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Citation Styles Guide | Examples for All Major Styles

Published on June 24, 2022 by Jack Caulfield . Revised on November 7, 2022.

A citation style is a set of guidelines on how to cite sources in your academic writing . You always need a citation whenever you quote , paraphrase , or summarize a source to avoid plagiarism . How you present these citations depends on the style you follow. Scribbr’s citation generator can help!

Different styles are set by different universities, academic associations, and publishers, often published in an official handbook with in-depth instructions and examples.

There are many different citation styles, but they typically use one of three basic approaches: parenthetical citations , numerical citations, or note citations.

Parenthetical citations

• Chicago (Turabian) author-date

CSE name-year

Numerical citations

CSE citation-name or citation-sequence

Note citations

• Chicago (Turabian) notes and bibliography

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Table of contents

Types of citation: parenthetical, note, numerical, which citation style should i use, parenthetical citation styles, numerical citation styles, note citation styles, frequently asked questions about citation styles.

The clearest identifying characteristic of any citation style is how the citations in the text are presented. There are three main approaches:

• Parenthetical citations: You include identifying details of the source in parentheses in the text—usually the author’s last name and the publication date, plus a page number if relevant ( author-date ). Sometimes the publication date is omitted ( author-page ).
• Numerical citations: You include a number in brackets or in superscript, which corresponds to an entry in your numbered reference list.
• Note citations: You include a full citation in a footnote or endnote, which is indicated in the text with a superscript number or symbol.

Citation styles also differ in terms of how you format the reference list or bibliography entries themselves (e.g., capitalization, order of information, use of italics). And many style guides also provide guidance on more general issues like text formatting, punctuation, and numbers.

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In most cases, your university, department, or instructor will tell you which citation style you need to follow in your writing. If you’re not sure, it’s best to consult your institution’s guidelines or ask someone. If you’re submitting to a journal, they will usually require a specific style.

Sometimes, the choice of citation style may be left up to you. In those cases, you can base your decision on which citation styles are commonly used in your field. Try reading other articles from your discipline to see how they cite their sources, or consult the table below.

The American Anthropological Association (AAA) recommends citing your sources using Chicago author-date style . AAA style doesn’t have its own separate rules. This style is used in the field of anthropology.

APA Style is defined by the 7th edition of the Publication Manual of the American Psychological Association . It was designed for use in psychology, but today it’s widely used across various disciplines, especially in the social sciences.

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The citation style of the American Political Science Association (APSA) is used mainly in the field of political science.

The citation style of the American Sociological Association (ASA) is used primarily in the discipline of sociology.

Chicago author-date

Chicago author-date style is one of the two citation styles presented in the Chicago Manual of Style (17th edition). It’s used mainly in the sciences and social sciences.

The citation style of the Council of Science Editors (CSE) is used in various scientific disciplines. It includes multiple options for citing your sources, including the name-year system.

Harvard style is often used in the field of economics. It is also very widely used across disciplines in UK universities. There are various versions of Harvard style defined by different universities—it’s not a style with one definitive style guide.

Check out Scribbr’s Harvard Reference Generator

MLA style is the official style of the Modern Language Association, defined in the MLA Handbook (9th edition). It’s widely used across various humanities disciplines. Unlike most parenthetical citation styles, it’s author-page rather than author-date.

Generate accurate MLA citations with Scribbr

The American Chemical Society (ACS) provides guidelines for a citation style using numbers in superscript or italics in the text, corresponding to entries in a numbered reference list at the end. It is used in chemistry.

The American Medical Association ( AMA ) provides guidelines for a numerical citation style using superscript numbers in the text, which correspond to entries in a numbered reference list. It is used in the field of medicine.

CSE style includes multiple options for citing your sources, including the citation-name and citation-sequence systems. Your references are listed alphabetically in the citation-name system; in the citation-sequence system, they appear in the order in which you cited them.

The Institute of Electrical and Electronics Engineers ( IEEE ) provides guidelines for citing your sources with IEEE in-text citations that consist of numbers enclosed in brackets, corresponding to entries in a numbered reference list. This style is used in various engineering and IT disciplines.

The National Library of Medicine (NLM) citation style is defined in Citing Medicine: The NLM Style Guide for Authors, Editors, and Publishers (2nd edition).

Vancouver style is also used in various medical disciplines. As with Harvard style, a lot of institutions and publications have their own versions of Vancouver—it doesn’t have one fixed style guide.

The Bluebook: A Uniform System of Citation is the main style guide for legal citations in the US. It’s widely used in law, and also when legal materials need to be cited in other disciplines.

Chicago notes and bibliography

Chicago notes and bibliography is one of the two citation styles presented in the Chicago Manual of Style (17th edition). It’s used mainly in the humanities.

The Oxford University Standard for the Citation of Legal Authorities ( OSCOLA ) is the main legal citation style in the UK (similar to Bluebook for the US).

There are many different citation styles used across different academic disciplines, but they fall into three basic approaches to citation:

• Parenthetical citations : Including identifying details of the source in parentheses —usually the author’s last name and the publication date, plus a page number if available ( author-date ). The publication date is occasionally omitted ( author-page ).
• Numerical citations: Including a number in brackets or superscript, corresponding to an entry in your numbered reference list.
• Note citations: Including a full citation in a footnote or endnote , which is indicated in the text with a superscript number or symbol.

Check if your university or course guidelines specify which citation style to use. If the choice is left up to you, consider which style is most commonly used in your field.

• APA Style is the most popular citation style, widely used in the social and behavioral sciences.
• MLA style is the second most popular, used mainly in the humanities.
• Chicago notes and bibliography style is also popular in the humanities, especially history.
• Chicago author-date style tends to be used in the sciences.

Other more specialized styles exist for certain fields, such as Bluebook and OSCOLA for law.

The most important thing is to choose one style and use it consistently throughout your text.

A scientific citation style is a system of source citation that is used in scientific disciplines. Some commonly used scientific citation styles are:

• Chicago author-date , CSE , and Harvard , used across various sciences
• ACS , used in chemistry
• AMA , NLM , and Vancouver , used in medicine and related disciplines
• AAA , APA , and ASA , commonly used in the social sciences

APA format is widely used by professionals, researchers, and students in the social and behavioral sciences, including fields like education, psychology, and business.

Be sure to check the guidelines of your university or the journal you want to be published in to double-check which style you should be using.

MLA Style  is the second most used citation style (after APA ). It is mainly used by students and researchers in humanities fields such as literature, languages, and philosophy.

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Caulfield, J. (2022, November 07). Citation Styles Guide | Examples for All Major Styles. Scribbr. Retrieved April 2, 2024, from https://www.scribbr.com/citing-sources/citation-styles/

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Citation styles: apa, mla, chicago, turabian, ieee.

• APA 7th Edition
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Quick Links

Listed below are a few quick links to resources that will aid you in citing sources.

• Sign up for a Mendeley, EndNote, or Zotero training class.
• APA 7th Edition Published in October 2019. Visit this page for links to resources and examples.
• MLA Need help with citing MLA style? Find information here along with links to books in PittCat and free online resources.
• Chicago/Turabian Need help with citing Chicago/Turabian style? Find examples here along with links to the online style manual and free online resources.

Getting Started: How to use this guide

This LibGuide was designed to provide you with assistance in citing your sources when writing an academic paper.

There are different styles which format the information differently. In each tab, you will find descriptions of each citation style featured in this guide along with links to online resources for citing and a few examples.

What is a citation and citation style?

A citation is a way of giving credit to individuals for their creative and intellectual works that you utilized to support your research. It can also be used to locate particular sources and combat plagiarism. Typically, a citation can include the author's name, date, location of the publishing company, journal title, or DOI (Digital Object Identifier).

A citation style dictates the information necessary for a citation and how the information is ordered, as well as punctuation and other formatting.

How to do I choose a citation style?

There are many different ways of citing resources from your research. The citation style sometimes depends on the academic discipline involved. For example:

• APA (American Psychological Association) is used by Education, Psychology, and Sciences
• MLA (Modern Language Association) style is used by the Humanities
• Chicago/Turabian style is generally used by Business, History, and the Fine Arts

*You will need to consult with your professor to determine what is required in your specific course.

Click the links below to find descriptions of each style along with a sample of major in-text and bibliographic citations, links to books in PittCat, online citation manuals, and other free online resources.

• APA Citation Style
• MLA Citation Style
• Chicago/Turabian Citation Style
• Tools for creating bibliographies (CItation Managers)

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How to Format a Citation

Examples of apa, mla, and chicago manual of style, citation styles: american psychological association (apa), citation styles: chicago, citation styles: modern language association (mla), example: direct quote cited in a book, example: reference within a journal article.

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There are two basic approaches to citation:

• In-text citations + a list of references at the end of the paper
• Endnotes or footnotes +/- a bibliography at the end of the paper

Scholars writing in the sciences and social sciences typically use in-text citations, while humanities scholars utilize endnotes/footnotes.

While the two basic approaches to citations are simple, there are many different citation styles.

What is a citation style?

The way that citations appear (format) depends on the citation style, which is a set of established rules and conventions for documenting sources.

Citation styles can be defined by an association, such as the Modern Language Association (MLA), publisher, such as the University of Chicago Press, or journal, such as The New England Journal of Medicine .

What citation style should I use?

The citation style that you use depends on the discipline in which you are writing, and where, or by whom, your work will be published or read.

When in doubt, ask your professor if there is a particular style that he/she would like you to use. 

Where can I find more information on how to cite a specific type of source in a particular style?

The library has style manuals in print and online for several commonly used styles such as American Psychological Association (APA), Modern Language Association (MLA) and Chicago.  In addition, there are several excellent citation style guides on the web. (See below)

For examples of APA and MLA and Chicago Manual of Style, visit Purdue's OWL (Online Writing Lab) site.

Frank, H. (2011). Wolves, Dogs, Rearing and Reinforcement: Complex Interactions Underlying Species Differences in Training and Problem-Solving Performance.  Behavior Genetics ,  41 (6), 830-839. 

• Publication Manual of the American Psychological Association Print manual for the APA style, available in the Sciences and Rockefeller libraries.
• Purdue University Online Writing Lab Well-organized, easy-to-follow guide, with numerous examples.
• APA Style American Psychological Association website for the APA Style. Provides tutorials, answers to frequently asked questions, and more.

Frank, H. 2011. "Wolves, Dogs, Rearing and Reinforcement: Complex Interactions Underlying Species Differences in Training and Problem-Solving Performance."   Behavior Genetics  41 (6):830-839. 

• The Chicago Manual of Style Older (15th edition) print manual, available at the Sciences, Rockefeller and Orwig libraries.
• The Chicago Manual of Style Online Current (16th) edition of the Chicago Manual of Style, and answers to frequently asked questions. Off-campus use requires Brown username and password.

Frank, H. "Wolves, Dogs, Rearing and Reinforcement: Complex Interactions Underlying Species Differences in Training and Problem-Solving Performance."  Behavior Genetics  41.6 (2011): 830-39. Print.

• MLA Style Manual and Guide to Scholarly Publishing Print manual for the MLA style. Available in the Rockefeller Library.
• MLA Handbook for Writers of Research Papers Print handbook for the MLA. Available in the Rockefeller Library.

Source: Gabriel, R. A. (2001). Gods of Our Fathers: The Memory of Egypt in Judaism & Christianity . Westport, CT, USA: Greenwood Press.

Source: Bradt, J., Potvin, N., Kesslick, A., Shim, M., Radl, D., Schriver, E., … Komarnicky-Kocher, L. T. (2015). The impact of music therapy versus music medicine on psychological outcomes and pain in cancer patients: a mixed methods study. Supportive Care in Cancer : Official Journal of the Multinational Association of Supportive Care in Cancer , 23 (5), 1261–71.

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If you are having trouble locating a specific resource please visit the  search page  or the  Site Map . The Citation Chart  provides a detailed overview of MLA Style, APA Style, and Chicago Manual of Style source documentation by category.

Conducting Research

These OWL resources will help you conduct research using primary source methods, such as interviews and observations, and secondary source methods, such as books, journals, and the Internet. This area also includes materials on evaluating research sources.

Using Research

These OWL resources will help you use the research you have conducted in your documents. This area includes material on quoting and paraphrasing your research sources, as well as material on how to avoid plagiarism.

APA Style (7th Edition)

These OWL resources will help you learn how to use the American Psychological Association (APA) citation and format style. This section contains resources on in-text citation and the References page, as well as APA sample papers, slide presentations, and the APA classroom poster.

These OWL resources will help you learn how to use the Modern Language Association (MLA) citation and format style. This section contains resources on in-text citation and the Works Cited page, as well as MLA sample papers, slide presentations, and the MLA classroom poster

Chicago Manual of Style

This section contains information on the Chicago Manual of Style method of document formatting and citation. These resources follow the 17th edition of the Chicago Manual of Style, which was issued in 2017.

Institute of Electrical and Electronics Engineers (IEEE) Style

These resources describe how to structure papers, cite sources, format references, and handle the complexities of tables and figures according to the latest Institute of Electrical and Electronics Engineers (IEEE) guidelines.

American Medical Association (AMA) Style

These resources provide guidance on how to cite sources using American Medical Association (AMA) Style, 10th Ed., including examples for print and electronic sources.

Research Overview

We live in an age overflowing with sources of information. With so many information sources at our fingertips, knowing where to start, sorting through it all and finding what we want can be overwhelming! This handout provides answers to the following research-related questions: Where do I begin? Where should I look for information? What types of sources are available?

Conducting Primary Research

Primary research involves collecting data about a given subject directly from the real world. This section includes information on what primary research is, how to get started, ethics involved with primary research and different types of research you can do. It includes details about interviews, surveys, observations, and analysis.

Evaluating Sources of Information

Evaluating sources of information is an important step in any research activity. This section provides information on evaluating bibliographic citations, aspects of evaluation, reading evaluation, print vs. online sources, and evaluating Internet sources.

Searching Online

This section covers finding information online. It includes information about search engines, Boolean operators, Web directories, and the invisible Web. It also includes an extensive, annotated links section.

Internet References

This page contains links and short descriptions of writing resources including dictionaries, style manuals, grammar handbooks, and editing resources. It also contains a list of online reference sites, indexes for writers, online libraries, books and e-texts, as well as links to newspapers, news services, journals, and online magazines.

Archival Research

This resource discusses conducting research in a variety of archives. It also discusses a number of considerations and best practices for conducting archival research.

This resources was developed in consultation with Purdue University Virginia Kelly Karnes Archives and Special Collections staff.

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Organizing Your Social Sciences Research Paper

• 6. The Methodology
• Purpose of Guide
• Design Flaws to Avoid
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• Glossary of Research Terms
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• Narrowing a Topic Idea
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• Common Grammar Mistakes
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The methods section describes actions taken to investigate a research problem and the rationale for the application of specific procedures or techniques used to identify, select, process, and analyze information applied to understanding the problem, thereby, allowing the reader to critically evaluate a study’s overall validity and reliability. The methodology section of a research paper answers two main questions: How was the data collected or generated? And, how was it analyzed? The writing should be direct and precise and always written in the past tense.

Kallet, Richard H. "How to Write the Methods Section of a Research Paper." Respiratory Care 49 (October 2004): 1229-1232.

Importance of a Good Methodology Section

You must explain how you obtained and analyzed your results for the following reasons:

• Readers need to know how the data was obtained because the method you chose affects the results and, by extension, how you interpreted their significance in the discussion section of your paper.
• Methodology is crucial for any branch of scholarship because an unreliable method produces unreliable results and, as a consequence, undermines the value of your analysis of the findings.
• In most cases, there are a variety of different methods you can choose to investigate a research problem. The methodology section of your paper should clearly articulate the reasons why you have chosen a particular procedure or technique.
• The reader wants to know that the data was collected or generated in a way that is consistent with accepted practice in the field of study. For example, if you are using a multiple choice questionnaire, readers need to know that it offered your respondents a reasonable range of answers to choose from.
• The method must be appropriate to fulfilling the overall aims of the study. For example, you need to ensure that you have a large enough sample size to be able to generalize and make recommendations based upon the findings.
• The methodology should discuss the problems that were anticipated and the steps you took to prevent them from occurring. For any problems that do arise, you must describe the ways in which they were minimized or why these problems do not impact in any meaningful way your interpretation of the findings.
• In the social and behavioral sciences, it is important to always provide sufficient information to allow other researchers to adopt or replicate your methodology. This information is particularly important when a new method has been developed or an innovative use of an existing method is utilized.

Bem, Daryl J. Writing the Empirical Journal Article. Psychology Writing Center. University of Washington; Denscombe, Martyn. The Good Research Guide: For Small-Scale Social Research Projects . 5th edition. Buckingham, UK: Open University Press, 2014; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008.

Structure and Writing Style

I.  Groups of Research Methods

There are two main groups of research methods in the social sciences:

• The e mpirical-analytical group approaches the study of social sciences in a similar manner that researchers study the natural sciences . This type of research focuses on objective knowledge, research questions that can be answered yes or no, and operational definitions of variables to be measured. The empirical-analytical group employs deductive reasoning that uses existing theory as a foundation for formulating hypotheses that need to be tested. This approach is focused on explanation.
• The i nterpretative group of methods is focused on understanding phenomenon in a comprehensive, holistic way . Interpretive methods focus on analytically disclosing the meaning-making practices of human subjects [the why, how, or by what means people do what they do], while showing how those practices arrange so that it can be used to generate observable outcomes. Interpretive methods allow you to recognize your connection to the phenomena under investigation. However, the interpretative group requires careful examination of variables because it focuses more on subjective knowledge.

II.  Content

The introduction to your methodology section should begin by restating the research problem and underlying assumptions underpinning your study. This is followed by situating the methods you used to gather, analyze, and process information within the overall “tradition” of your field of study and within the particular research design you have chosen to study the problem. If the method you choose lies outside of the tradition of your field [i.e., your review of the literature demonstrates that the method is not commonly used], provide a justification for how your choice of methods specifically addresses the research problem in ways that have not been utilized in prior studies.

The remainder of your methodology section should describe the following:

• Decisions made in selecting the data you have analyzed or, in the case of qualitative research, the subjects and research setting you have examined,
• Tools and methods used to identify and collect information, and how you identified relevant variables,
• The ways in which you processed the data and the procedures you used to analyze that data, and
• The specific research tools or strategies that you utilized to study the underlying hypothesis and research questions.

In addition, an effectively written methodology section should:

• Introduce the overall methodological approach for investigating your research problem . Is your study qualitative or quantitative or a combination of both (mixed method)? Are you going to take a special approach, such as action research, or a more neutral stance?
• Indicate how the approach fits the overall research design . Your methods for gathering data should have a clear connection to your research problem. In other words, make sure that your methods will actually address the problem. One of the most common deficiencies found in research papers is that the proposed methodology is not suitable to achieving the stated objective of your paper.
• Describe the specific methods of data collection you are going to use , such as, surveys, interviews, questionnaires, observation, archival research. If you are analyzing existing data, such as a data set or archival documents, describe how it was originally created or gathered and by whom. Also be sure to explain how older data is still relevant to investigating the current research problem.
• Explain how you intend to analyze your results . Will you use statistical analysis? Will you use specific theoretical perspectives to help you analyze a text or explain observed behaviors? Describe how you plan to obtain an accurate assessment of relationships, patterns, trends, distributions, and possible contradictions found in the data.
• Provide background and a rationale for methodologies that are unfamiliar for your readers . Very often in the social sciences, research problems and the methods for investigating them require more explanation/rationale than widely accepted rules governing the natural and physical sciences. Be clear and concise in your explanation.
• Provide a justification for subject selection and sampling procedure . For instance, if you propose to conduct interviews, how do you intend to select the sample population? If you are analyzing texts, which texts have you chosen, and why? If you are using statistics, why is this set of data being used? If other data sources exist, explain why the data you chose is most appropriate to addressing the research problem.
• Provide a justification for case study selection . A common method of analyzing research problems in the social sciences is to analyze specific cases. These can be a person, place, event, phenomenon, or other type of subject of analysis that are either examined as a singular topic of in-depth investigation or multiple topics of investigation studied for the purpose of comparing or contrasting findings. In either method, you should explain why a case or cases were chosen and how they specifically relate to the research problem.
• Describe potential limitations . Are there any practical limitations that could affect your data collection? How will you attempt to control for potential confounding variables and errors? If your methodology may lead to problems you can anticipate, state this openly and show why pursuing this methodology outweighs the risk of these problems cropping up.

NOTE :   Once you have written all of the elements of the methods section, subsequent revisions should focus on how to present those elements as clearly and as logically as possibly. The description of how you prepared to study the research problem, how you gathered the data, and the protocol for analyzing the data should be organized chronologically. For clarity, when a large amount of detail must be presented, information should be presented in sub-sections according to topic. If necessary, consider using appendices for raw data.

ANOTHER NOTE : If you are conducting a qualitative analysis of a research problem , the methodology section generally requires a more elaborate description of the methods used as well as an explanation of the processes applied to gathering and analyzing of data than is generally required for studies using quantitative methods. Because you are the primary instrument for generating the data [e.g., through interviews or observations], the process for collecting that data has a significantly greater impact on producing the findings. Therefore, qualitative research requires a more detailed description of the methods used.

YET ANOTHER NOTE :   If your study involves interviews, observations, or other qualitative techniques involving human subjects , you may be required to obtain approval from the university's Office for the Protection of Research Subjects before beginning your research. This is not a common procedure for most undergraduate level student research assignments. However, i f your professor states you need approval, you must include a statement in your methods section that you received official endorsement and adequate informed consent from the office and that there was a clear assessment and minimization of risks to participants and to the university. This statement informs the reader that your study was conducted in an ethical and responsible manner. In some cases, the approval notice is included as an appendix to your paper.

III.  Problems to Avoid

Irrelevant Detail The methodology section of your paper should be thorough but concise. Do not provide any background information that does not directly help the reader understand why a particular method was chosen, how the data was gathered or obtained, and how the data was analyzed in relation to the research problem [note: analyzed, not interpreted! Save how you interpreted the findings for the discussion section]. With this in mind, the page length of your methods section will generally be less than any other section of your paper except the conclusion.

Unnecessary Explanation of Basic Procedures Remember that you are not writing a how-to guide about a particular method. You should make the assumption that readers possess a basic understanding of how to investigate the research problem on their own and, therefore, you do not have to go into great detail about specific methodological procedures. The focus should be on how you applied a method , not on the mechanics of doing a method. An exception to this rule is if you select an unconventional methodological approach; if this is the case, be sure to explain why this approach was chosen and how it enhances the overall process of discovery.

Problem Blindness It is almost a given that you will encounter problems when collecting or generating your data, or, gaps will exist in existing data or archival materials. Do not ignore these problems or pretend they did not occur. Often, documenting how you overcame obstacles can form an interesting part of the methodology. It demonstrates to the reader that you can provide a cogent rationale for the decisions you made to minimize the impact of any problems that arose.

Literature Review Just as the literature review section of your paper provides an overview of sources you have examined while researching a particular topic, the methodology section should cite any sources that informed your choice and application of a particular method [i.e., the choice of a survey should include any citations to the works you used to help construct the survey].

It’s More than Sources of Information! A description of a research study's method should not be confused with a description of the sources of information. Such a list of sources is useful in and of itself, especially if it is accompanied by an explanation about the selection and use of the sources. The description of the project's methodology complements a list of sources in that it sets forth the organization and interpretation of information emanating from those sources.

Azevedo, L.F. et al. "How to Write a Scientific Paper: Writing the Methods Section." Revista Portuguesa de Pneumologia 17 (2011): 232-238; Blair Lorrie. “Choosing a Methodology.” In Writing a Graduate Thesis or Dissertation , Teaching Writing Series. (Rotterdam: Sense Publishers 2016), pp. 49-72; Butin, Dan W. The Education Dissertation A Guide for Practitioner Scholars . Thousand Oaks, CA: Corwin, 2010; Carter, Susan. Structuring Your Research Thesis . New York: Palgrave Macmillan, 2012; Kallet, Richard H. “How to Write the Methods Section of a Research Paper.” Respiratory Care 49 (October 2004):1229-1232; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008. Methods Section. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Rudestam, Kjell Erik and Rae R. Newton. “The Method Chapter: Describing Your Research Plan.” In Surviving Your Dissertation: A Comprehensive Guide to Content and Process . (Thousand Oaks, Sage Publications, 2015), pp. 87-115; What is Interpretive Research. Institute of Public and International Affairs, University of Utah; Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University; Methods and Materials. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College.

Writing Tip

Statistical Designs and Tests? Do Not Fear Them!

Don't avoid using a quantitative approach to analyzing your research problem just because you fear the idea of applying statistical designs and tests. A qualitative approach, such as conducting interviews or content analysis of archival texts, can yield exciting new insights about a research problem, but it should not be undertaken simply because you have a disdain for running a simple regression. A well designed quantitative research study can often be accomplished in very clear and direct ways, whereas, a similar study of a qualitative nature usually requires considerable time to analyze large volumes of data and a tremendous burden to create new paths for analysis where previously no path associated with your research problem had existed.

To locate data and statistics, GO HERE .

Another Writing Tip

Knowing the Relationship Between Theories and Methods

There can be multiple meaning associated with the term "theories" and the term "methods" in social sciences research. A helpful way to delineate between them is to understand "theories" as representing different ways of characterizing the social world when you research it and "methods" as representing different ways of generating and analyzing data about that social world. Framed in this way, all empirical social sciences research involves theories and methods, whether they are stated explicitly or not. However, while theories and methods are often related, it is important that, as a researcher, you deliberately separate them in order to avoid your theories playing a disproportionate role in shaping what outcomes your chosen methods produce.

Introspectively engage in an ongoing dialectic between the application of theories and methods to help enable you to use the outcomes from your methods to interrogate and develop new theories, or ways of framing conceptually the research problem. This is how scholarship grows and branches out into new intellectual territory.

Reynolds, R. Larry. Ways of Knowing. Alternative Microeconomics . Part 1, Chapter 3. Boise State University; The Theory-Method Relationship. S-Cool Revision. United Kingdom.

Yet Another Writing Tip

Methods and the Methodology

Do not confuse the terms "methods" and "methodology." As Schneider notes, a method refers to the technical steps taken to do research . Descriptions of methods usually include defining and stating why you have chosen specific techniques to investigate a research problem, followed by an outline of the procedures you used to systematically select, gather, and process the data [remember to always save the interpretation of data for the discussion section of your paper].

The methodology refers to a discussion of the underlying reasoning why particular methods were used . This discussion includes describing the theoretical concepts that inform the choice of methods to be applied, placing the choice of methods within the more general nature of academic work, and reviewing its relevance to examining the research problem. The methodology section also includes a thorough review of the methods other scholars have used to study the topic.

Bryman, Alan. "Of Methods and Methodology." Qualitative Research in Organizations and Management: An International Journal 3 (2008): 159-168; Schneider, Florian. “What's in a Methodology: The Difference between Method, Methodology, and Theory…and How to Get the Balance Right?” PoliticsEastAsia.com. Chinese Department, University of Leiden, Netherlands.

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Organizing Academic Research Papers: 6. The Methodology

• Purpose of Guide
• Design Flaws to Avoid
• Glossary of Research Terms
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• Executive Summary
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tertiary Sources
• What Is Scholarly vs. Popular?
• Qualitative Methods
• Quantitative Methods
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Annotated Bibliography
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• How to Manage Group Projects
• Multiple Book Review Essay
• Reviewing Collected Essays
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Research Proposal
• Acknowledgements

The methods section of a research paper provides the information by which a study’s validity is judged. The method section answers two main questions: 1) How was the data collected or generated? 2) How was it analyzed? The writing should be direct and precise and written in the past tense.

Importance of a Good Methodology Section

You must explain how you obtained and analyzed your results for the following reasons:

• Readers need to know how the data was obtained because the method you choose affects the results and, by extension, how you likely interpreted those results.
• Methodology is crucial for any branch of scholarship because an unreliable method produces unreliable results and it misappropriates interpretations of findings .
• In most cases, there are a variety of different methods you can choose to investigate a research problem. Your methodology section of your paper should make clear the reasons why you chose a particular method or procedure .
• The reader wants to know that the data was collected or generated in a way that is consistent with accepted practice in the field of study. For example, if you are using a questionnaire, readers need to know that it offered your respondents a reasonable range of answers to choose from.
• The research method must be appropriate to the objectives of the study . For example, be sure you have a large enough sample size to be able to generalize and make recommendations based upon the findings.
• The methodology should discuss the problems that were anticipated and the steps you took to prevent them from occurring . For any problems that did arise, you must describe the ways in which their impact was minimized or why these problems do not affect the findings in any way that impacts your interpretation of the data.
• Often in social science research, it is useful for other researchers to adapt or replicate your methodology. Therefore, it is important to always provide sufficient information to allow others to use or replicate the study . This information is particularly important when a new method had been developed or an innovative use of an existing method has been utilized.

Bem, Daryl J. Writing the Empirical Journal Article . Psychology Writing Center. University of Washington; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008.

Structure and Writing Style

I. Groups of Research Methods

There are two main groups of research methods in the social sciences:

• The empirical-analytical group approaches the study of social sciences in a similar manner that researchers study the natural sciences. This type of research focuses on objective knowledge, research questions that can be answered yes or no, and operational definitions of variables to be measured. The empirical-analytical group employs deductive reasoning that uses existing theory as a foundation for hypotheses that need to be tested. This approach is focused on explanation .
• The interpretative group is focused on understanding phenomenon in a comprehensive, holistic way . This research method allows you to recognize your connection to the subject under study. Because the interpretative group focuses more on subjective knowledge, it requires careful interpretation of variables.

II. Content

An effectively written methodology section should:

• Introduce the overall methodological approach for investigating your research problem . Is your study qualitative or quantitative or a combination of both (mixed method)? Are you going to take a special approach, such as action research, or a more neutral stance?
• Indicate how the approach fits the overall research design . Your methods should have a clear connection with your research problem. In other words, make sure that your methods will actually address the problem. One of the most common deficiencies found in research papers is that the proposed methodology is unsuited to achieving the stated objective of your paper.
• Describe the specific methods of data collection you are going to use , such as, surveys, interviews, questionnaires, observation, archival research. If you are analyzing existing data, such as a data set or archival documents, describe how it was originally created or gathered and by whom.
• Explain how you intend to analyze your results . Will you use statistical analysis? Will you use specific theoretical perspectives to help you analyze a text or explain observed behaviors?
• Provide background and rationale for methodologies that are unfamiliar for your readers . Very often in the social sciences, research problems and the methods for investigating them require more explanation/rationale than widely accepted rules governing the natural and physical sciences. Be clear and concise in your explanation.
• Provide a rationale for subject selection and sampling procedure . For instance, if you propose to conduct interviews, how do you intend to select the sample population? If you are analyzing texts, which texts have you chosen, and why? If you are using statistics, why is this set of statisics being used? If other data sources exist, explain why the data you chose is most appropriate.
• Address potential limitations . Are there any practical limitations that could affect your data collection? How will you attempt to control for potential confounding variables and errors? If your methodology may lead to problems you can anticipate, state this openly and show why pursuing this methodology outweighs the risk of these problems cropping up.

NOTE :  Once you have written all of the elements of the methods section, subsequent revisions should focus on how to present those elements as clearly and as logically as possibly. The description of how you prepared to study the research problem, how you gathered the data, and the protocol for analyzing the data should be organized chronologically. For clarity, when a large amount of detail must be presented, information should be presented in sub-sections according to topic.

III.  Problems to Avoid

Irrelevant Detail The methodology section of your paper should be thorough but to the point. Don’t provide any background information that doesn’t directly help the reader to understand why a particular method was chosen, how the data was gathered or obtained, and how it was analyzed. Unnecessary Explanation of Basic Procedures Remember that you are not writing a how-to guide about a particular method. You should make the assumption that readers possess a basic understanding of how to investigate the research problem on their own and, therefore, you do not have to go into great detail about specific methodological procedures. The focus should be on how you applied a method , not on the mechanics of doing a method. NOTE: An exception to this rule is if you select an unconventional approach to doing the method; if this is the case, be sure to explain why this approach was chosen and how it enhances the overall research process. Problem Blindness It is almost a given that you will encounter problems when collecting or generating your data. Do not ignore these problems or pretend they did not occur. Often, documenting how you overcame obstacles can form an interesting part of the methodology. It demonstrates to the reader that you can provide a cogent rationale for the decisions you made to minimize the impact of any problems that arose. Literature Review Just as the literature review section of your paper provides an overview of sources you have examined while researching a particular topic, the methodology section should cite any sources that informed your choice and application of a particular method [i.e., the choice of a survey should include any citations to the works you used to help construct the survey].

It’s More than Sources of Information! A description of a research study's method should not be confused with a description of the sources of information. Such a list of sources is useful in itself, especially if it is accompanied by an explanation about the selection and use of the sources. The description of the project's methodology complements a list of sources in that it sets forth the organization and interpretation of information emanating from those sources.

Azevedo, L.F. et al. How to Write a Scientific Paper: Writing the Methods Section. Revista Portuguesa de Pneumologia 17 (2011): 232-238; Butin, Dan W. The Education Dissertation A Guide for Practitioner Scholars . Thousand Oaks, CA: Corwin, 2010; Carter, Susan. Structuring Your Research Thesis . New York: Palgrave Macmillan, 2012; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008. Methods Section . The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Writing the Experimental Report: Methods, Results, and Discussion . The Writing Lab and The OWL. Purdue University; Methods and Materials . The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College.

Writing Tip

Statistical Designs and Tests? Do Not Fear Them!

Don't avoid using a quantitative approach to analyzing your research problem just because you fear the idea of applying statistical designs and tests. A qualitative approach, such as conducting interviews or content analysis of archival texts, can yield exciting new insights about a research problem, but it should not be undertaken simply because you have a disdain for running a simple regression. A well designed quantitative research study can often be accomplished in very clear and direct ways, whereas, a similar study of a qualitative nature usually requires considerable time to analyze large volumes of data and a tremendous burden to create new paths for analysis where previously no path associated with your research problem had existed.

Another Writing Tip

Knowing the Relationship Between Theories and Methods

There can be multiple meaning associated with the term "theories" and the term "methods" in social sciences research. A helpful way to delineate between them is to understand "theories" as representing different ways of characterizing the social world when you research it and "methods" as representing different ways of generating and analyzing data about that social world. Framed in this way, all empirical social sciences research involves theories and methods, whether they are stated explicitly or not. However, while theories and methods are often related, it is important that, as a researcher, you deliberately separate them in order to avoid your theories playing a disproportionate role in shaping what outcomes your chosen methods produce.

Introspectively engage in an ongoing dialectic between theories and methods to help enable you to use the outcomes from your methods to interrogate and develop new theories, or ways of framing conceptually the research problem. This is how scholarship grows and branches out into new intellectual territory.

Reynolds, R. Larry. Ways of Knowing. Alternative Microeconomics. Part 1, Chapter 3. Boise State University; The Theory-Method Relationship . S-Cool Revision. United Kingdom.

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• Next: Qualitative Methods >>
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Here's What You Need to Understand About Research Methodology

Table of Contents

Research methodology involves a systematic and well-structured approach to conducting scholarly or scientific inquiries. Knowing the significance of research methodology and its different components is crucial as it serves as the basis for any study.

Typically, your research topic will start as a broad idea you want to investigate more thoroughly. Once you’ve identified a research problem and created research questions , you must choose the appropriate methodology and frameworks to address those questions effectively.

What is the definition of a research methodology?

Research methodology is the process or the way you intend to execute your study. The methodology section of a research paper outlines how you plan to conduct your study. It covers various steps such as collecting data, statistical analysis, observing participants, and other procedures involved in the research process

The methods section should give a description of the process that will convert your idea into a study. Additionally, the outcomes of your process must provide valid and reliable results resonant with the aims and objectives of your research. This thumb rule holds complete validity, no matter whether your paper has inclinations for qualitative or quantitative usage.

Studying research methods used in related studies can provide helpful insights and direction for your own research. Now easily discover papers related to your topic on SciSpace and utilize our AI research assistant, Copilot , to quickly review the methodologies applied in different papers.

The need for a good research methodology

While deciding on your approach towards your research, the reason or factors you weighed in choosing a particular problem and formulating a research topic need to be validated and explained. A research methodology helps you do exactly that. Moreover, a good research methodology lets you build your argument to validate your research work performed through various data collection methods, analytical methods, and other essential points.

Just imagine it as a strategy documented to provide an overview of what you intend to do.

While undertaking any research writing or performing the research itself, you may get drifted in not something of much importance. In such a case, a research methodology helps you to get back to your outlined work methodology.

A research methodology helps in keeping you accountable for your work. Additionally, it can help you evaluate whether your work is in sync with your original aims and objectives or not. Besides, a good research methodology enables you to navigate your research process smoothly and swiftly while providing effective planning to achieve your desired results.

What is the basic structure of a research methodology?

Usually, you must ensure to include the following stated aspects while deciding over the basic structure of your research methodology:

1. Your research procedure

Explain what research methods you’re going to use. Whether you intend to proceed with quantitative or qualitative, or a composite of both approaches, you need to state that explicitly. The option among the three depends on your research’s aim, objectives, and scope.

2. Provide the rationality behind your chosen approach

Based on logic and reason, let your readers know why you have chosen said research methodologies. Additionally, you have to build strong arguments supporting why your chosen research method is the best way to achieve the desired outcome.

3. Explain your mechanism

The mechanism encompasses the research methods or instruments you will use to develop your research methodology. It usually refers to your data collection methods. You can use interviews, surveys, physical questionnaires, etc., of the many available mechanisms as research methodology instruments. The data collection method is determined by the type of research and whether the data is quantitative data(includes numerical data) or qualitative data (perception, morale, etc.) Moreover, you need to put logical reasoning behind choosing a particular instrument.

4. Significance of outcomes

The results will be available once you have finished experimenting. However, you should also explain how you plan to use the data to interpret the findings. This section also aids in understanding the problem from within, breaking it down into pieces, and viewing the research problem from various perspectives.

5. Reader’s advice

Anything that you feel must be explained to spread more awareness among readers and focus groups must be included and described in detail. You should not just specify your research methodology on the assumption that a reader is aware of the topic.  

All the relevant information that explains and simplifies your research paper must be included in the methodology section. If you are conducting your research in a non-traditional manner, give a logical justification and list its benefits.

6. Explain your sample space

Include information about the sample and sample space in the methodology section. The term "sample" refers to a smaller set of data that a researcher selects or chooses from a larger group of people or focus groups using a predetermined selection method. Let your readers know how you are going to distinguish between relevant and non-relevant samples. How you figured out those exact numbers to back your research methodology, i.e. the sample spacing of instruments, must be discussed thoroughly.

For example, if you are going to conduct a survey or interview, then by what procedure will you select the interviewees (or sample size in case of surveys), and how exactly will the interview or survey be conducted.

7. Challenges and limitations

This part, which is frequently assumed to be unnecessary, is actually very important. The challenges and limitations that your chosen strategy inherently possesses must be specified while you are conducting different types of research.

The importance of a good research methodology

You must have observed that all research papers, dissertations, or theses carry a chapter entirely dedicated to research methodology. This section helps maintain your credibility as a better interpreter of results rather than a manipulator.

A good research methodology always explains the procedure, data collection methods and techniques, aim, and scope of the research. In a research study, it leads to a well-organized, rationality-based approach, while the paper lacking it is often observed as messy or disorganized.

You should pay special attention to validating your chosen way towards the research methodology. This becomes extremely important in case you select an unconventional or a distinct method of execution.

Curating and developing a strong, effective research methodology can assist you in addressing a variety of situations, such as:

• When someone tries to duplicate or expand upon your research after few years.
• If a contradiction or conflict of facts occurs at a later time. This gives you the security you need to deal with these contradictions while still being able to defend your approach.
• Gaining a tactical approach in getting your research completed in time. Just ensure you are using the right approach while drafting your research methodology, and it can help you achieve your desired outcomes. Additionally, it provides a better explanation and understanding of the research question itself.
• Documenting the results so that the final outcome of the research stays as you intended it to be while starting.

Instruments you could use while writing a good research methodology

As a researcher, you must choose which tools or data collection methods that fit best in terms of the relevance of your research. This decision has to be wise.

There exists many research equipments or tools that you can use to carry out your research process. These are classified as:

a. Interviews (One-on-One or a Group)

An interview aimed to get your desired research outcomes can be undertaken in many different ways. For example, you can design your interview as structured, semi-structured, or unstructured. What sets them apart is the degree of formality in the questions. On the other hand, in a group interview, your aim should be to collect more opinions and group perceptions from the focus groups on a certain topic rather than looking out for some formal answers.

In surveys, you are in better control if you specifically draft the questions you seek the response for. For example, you may choose to include free-style questions that can be answered descriptively, or you may provide a multiple-choice type response for questions. Besides, you can also opt to choose both ways, deciding what suits your research process and purpose better.

c. Sample Groups

Similar to the group interviews, here, you can select a group of individuals and assign them a topic to discuss or freely express their opinions over that. You can simultaneously note down the answers and later draft them appropriately, deciding on the relevance of every response.

d. Observations

If your research domain is humanities or sociology, observations are the best-proven method to draw your research methodology. Of course, you can always include studying the spontaneous response of the participants towards a situation or conducting the same but in a more structured manner. A structured observation means putting the participants in a situation at a previously decided time and then studying their responses.

Of all the tools described above, it is you who should wisely choose the instruments and decide what’s the best fit for your research. You must not restrict yourself from multiple methods or a combination of a few instruments if appropriate in drafting a good research methodology.

Types of research methodology

A research methodology exists in various forms. Depending upon their approach, whether centered around words, numbers, or both, methodologies are distinguished as qualitative, quantitative, or an amalgamation of both.

1. Qualitative research methodology

When a research methodology primarily focuses on words and textual data, then it is generally referred to as qualitative research methodology. This type is usually preferred among researchers when the aim and scope of the research are mainly theoretical and explanatory.

The instruments used are observations, interviews, and sample groups. You can use this methodology if you are trying to study human behavior or response in some situations. Generally, qualitative research methodology is widely used in sociology, psychology, and other related domains.

2. Quantitative research methodology

If your research is majorly centered on data, figures, and stats, then analyzing these numerical data is often referred to as quantitative research methodology. You can use quantitative research methodology if your research requires you to validate or justify the obtained results.

In quantitative methods, surveys, tests, experiments, and evaluations of current databases can be advantageously used as instruments If your research involves testing some hypothesis, then use this methodology.

3. Amalgam methodology

As the name suggests, the amalgam methodology uses both quantitative and qualitative approaches. This methodology is used when a part of the research requires you to verify the facts and figures, whereas the other part demands you to discover the theoretical and explanatory nature of the research question.

The instruments for the amalgam methodology require you to conduct interviews and surveys, including tests and experiments. The outcome of this methodology can be insightful and valuable as it provides precise test results in line with theoretical explanations and reasoning.

The amalgam method, makes your work both factual and rational at the same time.

Final words: How to decide which is the best research methodology?

If you have kept your sincerity and awareness intact with the aims and scope of research well enough, you must have got an idea of which research methodology suits your work best.

Before deciding which research methodology answers your research question, you must invest significant time in reading and doing your homework for that. Taking references that yield relevant results should be your first approach to establishing a research methodology.

Moreover, you should never refrain from exploring other options. Before setting your work in stone, you must try all the available options as it explains why the choice of research methodology that you finally make is more appropriate than the other available options.

You should always go for a quantitative research methodology if your research requires gathering large amounts of data, figures, and statistics. This research methodology will provide you with results if your research paper involves the validation of some hypothesis.

Whereas, if  you are looking for more explanations, reasons, opinions, and public perceptions around a theory, you must use qualitative research methodology.The choice of an appropriate research methodology ultimately depends on what you want to achieve through your research.

Frequently Asked Questions (FAQs) about Research Methodology

1. how to write a research methodology.

You can always provide a separate section for research methodology where you should specify details about the methods and instruments used during the research, discussions on result analysis, including insights into the background information, and conveying the research limitations.

2. What are the types of research methodology?

There generally exists four types of research methodology i.e.

• Observation
• Experimental
• Derivational

3. What is the true meaning of research methodology?

The set of techniques or procedures followed to discover and analyze the information gathered to validate or justify a research outcome is generally called Research Methodology.

4. Where lies the importance of research methodology?

Your research methodology directly reflects the validity of your research outcomes and how well-informed your research work is. Moreover, it can help future researchers cite or refer to your research if they plan to use a similar research methodology.

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Home » Research Paper Format – Types, Examples and Templates

Research Paper Format – Types, Examples and Templates

Table of Contents

Research paper format is an essential aspect of academic writing that plays a crucial role in the communication of research findings . The format of a research paper depends on various factors such as the discipline, style guide, and purpose of the research. It includes guidelines for the structure, citation style, referencing , and other elements of the paper that contribute to its overall presentation and coherence. Adhering to the appropriate research paper format is vital for ensuring that the research is accurately and effectively communicated to the intended audience. In this era of information, it is essential to understand the different research paper formats and their guidelines to communicate research effectively, accurately, and with the required level of detail. This post aims to provide an overview of some of the common research paper formats used in academic writing.

Research Paper Formats

Research Paper Formats are as follows:

• APA (American Psychological Association) format
• MLA (Modern Language Association) format
• Chicago/Turabian style
• IEEE (Institute of Electrical and Electronics Engineers) format
• AMA (American Medical Association) style
• Harvard style
• Vancouver style
• ACS (American Chemical Society) style
• ASA (American Sociological Association) style
• APSA (American Political Science Association) style

APA (American Psychological Association) Format

Here is a general APA format for a research paper:

• Title Page: The title page should include the title of your paper, your name, and your institutional affiliation. It should also include a running head, which is a shortened version of the title, and a page number in the upper right-hand corner.
• Abstract : The abstract is a brief summary of your paper, typically 150-250 words. It should include the purpose of your research, the main findings, and any implications or conclusions that can be drawn.
• Introduction: The introduction should provide background information on your topic, state the purpose of your research, and present your research question or hypothesis. It should also include a brief literature review that discusses previous research on your topic.
• Methods: The methods section should describe the procedures you used to collect and analyze your data. It should include information on the participants, the materials and instruments used, and the statistical analyses performed.
• Results: The results section should present the findings of your research in a clear and concise manner. Use tables and figures to help illustrate your results.
• Discussion : The discussion section should interpret your results and relate them back to your research question or hypothesis. It should also discuss the implications of your findings and any limitations of your study.
• References : The references section should include a list of all sources cited in your paper. Follow APA formatting guidelines for your citations and references.

Some additional tips for formatting your APA research paper:

• Use 12-point Times New Roman font throughout the paper.
• Double-space all text, including the references.
• Use 1-inch margins on all sides of the page.
• Indent the first line of each paragraph by 0.5 inches.
• Use a hanging indent for the references (the first line should be flush with the left margin, and all subsequent lines should be indented).
• Number all pages, including the title page and references page, in the upper right-hand corner.

APA Research Paper Format Template

APA Research Paper Format Template is as follows:

Title Page:

• Title of the paper
• Author’s name
• Institutional affiliation
• A brief summary of the main points of the paper, including the research question, methods, findings, and conclusions. The abstract should be no more than 250 words.

Introduction:

• Background information on the topic of the research paper
• Research question or hypothesis
• Significance of the study
• Overview of the research methods and design
• Brief summary of the main findings
• Participants: description of the sample population, including the number of participants and their characteristics (age, gender, ethnicity, etc.)
• Materials: description of any materials used in the study (e.g., survey questions, experimental apparatus)
• Procedure: detailed description of the steps taken to conduct the study
• Presentation of the findings of the study, including statistical analyses if applicable
• Tables and figures may be included to illustrate the results

Discussion:

• Interpretation of the results in light of the research question and hypothesis
• Implications of the study for the field
• Limitations of the study
• Suggestions for future research

References:

• A list of all sources cited in the paper, in APA format

Formatting guidelines:

• Double-spaced
• 12-point font (Times New Roman or Arial)
• 1-inch margins on all sides
• Page numbers in the top right corner
• Headings and subheadings should be used to organize the paper
• The first line of each paragraph should be indented
• Quotations of 40 or more words should be set off in a block quote with no quotation marks
• In-text citations should include the author’s last name and year of publication (e.g., Smith, 2019)

APA Research Paper Format Example

APA Research Paper Format Example is as follows:

The Effects of Social Media on Mental Health

University of XYZ

This study examines the relationship between social media use and mental health among college students. Data was collected through a survey of 500 students at the University of XYZ. Results suggest that social media use is significantly related to symptoms of depression and anxiety, and that the negative effects of social media are greater among frequent users.

Social media has become an increasingly important aspect of modern life, especially among young adults. While social media can have many positive effects, such as connecting people across distances and sharing information, there is growing concern about its impact on mental health. This study aims to examine the relationship between social media use and mental health among college students.

Participants: Participants were 500 college students at the University of XYZ, recruited through online advertisements and flyers posted on campus. Participants ranged in age from 18 to 25, with a mean age of 20.5 years. The sample was 60% female, 40% male, and 5% identified as non-binary or gender non-conforming.

Data was collected through an online survey administered through Qualtrics. The survey consisted of several measures, including the Patient Health Questionnaire-9 (PHQ-9) for depression symptoms, the Generalized Anxiety Disorder-7 (GAD-7) for anxiety symptoms, and questions about social media use.

Procedure :

Participants were asked to complete the online survey at their convenience. The survey took approximately 20-30 minutes to complete. Data was analyzed using descriptive statistics, correlations, and multiple regression analysis.

Results indicated that social media use was significantly related to symptoms of depression (r = .32, p < .001) and anxiety (r = .29, p < .001). Regression analysis indicated that frequency of social media use was a significant predictor of both depression symptoms (β = .24, p < .001) and anxiety symptoms (β = .20, p < .001), even when controlling for age, gender, and other relevant factors.

The results of this study suggest that social media use is associated with symptoms of depression and anxiety among college students. The negative effects of social media are greater among frequent users. These findings have important implications for mental health professionals and educators, who should consider addressing the potential negative effects of social media use in their work with young adults.

References :

References should be listed in alphabetical order according to the author’s last name. For example:

• Chou, H. T. G., & Edge, N. (2012). “They are happier and having better lives than I am”: The impact of using Facebook on perceptions of others’ lives. Cyberpsychology, Behavior, and Social Networking, 15(2), 117-121.
• Twenge, J. M., Joiner, T. E., Rogers, M. L., & Martin, G. N. (2018). Increases in depressive symptoms, suicide-related outcomes, and suicide rates among U.S. adolescents after 2010 and links to increased new media screen time. Clinical Psychological Science, 6(1), 3-17.

Note: This is just a sample Example do not use this in your assignment.

MLA (Modern Language Association) Format

MLA (Modern Language Association) Format is as follows:

• Page Layout : Use 8.5 x 11-inch white paper, with 1-inch margins on all sides. The font should be 12-point Times New Roman or a similar serif font.
• Heading and Title : The first page of your research paper should include a heading and a title. The heading should include your name, your instructor’s name, the course title, and the date. The title should be centered and in title case (capitalizing the first letter of each important word).
• In-Text Citations : Use parenthetical citations to indicate the source of your information. The citation should include the author’s last name and the page number(s) of the source. For example: (Smith 23).
• Works Cited Page : At the end of your paper, include a Works Cited page that lists all the sources you used in your research. Each entry should include the author’s name, the title of the work, the publication information, and the medium of publication.
• Formatting Quotations : Use double quotation marks for short quotations and block quotations for longer quotations. Indent the entire quotation five spaces from the left margin.
• Formatting the Body : Use a clear and readable font and double-space your text throughout. The first line of each paragraph should be indented one-half inch from the left margin.

MLA Research Paper Template

MLA Research Paper Format Template is as follows:

• Use 8.5 x 11 inch white paper.
• Use a 12-point font, such as Times New Roman.
• Use double-spacing throughout the entire paper, including the title page and works cited page.
• Set the margins to 1 inch on all sides.
• Use page numbers in the upper right corner, beginning with the first page of text.
• Include a centered title for the research paper, using title case (capitalizing the first letter of each important word).
• Include your name, instructor’s name, course name, and date in the upper left corner, double-spaced.

In-Text Citations

• When quoting or paraphrasing information from sources, include an in-text citation within the text of your paper.
• Use the author’s last name and the page number in parentheses at the end of the sentence, before the punctuation mark.
• If the author’s name is mentioned in the sentence, only include the page number in parentheses.

Works Cited Page

• List all sources cited in alphabetical order by the author’s last name.
• Each entry should include the author’s name, title of the work, publication information, and medium of publication.
• Use italics for book and journal titles, and quotation marks for article and chapter titles.
• For online sources, include the date of access and the URL.

Here is an example of how the first page of a research paper in MLA format should look:

Headings and Subheadings

• Use headings and subheadings to organize your paper and make it easier to read.
• Use numerals to number your headings and subheadings (e.g. 1, 2, 3), and capitalize the first letter of each word.
• The main heading should be centered and in boldface type, while subheadings should be left-aligned and in italics.
• Use only one space after each period or punctuation mark.
• Use quotation marks to indicate direct quotes from a source.
• If the quote is more than four lines, format it as a block quote, indented one inch from the left margin and without quotation marks.
• Use ellipses (…) to indicate omitted words from a quote, and brackets ([…]) to indicate added words.

Works Cited Examples

• Book: Last Name, First Name. Title of Book. Publisher, Publication Year.
• Journal Article: Last Name, First Name. “Title of Article.” Title of Journal, volume number, issue number, publication date, page numbers.
• Website: Last Name, First Name. “Title of Webpage.” Title of Website, publication date, URL. Accessed date.

Here is an example of how a works cited entry for a book should look:

Smith, John. The Art of Writing Research Papers. Penguin, 2021.

MLA Research Paper Example

MLA Research Paper Format Example is as follows:

Your Professor’s Name

Course Name and Number

Date (in Day Month Year format)

Word Count (not including title page or Works Cited)

Title: The Impact of Video Games on Aggression Levels

Video games have become a popular form of entertainment among people of all ages. However, the impact of video games on aggression levels has been a subject of debate among scholars and researchers. While some argue that video games promote aggression and violent behavior, others argue that there is no clear link between video games and aggression levels. This research paper aims to explore the impact of video games on aggression levels among young adults.

Background:

The debate on the impact of video games on aggression levels has been ongoing for several years. According to the American Psychological Association, exposure to violent media, including video games, can increase aggression levels in children and adolescents. However, some researchers argue that there is no clear evidence to support this claim. Several studies have been conducted to examine the impact of video games on aggression levels, but the results have been mixed.

Methodology:

This research paper used a quantitative research approach to examine the impact of video games on aggression levels among young adults. A sample of 100 young adults between the ages of 18 and 25 was selected for the study. The participants were asked to complete a questionnaire that measured their aggression levels and their video game habits.

The results of the study showed that there was a significant correlation between video game habits and aggression levels among young adults. The participants who reported playing violent video games for more than 5 hours per week had higher aggression levels than those who played less than 5 hours per week. The study also found that male participants were more likely to play violent video games and had higher aggression levels than female participants.

The findings of this study support the claim that video games can increase aggression levels among young adults. However, it is important to note that the study only examined the impact of video games on aggression levels and did not take into account other factors that may contribute to aggressive behavior. It is also important to note that not all video games promote violence and aggression, and some games may have a positive impact on cognitive and social skills.

Conclusion :

In conclusion, this research paper provides evidence to support the claim that video games can increase aggression levels among young adults. However, it is important to conduct further research to examine the impact of video games on other aspects of behavior and to explore the potential benefits of video games. Parents and educators should be aware of the potential impact of video games on aggression levels and should encourage young adults to engage in a variety of activities that promote cognitive and social skills.

Works Cited:

• American Psychological Association. (2017). Violent Video Games: Myths, Facts, and Unanswered Questions. Retrieved from https://www.apa.org/news/press/releases/2017/08/violent-video-games
• Ferguson, C. J. (2015). Do Angry Birds make for angry children? A meta-analysis of video game influences on children’s and adolescents’ aggression, mental health, prosocial behavior, and academic performance. Perspectives on Psychological Science, 10(5), 646-666.
• Gentile, D. A., Swing, E. L., Lim, C. G., & Khoo, A. (2012). Video game playing, attention problems, and impulsiveness: Evidence of bidirectional causality. Psychology of Popular Media Culture, 1(1), 62-70.
• Greitemeyer, T. (2014). Effects of prosocial video games on prosocial behavior. Journal of Personality and Social Psychology, 106(4), 530-548.

Chicago/Turabian Style

Chicago/Turabian Formate is as follows:

• Margins : Use 1-inch margins on all sides of the paper.
• Font : Use a readable font such as Times New Roman or Arial, and use a 12-point font size.
• Page numbering : Number all pages in the upper right-hand corner, beginning with the first page of text. Use Arabic numerals.
• Title page: Include a title page with the title of the paper, your name, course title and number, instructor’s name, and the date. The title should be centered on the page and in title case (capitalize the first letter of each word).
• Headings: Use headings to organize your paper. The first level of headings should be centered and in boldface or italics. The second level of headings should be left-aligned and in boldface or italics. Use as many levels of headings as necessary to organize your paper.
• In-text citations : Use footnotes or endnotes to cite sources within the text of your paper. The first citation for each source should be a full citation, and subsequent citations can be shortened. Use superscript numbers to indicate footnotes or endnotes.
• Bibliography : Include a bibliography at the end of your paper, listing all sources cited in your paper. The bibliography should be in alphabetical order by the author’s last name, and each entry should include the author’s name, title of the work, publication information, and date of publication.
• Formatting of quotations: Use block quotations for quotations that are longer than four lines. Indent the entire quotation one inch from the left margin, and do not use quotation marks. Single-space the quotation, and double-space between paragraphs.
• Tables and figures: Use tables and figures to present data and illustrations. Number each table and figure sequentially, and provide a brief title for each. Place tables and figures as close as possible to the text that refers to them.
• Spelling and grammar : Use correct spelling and grammar throughout your paper. Proofread carefully for errors.

Chicago/Turabian Research Paper Template

Chicago/Turabian Research Paper Template is as folows:

Title of Paper

Name of Student

Professor’s Name

I. Introduction

A. Background Information

B. Research Question

C. Thesis Statement

II. Literature Review

A. Overview of Existing Literature

B. Analysis of Key Literature

C. Identification of Gaps in Literature

III. Methodology

A. Research Design

B. Data Collection

C. Data Analysis

IV. Results

A. Presentation of Findings

B. Analysis of Findings

C. Discussion of Implications

V. Conclusion

A. Summary of Findings

B. Implications for Future Research

C. Conclusion

VI. References

A. Bibliography

B. In-Text Citations

VII. Appendices (if necessary)

A. Data Tables

C. Additional Supporting Materials

Chicago/Turabian Research Paper Example

Title: The Impact of Social Media on Political Engagement

Name: John Smith

Class: POLS 101

Professor: Dr. Jane Doe

Date: April 8, 2023

I. Introduction:

Social media has become an integral part of our daily lives. People use social media platforms like Facebook, Twitter, and Instagram to connect with friends and family, share their opinions, and stay informed about current events. With the rise of social media, there has been a growing interest in understanding its impact on various aspects of society, including political engagement. In this paper, I will examine the relationship between social media use and political engagement, specifically focusing on how social media influences political participation and political attitudes.

II. Literature Review:

There is a growing body of literature on the impact of social media on political engagement. Some scholars argue that social media has a positive effect on political participation by providing new channels for political communication and mobilization (Delli Carpini & Keeter, 1996; Putnam, 2000). Others, however, suggest that social media can have a negative impact on political engagement by creating filter bubbles that reinforce existing beliefs and discourage political dialogue (Pariser, 2011; Sunstein, 2001).

III. Methodology:

To examine the relationship between social media use and political engagement, I conducted a survey of 500 college students. The survey included questions about social media use, political participation, and political attitudes. The data was analyzed using descriptive statistics and regression analysis.

Iv. Results:

The results of the survey indicate that social media use is positively associated with political participation. Specifically, respondents who reported using social media to discuss politics were more likely to have participated in a political campaign, attended a political rally, or contacted a political representative. Additionally, social media use was found to be associated with more positive attitudes towards political engagement, such as increased trust in government and belief in the effectiveness of political action.

V. Conclusion:

The findings of this study suggest that social media has a positive impact on political engagement, by providing new opportunities for political communication and mobilization. However, there is also a need for caution, as social media can also create filter bubbles that reinforce existing beliefs and discourage political dialogue. Future research should continue to explore the complex relationship between social media and political engagement, and develop strategies to harness the potential benefits of social media while mitigating its potential negative effects.

Vii. References:

• Delli Carpini, M. X., & Keeter, S. (1996). What Americans know about politics and why it matters. Yale University Press.
• Pariser, E. (2011). The filter bubble: What the Internet is hiding from you. Penguin.
• Putnam, R. D. (2000). Bowling alone: The collapse and revival of American community. Simon & Schuster.
• Sunstein, C. R. (2001). Republic.com. Princeton University Press.

IEEE (Institute of Electrical and Electronics Engineers) Format

IEEE (Institute of Electrical and Electronics Engineers) Research Paper Format is as follows:

• Title : A concise and informative title that accurately reflects the content of the paper.
• Abstract : A brief summary of the paper, typically no more than 250 words, that includes the purpose of the study, the methods used, the key findings, and the main conclusions.
• Introduction : An overview of the background, context, and motivation for the research, including a clear statement of the problem being addressed and the objectives of the study.
• Literature review: A critical analysis of the relevant research and scholarship on the topic, including a discussion of any gaps or limitations in the existing literature.
• Methodology : A detailed description of the methods used to collect and analyze data, including any experiments or simulations, data collection instruments or procedures, and statistical analyses.
• Results : A clear and concise presentation of the findings, including any relevant tables, graphs, or figures.
• Discussion : A detailed interpretation of the results, including a comparison of the findings with previous research, a discussion of the implications of the results, and any recommendations for future research.
• Conclusion : A summary of the key findings and main conclusions of the study.
• References : A list of all sources cited in the paper, formatted according to IEEE guidelines.

In addition to these elements, an IEEE research paper should also follow certain formatting guidelines, including using 12-point font, double-spaced text, and numbered headings and subheadings. Additionally, any tables, figures, or equations should be clearly labeled and referenced in the text.

AMA (American Medical Association) Style

AMA (American Medical Association) Style Research Paper Format:

• Title Page: This page includes the title of the paper, the author’s name, institutional affiliation, and any acknowledgments or disclaimers.
• Abstract: The abstract is a brief summary of the paper that outlines the purpose, methods, results, and conclusions of the study. It is typically limited to 250 words or less.
• Introduction: The introduction provides a background of the research problem, defines the research question, and outlines the objectives and hypotheses of the study.
• Methods: The methods section describes the research design, participants, procedures, and instruments used to collect and analyze data.
• Results: The results section presents the findings of the study in a clear and concise manner, using graphs, tables, and charts where appropriate.
• Discussion: The discussion section interprets the results, explains their significance, and relates them to previous research in the field.
• Conclusion: The conclusion summarizes the main points of the paper, discusses the implications of the findings, and suggests future research directions.
• References: The reference list includes all sources cited in the paper, listed in alphabetical order by author’s last name.

In addition to these sections, the AMA format requires that authors follow specific guidelines for citing sources in the text and formatting their references. The AMA style uses a superscript number system for in-text citations and provides specific formats for different types of sources, such as books, journal articles, and websites.

Harvard Style

Harvard Style Research Paper format is as follows:

• Title page: This should include the title of your paper, your name, the name of your institution, and the date of submission.
• Abstract : This is a brief summary of your paper, usually no more than 250 words. It should outline the main points of your research and highlight your findings.
• Introduction : This section should introduce your research topic, provide background information, and outline your research question or thesis statement.
• Literature review: This section should review the relevant literature on your topic, including previous research studies, academic articles, and other sources.
• Methodology : This section should describe the methods you used to conduct your research, including any data collection methods, research instruments, and sampling techniques.
• Results : This section should present your findings in a clear and concise manner, using tables, graphs, and other visual aids if necessary.
• Discussion : This section should interpret your findings and relate them to the broader research question or thesis statement. You should also discuss the implications of your research and suggest areas for future study.
• Conclusion : This section should summarize your main findings and provide a final statement on the significance of your research.
• References : This is a list of all the sources you cited in your paper, presented in alphabetical order by author name. Each citation should include the author’s name, the title of the source, the publication date, and other relevant information.

In addition to these sections, a Harvard Style research paper may also include a table of contents, appendices, and other supplementary materials as needed. It is important to follow the specific formatting guidelines provided by your instructor or academic institution when preparing your research paper in Harvard Style.

Vancouver Style

Vancouver Style Research Paper format is as follows:

The Vancouver citation style is commonly used in the biomedical sciences and is known for its use of numbered references. Here is a basic format for a research paper using the Vancouver citation style:

• Title page: Include the title of your paper, your name, the name of your institution, and the date.
• Abstract : This is a brief summary of your research paper, usually no more than 250 words.
• Introduction : Provide some background information on your topic and state the purpose of your research.
• Methods : Describe the methods you used to conduct your research, including the study design, data collection, and statistical analysis.
• Results : Present your findings in a clear and concise manner, using tables and figures as needed.
• Discussion : Interpret your results and explain their significance. Also, discuss any limitations of your study and suggest directions for future research.
• References : List all of the sources you cited in your paper in numerical order. Each reference should include the author’s name, the title of the article or book, the name of the journal or publisher, the year of publication, and the page numbers.

ACS (American Chemical Society) Style

ACS (American Chemical Society) Style Research Paper format is as follows:

The American Chemical Society (ACS) Style is a citation style commonly used in chemistry and related fields. When formatting a research paper in ACS Style, here are some guidelines to follow:

• Paper Size and Margins : Use standard 8.5″ x 11″ paper with 1-inch margins on all sides.
• Font: Use a 12-point serif font (such as Times New Roman) for the main text. The title should be in bold and a larger font size.
• Title Page : The title page should include the title of the paper, the authors’ names and affiliations, and the date of submission. The title should be centered on the page and written in bold font. The authors’ names should be centered below the title, followed by their affiliations and the date.
• Abstract : The abstract should be a brief summary of the paper, no more than 250 words. It should be on a separate page and include the title of the paper, the authors’ names and affiliations, and the text of the abstract.
• Main Text : The main text should be organized into sections with headings that clearly indicate the content of each section. The introduction should provide background information and state the research question or hypothesis. The methods section should describe the procedures used in the study. The results section should present the findings of the study, and the discussion section should interpret the results and provide conclusions.
• References: Use the ACS Style guide to format the references cited in the paper. In-text citations should be numbered sequentially throughout the text and listed in numerical order at the end of the paper.
• Figures and Tables: Figures and tables should be numbered sequentially and referenced in the text. Each should have a descriptive caption that explains its content. Figures should be submitted in a high-quality electronic format.
• Supporting Information: Additional information such as data, graphs, and videos may be included as supporting information. This should be included in a separate file and referenced in the main text.
• Acknowledgments : Acknowledge any funding sources or individuals who contributed to the research.

ASA (American Sociological Association) Style

ASA (American Sociological Association) Style Research Paper format is as follows:

• Title Page: The title page of an ASA style research paper should include the title of the paper, the author’s name, and the institutional affiliation. The title should be centered and should be in title case (the first letter of each major word should be capitalized).
• Abstract: An abstract is a brief summary of the paper that should appear on a separate page immediately following the title page. The abstract should be no more than 200 words in length and should summarize the main points of the paper.
• Main Body: The main body of the paper should begin on a new page following the abstract page. The paper should be double-spaced, with 1-inch margins on all sides, and should be written in 12-point Times New Roman font. The main body of the paper should include an introduction, a literature review, a methodology section, results, and a discussion.
• References : The reference section should appear on a separate page at the end of the paper. All sources cited in the paper should be listed in alphabetical order by the author’s last name. Each reference should include the author’s name, the title of the work, the publication information, and the date of publication.
• Appendices : Appendices are optional and should only be included if they contain information that is relevant to the study but too lengthy to be included in the main body of the paper. If you include appendices, each one should be labeled with a letter (e.g., Appendix A, Appendix B, etc.) and should be referenced in the main body of the paper.

APSA (American Political Science Association) Style

APSA (American Political Science Association) Style Research Paper format is as follows:

• Title Page: The title page should include the title of the paper, the author’s name, the name of the course or instructor, and the date.
• Abstract : An abstract is typically not required in APSA style papers, but if one is included, it should be brief and summarize the main points of the paper.
• Introduction : The introduction should provide an overview of the research topic, the research question, and the main argument or thesis of the paper.
• Literature Review : The literature review should summarize the existing research on the topic and provide a context for the research question.
• Methods : The methods section should describe the research methods used in the paper, including data collection and analysis.
• Results : The results section should present the findings of the research.
• Discussion : The discussion section should interpret the results and connect them back to the research question and argument.
• Conclusion : The conclusion should summarize the main findings and implications of the research.
• References : The reference list should include all sources cited in the paper, formatted according to APSA style guidelines.

In-text citations in APSA style use parenthetical citation, which includes the author’s last name, publication year, and page number(s) if applicable. For example, (Smith 2010, 25).

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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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.

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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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• Published: 26 March 2024

Predicting and improving complex beer flavor through machine learning

• Michiel Schreurs   ORCID: orcid.org/0000-0002-9449-5619 1 , 2 , 3   na1 ,
• Supinya Piampongsant 1 , 2 , 3   na1 ,
• Miguel Roncoroni   ORCID: orcid.org/0000-0001-7461-1427 1 , 2 , 3   na1 ,
• Lloyd Cool   ORCID: orcid.org/0000-0001-9936-3124 1 , 2 , 3 , 4 ,
• Beatriz Herrera-Malaver   ORCID: orcid.org/0000-0002-5096-9974 1 , 2 , 3 ,
• Christophe Vanderaa   ORCID: orcid.org/0000-0001-7443-5427 4 ,
• Florian A. Theßeling 1 , 2 , 3 ,
• Łukasz Kreft   ORCID: orcid.org/0000-0001-7620-4657 5 ,
• Alexander Botzki   ORCID: orcid.org/0000-0001-6691-4233 5 ,
• Philippe Malcorps 6 ,
• Luk Daenen 6 ,
• Tom Wenseleers   ORCID: orcid.org/0000-0002-1434-861X 4 &
• Kevin J. Verstrepen   ORCID: orcid.org/0000-0002-3077-6219 1 , 2 , 3  

Nature Communications volume  15 , Article number:  2368 ( 2024 ) Cite this article

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• Chemical engineering
• Gas chromatography
• Machine learning
• Metabolomics
• Taste receptors

The perception and appreciation of food flavor depends on many interacting chemical compounds and external factors, and therefore proves challenging to understand and predict. Here, we combine extensive chemical and sensory analyses of 250 different beers to train machine learning models that allow predicting flavor and consumer appreciation. For each beer, we measure over 200 chemical properties, perform quantitative descriptive sensory analysis with a trained tasting panel and map data from over 180,000 consumer reviews to train 10 different machine learning models. The best-performing algorithm, Gradient Boosting, yields models that significantly outperform predictions based on conventional statistics and accurately predict complex food features and consumer appreciation from chemical profiles. Model dissection allows identifying specific and unexpected compounds as drivers of beer flavor and appreciation. Adding these compounds results in variants of commercial alcoholic and non-alcoholic beers with improved consumer appreciation. Together, our study reveals how big data and machine learning uncover complex links between food chemistry, flavor and consumer perception, and lays the foundation to develop novel, tailored foods with superior flavors.

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Introduction

Predicting and understanding food perception and appreciation is one of the major challenges in food science. Accurate modeling of food flavor and appreciation could yield important opportunities for both producers and consumers, including quality control, product fingerprinting, counterfeit detection, spoilage detection, and the development of new products and product combinations (food pairing) 1 , 2 , 3 , 4 , 5 , 6 . Accurate models for flavor and consumer appreciation would contribute greatly to our scientific understanding of how humans perceive and appreciate flavor. Moreover, accurate predictive models would also facilitate and standardize existing food assessment methods and could supplement or replace assessments by trained and consumer tasting panels, which are variable, expensive and time-consuming 7 , 8 , 9 . Lastly, apart from providing objective, quantitative, accurate and contextual information that can help producers, models can also guide consumers in understanding their personal preferences 10 .

Despite the myriad of applications, predicting food flavor and appreciation from its chemical properties remains a largely elusive goal in sensory science, especially for complex food and beverages 11 , 12 . A key obstacle is the immense number of flavor-active chemicals underlying food flavor. Flavor compounds can vary widely in chemical structure and concentration, making them technically challenging and labor-intensive to quantify, even in the face of innovations in metabolomics, such as non-targeted metabolic fingerprinting 13 , 14 . Moreover, sensory analysis is perhaps even more complicated. Flavor perception is highly complex, resulting from hundreds of different molecules interacting at the physiochemical and sensorial level. Sensory perception is often non-linear, characterized by complex and concentration-dependent synergistic and antagonistic effects 15 , 16 , 17 , 18 , 19 , 20 , 21 that are further convoluted by the genetics, environment, culture and psychology of consumers 22 , 23 , 24 . Perceived flavor is therefore difficult to measure, with problems of sensitivity, accuracy, and reproducibility that can only be resolved by gathering sufficiently large datasets 25 . Trained tasting panels are considered the prime source of quality sensory data, but require meticulous training, are low throughput and high cost. Public databases containing consumer reviews of food products could provide a valuable alternative, especially for studying appreciation scores, which do not require formal training 25 . Public databases offer the advantage of amassing large amounts of data, increasing the statistical power to identify potential drivers of appreciation. However, public datasets suffer from biases, including a bias in the volunteers that contribute to the database, as well as confounding factors such as price, cult status and psychological conformity towards previous ratings of the product.

Classical multivariate statistics and machine learning methods have been used to predict flavor of specific compounds by, for example, linking structural properties of a compound to its potential biological activities or linking concentrations of specific compounds to sensory profiles 1 , 26 . Importantly, most previous studies focused on predicting organoleptic properties of single compounds (often based on their chemical structure) 27 , 28 , 29 , 30 , 31 , 32 , 33 , thus ignoring the fact that these compounds are present in a complex matrix in food or beverages and excluding complex interactions between compounds. Moreover, the classical statistics commonly used in sensory science 34 , 35 , 36 , 37 , 38 , 39 require a large sample size and sufficient variance amongst predictors to create accurate models. They are not fit for studying an extensive set of hundreds of interacting flavor compounds, since they are sensitive to outliers, have a high tendency to overfit and are less suited for non-linear and discontinuous relationships 40 .

In this study, we combine extensive chemical analyses and sensory data of a set of different commercial beers with machine learning approaches to develop models that predict taste, smell, mouthfeel and appreciation from compound concentrations. Beer is particularly suited to model the relationship between chemistry, flavor and appreciation. First, beer is a complex product, consisting of thousands of flavor compounds that partake in complex sensory interactions 41 , 42 , 43 . This chemical diversity arises from the raw materials (malt, yeast, hops, water and spices) and biochemical conversions during the brewing process (kilning, mashing, boiling, fermentation, maturation and aging) 44 , 45 . Second, the advent of the internet saw beer consumers embrace online review platforms, such as RateBeer (ZX Ventures, Anheuser-Busch InBev SA/NV) and BeerAdvocate (Next Glass, inc.). In this way, the beer community provides massive data sets of beer flavor and appreciation scores, creating extraordinarily large sensory databases to complement the analyses of our professional sensory panel. Specifically, we characterize over 200 chemical properties of 250 commercial beers, spread across 22 beer styles, and link these to the descriptive sensory profiling data of a 16-person in-house trained tasting panel and data acquired from over 180,000 public consumer reviews. These unique and extensive datasets enable us to train a suite of machine learning models to predict flavor and appreciation from a beer’s chemical profile. Dissection of the best-performing models allows us to pinpoint specific compounds as potential drivers of beer flavor and appreciation. Follow-up experiments confirm the importance of these compounds and ultimately allow us to significantly improve the flavor and appreciation of selected commercial beers. Together, our study represents a significant step towards understanding complex flavors and reinforces the value of machine learning to develop and refine complex foods. In this way, it represents a stepping stone for further computer-aided food engineering applications 46 .

To generate a comprehensive dataset on beer flavor, we selected 250 commercial Belgian beers across 22 different beer styles (Supplementary Fig.  S1 ). Beers with ≤ 4.2% alcohol by volume (ABV) were classified as non-alcoholic and low-alcoholic. Blonds and Tripels constitute a significant portion of the dataset (12.4% and 11.2%, respectively) reflecting their presence on the Belgian beer market and the heterogeneity of beers within these styles. By contrast, lager beers are less diverse and dominated by a handful of brands. Rare styles such as Brut or Faro make up only a small fraction of the dataset (2% and 1%, respectively) because fewer of these beers are produced and because they are dominated by distinct characteristics in terms of flavor and chemical composition.

Extensive analysis identifies relationships between chemical compounds in beer

For each beer, we measured 226 different chemical properties, including common brewing parameters such as alcohol content, iso-alpha acids, pH, sugar concentration 47 , and over 200 flavor compounds (Methods, Supplementary Table  S1 ). A large portion (37.2%) are terpenoids arising from hopping, responsible for herbal and fruity flavors 16 , 48 . A second major category are yeast metabolites, such as esters and alcohols, that result in fruity and solvent notes 48 , 49 , 50 . Other measured compounds are primarily derived from malt, or other microbes such as non- Saccharomyces yeasts and bacteria (‘wild flora’). Compounds that arise from spices or staling are labeled under ‘Others’. Five attributes (caloric value, total acids and total ester, hop aroma and sulfur compounds) are calculated from multiple individually measured compounds.

As a first step in identifying relationships between chemical properties, we determined correlations between the concentrations of the compounds (Fig.  1 , upper panel, Supplementary Data  1 and 2 , and Supplementary Fig.  S2 . For the sake of clarity, only a subset of the measured compounds is shown in Fig.  1 ). Compounds of the same origin typically show a positive correlation, while absence of correlation hints at parameters varying independently. For example, the hop aroma compounds citronellol, and alpha-terpineol show moderate correlations with each other (Spearman’s rho=0.39 and 0.57), but not with the bittering hop component iso-alpha acids (Spearman’s rho=0.16 and −0.07). This illustrates how brewers can independently modify hop aroma and bitterness by selecting hop varieties and dosage time. If hops are added early in the boiling phase, chemical conversions increase bitterness while aromas evaporate, conversely, late addition of hops preserves aroma but limits bitterness 51 . Similarly, hop-derived iso-alpha acids show a strong anti-correlation with lactic acid and acetic acid, likely reflecting growth inhibition of lactic acid and acetic acid bacteria, or the consequent use of fewer hops in sour beer styles, such as West Flanders ales and Fruit beers, that rely on these bacteria for their distinct flavors 52 . Finally, yeast-derived esters (ethyl acetate, ethyl decanoate, ethyl hexanoate, ethyl octanoate) and alcohols (ethanol, isoamyl alcohol, isobutanol, and glycerol), correlate with Spearman coefficients above 0.5, suggesting that these secondary metabolites are correlated with the yeast genetic background and/or fermentation parameters and may be difficult to influence individually, although the choice of yeast strain may offer some control 53 .

Spearman rank correlations are shown. Descriptors are grouped according to their origin (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)), and sensory aspect (aroma, taste, palate, and overall appreciation). Please note that for the chemical compounds, for the sake of clarity, only a subset of the total number of measured compounds is shown, with an emphasis on the key compounds for each source. For more details, see the main text and Methods section. Chemical data can be found in Supplementary Data  1 , correlations between all chemical compounds are depicted in Supplementary Fig.  S2 and correlation values can be found in Supplementary Data  2 . See Supplementary Data  4 for sensory panel assessments and Supplementary Data  5 for correlation values between all sensory descriptors.

Interestingly, different beer styles show distinct patterns for some flavor compounds (Supplementary Fig.  S3 ). These observations agree with expectations for key beer styles, and serve as a control for our measurements. For instance, Stouts generally show high values for color (darker), while hoppy beers contain elevated levels of iso-alpha acids, compounds associated with bitter hop taste. Acetic and lactic acid are not prevalent in most beers, with notable exceptions such as Kriek, Lambic, Faro, West Flanders ales and Flanders Old Brown, which use acid-producing bacteria ( Lactobacillus and Pediococcus ) or unconventional yeast ( Brettanomyces ) 54 , 55 . Glycerol, ethanol and esters show similar distributions across all beer styles, reflecting their common origin as products of yeast metabolism during fermentation 45 , 53 . Finally, low/no-alcohol beers contain low concentrations of glycerol and esters. This is in line with the production process for most of the low/no-alcohol beers in our dataset, which are produced through limiting fermentation or by stripping away alcohol via evaporation or dialysis, with both methods having the unintended side-effect of reducing the amount of flavor compounds in the final beer 56 , 57 .

Besides expected associations, our data also reveals less trivial associations between beer styles and specific parameters. For example, geraniol and citronellol, two monoterpenoids responsible for citrus, floral and rose flavors and characteristic of Citra hops, are found in relatively high amounts in Christmas, Saison, and Brett/co-fermented beers, where they may originate from terpenoid-rich spices such as coriander seeds instead of hops 58 .

Tasting panel assessments reveal sensorial relationships in beer

To assess the sensory profile of each beer, a trained tasting panel evaluated each of the 250 beers for 50 sensory attributes, including different hop, malt and yeast flavors, off-flavors and spices. Panelists used a tasting sheet (Supplementary Data  3 ) to score the different attributes. Panel consistency was evaluated by repeating 12 samples across different sessions and performing ANOVA. In 95% of cases no significant difference was found across sessions ( p  > 0.05), indicating good panel consistency (Supplementary Table  S2 ).

Aroma and taste perception reported by the trained panel are often linked (Fig.  1 , bottom left panel and Supplementary Data  4 and 5 ), with high correlations between hops aroma and taste (Spearman’s rho=0.83). Bitter taste was found to correlate with hop aroma and taste in general (Spearman’s rho=0.80 and 0.69), and particularly with “grassy” noble hops (Spearman’s rho=0.75). Barnyard flavor, most often associated with sour beers, is identified together with stale hops (Spearman’s rho=0.97) that are used in these beers. Lactic and acetic acid, which often co-occur, are correlated (Spearman’s rho=0.66). Interestingly, sweetness and bitterness are anti-correlated (Spearman’s rho = −0.48), confirming the hypothesis that they mask each other 59 , 60 . Beer body is highly correlated with alcohol (Spearman’s rho = 0.79), and overall appreciation is found to correlate with multiple aspects that describe beer mouthfeel (alcohol, carbonation; Spearman’s rho= 0.32, 0.39), as well as with hop and ester aroma intensity (Spearman’s rho=0.39 and 0.35).

Similar to the chemical analyses, sensorial analyses confirmed typical features of specific beer styles (Supplementary Fig.  S4 ). For example, sour beers (Faro, Flanders Old Brown, Fruit beer, Kriek, Lambic, West Flanders ale) were rated acidic, with flavors of both acetic and lactic acid. Hoppy beers were found to be bitter and showed hop-associated aromas like citrus and tropical fruit. Malt taste is most detected among scotch, stout/porters, and strong ales, while low/no-alcohol beers, which often have a reputation for being ‘worty’ (reminiscent of unfermented, sweet malt extract) appear in the middle. Unsurprisingly, hop aromas are most strongly detected among hoppy beers. Like its chemical counterpart (Supplementary Fig.  S3 ), acidity shows a right-skewed distribution, with the most acidic beers being Krieks, Lambics, and West Flanders ales.

Tasting panel assessments of specific flavors correlate with chemical composition

We find that the concentrations of several chemical compounds strongly correlate with specific aroma or taste, as evaluated by the tasting panel (Fig.  2 , Supplementary Fig.  S5 , Supplementary Data  6 ). In some cases, these correlations confirm expectations and serve as a useful control for data quality. For example, iso-alpha acids, the bittering compounds in hops, strongly correlate with bitterness (Spearman’s rho=0.68), while ethanol and glycerol correlate with tasters’ perceptions of alcohol and body, the mouthfeel sensation of fullness (Spearman’s rho=0.82/0.62 and 0.72/0.57 respectively) and darker color from roasted malts is a good indication of malt perception (Spearman’s rho=0.54).

Heatmap colors indicate Spearman’s Rho. Axes are organized according to sensory categories (aroma, taste, mouthfeel, overall), chemical categories and chemical sources in beer (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)). See Supplementary Data  6 for all correlation values.

Interestingly, for some relationships between chemical compounds and perceived flavor, correlations are weaker than expected. For example, the rose-smelling phenethyl acetate only weakly correlates with floral aroma. This hints at more complex relationships and interactions between compounds and suggests a need for a more complex model than simple correlations. Lastly, we uncovered unexpected correlations. For instance, the esters ethyl decanoate and ethyl octanoate appear to correlate slightly with hop perception and bitterness, possibly due to their fruity flavor. Iron is anti-correlated with hop aromas and bitterness, most likely because it is also anti-correlated with iso-alpha acids. This could be a sign of metal chelation of hop acids 61 , given that our analyses measure unbound hop acids and total iron content, or could result from the higher iron content in dark and Fruit beers, which typically have less hoppy and bitter flavors 62 .

Public consumer reviews complement expert panel data

To complement and expand the sensory data of our trained tasting panel, we collected 180,000 reviews of our 250 beers from the online consumer review platform RateBeer. This provided numerical scores for beer appearance, aroma, taste, palate, overall quality as well as the average overall score.

Public datasets are known to suffer from biases, such as price, cult status and psychological conformity towards previous ratings of a product. For example, prices correlate with appreciation scores for these online consumer reviews (rho=0.49, Supplementary Fig.  S6 ), but not for our trained tasting panel (rho=0.19). This suggests that prices affect consumer appreciation, which has been reported in wine 63 , while blind tastings are unaffected. Moreover, we observe that some beer styles, like lagers and non-alcoholic beers, generally receive lower scores, reflecting that online reviewers are mostly beer aficionados with a preference for specialty beers over lager beers. In general, we find a modest correlation between our trained panel’s overall appreciation score and the online consumer appreciation scores (Fig.  3 , rho=0.29). Apart from the aforementioned biases in the online datasets, serving temperature, sample freshness and surroundings, which are all tightly controlled during the tasting panel sessions, can vary tremendously across online consumers and can further contribute to (among others, appreciation) differences between the two categories of tasters. Importantly, in contrast to the overall appreciation scores, for many sensory aspects the results from the professional panel correlated well with results obtained from RateBeer reviews. Correlations were highest for features that are relatively easy to recognize even for untrained tasters, like bitterness, sweetness, alcohol and malt aroma (Fig.  3 and below).

RateBeer text mining results can be found in Supplementary Data  7 . Rho values shown are Spearman correlation values, with asterisks indicating significant correlations ( p  < 0.05, two-sided). All p values were smaller than 0.001, except for Esters aroma (0.0553), Esters taste (0.3275), Esters aroma—banana (0.0019), Coriander (0.0508) and Diacetyl (0.0134).

Besides collecting consumer appreciation from these online reviews, we developed automated text analysis tools to gather additional data from review texts (Supplementary Data  7 ). Processing review texts on the RateBeer database yielded comparable results to the scores given by the trained panel for many common sensory aspects, including acidity, bitterness, sweetness, alcohol, malt, and hop tastes (Fig.  3 ). This is in line with what would be expected, since these attributes require less training for accurate assessment and are less influenced by environmental factors such as temperature, serving glass and odors in the environment. Consumer reviews also correlate well with our trained panel for 4-vinyl guaiacol, a compound associated with a very characteristic aroma. By contrast, correlations for more specific aromas like ester, coriander or diacetyl are underrepresented in the online reviews, underscoring the importance of using a trained tasting panel and standardized tasting sheets with explicit factors to be scored for evaluating specific aspects of a beer. Taken together, our results suggest that public reviews are trustworthy for some, but not all, flavor features and can complement or substitute taste panel data for these sensory aspects.

Models can predict beer sensory profiles from chemical data

The rich datasets of chemical analyses, tasting panel assessments and public reviews gathered in the first part of this study provided us with a unique opportunity to develop predictive models that link chemical data to sensorial features. Given the complexity of beer flavor, basic statistical tools such as correlations or linear regression may not always be the most suitable for making accurate predictions. Instead, we applied different machine learning models that can model both simple linear and complex interactive relationships. Specifically, we constructed a set of regression models to predict (a) trained panel scores for beer flavor and quality and (b) public reviews’ appreciation scores from beer chemical profiles. We trained and tested 10 different models (Methods), 3 linear regression-based models (simple linear regression with first-order interactions (LR), lasso regression with first-order interactions (Lasso), partial least squares regressor (PLSR)), 5 decision tree models (AdaBoost regressor (ABR), extra trees (ET), gradient boosting regressor (GBR), random forest (RF) and XGBoost regressor (XGBR)), 1 support vector regression (SVR), and 1 artificial neural network (ANN) model.

To compare the performance of our machine learning models, the dataset was randomly split into a training and test set, stratified by beer style. After a model was trained on data in the training set, its performance was evaluated on its ability to predict the test dataset obtained from multi-output models (based on the coefficient of determination, see Methods). Additionally, individual-attribute models were ranked per descriptor and the average rank was calculated, as proposed by Korneva et al. 64 . Importantly, both ways of evaluating the models’ performance agreed in general. Performance of the different models varied (Table  1 ). It should be noted that all models perform better at predicting RateBeer results than results from our trained tasting panel. One reason could be that sensory data is inherently variable, and this variability is averaged out with the large number of public reviews from RateBeer. Additionally, all tree-based models perform better at predicting taste than aroma. Linear models (LR) performed particularly poorly, with negative R 2 values, due to severe overfitting (training set R 2  = 1). Overfitting is a common issue in linear models with many parameters and limited samples, especially with interaction terms further amplifying the number of parameters. L1 regularization (Lasso) successfully overcomes this overfitting, out-competing multiple tree-based models on the RateBeer dataset. Similarly, the dimensionality reduction of PLSR avoids overfitting and improves performance, to some extent. Still, tree-based models (ABR, ET, GBR, RF and XGBR) show the best performance, out-competing the linear models (LR, Lasso, PLSR) commonly used in sensory science 65 .

GBR models showed the best overall performance in predicting sensory responses from chemical information, with R 2 values up to 0.75 depending on the predicted sensory feature (Supplementary Table  S4 ). The GBR models predict consumer appreciation (RateBeer) better than our trained panel’s appreciation (R 2 value of 0.67 compared to R 2 value of 0.09) (Supplementary Table  S3 and Supplementary Table  S4 ). ANN models showed intermediate performance, likely because neural networks typically perform best with larger datasets 66 . The SVR shows intermediate performance, mostly due to the weak predictions of specific attributes that lower the overall performance (Supplementary Table  S4 ).

Model dissection identifies specific, unexpected compounds as drivers of consumer appreciation

Next, we leveraged our models to infer important contributors to sensory perception and consumer appreciation. Consumer preference is a crucial sensory aspects, because a product that shows low consumer appreciation scores often does not succeed commercially 25 . Additionally, the requirement for a large number of representative evaluators makes consumer trials one of the more costly and time-consuming aspects of product development. Hence, a model for predicting chemical drivers of overall appreciation would be a welcome addition to the available toolbox for food development and optimization.

Since GBR models on our RateBeer dataset showed the best overall performance, we focused on these models. Specifically, we used two approaches to identify important contributors. First, rankings of the most important predictors for each sensorial trait in the GBR models were obtained based on impurity-based feature importance (mean decrease in impurity). High-ranked parameters were hypothesized to be either the true causal chemical properties underlying the trait, to correlate with the actual causal properties, or to take part in sensory interactions affecting the trait 67 (Fig.  4A ). In a second approach, we used SHAP 68 to determine which parameters contributed most to the model for making predictions of consumer appreciation (Fig.  4B ). SHAP calculates parameter contributions to model predictions on a per-sample basis, which can be aggregated into an importance score.

A The impurity-based feature importance (mean deviance in impurity, MDI) calculated from the Gradient Boosting Regression (GBR) model predicting RateBeer appreciation scores. The top 15 highest ranked chemical properties are shown. B SHAP summary plot for the top 15 parameters contributing to our GBR model. Each point on the graph represents a sample from our dataset. The color represents the concentration of that parameter, with bluer colors representing low values and redder colors representing higher values. Greater absolute values on the horizontal axis indicate a higher impact of the parameter on the prediction of the model. C Spearman correlations between the 15 most important chemical properties and consumer overall appreciation. Numbers indicate the Spearman Rho correlation coefficient, and the rank of this correlation compared to all other correlations. The top 15 important compounds were determined using SHAP (panel B).

Both approaches identified ethyl acetate as the most predictive parameter for beer appreciation (Fig.  4 ). Ethyl acetate is the most abundant ester in beer with a typical ‘fruity’, ‘solvent’ and ‘alcoholic’ flavor, but is often considered less important than other esters like isoamyl acetate. The second most important parameter identified by SHAP is ethanol, the most abundant beer compound after water. Apart from directly contributing to beer flavor and mouthfeel, ethanol drastically influences the physical properties of beer, dictating how easily volatile compounds escape the beer matrix to contribute to beer aroma 69 . Importantly, it should also be noted that the importance of ethanol for appreciation is likely inflated by the very low appreciation scores of non-alcoholic beers (Supplementary Fig.  S4 ). Despite not often being considered a driver of beer appreciation, protein level also ranks highly in both approaches, possibly due to its effect on mouthfeel and body 70 . Lactic acid, which contributes to the tart taste of sour beers, is the fourth most important parameter identified by SHAP, possibly due to the generally high appreciation of sour beers in our dataset.

Interestingly, some of the most important predictive parameters for our model are not well-established as beer flavors or are even commonly regarded as being negative for beer quality. For example, our models identify methanethiol and ethyl phenyl acetate, an ester commonly linked to beer staling 71 , as a key factor contributing to beer appreciation. Although there is no doubt that high concentrations of these compounds are considered unpleasant, the positive effects of modest concentrations are not yet known 72 , 73 .

To compare our approach to conventional statistics, we evaluated how well the 15 most important SHAP-derived parameters correlate with consumer appreciation (Fig.  4C ). Interestingly, only 6 of the properties derived by SHAP rank amongst the top 15 most correlated parameters. For some chemical compounds, the correlations are so low that they would have likely been considered unimportant. For example, lactic acid, the fourth most important parameter, shows a bimodal distribution for appreciation, with sour beers forming a separate cluster, that is missed entirely by the Spearman correlation. Additionally, the correlation plots reveal outliers, emphasizing the need for robust analysis tools. Together, this highlights the need for alternative models, like the Gradient Boosting model, that better grasp the complexity of (beer) flavor.

Finally, to observe the relationships between these chemical properties and their predicted targets, partial dependence plots were constructed for the six most important predictors of consumer appreciation 74 , 75 , 76 (Supplementary Fig.  S7 ). One-way partial dependence plots show how a change in concentration affects the predicted appreciation. These plots reveal an important limitation of our models: appreciation predictions remain constant at ever-increasing concentrations. This implies that once a threshold concentration is reached, further increasing the concentration does not affect appreciation. This is false, as it is well-documented that certain compounds become unpleasant at high concentrations, including ethyl acetate (‘nail polish’) 77 and methanethiol (‘sulfury’ and ‘rotten cabbage’) 78 . The inability of our models to grasp that flavor compounds have optimal levels, above which they become negative, is a consequence of working with commercial beer brands where (off-)flavors are rarely too high to negatively impact the product. The two-way partial dependence plots show how changing the concentration of two compounds influences predicted appreciation, visualizing their interactions (Supplementary Fig.  S7 ). In our case, the top 5 parameters are dominated by additive or synergistic interactions, with high concentrations for both compounds resulting in the highest predicted appreciation.

To assess the robustness of our best-performing models and model predictions, we performed 100 iterations of the GBR, RF and ET models. In general, all iterations of the models yielded similar performance (Supplementary Fig.  S8 ). Moreover, the main predictors (including the top predictors ethanol and ethyl acetate) remained virtually the same, especially for GBR and RF. For the iterations of the ET model, we did observe more variation in the top predictors, which is likely a consequence of the model’s inherent random architecture in combination with co-correlations between certain predictors. However, even in this case, several of the top predictors (ethanol and ethyl acetate) remain unchanged, although their rank in importance changes (Supplementary Fig.  S8 ).

Next, we investigated if a combination of RateBeer and trained panel data into one consolidated dataset would lead to stronger models, under the hypothesis that such a model would suffer less from bias in the datasets. A GBR model was trained to predict appreciation on the combined dataset. This model underperformed compared to the RateBeer model, both in the native case and when including a dataset identifier (R 2  = 0.67, 0.26 and 0.42 respectively). For the latter, the dataset identifier is the most important feature (Supplementary Fig.  S9 ), while most of the feature importance remains unchanged, with ethyl acetate and ethanol ranking highest, like in the original model trained only on RateBeer data. It seems that the large variation in the panel dataset introduces noise, weakening the models’ performances and reliability. In addition, it seems reasonable to assume that both datasets are fundamentally different, with the panel dataset obtained by blind tastings by a trained professional panel.

Lastly, we evaluated whether beer style identifiers would further enhance the model’s performance. A GBR model was trained with parameters that explicitly encoded the styles of the samples. This did not improve model performance (R2 = 0.66 with style information vs R2 = 0.67). The most important chemical features are consistent with the model trained without style information (eg. ethanol and ethyl acetate), and with the exception of the most preferred (strong ale) and least preferred (low/no-alcohol) styles, none of the styles were among the most important features (Supplementary Fig.  S9 , Supplementary Table  S5 and S6 ). This is likely due to a combination of style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original models, as well as the low number of samples belonging to some styles, making it difficult for the model to learn style-specific patterns. Moreover, beer styles are not rigorously defined, with some styles overlapping in features and some beers being misattributed to a specific style, all of which leads to more noise in models that use style parameters.

Model validation

To test if our predictive models give insight into beer appreciation, we set up experiments aimed at improving existing commercial beers. We specifically selected overall appreciation as the trait to be examined because of its complexity and commercial relevance. Beer flavor comprises a complex bouquet rather than single aromas and tastes 53 . Hence, adding a single compound to the extent that a difference is noticeable may lead to an unbalanced, artificial flavor. Therefore, we evaluated the effect of combinations of compounds. Because Blond beers represent the most extensive style in our dataset, we selected a beer from this style as the starting material for these experiments (Beer 64 in Supplementary Data  1 ).

In the first set of experiments, we adjusted the concentrations of compounds that made up the most important predictors of overall appreciation (ethyl acetate, ethanol, lactic acid, ethyl phenyl acetate) together with correlated compounds (ethyl hexanoate, isoamyl acetate, glycerol), bringing them up to 95 th percentile ethanol-normalized concentrations (Methods) within the Blond group (‘Spiked’ concentration in Fig.  5A ). Compared to controls, the spiked beers were found to have significantly improved overall appreciation among trained panelists, with panelist noting increased intensity of ester flavors, sweetness, alcohol, and body fullness (Fig.  5B ). To disentangle the contribution of ethanol to these results, a second experiment was performed without the addition of ethanol. This resulted in a similar outcome, including increased perception of alcohol and overall appreciation.

Adding the top chemical compounds, identified as best predictors of appreciation by our model, into poorly appreciated beers results in increased appreciation from our trained panel. Results of sensory tests between base beers and those spiked with compounds identified as the best predictors by the model. A Blond and Non/Low-alcohol (0.0% ABV) base beers were brought up to 95th-percentile ethanol-normalized concentrations within each style. B For each sensory attribute, tasters indicated the more intense sample and selected the sample they preferred. The numbers above the bars correspond to the p values that indicate significant changes in perceived flavor (two-sided binomial test: alpha 0.05, n  = 20 or 13).

In a last experiment, we tested whether using the model’s predictions can boost the appreciation of a non-alcoholic beer (beer 223 in Supplementary Data  1 ). Again, the addition of a mixture of predicted compounds (omitting ethanol, in this case) resulted in a significant increase in appreciation, body, ester flavor and sweetness.

Predicting flavor and consumer appreciation from chemical composition is one of the ultimate goals of sensory science. A reliable, systematic and unbiased way to link chemical profiles to flavor and food appreciation would be a significant asset to the food and beverage industry. Such tools would substantially aid in quality control and recipe development, offer an efficient and cost-effective alternative to pilot studies and consumer trials and would ultimately allow food manufacturers to produce superior, tailor-made products that better meet the demands of specific consumer groups more efficiently.

A limited set of studies have previously tried, to varying degrees of success, to predict beer flavor and beer popularity based on (a limited set of) chemical compounds and flavors 79 , 80 . Current sensitive, high-throughput technologies allow measuring an unprecedented number of chemical compounds and properties in a large set of samples, yielding a dataset that can train models that help close the gaps between chemistry and flavor, even for a complex natural product like beer. To our knowledge, no previous research gathered data at this scale (250 samples, 226 chemical parameters, 50 sensory attributes and 5 consumer scores) to disentangle and validate the chemical aspects driving beer preference using various machine-learning techniques. We find that modern machine learning models outperform conventional statistical tools, such as correlations and linear models, and can successfully predict flavor appreciation from chemical composition. This could be attributed to the natural incorporation of interactions and non-linear or discontinuous effects in machine learning models, which are not easily grasped by the linear model architecture. While linear models and partial least squares regression represent the most widespread statistical approaches in sensory science, in part because they allow interpretation 65 , 81 , 82 , modern machine learning methods allow for building better predictive models while preserving the possibility to dissect and exploit the underlying patterns. Of the 10 different models we trained, tree-based models, such as our best performing GBR, showed the best overall performance in predicting sensory responses from chemical information, outcompeting artificial neural networks. This agrees with previous reports for models trained on tabular data 83 . Our results are in line with the findings of Colantonio et al. who also identified the gradient boosting architecture as performing best at predicting appreciation and flavor (of tomatoes and blueberries, in their specific study) 26 . Importantly, besides our larger experimental scale, we were able to directly confirm our models’ predictions in vivo.

Our study confirms that flavor compound concentration does not always correlate with perception, suggesting complex interactions that are often missed by more conventional statistics and simple models. Specifically, we find that tree-based algorithms may perform best in developing models that link complex food chemistry with aroma. Furthermore, we show that massive datasets of untrained consumer reviews provide a valuable source of data, that can complement or even replace trained tasting panels, especially for appreciation and basic flavors, such as sweetness and bitterness. This holds despite biases that are known to occur in such datasets, such as price or conformity bias. Moreover, GBR models predict taste better than aroma. This is likely because taste (e.g. bitterness) often directly relates to the corresponding chemical measurements (e.g., iso-alpha acids), whereas such a link is less clear for aromas, which often result from the interplay between multiple volatile compounds. We also find that our models are best at predicting acidity and alcohol, likely because there is a direct relation between the measured chemical compounds (acids and ethanol) and the corresponding perceived sensorial attribute (acidity and alcohol), and because even untrained consumers are generally able to recognize these flavors and aromas.

The predictions of our final models, trained on review data, hold even for blind tastings with small groups of trained tasters, as demonstrated by our ability to validate specific compounds as drivers of beer flavor and appreciation. Since adding a single compound to the extent of a noticeable difference may result in an unbalanced flavor profile, we specifically tested our identified key drivers as a combination of compounds. While this approach does not allow us to validate if a particular single compound would affect flavor and/or appreciation, our experiments do show that this combination of compounds increases consumer appreciation.

It is important to stress that, while it represents an important step forward, our approach still has several major limitations. A key weakness of the GBR model architecture is that amongst co-correlating variables, the largest main effect is consistently preferred for model building. As a result, co-correlating variables often have artificially low importance scores, both for impurity and SHAP-based methods, like we observed in the comparison to the more randomized Extra Trees models. This implies that chemicals identified as key drivers of a specific sensory feature by GBR might not be the true causative compounds, but rather co-correlate with the actual causative chemical. For example, the high importance of ethyl acetate could be (partially) attributed to the total ester content, ethanol or ethyl hexanoate (rho=0.77, rho=0.72 and rho=0.68), while ethyl phenylacetate could hide the importance of prenyl isobutyrate and ethyl benzoate (rho=0.77 and rho=0.76). Expanding our GBR model to include beer style as a parameter did not yield additional power or insight. This is likely due to style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original model, as well as the smaller sample size per style, limiting the power to uncover style-specific patterns. This can be partly attributed to the curse of dimensionality, where the high number of parameters results in the models mainly incorporating single parameter effects, rather than complex interactions such as style-dependent effects 67 . A larger number of samples may overcome some of these limitations and offer more insight into style-specific effects. On the other hand, beer style is not a rigid scientific classification, and beers within one style often differ a lot, which further complicates the analysis of style as a model factor.

Our study is limited to beers from Belgian breweries. Although these beers cover a large portion of the beer styles available globally, some beer styles and consumer patterns may be missing, while other features might be overrepresented. For example, many Belgian ales exhibit yeast-driven flavor profiles, which is reflected in the chemical drivers of appreciation discovered by this study. In future work, expanding the scope to include diverse markets and beer styles could lead to the identification of even more drivers of appreciation and better models for special niche products that were not present in our beer set.

In addition to inherent limitations of GBR models, there are also some limitations associated with studying food aroma. Even if our chemical analyses measured most of the known aroma compounds, the total number of flavor compounds in complex foods like beer is still larger than the subset we were able to measure in this study. For example, hop-derived thiols, that influence flavor at very low concentrations, are notoriously difficult to measure in a high-throughput experiment. Moreover, consumer perception remains subjective and prone to biases that are difficult to avoid. It is also important to stress that the models are still immature and that more extensive datasets will be crucial for developing more complete models in the future. Besides more samples and parameters, our dataset does not include any demographic information about the tasters. Including such data could lead to better models that grasp external factors like age and culture. Another limitation is that our set of beers consists of high-quality end-products and lacks beers that are unfit for sale, which limits the current model in accurately predicting products that are appreciated very badly. Finally, while models could be readily applied in quality control, their use in sensory science and product development is restrained by their inability to discern causal relationships. Given that the models cannot distinguish compounds that genuinely drive consumer perception from those that merely correlate, validation experiments are essential to identify true causative compounds.

Despite the inherent limitations, dissection of our models enabled us to pinpoint specific molecules as potential drivers of beer aroma and consumer appreciation, including compounds that were unexpected and would not have been identified using standard approaches. Important drivers of beer appreciation uncovered by our models include protein levels, ethyl acetate, ethyl phenyl acetate and lactic acid. Currently, many brewers already use lactic acid to acidify their brewing water and ensure optimal pH for enzymatic activity during the mashing process. Our results suggest that adding lactic acid can also improve beer appreciation, although its individual effect remains to be tested. Interestingly, ethanol appears to be unnecessary to improve beer appreciation, both for blond beer and alcohol-free beer. Given the growing consumer interest in alcohol-free beer, with a predicted annual market growth of >7% 84 , it is relevant for brewers to know what compounds can further increase consumer appreciation of these beers. Hence, our model may readily provide avenues to further improve the flavor and consumer appreciation of both alcoholic and non-alcoholic beers, which is generally considered one of the key challenges for future beer production.

Whereas we see a direct implementation of our results for the development of superior alcohol-free beverages and other food products, our study can also serve as a stepping stone for the development of novel alcohol-containing beverages. We want to echo the growing body of scientific evidence for the negative effects of alcohol consumption, both on the individual level by the mutagenic, teratogenic and carcinogenic effects of ethanol 85 , 86 , as well as the burden on society caused by alcohol abuse and addiction. We encourage the use of our results for the production of healthier, tastier products, including novel and improved beverages with lower alcohol contents. Furthermore, we strongly discourage the use of these technologies to improve the appreciation or addictive properties of harmful substances.

The present work demonstrates that despite some important remaining hurdles, combining the latest developments in chemical analyses, sensory analysis and modern machine learning methods offers exciting avenues for food chemistry and engineering. Soon, these tools may provide solutions in quality control and recipe development, as well as new approaches to sensory science and flavor research.

Beer selection

250 commercial Belgian beers were selected to cover the broad diversity of beer styles and corresponding diversity in chemical composition and aroma. See Supplementary Fig.  S1 .

Chemical dataset

Sample preparation.

Beers within their expiration date were purchased from commercial retailers. Samples were prepared in biological duplicates at room temperature, unless explicitly stated otherwise. Bottle pressure was measured with a manual pressure device (Steinfurth Mess-Systeme GmbH) and used to calculate CO 2 concentration. The beer was poured through two filter papers (Macherey-Nagel, 500713032 MN 713 ¼) to remove carbon dioxide and prevent spontaneous foaming. Samples were then prepared for measurements by targeted Headspace-Gas Chromatography-Flame Ionization Detector/Flame Photometric Detector (HS-GC-FID/FPD), Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS), colorimetric analysis, enzymatic analysis, Near-Infrared (NIR) analysis, as described in the sections below. The mean values of biological duplicates are reported for each compound.

HS-GC-FID/FPD

HS-GC-FID/FPD (Shimadzu GC 2010 Plus) was used to measure higher alcohols, acetaldehyde, esters, 4-vinyl guaicol, and sulfur compounds. Each measurement comprised 5 ml of sample pipetted into a 20 ml glass vial containing 1.75 g NaCl (VWR, 27810.295). 100 µl of 2-heptanol (Sigma-Aldrich, H3003) (internal standard) solution in ethanol (Fisher Chemical, E/0650DF/C17) was added for a final concentration of 2.44 mg/L. Samples were flushed with nitrogen for 10 s, sealed with a silicone septum, stored at −80 °C and analyzed in batches of 20.

The GC was equipped with a DB-WAXetr column (length, 30 m; internal diameter, 0.32 mm; layer thickness, 0.50 µm; Agilent Technologies, Santa Clara, CA, USA) to the FID and an HP-5 column (length, 30 m; internal diameter, 0.25 mm; layer thickness, 0.25 µm; Agilent Technologies, Santa Clara, CA, USA) to the FPD. N 2 was used as the carrier gas. Samples were incubated for 20 min at 70 °C in the headspace autosampler (Flow rate, 35 cm/s; Injection volume, 1000 µL; Injection mode, split; Combi PAL autosampler, CTC analytics, Switzerland). The injector, FID and FPD temperatures were kept at 250 °C. The GC oven temperature was first held at 50 °C for 5 min and then allowed to rise to 80 °C at a rate of 5 °C/min, followed by a second ramp of 4 °C/min until 200 °C kept for 3 min and a final ramp of (4 °C/min) until 230 °C for 1 min. Results were analyzed with the GCSolution software version 2.4 (Shimadzu, Kyoto, Japan). The GC was calibrated with a 5% EtOH solution (VWR International) containing the volatiles under study (Supplementary Table  S7 ).

HS-SPME-GC-MS

HS-SPME-GC-MS (Shimadzu GCMS-QP-2010 Ultra) was used to measure additional volatile compounds, mainly comprising terpenoids and esters. Samples were analyzed by HS-SPME using a triphase DVB/Carboxen/PDMS 50/30 μm SPME fiber (Supelco Co., Bellefonte, PA, USA) followed by gas chromatography (Thermo Fisher Scientific Trace 1300 series, USA) coupled to a mass spectrometer (Thermo Fisher Scientific ISQ series MS) equipped with a TriPlus RSH autosampler. 5 ml of degassed beer sample was placed in 20 ml vials containing 1.75 g NaCl (VWR, 27810.295). 5 µl internal standard mix was added, containing 2-heptanol (1 g/L) (Sigma-Aldrich, H3003), 4-fluorobenzaldehyde (1 g/L) (Sigma-Aldrich, 128376), 2,3-hexanedione (1 g/L) (Sigma-Aldrich, 144169) and guaiacol (1 g/L) (Sigma-Aldrich, W253200) in ethanol (Fisher Chemical, E/0650DF/C17). Each sample was incubated at 60 °C in the autosampler oven with constant agitation. After 5 min equilibration, the SPME fiber was exposed to the sample headspace for 30 min. The compounds trapped on the fiber were thermally desorbed in the injection port of the chromatograph by heating the fiber for 15 min at 270 °C.

The GC-MS was equipped with a low polarity RXi-5Sil MS column (length, 20 m; internal diameter, 0.18 mm; layer thickness, 0.18 µm; Restek, Bellefonte, PA, USA). Injection was performed in splitless mode at 320 °C, a split flow of 9 ml/min, a purge flow of 5 ml/min and an open valve time of 3 min. To obtain a pulsed injection, a programmed gas flow was used whereby the helium gas flow was set at 2.7 mL/min for 0.1 min, followed by a decrease in flow of 20 ml/min to the normal 0.9 mL/min. The temperature was first held at 30 °C for 3 min and then allowed to rise to 80 °C at a rate of 7 °C/min, followed by a second ramp of 2 °C/min till 125 °C and a final ramp of 8 °C/min with a final temperature of 270 °C.

Mass acquisition range was 33 to 550 amu at a scan rate of 5 scans/s. Electron impact ionization energy was 70 eV. The interface and ion source were kept at 275 °C and 250 °C, respectively. A mix of linear n-alkanes (from C7 to C40, Supelco Co.) was injected into the GC-MS under identical conditions to serve as external retention index markers. Identification and quantification of the compounds were performed using an in-house developed R script as described in Goelen et al. and Reher et al. 87 , 88 (for package information, see Supplementary Table  S8 ). Briefly, chromatograms were analyzed using AMDIS (v2.71) 89 to separate overlapping peaks and obtain pure compound spectra. The NIST MS Search software (v2.0 g) in combination with the NIST2017, FFNSC3 and Adams4 libraries were used to manually identify the empirical spectra, taking into account the expected retention time. After background subtraction and correcting for retention time shifts between samples run on different days based on alkane ladders, compound elution profiles were extracted and integrated using a file with 284 target compounds of interest, which were either recovered in our identified AMDIS list of spectra or were known to occur in beer. Compound elution profiles were estimated for every peak in every chromatogram over a time-restricted window using weighted non-negative least square analysis after which peak areas were integrated 87 , 88 . Batch effect correction was performed by normalizing against the most stable internal standard compound, 4-fluorobenzaldehyde. Out of all 284 target compounds that were analyzed, 167 were visually judged to have reliable elution profiles and were used for final analysis.

Discrete photometric and enzymatic analysis

Discrete photometric and enzymatic analysis (Thermo Scientific TM Gallery TM Plus Beermaster Discrete Analyzer) was used to measure acetic acid, ammonia, beta-glucan, iso-alpha acids, color, sugars, glycerol, iron, pH, protein, and sulfite. 2 ml of sample volume was used for the analyses. Information regarding the reagents and standard solutions used for analyses and calibrations is included in Supplementary Table  S7 and Supplementary Table  S9 .

NIR analyses

NIR analysis (Anton Paar Alcolyzer Beer ME System) was used to measure ethanol. Measurements comprised 50 ml of sample, and a 10% EtOH solution was used for calibration.

Correlation calculations

Pairwise Spearman Rank correlations were calculated between all chemical properties.

Sensory dataset

Trained panel.

Our trained tasting panel consisted of volunteers who gave prior verbal informed consent. All compounds used for the validation experiment were of food-grade quality. The tasting sessions were approved by the Social and Societal Ethics Committee of the KU Leuven (G-2022-5677-R2(MAR)). All online reviewers agreed to the Terms and Conditions of the RateBeer website.

Sensory analysis was performed according to the American Society of Brewing Chemists (ASBC) Sensory Analysis Methods 90 . 30 volunteers were screened through a series of triangle tests. The sixteen most sensitive and consistent tasters were retained as taste panel members. The resulting panel was diverse in age [22–42, mean: 29], sex [56% male] and nationality [7 different countries]. The panel developed a consensus vocabulary to describe beer aroma, taste and mouthfeel. Panelists were trained to identify and score 50 different attributes, using a 7-point scale to rate attributes’ intensity. The scoring sheet is included as Supplementary Data  3 . Sensory assessments took place between 10–12 a.m. The beers were served in black-colored glasses. Per session, between 5 and 12 beers of the same style were tasted at 12 °C to 16 °C. Two reference beers were added to each set and indicated as ‘Reference 1 & 2’, allowing panel members to calibrate their ratings. Not all panelists were present at every tasting. Scores were scaled by standard deviation and mean-centered per taster. Values are represented as z-scores and clustered by Euclidean distance. Pairwise Spearman correlations were calculated between taste and aroma sensory attributes. Panel consistency was evaluated by repeating samples on different sessions and performing ANOVA to identify differences, using the ‘stats’ package (v4.2.2) in R (for package information, see Supplementary Table  S8 ).

Online reviews from a public database

The ‘scrapy’ package in Python (v3.6) (for package information, see Supplementary Table  S8 ). was used to collect 232,288 online reviews (mean=922, min=6, max=5343) from RateBeer, an online beer review database. Each review entry comprised 5 numerical scores (appearance, aroma, taste, palate and overall quality) and an optional review text. The total number of reviews per reviewer was collected separately. Numerical scores were scaled and centered per rater, and mean scores were calculated per beer.

For the review texts, the language was estimated using the packages ‘langdetect’ and ‘langid’ in Python. Reviews that were classified as English by both packages were kept. Reviewers with fewer than 100 entries overall were discarded. 181,025 reviews from >6000 reviewers from >40 countries remained. Text processing was done using the ‘nltk’ package in Python. Texts were corrected for slang and misspellings; proper nouns and rare words that are relevant to the beer context were specified and kept as-is (‘Chimay’,’Lambic’, etc.). A dictionary of semantically similar sensorial terms, for example ‘floral’ and ‘flower’, was created and collapsed together into one term. Words were stemmed and lemmatized to avoid identifying words such as ‘acid’ and ‘acidity’ as separate terms. Numbers and punctuation were removed.

Sentences from up to 50 randomly chosen reviews per beer were manually categorized according to the aspect of beer they describe (appearance, aroma, taste, palate, overall quality—not to be confused with the 5 numerical scores described above) or flagged as irrelevant if they contained no useful information. If a beer contained fewer than 50 reviews, all reviews were manually classified. This labeled data set was used to train a model that classified the rest of the sentences for all beers 91 . Sentences describing taste and aroma were extracted, and term frequency–inverse document frequency (TFIDF) was implemented to calculate enrichment scores for sensorial words per beer.

The sex of the tasting subject was not considered when building our sensory database. Instead, results from different panelists were averaged, both for our trained panel (56% male, 44% female) and the RateBeer reviews (70% male, 30% female for RateBeer as a whole).

Beer price collection and processing

Beer prices were collected from the following stores: Colruyt, Delhaize, Total Wine, BeerHawk, The Belgian Beer Shop, The Belgian Shop, and Beer of Belgium. Where applicable, prices were converted to Euros and normalized per liter. Spearman correlations were calculated between these prices and mean overall appreciation scores from RateBeer and the taste panel, respectively.

Pairwise Spearman Rank correlations were calculated between all sensory properties.

Machine learning models

Predictive modeling of sensory profiles from chemical data.

Regression models were constructed to predict (a) trained panel scores for beer flavors and quality from beer chemical profiles and (b) public reviews’ appreciation scores from beer chemical profiles. Z-scores were used to represent sensory attributes in both data sets. Chemical properties with log-normal distributions (Shapiro-Wilk test, p  <  0.05 ) were log-transformed. Missing chemical measurements (0.1% of all data) were replaced with mean values per attribute. Observations from 250 beers were randomly separated into a training set (70%, 175 beers) and a test set (30%, 75 beers), stratified per beer style. Chemical measurements (p = 231) were normalized based on the training set average and standard deviation. In total, three linear regression-based models: linear regression with first-order interaction terms (LR), lasso regression with first-order interaction terms (Lasso) and partial least squares regression (PLSR); five decision tree models, Adaboost regressor (ABR), Extra Trees (ET), Gradient Boosting regressor (GBR), Random Forest (RF) and XGBoost regressor (XGBR); one support vector machine model (SVR) and one artificial neural network model (ANN) were trained. The models were implemented using the ‘scikit-learn’ package (v1.2.2) and ‘xgboost’ package (v1.7.3) in Python (v3.9.16). Models were trained, and hyperparameters optimized, using five-fold cross-validated grid search with the coefficient of determination (R 2 ) as the evaluation metric. The ANN (scikit-learn’s MLPRegressor) was optimized using Bayesian Tree-Structured Parzen Estimator optimization with the ‘Optuna’ Python package (v3.2.0). Individual models were trained per attribute, and a multi-output model was trained on all attributes simultaneously.

Model dissection

GBR was found to outperform other methods, resulting in models with the highest average R 2 values in both trained panel and public review data sets. Impurity-based rankings of the most important predictors for each predicted sensorial trait were obtained using the ‘scikit-learn’ package. To observe the relationships between these chemical properties and their predicted targets, partial dependence plots (PDP) were constructed for the six most important predictors of consumer appreciation 74 , 75 .

The ‘SHAP’ package in Python (v0.41.0) was implemented to provide an alternative ranking of predictor importance and to visualize the predictors’ effects as a function of their concentration 68 .

Validation of causal chemical properties

To validate the effects of the most important model features on predicted sensory attributes, beers were spiked with the chemical compounds identified by the models and descriptive sensory analyses were carried out according to the American Society of Brewing Chemists (ASBC) protocol 90 .

Compound spiking was done 30 min before tasting. Compounds were spiked into fresh beer bottles, that were immediately resealed and inverted three times. Fresh bottles of beer were opened for the same duration, resealed, and inverted thrice, to serve as controls. Pairs of spiked samples and controls were served simultaneously, chilled and in dark glasses as outlined in the Trained panel section above. Tasters were instructed to select the glass with the higher flavor intensity for each attribute (directional difference test 92 ) and to select the glass they prefer.

The final concentration after spiking was equal to the within-style average, after normalizing by ethanol concentration. This was done to ensure balanced flavor profiles in the final spiked beer. The same methods were applied to improve a non-alcoholic beer. Compounds were the following: ethyl acetate (Merck KGaA, W241415), ethyl hexanoate (Merck KGaA, W243906), isoamyl acetate (Merck KGaA, W205508), phenethyl acetate (Merck KGaA, W285706), ethanol (96%, Colruyt), glycerol (Merck KGaA, W252506), lactic acid (Merck KGaA, 261106).

Significant differences in preference or perceived intensity were determined by performing the two-sided binomial test on each attribute.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The data that support the findings of this work are available in the Supplementary Data files and have been deposited to Zenodo under accession code 10653704 93 . The RateBeer scores data are under restricted access, they are not publicly available as they are property of RateBeer (ZX Ventures, USA). Access can be obtained from the authors upon reasonable request and with permission of RateBeer (ZX Ventures, USA).  Source data are provided with this paper.

Code availability

The code for training the machine learning models, analyzing the models, and generating the figures has been deposited to Zenodo under accession code 10653704 93 .

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Acknowledgements

We thank all lab members for their discussions and thank all tasting panel members for their contributions. Special thanks go out to Dr. Karin Voordeckers for her tremendous help in proofreading and improving the manuscript. M.S. was supported by a Baillet-Latour fellowship, L.C. acknowledges financial support from KU Leuven (C16/17/006), F.A.T. was supported by a PhD fellowship from FWO (1S08821N). Research in the lab of K.J.V. is supported by KU Leuven, FWO, VIB, VLAIO and the Brewing Science Serves Health Fund. Research in the lab of T.W. is supported by FWO (G.0A51.15) and KU Leuven (C16/17/006).

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These authors contributed equally: Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni.

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VIB—KU Leuven Center for Microbiology, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni, Lloyd Cool, Beatriz Herrera-Malaver, Florian A. Theßeling & Kevin J. Verstrepen

CMPG Laboratory of Genetics and Genomics, KU Leuven, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Leuven Institute for Beer Research (LIBR), Gaston Geenslaan 1, B-3001, Leuven, Belgium

Laboratory of Socioecology and Social Evolution, KU Leuven, Naamsestraat 59, B-3000, Leuven, Belgium

Lloyd Cool, Christophe Vanderaa & Tom Wenseleers

VIB Bioinformatics Core, VIB, Rijvisschestraat 120, B-9052, Ghent, Belgium

Łukasz Kreft & Alexander Botzki

AB InBev SA/NV, Brouwerijplein 1, B-3000, Leuven, Belgium

Philippe Malcorps & Luk Daenen

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Contributions

S.P., M.S. and K.J.V. conceived the experiments. S.P., M.S. and K.J.V. designed the experiments. S.P., M.S., M.R., B.H. and F.A.T. performed the experiments. S.P., M.S., L.C., C.V., L.K., A.B., P.M., L.D., T.W. and K.J.V. contributed analysis ideas. S.P., M.S., L.C., C.V., T.W. and K.J.V. analyzed the data. All authors contributed to writing the manuscript.

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Correspondence to Kevin J. Verstrepen .

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Schreurs, M., Piampongsant, S., Roncoroni, M. et al. Predicting and improving complex beer flavor through machine learning. Nat Commun 15 , 2368 (2024). https://doi.org/10.1038/s41467-024-46346-0

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Article Contents

Introduction, the analytical framework: linking climate change, vulnerability, and conflict, methodology: a systematic review, pathways between climate change and violent conflict in the mena region, evaluating the “pathways” framework in the mena region.

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Climate Change and Violent Conflict in the Middle East and North Africa

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Kyungmee Kim, Tània Ferré Garcia, Climate Change and Violent Conflict in the Middle East and North Africa, International Studies Review , Volume 25, Issue 4, December 2023, viad053, https://doi.org/10.1093/isr/viad053

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Previous research has demonstrated that climate change can escalate the risks for violent conflict through various pathways. Existing evidence suggests that contextual factors, such as migration and livelihood options, governance arrangements, and existing conflict dynamics, can influence the pathways through which climate change leads to conflict. This important insight leads to an inquiry to identify sets of conditions and processes that make climate-related violent conflict more likely. In this analytic essay, we conduct a systematic review of scholarly literature published during the period 1989–2022 and explore the climate-conflict pathways in the Middle East and North Africa (MENA) region. Through the systematic review of forty-one peer-reviewed publications in English, we identify that society’s ability to cope with the changing climate and extreme weather events is influenced by a range of factors, including preceding government policies that led to the mismanagement of land and water and existing conflict dynamics in the MENA region. Empirical research to unpack the complex and diverse relationship between the climate shocks and violent conflict in the MENA region needs advancing. Several avenues for future research are highlighted such as more studies on North Africa and the Gulf region, with focus on the implications of floods and heatwaves, and exploring climate implications on non-agriculture sectors including the critical oil sector.

Investigaciones previas que han demostrado que el cambio climático puede llegar a aumentar la probabilidad del riesgo de conflictos violentos a través de diversos mecanismos. Las pruebas existentes sugieren que los factores contextuales, tales como la migración y las opciones de medios de subsistencia, los acuerdos de gobernanza y la dinámica de conflicto existente, pueden influir en las vías a través de las cuales el cambio climático conduce a los conflictos. Esta percepción motiva una investigación con el objetivo de identificar una serie de condiciones y procesos que hacen que incrementan la probabilidad de conflictos violentos relacionados con el clima. En este ensayo analítico, llevamos a cabo una revisión sistemática de la literatura académica publicada durante el período entre 1989 y 2022. El estudio explora las vías de conflicto climático en la región de Oriente Medio y el Norte de África (MENA, por sus siglas en inglés). A través de la revisión sistemática de 41 publicaciones en inglés revisadas por expertos, fenómenos meteorológicos extremos está influenciada por una serie de factores, que incluyen tanto las políticas gubernamentales precedentes que condujeron a la mala gestión de la tierra y el agua como la dinámica de conflicto existente en la región MENA. Es esencial avanzar en la investigación empírica para poder desentrañar la compleja y diversa relación existente entre las perturbaciones climáticas y los conflictos violentos en la región de Oriente Medio y el Norte de África. Destacamos varias vías de investigación futura, como la realización de un mayor número estudios sobre el norte de África y la región del Golfo, con un enfoque en las implicaciones de las inundaciones y las olas de calor, así como la exploración de las implicaciones climáticas en los sectores no agrícolas, incluido el sector petrolero, de crítica importancia.

Des travaux de recherche antérieurs ont montré que le changement climatique pouvait aggraver les risques de conflits violents de bien des façons. Les éléments probants existants indiquent que les facteurs contextuels, comme les possibilités d'immigration et de moyens de subsistance, les arrangements gouvernementaux et les dynamiques de conflit existantes, peuvent avoir une incidence sur les mécanismes par lesquels le changement climatique peut créer des conflits. Cette information importante nous pousse à chercher les ensembles de conditions et de processus qui augmentent la probabilité des conflits violents en lien avec le climat. Dans cet article analytique, nous conduisons un examen systématique de la littérature académique publiée entre 1989 et 2022 pour nous intéresser aux liens entre climat et conflits dans la région du Moyen-Orient et de l'Afrique du Nord (MENA). En examinant de façon systématique 41 publications en anglais vérifiées par des pairs, nous remarquons que la capacité d'une société à gérer l’évolution du climat et les phénomènes météorologiques extrêmes est liée à un éventail de facteurs, y compris les politiques précédentes du gouvernement qui ont engendré une mauvaise gestion des terres et de l'eau et les dynamiques de conflit existantes dans la région MENA. La recherche empirique pour décortiquer la relation complexe et plurielle entre les crises climatiques et les conflits violents dans la région MENA doit avancer. Plusieurs pistes de recherches ultérieures sont présentées, comme davantage d’études dans la région de l'Afrique du Nord et du Golfe, en se concentrant plus particulièrement sur les implications des inondations et des vagues de chaleur, et l'analyse des conséquences climatiques sur les secteurs hors agriculture, notamment le secteur décisif du pétrole.

Climate change contributes to conflict risk and undermines livelihoods and human security. The impact of climate change overburdens countries in demanding security environments and exacerbates political instability, which may lead to violent conflict. Researchers have sought to explain the relationship between climate change and violent conflict and climate change as a growing factor for security risks ( Gleditsch 2012 ; Meierding 2013 ; Sakaguchi, Varughese, and Auld 2017 ; Ide 2018 ; Van Baalen and Mobjörk 2018 ). There is a greater consensus that climate change has an impact on human security and sustaining peace ( Abrahams 2020 ; Black et al. 2022 ; Morales-Muñoz et al. 2022 ). The evidence has been gathered on the physical changes in diverse livelihood systems and human migration and the negative effects on human adaptation capacities ( IPCC 2022 ). The debate may have to move on from whether climate change has been the primary cause of a war or not ( Verhoeven 2011 ; e.g., Selby et al. 2017 ). Our understanding of what context climate change matters for conflict and security and how relevant factors play out in local contexts should be based on comprehensive and systematic research that considers various scales, time periods, and localities.

Moreover, existing evidence suggests that climate-related security risks are context specific, and there are multiple pathways by which climate change influences the onsets and patterns of armed conflict ( Brzoska and Fröhlich 2016 ; Mobjörk, Krampe, and Tarif 2020 ). The “climate insecurity pathway” framework assumes that climate change may not be the only contributor to violent conflict but also other factors leading to insecurity such as internal and international migration, livelihood options, and governance arrangements ( Van Baalen and Mobjörk 2018 ). Existing conflict dynamics and security environments can exacerbate climate-related security risks. This analytic essay contributes to the debate on how climate change affects the risk of violent conflict by conducting a systematic review of the literature directly or indirectly linking climate change of violent conflict focusing on the Middle East and North Africa (MENA), a region that has been severely impacted by both. 1 By conducting a systematic literature review, we are particularly interested in synthesizing existing evidence to better understand the climate-conflict links in the MENA region. We included forty-one peer-reviewed articles published between 1989 and 2022 in the analysis. Based on the review, we conclude that the relationship between climate change and violent conflict is predominantly indirect and diverse, highlighting the need to avoid oversimplified assumptions. Climate change’s contribution to conflict risk in the MENA region is further mediated by political economy, institutional weaknesses, elite competition, and existing socio-political relations. A careful examination of evidence is crucial for comprehensive climate security discussions in general and policy considerations for the MENA region. The following systematic review of literature showcases the linkages between climate exposure and various sources of vulnerability in the MENA region.

Climate Exposure and Social Vulnerability in the MENA Region

The MENA region is facing major security challenges from its vulnerability to climate change and violent conflict. The region is the world’s most water-stressed region, hosting thirteen of the world’s twenty most water-stressed countries, with currently over 82 percent of its terrain covered in desert ( Sieghart and Betre 2018 ). Indeed, water rationing and the limitation of water supplies are already a reality in parts of Algeria, Lebanon, Iraq, Palestine, and Jordan ( Sowers, Vengosh, and Weinthal 2011 ). Recent climate science predicts an average global warming of 1.5°C under the business-as-usual scenario, while in the MENA region, it is expected to increase up to 4°C ( Gaub and Lienard 2021 ). Furthermore, the level of mean precipitation is also expected to decrease in the region ( Zittis , et al. 2020 ). By the end of the century, about half of the MENA population could be annually exposed to super- and ultra-extreme heatwaves ( Zittis et al. 2021 ). In essence, the region is likely to become drier and experience extremely high temperatures, followed by extreme and chronic water shortages becoming more frequent.

Many countries in the MENA region are vulnerable to the effects of climate change due to their weak adaptive capacity ( Sowers et al. 2011 ; Namdar, Karami, and Keshavarz 2021 ). The adaptive capacity to climate change varies across the MENA region. While oil-exporting Gulf states have the financial resources for investments in water desalination and wastewater technologies, others suffer from a lack of financial resources and water conservation policies ( Sowers et al. 2011 ). The adverse effect of climate change on agricultural productivity is likely to affect the livelihood conditions of rural populations and may contribute to rural-to-urban migration in some cases ( Waha et al. 2017 ). Changes in precipitation and extreme weather events can reduce the region’s agriculture yields, as up to 70 percent of the crops are rain-fed ( Waha et al. 2017 ). Climate change impacts present a threat to food security in the MENA region and exacerbate the vulnerability to global food price volatility, including Egypt and Lebanon. Countries with a high level of imported grain dependency witness significant inflations in cereal prices that can be a source of political instability ( Tanchum 2021 ). Food price volatility has contributed to political stability in the past, especially during the Arab Spring, and the combined effect of reduced water discharge with the demographic trend of the youth bulge could present a challenge to the political stability of a region ( Borghesi and Ticci 2019 ).

Over the past decade, several of the world’s deadliest conflicts flared up in the MENA region, particularly in Syria, Yemen, Iraq, and Turkey ( Palik et al. 2020 ). The intractable conflict between Israel and Palestine has caused immense human suffering and disrupted regional stability. These conflicts are linked to long-running inequalities and grievances and economic and political instability, which make conflict resolution exceptionally challenging. Deterioration of the physical environment and land degradation further exacerbate risks of communal conflict and political instability in the future. Violent conflict, on the other hand, has been destructive to the adjoining environment. For instance, the effect of intense armed conflict has been significant in Syria’s already declining land and water resources ( Mohamed, Anders, and Schneider 2020 ). Environmental degradation leading to water and food insecurity has adversely affected the livelihoods of the population.

The linkages between conflict and the environment are an integral component that constitutes peace and security in the MENA region. The arid natural environment of the region and the changing climate are part of consideration when analyzing conflict in the region ( Smith and Krampe 2019 ). This article focuses on the MENA region and analyzes the role of climate-related environmental factors in violent conflict by drawing evidence from existing research. This systematic review provides an overview of conditions and processes in the climate-conflict nexus. The findings demonstrate that indirect pathways between climate change and violent conflict that are found in other regions such as East Africa, South Asia, and Southeast Asia, and West Africa are also applicable to the MENA region. In addition, downstream impacts of water development projects such as dams and irrigation projects in transboundary river basins, weaponization of water by armed groups, and the government’s mismanagement of water and land have particularly affected vulnerability to climate change in the MENA region. Climate change exacerbates water scarcity in the MENA region, which in turn can incentivize policies such as unilaterally building water storages and weaponization of water as an instrument for leverage during armed conflicts. These MENA region-specific dimensions of climate-conflict pathways appear to be influenced by the region’s internal politics, relations between neighboring countries, and conflict dynamics.

The article is organized in the following order. We present the analytical framework of a set of pathways that connects climate change and violent conflict and then an outline of the methodology for a systematic review, which includes the operationalization of the variables and the sampling strategy. This is followed by the description of the methodology for conducting a systematic review. The review of literature is organized into four categories that are specified in the analytical framework, and then a synthesized analysis is detailed. Finally, we conclude by summarizing policy and research relevant implications from the finding in the MENA regional contexts with a set of recommendations.

The climate-conflict nexus is complex. Climate change has implications for various forms of interstate and intrastate conflict, including communal violence, insurgencies, mass civil resistance campaigns, protests, and interpersonal disputes ( Hendrix et al. 2023 ). Specific contexts of environment, socio-political systems, and pre-existing conflict matter when examining the connection between climate-related environmental changes and conflict. The analytical framework is based on a premise that the relationship between climate change and conflict is mediated by social, political, and ecological vulnerability ( Daoudy 2021 ). When climate impacts contribute to social outcomes such as deteriorating livelihood conditions, migration, escalation of armed groups’ tactics, and elite capture, risks of violent conflict can increase. The following outlines four “pathways” between climate change and conflict ( Figure 1 ).

A framework of climate insecurity pathways

The deterioration of livelihood conditions is a centerpiece in linking environmental changes and violent conflict. Climate-exposed sectors such as agriculture, forestry, fishery, energy, and tourism are highly likely to suffer from economic damages from climate change ( IPCC 2022 , SPM-11). Consequently, people whose livelihoods are dependent on the natural environment are subjected to additional economic burdens due to the changing climate or climate shocks. Extreme weather events such as droughts, heatwaves, sandstorms, flooding, and long-term changes in the environment can affect the income from the aforementioned sectors ( IPCC 2022 , SPM-11). Populations with low adaptive capacity including marginalized groups are disproportionately affected and vulnerable to short-term economic damages related to climate change ( IPCC 2022 , SPM-8). Demographic changes may accelerate the deterioration of livelihood conditions. Population growth in the MENA region has been rapid from 105 million in 1960 to 486 million in 2021 ( World Bank 2022 ), which means more land and water are required for livelihoods. Climate change can worsen coastal erosion and decline tin he productivity of coastal plains in Israel and Morocco, which are important for food production. Sea-level rise has negative impacts on deltas, coastal plains, and human settlements, and tourism and industrial activities are also expected to decline due to heatwaves and worsening water shortages ( Sowers et al. 2011 ).

Existing studies focus on various socio-economic outcomes of climate and environmental changes and their implications on conflict mobilization. Agriculture, fisheries, and livestock sectors are particularly susceptible to the loss of income due to climate shocks such as prolonged droughts ( von Uexkull 2014 ; Schmidt and Pearson 2016 ). Loss of income due to the deterioration of livelihood conditions can lead individuals to seek alternative sources of livelihood, and some may turn to illicit activities, including joining non-state armed groups ( Barnett and Adger 2007 , 644; Seter 2016 , 5).

Another category of social outcomes includes changes in migration and mobility patterns. Migration is one of the climate adaptation strategies, and subsequent socioeconomic and political impacts of migration can be linked to conflict. Declining livelihood conditions can trigger rural-to-urban migration in search for alternative livelihoods ( Rüttinger et al. 2015 , 27). Long-term climate change and weather shocks may accelerate environmental degradation and declining livelihood conditions. The increased migration flow accelerates urbanization and creates instability in hosting cities with inadequate infrastructure for public services ( Balsari, Dresser, and Leaning 2020 ).

Changing migratory patterns of pastoralist or agropastoral groups, influenced by the availability of grazing land and water, can be linked to clashes with other communities ( Abroulaye et al. 2015 ; Mohammed Ali 2019 ). Violent communal clashes and livestock raiding, which have become increasingly lethal, are linked to intensified competition over scarce resources for pastoralist populations ( Detges 2014 ). For instance, farmer-herder conflicts in the Sahel region have become increasingly lethal during recent decades, especially in areas with a higher population and livestock density.

Previous research also focuses on the role of elites who have leveraged social outcomes of climate change for their benefit. Here, elite actors include traditional elites, privileged groups with economic and political power, and even armed group leaders. More frequent and intense climate-related extreme weather events can provide additional opportunities for local elites to capture resources. When climate-induced disasters such as droughts and floods cause humanitarian crises, their basic needs and post-disaster reconstruction would bring in additional resources to the disaster-hit regions, which can be exploited by local elites. Humanitarian aid delivery often needs to cooperate with local elites, whose influence over the aid provision can further strengthen the client-patronage relationship, which is a source of tension ( Uson 2017 ). Elite capture of resources, particularly land, is likely to generate strains within and between communities ( Zaman 1991 ). Local grievances over land rights can be exploited in intercommunal conflict or national conflicts ( Chavunduka and Bromley 2011 ). National elites can exploit local grievances of a population segment that are closely related to climate change. Inadequate government responses to Cyclone Bhola in 1970 led to a devastating human toll in the Bay of Bengal and contributed to the rise of the independence movement, which subsequently led to the secession of Bangladesh ( Busby 2022 , 181).

Changing environmental conditions by climate change may influence armed group tactics and behaviors. Armed groups have utilized the local grievances for a recruitment drive for the youth ( Benjaminsen and Ba 2019 ). Climate change also affects the way of wars are to be fought. In warm climates, prolonged and unpredictable rainy seasons can alter the fighting season and patterns. Due to the reduced water availability in some areas, the strategic importance of water access points and infrastructure may have become more salient. Armed groups can escalate the conflict by weaponizing water by flooding farmland and cities or depriving the population of water ( King 2015 ). Amid droughts and unreliable rainfalls, armed groups may consider water weaponization as a more effective tactic in order to influence and control communities already experiencing water scarcity.

The analytical framework of climate-conflict pathways is applied to analyze findings from existing research relevant to the MENA region. The following details a method of a systematic review of the literature.

This paper leverages from existing evidence by conducting a systematic review of existing studies. Systematic review method has been extensively employed in examining the linkage between climate change and violent conflict ( Ide 2018 ; Nordqvist and Krampe 2018 ; Van Baalen and Mobjörk 2018 ; Tarif 2022 ). Systematic reviews differ from a traditional sense of literature review in a way that it is “focused” and “systematic”; it zooms on a specific research question; and is based on pre-established sets of principles for literature selection. Systematic and focused nature of the review is helpful to “locate previous research, select relevant literature, evaluate contributions and analyses, and synthesize data” ( Denyer and Tranfield 2009 , 671). This approach is particularly useful to yield new insights and provide clarification on frequently debated issues ( Dacombe 2018 , 155). In addition, the method is a highly relevant policy tool that promotes evidence-based policymaking.

We have used the following set of principles for locating, selecting, and evaluating the literature. A Boolean search string containing keywords was composed with keywords from climate change and violent conflict. 2 Search words for climate-related environmental conditions include terms related to the effects of extreme weather events or long-term environmental changes on nature-based livelihoods and water and food insecurity, involuntary displacement, which are adopted from previous research done in a similar scope ( Nordqvist and Krampe 2018 ; Van Baalen and Mobjörk 2018 ; Tarif 2022 ). Several social outcomes are theorized as consequences of climate change such as internal and cross-border migration and elite exploitation of changing environmental conditions. In the paper, violent conflict is defined as the situation when one or more actors engaged in violence against hostile groups due to incompatibilities. This broad definition allows include interstate wars, terrorism to communal clashes involving violence. The definition does not include protests and non-violent actions, which are a crucial class of social phenomena leading to political instability and violence. We paid attention to this element in the analysis but excluded studies exclusively focusing on non-violent conflict (e.g., Ide et al. 2021 ). We used specific keywords relevant to conflict actors and types of conflict in the MENA region.

The Boolean search string was used in searching the abstracts of existing studies in English published during 1989–2020 from Web of Science, a major database of scholarly literature. From the search results, we read the abstracts and selected items with relevance to the relationship between climate-related environmental changes and conflict. The initial screening found 141 articles, which then were reviewed manually for their relevance to the inquiry (see the Online Appendix). In the screening process, we excluded a number of studies that focused on the impact of armed conflict on the environment and studies that did not explicitly focus on violent conflict. Similarly, studies that do not explicitly focus on climate change as in long-term climate trends, climate hazards, and weather events were excluded. Another set of articles that were removed from the list were commentaries and reviews that were not based on either qualitative or quantitative empirical material. While all the selected articles either have at least one country in the MENA region or adapt a regional focus on the MENA, the specific definition of these regions varies. In our literature review, we adhere to a specific list of countries that we recognize as part of the region. 3 After the screening, we manually searched the bibliographies of the selected articles and included eleven relevant articles. In total, forty-one articles are reviewed with a focus on a set of categories stemmed from the analytical framework for explaining the relationship between climate-related environmental change and violent conflict ( Figure 2 ).

Peer-reviewed articles reviewed

The geographical focus of the reviewed studies demonstrates that much of the scholarship focuses on Syria and Iraq. In contrast, North African countries and Gulf countries have received relatively limited attention ( Figure 3 ). The high number of research works focusing on Syria can be explained by the high profile of the contested linkage between climate change and the Syrian civil war. While media narratives have regarded Syria as a prime example of an armed conflict fuelled by climate change and several prominent public figures have publicized it as an illustration of the nexus, it is worth noting that scholarly research has presented differing perspectives on the direct causative role of climate change in conflict escalation ( Miller 2015 ; “Climate Wars - Syria” with Thomas Friedman 2017 ; VICE 2017 ).

The distribution of geographical focus of the reviewed studies

Source: a map drawn by authors.

In this section, we discuss existing explanations from previous research that connect climate-related environmental changes and violent conflict in the MENA region. The linkages between the environmental changes related to climate change and violent conflict constitute a complex chain of events (e.g., Gleditsch 1998 ). Most empirical research contributes to examine parts of the chain under specific temporal and spatial scopes, and this is one reason why it is important to consider the broader implication of each piece of evidence, which then can contribute to the better understanding of the climate-conflict pathways as a larger phenomenon. For clarity and focus, we organized a set of findings from previous studies under four pre-determined analytical categories: worsening livelihood conditions, migration and mobility, armed groups, and elite exploitation. As explained earlier, these categories are not mutually exclusive; rather, explanations under different categories are interlinked and can mutually reinforce each other in different stages of mobilization and conflict.

Direct Link between Climate Change and Violent Conflict

Scholars have examined whether climate impacts such as warmer temperatures and precipitation anomalies are statistically correlated to violent conflict, and several studies have focused on specific countries within the MENA region ( Feizi, Janatabadi, and Torshizi 2019 ; Döring 2020 ; Helman and Zaitchik 2020 ; Helman, Zaitchik, and Funk 2020 ; Sofuoglu and Ay 2020 ; Linke and Ruether 2021 ). Findings from existing research on the direct impact of climate-related factors on violent conflict and political instability suggest that the relationship is not always linear and varied in specific country contexts ( Helman and Zaitchik 2020 ; Helman et al. 2020 ). Water scarcity, for instance, is not only associated with increased communal conflict but also cooperation ( Döring 2020 ). Warming did not unitarily increase or decrease conflict risk—warmer temperatures increased risks of violence in Africa but decreased in the Middle East, and warming did not have a linear effect but had a greater effect on conflict risk in warmer regions ( Helman et al. 2020 ). Increased temperatures and rainfall anomalies are positively associated with political instability in the MENA region ( Helman and Zaitchik 2020 ; Sofuoglu and Ay 2020 ). These findings caution against generalized or simplistic assumptions about the relationship between climate change and violent conflict.

Studies have found an insignificant relationship between water scarcity and violent conflict. Precipitation levels and droughts do not have a direct impact on communal violence in a model including the Middle East and Africa ( Döring 2020 ). The same study also found that communal conflict is more likely to occur in areas with lower rainfalls and limited groundwater availability. Groundwater is less affected by short-term droughts, but prolonged droughts and unsustainable extraction can lead to groundwater shortages, which is the case in northern Syria ( Kelley et al. 2015 ) and Yemen ( Weiss 2015 ). Rainfall variability does not seem to have significantly affected the intensity of civil war violence during the 2011–2019 Syrian civil war ( Linke and Ruether 2021 ). The discussion on climate change’s impact on armed group tactics and behavior is followed in the later part of the paper.

Droughts and water scarcity seem to be a source of social disputes and non-violent conflict ( Feizi et al. 2019 ; Bijani et al. 2020 ; Ide et al. 2021 ). Whether the tension over water scarcity escalates to non-violent conflict or not seems to be contingent on the pre-existing negative socio-political relationships between groups and the types of political systems ( Ide et al. 2021 ). In Iran, irregular rainfalls and water scarcity at the local level are linked to interpersonal conflict and communal tensions and can degrade state legitimacy and contribute to political instability ( Feizi et al. 2019 ; Bijani et al. 2020 ).

Evidence from existing studies on the direct climate-conflict link also alludes to the need to further explore the mechanisms between physical environmental changes and social outcomes. Both large- N and small- N studies can contribute to the understanding of the underlying mechanisms or indirect pathways connecting climate change and conflict. The following sections discuss livelihoods, migration, inadequate management, and armed group behaviors as the pathways between climate-related environmental changes and violent conflict.

Deteriorating Livelihood Conditions

Several studies evaluating the worsening livelihood mechanism in the MENA region focus on the relationship between droughts’ impacts on agriculture and conflict. Severe and frequent droughts due to climate change may affect the region’s food security and livelihoods. In the MENA countries, agriculture, fisheries, and livestock accounts for roughly 15 percent of the total population’s livelihood ( World Bank 2023 ). Agriculture dependency is one of the best predictors of violent conflict ( von Uexkull et al. 2016 ). Indeed, evidence from a study focusing on the MENA region and Africa shows a consistent result that conflict risk is higher in areas where the population depends on agriculture for their livelihoods ( Helman and Zaitchik 2020 ).

Droughts’ impact on agriculture is an important area of research in the implications of the changing climate on the deterioration of livelihood conditions. During the last three decades, droughts in the MENA region have become more frequent and severe. Three out of four most severe multi-year droughts in the Fertile Crescent region referring to parts of Iraq, Syria, Lebanon, Palestine, Israel, Jordan, and Egypt occurred during 1990–2015 ( Kelley et al. 2015 , 3243). The sub-region has historically witnessed periodic droughts; therefore, their agricultural systems are to a degree adaptive to drought conditions and low rainfalls. More frequent and intensifying droughts and drying conditions may jeopardize the population’s adaptive capacity, leading to far-reaching and consequential disruptions in societies. In particular, the 2007–2008 drought severely affected the agricultural production in the Fertile Crescent region. Annual wheat production in Iraq during 2008–2009 declined by 35 percent ( Selby 2019 , 264). Jordan and the West Bank in Palestine also experienced a reduction in agricultural production after the 2007–2008 drought ( Feitelson and Tubi 2017 ). However, none of these countries experienced the same extent of “shock” as in Syria, whose effects some refer to as the “collapse” of the agricultural sector. The 2007–2008 drought is considered “the worst drought in the instrumental record, causing widespread crop failure” and decimation of livestock populations in northeast Syria ( Kelley et al. 2015 , 3241).

A dozen of the reviewed authors have probed the linkage between the 2007 and 2008 multi-year droughts and their impacts on agricultural and livestock production and the Syrian conflict using quantitative and qualitative methods ( De Châtel 2014 ; Gleick 2014 ; Kelley et al. 2015 ; Eklund and Thompson 2017 ; Selby et al. 2017 ; Ide 2018 ; Karnieli et al. 2019 ; Ash and Obradovich 2020 ; Daoudy 2020a , 2021 ; Eklund et al. 2022 ). These reviewed research works have disagreed on what extent the drought’s contribution to the sharp decline in agricultural production and rural livelihood in Syria. Kelley et al. (2015 ) is one of the major empirical studies that argues for the linkage between the multi-year drought and the political instability, which argument is similar to Gleick (2014 ). Other studies have refuted the causal linkage between the drought and the Syrian civil war, but their core reasons for arguing against it have varied.

Several authors point out that the impact of climate shocks on livelihoods is mediated by water governance decisions. This argument downplays the role of climate change as the main driver of livelihood deterioration rather than a contributing factor. Despite being affected by similar rainfall deficits during 2007–2008, farmers in northern Syria generally experienced far worse consequences in productivity compared to northwest Iraq and southeast Turkey ( Eklund and Thompson 2017 ). Turkey’s substantial investment in water infrastructure and placing policies for better management during the 1990s and 2000s seem to have reduced their vulnerability to droughts ( Kelley et al. 2015 ; Eklund and Thompson 2017 ). On the contrary, the Syrian regime’s agricultural expansion policy, unsustainable groundwater use, and economic policy have exacerbated the population’s drought vulnerability. Agricultural expansion schemes in Syria more than doubled the irrigated area from 650,000 ha in 1985 to 1.4 million ha in 2005, driven by “a vision of development through agrarian modernization” ( Selby 2019 , 268). The policy overlooked physical limitations of groundwater resources by over-extracting water from aquifers at a rate of 300 percent or more than the basin’s yield and depleting aquifers prior to the 2007–2008 drought ( Selby 2019 , 266). Groundwater depletion in the region has a major effect on drought vulnerability because groundwater is an important source of water during low rainfall years ( Kelley et al. 2015 ).

Weiss (2015 ) makes a similar observation in Yemen, indicating that governance issues are mainly responsible for groundwater depletion in the country rather than climate-related environmental changes. Factors related to agrarian political economy and governance capacities further affect the vulnerability. The government’s capacity to deal with environmental changes and their impact on local economies and livelihoods is pointed out to be a key mediating factor in the linkage between climate change and violent conflict. The issues related to mismanagement and elite exploitation of climate change are further discussed in the later section of the article.

A few studies found differing climate impacts based on gender and ethnicity. Vulnerability to climate change varies between communities and countries, and intersectional identities of the affected people such as gender, age, and ethnicity influence their capacity to adapt to climate change and resilience ( Thomas et al. 2019 ). Evidence from Iran shows how women are forced to carry the “double burden” of doing off-farm work activities such as weeding or thinning cotton for minimal wages, in addition to the regular household and on-farm tasks ( Keshavarz, Karami, and Vanclay 2013 ). In Syria, the mechanization of agriculture has led to a significant loss of rural employment and disproportionately affected women ( Selby 2019 , 267). The disproportionate effect on women is related to structural gender inequality restricting women’s economic opportunities and wealth accumulation ( Selby 2019 ). This finding aligns with previous literature linking gender and climate change indicating that women are often worse affected by climate impacts due to restrictive norms and rights ( Denton 2002 ). In Israel, pastoralists are often disadvantaged due to the Israeli state’s resource allocation policies prioritizing farmers. In the northern Negev region, the state’s land appropriation disproportionately affected agri-pastoralist Bedouin tribes during the early 1900s. This has led to higher vulnerability of the Bedouins during droughts ( Tubi and Feitelson 2016 ). A similar pattern of marginalization is found in Hasakah, a region in northern Syria, where the state turned open range lands into farmlands ( Selby 2019 ). The findings on differing vulnerability and impacts on livelihoods are based on a handful of studies, and intersectional approaches are generally absent in most studies reviewed in the analytic essay.

Changes in Migration and Mobility Patterns

Migration represents a critical adaptation strategy for populations affected by climate-induced environmental changes. Existing research examines various linkages between climate-induced environmental changes and migration in the MENA region. The main discussions are related to the contribution of climate shocks in internal and international migration and migration as a source of political instability and conflict. Existing evidence in the reviewed studies does not fully confirm that climate shocks and changing climate conditions are the primary drivers of internal or international migration. The link between displacement and violent conflict seems to be contested as well.

One of the predominant narratives links climate, migration, and insecurity theorizes worsening of livelihood conditions due to climate change has led to distressed migration of rural population to urban or peri-urban areas, which can contribute to greater political instability ( Gleick 2014 ; Kelley et al. 2015 ; Feitelson and Tubi 2017 ; Ash and Obradovich 2020 ). This argument gained prominence after out-migration from drought-affected regions in northern Syria in 2008 and the agricultural sector collapse in 2010 preceded the 2011 uprising.

Several studies focus on empirically examining the migration patterns after the 2007–2008 droughts in the Levant ( De Châtel 2014 ; Gleick 2014 ; Kelley et al. 2015 ; Ash and Obradovich 2020 ). There seems to be a wide-ranging estimation of the scale of internal migration in Syria during this time ( Ide 2018 ). While acknowledging the multiple factors contributing to migration, researchers have debated on the number of displaced people in northern Syria and Iraq amid the 2007–2008 drought. While Gleick (2014 , 334) and  Kelley et al . ( 2015 , 3241–2) estimate ∼1.5 million people to be internally displaced, others suggest 40–60,000 households or ∼ 300,000 displaced people ( Selby et al. 2017 , 254). Several methods are employed in estimating drought-induced migration. For instance, Ash and Obradovich (2020 ) used nightlight intensity as a proxy measure for population change, which seemed to detect the changes in population in drought-affected regions. Satellite imagery can be analyzed for measuring agricultural land use, which can be a proxy indicator for out-migration ( Eklund et al. 2022 ). Others relied on official statistics and survey data, which are based on a combination of census, fieldwork, and expert assessment (e.g., OCHA 2009 ). Nightlight intensity and satellite imagery are effective measurements of population changes, but remote sensing data provide little context about who moved, to where, and why. Fieldwork-based studies such as De Châtel (2014 ) provide insights into the socio-economic circumstances of migrants and their political orientation. A UN rapid assessment report is based on various UN-led field reports and assessments during 2006–2008 and supplies valuable on-the-ground information including changing migration patterns, children’s school enrollment, and water availability ( OCHA 2009 ). The evidence indicates that migration after the drought was indeed significant, although we cannot exactly say the scale of it. The question is whether these migrants play a role in the subsequent uprising and civil war.

Critics of this narrative argue that the Syrian uprising emerged due to political discontent, economic recession, youth unemployment, discrimination, and injustice, not because of the mass climate migrants ( De Châtel 2014 ; Selby et al. 2017 ; Daoudy 2020a ). Eklund et al. (2022 ) suggest migration triggered by the 2007–2008 droughts did not play a significant role in the uprising because migrants were likely to have returned as early as 2010 based on the satellite images showing full recovery of agricultural activities in drought-affected areas ( Eklund et al. 2022 ). Rural-to-urban migration in the MENA region is rather influenced by pre-existing socio-economic conditions and political decisions. For example, in Syria, the introduction of neoliberal agrarian policies by the government generated a significant degree of insecurity in the rural populations and prompted rural-to-urban migration ( De Châtel 2014 ; Selby 2019 ). And region’s demographic trend has a much greater and long-lasting impact on the pressure in urban areas. For instance, the urban population in Syria is estimated to have grown from 8.9 million in 2002 to 13.8 million in 2010, and most migrants lived in informal settlements with poor infrastructure and no jobs ( Kelley et al. 2015 ).

The narratives on climate change and migration in the MENA region in existing literature reflect how countries perceive climate-induced migration as a source of conflict and insecurity. Jordan, for instance, fears the influx of migration from the MENA region, mostly Palestine, Iraq, and Syria, would worsen the country’s water scarcity and thus security ( Weinthal, Zawahri, and Sowers 2015 ). Fears of “climate refugees” from Africa have shaped Israel’s discriminatory discourses and practices against African refugees and Bedouin communities inside the country ( Weinthal et al. 2015 ). Media reports have suggested that climate shocks in the MENA regions, where asylum seekers and irregular migrants originated from, have affected their decision to migrate ( O'Hagan 2015 ). More than 2.2 million migrants without legal permits have amassed at EU external borders during 2009–2017, and most migrants during this period were from the MENA region ( Cottier and Salehyan 2021 , 2).

Findings from existing research refute the idea of climate shocks would trigger refugee flows from the MENA region. Climate shocks and precipitation deficits are not linked to the increase of out-migration from the MENA region to Europe ( Abel et al. 2019 ; Cottier and Salehyan 2021 ). Severe droughts and drier weather conditions in the MENA region are associated with the reduced migration flow to Europe, which is contradictory from the popular media narrative about “climate refugees” ( Cottier and Salehyan 2021 ). This finding alone suggests that migration can be an “investment,” because the extra income generated from additional rain reduces financial barriers to emigrating ( Cottier and Salehyan 2021 , 6). The correlation between rainfall variability and asylum-seeking flows has been found during 2010–2012 when the Arab Spring swept a dozen MENA countries but not during other periods between 2006 and 2015 ( Abel et al. 2019 ). This finding demonstrates that the impact of climate change on generating asylum-seeking flows seems to be conditional on the origin country’s political stability.

Armed Group’s Tactical Considerations

Existing research specifically focusing on how climate change affects armed groups’ tactics is sparse in the MENA region (exception of Linke and Ruether 2021 ), but several research works demonstrate that armed groups may escalate their tactics due to the increased environmental stress on water and agricultural land. Changing climate conditions and weather shocks adversely affect water availability for agriculture. This trend underscores the notion that the strategic importance of controlling water and water infrastructure could emerge as an effective instrument for exerting pressure to local populations in times of armed conflicts. Previous research supplies evidence on how water is weaponized by armed groups, which is a case of escalation of tactics ( Grech-Madin 2020 ). Water weaponization is defined as the “intentional or unintentional damage or destruction of (sensitive) components of the water infrastructure like dams, treatment plants, pumping stations, piping and canal systems, sewage plants, reservoirs, wells, etc” ( von Lossow 2016 , 84).

Water has been used as both a target and a weapon by state and non-state actors. Existing studies focus on how non-state armed groups and government militaries have strategically attacked or captured water and other environmental infrastructure ( King 2015 ; von Lossow 2016 ; Sowers, Weinthal, and Zawahri 2017 ; Gleick 2019 ; Daoudy 2020b ). Water scarcity in the region is an incentivizing factor for government troops and armed groups to use water to incur damage to the local population. Attacks on water pipes, sanitation and desalination plants, water treatment, pumping and distribution facilities, and dams have occurred in Syria, Libya, and Yemen during civil wars. Targeting of water infrastructure also occurs in protracted conflict situations such as the Israel and Palestine conflict when Israel was accused of attacking wells in Gaza City ( von Lossow 2016 , 84). Particular attention has been drawn to rebel groups’ ability to use water for strategic but as well psychological terrorism ( King 2015 ).

The weaponization of water is not limited to targeting water infrastructure during wartime. Increasing water scarcity and the importance of water access influence the strategic calculation by armed groups on when and where they would deploy violence ( King 2015 ). Non-state armed groups such as the Islamic State in Syria and Iraq are known to have fought over the control of water infrastructure in the Euphrates and Tigris Rivers as part of their expansion strategy ( von Lossow 2016 ). Armed groups fight more intensely during the growing season, which is linked to tax revenue from agricultural harvest and control of the population who rely on farming ( Linke and Ruether 2021 , 116).

Armed groups can also use water as a tool of governance. By providing water and electricity to the local population, the Islamic State achieved ideological credibility as well as legitimacy over the local population, which was a core component of the IS claim of statehood ( King 2015 ; von Lossow 2016 ). Supplying water is a crucial governance function, so armed groups can obstruct water infrastructure to damage the conflict party’s control and legitimacy.

Elite Exploitation

Previous research demonstrates how elite exploitation is linked to protests and violent conflict by focusing on corruption, elite capture of disaster relief, and elite bias in the MENA region. Political patronage and ethnic, tribal, and religious networks for political mobilization shape elite behavior in the region. Political patronage is not unique to the MENA region, but clientelism explains the viability of political networks of some political elites in the MENA region who maintained power through providing resources and preferential treatment in return for votes, loyalty, and compliance ( Herb 1999 ; Haddad 2012 ). Social fabrics of the MENA are woven with diverse ethnic, tribal, and religious groups, and these minorities have also been part of political cleavage structures ( Belge and Karakoç 2015 ). Political mobilization along ethnic, tribal, and religious lines has been effective in the contexts when these identities are contested ( Yiftachel 1996 ). In the following, three main findings from existing research are outlined.

Climate change may increase opportunities for elites to appropriate humanitarian aid for their benefit, and elite exploitation can worsen the conflict risk amid climate-induced disasters and environmental scarcities. The risk of politicization of humanitarian and development aid has been extensively studied ( Doocy and Lyles 2018 ; Alqatabry and Butcher 2020 ). In situations of climate-induced disasters, local and central elites can have a significant influence on the planning and distribution of humanitarian aid. Political elites can be biased in their relationship with local elites, and this elite bias can have implications for local-level politics ( Brosché and Elfversson 2012 ). After the 2007–2008 drought in Syria, the Assad government directed the UN-led relief efforts to almost entirely focus on the Arab district of Al-Shaddadi, although the Kurdish communities were equally or worse affected ( Selby 2019 , 270). Unequal aid distribution can increase intercommunal tensions during droughts. State intervention can reduce the risk of conflict amid climate-related natural disasters. Tubi and Feitelson (2016 ) demonstrate how proactive relief provisions during droughts have reduced communal violence between Bedouin herders and Jewish farmers in Israel. The findings from Tubi and Feitelson (2016 ) confirm that the state’s capacity to adapt and absorb shocks remains essential for the inhabitants’ perceived marginal benefits and the opportunity cost of conflict ( Post et al. 2016 ).

Powerful elites compete over acquiring land and water resources from weak and vulnerable groups. Mismanagement and corruption in the public sector are other factors that affect the population’s access to water and basic services, which are simultaneously hampered by climate change ( Kim and Swain 2017 ). In Yemen, most communal conflict occurs over water and land when tribal elites compete with one another ( Weiss 2015 ). In southern Iraq, a large volume of water is illegally diverted for commercial farms owned by elites, which worsens water scarcity ( Mason 2022 ). Donor-funded projects for repairing Basra’s aging water infrastructure after the 2003 invasion, worth 2 billion USD over nearly two decades, were succumbed to widespread corruption ( Mason 2022 ). Bureaucratic procedures endow opportunities for officials to extort bribes such as well-licensing in Syria and water development project licensing in Lebanon ( De Châtel 2014 ; Mason and Khawlie 2016 ). In Syria, the government’s requirement to annually renew well licences was an opportunity for security personnel and local officials to collect bribes ( De Châtel 2014 , 12). Protestors in Dara’a, Syria initially demanded to end corruption in the water sector ( De Châtel 2014 ). In Iraq, the epidemic of corruption in the water sector endowed youth and urban poor grievances against the state, which led to widespread protests ( Human Rights Watch 2019 ).

Although the MENA region is a climate change hotspot, governance failures, and mismanagement account for declining water access ( Mason and Khawlie 2016 ; Selby et al. 2017 ; Daoudy 2021 ). Elites in the MENA region have leveraged climate change to explain some of the governance failures in the water and agriculture sectors. The Syrian state and security apparatus have exploited the narratives around climate change by portraying Syria as a “naturally water-scarce” country, although the reality on the ground shows a man-made water crisis due to corruption and inefficient management by the government authorities ( De Châtel 2014 , 9). Similarly, the Lebanese government blamed climate change for the reduction of water flow in the Hasbani Basin, while civil society representatives accused the government of “systematically neglecting their concerns” about water access ( Mason and Khawlie 2016 , 1352–3).

Tensions over transboundary water sharing may continue to rise in the MENA region ( Bulloch and Darwish 1993 ; Amery 2002 ). The Euphrates River and Tigris River are important water sources for Turkey, Iraq, Syria, and Iran, and Turkey controls the water flow through the investment in the Southeastern Anatolia Project consisting of twenty-two large reservoirs and nineteen hydroelectric power stations on the upper tributaries of the Euphrates and Tigris Rivers. Karnieli et al. (2019 ) argue that Turkey’s transboundary investment and dam filling to be the primary driver of 2007–2008 droughts in Syria instead of climate change. This might be inconsequential because Turkey released additional water to Syria during the drought (see Kibaroglu and Scheumann 2011 , 297). As long as the downstream countries, Syria, Iraq, and Iran, see their domestic water problems to be attributed to the upstream dams in Turkey (e.g., Al-Muqdadi et al. 2016 ), transboundary rivers can be a source of interstate tension—although it is unlikely to develop into a full-scale armed conflict ( Bencala and Dabelko 2008 ). The impact of climate change in transboundary water governance is still an under-researched area that deserves more attention. Another area that can be a subject for further research is a growing sub-national competition over water such as brewing tension within Iraq due to the Kurdish Regional Government’s dam building plans ( Tinti 2023 ).

Existing evidence demonstrates that climate impacts, particularly droughts and drying trends, contribute to armed conflict in various ways. This section weighs in on the findings from the analysis to evaluate the overall framework of pathways to climate insecurity in the MENA region. The synthesis of findings highlights consensus and disagreement in existing studies and identifies the areas for further research.

Water scarcity in the MENA region is apparent at multiple scales, from domestic to transboundary, and has various implications for social vulnerability and political stability. The region’s water insecurity is as much driven by governance challenges as climatic and environmental trends. Severe droughts in the Levant during 2007–2009 appear to have led to the decline in agricultural production in the affected areas, but the drought vulnerability is mediated by groundwater availability, the viability of irrigation systems, and the capacity of water infrastructure ( Kelley et al. 2015 ). Decades of mismanagement of water resources and institutional failings undermine adaptive capacities in the region, demonstrated in examples from Lebanon, Yemen, Syria, and Iraq ( Weiss 2015 ; Mason and Khawlie 2016 ; Selby 2019 ; Mason 2022 ).

The depletion of groundwater in parts of the MENA region is largely attributed to the government’s unsustainable agricultural and water policies. Groundwater offers an important source of reserve during droughts, and the unsustainable use of groundwater adversely affects farmers’ drought vulnerability. Government subsidies on fuels encouraged farmers to install diesel pumps to use groundwater for irrigation, without consideration for sustainability in Yemen and Syria ( Weiss 2015 ; Selby 2019 ). These governments’ agricultural and economic policies resulted in farmers growing more water-intensive crops such as cotton and citrus fruits, which accelerated groundwater depletion. Political elites used fuel subsidies to ensure support from farmers at the expense of the environment. These unsustainable water and agricultural policies are not technical “mismanagement” but embedded in a much larger political context and ideology ( Daoudy 2021 , 13). Considering political factors in climate vulnerability is an important aspect to understand the climate-conflict nexus in the MENA region.

This analytic essay also looks into the important debate about the contribution of droughts in the Syrian uprising and subsequent civil war. Fourteen out of thirty-nine existing studies focus on the Syrian conflict and examine various linkages between the conflict and climate-related environmental factors. The popular narrative portrays the Syrian civil war as a climate conflict that is triggered by climate-induced agricultural collapse resulting in mass displacement ( Gleick 2014 ; Werrell, Femia, and Sternberg 2015 ). Research refutes this narrative by contesting the empirical foundations. Drought-displaced people in urban or peri-urban areas did not participate in street protests ( De Châtel 2014 ), and a significant proportion of the displaced returned to northern Syria before the revolution began ( Eklund and Thompson 2017 ; Eklund et al. 2022 ). Reviewing the literature demonstrates that attributing the onset of the Syrian civil war solely to climate change lacks empirical substantiation. Nevertheless, climate-related environmental changes, such as falling groundwater levels, have significant impact on natural resources and livelihoods, which can consequently undermine human and environment security.

Internal migration is more prominent than international migration in the research focusing on climate-induced mobility in the MENA region. This is similar to other studies with different regional focus (e.g., Burrows and Kinney 2016 ). The disruption of the rural livelihoods appears to be a strong push factor in Syria, which can be worsened by droughts ( Fröhlich 2016 ). Data on migration seem to be a challenge in unpacking this complex phenomenon. It is challenging to disentangle environmental changes from economic drivers in migration decision-making. Satellite-based data provide reasonable proxy measures for in- and out-migration in locations (e.g., Ash and Obradovich 2020 ), but they do not offer insights on who moved from where to where and why. More studies incorporating qualitative data are needed to further the understanding of climate-induced internal migration.

There is clear evidence that armed groups have escalated their tactics by weaponizing water in the MENA region. Several studies demonstrate how armed groups escalate their tactics by weaponizing water. Such a wartime trend indicates a heightened risk for civilians and long-term consequences by destructing key water infrastructures. This finding is highly policy relevant for strengthening and enforcing international laws for civilian protection during armed conflict (see Grech-Madin 2021 ). In relation to the armed group’s tactics, more research is needed to unpack the role of climate-related environmental factors in the armed group’s recruitment and tactical decisions.

The findings on differing vulnerability and gendered impacts on livelihoods are based on a handful of studies, and intersectional approaches are generally absent in most studies reviewed in the analytic essay. How climate shocks have varying impacts on people based on their gender, age, livelihoods, ethnicity, and combinations of these identities is missing. If marginalization and grievances are key processes of climate-induced conflict, how climate change affects different segments of the population differently needs better understanding.

The relationship between climate change and violent conflict is primarily indirect and varied, cautioning against generalized assumptions. How climate change influences the risk of violent conflict in the MENA region is mediated by political economy, institutional shortcomings, and elite competition. The risk of violent conflict is contingent on pre-existing negative socio-political relationships, types of political systems, and different climate vulnerabilities of various social groups. Gendered climate vulnerabilities need better understanding for establishing the linkage between climate vulnerability and insecurity. Carefully examining existing evidence is important for both over general climate security discussions as well as for the policy discussions on the MENA region, which has remained a focal point of scholarly and policy debates concerning climate security ( Daoudy, Sowers, and Weinthal 2022 , 7).

Disentangling specific climate impacts is also crucial for enhancing government’s climate adaptation and disaster mitigation policies in the MENA region. Civil society representatives from the MENA region have been concerned that states and political elites blame climate change to legitimize inequalities and to devoid accountability ( Selby et al. 2017 ; Kausch 2022 ). As existing research demonstrated, water and food insecurity in the region is driven by a lack of state capacity to properly manage natural resources and the integrity of public institutions in the MENA region.

Future research should pay attention to other types of climate hazards, including floods, heatwaves, and dust storms. Existing research primarily focuses on droughts and precipitation deficits, failing to account for heatwaves and flooding, which also are common in the MENA region. Floods are understudied despite their severe humanitarian impact. For instance, heavy flooding forced more than 84,000 people to displacement in Yemen, 13,000 people in Iran, and 5,000 people in northern Iraq in 2021 ( IDMC 2023 ). How flooding affects livelihood conditions and social vulnerability would be considerably different from droughts. Studies from other regions suggest floods are not associated with communal violence ( Petrova 2022 ). Ultra-heatwaves are likely to worsen without substantial government interventions ( Zittis et al. 2021 ), and their impact on oil exploitation, tourism, and urban areas demands more research. Oil and tourism industries are economic backbones of several MENA countries, and adverse impact on these sectors is likely lead to ripple effects on the society. A decrease in oil production due to extreme heatwaves and dust storms will affect public service provisions by the governments, which can be a source of instability as previous research points out (e.g., Mason 2022 ).

Future research should look at non-violent conflicts, especially protests linked to climate change in the MENA region. There is already a substantial debate on the role of food security in political stability, such as in the Arab Spring ( Werrell and Femia 2013 ; Schilling et al. 2020 ). And few studies focus on under what conditions droughts and floods can lead to non-violent conflicts such as political unrest and protests ( Ide, Kristensen, and Bartusevičius 2021 ; Ide et al. 2021 ). Youth climate activists in the region have demanded their respective governments to take proactive climate actions ( Altaeb 2022 ). Climate change is becoming a politically salient topic, and the MENA region’s civil society has voiced its concerns about the inaction and growing uncertainty about the future. How the region’s climate activism interacts with politics appears to be an important area for future research.

The narrative about climate change and conflict in the MENA region is shaped by both scientific projections but also a “long history of colonial and postcolonial scholarship invoking environmental determinism as an explanation for underdevelopment” ( Daoudy et al. 2022 , 7). This calls for more “open” and critical approaches in researching the climate-conflict nexus in the region. The evidence from existing studies shows that current water and food insecurity in the MENA region are outcomes of domestic politics and institutional shortcomings rather than past climate change. This highlights the importance of governance reforms for enhancing adaptative capacity in the region ( Sowers et al. 2011 ). Improved understanding of how vulnerability to climate change interacts with political systems, institutions, and social relations can inform policy development. This enhanced understanding can equip relevant stakeholders to more effectively anticipate, prevent, and respond to the intricate web of risks entwining climate change and violent conflict, while concurrently enhancing resilience-building efforts.

We adopt SIPRI’s definition of the MENA region, which includes Bahrain, Egypt, Iran, Iraq, Israel, Jordan, Kuwait, Lebanon, Oman, Palestine, Qatar, Saudi Arabia, Syria, Turkey, the United Arab Emirates (UAE), North Yemen (–1990), South Yemen (–1990) and Yemen; (NA) Algeria, Libya, Morocco, and Tunisia. See “Regional coverage,” See SIPRI databases at https://www.sipri.org/databases/regional-coverage .

The search string was the following: AB=((climat* OR "climat* change" OR "climat* variability" OR rainfall OR precipitation OR drought OR "water scarcity" OR "land degradation" OR weather OR disaster OR temperature OR warming OR "sea level rise" OR desertification OR famine OR “soil erosion” OR flood*) AND (conflict OR jihad* OR armed OR insurgen* OR rebel* OR terror* OR violen* OR war) AND ("middle east*" OR “north africa*” OR MENA OR algeria OR bahrain OR egypt OR iran OR Iraq OR israel OR jordan OR kuwait OR lebanon OR libya OR morocco OR oman OR palestin* OR qatar OR “saudi arabia” OR syria OR tunisia OR “united arab emirates” OR yemen OR “western sahara”)).

Here, we use SIPRI’s definition of the MENA region, which includes Bahrain, Egypt, Iran, Iraq, Israel, Jordan, Kuwait, Lebanon, Oman, Palestine, Qatar, Saudi Arabia, Syria, Turkey, the United Arab Emirates (UAE), North Yemen (–1990), South Yemen (–1990) and Yemen; (NA) Algeria, Libya, Morocco, and Tunisia.

Author’s note : This work is supported by funding from the Swedish Ministry for Foreign Affairs as part of SIPRI’s Climate Change and Security Project and the Norwegian Ministry of Foreign Affairs for SIPRI’s Climate-Related Security and Development Risks Project. We would like to thank two anonymous reviewers for their constructive feedback for improving the manuscript. We are indebted to Florian Krampe, Farah Hegazi, and Kheira Tarif for their helpful comments throughout the writing process.

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This paper is in the following e-collection/theme issue:

Published on 3.4.2024 in Vol 26 (2024)

Ambiguity in Statistical Analysis Methods and Nonconformity With Prespecified Commitment to Data Sharing in a Cluster Randomized Controlled Trial

Authors of this article:

Letter to the Editor

• Yasaman Jamshidi-Naeini 1 , PhD   ; 
• Lilian Golzarri-Arroyo 1 , MS   ; 
• Deependra K Thapa 1 , PhD   ; 
• Andrew W Brown 2, 3 , PhD   ; 
• Daniel E Kpormegbey 1 , PhD   ; 
• David B Allison 1 , PhD  

1 Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, Bloomington, IN, United States

2 Department of Biostatistics, University of Arkansas for Medical Sciences, Little Rock, AR, United States

3 Arkansas Children's Research Institute, Little Rock, AR, United States

Corresponding Author:

David B Allison, PhD

Department of Epidemiology and Biostatistics

School of Public Health

Indiana University Bloomington

1025 E 7th St, PH 111

Bloomington, IN, 47405

United States

Phone: 1 8128551250

Email: [email protected]

In a published cluster randomized controlled trial (cRCT) [ 1 ] on the effects of desks on physical behaviors, 66 individuals were randomized into 3 groups by their office space (ie, clusters): the seated desk control (n=21; 8 clusters), sit-to-stand desk (n=23; 9 clusters), or treadmill desk (n=22; 7 clusters) group.

In the article, there is ambiguity regarding whether clustering (potential nonindependence of observations within the same office space) and nesting (due to the hierarchical structure of the data; see definitions from Jamshidi-Naeini et al [ 2 ]) have been accounted for. Furthermore, it appears that the data underlying the published results are not available to other researchers, which is contrary to the journal’s policy indicating “a submission to JMIR journals implies that…all relevant raw data, will be freely available to any researcher wishing to use them for non-commercial purposes….”

The description of methods indicates using random intercept mixed linear models accounting for repeated measures and clusters. However, it is stated elsewhere that “[t]he cluster effect did not significantly (all P values >.05) account for the variability in any of the outcome variables….Therefore, aim 1 and aim 2 outcome observations…were analyzed at the participant level instead of cluster…” [ 1 ].

The statement regarding accounting for the clustering effect is ambiguous. If the authors ignored the clustering effect based on the reasoning that participant outcomes within the same cluster are unrelated, as indicated by intraclass correlation coefficient (ICC) values that one chooses to describe as “small” or nonstatistical significance of the ICC values at some nominal α level (eg, 0.05), such reasoning is erroneous. A sample ICC or its associated P value is not an appropriate metric on which to determine whether one should account for clustering. Ignoring clustering, regardless of a sample ICC’s magnitude or associated P value, potentially leads to miscalculation of variance components and type I error rates above the nominal significance level [ 3 , 4 ].

In a cRCT with such unequal cluster sizes (ranging from 1 to 11 participants), there is no exact size α test, and type I error inflation may occur. Therefore, to ensure control of type I error rate, it is essential to apply appropriate weighting for unequal cluster sizes. In addition, the nesting effect that arises from the hierarchical structure of the data in cRCTs was not considered in the statistical analyses. Adjusting df (eg, by between-within determination [ 4 ]) could have accounted for this nesting effect.

We requested the raw data to reproduce the analyses (see definition of “reproducing” in Reproducibility and Replicability in Science [ 5 ]) and potentially use alternative corrected methods to reanalyze the data. Data were not shared with us. The authors stated that this decision was made “to ensure the integrity of ongoing research being conducted using the same dataset.” Nevertheless, sharing data for the purpose of reproducing published results does not compromise the integrity of further analyses on the same data set. Withholding data, on the other hand, renders the study irreproducible and thus compromises the trustworthiness of the published results.

The concerns raised herein should be addressed to ensure the integrity, transparency, and reproducibility of the published findings.

Acknowledgments

The authors are supported in part by R25DK099080, R25HL124208, R25GM141507, and the Gordon and Betty Moore Foundation. The opinions expressed are those of the authors and do not necessarily represent those of the National Institutes of Health (NIH) or any other organization.

Conflicts of Interest

Collectively, the authors and their institutions have received payments for consultations, grants, contracts, in-kind donations, and contributions from multiple for-profit and not-for-profit entities interested in statistical design and analysis of experiments but not directly related to the research questions addressed in the paper in question.

• Arguello D, Cloutier G, Thorndike AN, Castaneda Sceppa C, Griffith J, John D. Impact of sit-to-stand and treadmill desks on patterns of daily waking physical behaviors among overweight and obese seated office workers: cluster randomized controlled trial. J Med Internet Res. May 16, 2023;25:e43018. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Jamshidi-Naeini Y, Brown AW, Mehta T, Glueck DH, Golzarri-Arroyo L, Muller KE, et al. A practical decision tree to support editorial adjudication of submitted parallel cluster randomized controlled trials. Obesity (Silver Spring). Mar 2022;30(3):565-570. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Brown AW, Li P, Brown MMB, Kaiser KA, Keith SW, Oakes JM, et al. Best (but oft-forgotten) practices: designing, analyzing, and reporting cluster randomized controlled trials. Am J Clin Nutr. Aug 2015;102(2):241-248. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Golzarri-Arroyo L, Dickinson SL, Jamshidi-Naeini Y, Zoh RS, Brown AW, Owora AH, et al. Evaluation of the type I error rate when using parametric bootstrap analysis of a cluster randomized controlled trial with binary outcomes and a small number of clusters. Comput Methods Programs Biomed. Mar 2022;215:106654. [ FREE Full text ] [ CrossRef ] [ Medline ]
• National Academies of Sciences, Engineering, and Medicine. Reproducibility and Replicability in Science. Washington, DC. The National Academies Press; 2019.

Abbreviations

Edited by T Leung; This is a non–peer-reviewed article. submitted 29.10.23; accepted 22.02.24; published 03.04.24.

©Yasaman Jamshidi-Naeini, Lilian Golzarri-Arroyo, Deependra K Thapa, Andrew W Brown, Daniel E Kpormegbey, David B Allison. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 03.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

This paper is in the following e-collection/theme issue:

Published on 1.4.2024 in Vol 10 (2024)

Using Project Extension for Community Healthcare Outcomes to Enhance Substance Use Disorder Care in Primary Care: Mixed Methods Study

Authors of this article:

Original Paper

• MacKenzie Koester, MPH   ; 
• Rosemary Motz, MPH, MA, RDN   ; 
• Ariel Porto, MPH   ; 
• Nikita Reyes Nieves, MPH   ; 
• Karen Ashley, EdD  

Weitzman Institute, Moses Weitzman Health System, Washington, DC, United States

Corresponding Author:

MacKenzie Koester, MPH

Weitzman Institute

Moses Weitzman Health System

1575 I Street Northwest

Washington, DC, 20005

United States

Phone: 1 8603476971

Email: [email protected]

Background: Substance use and overdose deaths make up a substantial portion of injury-related deaths in the United States, with the state of Ohio leading the nation in rates of diagnosed substance use disorder (SUD). Ohio’s growing epidemic has indicated a need to improve SUD care in a primary care setting through the engagement of multidisciplinary providers and the use of a comprehensive approach to care.

Objective: The purpose of this study was to assess the ability of the Weitzman Extension for Community Healthcare Outcomes (ECHO): Comprehensive Substance Use Disorder Care program to both address and meet 7 series learning objectives and address substances by analyzing (1) the frequency of exposure to the learning objective topics and substance types during case discussions and (2) participants’ change in knowledge, self-efficacy, attitudes, and skills related to the treatment of SUDs pre- to postseries. The 7 series learning objective themes included harm reduction, team-based care, behavioral techniques, medication-assisted treatment, trauma-informed care, co-occurring conditions, and social determinants of health.

Methods: We used a mixed methods approach using a conceptual content analysis based on series learning objectives and substances and a 2-tailed paired-samples t test of participants’ self-reported learner outcomes. The content analysis gauged the frequency and dose of learning objective themes and illicit and nonillicit substances mentioned in participant case presentations and discussions, and the paired-samples t test compared participants’ knowledge, self-efficacy, attitudes, and skills associated with learning objectives and medication management of substances from pre- to postseries.

Results: The results of the content analysis indicated that 3 learning objective themes—team-based care, harm reduction, and social determinants of health—resulted in the highest frequencies and dose, appearing in 100% (n=22) of case presentations and discussions. Alcohol had the highest frequency and dose among the illicit and nonillicit substances, appearing in 81% (n=18) of case presentations and discussions. The results of the paired-samples t test indicated statistically significant increases in knowledge domain statements related to polysubstance use ( P =.02), understanding the approach other disciplines use in SUD care ( P =.02), and medication management strategies for nicotine ( P =.03) and opioid use disorder ( P =.003). Statistically significant increases were observed for 2 self-efficacy domain statements regarding medication management for nicotine ( P =.002) and alcohol use disorder ( P =.02). Further, 1 statistically significant increase in the skill domain was observed regarding using the stages of change theory in interventions ( P =.03).

Conclusions: These findings indicate that the ECHO program’s content aligned with its stated learning objectives; met its learning objectives for the 3 themes where significant improvements were measured; and met its intent to address multiple substances in case presentations and discussions. These results demonstrate that Project ECHO is a potential tool to educate multidisciplinary providers in a comprehensive approach to SUD care.

Introduction

In the United States, overdose deaths continue to be a major cause of injury-related deaths. Since the onset of the COVID-19 pandemic, numbers have only accelerated, and the state of Ohio has led the nation in high substance use disorder (SUD) rates, including drug use and prescription drug use. The Centers for Disease Control and Prevention ranks the state among the top 5 across the United States with the highest rates of opioid overdose deaths [ 1 ]. While research has shown an increase in the number of people enrolled in substance use treatment in Ohio between 2015 and 2019 there was still a notable high increase in the annual average prevalence of past-year illicit drug use disorder in Ohio (3.6%) compared to the regional average (3%) and the national average (2.9%) [ 2 ]. In addition, past-month alcohol use disorder (9.3%), cannabis use disorder (5.8%), and tobacco use disorder (35.2%) were higher than the national average among young adults aged 18-25 years [ 2 ]. Ohio’s growing epidemic has highlighted the need to improve SUD care in a primary care setting by training providers to better address differences in care and social determinants of health through the use of behavioral techniques, harm-reduction philosophy of care, medication management, and a team-based care approach.

Weitzman Extension for Community Healthcare Outcomes: Comprehensive Substance Use Disorder Care Program

Beginning in 2021, Buckeye Health Plan and Ohio University Heritage College of Osteopathic Medicine have partnered with the Weitzman Institute (WI), a national primary care research, policy, and education institute, to provide targeted support and education to Ohio primary care medical and behavioral health providers working with underserved patients, especially those in the rural, southeastern Appalachian region, using the evidence-based Project Extension for Community Healthcare Outcomes (ECHO) learning model. Project ECHO uses frequent videoconference sessions to connect a target audience of learners with subject matter experts for didactic and case-based instruction and engaged discussion [ 3 ]. Through regular attendance at these sessions, Project ECHO aims to equip learners with the knowledge, confidence, and skills to better manage complex cases.

WI has over 11 years of experience in developing and delivering Project ECHO programs to meet the needs of providers working in resource-limited settings. As an early adopter of the model in 2012, Weitzman ECHO programs have been offered in 22 topic areas to over 8000 health care professionals across all 50 states, Washington D.C., and Puerto Rico. Working in collaboration, Buckeye Health Plan and Ohio University aimed to leverage this expertise and offer multiple Project ECHO programs each year for providers in topics of the greatest need and interest.

As described, one of Ohio’s most dire population health needs is to improve outcomes for patients experiencing addiction. Thus, SUD was selected as the second ECHO program developed through this partnership. More specifically, opioids are a heightened concern throughout both Ohio and the United States, and the opioid epidemic has spurred significant funding allocations, such as the Biden Administration’s US $1.5 billion award to states and territories to end the epidemic [ 4 ]. However, there are many additional substances of concern, both illicit and nonillicit, such as alcohol, tobacco, cannabis, methamphetamine, and cocaine [ 5 ], which may receive less attention given the directed funding for opioids. For this reason, it was decided that the ECHO would address not only opioids, or any one substance, but rather be designed to provide techniques to help providers address SUD overall through a comprehensive, team-based lens and a harm reduction philosophy of care. Reflecting this broad topical approach, the program was titled the Weitzman ECHO: Comprehensive Substance Use Disorder Care (CSUDC ECHO) program.

CSUDC ECHO consisted of 24 twice-monthly sessions held between July 2021 and July 2022. Each 1-hour session included a 20- to 25-minute didactic presentation followed by 1 patient case submitted by a participant before the session and discussed live for the remaining 35-40 minutes. Textbox 1 outlines the didactic presentation topics for each session. A multidisciplinary core faculty facilitated each session and was comprised of 1 physician with dual board certification in family medicine and addiction medicine and experienced in treating SUDs at federally qualified health centers; 1 nurse practitioner who developed and leads a federally qualified health center medication-assisted treatment (MAT) program; 1 supervisory licensed counselor; and 1 population health expert. Together, the faculty built a 12-month curriculum covering diverse topics such as medication management, team-based care, trauma-informed care, stages of change and motivational interviewing, polysubstance use and co-occurring conditions, and coordinating levels of care.

Session and didactic topic

• Philosophy of care (no case presentation).
• Harm reduction strategies.
• Principals of medication management.
• Team-based care: care provision partners.
• Trauma-informed care: an overview.
• Motivational interviewing.
• Stages of change for addiction.
• Assessing stages of change and stage-based interventions.
• Medications for opioid use disorder basics.
• Behavioral health and primary care coordination.
• Transitions of care.
• Polysubstance use.
• Social determinants of health including barriers or challenges (no case presentation).
• Adverse childhood experiences.
• Legal factors and access.
• Mental health crisis and coordination of care.
• Medication-assisted treatment for alcohol and tobacco use disorders.
• Self-determination and strength-based approaches.
• Contingency management for substance use disorder.
• HIV and hepatitis C virus in patients with substance use disorder.
• Screening, brief intervention and referral to treatment into primary care.
• Stimulant use disorder treatment and medication management.
• Co-occurring mental health substance use disorder.
• Tobacco cessation for polysubstance patients.

Participants were recruited by email blasts targeted to each partner’s network of Ohio primary care providers and other members of the care team. A total of 109 participants attended at least one session, 16 participants attended between 7 and 11 sessions, and 23 participants attended over 12 (half) of the sessions. On average, there were 32 attendees at each session. Continuing education credits were offered to medical providers, behavioral health providers, and nurses.

Purpose of Study

The purpose of this study was to assess the ability of CSUDC ECHO to both address and meet 7 learning objectives ( Textbox 2 ) and address multiple substances by analyzing (1) the frequency of exposure to the learning objective topics and substance types during case discussions and (2) participants’ knowledge, self-efficacy, skills, and attitudes related to the treatment of SUDs pre- to postprogram.

• Project a harm reduction philosophy of care into your treatment of patients experiencing substance use disorders and explain this concept to peers.
• Use the care team more effectively to improve the management of patients experiencing substance use disorders.
• Use motivational interviewing and other behavioral techniques to improve patient outcomes related to substance use disorders.
• Better differentiate and implement medication management strategies for patients experiencing substance use disorders.
• Illustrate trauma-informed practices in the screening, assessment, and treatment of patients experiencing substance use disorders.
• Describe and manage common co-occurring conditions and polysubstance use more effectively in patients experiencing substance use disorders.
• Distinguish and address factors related to social determinants of health faced by specific populations experiencing substance use disorders.

Study Design and Data Collection

This study used a mixed methods design, using a conceptual content analysis [ 6 ] analyzing ECHO participant-led case presentations, as well as a 2-tailed paired-samples t test of participant self-reported learner outcomes. All ECHO attendees who registered and attended the Project ECHO CSUDC sessions are included in the deductive content analysis. All ECHO attendees who registered before and through the first session of the series were invited to complete a preseries survey (n=106) via Qualtrics survey software (Qualtrics). The preseries survey remained open for 3 weeks from June 25, 2021, to July 18, 2021. A total of 79 responses were received (n=79) for a response rate of 75%. Upon completion of the ECHO series, active attendees (ie, those that were still active at the conclusion of the series and did not officially drop from the series, as well as those who enrolled throughout the series) were invited to complete a postseries survey via Qualtrics Survey Software (n=90). The postseries survey remained open for 4 weeks from July 7, 2022, to August 2, 2022. A total of 25 responses were received (n=25) for a response rate of 28%. A total of 16 consented participants completed both the preseries and postseries surveys (n=16) and are included in the paired-samples t tests statistical analysis.

Ethical Considerations

This study was approved by the Community Health Center, Inc, Institutional Review Board (IRB; 1190) on January 6, 2022. Informed consent was accounted for by the authors through the administration of a consent form on the postseries survey gathering participant consent to use their deidentified survey data for the paired-samples t test analysis. The deductive content analysis was considered a secondary analysis and was given exempt status. All data used in this study were deidentified, accounting for privacy and confidentially. No compensation for participation in this study was deemed necessary by the IRB.

Survey Tools

The preseries and postseries surveys were internally created and based on the Consolidated Framework for Implementation Research (CFIR) [ 7 ] and Moore’s Model of Outcomes Assessment Framework [ 8 ]. The specific CFIR domains assessed for include intervention characteristics, outer setting, inner setting, characteristics of individuals, and process [ 7 ]. Additionally, the levels of Moore’s Model of Outcomes Assessment Framework assessed for include level 2 (satisfaction), level 3a (declarative knowledge), level 3b (procedural knowledge), level 4 (competence), level 5 (performance), and level 6 (patient health) [ 8 ]. The surveys assessed changes in participants’ self-reported knowledge, attitudes, self-efficacy, and skills through statements centered on the series’ learning objectives. The preseries survey also collected participant characteristics including provider type and years of experience working with patients diagnosed with SUDs, as well as team-based care practices. Additionally, the postseries survey collected information on engagement and practice changes. The preseries survey instrument is presented in Multimedia Appendix 1 and the postseries survey instrument is presented in Multimedia Appendix 2 .

While the preseries survey and postseries survey tools were based on CFIR [ 7 ] and Moore’s Model of Outcomes Assessment Framework [ 8 ], both surveys were internally designed. The internal research and evaluation and CSUDC ECHO programmatic teams created the survey tools through several iterations of the internal review, which also consisted of selecting the appropriate domain (ie, knowledge, attitudes, self-efficacy, and skills) to assess each series’ learning objective. Each domain used a 5-point Likert scale to assess responses. The surveys were then presented to the CSUDC ECHO series stakeholders and faculty for review and approval before administering the surveys to the ECHO attendees. See Multimedia Appendices 1 and 2 for the domain placement of learning objectives and the 5-point Likert scales.

Conceptual Content Analysis

To further evaluate Weitzman ECHO CSUDC aims, researchers conducted a conceptual content analysis [ 6 ] using a set of a priori themes extracted from the series’ learning objectives. Series’ learning objectives are detailed in Textbox 2 . To establish a priori themes, researchers met before the launch of the ECHO to examine the series’ 7 learning objectives and extracted 7 themes for the content analysis. The themes were: harm reduction, team-based care, behavioral techniques, MAT, trauma-informed care, co-occurring conditions, and social determinants of health. To assess the frequency to which multiple substances were discussed, the themes also included 5 illicit and nonillicit substances of concern: alcohol, stimulants, opioids, cannabis, tobacco, or nicotine, plus polysubstance use when any 2 or more of these substances were identified. A conceptual analysis approach was used to gauge the dose and frequency of all learning objective themes and selected illicit and nonillicit substances. The content analysis aimed to confirm the discussion of the series’ learning objectives during case presentations and to determine to what extent multiple substances were able to be addressed.

Researchers evaluated all 22 participant-led ECHO case presentations and discussions for the presence of the selected themes in the prepared participant cases, faculty recommendations, and participant recommendations. Case presentations and discussions consisted of participants independently preparing a patient case to present and receive participant and faculty guidance for a patient treatment plan. Case presentations were recorded and transcribed using Zoom videoconferencing software (Zoom Video Communications, Inc). The transcriptions were then used for the conceptual content analysis.

To ensure coding accuracy, 4 researchers independently coded 27% (n=6) of the case presentations and met to reconcile discrepancies and better establish coding parameters. After reconciling discrepancies, 1 researcher coded the remaining 16 case presentations and discussion transcripts. The content analysis themes and descriptions are presented in Table 1 .

Paired-Samples t Test

To determine if Project ECHO CSUDC affected participant learner outcomes, researchers calculated mean scores reported on a Likert scale of 1 to 5 and conducted a paired-samples t test to compare pre- and postseries scores at a .05 significance level. The surveys consisted of matching statements assessing knowledge, self-efficacy, attitudes, and skills associated with the series’ learning objectives. The data were assessed for normality and homogeneity of variance and the assumptions were met. The data analysis was conducted using SPSS Statistics for Windows (version 26.0; IBM Corp).

Participant Characteristics

CSUDC ECHO participants were asked to report their role type on the preseries survey. Of the participants that responded to the survey items (n=79), a majority were other care team members (n = 32; 41%) followed by behavioral health providers (n = 30; 38%) and medical providers (n = 16; 21%). Additionally, participants were asked to indicate their years of experience working with SUDs. Most participants had between 1 and 5 years of experience (n=23; 29%) followed by 6-10 years (n=15; 19%), 11-20 years (n=14; 18%), less than 1 year (n=13; 16%), 7 participants indicated they do not work directly with patients (n=7; 9%), 21-30 years (n=4; 5%), 31-40 years (n=2; 3%), and more than 40 years of experience (n=1; 1%). Full participant characteristics of the entire CSUDC ECHO attendees, excluding the paired-samples t test sample, the paired-samples t test sample only, and all combined CSUDC ECHO attendees are provided in Table 2 .

The attendance data of participants included in the paired-samples t test analysis were analyzed. Further, 6 (n=6; 38%) of the paired-samples t test participants attended 1% (n=1) to 25% (n=6) of the 24 CSUDC ECHO sessions, 3 (n = 3; 19%) attended 26% (n=7) to 49% (n=11) of the sessions, 4 (n = 4; 25%) attended 50% (n=12) to 75% (n=18) of the sessions, and 3 (n = 3; 19%) attended 76% (n=19) to 100% (n=24) of the sessions.

a ECHO: Extension for Community Healthcare Outcomes.

b CSUDC: Comprehensive Substance Use Disorder Care.

c SUD: substance use disorder.

The conceptual content analysis indicated that all of the a priori themes relating to the learning objectives resulted in high frequencies and doses, appearing in a majority of case presentations and discussions. Further, 3 themes appeared in 100% (n = 22) of case presentations and discussions, including team-based care at a frequency of 156, followed by harm reduction at a frequency of 152, and social determinants of health at a frequency of 135. In total, 4 themes appeared in less than 100% (n=22) of case presentations and discussions, but above 81% (n=18), including co-occurring conditions with a frequency of 118 and appearing in 95% (n = 21) of case presentations and discussions, followed by behavioral techniques at a frequency of 108 and appearing in 91% (n = 20) of case presentations and discussions, MAT at a frequency of 89 and appearing in 86% (n = 19) of case presentations and discussions, and trauma-informed care at a frequency of 79 and appearing in 82% (n=18) case presentations and discussions. Additionally, multiple substances were represented but at differing frequencies. The substance that resulted in the highest frequency and dose was alcohol at a frequency of 64 and appeared in 81% (n = 18) of case presentations and discussions, followed by stimulants at a frequency of 55 and 77% (n=17) of case presentations and discussions, opioids at a frequency of 49 and 59% (n = 13) of case presentations and discussions. Cannabis resulted with a frequency of 38 but appeared in 64% (n = 14) of case presentations and discussions. Finally, tobacco and nicotine resulted in the lowest frequency at 11 and dose appearing in 27% (n = 6) of case presentations and discussions. When evaluating polysubstance use, which was limited to the use of two or more of the listed substances, we found a dose of 95% (n = 21) of case presentations and discussions. The frequency of polysubstance use was not included in the conceptual content analysis since it was not a learning objective theme and the emphasis of the conceptual content analysis was focused on the specific illicit and nonillicit substance types. The results of the conceptual content analysis are presented in Table 3 .

a MAT: medication-assisted treatment.

b —: not available.

In total, 4 knowledge domain statements resulted in statistically significant increases: understanding polysubstance use in patients experiencing SUD ( P =.02), understanding the approach colleagues in other disciplines use to address SUD ( P =.02), knowledge of medication management strategies for nicotine use disorder ( P =.03), and knowledge of medication management strategies for opioid use disorder (OUD; P =.003). Additionally, all knowledge domain statements resulted in an increased change in mean score from preseries to postseries. The results of the knowledge domain preseries and postseries scores are presented in Table 4 .

No attitudes domain statements resulted as statistically significant. All attitudes domain statements resulted in an increased change in mean score from preseries to postseries except the statement about a treatment plan for a patient experiencing an illicit SUD only being successful if abstinence is maintained, which resulted in a negative change in mean score. The negative change in mean score from preseries to postseries was the appropriate direction of change for alignment with promoting a harm reduction philosophy. The results of the attitudes domain preseries and postseries scores are presented in Table 5 .

Self-Efficacy

In total, 2 self-efficacy statements resulted in statistically significant increases: choosing a medication management strategy for nicotine use disorder ( P =.002) and alcohol use disorder ( P =.02). Additionally, all self-efficacy domain statements resulted in an increased change in mean score from preseries to postseries. The results of the self-efficacy domain preseries and postseries scores are presented in Table 6 .

a SMART: specific, measurable, achievable, relevant, timely.

In total, 1 skill domain statement resulted in a statistically significant increase: using the stages of change theory to provide stage-based interventions to patients experiencing SUDs ( P =.03). Additionally, all skill domain statements resulted in an increased change in mean score from preseries to postseries. The results of the skill domain preseries and postseries scores are presented in Table 7 .

a IOP: intensive outpatient.

Principal Findings

Ohio’s annual average prevalence of tobacco use, heroin use, use of prescription pain relievers, OUDs, illicit drug use disorder, and SUD have been higher compared to both regional and national averages [ 2 ]. Considering the need to address this public health concern, CSUDC ECHO was implemented to train Ohio providers and care team members in substance use care. CSUDC ECHO enhanced the Project ECHO work in this field by focusing content and learning objectives on a comprehensive, team-based lens and a harm reduction philosophy of care to address multiple illicit and nonillicit substances including opioids, alcohol, nicotine, cannabis, and stimulants. To assess the ability of the CSUDC ECHO program to meet its 7 program learning objectives ( Textbox 2 ) and address multiple substances, this study analyzed (1) the frequency of exposure to learning objective themes and substance types during case presentations and discussions and (2) participating providers’ change in knowledge, attitudes, self-efficacy, and skills related to the treatment of SUDs.

Study results demonstrate that all 7 learning objectives were frequently addressed in the content of case presentations and discussions throughout the program, with team-based care being the most frequently mentioned, 3 objectives appearing in 100% (n=22) of case discussions (eg, team-based care, harm reduction, and co-occurring conditions), and all 7 objectives appearing in >81% (n=18) of all cases discussed. This may have resulted in the learner outcome improvement pre- to postprogram for multiple learner domains (eg, knowledge, self-efficacy, and skill) for the following themes: team-based care, MAT, polysubstance use, and behavioral techniques. No pattern emerged among the participants included in the paired-samples t test analysis exposure to didactic topics and changes in learner outcomes.

Alcohol, stimulants, opioids, cannabis, and nicotine were addressed in the content of case presentations and discussions throughout CSUDC ECHO with alcohol being the most frequently mentioned and most common substance appearing in cases, 4 substances appearing in >59% (n=13) of case discussions (eg, alcohol, stimulants, opioid, and cannabis), and all coded substances appearing in at least a quarter of cases. The dialogue about these substances during case discussions likely resulted in improvements to the following learner outcomes related to medication management: alcohol use disorder, OUD, and nicotine use disorder. Medication management of cannabis use disorder was not assessed in the pre- to postsurveys. Additionally, the didactic presentation topics that centered on alcohol, opioid, and nicotine use disorder resulted in a higher attendance rate with about 40% (n=6) to 50% (n=8) of the participants included in the paired-samples t test analysis attending the sessions, as compared to only 20% (n=3) of the aforementioned participant sample having attended the session centered on stimulant use disorder.

These findings indicate that the ECHO program’s content aligned with its stated learning objectives; met its learning objectives for the 3 themes where significant improvements were measured; and met its intent to address multiple substances in case presentations and discussions. While case presentations and discussions comprise from half to the majority of content in the sessions (30-35 minutes of a 60-minute session), content during sessions also includes faculty didactic presentations (20-25 minutes), which also addresses these 7 learning objectives and various substances but was not a part of the content analysis. Therefore, learner outcome improvements may also be a result of content addressed in didactic presentations.

While the Project ECHO model has been shown to be effective in training the primary care workforce [ 9 ], specifically on OUD [ 10 , 11 ] and addiction medicine [ 12 , 13 ], there has been no documentation, to our knowledge, of the ability of a team-based, comprehensive SUD and polysubstance-focused Project ECHO designed to improve learner outcomes (eg, knowledge, self-efficacy, and skills). Although Komaromy and colleagues [ 14 ] investigated the frequency of cases presented based on substance type in a comprehensive SUD-focused ECHO, a content analysis of the case presentation and discussion transcripts was not analyzed to either assess the frequency of substances or learning objectives. Furthermore, to our knowledge, this process has not been combined in a mixed method approach to compare learner outcomes with a content analysis to gauge the ability of an SUD-focused Project ECHO program to meet its stated learning objectives. Our results reported here align with this literature and expand to demonstrate that Project ECHO is a potential tool to effectively educate multidisciplinary providers in a comprehensive approach to SUD care.

This study has several strengths which promote the ability of the Project ECHO model in enhancing health care providers’ knowledge, self-efficacy, and skill associated with comprehensive SUD care. The focus of this study is unique as there is minimal research exploring the benefits and training ability of Project ECHO with a comprehensive SUD care focus. This study’s noteworthy strength is the use of a mixed methods design that presents a comprehensive evaluation correlating the content addressed in the case presentations and discussions to statistically significant learner outcomes to demonstrate how this telementoring continuing education series improved provider’s knowledge, skills, and self-efficacy to benefit participating providers and their practices.

Limitations

This study faced several limitations during data collection and analysis. The first limitation of this study was the limited sample size and low response rate. There was a decline between the number of participants who completed the preseries survey and postseries survey, resulting in a low comparative sample, which restricted the options for statistical analysis. Another limitation was generalizability; the results of this Project ECHO are limited to the target audience of medical providers, behavioral health providers, and care team members from the state of Ohio, which is not a representative sample of broader populations nationally. Additionally, participants self-selected to take part in the Project ECHO series, which presents the potential for self-selection bias. Another limitation this study faced was the lack of available or reliable data on Project ECHO and its ability to meet learning objectives and address multiple substances through providers’ knowledge, self-efficacy, skill, and attitudes. Furthermore, self-reported data to assess knowledge and skills, and self-reported data in general, could present participant biases and is difficult to corroborate with outcomes. The use of internally designed survey instruments instead of using validated instruments presents as a limitation. In light of these limitations, future studies in this subject matter should include a larger data set. Additionally, future studies using a nested analysis approach might provide more insight into how the learning objective themes coincide with the various illicit and nonillicit substance types and would be a useful analysis to contribute to the knowledge base. Another recommendation for future studies in this subject matter should include a deeper analysis of attendance dose and exposure to didactic topics to better understand the impact on changes in learner outcomes. Future research with greater validity will contribute to the significant gaps in literature regarding this subject.

Conclusions

The purpose of this research study was to assess the ability of CSUDC ECHO to both address and meet 7 learning objectives ( Textbox 2 ) and address multiple substances by analyzing (1) the frequency of exposure to the learning objective topics and substance types during case presentations and discussions and (2) participants’ knowledge, self-efficacy, skills, and attitudes related to the treatment of SUDs from pre- to postprogram. The results of this study indicate that CSUDC ECHO was able to both address and meet its learning objectives while addressing multiple substances, as demonstrated by improvements in learner knowledge, self-efficacy, and skills. All learning objective themes resulted in high frequencies and doses, appearing in a majority of case presentations throughout the series. These promising results suggest that Project ECHO is a potential tool to educate primary care providers, behavioral health providers, and care team members in a comprehensive approach to SUD assessment and treatment through complex case discussions combined with didactic learning for certain settings. As Project ECHO programs continue to be established globally and existing programs strengthen, further research examining the model’s ability to achieve positive learning outcomes and factors that may contribute to these outcomes (eg, frequency of topic dose) is needed to confirm the outcomes in larger population samples, additional topics of focus, and other geographical settings.

Acknowledgments

The authors would like to acknowledge our partners at Buckeye Health Plan and Ohio University Heritage College of Osteopathic Medicine. We would like to thank our funders, Centene Corporation through its subsidiary, Buckeye Health Plan; without their financial support, this work would not have been possible. We would like to thank the faculty that led the ECHO sessions, delivered didactic presentations, and provided high-quality case recommendations, including core faculty members Dana Vallangeon, doctor of medicine, Tracy Plouck, master of public administration, Amy Black, master of science in nursing, advanced practice registered nurse, nurse practitioner-certified, Ericka Ludwig, licensed professional clinical counselors applying for training supervision designation, licensed independent chemical dependency counselor, as well as guest faculty members. We would also like to thank our Weitzman Institute colleagues who helped with the content analysis: Zeba Kokan, Claire Newby, and Reilly Orner. To learn more about Weitzman Extension for Community Healthcare Outcomes programs, visit their website [ 15 ]. This project was supported by Buckeye Health Plan, a subsidiary of Centene Corporation. The views, opinions, and content expressed in this paper do not necessarily reflect the views, opinions, or policies of Buckeye Health Plan or Centene Corporation. The authors did not use generative artificial intelligence in any portion of this paper.

Data Availability

The data sets generated and analyzed during this study are not publicly available due to a portion of the data being deemed as exempt by the institutional review board and the institutional review board approving a waiver of informed consent for the exempt data, as well as the sensitive nature of the data, but are available from the corresponding author on reasonable request.

Authors' Contributions

MK wrote this paper, reviewed this paper, designed the evaluation plan, and performed the qualitative and statistical analyses. AP wrote this paper, reviewed this paper, and assisted with the evaluation design and approval. RM wrote this paper, reviewed this paper, performed the literature review, and assisted with the evaluation design and approval. NRN wrote this paper, reviewed this paper, and performed the literature review. KA critically reviewed this paper.

Conflicts of Interest

None declared.

Weitzman Extension for Community Healthcare Outcomes: Comprehensive Substance Use Disorder Care preseries survey instrument.

Weitzman Extension for Community Healthcare Outcomes: Comprehensive Substance Use Disorder Care postseries survey instrument.

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Abbreviations

Edited by T de Azevedo Cardoso; submitted 12.04.23; peer-reviewed by A Arbabisarjou, J Ford Ii; comments to author 12.09.23; revised version received 06.11.23; accepted 29.02.24; published 01.04.24.

©MacKenzie Koester, Rosemary Motz, Ariel Porto, Nikita Reyes Nieves, Karen Ashley. Originally published in JMIR Medical Education (https://mededu.jmir.org), 01.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Education, is properly cited. The complete bibliographic information, a link to the original publication on https://mededu.jmir.org/, as well as this copyright and license information must be included.

Cardiac Ultrasonic Tissue Characterization in Myocardial Infarction Based on Deep Transfer Learning and Radiomics Features

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Objective Acute myocardial infarction (MI) alters cardiomyocyte geometry and architecture, leading to changes in the acoustic properties of the myocardium. This study examines ultrasomics — a novel cardiac ultrasound-based radiomics technique to extract high-throughput pixel-level information from images—for identifying infarcted myocardium.

Methodology A retrospective multicenter cohort of 380 participants was split into two groups: a model development cohort (n=296; 101 MI cases, 195 controls) and an external validation cohort (n=84; 40 MI cases, 44 controls). Handcrafted and transfer learning-derived deep ultrasomics features were extracted from 2-chamber and 4-chamber echocardiographic views and ML models were built to detect patients with MI and infarcted myocardium within individual views. Myocardial infarct localization via texture features was determined using Shapley additive explanations. All the ML models were trained using 10-fold cross-validation and assessed on an external test dataset, using the area under the curve (AUC).

Results The ML model, leveraging segment-level handcrafted ultrasomics features identified MI with AUCs of 0.93 (95% CI: 0.97-0.97) and 0.83 (95% CI: 0.74-0.89) at the patient-level and view-level, respectively. A model combining handcrafted and deep ultrasomics provided incremental information over deep ultrasomics alone (AUC: 0.79, 95% CI: 0.71-0.85 vs. 0.75, 95% CI: 0.66-0.82). Using a view-level ultrasomic model we identified texture features that effectively discriminated between infarcted and non-infarcted segments (p<0.001) and facilitated parametric visualization of infarcted myocardium.

Conclusion This pilot study highlights the potential of cardiac ultrasomics in distinguishing healthy and infarcted myocardium and opens new opportunities for advancing myocardial tissue characterization using echocardiography.

Competing Interest Statement

Dr. Sengupta is a consultant for RCE Technologies. Dr. Yanamala is an advisor to Research Spark Hub Inc., Turnkey Learning, LLC, and Turnkey Insights (I) Pvt Ltd. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.

Funding Statement

This work was supported by a National Science Foundation grant (Grant Number: 2125872).

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Institutional Review Board (IRB) at Robert Wood Johnson University Hospital (RWJBH). Study is exempt under category 4(iii) with approval ID: FWA00003913

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Disclosures : Dr. Sengupta is a consultant for RCE Technologies. Dr. Yanamala is an advisor to Research Spark Hub Inc., Turnkey Learning, LLC, and Turnkey Insights (I) Pvt Ltd. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.

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Title: realm: reference resolution as language modeling.

Abstract: Reference resolution is an important problem, one that is essential to understand and successfully handle context of different kinds. This context includes both previous turns and context that pertains to non-conversational entities, such as entities on the user's screen or those running in the background. While LLMs have been shown to be extremely powerful for a variety of tasks, their use in reference resolution, particularly for non-conversational entities, remains underutilized. This paper demonstrates how LLMs can be used to create an extremely effective system to resolve references of various types, by showing how reference resolution can be converted into a language modeling problem, despite involving forms of entities like those on screen that are not traditionally conducive to being reduced to a text-only modality. We demonstrate large improvements over an existing system with similar functionality across different types of references, with our smallest model obtaining absolute gains of over 5% for on-screen references. We also benchmark against GPT-3.5 and GPT-4, with our smallest model achieving performance comparable to that of GPT-4, and our larger models substantially outperforming it.

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