and lived experience [ Figure 1 ]. Such research can not only provide robust and reliable data but can also humanize and add richness to our understanding of the ways in which people in different parts of the world perceive and experience illness and how they interact with medical institutions, systems, and therapeutics.

Examples of qualitative research techniques

Qualitative research methods should not be seen as tools that can be applied independently of theory. It is important for these tools to be based on more than just method. In their research, social scientists and scholars in the humanities emphasize social theory. Departing from a reductionist psychological model of individual behavior that often blames people for their illness, social theory focuses on relations – disease happens not simply in people but between people. This type of theoretically informed and empirically grounded research thus examines not just patients but interactions between a wide range of actors (e.g., patients, family members, friends, neighbors, local politicians, medical practitioners at all levels, and from many systems of medicine, researchers, policymakers) to give voice to the lived experiences, motivations, and constraints of all those who are touched by disease.

PHILOSOPHICAL FOUNDATIONS OF QUALITATIVE RESEARCH METHODS

In identifying the factors that contribute to the occurrence and persistence of a phenomenon, it is paramount that we begin by asking the question: what do we know about this reality? How have we come to know this reality? These two processes, which we can refer to as the “what” question and the “how” question, are the two that all scientists (natural and social) grapple with in their research. We refer to these as the ontological and epistemological questions a research study must address. Together, they help us create a suitable methodology for any research study[ 1 ] [ Figure 2 ]. Therefore, as with quantitative methods, there must be a justifiable and logical method for understanding the world even for qualitative methods. By engaging with these two dimensions, the ontological and the epistemological, we open a path for learning that moves away from commonsensical understandings of the world, and the perpetuation of stereotypes and toward robust scientific knowledge production.

Developing a research methodology

Every discipline has a distinct research philosophy and way of viewing the world and conducting research. Philosophers and historians of science have extensively studied how these divisions and specializations have emerged over centuries.[ 1 , 2 , 3 ] The most important distinction between quantitative and qualitative research techniques lies in the nature of the data they study and analyze. While the former focus on statistical, numerical, and quantitative aspects of phenomena and employ the same in data collection and analysis, qualitative techniques focus on humanistic, descriptive, and qualitative aspects of phenomena.[ 4 ]

For the findings of any research study to be reliable, they must employ the appropriate research techniques that are uniquely tailored to the phenomena under investigation. To do so, researchers must choose techniques based on their specific research questions and understand the strengths and limitations of the different tools available to them. Since clinical work lies at the intersection of both natural and social phenomena, it means that it must study both: biological and physiological phenomena (natural, quantitative, and objective phenomena) and behavioral and cultural phenomena (social, qualitative, and subjective phenomena). Therefore, clinical researchers can gain from both sets of techniques in their efforts to produce medical knowledge and bring forth scientifically informed change.

KEY FEATURES AND CONTRIBUTIONS OF QUALITATIVE RESEARCH METHODS

In this section, we discuss the key features and contributions of qualitative research methods [ Figure 3 ]. We describe the specific strengths and limitations of these techniques and discuss how they can be deployed in scientific investigations.

Key features of qualitative research methods

One of the most important contributions of qualitative research methods is that they provide rigorous, theoretically sound, and rational techniques for the analysis of subjective, nebulous, and difficult-to-pin-down phenomena. We are aware, for example, of the role that social factors play in health care but find it hard to qualify and quantify these in our research studies. Often, we find researchers basing their arguments on “common sense,” developing research studies based on assumptions about the people that are studied. Such commonsensical assumptions are perhaps among the greatest impediments to knowledge production. For example, in trying to understand stigma, surveys often make assumptions about its reasons and frequently associate it with vague and general common sense notions of “fear” and “lack of information.” While these may be at work, to make such assumptions based on commonsensical understandings, and without conducting research inhibit us from exploring the multiple social factors that are at work under the guise of stigma.

In unpacking commonsensical understandings and researching experiences, relationships, and other phenomena, qualitative researchers are assisted by their methodological commitment to open-ended research. By open-ended research, we mean that these techniques take on an unbiased and exploratory approach in which learnings from the field and from research participants, are recorded and analyzed to learn about the world.[ 5 ] This orientation is made possible by qualitative research techniques that are particularly effective in learning about specific social, cultural, economic, and political milieus.

Second, qualitative research methods equip us in studying complex phenomena. Qualitative research methods provide scientific tools for exploring and identifying the numerous contributing factors to an occurrence. Rather than establishing one or the other factor as more important, qualitative methods are open-ended, inductive (ground-up), and empirical. They allow us to understand the object of our analysis from multiple vantage points and in its dispersion and caution against predetermined notions of the object of inquiry. They encourage researchers instead to discover a reality that is not yet given, fixed, and predetermined by the methods that are used and the hypotheses that underlie the study.

Once the multiple factors at work in a phenomenon have been identified, we can employ quantitative techniques and embark on processes of measurement, establish patterns and regularities, and analyze the causal and correlated factors at work through statistical techniques. For example, a doctor may observe that there is a high patient drop-out in treatment. Before carrying out a study which relies on quantitative techniques, qualitative research methods such as conversation analysis, interviews, surveys, or even focus group discussions may prove more effective in learning about all the factors that are contributing to patient default. After identifying the multiple, intersecting factors, quantitative techniques can be deployed to measure each of these factors through techniques such as correlational or regression analyses. Here, the use of quantitative techniques without identifying the diverse factors influencing patient decisions would be premature. Qualitative techniques thus have a key role to play in investigations of complex realities and in conducting rich exploratory studies while embracing rigorous and philosophically grounded methodologies.

Third, apart from subjective, nebulous, and complex phenomena, qualitative research techniques are also effective in making sense of irrational, illogical, and emotional phenomena. These play an important role in understanding logics at work among patients, their families, and societies. Qualitative research techniques are aided by their ability to shift focus away from the individual as a unit of analysis to the larger social, cultural, political, economic, and structural forces at work in health. As health-care practitioners and researchers focused on biological, physiological, disease and therapeutic processes, sociocultural, political, and economic conditions are often peripheral or ignored in day-to-day clinical work. However, it is within these latter processes that both health-care practices and patient lives are entrenched. Qualitative researchers are particularly adept at identifying the structural conditions such as the social, cultural, political, local, and economic conditions which contribute to health care and experiences of disease and illness.

For example, the decision to delay treatment by a patient may be understood as an irrational choice impacting his/her chances of survival, but the same may be a result of the patient treating their child's education as a financial priority over his/her own health. While this appears as an “emotional” choice, qualitative researchers try to understand the social and cultural factors that structure, inform, and justify such choices. Rather than assuming that it is an irrational choice, qualitative researchers try to understand the norms and logical grounds on which the patient is making this decision. By foregrounding such logics, stories, fears, and desires, qualitative research expands our analytic precision in learning about complex social worlds, recognizing reasons for medical successes and failures, and interrogating our assumptions about human behavior. These in turn can prove useful in arriving at conclusive, actionable findings which can inform institutional and public health policies and have a very important role to play in any change and transformation we may wish to bring to the societies in which we work.

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• Open access
• Published: 25 March 2024

Research on helmet wearing detection method based on deep learning

• Lihong Wei 1 ,
• Panpan Liu 2 ,
• Haihui Ren 2 &
• Dong Xiao 2 , 3  

Scientific Reports volume  14 , Article number:  7010 ( 2024 ) Cite this article

Metrics details

• Civil engineering
• Electrical and electronic engineering
• Engineering
• Mechanical engineering

The vigorous development of the construction industry has also brought unprecedented safety risks. The wearing of safety helmets at the construction site can effectively reduce casualties. As a result, this paper suggests employing a deep learning-based approach for the real-time detection of safety helmet usage among construction workers. Based on the selected YOLOv5s network through experiments, this paper analyzes its training results. Considering its poor detection effect on small objects and occluded objects. Therefore, multiple attention mechanisms are used to improve the YOLOv5s network, the feature pyramid network is improved into a BiFPN bidirectional feature pyramid network, and the post-processing method NMS is improved into Soft-NMS. Based on the above-improved method, the loss function is improved to enhance the convergence speed of the model and improve the detection speed. We propose a network model called BiFEL-YOLOv5s, which combines the BiFPN network and Focal-EIoU Loss to improve YOLOv5s. The average precision of the model is increased by 0.9% the recall rate is increased by 2.8%, and the detection speed of the model does not decrease too much. It is better suited for real-time safety helmet object detection, addressing the requirements of helmet detection across various work scenarios.

Introduction

China is a big country in infrastructure construction. In the past 70 years, China has made remarkable achievements in infrastructure construction. The rapid development of the construction industry also drives the rapid development of the national economy. However, the construction site is very dangerous. According to statistics, the probability of accidents in the construction industry ranks first among all work industries 1 , 2 . Safety accidents can result in significant human and property losses. Therefore, while pursuing rapid economic development and vigorously implementing infrastructure projects, greater emphasis should be placed on the safety of construction workers’ lives. Among many construction safety accidents, the accidents of falling from high altitude account for more than half, and also include object impact accidents, collapse accidents, lifting equipment injury accidents, construction equipment injury accidents, and so on 3 . Wearing safety helmets is the most important means of preventing high fall accidents, which can greatly reduce casualties on account of construction safety accidents. As object detection and deep learning technology continue to advance, a multitude of scholars have begun to conduct research on object detection at construction sites 4 , 5 . However, because of the intricate conditions within the construction site, changeable weather and light, occlusion, dense personnel, and inconsistent object size, real-time detection on the site is difficult. At present, although there are many related studies, most of them can only be used for simple recognition in ideal environments, and it is difficult to carry out specific applications 6 .

As computer technology advances, the application of machine learning techniques has become increasingly prevalent in the detection of safety helmet usage. Currently, the detection algorithm for safety helmet usage can be categorized into two main types: the target detection algorithm that relies on traditional machine learning and the target detection algorithm that relies on deep learning. The conventional algorithm for detecting safety helmet usage primarily relies on recognizing color and shape features. In 2014, Liu et al. 7 employed a hybrid approach that combined a Support Vector Machine (SVM) with skin tone detection to realize safety helmet recognition.In 2015, Shrestha et al. 8 used Haar-like features to detect faces and used an edge detection algorithm to find helmet contour features. In 2016, Rubaiya et al. 9 , 10 utilized the Histogram of Orientation Gradient (HOG) algorithm for detecting construction workers by combining frequency domain information in images. They further employed the Circle Hough Transform (CHT) feature extraction technique to determine whether the workers were wearing helmets. While helmet detection algorithms based on traditional machine learning, such as those mentioned above, have faster detection speeds, they require manual feature design and classifier training for specific detection objects. Additionally, due to the limited feature set and poor generalization ability, these algorithms cannot effectively detect targets in complex construction environments, leading to inaccurate detection results.

For the problem of helmet wear monitoring, research on deep learning-based target detection algorithms is the mainstream approach. Deep learning-based target detection algorithms can be categorized into two types: two-stage detection algorithms based on candidate frames and one-stage detection algorithms based on regression. Two-stage detection methods, such as RCNN 11 , Fast-RCNN 12 , and FasterRCNN 13 , use Region Proposal in the first stage to generate candidate regions and extract feature vectors from them. In the second stage, a convolutional neural network is utilized to forecast the category and position of the object, enabling accurate detection and localization of the target. On the other hand, single-stage-based detection methods, including YOLO 14 and SSD 15 , do not require generating candidate regions. Instead, they directly input images and output the position and label of the entity, which significantly enhances the detection performance of the algorithm through the end-to-end technique. Compared to the two-stage target detection algorithm, the one-stage target detection algorithm achieves target location and classification results through a single forward inference from the network. As a result, the detection speed is significantly faster than the two-stage detection algorithm. In 2018, Fang et al. 16 applied the Faster RCNN algorithm to helmet-wearing detection for the first time, and although the test accuracy was improved, it still could not meet the real-time demand. In 2021, Zhou et al. 17 and 2022, Kisaezehra et al. 18 used the YOLOv5 model for helmet detection to achieve high accuracy and speed to meet the real-time requirements, and the model generalization ability was poor due to the small sample of real scenes in the construction site of the dataset. In addition, some scholars have improved the classical target detection algorithm to achieve improved detection performance of the algorithm model. In 2022, Yang et al. 19 made enhancements to the model backbone network based on YOLOv4. They utilized MCM modules with convolutional kernels of different sizes to boost the multi-scale feature extraction capability of the backbone network. Additionally, they incorporated the channel attention module to continuously concentrate on the characteristics of the channels for the detected tiny and indistinct objects. Finally, they replaced the CIOU loss function with EIOU to boost the model’s velocity of convergence and correctness of regression. In 2023, Chen et al. 20 proposed to enhance the backbone network of YOLOv4. To boost the feature information, the lightweight network PP LCNet was utilized, and the coordinate attention mechanism module was integrated into the three output feature layers of the backbone network. Finally, they replaced the loss function with SIOU, which significantly reduced the model size while improving the detection speed after the enhancement. In 2023, a new super-resolution reconstruction module was designed by Han et al. 21 for high-speed detection of high-resolution helmets. The module utilized a multi-channel attention mechanism to enhance the range of feature recognition. Additionally, a new CSP (cross-stage partial) module was proposed to alleviate information loss and gradient confusion.

Although the above method has optimized and improved the algorithm, it also has shortcomings in detecting small and dense targets, for the shortcomings, shortcomings of the existing technology, this paper proposes an improved helmet-wearing detection model BiFEL-YOLOv5s. Firstly, to effectively solve the problem of detecting objects with obvious size differences, the feature pyramid network is improved by using a weighted bi-directional feature pyramid network Bi-FPN instead of PANet, which introduces the concept of weights to improve the model performance. Secondly, the SENet attention mechanism is introduced to enhance the attention to the detection target, and comparative experiments are conducted in the dataset to verify its effectiveness. The Focal-EIoU Loss loss function is also used instead of the CIoU Loss loss function to improve the convergence of the model. Improved post-processing method using Soft-NMS to address the presence of occlusion in helmet targets. The improved algorithm, compared with the traditional detection methods, improves the precision and recall while also effectively improving the problem in scenarios with dense targets, small targets, and occluded targets, and meets the performance requirements for helmet wear detection at construction sites.

Proposed methods

The YOLO family of methods is today’s most popular single-stage detection technique, compared with other two-stage target detection algorithms, they do not need to generate a candidate region, but rather, the image will be input from the input side, and then in the output side of the output of the target’s location and category, this end-to-end technology greatly improves the detection performance of the algorithm. Meanwhile, with the continuous development of the YOLO series of algorithms, several algorithms from YOLOv1 to v7 have been produced, and the performance has been continuously improved. Among them, YOLOv5 22 is currently the most widely used, with high accuracy, speed, comprehensiveness, and many other advantages. Compared to YOLOv6 and YOLOv7, the YOLOv5 model is the smallest, consumes the least amount of resources, is the fastest in training and inference, and only slightly inferior in detection accuracy. At the same time, due to the limited performance of most of the equipment applicable in the construction environment and the small budget, it is necessary to choose the appropriate detection model, i.e., a smaller number of parameters, a smaller amount of computation and moderate accuracy, and to ensure that it can be used in some simple application scenarios, so in this paper, we choose the YOLOv5 model through comprehensive consideration.

Loss function

Within the realm of object detection, it is customary to employ Intersection over Union (IoU) for assessing the discrepancy between the predicted bounding box and the ground truth box during loss function computation. IoU is defined as the ratio of intersection and union of two bounding boxes. The detection box is denoted as A, and the true box is denoted as B. Then IoULoss is the negative logarithm of IoU, often written as \(IoULoss = 1 - IoU = 1 - \left( {A \cap B} \right)/\left( {A \cup B} \right)\) . Compared with L1/L2 Loss using 4 coordinate points for regression, IoU Loss is scale-invariant and its output value is between (0,1), so it can better reflect the deviation between the predicted box and the ground truth.

The bounding box loss of YOLOv5 uses CIoU loss, although the overlapping area of the predicted box and the real box, the distance from the center point, and the aspect ratio are taken into account, the parameter v in the formula representing the consistency of the aspect ratio indicates only the difference in the horizontal and vertical ratios and does not take into account the width and the height separately. Therefore, if the predicted frame has exactly the same aspect ratio as the real frame, it will also cause its penalty term to remain at 0. The gradient of w、h with respect to v in CIoU loss is a pair of opposites, w and h cannot increase or decrease at the same time.YOLOv5 also uses CIoU Loss as the loss function, and its formula is shown in Eq. ( 1 ):

where v is a parameter representing the consistency of aspect ratio as shown in Eq. ( 2 ):

While CIoU Loss considers aspects such as the overlap area, center point distance, aspect ratio, and other factors, the parameter “v” in the formula only signifies the dissimilarity in aspect ratio, without individual consideration of width and height. Therefore, EIoU Loss is proposed to consider the aspect ratio separately. In addition, Focal Loss is added to make the regression process focus on high-quality anchors, which solves the problem of loss value oscillation caused by low-quality samples. The Loss function of EIoU Loss is mainly composed of three parts, IoU loss \(L_{IoU}\) , center distance loss \(L_{dis}\) , and width and height loss \(L_{asp}\) . In the calculation of width and height loss, the side length is directly used as the penalty term. The formula of EIoU Loss is shown in Eq. ( 3 ):

Here, \(C_{w}\) and \(C_{h}\) are the width and height of the minimum bounding rectangle of the two bounding boxes, respectively. On the basis of EIoU Loss, the authors introduce Focal Loss, whose penalty formula is shown in Eq. ( 4 ):

The CIoU Loss function is employed as the bounding box loss in the YOLOv5 model. However, considering that CIoU Loss is only a regression on aspect ratio, in this paper, the Focal-EIoU Loss function is implemented as the bounding box loss in YOLOv5 predictions to enhance its performance. Not only the length and width are considered separately, directly using the side length for regression will further accelerate the regression process, but also the added Focal Loss model can optimize the sample imbalance problem in the bounding box regression task, further improving the performance of the model.

• Attention mechanism

The primary function of the attention mechanism is to determine which portions of the input require focused consideration, allocate resources judiciously, and allocate greater resources to the critical components. In the domain of object detection, the introduction of an attention mechanism notably amplifies the detection performance for small objects. Therefore, this paper compares the effect of three attention mechanisms for safety helmet object detection. The three attention mechanisms are SeNet, CBAM, and CA attention mechanisms. These three attention mechanisms were chosen because they are typical of channel domains and hybrid domains. The attention mechanism is analyzed according to the model structure and is divided into three major attention domains: (1) channel domain: similar to the signal on each channel added a weight to represent the channel with the key information of the relevance of the words, the larger this weight, the higher the relevance is indicated. A mask mask is generated for the channel, scored, and represented as SeNet. (2) Hybrid domain: the attention of the spatial domain is to ignore the information in the channel domain and treat the picture features in each channel equally, this practice will limit the spatial domain transformation method to the original picture feature extraction stage, and the interpretability of the application to other layers of the neural network layer is not strong. The attention of the channel domain is a direct global average pooling of the information in a channel while ignoring the local information in each channel, this practice is actually also a more violent behavior. So by combining the two ideas, a hybrid domain model of the attention mechanism can be designed. Simultaneously evaluating and scoring channel attention and spatial attention is represented by CBAM.

SeNet attention module

SeNet (Squeeze-and-Excitation Networks) attention module 23 was proposed by Hu et al., in 2017, and its network structure is shown in Fig.  1 23 . SeNet is categorized as a channel attention mechanism, prioritizing the inter-channel relationships for weight learning among different channels. The SeNet module comprises three components: the squeeze operation, excitation operation, and scale operation.

The structure of SeNet module.

The feature map is compressed using the global average pooling technique. The size of the feature map is compressed from \(H \times W \times C\) to \(1 \times 1 \times C\) , where C represents the number of channels of the input feature map. The channel global spatial feature is transformed into a global feature by squeeze operation. In excitation operation, two fully connected layers are used to realize the transformation of the size of the feature map from \(1 \times 1 \times C\) to \(1 \times 1 \times C^{*}\) and then to \(1 \times 1 \times C\) , where \(C^{*} = C/16\) can achieve a balance between computational complexity and performance. The initial fully connected layer decreases the dimension of the feature map and feeds it into the ReLU function to apply nonlinear activation. The second fully connected layer elevates the dimensionality of the feature map and directs it into the sigmoid function to constrain the output values within the range of 0 to 1. The excitation operation aims to recalibrate features, reduce the complexity of model parameters, and enhance the generalization ability of the model. In the scale operation, the weight parameters of the output \(1 \times 1 \times C\) in the excitation operation are multiplied by the corresponding channels with the input \(H \times W \times C\) feature map to achieve the assignment of weights.

CBAM attention module

CBAM (Convolutional Block Attention Module) attention module 24 is an attention mechanism that integrates channel attention and spatial attention proposed by Woo et al., in 2018, which is an extension of SeNet attention. The network’s capability for feature extraction is further enhanced, and its structural diagram is depicted in Fig.  2 24 .

The structure of CBAM module.

The CBAM attention module comprises two components: the channel attention module and the spatial attention module, which assign weights to the channel layer and spatial layer, respectively. In the first part, the channel attention module is similar to the SeNet attention module. However, the global Max pooling and global average pooling are respectively applied to the \(H \times W \times C\) dimensional feature maps in CBAM to obtain two \(1 \times 1 \times C\) dimensional feature maps, which are still input into the two fully connected layers for dimension reduction and dimension enhancement. The two acquired feature maps are summed and fed into the sigmoid activation function, after which the final weight parameter is applied to the input feature map through multiplication. The inclusion of global average pooling encourages the neural network to prioritize global information within the image and enhance the semantic content of the primary channel.

In the second part, spatial attention also inputs the \(H \times W \times C\) dimension feature maps into the global Max pooling and global average pooling to obtain two \(H \times W \times 1\) feature maps, concatenate the two feature maps, and input the obtained \(H \times W \times 2\) feature maps into a convolution layer to obtain \(H \times W \times 1\) feature maps. It is fed into the sigmoid function to derive a weight parameter with an output value ranging between 0 and 1, and this parameter is then multiplied with the feature map output from the channel attention mechanism to obtain the ultimate attention feature. CBAM can be embedded into the residual network and combined with the conv module or C3 module in YOLOv5 for improvement.

CA Attention module

CA (Coordinate Attention) attention module 25 is an attention module proposed by Hou et al., which considers channel relationship and location information simultaneously. It is an improved strategy based on SeNet module, and its structural diagram is illustrated in Fig.  3 25 . By encoding the lateral and vertical position information into channel attention, we can focus on a wide range of position information while only adding a small amount of computation.

The structure of CA module.

The CA module consists of two stages: coordinate information embedding and coordinate attention generation. In the CA module, two pooling kernels with dimensions of \(\left( {{\text{H}},1} \right)\) and \(\left( {1,{\text{ W}}} \right)\) are used to channel encode the input feature maps along the vertical and horizontal coordinates, respectively, to obtain a pair of direction-aware feature maps, which can obtain accurate position information and enhance the positioning accuracy of the network. The output of the CTH channel at the height “h” and width “w” is represented in Eqs. ( 5 ) and ( 6 ), correspondingly.

During the coordinate attention generation phase, the coordinate information is embedded into the results in the module for concatenate operation and 1 × 1 convolution transformation operation. Among them, the concatenate operation performs encoding operations along both horizontal and vertical directions to generate intermediate feature maps for spatial information encoding. To mitigate the model’s complexity, the number of channels is reduced to 1/32 of the original, the intermediate feature map is divided into two tensors along the spatial dimension, and the 1 × 1 convolution operation is used again. The output result obtained is used as the attention weight, and the horizontal and vertical weights are added to the input feature map at the same time in the way of the product.

Comparison of three attention mechanisms

The SE attention mechanism enhances the useful information in the feature map by learning the importance of global channels. Its advantage is that it is computationally simple and can efficiently extract global features, but its disadvantage is that it does not consider spatial correlation.

The advantage of CA attention mechanism is that not only channel information but also direction-related position information is considered. The disadvantage is that it requires additional computation with high computational overhead. In addition, it is not possible to capture long-range dependencies because it requires the computation of attention weights for the whole feature map.

The advantage of CBAM attention mechanism is that it introduces two analysis dimensions, spatial attention and channel attention, and realizes the sequential attention structure from channel to space. The disadvantage is that it requires more computational resources and higher computational.

Non-maximum suppression

In object detection, there may be multiple prediction boxes for the same detection object. In order to retain the optimal prediction box, the Non-Maximum Suppression (NMS) technique is applied during post-processing to eliminate redundant bounding boxes and yield the ultimate detection outcome. The main process of NMS is to first sort all the predicted boxes according to their confidence scores, find the bounding box with the highest score, calculate the overlap degree (IoU value) between the remaining bounding boxes and the highest scoring bounding boxes, eliminate the bounding boxes that are greater than the threshold, and then find the highest confidence score in the remaining bounding boxes, and continue the above process until all the remaining bounding boxes are eliminated. This NMS is adopted as a post-processing method in YOLOv5.

However, the NMS method, on the one hand, is difficult to determine the threshold of IoU, on the other hand, it will produce false suppression for different objects with large overlap. Therefore, this paper proposes to use Soft-NMS to improve the YOLOv5s model. In the improved method, Soft-NMS is applied to the inference stage, while NMS is still used for computation in the training stage. The Soft-NMS method mainly improves the problem of false deletion of overlapping objects in NMS. In the NMS algorithm, the detection boxes whose IoU value with the detection box M is greater than or equal to the threshold \(N_{t}\) will be deleted directly, so the corresponding false deletion problem will be caused. For the NMS method, the formula for calculating the confidence score is shown in Eq. ( 7 ):

The improvement of Soft-NMS is that the overlapping detection boxes are not deleted directly, but their confidence scores are reduced, and the corresponding strategy is designed to ensure that the overlapping object detection boxes are retained while multiple detection boxes of the same object are deleted. The confidence score of Soft-NMS is calculated as shown in Eq. ( 8 ):

The calculation formula of Gaussian function \(s_{i}\) is shown in Eq. ( 9 ):

Improved feature pyramid networks

YOLOv5s Network adopts the structure of FPN + PANet in the Neck part. In order to further improve the efficiency of the multi-scale fusion of the model, this paper intends to use BiFPN (Bidirectional Feature Pyramid Network) 26 to improve the neck network of YOLOv5, and its structure is shown in Fig.  4 a 26 . In BiFPN, considering that there is no feature fusion, the node with only one input edge will have little contribution to the feature network fusion of different features. Therefore, on the basis of the PANet network, as shown in Fig.  4 b 26 , the intermediate nodes of P3 and P7 are removed to obtain a simplified feature fusion network. Jump connections are added to the input and output nodes of the same scale of the simplified network, as the purple arrows in Fig.  4 c 26 , to fuse more features. Finally, for feature fusion of different resolutions, the previous network treats the input features with the same weight, but in fact, the contribution of features of different resolutions to the output features is not equal. Therefore, this paper proposes to increase the weight value of each input feature for feature fusion, so that the network learns the importance of different input features. Using Fast normalized fusion, the weight values are normalized and scaled to between [0, 1], similar to the Softmax method but with higher training speed.

Network structure diagram of the three networks FPN, PANet, and BiFPN.

Statement of informed consent

The helmet images used in the manuscript were reportedly taken from the open-source helmet dataset SHWD, and all human participants involved in the images agreed to participate in the study and also agreed to the publication of identifiable information/images in an online open access publication.

Dataset and results

Selection and description of the dataset.

In this study, an open-source safety helmet dataset SHWD (Safe Helmet Wearing Dataset, https://github.com/njvisionpower/Safety-Helmet-Wearing-Dataset ) available on the internet, comprising a total of 3241 images, is utilized. The data set contains a certain number of positive and negative object samples of various types of safety helmets, such as small objects, objects with occlusion, dense objects, fuzzy objects, and interference objects wearing other hats, as shown in Fig.  5 .

Different kinds of samples in public datasets.

The dataset contains two kinds of labels, hat, and person, indicating the staff wearing safety helmets and the staff not wearing safety helmets. In the helmet dataset used in this paper, the training set accounts for 80% of the total dataset, and the validation and test sets each account for 10%. The labeling method of the picture is consistent with the previous labeling method. The previous labeling method is for the helmet picture labeling is generally divided into two categories, one is wearing a helmet personnel, with “hat” labeling, the helmet and the head as a whole to labeling, and the other is not wearing a helmet personnel, with “person” labeling, construction personnel head labeling. The label file of the picture is in xml format, and its category and border position can be easily read out. As shown in Fig.  6 , Fig.  6 a is the object detection picture of the safety helmet, Fig.  6 b is the tag information of its xml file, which can be read from the figure, the picture is an RGB image, the size is 800 × 1158, the picture contains two bounding boxes, hat, and person, where the coordinate of hat is (86, 167, 444, 546), and the coordinate of hat is (86, 167, 444, 546). Person’s coordinate is (589, 214, 800, 625). Read the information in the xml file, mark hat and person as 0 and 1 respectively, calculate the center point coordinates of the bounding box and its width and height value and normalize them. Each line represents an object, which is the category, the center point coordinates, and the width and height value, as shown in Fig.  6 c, thus converting the xml file into a txt file suitable for the YOLO model.

Safety helmet target detection image and its corresponding xml, txt annotation information.

Experimental environment

In the experimental stage, this paper conducts experiments on the selection of the basic network, the selection of the attention mechanism, and the overall improvement effect of the network, respectively, and describes the results and the reasons for the selection in detail. The hardware configuration used in the experiment is the CPU model of 11th Gen Intel(R) Core(TM) i7-11800H, and the refresh frequency is 2.30GHz. The GPU version is NVIDIA GeForce RTX 3050 Ti Laptop GPU with 4GB of memory. The main hard disk model is Micron MTFDKBA512TFH with a capacity of 512G. The main board model is LNVNB161216. In this paper, the Pytorch deep learning framework is used when training the safety helmet object detection model. The running environment is Windows 10, the 64-bit operating system, the python version number is 3.9, the pytorch version number is 1.12.1, and the CUDA version number is 11.3.

During model training, the pre-trained weights trained by the public dataset MS COCO are used, which contains more than 330K images, 1.5 million objects, and 80 categories. The pre-trained outcomes serve as initialization parameters in the training process, followed by fine-tuning the parameters using the safety hat dataset introduced in section “ Attention mechanism ” of this paper. The configuration of the model's fundamental parameters is outlined in Table 1 .

Evaluation index

Throughout the experimental procedure, Precision, Recall, Average Precision (AP), and mean Average Precision (mAP) are chosen as the assessment metrics for detection accuracy, while Frames Per Second (FPS) is used as the speed evaluation criterion.

If the intersection ratio between the predicted outcome and the True value exceeds a specific threshold, the prediction is considered True. Employing this approach, the predicted results can be categorized into four scenarios: True Positive (TP), False Positive (FP), True Negative (TN), and False Negative (FN).

Among them, accuracy refers to the probability of correct detection among all detected objects, so its formula is expressed as Eq. ( 10 ):

Recall refers to the probability of correct detection among all positive samples, so its formula is expressed as Eq. ( 11 ):

The Precision-Recall curve (PR curve) is a graphical representation that plots Recall (R) on the horizontal axis and Precision (P) on the vertical axis. Average Precision (AP) represents the model accuracy under different recall rates, which refers to the area enclosed by the PR curve and the horizontal axis on the graph. Therefore, for a continuous PR curve, the formula is expressed as Eq. ( 12 ):

Mean Average Precision (mAP) is the average of the precision values associated with various object categories, with its formula defined as depicted in Eq. ( 13 ). In this paper, n equals 2, representing hat and person, respectively.

Frames per second (FPS) represents the quantity of images processed in a single second, serving as a metric for evaluating the speed of object detection. When evaluating this metric, you need to ensure that the test environment is consistent. It can not only compare the number of images processed per unit of time, the unit is one, the larger the faster the detection speed, but also compare the detection time required to process a single image, the general unit is ms, the smaller is the faster detection speed.

Experiments on the selection of the base network

The data set mentioned in 2.2 of this paper is used to train and test YOLOv5, YOLOv6, and YOLOv7, and the training results and training time are shown in Fig.  7 , where (a), (b), and (c) denote the change of average accuracy of YOLOv5, YOLOv6, and YOLOv7 in the training process with IoU of 0.5 and 0.5: 0.95 respectively.

Training results and training time using YOLOv5-v7 respectively under the same conditions.

The trained model undergoes testing on the identical test dataset to compare the average accuracy and detection speed among the three models, with the results presented in Table 2 .

It can be seen from the experimental results that the average accuracy of YOLOv5 network is 81.9%, and the frame per second rate is 96.2, which achieves a satisfactory accuracy under the condition of high detection speed and less training time. The detection speed of YOLOv6 network is greatly reduced when the average accuracy is not improved. Although the average accuracy of YOLOv7 network is higher than that of YOLOv5, its detection speed is almost halved, and the training time of YOLOv7 network is twice that of YOLOv5 network. More importantly, its hardware equipment, such as video memory and other conditions, is not suitable for a wide range of popularity in the industrial safety helmet detection scene. Therefore, this paper chooses YOLOv5s network as the basic network for further research and improvement.

Selection of attention mechanism

Regarding the enhancement of the foundational network, this paper first considers the shortcomings of YOLOv5s network for small object detection, so three different attention mechanisms SE, CBAM, and CA are added to the backbone of YOLOv5s network, and the detection effects of the three attention mechanisms are compared. The test outcomes are displayed in Table 3 . The CA attention mechanism will slightly improve the accuracy but cause a large decrease in the average precision. The CBAM attention mechanism does not result in a discernible enhancement in the overall accuracy of the network. The SE attention mechanism achieves an average accuracy increase of 0.9% while the detection speed and accuracy are almost unchanged. Therefore, it is finally decided to choose the SE attention mechanism to combine with other methods for further improvement. Figure  8 illustrates the contrast between detection results before and after the inclusion of the SE attention mechanism.

Detection results before and after adding SE attention mechanism.

Improvement of YOLOv5s and ablation experiments

After the attention mechanism is selected, this paper continues to analyze and compare the detection results. To address the issue of occlusion in the results, Soft-NMS is employed for enhancement, while the utilization of the BiFPN network aims to augment the model’s feature extraction capability. Therefore, the CIoU Loss is improved to Focal-EIoU Loss in the model, the improved method is added to the YOLOv5s network, and the ablation experiment is carried out, and the outcomes are displayed in Table 4 .

From the experimental results, among the four improved methods, SE attention mechanism and BiFPN network will improve the average accuracy of the network, Focal-EIoU Loss and Soft-NMS will improve the accuracy of the network. Therefore, in this case, this paper combines the two methods of improvement direction in pairs. For safety helmet detection, with a view to ensure the safety of construction workers, the recall rate of the model should be guaranteed to be high enough to detect as many people as possible who do not wear safety helmets. From the comparison results, the method of improving BiFPN in Neck based on YOLOv5s and combining Focal-EIoU Loss has a better effect on safety helmet detection, with an average precision increase of 0.9% and a recall rate increase of 2.8%, and the detection speed of the model does not decrease too much. We have christened it “BiFEL-YOLOv5s” as a novel approach for safety helmet object detection, and Fig.  9 illustrates the comparison of its detection outcomes.

Comparison of network detection results between BiFEL-YOLOv5s and original YOLOv5s in different scenarios respectively.

Since there is no public dataset in the literature 16 , 17 , 18 , 19 , 20 , and each literature uses different datasets to get different results, in order to ensure the validity and intuition of the comparison results, a unified dataset is now used, i.e., the SHWD dataset used in this paper. The comparison results are shown in Table 5 and Fig.  10 .

Comparison results with major helmet improvement algorithms.

From the Table 5 and Fig.  10 , it can be seen that the detection accuracy of literature 16 is higher, but due to the two-stage target detection algorithm, the detection speed is low, and there is a serious leakage of small target samples; Literature 17 has a higher detection speed, but there is a certain degree of leakage; Literature 19 has a moderate detection accuracy and speed, but there is a misdetection. Comprehensive comparison, this paper's proposed detection algorithm has the highest accuracy, and the detection speed is lower than that of Literature 17 , at the same time there is a significant improvement in the leakage of small targets and the misdetection of the situation.

This paper takes the safety helmet object in the construction environment as the research object and studies the safety helmet object detection method based on deep learning. Firstly, this paper analyzes the significance of real-time detection of safety helmet objects, understands the related object detection methods, and determines that YOLO algorithm is more suitable for safety helmet object detection. The running environment of Python and Pytorch is built, different versions of YOLO network are used to train it, and the experimental results are compared and analyzed. Considering the long training time of YOLOv7 and the high requirements for hardware equipment, YOLOv5s is selected as the basic network. Aiming at the improvement of YOLOv5s basic network, considering its shortcomings for small objects and occluded objects, SE, CBAM, and CA attention mechanisms are added to the backbone network respectively. By analyzing and comparing experimental outcomes, it is concluded that the channel attention mechanism, SE, is better suited for safety helmet object detection. Focal-EIoU is employed to enhance the loss function, while Soft-NMS is utilized to enhance post-processing non-maximum suppression, thus improving both model accuracy and detection speed. The final experimental results show that our proposed BiFEL-YOLOv5s method has a better effect on safety helmet object detection, and can meet the real-time object detection in complex work scenes.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported in part by Intelligent recognition of open-pit mining based on deep learning and remote sensing big data—Taking the eastern Mongolian region as an example under Grant NJZY22278; in part by Research on Convolutional Neural Network Algorithm Based on Big Data Analysis and Urban Structure Type Recognition under Grant 2022ZKZD05; in part by the National Key Research and Development Program of China under Grant 2020AAA0109200; in part by the Liaoning Revitalization Talents Program under Grant XLYC2008020; in part by the National Natural Science Foundation of China under Grant 52074064; in part by the Natural Science Foundation of Science and Technology Department of Liaoning Province under Grant 2021-BS-054; in part by the Fundamental Research Funds for the Central Universities of China under Grant N2204006, Grant N2104026, Grant N2018008, and Grant N2001002.

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Lihong Wei designed the study and writed the main manuscript text. Panpan Liu collected data. Haihui Ren analyzed and interpreted data. Dong Xiao prepared figures and tables. All authors reviewed the manuscript.

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Wei, L., Liu, P., Ren, H. et al. Research on helmet wearing detection method based on deep learning. Sci Rep 14 , 7010 (2024). https://doi.org/10.1038/s41598-024-57433-z

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• Volume 41, Issue 4
• Perceived barriers and opportunities to improve working conditions and staff retention in emergency departments: a qualitative study
• Article Text
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• http://orcid.org/0000-0003-3067-9416 Jo Daniels 1 , 2 ,
• http://orcid.org/0000-0002-8013-3297 Emilia Robinson 1 ,
• http://orcid.org/0000-0001-5686-5132 Elizabeth Jenkinson 3 ,
• http://orcid.org/0000-0002-2064-4618 Edward Carlton 4 , 5
• 1 Department of Psychology , University of Bath , Bath , UK
• 2 Psychology , North Bristol NHS Trust , Westbury on Trym , Bristol , UK
• 3 Department of Health and Social Sciences , University of the West of England , Bristol , UK
• 4 Emergency Department, Southmead Hospital , North Bristol NHS Trust , Westbury on Trym , UK
• 5 Bristol Medical School , University of Bristol , Bristol , UK
• Correspondence to Dr Jo Daniels, Department of Psychology, University of Bath, Bath, UK; j.daniels{at}bath.ac.uk

Background Staff retention in Emergency Medicine (EM) is at crisis level and could be attributed in some part to adverse working conditions. This study aimed to better understand current concerns relating to working conditions and working practices in Emergency Departments (EDs).

Methods A qualitative approach was taken, using focus groups with ED staff (doctors, nurses, advanced care practitioners) of all grades, seniority and professional backgrounds from across the UK. Snowball recruitment was undertaken using social media and Royal College of Emergency Medicine communication channels. Focus group interviews were conducted online and organised by profession. A semi-structured topic guide was used to explore difficulties in the work environment, impact of these difficulties, barriers and priorities for change. Data were analysed using a directive content analysis to identify common themes.

Results Of the 116 clinical staff who completed the eligibility and consent forms, 46 met criteria and consented, of those, 33 participants took part. Participants were predominantly white British (85%), females (73%) and doctors (61%). Four key themes were generated: ‘culture of blame and negativity’, ‘untenable working environments’, ‘compromised leadership’ and ‘striving for support’. Data pertaining to barriers and opportunities for change were identified as sub-themes. In particular, strong leadership emerged as a key driver of change across all aspects of working practices.

Conclusion This study identified four key themes related to workplace concerns and their associated barriers and opportunities for change. Culture, working environment and need for support echoed current narratives across healthcare settings. Leadership emerged more prominently than in prior studies as both a barrier and opportunity for well-being and retention in the EM workplace. Further work is needed to develop leadership skills early on in clinical training, ensure protected time to deliver the role, ongoing opportunities to refine leadership skills and a clear pathway to address higher levels of management.

• qualitative research
• staff support

Data availability statement

Data are available upon reasonable request. Requests go to the corresponding author - Jo Daniels ([email protected], University of Bath, UK). De-identified participant data can be made available upon reasonable request.

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/emermed-2023-213189

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WHAT IS ALREADY KNOWN ON THIS TOPIC

Retention of staff in emergency medicine is at crisis level and has been a high priority area for over a decade.

Multiple guidelines have been published to outline improvements that need to be made to retain staff; however, little improvement has been seen on the ground in EDs.

Key factors such as staff burnout and poor working conditions are known to influence intention to leave; however, it is unclear why change has not taken place despite knowledge of these problems and existing guidelines seeking to address these issues.

WHAT THIS STUDY ADDS

This qualitative study assessed perceived barriers that may be inhibiting the implementation to working conditions and working practices in EDs.

Leadership is identified as an important driver of change in working practices and can play an important role in workplace well-being and retention.

Key recommendations for avenues of improvement are made, identifying key actions at government, professional, organisational and personal level.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

This study identifies leadership as a key opportunity for change and as a result makes specific recommendations for policy and practice regarding leadership in emergency medicine.

Introduction

Emergency Medicine (EM) is facing a global staffing crisis. 1 Record numbers of staff continue to leave the UK NHS with EM the most affected specialty. 2 EM reports the highest work intensity of all medical specialties, 3 with ‘intensity’ recognised as one of the leading factors in job dissatisfaction, attrition and career burnout. 3–5 These factors are amplified in an already stretched workforce. 2 Psychological well-being of the EM workforce is compromised, with working conditions recognised as playing a key role. 6 7 Staff attrition has a systemic impact: lower staff ratios lead to higher workloads, reduced quality of care, 8 higher levels of medical errors 9 and poorer staff well-being, 10 all factors associated with staff absence and intention to leave. 11 The landscape of EM has also changed; increased prevalence of high patient acuity, multimorbidity and an ageing population all bear considerable impact.

Key sector stakeholder initiatives and policy recommendations relating to retention and well-being 12–14 are largely generic and forfeit relevance to the specialty due to the lack of specificity to the clinical context within which these guidelines need to be implemented. Retention improvement programmes suggest approaches should be tailored per organisation, 12 however, this assumes that the challenges faced by staff across specialities and disciplines are homogeneous. In a specialty which reports the highest pressured environment, highest attrition and rates of burnout, 15 considerations of workplace context and specificity of policy recommendations are likely to be crucial. Interventions or initiatives must take account of the unique demands of the EM working environment, and how feasible it is to implement recommendations.

The James Lind Alliance (JLA) priority setting partnership in EM 16 identified initiatives to improve staff retention as research priorities in 2017 and again in the 2022 JLA refresh, 17 signalling the need for further research in this area due to a deepening workforce crisis. Current guidelines and initiatives target working conditions which are known to be associated with retention; however, these initiatives have been poorly implemented or enforced, with few formal evaluations of such interventions. 5 Moreover, current research is limited to the perspectives of specific professional groups and most are survey-based studies. 18

In order to better address current working conditions, with a view to improving retention, this research was aimed at determining practical barriers and opportunities for change in the ED working environment as perceived by professional staff working in this environment. This will tooffer insight into the shared experiences, constraints and priorities of those working within the ED.

Enhanced understanding of these issues can provide a firm basis from which to shape, inform and underpin future policies and workplace initiatives, ensuring that practical barriers and opportunities for change are embedded in a way that optimises relevance and feasibility of implementation in the ED working environment.

Study aims and objectives

This study sought to engage three core professional groups (doctors, nurses, advanced care practitioners; ACPs) who work within an EM context to better understand (a) primary concerns relating to working conditions; (b) perceived barriers to implementing change and (c) perceived opportunities and targets for change. Findings will be used to underpin key recommendations that are tailored to the needs of an over-burdened and under-resourced ED.

This qualitative study forms part of a larger collaborative project between the University of Bath and the Royal College of Emergency Medicine (RCEM), funded by a UKRI Policy Fund. The full recommendations relating to the four core themes are available on the RCEM website (Psychologically Informed Practice and Policy (PIPP) | RCEM).

Methodology

This study uses a qualitative approach involving online focus groups in order to gain a rich and detailed understanding of participant perspectives and views, unrestricted by closed question responses. Focus groups offer the opportunity to gain an understanding of shared experiences and narratives, using a dynamic approach to the subject matter, allowing further probing for clarification and participant interaction for deeper insights. The COVID Clinicians Cohort (CoCCo) study 19 was used to organise data into key categories; this model mirrors Maslow’s Hierarchy of Needs 20 from a workplace perspective.

Participants

To be eligible for participation, ED staff must have been currently employed in a UK NHS ED as either a doctor, nurse or ACP.

ACPs are a recently developed workforce of accredited clinicians who have received advanced training to expand the scope of their usual role (eg, paramedic, nurse), permitting them to take on additional clinical responsibility in the ED.

These three groups are core affiliates of the RCEM and represent the majority of the workforce in the ED. The ED setting was used as the focus (rather than all acute care settings) as this represents the core and central setting for EM.

Recruitment and procedure

Online adverts and qualtrics survey links were distributed through social media (ie, Twitter) and RCEM communication channels using snowball recruitment methods. Profession-specific focus group interviews were conducted online using MS teams by two study researchers (JD, ER) using a semi-structured topic guide (see online supplemental materials ). The guide was shaped by the scope of study aims and the current evidence base and explored difficulties in the work environment, impact of these difficulties, barriers and priorities for change. Focus groups were 60–90 min in duration and were recorded using encrypted audio recorders, transcribed and stored securely. Participants were given debrief information sheets following the focus group. Transcripts were not returned to participants and no repeat focus groups were carried out.

Supplemental material

Directive content analysis was applied to the data. 21 This analysis strategy was used to identify common themes from participant responses, using deductive codes by identifying key concepts from existing theory 19 and prior research. Two researchers (ER, JD) read through each transcript, highlighting passages that could be categorised in the pre-determined codes. Any passages that could not be categorised within the initial coding theme were given new codes. Further coding was then conducted and this iteration was reviewed and updated. After coding was completed, initial notes from the focus groups were revisited to ensure all reflective notes were incorporated where relevant. Final themes were refined through an iterative process between JD, ER and EJ (qualitative analysis expert), with all stages of analysis reaching consensus agreement with regard to the content and labelling of codes and themes.

Patient and public involvement

As this study focused on staff experiences in an EM workplace, a Clinical Advisory Group (CAG) was used in place of patient or public involvement. The CAG comprised of five clinicians working in the ED who advised on the scope and priorities of the study. This included two medical consultants, one charge nurse, one trainee and one specialty grade doctor. Of those, three were males and two were females. All CAG members were offered renumeration for their time.

Of the 117 total responses to the study advert, 16 respondents were eligible but not available to attend focus groups and 55 either did not consent or were not eligible based on their role and/or department. From the remaining 46 respondents, 13 of these could not attend or cancelled, leaving a final sample of N=33 (28% of total responses). Due to higher response rates from doctors, these focus groups were further grouped by grade; nurses and ACPs were grouped by profession only and were organised base on availability. There were 11 groups in total (see table 1 ). Participants were mostly female, and from a white British background. Ages were spread fairly evenly across the categories, except ages 35–44 which included substantially fewer participants.

• View inline

Participant and focus group characteristics

Following analysis of the qualitative data, four key themes were generated. These were termed: ‘culture of blame and negativity’, ‘untenable working environments’, ‘compromised leadership’ and ‘striving for support’. Data within these themes that were identified as ‘barriers’ or ‘opportunities’ for change were extracted ( table 2 ). Illustrative participant quotes are identified by researcher codes, which reflect the profession and a recoded group number, to preserve anonymity.

Primary concerns, barriers and opportunities for change

Culture of blame and negativity

When asked about the most difficult aspects of their working conditions, participants commonly reported a culture of blame and negativity in the ED. The work culture not only felt unsupportive and ‘toxic’ but had a marked effect on well-being. Participants described a culture which was quick to blame rather than support:

You worry about making a mistake, and if you did make a mistake who would have your back. (ACP, G7) You very rarely get anyone saying that was a good job. (SAS doctor, G8)

This was particularly felt top-down, where those in management position were perceived to take an unsympathetic view of extended waiting times and unmet targets, despite the tangible constraints of operating at overcapacity and ‘exit block’, problems that participants perceived to be out of their control. Participants in all groups indicated that the negative culture instils anxiety over how they might be perceived by peers, but particularly by senior colleagues:

That’s a classic example… she’s a senior member of the team, really knows her job…. She was quite critical really, in a very negative way about how you managed that patient. (Nurse, G11)

Some participants reported senior colleagues having unrealistic expectations of the more junior staff, with little consideration of the increased pressures that have arisen in recent years:

It’s ridiculous to compare the needs, even for our senior colleagues who were registrars five years ago, the reality of running the department overnight is not the same as it was then. (SAS doctor, G1)

Existing structures and working practices of the NHS were described as ‘archaic’ and ‘old fashioned’, leading staff to feel blamed if they could not cope with the pressures and disempowered to seek support due to the expectation that they should be ‘unbreakable’ (Trainee, G9). Participants also voiced that they were unclear on lines of accountability, who to approach for what problem. This barrier to escalating their concerns was further compounded by the belief that both clinical leadership and higher management were generally overburdened and unreceptive to discussions on workplace concerns.

Increasing pressure and longer waiting times were described as driving antisocial behaviour from patients, exposing staff to risks to physical and psychological well-being:

So the long wait causes verbal or physical violence and aggression, which has a massive impact on staff well-being. (Nurse, G11)

Participants highlighted the desire to be supported to learn from difficult experiences and develop in light of them, suggesting that a simple checking in on how individual staff members are progressing would be well received and beneficial to well-being:

We have intermittent debriefs… but it’s not every time. It doesn’t necessarily need to be every time, but it’s not as frequent as it should be. Even if it is just ask are you okay? (Trainee, G5)

Interprofessional respect and development of a more empathic culture of shared responsibility were flagged as key opportunities for change that would support better team cohesion:

We need to change how we speak and respect each group, and we need to try and understand each other’s point of view, and if we could get better ways of working, but just talking to each other about what are my problems, what are your problems, why is this stressing you, what’s stressing us, how can we work together to do that. (ACP, G2)

Findings suggest that EM professionals are confronted with outdated perceptions of clinical demand from within teams and systems, with unrealistic expectations which compound a blame and shame culture when expectations are not met. Operating within this chronically under-resourced system was framed as compromising workforce well-being and risking burnout, yet participants indicated that simple interventions such as check-ins, clearer lines of accountability and a more civil and respectful culture would offer key opportunities for growth and sustainability even in the face of a staffing crisis.

Untenable work environments

The complex work environment within the ED was described as being of significant concern, compromising care and leaving staff feeling undervalued due to basic needs being unmet. Participants frequently reported poor quality or inadequate facilities, such as provision of toilets, lockers and changing rooms, hot food only available within limited hours, poorly functioning IT systems and rest spaces being in a different building.

So you’re just basically sharing (toilets) with the patients. In the urgent care centre there’s two toilets for the whole of the department in there, often one of those is broken…and not enough lockers for every member of staff. (ACP, G2) Stuff like working computers, a consistently working POD system… those little things I think make a bigger impact on your life than how many people come in through the front door. (Trainee, G5)

A lack of physical space for administrative tasks was highlighted by many clinical staff, being described as ‘woefully inadequate’ (ACP, G2). Wards were described as ‘unfit for purpose ’ (Nurse, G11), which was attributed, in part, to higher management lacking understanding of the needs and practices of the ED. One example highlighted the long-term impact of ED workspace changes that were not fit for purpose:

…it was clear that no clinical staff had been involved. Doors were in the wrong space, no sinks in the right place, not enough storage, poor flow, poor layout (ACP, G2)

Existing rest spaces or staff rooms were reported to be taken over to provide more clinical room, limiting the space for staff to change, rest and decompress.

The nurses were getting changed in a corridor, now they seem to have a cubicle they can get changed in. But the facilities for the same trust are really very different. (Nurse, G10)

This was perceived to be particularly important due to working in the high-pressure environments of a crowded ED, where staff voiced concerns regarding the sustainability of working with a high workload safely without private spaces.

EDs were perceived to be more busy, for reasons associated with shifts in societal expectations and perceptions of the scope and role of ED:

Go back ten years ago in the emergency department and people would try their best at home, would take painkillers, will see how it goes, not wanting to trouble A&E, but seems like now it seems like A&E is the open door for everybody just to come in with everything. (ACP, G7)

Participants used emotionally laden language when describing the intensity of the workload itself, with parallels drawn between being at war and working on the NHS frontline, where staff worked under similar levels of intensity but longer term and without rest.

…when people are deployed (in the forces) they are deployed for 6 months…because that 6 months is intense, it’s intense on your body, it’s intense on your mind, it’s intense on your family, it’s intense on everything about you, and that’s while you were deployed for 6 months, and then there’s some recovery time coming back. (Consultant, G4)

Comparisons were also made to the sinking of ‘the Titanic’:

There is the jollying everybody along, being the redcoat on the shift, cheering everybody up, saying everything is going to be okay, but feeling like you’re just rearranging the deckchairs on the Titanic (Nurse, G10)

The impact of a consistently high workload was described as being compacted by a lack of agency and autonomy over working patterns, which was perceived to be related to non-clinical staff making decisions about shifts without understanding the inherent pressures:

The people who control our rotas are… her job is a rota co-ordinator, she works in an office, she is administrative, and the person who signs that off is the manager for the department, again non-clinical, and getting leave is a nightmare, it’s awful. (Trainee doctor, G5)

Consultants identified that there were limited options to reduce workload when approaching retirement, and they did not necessarily feel well-equipped to continue operating under high pressure and for long hours. Those in training posts reported insufficient time to meet requirements or study due to workload, influencing both career progression and confidence in the role.

You are getting no progression because you’re not getting your training, and I know that personally in the last year I made my decision that I will not continue to work clinically, I will step back in the next few years because there’s… why would I stay doing something that there’s no reward for? (Nurse, G11)

Participants agreed that there was both a need and an opportunity for the ED to be a ‘nicer place to work’ (ACP, G2). Specific suggestions included a full staffing quota, ensuring staff are adequately rested to return to work and the opportunity for peer support:

My top three things would be coming on with a full staffing quote so you know there’s no gaps in the rota, so you’re all there. Everyone is well rested and ready for the shift, just being able to talk to each other on the shop floor and being quite open with each other on how everyone is feeling. (ACP, G7)

Many of the suggested changes directed at making working conditions in the ED more sustainable related to basic needs such as being able to take breaks, access healthy food and functioning IT when needed:

…having those opportunities to go off and have a five minutes when you need to, to be able to continue your shift. (ACP, G7) It would be really nice to be able to have some healthy nice food in the department. (Nurse, G11) As more and more of our job goes electronic, electronic notes, electronic prescribing, actually having IT systems that are fit for purpose, everyone has access to (Trainee doctor, G9)

Self-rostering was frequently mentioned as a positive experience for participants and a useful avenue to help participants to deliver better care and improve well-being:

One day off between a set of shifts is not enough to decompress and be re-energised to start back on your next set of shifts. So I think the rota, we have moved to a more self-rostering method now, and I think that’s helping with staff well-being, especially in our team. (A7)

Overall, working in existing ED environments was described as ‘untenable’ and ‘unsustainable’ in terms of both the working environment and the lack of agency and autonomy over high-intensity workloads. Many of the problems and solutions relate to provision of resources to meet basic needs, many of which are subject to professional and NHS regulations; however, due to pressures this is not being implemented.

Compromised leadership

Clinical leads in the ED were perceived to hold responsibility for setting the tone for culture and behaviour in the ED, leading by example:

And you lead by example as well, so if your consultant in charge is not taking a break you feel like you can’t ask to take a break. It’s the same with the nurses, if the nurse in charge is not taking a break then a lot of the junior nurses won’t come and ask for a break because again you’re guided by the leadership aren’t you? (A7)

The clinical lead in the ED is a key conduit for change, from a cultural and environmental perspective especially. However, participants expressed frustration about feeling that their voices were not heard or valued outside of the department, in part due to clinical leads being reluctant to escalate their concerns due to the discrepancies between clinical priorities within the ED and the priorities expressed by trust level executive management:

You’ve got the clinical side, and we are to one degree or another worried about the patients, and then you have got the management side and they are worried about figures, times or money, and those two things don’t really mesh together (ACP, G2)

Yet, within the EDs, leadership was described as being poorly supported in terms of protected time to train and deliver the role fully. Consultants voiced reluctance to take on a leadership role due to lack of ‘visible leaders’ to provide inspiration or exemplar: ‘There is no one for us to look up to, to lead us’ (Consultant, G4), ‘We need compassionate leadership’ (SAS doctor, G1).

A lack of definition or clear understanding of what the clinical role entailed was reported to make it difficult for clinical leads to be effective in their role:

People tell you that you’re there to lead, and you’re like I know but what does that mean? And then you don’t know if you’ve got to go to all these meetings, which ones you really need to go to, which ones can I not go to, also for me I do the job on my own. (Clinical lead, G6)

Participants emphasised they need a ‘clear definition of what the college would see the role to be, and how much time they would expect it to take of your job ’ (Clinical lead, G6). Any possibility for growth was hampered by a lack of training or support from colleagues to help with even the practicalities of the role (such as recruitment and personnel management):

I have literally started last week on a leadership course that’s been for other clinical leads in the organisation. But I feel a bit could have done with this maybe earlier. But that’s more about your leadership qualities and conflict resolution, it’s all that side of it as opposed to the actual practicalities of the job. (Clinical Lead, G6)

When considering possible solutions to these difficulties, participants suggested that an accessible time to do the job and an online repository may offer an opportunity to share resources, learn from one another and foster development:

I think sharing all the stuff we shared on the WhatsApp, trying to share stuff, so how to write a business case, what you need to do. (Clinical lead, G6) I should be doing work at a time I am getting paid, so you need to give me that time. (Trainee doctor, G9)

Mentorship was also deemed to be important for successful delivery of the role:

I think personally as leads and stuff we should all have some kind of mentoring type…Supervision, that’s the thing, we don’t get any. (Nurses, G10)

Participants described having difficulties feeding into emerging issues to address unmet need, blocked from communication with leaders by ‘layers of bureaucratic sediment’. This was compounded by the career trajectory of NHS management, where often those in post would swiftly move on for promotion.

Overall, clinical leadership within the ED was described as compromised, unsupported and, ultimately, a key barrier or missed opportunity for change in culture and working practices in the ED. However, there were clear indications of opportunities for growth and change, including a need for compassionate leadership, shared resources, time to do the job and mentorship.

Striving for support

This final theme encompasses the concerns raised by participants regarding well-being and staff support, specifically the barriers to accessing well-being support and their preferences in relation to what changes are likely to improve their well-being. Common barriers included having to attend support or well-being services during time off, with the scheduling of support geared to a ‘nine to five’ non-clinical workforce (ACP, G2). Mental health stigma in the ED was also cited as a key barrier.

I think for me it still feels like a bit of a stigma about saying I am struggling what should I do next. (Nurses, G11) There’s nowhere that I can express how I am feeling or even understand how I am feeling. (Consultant, G4)

This was reinforced by well-being not being viewed as a priority, with team check-ins or formal appraisals described as having ‘nothing in there about wellbeing’ (Clinical lead, G6), despite suggestions that simple well-being check-ins would suffice.

Participants suggested that support should not be purely accessed after the fact but something that should be prioritised and routinely available to staff to safeguard mental health:

… psychological support…it shouldn’t be something that we access when there is a problem, it should be something where we go well every month on a Friday at this time I go and talk to someone about what I have seen. (Trainee, G9)

Participants’ lack of understanding about which services were being offered was raised by many, with participants often able to list services available, or where the staff support centre was based, but not how or when one might access them. This offers a key opportunity for collaboration between staff support services and the ED to develop clearer pathways or a clear role for a departmental well-being lead.

Peer support was consistently highlighted as a highly valued resource that should be considered part of supportive culture ‘gives you somebody else to share the load with, and not be that single voice’ (Trainee doctor, G9). However, limited physical space and time to engage in peer activities were cited as barriers:

Well yeah it would be lovely to sit down and chat with my peers, apart from the fact that 1) we’re constantly busy, 2) we don’t have anywhere where we can sit and have a confidential gas. (SAS doctor, G8)

Overall, accounts suggested that existing support was largely unfit for purpose, and where it was easy to access (such as peer support) and available, it was often incompatible with ED working practices and within a culture where seeking support was often stigmatised.

Some participants expressed that having a psychologist embedded within the department was highly valued as a resource, particularly the different levels of support dependent on need:

…(during the pandemic) we setup weekly drop-in sessions with the psychologist… and it was really great for a lot of people to be able to drop-in, and then that led on to having one to one for people who felt they needed that, and also within ED we had a psychologist come round to our supervision when we needed them. (ACP, G7)

Participants reflected that psychological input introduced in response to the impact of the COVID-19 pandemic was highly valued. While many were open to discussion about their mental health and well-being, for many, stigma still permeates the ED culture and is further compounded by poor understanding and communication of available resources. Appointment of well-being leads, more value placed on well-being (including informal peer support) and routine access to psychology are suggested as opportunities to make strides towards improved well-being.

This study identified four key themes describing the difficulties in the ED work place. Working culture, physical working environment, pathways to care and leadership represent the core workplace concerns within our sample. These issues were perceived to play an instrumental role in their ability to sustain good working practices, well-being and, importantly, their intention to leave. Participants identified key barriers and opportunities within their work contexts which resonate with existing research and policy and can be used to shape the future policy and research development. 22 , 2 5 These findings act as a basis for the development of specialty-specific targets for change that are aligned with the views and voices of those working in this working environment and also take account the barriers and opportunities faced in the fast-paced unique environment of the ED. For a full set of EM-specific recommendations to underpin change across all of these four areas, see the Psychologically Informed Practice and Policy (PIPP) recommendations ( https://rcem.ac.uk/workforce/psychologically-informed-practice-and-policy-pipp/ )

Several of our findings have been noted in previous studies, particularly the role of culture, environment and access to support. 22 Most of the research examining factors associated with working conditions and retention in EM are profession specific 3 6 18 19 and are not readily generalisable to other professional groups in the ED. However, our study included doctors, nurses and ACPs from which emerged common cross-cutting themes affecting all of these professions working in the ED, themes which are consistent with the broader literature 9 10 but specific to the EM working environment.

As reflected in the work by Darbyshire et al , 5 the nature of the problems described were systemic; the workplace challenges were interrelated and appeared reciprocal in influence, arguably maintaining one another. The cyclical nature itself proves a key barrier to change, which raises the question: which is the primary target to effect most change? Leadership has a pivotal influence across these themes and is unequivocally vital to workforce transformation; however, this is an area that has been largely neglected in EM, with very little research seeking to develop or evaluate leadership interventions in this environment. Indeed, there is an assumption that leadership naturally develops over time and is fully formed on appointment to the role. 23 However, leadership within the ED is particularly complex and demanding due to the range of competencies required (clinical, managerial and administrative) 23 and the high-pressured environment within which this role needs to be delivered. This warrants tailored training and support to fully succeed. In settings where the nature of the work is unpredictable and at times clinically critical, leadership is pivotal to patient outcomes and team functioning, 23 24 which are particularly crucial in the ED setting. Leadership has the potential to be a powerful driver in workforce transformation, cultural change 25 and team functioning within these highly skilled, professionally interdependent teams. 26 To fully harness the capacity of leaders as agents of change, those in leadership positions must be sufficiently skilled, 27 feel supported to act on important issues 27 and have time to do the job. Yet, participants in this study reported poor role definition, lack of training and absence of protected time to deliver the role. This was compounded by blurred lines of accountability that led to impotence to effect change.

Implications

The development of leadership in EM should now be a primary focus. There are clear steps that can be taken to begin to mobilise and maximise the pivotal influence of leadership in effecting change, across government, professional, organisational and individual levels.

On a public policy level, there has been a rapid growth of government level publications and resources to recognise the role of leadership as a conduit to better patient and team health. 28 However, recommended leadership training is often generic and never mandated. This is surprising given the clear links with patient safety and team functioning. 23 24 Leadership training in healthcare should be mandated by government bodies, not least due to links with patient safety. 29

Significant work has been undertaken by RCEM to integrate and embed mandatory leadership training into the training curriculum for EM trainees, without which they cannot progress. While this demonstrates forward thinking and some future-proofing for the medical profession, it cannot cease at this point, it must be supported with continuing professional development post-training. The relevant professional bodies provide access to good quality leadership training such as the RCEM EM Leaders Programme and the RCN Leadership Programme, however, this is largely online without protected time to access or support development. More work is needed to ensure leadership training is visible, supported as part of a workplan, and a priority area championed by all relevant professional bodies.

Further work is needed to ensure that leadership competencies are introduced at an early stage of training 23 so the necessary skills are embedded and cultivated on the pathway towards and within leadership roles, rather than ad hoc when necessity dictates. This falls to both training and professional bodies to work together to ensure that theory-driven leadership is a core part of the teaching curriculum, with mentorship and practical resources (such as role definition, a personal development plan, human resource support) to complement and facilitate the necessary continuing professional development throughout a clinical career. Responsibility then moves to the employing local NHS trusts to support the development of those individuals within leadership positions. It is at this level that ED clinical leads and their teams can harness their influence; local NHS trust policies are driven by guidance from government and professional bodies, however, they have the power to shape local policy and mandate change in view of the needs of a service. We summarise key recommendations to underpin change at a local NHS level in Box 1 .

Key leadership recommendations for local NHS trust level commissioning

Those in leadership positions should be supported to attend leadership training as part of their workplan, within their workplace hours. This would include top-up training and training assignments.

Support to engage with a leadership mentorship or coaching programme as part of their workplan, with a view to continuing professional leadership development and creating safe spaces to problem-solve, reflect and seek support.

Access to the consultation service within the local NHS staff support services.

Appointment of a designated ‘Wellbeing Lead’ with protected time and support to deliver the role.

Clear description of roles and responsibilities, to include protected time dedicated to undertaking additional responsibilities associated with a leadership role and a professional development plan that is reviewed annually.

Support to engage with the EM clinical lead network in order to access resources to support the delivery of the role and access peer support when necessary.

Clear lines of accountability at an NHS organisational level with identified pathways to escalate concerns.

EM, emergency medicine.

Appointment of well-being leads within the ED, as outlined in the RCEM PIPP recommendations 30 and other key documents, 22 is also a key step towards workplace transformation through leadership; however, it is imperative this role is also supported with protected time and development. A well-being lead with a clearly defined remit and role would play a pivotal gatekeeper role in encouraging attitudes towards well-being in the ED by delivering ‘warm handovers’ and well-being initiatives, such as informal check-ins, staff team activities (ie, safety huddles), and well-being surveys.

On an individual level, those in leadership positions are more likely to succeed by harnessing the influence and opportunity that accompanies the role, identifying and taking inventory of challenges and barriers, clarifying lines of accountability to drive forward change and advocating for the needs of their team. Two mechanisms by which leadership bears the greatest influence include leading and prioritising a continuous cycle of quality improvement (eg, autonomy over work patterns, access to rest spaces, patient flow, taking steps to address the diversity gap) and role modelling of positive professional behaviours. 26 The latter includes compassionate and inclusive attributes but also speaks to the necessity to meet basic needs: taking breaks, adhering to annual leave, destigmatising views on mental health and openness to learning and change. Those in leadership roles should be encouraged to engage with the leadership networks, broadened to encompass a platform or virtual environment (ie, repository) to share and access resources and be granted access to leadership consultation with the well-being team as and when necessary. Those in leadership positions should also be provided with clear referral processes and internal professional standards to help address any incivility, including bullying, harassment and issues of inclusion. This would help promote a culture of care and interprofessional valuing and respect, improving team cohesion.

Finally, it is imperative that lines of accountability are clear for those in a leadership position. While many NHS trusts differ in their management structures, each trust will have communication pathways to divisional and executive management leadership teams. In order to drive the full potential of leaders to action change through these mechanisms, it is fundamental that pathways from ‘shop floor’ to the chief executive are clear and opinions and concerns of ED leadership are welcomed.

Flow through the ED, staff ratios, pay and pension structures are of course prime targets for change and where the current high-profile focus lies. However, leadership is a key conduit to change and those with mandatory powers must now move to recognise this in order to unlock the full potential of this role.

Limitations and future directions

There are inherent limitations in the small size of some of the participant groups, and as such the views and opinions expressed cannot be considered transferable across their respective professions. While many prospective participants did not proceed to focus group meetings due to last minute requests to cover shifts, the participant pool was comfortably within the bounds of what is acceptable for a qualitative study.

Findings should be interpreted in light of the sample consisting mainly of white women, therefore the views of males and minority groups may not be fully represented. Doctors made up a higher proportion of the final sample; this may be a consequence of using RCEM communication channels as a primary recruitment method, which has more members registered as doctors than nurses. As not all professions working in ED were included (eg, physiotherapy, psychology) it is possible that additional themes or differences might have been missed.

The geographical spread reflects a broad reach; however, there was a preponderance towards the South West, where the research was conducted. While none of the interviewees were known to the research team, those in the South West may have been more exposed to recruitment drives through mutual connections.

The development and testing of leadership training and packages should be a priority for professional bodies and at organisational level. This should take account of the overlapping and competing competencies required of ED leadership, including managerial, administrative and clinical components and the high-pressured context within which these skills are required.

This study identified key themes in understanding workplace concerns in the ED, and their associated barriers and opportunities for change. Leadership in EM should now be a primary focus, with further investment and support to target the development of leadership skills early on in training and provide protected time to refine these leadership skills and qualities across the working lifetime. This will serve to harness the pivotal influence of leadership in EM, which, if properly supported, holds the potential to act as a conduit for change across all areas of focus.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

This study involves human participants and was approved by University of Bath Psychology Research Ethics Committee (22-039). The Health Research Authority toolkit confirmed further approval was not required. Participants gave informed consent to participate in the study before taking part.

Acknowledgments

The study authors would like to extend thanks to all who contributed to this project including participants and the clinical advisory group. The authors would also like to acknowledge and thank RCEM President (AB) and policy advisor (SMcI) who advised on the policy priorities of RCEM and wellbeing clinical leads (Dr Jo Poitier, Consultant Clinical Psychologist at Alder Hey Children's NHS Foundation Trust; Dr Olivia Donnelly, Consultant Clinical Psychologist at North Bristol NHS Trust) who were consulted on their respective areas of expertise. They also thank Rita De Nicola for help in preparing the manuscript.

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1
• Data supplement 2
• Data supplement 3

Handling editor Caroline Leech

Twitter @drjodaniels

Contributors The original concept for the paper was developed by JD and shaped in consultation with EC and the RCEM President AB. JD was the primary contributor, guarantor and lead for the content and refinement of the paper. EJ gave expert methodological advice and contributed to the reporting and refinement of results. ER and JD performed the analysis, both contributing to the reporting of the results. ER prepared the manuscript for publication. EC gave expert advice on all aspects of the study from an Emergency Medicine standpoint and also contributed to the write-up of the paper. All authors contributed to the final version of the paper and approved for publication.

Funding This research has been carried out through funding from the UK Research and Innovation Policy (UKRI) Support Fund. The funder did not provide a grant number for this project, it is part of block 'UKRI Policy Support' funding from UKRI directly to Universities who distribute within their institutions. The funders had no role in considering the study design or in the collection, analysis or interpretation of data; the writing of the report or the decision to submit the article for publication.

Competing interests None declared.

Patient and public involvement Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods section for further details.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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Title: one-step image translation with text-to-image models.

Abstract: In this work, we address two limitations of existing conditional diffusion models: their slow inference speed due to the iterative denoising process and their reliance on paired data for model fine-tuning. To tackle these issues, we introduce a general method for adapting a single-step diffusion model to new tasks and domains through adversarial learning objectives. Specifically, we consolidate various modules of the vanilla latent diffusion model into a single end-to-end generator network with small trainable weights, enhancing its ability to preserve the input image structure while reducing overfitting. We demonstrate that, for unpaired settings, our model CycleGAN-Turbo outperforms existing GAN-based and diffusion-based methods for various scene translation tasks, such as day-to-night conversion and adding/removing weather effects like fog, snow, and rain. We extend our method to paired settings, where our model pix2pix-Turbo is on par with recent works like Control-Net for Sketch2Photo and Edge2Image, but with a single-step inference. This work suggests that single-step diffusion models can serve as strong backbones for a range of GAN learning objectives. Our code and models are available at this https URL .

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What the Data Says About Pandemic School Closures, Four Years Later

The more time students spent in remote instruction, the further they fell behind. And, experts say, extended closures did little to stop the spread of Covid.

By Sarah Mervosh ,  Claire Cain Miller and Francesca Paris

Four years ago this month, schools nationwide began to shut down, igniting one of the most polarizing and partisan debates of the pandemic.

Some schools, often in Republican-led states and rural areas, reopened by fall 2020. Others, typically in large cities and states led by Democrats, would not fully reopen for another year.

A variety of data — about children’s academic outcomes and about the spread of Covid-19 — has accumulated in the time since. Today, there is broad acknowledgment among many public health and education experts that extended school closures did not significantly stop the spread of Covid, while the academic harms for children have been large and long-lasting.

While poverty and other factors also played a role, remote learning was a key driver of academic declines during the pandemic, research shows — a finding that held true across income levels.

Source: Fahle, Kane, Patterson, Reardon, Staiger and Stuart, “ School District and Community Factors Associated With Learning Loss During the COVID-19 Pandemic .” Score changes are measured from 2019 to 2022. In-person means a district offered traditional in-person learning, even if not all students were in-person.

“There’s fairly good consensus that, in general, as a society, we probably kept kids out of school longer than we should have,” said Dr. Sean O’Leary, a pediatric infectious disease specialist who helped write guidance for the American Academy of Pediatrics, which recommended in June 2020 that schools reopen with safety measures in place.

There were no easy decisions at the time. Officials had to weigh the risks of an emerging virus against the academic and mental health consequences of closing schools. And even schools that reopened quickly, by the fall of 2020, have seen lasting effects.

But as experts plan for the next public health emergency, whatever it may be, a growing body of research shows that pandemic school closures came at a steep cost to students.

The longer schools were closed, the more students fell behind.

At the state level, more time spent in remote or hybrid instruction in the 2020-21 school year was associated with larger drops in test scores, according to a New York Times analysis of school closure data and results from the National Assessment of Educational Progress , an authoritative exam administered to a national sample of fourth- and eighth-grade students.

At the school district level, that finding also holds, according to an analysis of test scores from third through eighth grade in thousands of U.S. districts, led by researchers at Stanford and Harvard. In districts where students spent most of the 2020-21 school year learning remotely, they fell more than half a grade behind in math on average, while in districts that spent most of the year in person they lost just over a third of a grade.

( A separate study of nearly 10,000 schools found similar results.)

Such losses can be hard to overcome, without significant interventions. The most recent test scores, from spring 2023, show that students, overall, are not caught up from their pandemic losses , with larger gaps remaining among students that lost the most ground to begin with. Students in districts that were remote or hybrid the longest — at least 90 percent of the 2020-21 school year — still had almost double the ground to make up compared with students in districts that allowed students back for most of the year.

Some time in person was better than no time.

As districts shifted toward in-person learning as the year went on, students that were offered a hybrid schedule (a few hours or days a week in person, with the rest online) did better, on average, than those in places where school was fully remote, but worse than those in places that had school fully in person.

Students in hybrid or remote learning, 2020-21

80% of students

Some schools return online, as Covid-19 cases surge. Vaccinations start for high-priority groups.

Teachers are eligible for the Covid vaccine in more than half of states.

Most districts end the year in-person or hybrid.

Source: Burbio audit of more than 1,200 school districts representing 47 percent of U.S. K-12 enrollment. Note: Learning mode was defined based on the most in-person option available to students.

Income and family background also made a big difference.

A second factor associated with academic declines during the pandemic was a community’s poverty level. Comparing districts with similar remote learning policies, poorer districts had steeper losses.

But in-person learning still mattered: Looking at districts with similar poverty levels, remote learning was associated with greater declines.

A community’s poverty rate and the length of school closures had a “roughly equal” effect on student outcomes, said Sean F. Reardon, a professor of poverty and inequality in education at Stanford, who led a district-level analysis with Thomas J. Kane, an economist at Harvard.

Score changes are measured from 2019 to 2022. Poorest and richest are the top and bottom 20% of districts by percent of students on free/reduced lunch. Mostly in-person and mostly remote are districts that offered traditional in-person learning for more than 90 percent or less than 10 percent of the 2020-21 year.

But the combination — poverty and remote learning — was particularly harmful. For each week spent remote, students in poor districts experienced steeper losses in math than peers in richer districts.

That is notable, because poor districts were also more likely to stay remote for longer .

Some of the country’s largest poor districts are in Democratic-leaning cities that took a more cautious approach to the virus. Poor areas, and Black and Hispanic communities , also suffered higher Covid death rates, making many families and teachers in those districts hesitant to return.

“We wanted to survive,” said Sarah Carpenter, the executive director of Memphis Lift, a parent advocacy group in Memphis, where schools were closed until spring 2021 .

“But I also think, man, looking back, I wish our kids could have gone back to school much quicker,” she added, citing the academic effects.

Other things were also associated with worse student outcomes, including increased anxiety and depression among adults in children’s lives, and the overall restriction of social activity in a community, according to the Stanford and Harvard research .

Even short closures had long-term consequences for children.

While being in school was on average better for academic outcomes, it wasn’t a guarantee. Some districts that opened early, like those in Cherokee County, Ga., a suburb of Atlanta, and Hanover County, Va., lost significant learning and remain behind.

At the same time, many schools are seeing more anxiety and behavioral outbursts among students. And chronic absenteeism from school has surged across demographic groups .

These are signs, experts say, that even short-term closures, and the pandemic more broadly, had lasting effects on the culture of education.

“There was almost, in the Covid era, a sense of, ‘We give up, we’re just trying to keep body and soul together,’ and I think that was corrosive to the higher expectations of schools,” said Margaret Spellings, an education secretary under President George W. Bush who is now chief executive of the Bipartisan Policy Center.

Closing schools did not appear to significantly slow Covid’s spread.

Perhaps the biggest question that hung over school reopenings: Was it safe?

That was largely unknown in the spring of 2020, when schools first shut down. But several experts said that had changed by the fall of 2020, when there were initial signs that children were less likely to become seriously ill, and growing evidence from Europe and parts of the United States that opening schools, with safety measures, did not lead to significantly more transmission.

“Infectious disease leaders have generally agreed that school closures were not an important strategy in stemming the spread of Covid,” said Dr. Jeanne Noble, who directed the Covid response at the U.C.S.F. Parnassus emergency department.

Politically, though, there remains some disagreement about when, exactly, it was safe to reopen school.

Republican governors who pushed to open schools sooner have claimed credit for their approach, while Democrats and teachers’ unions have emphasized their commitment to safety and their investment in helping students recover.

“I do believe it was the right decision,” said Jerry T. Jordan, president of the Philadelphia Federation of Teachers, which resisted returning to school in person over concerns about the availability of vaccines and poor ventilation in school buildings. Philadelphia schools waited to partially reopen until the spring of 2021 , a decision Mr. Jordan believes saved lives.

“It doesn’t matter what is going on in the building and how much people are learning if people are getting the virus and running the potential of dying,” he said.

Pandemic school closures offer lessons for the future.

Though the next health crisis may have different particulars, with different risk calculations, the consequences of closing schools are now well established, experts say.

In the future, infectious disease experts said, they hoped decisions would be guided more by epidemiological data as it emerged, taking into account the trade-offs.

“Could we have used data to better guide our decision making? Yes,” said Dr. Uzma N. Hasan, division chief of pediatric infectious diseases at RWJBarnabas Health in Livingston, N.J. “Fear should not guide our decision making.”

Source: Fahle, Kane, Patterson, Reardon, Staiger and Stuart, “ School District and Community Factors Associated With Learning Loss During the Covid-19 Pandemic. ”

The study used estimates of learning loss from the Stanford Education Data Archive . For closure lengths, the study averaged district-level estimates of time spent in remote and hybrid learning compiled by the Covid-19 School Data Hub (C.S.D.H.) and American Enterprise Institute (A.E.I.) . The A.E.I. data defines remote status by whether there was an in-person or hybrid option, even if some students chose to remain virtual. In the C.S.D.H. data set, districts are defined as remote if “all or most” students were virtual.

An earlier version of this article misstated a job description of Dr. Jeanne Noble. She directed the Covid response at the U.C.S.F. Parnassus emergency department. She did not direct the Covid response for the University of California, San Francisco health system.

How we handle corrections

Sarah Mervosh covers education for The Times, focusing on K-12 schools. More about Sarah Mervosh

Claire Cain Miller writes about gender, families and the future of work for The Upshot. She joined The Times in 2008 and was part of a team that won a Pulitzer Prize in 2018 for public service for reporting on workplace sexual harassment issues. More about Claire Cain Miller

Francesca Paris is a Times reporter working with data and graphics for The Upshot. More about Francesca Paris

NIH grant funds research on work-related asthma among nurses

A researcher at the Zuckerman College of Public Health will use a $750,000 grant to develop methodology to inform cleaning and disinfecting protocols and help educate nurses about asthma risks.

A National Institute of Health grant will fund a new study, “Work-related Asthma Risk for Nursing Staff Conducting Cleaning and Disinfection: Translation of Risk-risk Tradeoff Methodology,” at the Mel and Enid Zuckerman College of Public Health.

A researcher at the  University of Arizona Mel and Enid Zuckerman College of Public Health received a $750,000 National Institutes of Health grant to study the asthma risks associated with the use of cleaning and disinfecting products among nurses.

Cleaning processes in health care facilities involve an inherent “risk-risk tradeoff.” Increased use of cleaning and disinfection products leads to increased work-related asthma risks and simultaneously a decrease in occupational-infection risks.

Preliminary survey data indicate that nurses are generally willing to increase infection risks to maintain lower asthma risks if they think they will recover.

Early career researchers Amanda Wilson, PhD, will use a mentoring grant to investigate cleaning and disinfecting protocols used by nurses and their work-related asthma risks.

“Translating these concerns into cleaning and disinfection protocol changes is challenging due to logistical constraints and the lack of awareness about asthma risks,” said principal investigator  Amanda Wilson, PhD , an assistant professor in the Zuckerman College of Public Health’s  Department of Community, Environment and Policy .

Wilson will use the five-year National Heart, Lung and Blood Institute grant to gather data that can advance methodologies for relating tolerable occupational respiratory-disease risks to nurses and guide public health policies. 

In 2022, there were more than 3 million registered nurses in the United States and more than 56,000 of them worked in Arizona, according to the U.S. Bureau of Labor Statistics. Asthma affects more than 27 million people in the United States, or about 1 in 12 Americans, according to the Asthma and Allergy Foundation of America.

The study, “Work-related Asthma Risk for Nursing Staff Conducting Cleaning and Disinfection: Translation of Risk-risk Tradeoff Methodology,” is funded through a K01 Mentored Research Scientist Career Development Award that provides support for early-career researchers.

“Dr. Amanda Wilson earned her PhD with us here in the Zuckerman College of Public Health, and she is on her way to becoming an outstanding public health researcher,” said  Iman Hakim, MD, PhD, MPH , dean of the Zuckerman College of Public Health. “We are so proud of all she has accomplished. This new award shows that she is on her way to making a real difference in public health and respiratory research.”

Wilson’s mentors from the Zuckerman College of Public Health include adjunct professor  Lynn Gerald, PhD ; professor  Paloma Beamer, PhD ; and adjunct professor  Phil Harber, MD, MPH . Other mentors include Benjamin Wilfond, MD, of the University of Washington, Susan Chilton of Newcastle University and David Resnik, JD, PhD, of the National Institute of Environmental Health Sciences.

This research is supported in part by the National Heart, Lung and Blood Institute, a division of the National Institutes of Health, under award number K01HL168014.

Shipherd Reed Mel and Enid Zuckerman College of Public Health 520-626-9669, [email protected]

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• Introduction
• Finding sources

Evaluating sources

• Integrating sources

Citing sources

Tools and resources, a quick guide to working with sources.

Working with sources is an important skill that you’ll need throughout your academic career.

It includes knowing how to find relevant sources, assessing their authority and credibility, and understanding how to integrate sources into your work with proper referencing.

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Finding relevant sources

Sources commonly used in academic writing include academic journals, scholarly books, websites, newspapers, and encyclopedias. There are three main places to look for such sources:

• Research databases: Databases can be general or subject-specific. To get started, check out this list of databases by academic discipline . Another good starting point is Google Scholar .
• Your institution’s library: Use your library’s database to narrow down your search using keywords to find relevant articles, books, and newspapers matching your topic.
• Other online resources: Consult popular online sources like websites, blogs, or Wikipedia to find background information. Be sure to carefully evaluate the credibility of those online sources.

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In academic writing, your sources should be credible, up to date, and relevant to your research topic. Useful approaches to evaluating sources include the CRAAP test and lateral reading.

CRAAP is an abbreviation that reminds you of a set of questions to ask yourself when evaluating information.

• Currency: Does the source reflect recent research?
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• Authority: Is it a respected publication? Is the author an expert in their field?
• Accuracy: Does the source support its arguments and conclusions with evidence?
• Purpose: What is the author’s intention?

Lateral reading

Lateral reading means comparing your source to other sources. This allows you to:

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If a source is using methods or drawing conclusions that are incompatible with other research in its field, it may not be reliable.

Integrating sources into your work

Once you have found information that you want to include in your paper, signal phrases can help you to introduce it. Here are a few examples:

Following the signal phrase, you can choose to quote, paraphrase or summarize the source.

• Quoting : This means including the exact words of another source in your paper. The quoted text must be enclosed in quotation marks or (for longer quotes) presented as a block quote . Quote a source when the meaning is difficult to convey in different words or when you want to analyze the language itself.
• Paraphrasing : This means putting another person’s ideas into your own words. It allows you to integrate sources more smoothly into your text, maintaining a consistent voice. It also shows that you have understood the meaning of the source.
• Summarizing : This means giving an overview of the essential points of a source. Summaries should be much shorter than the original text. You should describe the key points in your own words and not quote from the original text.

Whenever you quote, paraphrase, or summarize a source, you must include a citation crediting the original author.

Citing your sources is important because it:

• Allows you to avoid plagiarism
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• Backs up your arguments with evidence
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The most common citation styles are APA, MLA, and Chicago style. Each citation style has specific rules for formatting citations.

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