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The General Theory of Terminology: A Literature Review and a Critical discussion

Research Terminology

Refer to this page for definitions and explanations of common terms used in research. This list is not exhaustive and is intended as a quick reference. Main menu | Comments/Suggestions

Confusion to Clarity: Definition of Terms in a Research Paper

Explore the definition of terms in research paper to enhance your understanding of crucial scientific terminology and grow your knowledge.

Have you ever come across a research paper and found yourself scratching your head over complex synonyms and unfamiliar terms? It’s a hassle as you have to fetch a dictionary and then ruffle through it to find the meaning of the terms.

To avoid that, an exclusive section called ‘ Definition of Terms in a Research Paper ’ is introduced which contains the definitions of terms used in the paper. Let us learn more about it in this article.

What Is The “Definition Of Terms” In A Research Paper?

The definition of terms section in a research paper provides a clear and concise explanation of key concepts, variables, and terminology used throughout the study.

In the definition of terms section, researchers typically provide precise definitions for specific technical terms, acronyms, jargon, and any other domain-specific vocabulary used in their work. This section enhances the overall quality and rigor of the research by establishing a solid foundation for communication and understanding.

Purpose Of Definition Of Terms In A Research Paper

This section aims to ensure that readers have a common understanding of the terminology employed in the research, eliminating confusion and promoting clarity. The definitions provided serve as a reference point for readers, enabling them to comprehend the context and scope of the study. It serves several important purposes:

Enhancing clarity
Establishing a shared language
Providing a reference point
Setting the scope and context
Ensuring consistency

Benefits Of Having A Definition Of Terms In A Research Paper

Having a definition of terms section in a research paper offers several benefits that contribute to the overall quality and effectiveness of the study. These benefits include:

Clarity And Comprehension

Clear definitions enable readers to understand the specific meanings of key terms, concepts, and variables used in the research. This promotes clarity and enhances comprehension, ensuring that readers can follow the study’s arguments, methods, and findings more easily.

Consistency And Precision

Definitions provide a consistent framework for the use of terminology throughout the research paper. By clearly defining terms, researchers establish a standard vocabulary, reducing ambiguity and potential misunderstandings. This precision enhances the accuracy and reliability of the study’s findings.

Common Understanding

The definition of terms section helps establish a shared understanding among readers, including those from different disciplines or with varying levels of familiarity with the subject matter. It ensures that readers approach the research with a common knowledge base, facilitating effective communication and interpretation of the results.

Avoiding Misinterpretation

Without clear definitions, readers may interpret terms and concepts differently, leading to misinterpretation of the research findings. By providing explicit definitions, researchers minimize the risk of misunderstandings and ensure that readers grasp the intended meaning of the terminology used in the study.

Accessibility For Diverse Audiences

Research papers are often read by a wide range of individuals, including researchers, students, policymakers, and professionals. Having a definition of terms in a research paper helps the diverse audience understand the concepts better and make appropriate decisions.

Types Of Definitions

There are several types of definitions that researchers can employ in a research paper, depending on the context and nature of the study. Here are some common types of definitions:

Lexical Definitions

Lexical definitions provide the dictionary or commonly accepted meaning of a term. They offer a concise and widely recognized explanation of a word or concept. Lexical definitions are useful for establishing a baseline understanding of a term, especially when dealing with everyday language or non-technical terms.

Operational Definitions

Operational definitions define a term or concept about how it is measured or observed in the study. These definitions specify the procedures, instruments, or criteria used to operationalize an abstract or theoretical concept. Operational definitions help ensure clarity and consistency in data collection and measurement.

Conceptual Definitions

Conceptual definitions provide an abstract or theoretical understanding of a term or concept within a specific research context. They often involve a more detailed and nuanced explanation, exploring the underlying principles, theories, or models that inform the concept. Conceptual definitions are useful for establishing a theoretical framework and promoting deeper understanding.

Descriptive Definitions

Descriptive definitions describe a term or concept by providing characteristics, features, or attributes associated with it. These definitions focus on outlining the essential qualities or elements that define the term. Descriptive definitions help readers grasp the nature and scope of a concept by painting a detailed picture.

Theoretical Definitions

Theoretical definitions explain a term or concept based on established theories or conceptual frameworks. They situate the concept within a broader theoretical context, connecting it to relevant literature and existing knowledge. Theoretical definitions help researchers establish the theoretical underpinnings of their study and provide a foundation for further analysis.

Also read: Understanding What is Theoretical Framework

Types Of Terms

In research papers, various types of terms can be identified based on their nature and usage. Here are some common types of terms:

A key term is a term that holds significant importance or plays a crucial role within the context of a research paper. It is a term that encapsulates a core concept, idea, or variable that is central to the study. Key terms are often essential for understanding the research objectives, methodology, findings, and conclusions.

Technical Term

Technical terms refer to specialized vocabulary or terminology used within a specific field of study. These terms are often precise and have specific meanings within their respective disciplines. Examples include “allele,” “hypothesis testing,” or “algorithm.”

Legal Terms

Legal terms are specific vocabulary used within the legal field to describe concepts, principles, and regulations. These terms have particular meanings within the legal context. Examples include “defendant,” “plaintiff,” “due process,” or “jurisdiction.”

Definitional Term

A definitional term refers to a word or phrase that requires an explicit definition to ensure clarity and understanding within a particular context. These terms may be technical, abstract, or have multiple interpretations.

Career Privacy Term

Career privacy term refers to a concept or idea related to the privacy of individuals in the context of their professional or occupational activities. It encompasses the protection of personal information, and confidential data, and the right to control the disclosure of sensitive career-related details.

A broad term is a term that encompasses a wide range of related concepts, ideas, or objects. It has a broader scope and may encompass multiple subcategories or specific examples.

Also read: Keywords In A Research Paper: The Importance Of The Right Choice

Steps To Writing Definitions Of Terms

When writing the definition of terms section for a research paper, you can follow these steps to ensure clarity and accuracy:

Step 1: Identify Key Terms

Review your research paper and identify the key terms that require definition. These terms are typically central to your study, specific to your field or topic, or may have different interpretations.

Step 2: Conduct Research

Conduct thorough research on each key term to understand its commonly accepted definition, usage, and any variations or nuances within your specific research context. Consult authoritative sources such as academic journals, books, or reputable online resources.

Step 3: Craft Concise Definitions

Based on your research, craft concise definitions for each key term. Aim for clarity, precision, and relevance. Define the term in a manner that reflects its significance within your research and ensures reader comprehension.

Step 4: Use Your Own Words

Paraphrase the definitions in your own words to avoid plagiarism and maintain academic integrity. While you can draw inspiration from existing definitions, rephrase them to reflect your understanding and writing style. Avoid directly copying from sources.

Step 5: Provide Examples Or Explanations

Consider providing examples, explanations, or context for the defined terms to enhance reader understanding. This can help illustrate how the term is applied within your research or clarify its practical implications.

Step 6: Order And Format

Decide on the order in which you present the definitions. You can follow alphabetical order or arrange them based on their importance or relevance to your research. Use consistent formatting, such as bold or italics, to distinguish the defined terms from the rest of the text.

Step 7: Revise And Refine

Review the definitions for clarity, coherence, and accuracy. Ensure that they align with your research objectives and are tailored to your specific study. Seek feedback from peers, mentors, or experts in your field to further refine and improve the definitions.

Step 8: Include Proper Citations

If you have drawn ideas or information from external sources, remember to provide proper citations for those sources. This demonstrates academic integrity and acknowledges the original authors.

Step 9: Incorporate The Section Into Your Paper

Integrate the definition of terms section into your research paper, typically as an early section following the introduction. Make sure it flows smoothly with the rest of the paper and provides a solid foundation for understanding the subsequent content.

By following these steps, you can create a well-crafted and informative definition of terms section that enhances the clarity and comprehension of your research paper.

In conclusion, the definition of terms in a research paper plays a critical role by providing clarity, establishing a common understanding, and enhancing communication among readers. The definition of terms section is an essential component that contributes to the overall quality, rigor, and effectiveness of a research paper.

Also read: Beyond The Main Text: The Value Of A Research Paper Appendix

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Sowjanya is a passionate writer and an avid reader. She holds MBA in Agribusiness Management and now is working as a content writer. She loves to play with words and hopes to make a difference in the world through her writings. Apart from writing, she is interested in reading fiction novels and doing craftwork. She also loves to travel and explore different cuisines and spend time with her family and friends.

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Key research terms.

bias: any influence that may distort the results of a research study and lead to error; the loss of balance and accuracy in the use of research methods.

case study: presentation of data about selected settings, persons, groups or events. Data can have been gathered using variety of different research methods (e.g., questionnaire, observation, historical or literary analysis). Is chiefly descriptive and analytical, usually based on qualitative data, though statistics such as survey findings may be included.

causal relationship: relationship between variables where movements in one or more variable(s) are held to cause changes in the other (s).

coded data: data are put into groups or categories, such as age groups, and each category is given a code number. Data are usually coded for convenience, speed, and handling to enable statistical analysis. construct: a mental state that can’t be directly observed or manipulated, such as love, intelligence, hunger, feeling warm, and aggression; a concept developed (constructed) for describing relations among or between phenomena or for other research purposes.

construct validity: the degree to which the study actually measures and manipulates the elements that the researcher claims to be measuring and manipulating. If the operational definitions of the constructs are poor, the study will not have good construct validity. For example, a test claiming to measure “aggressiveness” would not have construct validity if it really measured assertiveness.

internal validity: the degree to which the study demonstrates that a particular factor caused a change in behavior. If a study lacks internal validity, the researcher may falsely believe that a factor causes an effect when it really doesn’t. Most studies involving humans do not have internal validity because they can’t rule out the possibility that some other factor may have been responsible for the effect.

controls: processes used to make uniform or constant the conditions for carrying out an investigation.

control group: in experimental research, the group or item which does not receive the treatment or intervention under investigation and is used to compare outcomes with the one that does. correlation: the extent to which two or more factors vary in relationship to one another; the extent of association between two or more variables. Correlation does not equal causation. For example, might suggest relationship between academic success and self-esteem, but cannot prove that a change in first variable causes a change in second variable. correlation coefficient: a measure of the degree of relationship between two variables. It usually lies between +1 (showing a perfect positive relationship), 0 (showing no relationship), to -1.0 (showing a perfect negative relationship). dependent variable: variable thought to be determined or influenced by others.

experiment: a special type of study (not all studies are experiments!) that allows researchers to determine the cause of an effect; usually involves randomly assigning participants to groups.

external validity: the degree to which the results of the study can be generalized to other places, people, or times.

hypothesis: a proposition which research sets out to prove or disprove: “experimental” where the hypothesis is a positive statement, or “null” where statement contains a negative.

independent variable: a variable that researcher believes precedes, influences or predicts the dependent variable.

informed consent: giving potential participants information about the study, especially in terms of factors that might lead them to refuse to be in the study, before they decide whether to participate. Institutional Review Board (IRB): a committee of at least five members--one of whom must be a nonscientist--that review proposed research and monitor approved research in an effort to protect human research participants.

literature review: often the first step in the research process, it is a review of the literature on and around the subject of inquiry. Its main purposes are to avoid duplication, to identify gaps in research and to place the researcher’s approach within the work and approaches of others.

primary/secondary sources: primary sources are original firsthand records or materials relating to an event or happening. They may include, for example, official minutes of meetings, diaries, verbatim transcripts of interviews, completed questionnaires or records of the results of experiments. Secondary sources are accounts bases upon these, which usually offer an interpretation, commentary, analysis, or restatement of the primary sources. They can include, for example, books, journal articles, and conference papers.

qualitative data: information gathered in narrative, non-numerical form (e.g., transcript of an interview). Qualitative research used for exploratory (hypothesis-generating) purposes or explaining puzzling quantitative results, while quantitative methods are used to test hypotheses.

quantitative data: information gathered in numerical form. reliability: extent to which the same result will be repeated/achieved by using the same measure.

statistical significance: tests used to estimate the likelihood that the finding in a sample is true of the population from which the sample is derived and not due to chance.

simple experiment: used to establish cause and effect, so this type often used to determine effect of treatment. Participants randomly assigned to either control group with no treatment, while the experimental group receives treatment.

validity: extent to which research findings can be said to be accurate and reliable; degree to which conclusions are justified. Internal validity is extent to which researchers can show that they have evidence for the statements they make; external validity refers to a study’s generalizability.

Child Care and Early Education Research Connections

Research glossary.

The research glossary defines terms used in conducting social science and policy research, for example those describing methods, measurements, statistical procedures, and other aspects of research; the child care glossary defines terms used to describe aspects of child care and early education practice and policy.

USC Libraries
Research Guides

Organizing Your Social Sciences Research Paper

Academic Writing Style
Purpose of Guide
Design Flaws to Avoid
Independent and Dependent Variables
Glossary of Research Terms
Reading Research Effectively
Narrowing a Topic Idea
Broadening a Topic Idea
Extending the Timeliness of a Topic Idea
Applying Critical Thinking
Choosing a Title
Making an Outline
Paragraph Development
Research Process Video Series
Executive Summary
The C.A.R.S. Model
Background Information
The Research Problem/Question
Theoretical Framework
Citation Tracking
Content Alert Services
Evaluating Sources
Primary Sources
Secondary Sources
Tiertiary Sources
Scholarly vs. Popular Publications
Qualitative Methods
Quantitative Methods
Insiderness
Using Non-Textual Elements
Limitations of the Study
Common Grammar Mistakes
Writing Concisely
Avoiding Plagiarism
Footnotes or Endnotes?
Further Readings
Generative AI and Writing
USC Libraries Tutorials and Other Guides
Bibliography

Academic writing refers to a style of expression that researchers use to define the intellectual boundaries of their disciplines and specific areas of expertise. Characteristics of academic writing include a formal tone, use of the third-person rather than first-person perspective (usually), a clear focus on the research problem under investigation, and precise word choice. Like specialist languages adopted in other professions, such as, law or medicine, academic writing is designed to convey agreed meaning about complex ideas or concepts within a community of scholarly experts and practitioners.

Academic Writing. Writing Center. Colorado Technical College; Hartley, James. Academic Writing and Publishing: A Practical Guide . New York: Routledge, 2008; Ezza, El-Sadig Y. and Touria Drid. T eaching Academic Writing as a Discipline-Specific Skill in Higher Education . Hershey, PA: IGI Global, 2020.

Importance of Good Academic Writing

The accepted form of academic writing in the social sciences can vary considerable depending on the methodological framework and the intended audience. However, most college-level research papers require careful attention to the following stylistic elements:

I. The Big Picture Unlike creative or journalistic writing, the overall structure of academic writing is formal and logical. It must be cohesive and possess a logically organized flow of ideas; this means that the various parts are connected to form a unified whole. There should be narrative links between sentences and paragraphs so that the reader is able to follow your argument. The introduction should include a description of how the rest of the paper is organized and all sources are properly cited throughout the paper.

II. Tone The overall tone refers to the attitude conveyed in a piece of writing. Throughout your paper, it is important that you present the arguments of others fairly and with an appropriate narrative tone. When presenting a position or argument that you disagree with, describe this argument accurately and without loaded or biased language. In academic writing, the author is expected to investigate the research problem from an authoritative point of view. You should, therefore, state the strengths of your arguments confidently, using language that is neutral, not confrontational or dismissive.

III. Diction Diction refers to the choice of words you use. Awareness of the words you use is important because words that have almost the same denotation [dictionary definition] can have very different connotations [implied meanings]. This is particularly true in academic writing because words and terminology can evolve a nuanced meaning that describes a particular idea, concept, or phenomenon derived from the epistemological culture of that discipline [e.g., the concept of rational choice in political science]. Therefore, use concrete words [not general] that convey a specific meaning. If this cannot be done without confusing the reader, then you need to explain what you mean within the context of how that word or phrase is used within a discipline.

IV. Language The investigation of research problems in the social sciences is often complex and multi- dimensional . Therefore, it is important that you use unambiguous language. Well-structured paragraphs and clear topic sentences enable a reader to follow your line of thinking without difficulty. Your language should be concise, formal, and express precisely what you want it to mean. Do not use vague expressions that are not specific or precise enough for the reader to derive exact meaning ["they," "we," "people," "the organization," etc.], abbreviations like 'i.e.' ["in other words"], 'e.g.' ["for example"], or 'a.k.a.' ["also known as"], and the use of unspecific determinate words ["super," "very," "incredible," "huge," etc.].

V. Punctuation Scholars rely on precise words and language to establish the narrative tone of their work and, therefore, punctuation marks are used very deliberately. For example, exclamation points are rarely used to express a heightened tone because it can come across as unsophisticated or over-excited. Dashes should be limited to the insertion of an explanatory comment in a sentence, while hyphens should be limited to connecting prefixes to words [e.g., multi-disciplinary] or when forming compound phrases [e.g., commander-in-chief]. Finally, understand that semi-colons represent a pause that is longer than a comma, but shorter than a period in a sentence. In general, there are four grammatical uses of semi-colons: when a second clause expands or explains the first clause; to describe a sequence of actions or different aspects of the same topic; placed before clauses which begin with "nevertheless", "therefore", "even so," and "for instance”; and, to mark off a series of phrases or clauses which contain commas. If you are not confident about when to use semi-colons [and most of the time, they are not required for proper punctuation], rewrite using shorter sentences or revise the paragraph.

VI. Academic Conventions Among the most important rules and principles of academic engagement of a writing is citing sources in the body of your paper and providing a list of references as either footnotes or endnotes. The academic convention of citing sources facilitates processes of intellectual discovery, critical thinking, and applying a deliberate method of navigating through the scholarly landscape by tracking how cited works are propagated by scholars over time . Aside from citing sources, other academic conventions to follow include the appropriate use of headings and subheadings, properly spelling out acronyms when first used in the text, avoiding slang or colloquial language, avoiding emotive language or unsupported declarative statements, avoiding contractions [e.g., isn't], and using first person and second person pronouns only when necessary.

VII. Evidence-Based Reasoning Assignments often ask you to express your own point of view about the research problem. However, what is valued in academic writing is that statements are based on evidence-based reasoning. This refers to possessing a clear understanding of the pertinent body of knowledge and academic debates that exist within, and often external to, your discipline concerning the topic. You need to support your arguments with evidence from scholarly [i.e., academic or peer-reviewed] sources. It should be an objective stance presented as a logical argument; the quality of the evidence you cite will determine the strength of your argument. The objective is to convince the reader of the validity of your thoughts through a well-documented, coherent, and logically structured piece of writing. This is particularly important when proposing solutions to problems or delineating recommended courses of action.

VIII. Thesis-Driven Academic writing is “thesis-driven,” meaning that the starting point is a particular perspective, idea, or position applied to the chosen topic of investigation, such as, establishing, proving, or disproving solutions to the questions applied to investigating the research problem. Note that a problem statement without the research questions does not qualify as academic writing because simply identifying the research problem does not establish for the reader how you will contribute to solving the problem, what aspects you believe are most critical, or suggest a method for gathering information or data to better understand the problem.

IX. Complexity and Higher-Order Thinking Academic writing addresses complex issues that require higher-order thinking skills applied to understanding the research problem [e.g., critical, reflective, logical, and creative thinking as opposed to, for example, descriptive or prescriptive thinking]. Higher-order thinking skills include cognitive processes that are used to comprehend, solve problems, and express concepts or that describe abstract ideas that cannot be easily acted out, pointed to, or shown with images. Think of your writing this way: One of the most important attributes of a good teacher is the ability to explain complexity in a way that is understandable and relatable to the topic being presented during class. This is also one of the main functions of academic writing--examining and explaining the significance of complex ideas as clearly as possible. As a writer, you must adopt the role of a good teacher by summarizing complex information into a well-organized synthesis of ideas, concepts, and recommendations that contribute to a better understanding of the research problem.

Academic Writing. Writing Center. Colorado Technical College; Hartley, James. Academic Writing and Publishing: A Practical Guide . New York: Routledge, 2008; Murray, Rowena and Sarah Moore. The Handbook of Academic Writing: A Fresh Approach . New York: Open University Press, 2006; Johnson, Roy. Improve Your Writing Skills . Manchester, UK: Clifton Press, 1995; Nygaard, Lynn P. Writing for Scholars: A Practical Guide to Making Sense and Being Heard . Second edition. Los Angeles, CA: Sage Publications, 2015; Silvia, Paul J. How to Write a Lot: A Practical Guide to Productive Academic Writing . Washington, DC: American Psychological Association, 2007; Style, Diction, Tone, and Voice. Writing Center, Wheaton College; Sword, Helen. Stylish Academic Writing . Cambridge, MA: Harvard University Press, 2012.

Strategies for...

Understanding Academic Writing and Its Jargon

The very definition of research jargon is language specific to a particular community of practitioner-researchers . Therefore, in modern university life, jargon represents the specific language and meaning assigned to words and phrases specific to a discipline or area of study. For example, the idea of being rational may hold the same general meaning in both political science and psychology, but its application to understanding and explaining phenomena within the research domain of a each discipline may have subtle differences based upon how scholars in that discipline apply the concept to the theories and practice of their work.

Given this, it is important that specialist terminology [i.e., jargon] must be used accurately and applied under the appropriate conditions . Subject-specific dictionaries are the best places to confirm the meaning of terms within the context of a specific discipline. These can be found by either searching in the USC Libraries catalog by entering the disciplinary and the word dictionary [e.g., sociology and dictionary] or using a database such as Credo Reference [a curated collection of subject encyclopedias, dictionaries, handbooks, guides from highly regarded publishers] . It is appropriate for you to use specialist language within your field of study, but you should avoid using such language when writing for non-academic or general audiences.

Problems with Opaque Writing

A common criticism of scholars is that they can utilize needlessly complex syntax or overly expansive vocabulary that is impenetrable or not well-defined. When writing, avoid problems associated with opaque writing by keeping in mind the following:

1. Excessive use of specialized terminology . Yes, it is appropriate for you to use specialist language and a formal style of expression in academic writing, but it does not mean using "big words" just for the sake of doing so. Overuse of complex or obscure words or writing complicated sentence constructions gives readers the impression that your paper is more about style than substance; it leads the reader to question if you really know what you are talking about. Focus on creating clear, concise, and elegant prose that minimizes reliance on specialized terminology.

2. Inappropriate use of specialized terminology . Because you are dealing with concepts, research, and data within your discipline, you need to use the technical language appropriate to that area of study. However, nothing will undermine the validity of your study quicker than the inappropriate application of a term or concept. Avoid using terms whose meaning you are unsure of--do not just guess or assume! Consult the meaning of terms in specialized, discipline-specific dictionaries by searching the USC Libraries catalog or the Credo Reference database [see above].

Additional Problems to Avoid

In addition to understanding the use of specialized language, there are other aspects of academic writing in the social sciences that you should be aware of. These problems include:

Personal nouns . Excessive use of personal nouns [e.g., I, me, you, us] may lead the reader to believe the study was overly subjective. These words can be interpreted as being used only to avoid presenting empirical evidence about the research problem. Limit the use of personal nouns to descriptions of things you actually did [e.g., "I interviewed ten teachers about classroom management techniques..."]. Note that personal nouns are generally found in the discussion section of a paper because this is where you as the author/researcher interpret and describe your work.
Directives . Avoid directives that demand the reader to "do this" or "do that." Directives should be framed as evidence-based recommendations or goals leading to specific outcomes. Note that an exception to this can be found in various forms of action research that involve evidence-based advocacy for social justice or transformative change. Within this area of the social sciences, authors may offer directives for action in a declarative tone of urgency.
Informal, conversational tone using slang and idioms . Academic writing relies on excellent grammar and precise word structure. Your narrative should not include regional dialects or slang terms because they can be open to interpretation. Your writing should be direct and concise using standard English.
Wordiness. Focus on being concise, straightforward, and developing a narrative that does not have confusing language . By doing so, you help eliminate the possibility of the reader misinterpreting the design and purpose of your study.
Vague expressions (e.g., "they," "we," "people," "the company," "that area," etc.). Being concise in your writing also includes avoiding vague references to persons, places, or things. While proofreading your paper, be sure to look for and edit any vague or imprecise statements that lack context or specificity.
Numbered lists and bulleted items . The use of bulleted items or lists should be used only if the narrative dictates a need for clarity. For example, it is fine to state, "The four main problems with hedge funds are:" and then list them as 1, 2, 3, 4. However, in academic writing, this must then be followed by detailed explanation and analysis of each item. Given this, the question you should ask yourself while proofreading is: why begin with a list in the first place rather than just starting with systematic analysis of each item arranged in separate paragraphs? Also, be careful using numbers because they can imply a ranked order of priority or importance. If none exists, use bullets and avoid checkmarks or other symbols.
Descriptive writing . Describing a research problem is an important means of contextualizing a study. In fact, some description or background information may be needed because you can not assume the reader knows the key aspects of the topic. However, the content of your paper should focus on methodology, the analysis and interpretation of findings, and their implications as they apply to the research problem rather than background information and descriptions of tangential issues.
Personal experience. Drawing upon personal experience [e.g., traveling abroad; caring for someone with Alzheimer's disease] can be an effective way of introducing the research problem or engaging your readers in understanding its significance. Use personal experience only as an example, though, because academic writing relies on evidence-based research. To do otherwise is simply story-telling.

NOTE: Rules concerning excellent grammar and precise word structure do not apply when quoting someone. A quote should be inserted in the text of your paper exactly as it was stated. If the quote is especially vague or hard to understand, consider paraphrasing it or using a different quote to convey the same meaning. Consider inserting the term "sic" in brackets after the quoted text to indicate that the quotation has been transcribed exactly as found in the original source, but the source had grammar, spelling, or other errors. The adverb sic informs the reader that the errors are not yours.

Academic Writing. The Writing Lab and The OWL. Purdue University; Academic Writing Style. First-Year Seminar Handbook. Mercer University; Bem, Daryl J. Writing the Empirical Journal Article. Cornell University; College Writing. The Writing Center. University of North Carolina; Murray, Rowena and Sarah Moore. The Handbook of Academic Writing: A Fresh Approach . New York: Open University Press, 2006; Johnson, Eileen S. “Action Research.” In Oxford Research Encyclopedia of Education . Edited by George W. Noblit and Joseph R. Neikirk. (New York: Oxford University Press, 2020); Oppenheimer, Daniel M. "Consequences of Erudite Vernacular Utilized Irrespective of Necessity: Problems with Using Long Words Needlessly." Applied Cognitive Psychology 20 (2006): 139-156; Ezza, El-Sadig Y. and Touria Drid. T eaching Academic Writing as a Discipline-Specific Skill in Higher Education . Hershey, PA: IGI Global, 2020; Pernawan, Ari. Common Flaws in Students' Research Proposals. English Education Department. Yogyakarta State University; Style. College Writing. The Writing Center. University of North Carolina; Invention: Five Qualities of Good Writing. The Reading/Writing Center. Hunter College; Sword, Helen. Stylish Academic Writing . Cambridge, MA: Harvard University Press, 2012; What Is an Academic Paper? Institute for Writing Rhetoric. Dartmouth College.

Structure and Writing Style

I. Improving Academic Writing

To improve your academic writing skills, you should focus your efforts on three key areas: 1. Clear Writing . The act of thinking about precedes the process of writing about. Good writers spend sufficient time distilling information and reviewing major points from the literature they have reviewed before creating their work. Writing detailed outlines can help you clearly organize your thoughts. Effective academic writing begins with solid planning, so manage your time carefully. 2. Excellent Grammar . Needless to say, English grammar can be difficult and complex; even the best scholars take many years before they have a command of the major points of good grammar. Take the time to learn the major and minor points of good grammar. Spend time practicing writing and seek detailed feedback from professors. Take advantage of the Writing Center on campus if you need help. Proper punctuation and good proofreading skills can significantly improve academic writing [see sub-tab for proofreading you paper ].

Refer to these three basic resources to help your grammar and writing skills:

A good writing reference book, such as, Strunk and White’s book, The Elements of Style or the St. Martin's Handbook ;
A college-level dictionary, such as, Merriam-Webster's Collegiate Dictionary ;
The latest edition of Roget's Thesaurus in Dictionary Form .

3. Consistent Stylistic Approach . Whether your professor expresses a preference to use MLA, APA or the Chicago Manual of Style or not, choose one style manual and stick to it. Each of these style manuals provide rules on how to write out numbers, references, citations, footnotes, and lists. Consistent adherence to a style of writing helps with the narrative flow of your paper and improves its readability. Note that some disciplines require a particular style [e.g., education uses APA] so as you write more papers within your major, your familiarity with it will improve.

II. Evaluating Quality of Writing

A useful approach for evaluating the quality of your academic writing is to consider the following issues from the perspective of the reader. While proofreading your final draft, critically assess the following elements in your writing.

It is shaped around one clear research problem, and it explains what that problem is from the outset.
Your paper tells the reader why the problem is important and why people should know about it.
You have accurately and thoroughly informed the reader what has already been published about this problem or others related to it and noted important gaps in the research.
You have provided evidence to support your argument that the reader finds convincing.
The paper includes a description of how and why particular evidence was collected and analyzed, and why specific theoretical arguments or concepts were used.
The paper is made up of paragraphs, each containing only one controlling idea.
You indicate how each section of the paper addresses the research problem.
You have considered counter-arguments or counter-examples where they are relevant.
Arguments, evidence, and their significance have been presented in the conclusion.
Limitations of your research have been explained as evidence of the potential need for further study.
The narrative flows in a clear, accurate, and well-organized way.

Boscoloa, Pietro, Barbara Arféb, and Mara Quarisaa. “Improving the Quality of Students' Academic Writing: An Intervention Study.” Studies in Higher Education 32 (August 2007): 419-438; Academic Writing. The Writing Lab and The OWL. Purdue University; Academic Writing Style. First-Year Seminar Handbook. Mercer University; Bem, Daryl J. Writing the Empirical Journal Article. Cornell University; Candlin, Christopher. Academic Writing Step-By-Step: A Research-based Approach . Bristol, CT: Equinox Publishing Ltd., 2016; College Writing. The Writing Center. University of North Carolina; Style . College Writing. The Writing Center. University of North Carolina; Invention: Five Qualities of Good Writing. The Reading/Writing Center. Hunter College; Sword, Helen. Stylish Academic Writing . Cambridge, MA: Harvard University Press, 2012; What Is an Academic Paper? Institute for Writing Rhetoric. Dartmouth College.

Writing Tip

Considering the Passive Voice in Academic Writing

In the English language, we are able to construct sentences in the following way: 1. "The policies of Congress caused the economic crisis." 2. "The economic crisis was caused by the policies of Congress."

The decision about which sentence to use is governed by whether you want to focus on “Congress” and what they did, or on “the economic crisis” and what caused it. This choice in focus is achieved with the use of either the active or the passive voice. When you want your readers to focus on the "doer" of an action, you can make the "doer"' the subject of the sentence and use the active form of the verb. When you want readers to focus on the person, place, or thing affected by the action, or the action itself, you can make the effect or the action the subject of the sentence by using the passive form of the verb.

Often in academic writing, scholars don't want to focus on who is doing an action, but on who is receiving or experiencing the consequences of that action. The passive voice is useful in academic writing because it allows writers to highlight the most important participants or events within sentences by placing them at the beginning of the sentence.

Use the passive voice when:

You want to focus on the person, place, or thing affected by the action, or the action itself;
It is not important who or what did the action;
You want to be impersonal or more formal.

Form the passive voice by:

Turning the object of the active sentence into the subject of the passive sentence.
Changing the verb to a passive form by adding the appropriate form of the verb "to be" and the past participle of the main verb.

NOTE: Consult with your professor about using the passive voice before submitting your research paper. Some strongly discourage its use!

Active and Passive Voice. The Writing Lab and The OWL. Purdue University; Diefenbach, Paul. Future of Digital Media Syllabus. Drexel University; Passive Voice. The Writing Center. University of North Carolina.

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Terminology, the importance of defining

Affiliations.

1 Van Mil Consultancy, Zuidlaren, The Netherlands. [email protected].
2 School of Pharmacy & Pharmaceutical Sciences, Faculty of Health Sciences, Trinity College Dublin, Dublin, Ireland.
PMID: 27073076
DOI: 10.1007/s11096-016-0294-5

Multiple terms and definitions exist to describe specific aspects of pharmacy practice and service provision. This commentary explores the reasons for different interpretations of words and concepts in pharmaceutical care and pharmacy practice research. Reasons for this variation can be found in language, culture, profession and may also depend on developments over time. A list of words is provided where the authors think that currently multiple interpretations are possible. To make sure that the reader understands the essence, it seems imperative that authors include a definition of the topics that they actually study in their papers, and that they clearly cite existing definitions or refer to collections of definitions such as existing glossaries. It is important that presenters, authors and reviewers of pharmacy practice papers pay more attention to this aspect of describing studies.

Keywords: Pharmacy practice; Research; Terminology; Translation.

Community Pharmacy Services
Pharmacy Research / standards*
Terminology as Topic*

Your Guide to Understanding Common Research Terms

Demystifying Clinical Trials -- Part 2

The clinical research world can sometimes seem confusing. Research teams have many people in different roles, and they may use words that are unfamiliar to people outside of research work.

The guide below defines some commonly used words and phrases. Let us know in the comments below or on our Facebook , Twitter , or Instagram pages if you’d like definitions of other words or parts of the research process!

Accrual – the number of subjects who have completed or are actively in the process of completing a study. The accrual goal is how many subjects are needed to finish the study (2).

Adverse event (AE) – a negative symptom or experience encountered by an subject during the course of a clinical trial. Adverse events can be expected or unexpected.

Assent – a minor child’s affirmative agreement to participate in a clinical trial. Failure to object may not be taken as assent.

Clinical research coordinator – a study team member who manages the day-to-day study tasks as directed by the principal investigator. (3)

Consent form – a document explaining all relevant study information to assist the study subject in understanding the expectations and requirements of participating in the trial. This document is presented to and signed by the study subject.

Control arm/group – a comparison group of study subjects who are not treated with the investigational agent. The subjects in this group have the same disease or condition under study, but receive either a different treatment, no treatment, or a placebo.

Data – the objective information gathered during a research study that is used to determine the results of the study.

De-identification – the process of removing identifiers (personal names, dates, social security numbers, etc.) that directly or indirectly point to a person, and removing those identifiers from the data. De-identification of protected health information is essential for protecting patient privacy (4).

Enroll/Enrollment – the process of an eligible participant signing a consent form and voluntarily agreeing to participate in a research study (2).

Ethics committee – an independent group of both medical and non-medical professionals who are responsible for verifying the integrity of a study and ensuring the safety, integrity, and human rights of the study participants.

Food and Drug Administration (FDA) – the agency within the Department of Health and Human Services (DHHS) that enforces public health laws related to research conduct.

Greater than minimal risk – the research involves more than minimal risk to subjects (2).

Health Insurance Portability and Accountability Act of 1996 (HIPAA) – required the Department of Health & Human Services to develop regulations protecting the privacy and security of certain health information (5). The HIPAA Privacy Rule established the conditions under which health information may be used or disclosed by approved entities for research purposes (6).

Hypothesis – a specific, clear, and testable proposition or prediction about the possible outcome of a scientific research study (7).

Informed consent – the process of discussing a clinical trial that goes beyond signing the consent form. The discussion should provide sufficient information so that a subject can make an informed decision about whether or not to enroll in a study, or continue participation in a study. Informed consent is a voluntary agreement to participate in research, and should be an ongoing conversation throughout a subject’s entire time in the study (8).

Investigational New Drug Application (IND) – the process through which an investigator requests the FDA to allow human testing of a new drug.

Institutional Review Board (IRB) – an independent group of professionals designated to review and approve the clinical protocol, informed consent forms, study advertisements, and patient brochures to ensure that the study is safe for human participation. It is also the IRB’s responsibility to ensure that the study adheres to the FDA’s regulations.

Minimal risk – the probability that harm or discomfort anticipated in the research study are not greater than those encountered in daily life or during routine physical examinations (2).

National Institutes of Health (NIH) – agency within DHHS that provides funding for research, conducts studies, and funds multi-site national studies.

Protected Health Information (PHI) – individually identifiable health information. HIPAA provides federal protections for personal health information and gives patients more control over their health information. It also sets boundaries for how entities and institutions can use and release health records (9).

Placebo – an inactive substance designed to resemble the drug being tested. It is used as a control to rule out any psychological effects testing may present. Most advanced clinical trials include a control group that is unknowingly taking a placebo.

Principal Investigator – the primary individual responsible for conducting a clinical trial and adhering to federal regulations, institutional policies, and IRB regulations (2).

Protocol – a detailed plan that sets out the objectives, study design, and methodology for a clinical trial. A study protocol must be approved by an IRB before research may begin on human subjects.

Randomization – study participants are assigned to groups in such a way that each participant has an equal chance of being assigned to each treatment or control group. Since randomization ensures that no specific criteria are used to assign any patients to a particular group, all the groups will be equally comparable.

Research – systematic investigation designed to develop or contribute to generalizable knowledge.

Standard treatment/Standard of care – the currently accepted treatment or intervention considered to be effective in the treatment of a specific disease or condition.

Statistical significance – the probability that an event or difference was occurred by chance alone. In clinical trials, the level of statistical significance depends on the number or participants studied and the observations made, as well as the magnitude of differences observed.

Subject/Participant – a patient or healthy individual participating in a research study.

Treatment arm/group – a group of study subjects who are treated with the investigational agent.

Visit schedule/Test schedule – the number, frequency, and type of exams, tests, and procedures that research subjects will be expected to undergo during the study. Some visits may be the same as normal clinical care visits, while others may be required just for the purpose of collecting data for the research study.

Definitions taken from https://www.centerwatch.com/health-resources/glossary/ unless otherwise cited.

(2) https://www.mayo.edu/research/institutional-review-board/definition-terms

(3) https://acrpnet.org/2018/08/14/the-anatomy-of-a-great-clinical-research-coordinator/

(4) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5977668/

(5) https://www.hhs.gov/hipaa/for-professionals/privacy/index.html

(6) https://www.hhs.gov/hipaa/for-professionals/special-topics/research/index.html

(7) https://methods.sagepub.com/Reference//encyclopedia-of-survey-research-methods/n472.xml

(8) https://oprs.usc.edu/files/2017/04/Informed-Consent-Booklet-4.4.13.pdf

(9) https://www.hhs.gov/hipaa/for-individuals/faq/187/what-does-the-hipaa-privacy-rule-do/index.html

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5. CONDUCTING RESEARCH

5.1 Research Terminology

You will undoubtedly be required to “conduct research” for a course assignment or “include research” to support your ideas. While this may seem a bit intimidating, remember that engaging in research is basically just using a systematic process to find out more information about your topic. Nicholas Walliman, in his handbook Research Methods: The Basics , defines research methods as “the tools and techniques for doing research.” [1] These techniques include collecting, sorting, and analyzing the information and data you find. The better the tools and more comprehensive the techniques you employ, the more effective your research will be. By extension, the more effective your research is, the more credible and persuasive your argument will be.

Here are some basic terms and definitions you should be familiar with:

Research : the systematic process of finding out more about something than you already know, ideally so that you can prove a hypothesis, produce new knowledge and understanding, and make evidence-based decisions.

Research Methods: techniques of collecting, sorting, and analyzing information/data.

Data: bits of information.

The typical kinds of research sources you will use can be grouped into three broad categories:

Primary Sources: research you might conduct yourself in lab experiments and product testing, through surveys, observations, measurements, interviews, site visits, prototype testing, beta testing, etc . These can also include published raw statistical data, historical records, legal documents, firsthand historical accounts, and original creative works.
Secondary Sources : written sources that discuss, analyze, and interpret primary data, such as published research and studies, reviews of these studies, meta-analyses, and formal critiques.
Tertiary Sources : reference sources such as dictionaries, encyclopedias, and handbooks that provide a consolidation of primary and secondary information. They are useful to gain a general understanding of your topic and major concepts, lines of inquiry, or schools of thought in the field.

Data can be categorized in several ways:

Research methods are often categorized as quantitative, qualitative or “mixed method.” Some projects, like a science, require the use of the scientific method of inquiry, observation, quantitative data collection, analysis and conclusions to test a hypothesis. Other kinds of projects take a more deductive approach and gather both quantitative and qualitative evidence to support a thesis, position, or recommendation. The research methods you choose will be determined by the goals and scope of your project, and by your intended audience’s expectations. More specific methodologies, such as ways to structure the analysis of your data, include the following:

Cost/benefit Analysis : determines how much something will cost vs what measurable benefits it will create, and may lead to a calculation of “return on investment” (ROI).
Life-cycle Analysis : determines overall sustainability of a product or process, from manufacturing, through lifetime use, to disposal (you can also perform comparative life-cycle analyses, or specific life cycle stage analysis)
Comparative Analysis : compares two or more options to determine which is the “best” solution (given specific problem criteria such as goals, objectives, and constraints)
Process Analysis : studies each aspect of a process to determine if all parts and steps work efficiently together to create the desired outcome.
Sustainability Analysis : uses concepts such as the “triple bottom line” or “ three pillars of sustainability ” to analyze whether a product or process is environmentally, economically, and socially sustainable.

In all cases, the way you collect, analyze, and use data must be ethical and consistent with professional standards of honesty and integrity. Lapses in integrity can not only lead to poor quality reports in an academic context (poor grades and academic dishonesty penalties), but in the workplace, these lapses can also lead to lawsuits, loss of job, and even criminal charges. Some examples of these lapses include

Fabricating your own data (making it up to suit your purpose)
Ignoring data that disproves or contradicts your ideas
Misrepresenting someone else’s data or ideas
Using data or ideas from another source without acknowledgment or citation of the source.

Failing to cite quoted, paraphrased, or summarized sources properly is one of the most common lapses in academic integrity, which is why your previous academic writing class spent considerable time and effort to give you a sophisticated understanding of how and why to avoid plagiarizing, as well as the consequences of doing so. If you would like to review this information, see Appendix C: Integrating Source Evidence into Your Writing , and consult the University of Victoria’s policy on Academic Integrity .

N. Walliman, Research Methods: The Basics . New York: Routledge, 2011 ↵

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100+ Research Vocabulary Words & Phrases

The academic community can be conservative when it comes to enforcing academic writing style , but your writing shouldn’t be so boring that people lose interest midway through the first paragraph! Given that competition is at an all-time high for academics looking to publish their papers, we know you must be anxious about what you can do to improve your publishing odds.

To be sure, your research must be sound, your paper must be structured logically, and the different manuscript sections must contain the appropriate information. But your research must also be clearly explained. Clarity obviously depends on the correct use of English, and there are many common mistakes that you should watch out for, for example when it comes to articles , prepositions , word choice , and even punctuation . But even if you are on top of your grammar and sentence structure, you can still make your writing more compelling (or more boring) by using powerful verbs and phrases (vs the same weaker ones over and over). So, how do you go about achieving the latter?

Below are a few ways to breathe life into your writing.

1. Analyze Vocabulary Using Word Clouds

Have you heard of “Wordles”? A Wordle is a visual representation of words, with the size of each word being proportional to the number of times it appears in the text it is based on. The original company website seems to have gone out of business, but there are a number of free word cloud generation sites that allow you to copy and paste your draft manuscript into a text box to quickly discover how repetitive your writing is and which verbs you might want to replace to improve your manuscript.

Seeing a visual word cloud of your work might also help you assess the key themes and points readers will glean from your paper. If the Wordle result displays words you hadn’t intended to emphasize, then that’s a sign you should revise your paper to make sure readers will focus on the right information.

As an example, below is a Wordle of our article entitled, “ How to Choose the Best title for Your Journal Manuscript .” You can see how frequently certain terms appear in that post, based on the font size of the text. The keywords, “titles,” “journal,” “research,” and “papers,” were all the intended focus of our blog post.

2. Study Language Patterns of Similarly Published Works

Study the language pattern found in the most downloaded and cited articles published by your target journal. Understanding the journal’s editorial preferences will help you write in a style that appeals to the publication’s readership.

Another way to analyze the language of a target journal’s papers is to use Wordle (see above). If you copy and paste the text of an article related to your research topic into the applet, you can discover the common phrases and terms the paper’s authors used.

For example, if you were writing a paper on links between smoking and cancer , you might look for a recent review on the topic, preferably published by your target journal. Copy and paste the text into Wordle and examine the key phrases to see if you’ve included similar wording in your own draft. The Wordle result might look like the following, based on the example linked above.

research words and phrases word cloud, cancer study

If you are not sure yet where to publish and just want some generally good examples of descriptive verbs, analytical verbs, and reporting verbs that are commonly used in academic writing, then have a look at this list of useful phrases for research papers .

3. Use More Active and Precise Verbs

Have you heard of synonyms? Of course you have. But have you looked beyond single-word replacements and rephrased entire clauses with stronger, more vivid ones? You’ll find this task is easier to do if you use the active voice more often than the passive voice . Even if you keep your original sentence structure, you can eliminate weak verbs like “be” from your draft and choose more vivid and precise action verbs. As always, however, be careful about using only a thesaurus to identify synonyms. Make sure the substitutes fit the context in which you need a more interesting or “perfect” word. Online dictionaries such as the Merriam-Webster and the Cambridge Dictionary are good sources to check entire phrases in context in case you are unsure whether a synonym is a good match for a word you want to replace.

To help you build a strong arsenal of commonly used phrases in academic papers, we’ve compiled a list of synonyms you might want to consider when drafting or editing your research paper . While we do not suggest that the phrases in the “Original Word/Phrase” column should be completely avoided, we do recommend interspersing these with the more dynamic terms found under “Recommended Substitutes.”

A. Describing the scope of a current project or prior research

B. outlining a topic’s background, c. describing the analytical elements of a paper, d. discussing results, e. discussing methods, f. explaining the impact of new research, wordvice writing resources.

For additional information on how to tighten your sentences (e.g., eliminate wordiness and use active voice to greater effect), you can try Wordvice’s FREE APA Citation Generator and learn more about how to proofread and edit your paper to ensure your work is free of errors.

Before submitting your manuscript to academic journals, be sure to use our free AI proofreader to catch errors in grammar, spelling, and mechanics. And use our English editing services from Wordvice, including academic editing services , cover letter editing , manuscript editing , and research paper editing services to make sure your work is up to a high academic level.

We also have a collection of other useful articles for you, for example on how to strengthen your writing style , how to avoid fillers to write more powerful sentences , and how to eliminate prepositions and avoid nominalizations . Additionally, get advice on all the other important aspects of writing a research paper on our academic resources pages .

Home » Research Paper – Structure, Examples and Writing Guide

Research Paper – Structure, Examples and Writing Guide

Table of Contents

Research Paper

Definition:

Research Paper is a written document that presents the author’s original research, analysis, and interpretation of a specific topic or issue.

It is typically based on Empirical Evidence, and may involve qualitative or quantitative research methods, or a combination of both. The purpose of a research paper is to contribute new knowledge or insights to a particular field of study, and to demonstrate the author’s understanding of the existing literature and theories related to the topic.

Structure of Research Paper

The structure of a research paper typically follows a standard format, consisting of several sections that convey specific information about the research study. The following is a detailed explanation of the structure of a research paper:

The title page contains the title of the paper, the name(s) of the author(s), and the affiliation(s) of the author(s). It also includes the date of submission and possibly, the name of the journal or conference where the paper is to be published.

The abstract is a brief summary of the research paper, typically ranging from 100 to 250 words. It should include the research question, the methods used, the key findings, and the implications of the results. The abstract should be written in a concise and clear manner to allow readers to quickly grasp the essence of the research.

Introduction

The introduction section of a research paper provides background information about the research problem, the research question, and the research objectives. It also outlines the significance of the research, the research gap that it aims to fill, and the approach taken to address the research question. Finally, the introduction section ends with a clear statement of the research hypothesis or research question.

Literature Review

The literature review section of a research paper provides an overview of the existing literature on the topic of study. It includes a critical analysis and synthesis of the literature, highlighting the key concepts, themes, and debates. The literature review should also demonstrate the research gap and how the current study seeks to address it.

The methods section of a research paper describes the research design, the sample selection, the data collection and analysis procedures, and the statistical methods used to analyze the data. This section should provide sufficient detail for other researchers to replicate the study.

The results section presents the findings of the research, using tables, graphs, and figures to illustrate the data. The findings should be presented in a clear and concise manner, with reference to the research question and hypothesis.

The discussion section of a research paper interprets the findings and discusses their implications for the research question, the literature review, and the field of study. It should also address the limitations of the study and suggest future research directions.

The conclusion section summarizes the main findings of the study, restates the research question and hypothesis, and provides a final reflection on the significance of the research.

The references section provides a list of all the sources cited in the paper, following a specific citation style such as APA, MLA or Chicago.

How to Write Research Paper

You can write Research Paper by the following guide:

Choose a Topic: The first step is to select a topic that interests you and is relevant to your field of study. Brainstorm ideas and narrow down to a research question that is specific and researchable.
Conduct a Literature Review: The literature review helps you identify the gap in the existing research and provides a basis for your research question. It also helps you to develop a theoretical framework and research hypothesis.
Develop a Thesis Statement : The thesis statement is the main argument of your research paper. It should be clear, concise and specific to your research question.
Plan your Research: Develop a research plan that outlines the methods, data sources, and data analysis procedures. This will help you to collect and analyze data effectively.
Collect and Analyze Data: Collect data using various methods such as surveys, interviews, observations, or experiments. Analyze data using statistical tools or other qualitative methods.
Organize your Paper : Organize your paper into sections such as Introduction, Literature Review, Methods, Results, Discussion, and Conclusion. Ensure that each section is coherent and follows a logical flow.
Write your Paper : Start by writing the introduction, followed by the literature review, methods, results, discussion, and conclusion. Ensure that your writing is clear, concise, and follows the required formatting and citation styles.
Edit and Proofread your Paper: Review your paper for grammar and spelling errors, and ensure that it is well-structured and easy to read. Ask someone else to review your paper to get feedback and suggestions for improvement.
Cite your Sources: Ensure that you properly cite all sources used in your research paper. This is essential for giving credit to the original authors and avoiding plagiarism.

Research Paper Example

Note : The below example research paper is for illustrative purposes only and is not an actual research paper. Actual research papers may have different structures, contents, and formats depending on the field of study, research question, data collection and analysis methods, and other factors. Students should always consult with their professors or supervisors for specific guidelines and expectations for their research papers.

Research Paper Example sample for Students:

Title: The Impact of Social Media on Mental Health among Young Adults

Abstract: This study aims to investigate the impact of social media use on the mental health of young adults. A literature review was conducted to examine the existing research on the topic. A survey was then administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO (Fear of Missing Out) are significant predictors of mental health problems among young adults.

Introduction: Social media has become an integral part of modern life, particularly among young adults. While social media has many benefits, including increased communication and social connectivity, it has also been associated with negative outcomes, such as addiction, cyberbullying, and mental health problems. This study aims to investigate the impact of social media use on the mental health of young adults.

Literature Review: The literature review highlights the existing research on the impact of social media use on mental health. The review shows that social media use is associated with depression, anxiety, stress, and other mental health problems. The review also identifies the factors that contribute to the negative impact of social media, including social comparison, cyberbullying, and FOMO.

Methods : A survey was administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The survey included questions on social media use, mental health status (measured using the DASS-21), and perceived impact of social media on their mental health. Data were analyzed using descriptive statistics and regression analysis.

Results : The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO are significant predictors of mental health problems among young adults.

Discussion : The study’s findings suggest that social media use has a negative impact on the mental health of young adults. The study highlights the need for interventions that address the factors contributing to the negative impact of social media, such as social comparison, cyberbullying, and FOMO.

Conclusion : In conclusion, social media use has a significant impact on the mental health of young adults. The study’s findings underscore the need for interventions that promote healthy social media use and address the negative outcomes associated with social media use. Future research can explore the effectiveness of interventions aimed at reducing the negative impact of social media on mental health. Additionally, longitudinal studies can investigate the long-term effects of social media use on mental health.

Limitations : The study has some limitations, including the use of self-report measures and a cross-sectional design. The use of self-report measures may result in biased responses, and a cross-sectional design limits the ability to establish causality.

Implications: The study’s findings have implications for mental health professionals, educators, and policymakers. Mental health professionals can use the findings to develop interventions that address the negative impact of social media use on mental health. Educators can incorporate social media literacy into their curriculum to promote healthy social media use among young adults. Policymakers can use the findings to develop policies that protect young adults from the negative outcomes associated with social media use.

References :

Twenge, J. M., & Campbell, W. K. (2019). Associations between screen time and lower psychological well-being among children and adolescents: Evidence from a population-based study. Preventive medicine reports, 15, 100918.
Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, J. B., … & James, A. E. (2017). Use of multiple social media platforms and symptoms of depression and anxiety: A nationally-representative study among US young adults. Computers in Human Behavior, 69, 1-9.
Van der Meer, T. G., & Verhoeven, J. W. (2017). Social media and its impact on academic performance of students. Journal of Information Technology Education: Research, 16, 383-398.

Appendix : The survey used in this study is provided below.

Social Media and Mental Health Survey

How often do you use social media per day?
Less than 30 minutes
30 minutes to 1 hour
1 to 2 hours
2 to 4 hours
More than 4 hours
Which social media platforms do you use?
Others (Please specify)
How often do you experience the following on social media?
Social comparison (comparing yourself to others)
Cyberbullying
Fear of Missing Out (FOMO)
Have you ever experienced any of the following mental health problems in the past month?
Do you think social media use has a positive or negative impact on your mental health?
Very positive
Somewhat positive
Somewhat negative
Very negative
In your opinion, which factors contribute to the negative impact of social media on mental health?
Social comparison
In your opinion, what interventions could be effective in reducing the negative impact of social media on mental health?
Education on healthy social media use
Counseling for mental health problems caused by social media
Social media detox programs
Regulation of social media use

Thank you for your participation!

Applications of Research Paper

Research papers have several applications in various fields, including:

Advancing knowledge: Research papers contribute to the advancement of knowledge by generating new insights, theories, and findings that can inform future research and practice. They help to answer important questions, clarify existing knowledge, and identify areas that require further investigation.
Informing policy: Research papers can inform policy decisions by providing evidence-based recommendations for policymakers. They can help to identify gaps in current policies, evaluate the effectiveness of interventions, and inform the development of new policies and regulations.
Improving practice: Research papers can improve practice by providing evidence-based guidance for professionals in various fields, including medicine, education, business, and psychology. They can inform the development of best practices, guidelines, and standards of care that can improve outcomes for individuals and organizations.
Educating students : Research papers are often used as teaching tools in universities and colleges to educate students about research methods, data analysis, and academic writing. They help students to develop critical thinking skills, research skills, and communication skills that are essential for success in many careers.
Fostering collaboration: Research papers can foster collaboration among researchers, practitioners, and policymakers by providing a platform for sharing knowledge and ideas. They can facilitate interdisciplinary collaborations and partnerships that can lead to innovative solutions to complex problems.

When to Write Research Paper

Research papers are typically written when a person has completed a research project or when they have conducted a study and have obtained data or findings that they want to share with the academic or professional community. Research papers are usually written in academic settings, such as universities, but they can also be written in professional settings, such as research organizations, government agencies, or private companies.

Here are some common situations where a person might need to write a research paper:

For academic purposes: Students in universities and colleges are often required to write research papers as part of their coursework, particularly in the social sciences, natural sciences, and humanities. Writing research papers helps students to develop research skills, critical thinking skills, and academic writing skills.
For publication: Researchers often write research papers to publish their findings in academic journals or to present their work at academic conferences. Publishing research papers is an important way to disseminate research findings to the academic community and to establish oneself as an expert in a particular field.
To inform policy or practice : Researchers may write research papers to inform policy decisions or to improve practice in various fields. Research findings can be used to inform the development of policies, guidelines, and best practices that can improve outcomes for individuals and organizations.
To share new insights or ideas: Researchers may write research papers to share new insights or ideas with the academic or professional community. They may present new theories, propose new research methods, or challenge existing paradigms in their field.

Purpose of Research Paper

The purpose of a research paper is to present the results of a study or investigation in a clear, concise, and structured manner. Research papers are written to communicate new knowledge, ideas, or findings to a specific audience, such as researchers, scholars, practitioners, or policymakers. The primary purposes of a research paper are:

To contribute to the body of knowledge : Research papers aim to add new knowledge or insights to a particular field or discipline. They do this by reporting the results of empirical studies, reviewing and synthesizing existing literature, proposing new theories, or providing new perspectives on a topic.
To inform or persuade: Research papers are written to inform or persuade the reader about a particular issue, topic, or phenomenon. They present evidence and arguments to support their claims and seek to persuade the reader of the validity of their findings or recommendations.
To advance the field: Research papers seek to advance the field or discipline by identifying gaps in knowledge, proposing new research questions or approaches, or challenging existing assumptions or paradigms. They aim to contribute to ongoing debates and discussions within a field and to stimulate further research and inquiry.
To demonstrate research skills: Research papers demonstrate the author’s research skills, including their ability to design and conduct a study, collect and analyze data, and interpret and communicate findings. They also demonstrate the author’s ability to critically evaluate existing literature, synthesize information from multiple sources, and write in a clear and structured manner.

Characteristics of Research Paper

Research papers have several characteristics that distinguish them from other forms of academic or professional writing. Here are some common characteristics of research papers:

Evidence-based: Research papers are based on empirical evidence, which is collected through rigorous research methods such as experiments, surveys, observations, or interviews. They rely on objective data and facts to support their claims and conclusions.
Structured and organized: Research papers have a clear and logical structure, with sections such as introduction, literature review, methods, results, discussion, and conclusion. They are organized in a way that helps the reader to follow the argument and understand the findings.
Formal and objective: Research papers are written in a formal and objective tone, with an emphasis on clarity, precision, and accuracy. They avoid subjective language or personal opinions and instead rely on objective data and analysis to support their arguments.
Citations and references: Research papers include citations and references to acknowledge the sources of information and ideas used in the paper. They use a specific citation style, such as APA, MLA, or Chicago, to ensure consistency and accuracy.
Peer-reviewed: Research papers are often peer-reviewed, which means they are evaluated by other experts in the field before they are published. Peer-review ensures that the research is of high quality, meets ethical standards, and contributes to the advancement of knowledge in the field.
Objective and unbiased: Research papers strive to be objective and unbiased in their presentation of the findings. They avoid personal biases or preconceptions and instead rely on the data and analysis to draw conclusions.

Advantages of Research Paper

Research papers have many advantages, both for the individual researcher and for the broader academic and professional community. Here are some advantages of research papers:

Contribution to knowledge: Research papers contribute to the body of knowledge in a particular field or discipline. They add new information, insights, and perspectives to existing literature and help advance the understanding of a particular phenomenon or issue.
Opportunity for intellectual growth: Research papers provide an opportunity for intellectual growth for the researcher. They require critical thinking, problem-solving, and creativity, which can help develop the researcher’s skills and knowledge.
Career advancement: Research papers can help advance the researcher’s career by demonstrating their expertise and contributions to the field. They can also lead to new research opportunities, collaborations, and funding.
Academic recognition: Research papers can lead to academic recognition in the form of awards, grants, or invitations to speak at conferences or events. They can also contribute to the researcher’s reputation and standing in the field.
Impact on policy and practice: Research papers can have a significant impact on policy and practice. They can inform policy decisions, guide practice, and lead to changes in laws, regulations, or procedures.
Advancement of society: Research papers can contribute to the advancement of society by addressing important issues, identifying solutions to problems, and promoting social justice and equality.

Limitations of Research Paper

Research papers also have some limitations that should be considered when interpreting their findings or implications. Here are some common limitations of research papers:

Limited generalizability: Research findings may not be generalizable to other populations, settings, or contexts. Studies often use specific samples or conditions that may not reflect the broader population or real-world situations.
Potential for bias : Research papers may be biased due to factors such as sample selection, measurement errors, or researcher biases. It is important to evaluate the quality of the research design and methods used to ensure that the findings are valid and reliable.
Ethical concerns: Research papers may raise ethical concerns, such as the use of vulnerable populations or invasive procedures. Researchers must adhere to ethical guidelines and obtain informed consent from participants to ensure that the research is conducted in a responsible and respectful manner.
Limitations of methodology: Research papers may be limited by the methodology used to collect and analyze data. For example, certain research methods may not capture the complexity or nuance of a particular phenomenon, or may not be appropriate for certain research questions.
Publication bias: Research papers may be subject to publication bias, where positive or significant findings are more likely to be published than negative or non-significant findings. This can skew the overall findings of a particular area of research.
Time and resource constraints: Research papers may be limited by time and resource constraints, which can affect the quality and scope of the research. Researchers may not have access to certain data or resources, or may be unable to conduct long-term studies due to practical limitations.

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Muhammad Hassan

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What It Means To Be Asian in America

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

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

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

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

Introduction

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

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

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

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

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

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

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

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

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

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

A chart listing the 18 ethnic origins included in Pew Research Center's 66 focus groups, and the composition of the focus groups by income and birth place.

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

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

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

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

Illustrations by Jing Li

Publications from the Being Asian in America project

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

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

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

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

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

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

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

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

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

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

–Immigrant woman of Nepalese origin in late 20s

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

–Immigrant woman of Bhutanese origin in late 40s

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Other identities beyond race or ethnicity are also important

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

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

–Immigrant woman of Japanese origin in mid-30s

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

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

–Immigrant woman of Filipino origin in early 20s

Immigrant identity is tied to their ethnic heritage

A chart showing how participants in the focus groups described the differences between race-centered and ethnicity-centered identities.

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

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

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

–Immigrant man of Cambodian origin in late 50s

Navigating life in America is an adjustment

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

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

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

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

–Immigrant woman of Korean origin in mid-40s

And a Bhutanese immigrant explained:

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

–Immigrant man of Bhutanese origin in early 40s

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

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

–Immigrant woman of Bangladeshi origin in late 20s

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

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

Balancing between individualistic and collective thinking

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Identity is influenced by birthplace

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

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

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

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

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

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

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

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

–Immigrant man of Burmese origin in mid-30s

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

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

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

–Immigrant woman of Filipino origin in early 30s

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

A Venn diagram showing how participants in the focus group study described their racial or ethnic identity overlaps with their American identity

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

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

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

–Immigrant man of Japanese origin in mid-40s

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

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

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

All people see is ‘Asian’

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

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

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

–Immigrant woman of Korean origin in early 30s

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

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

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

The model minority myth and its impact

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

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

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

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

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

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

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

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

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

–Immigrant man of Thai origin in early 40s

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

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

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

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

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

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

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

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

–Immigrant woman of Filipino origin in mid-30s

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

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

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

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

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

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

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

Ignorance and misinformation about Asian identity can lead to contentious encounters

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

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

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

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

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

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

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

–Immigrant man of Pakistani origin in mid-60s

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

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

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

Taking pride in their unique cultures

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

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

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

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

Where home is

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

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

–Immigrant man of Hmong origin in late 30s

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

–Immigrant woman of Laotian origin in early 50s

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

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

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

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

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

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

–Immigrant man of Bangladeshi origin in mid-50s

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

–Immigrant man of Indian origin in late 40s

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

Read sortable quotes from our focus groups

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

Video documentary

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

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

Share the story of your family and your identity

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

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

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

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

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

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

Published on 25.4.2024 in Vol 26 (2024)

Smartphone-Based Speech Therapy for Poststroke Dysarthria: Pilot Randomized Controlled Trial Evaluating Efficacy and Feasibility

Authors of this article:

Original Paper

Yuyoung Kim 1 , MSc ;
Minjung Kim 1, 2 , MS ;
Jinwoo Kim 1, 2 , PhD ;
Tae-Jin Song 3 , MD, PhD

1 Human Computer Interaction Lab, Graduate Program in Cognitive Science, Yonsei University, Seoul, Republic of Korea

2 HAII Corporation, Seoul, Republic of Korea

3 Department of Neurology, Seoul Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea

Corresponding Author:

Tae-Jin Song, MD, PhD

Department of Neurology

Seoul Hospital

Ewha Womans University College of Medicine

22, Ewhayeodae 1an-gil, Seodaemun-gu

Seoul, 03766

Republic of Korea

Phone: 82 10 8919 8764

Email: [email protected]

Background: Dysarthria is a common poststroke speech disorder affecting communication and psychological well-being. Traditional speech therapy is effective but often poses challenges in terms of accessibility and patient adherence. Emerging smartphone-based therapies may offer promising alternatives for the treatment of poststroke dysarthria.

Objective: This study aimed to assess the efficacy and feasibility of smartphone-based speech therapy for improving speech intelligibility in patients with acute and early subacute poststroke dysarthria. This study also explored the impact of the intervention on psychological well-being, user experience, and overall feasibility in a clinical setting.

Methods: Participants were divided into 2 groups for this randomized, evaluator-blinded trial. The intervention group used a smartphone-based speech therapy app for 1 hour per day, 5 days per week, for 4 weeks, with guideline-based standard stroke care. The control group received standard guideline-based stroke care and rehabilitation. Speech intelligibility, psychological well-being, quality of life, and user acceptance were assessed using repeated measures ANOVA.

Results: In this study, 40 patients with poststroke dysarthria were enrolled, 32 of whom completed the trial (16 in each group). The intervention group showed significant improvements in speech intelligibility compared with the control group. This was evidenced by improvements from baseline ( F 1,30 =34.35; P <.001), between-group differences ( F 1,30 =6.18; P =.02), and notable time-by-group interactions ( F 1,30 =6.91; P =.01). Regarding secondary outcomes, the intervention led to improvements in the percentage of correct consonants over time ( F 1,30 =5.57; P =.03). In addition, significant reductions were noted in the severity of dysarthria in the intervention group over time ( F 1,30 =21.18; P <.001), with a pronounced group effect ( F 1,30 =5.52; P =.03) and time-by-group interaction ( F 1,30 =5.29; P =.03). Regarding quality of life, significant improvements were observed as measured by the EQ-5D-3L questionnaire ( F 1,30 =13.25; P <.001) and EQ-VAS ( F 1,30 =7.74; P =.009) over time. The adherence rate to the smartphone-based app was 64%, with over half of the participants completing all the sessions. The usability of the app was rated high (system usability score 80.78). In addition, the intervention group reported increased self-efficacy in using the app compared with the control group ( F 1,30 =10.81; P =.003).

Conclusions: The smartphone-based speech therapy app significantly improved speech intelligibility, articulation, and quality of life in patients with poststroke dysarthria. These findings indicate that smartphone-based speech therapy can be a useful assistant device in the management of poststroke dysarthria, particularly in the acute and early subacute stroke stages.

Trial Registration: ClinicalTrials.gov NCT05146765; https://clinicaltrials.gov/ct2/show/NCT05146765

Introduction

Stroke is a leading cause of mortality and morbidity worldwide [ 1 ]. Approximately 40% of people who had survived a stroke experience disabilities [ 2 , 3 ], and over half of the patients with acute stroke develop motor speech disorders, particularly dysarthria [ 4 ]. Poststroke dysarthria results from weakened, slow, or impaired speech production muscles caused by cranial nerve damage [ 5 ]. Poststroke dysarthria can cause abnormalities in vocal quality, pace, strength, and volume, ultimately leading to reduced speech intelligibility. Consequently, decreased speech intelligibility may trigger communication problems, impaired social interactions, anxiety, depression, and decreased quality of life [ 6 , 7 ].

Starting speech therapy immediately after a stroke can enhance recovery [ 8 - 10 ]. Evidence indicates that early, consistent, intensive treatment yields significantly better outcomes [ 11 , 12 ]. However, despite the recognized importance of early intervention, there is a notable lack of clinical studies that specifically address poststroke dysarthria, particularly in the early stages of stroke. The lack of evidence underscores the need for further studies. In animal studies, neuroplastic changes after an ischemic stroke have been shown to aid neural recovery. However, the direct applicability of these findings in human patients remains uncertain [ 13 , 14 ]. Therefore, further research is needed to define the benefits and risks of early interventions after stroke [ 10 ].

Unfortunately, treatment adherence is negatively affected by the perception that current speech treatments are tedious and repetitive [ 15 ]. Furthermore, patients may face restrictions regarding therapeutic resources because speech therapy requires substantial time and effort by clinicians or speech-language pathologists (SLPs) [ 16 ]. Approximately one-third of the patients received sufficient speech therapy. Additionally, the amount and frequency of therapy received varies among patients [ 17 ].

Digital speech therapy apps may offer significant advancements over traditional approaches [ 18 , 19 ]. They also enhance therapeutic accessibility and patient engagement. Additionally, they deliver effective therapeutic dosages and offer tailored feedback to patients [ 6 ]. Most importantly, smartphone-based speech therapy apps offer flexibility and ease of access. This is particularly beneficial for patients with stroke who find clinic visits challenging. In addition, smartphone-based speech therapy apps can reduce time and economic burden [ 20 ]. Smartphone-based speech therapy can play a crucial role in increasing therapy intensity. High-intensity practice leads to better outcomes in poststroke dysarthria treatment [ 5 , 21 ]. Smartphone-based speech therapy can be delivered using multimedia resources. This approach enhances patient engagement through repetitive practice. Finally, smartphone-based speech therapy enables patients to practice speech independently by measuring various vocal parameters and providing tailored feedback [ 22 ]. Real-time feedback can assist patients in recognizing and correcting inappropriate speech patterns [ 23 , 24 ]. This system can enhance the effectiveness of speech therapy by providing valuable insights and improving motivation. Additionally, such feedback is crucial to enhance patient self-efficacy and promote positive behavioral changes [ 25 ].

Our primary aim was to evaluate the effect of smartphone-based speech therapy on speech intelligibility, particularly in patients with poststroke dysarthria in the acute and early subacute stroke stages. Additionally, we focused on articulation function, dysarthria severity, and psychological well-being, including depression, anxiety, and quality of life. This study also assessed the feasibility of the trial by examining the adherence, recruitment, and dropout rates. Furthermore, we evaluated the usability and self-efficacy of the app experienced by the participants. This study aimed to explore the efficacy of early intervention and assess how digital tools can enhance speech therapy outcomes in patients with poststroke dysarthria.

Study Design

This was a prospective, randomized, evaluator-blinded trial study. Participants were allocated to intervention and control groups. They were recruited from 2 stroke centers in South Korea: Ewha Womans University Seoul Hospital and Mokdong Hospital. The trial was registered at ClinicalTrials.gov (NCT05146765).

The participants were screened for eligibility and randomly allocated to the intervention or control groups. Demographic and clinical characteristics were recorded, and a detailed baseline assessment of poststroke dysarthria was conducted. After 4 weeks, the participants underwent a postevaluation to reassess their condition and measure the efficacy of the intervention. The trial was designed according to the CONSORT-EHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile Health Applications and Online Telehealth) checklist ( Multimedia Appendix 1 ).

Ethical Considerations

In adherence to our commitment to ethical research standards, we observed several vital considerations throughout this study. Our adherence to these ethical principles was fundamental to ensuring all participants’ dignity, rights, safety, and well-being. Upon receiving ethics approval from the Ewha Womans University Seoul Hospital Institutional Review Board (approval SEUMC 2021-12-011), we ensured that all research procedures strictly adhered to the guidelines outlined in the Declaration of Helsinki [ 26 ]. Before participating, participants identified as neurologically stable and survived a stroke in the acute and early subacute stages received detailed information about the study’s goals, procedures, and potential benefits and risks. Each participant provided written informed consent to affirm their voluntary participation and understanding of the study. This consent process was necessary to ensure participants were fully informed and their autonomy respected. Next, strict data protection measures were implemented to safeguard our participants’ privacy and confidentiality. All collected data were anonymized throughout the research process to preserve participants’ privacy. We offered participants a monetary compensation of ₩50,000 (US $40) for their involvement in the study, which amounts to ₩25,000 (US $20) per visit. This compensation was offered as a token of appreciation for their valuable contribution to our research and to acknowledge the personal investment each participant made by dedicating their time to our study.

Participants

A principal investigator (TJS), specializing in stroke, screened and enrolled the eligible participants. The inclusion criteria were as follows: (1) patients diagnosed with dysarthria by a stroke specialist [ 27 ], (2) patients who are neurologically stable, (3) first-time patients with stroke, (4) patients who are in the acute or early subacute phase of stroke defined as having experienced their initial stroke event within the past 1 month, (5) patients with sufficient cognitive abilities to use a smartphone-based speech therapy app (Mini-Mental State Examination score ≥26) [ 28 ], and (6) patients with adequate vision [ 29 ], hearing [ 30 ], communication skills, and motor skills [ 27 ]. The exclusion criteria were as follows: (1) coexisting language disorders (eg, aphasia) or neurological disorders (eg, dementia, Pick disease, Huntington disease, Parkinson disease, or Parkinsonism) that could influence dysarthria, (2) history of severe mental disorders (eg, depression, schizophrenia, alcohol addiction, or drug addiction), (3) inability to use or access smartphone technology, (4) illiteracy, and (5) inability to communicate in Korean, the primary language of the study location.

Randomization and Masking

An independent researcher managed the randomization. A computerized system with permuted block sizes of 2 and 4 was used to ensure a balanced and unpredictable group distribution [ 31 ]. The block sizes were disclosed to the participants or researchers at the end of the trial to ensure randomization.

Given the intervention’s interactive nature, it was impossible to blind the participants to their group assignments [ 32 ]. However, independent evaluators and those not involved in the treatment process were blinded to the group allocation to minimize potential bias. This masking was crucial to maintain the integrity of the assessment. To preserve the integrity of the blinded assessment, participants were instructed not to disclose any intervention-related details during the evaluation sessions.

Intervention

Intervention group.

Participants in the intervention group received a smartphone-based speech therapy app and standard guideline-based stroke care. This app allowed participants to achieve speech therapy independently without the support of caregivers or therapists. The participants were instructed to use the app for 1 hour daily for at least 5 days per week for 4 consecutive weeks. The intervention could be completed in a single session or distributed across multiple daily sessions.

The app was tailored for older adults with poststroke dysarthria and optimized for users facing age-related challenges [ 33 ]. The interface was designed to minimize unintentional interactions for participants with motor impairments. Intuitive design elements, such as sequential tabs and text-labeled buttons, were included to enhance usability for older adults [ 34 ]. Moreover, button size and spacing were adjusted to facilitate ease of use and reduce inadvertent presses.

The app provided 6 components of speech exercises for 1 day based on established behavioral therapies [ 5 , 10 ]. These included oro-motor exercises, sustained sound, pitch variation, velopharyngeal closure, reading practice, and syllable repetition ( Table 1 ). The primary goal of these exercises was to improve overall speech intelligibility and enhance articulation.

Speech exercises such as sustaining sounds, repeating syllables, and reading provided real-time auditory and visual feedback. Real-time feedback was provided throughout the sessions to promote attention and self-awareness during speech therapy. Pronunciations and speech signals were transmitted during speech exercises. Our engine analyzed the speech parameters and provided feedback. The feedback results were displayed on the participants’ devices. For instance, the “sustaining sound” task required participants to sustain a vowel sound, such as /ah/, for 5-15 seconds. Subsequently, real-time feedback on the loudness, sound length, and pitch was provided. Participants could address speech errors through insights gained from the feedback (eg, “Speak more loudly!” in Figure 1 B).

The treatment results are presented in 2 formats as shown in Figure 2 . First, a summary of each therapy session focused on speech outcomes, including pronunciation accuracy, loudness, and pitch. The participants understood these outcomes better through voice- and text-guided interactions. After the exercise, they listened to their recorded voices and provided feedback. This feedback helped them assess their progress ( Figure 2 A). Second, the app provided cumulative analysis. The analysis included the daily treatment results, weekly and monthly progress, and speech outcome scores ( Figure 2 B).

The app automatically logged all the results in a database. The researchers could access these results using a data-logging system. Researchers monitored the participants’ adherence to the intervention and offered coaching for lapses or technical issues. The researchers evaluated the app use every evening to monitor participants’ adherence. If reduced adherence was observed, the researchers contacted the participants the following day via phone call or SMS text message to encourage therapeutic engagement. The participants were encouraged to report any app-related issues, which were promptly addressed by the researchers.

Control Group

Participants in the control group received standard stroke care for 4 weeks. Standard stroke care includes medical treatment, routine stroke therapy, and rehabilitative exercises, as outlined in the basic guidelines [ 35 , 36 ]. This care encompassed the conventional speech treatment recommended in standard protocols, such as vocal and articulation exercises. Care was provided by clinicians and SLPs who adhered to the conventional stroke therapy methods. Treatment was tailored to each participant’s clinical needs, established through a collaborative agreement between clinicians and participants, and modified to reflect their progress. Additionally, after the 4-week study period, participants in the control group were allowed to use a smartphone-based speech therapy app.

Outcome Measures

Assessments were conducted at 2 time points: at baseline and then immediately after the 4-week intervention period.

Primary Outcome: Speech Intelligibility

The primary outcome of this study was a change in speech intelligibility. To evaluate speech intelligibility, participants were asked to read the “Gaeul” passage, a standardized tool used in Korean paragraph reading tests for speakers with motor speech disorders, developed by Kim [ 37 ]. This passage consists of 369 syllables representing the frequency of occurrence of Korean vowels and consonants.

Participants were instructed to read the passage aloud at their natural pitch and loudness. Recordings were made using a high-quality digital recorder (Sony ICD-UX560F) positioned 30 cm from the participants in a quiet room. The evaluation was carried out in an environment free from noise, which ensured that the conditions were consistent for every assessment [ 38 ]. Participants were seated close to the evaluator to ensure optimal sound quality. The primary SLP evaluator conducted the assessment in the room during the recording. Subsequently, experienced SLPs, who were blinded to the participants’ group allocation, listened to each recording and assessed the speech intelligibility. All 3 SLPs who conducted the assessment possessed over 6 years of clinical experience, specialized in poststroke dysarthria, and held certifications in Korean speech-language pathology. Additionally, they had experienced specialized training in poststroke dysarthria. Speech intelligibility was rated on a scale ranging from 0 (intelligible, can be understood without difficulty) to 6 (unintelligible, cannot be understood at all) [ 39 ]. The other 2 evaluators assessed speech intelligibility based on the recorded audio. The average score from the 3 SLPs was used to determine each participant’s final speech intelligibility score.

Secondary Outcomes

Secondary outcomes were measured to assess factors related to dysarthria and psychological well-being. First, the Urimal Test of Articulation and Phonology 2 (U-TAP2) was used [ 40 ]. This measurement was used to identify the percentage of consonants correct for detecting articulation anomalies [ 41 ]. Participants were asked to read 30 words from U-TAP2 in a quiet room. The SLPs then recorded these readings and calculated the percentage of consonants correct by marking misarticulated consonants (94 in total) and converting them into a percentage score.

Stroke-related neurological deficits were measured using the National Institute of Health Stroke Scale [ 27 ], with a specific focus on components related to dysarthria. Stroke specialists quickly evaluated the severity of dysarthric speech. As the participants spoke specific words, the severity was rated on a 3-point scale: 0=normal, 1=mild to moderate, and 2=severe. This assessment was conducted by a seasoned neurologist with over 20 years of experience in stroke specialization and certified in the Korean National Institute of Health Stroke Scale.

Finally, participants’ psychological well-being was measured using self-reported questionnaires. The Patient Health Questionnaire-9 [ 42 , 43 ] and the Generalized Anxiety Disorder 7-Item Scale [ 44 , 45 ] were used to evaluate depressive and anxiety symptoms. Furthermore, the EQ-5D-3L questionnaire [ 46 ] was used to assess the participants’ quality of life across 5 different areas: their ability to move around, care for themselves, perform their usual activities, levels of pain or discomfort, and mood. To assign specific values to these quality-of-life measures, we applied weights based on the preferences of the South Korean population. These weights were calculated using the time trade-off method and scores from a visual analog scale [ 47 ].

Feasibility and User Acceptance

Feasibility was assessed based on several aspects. The participant recruitment rates were documented to reflect the level of engagement. Adherence to the intervention was evaluated by tracking the completion rates of the prescribed speech therapy sessions within the app, the frequency of app use, and the average duration of each session. These data, which were transmitted to a dedicated web system, allowed for a detailed analysis of adherence. Potential adverse events and safety concerns were continuously monitored. Any reported issues with app use or challenges faced by the participants were investigated by analyzing the app’s log data.

The usability and acceptance of smartphone-based speech therapy apps were measured using 2 surveys: the System Usability Scale (SUS) [ 48 ] and the Modified Computer Self-Efficacy Scale (mCSES) [ 49 ]. The usability of the app was evaluated using a 10-item, 5-point Likert scale that measured effectiveness, efficiency, and satisfaction. The mCSES was used to gauge participants’ confidence in using the new technology, especially tailored for older patients and those with disabilities.

Statistical Analysis

Power analysis focused on measuring the changes in speech intelligibility. We initially calculated that 32 participants were required to achieve 80% power [ 50 ] to detect a moderate effect size of 0.29 [ 51 ] with a significance level set at .02. However, we aimed to enroll 8 more participants to account for an anticipated dropout rate of 25%. Therefore, our goal was to recruit 40 participants with 20 participants per group [ 52 ].

Descriptive statistics (mean, SD, and percentage) were used to summarize the clinical and demographic characteristics of the participants. To ensure homogeneity between the intervention and control groups, a 2-tailed independent sample t test was conducted for continuous variables, whereas a chi-square test was used for categorical variables. Following the intention-to-treat principle, repeated measures ANOVA was applied to detect changes in outcome measures between and within groups. This analysis incorporated fixed effects for time, group, and time-by-group interactions, with measures taken at baseline and 4 weeks after the intervention. All analyses were performed using SPSS (version 27.0; IBM Corp). Statistical significance was set at P <.05 and was considered statistically significant.

Data Management

All data were encrypted to ensure privacy. After encryption, the system was securely transmitted to a dedicated web system. This process maintained the confidentiality and safety of the data. Real-time data such as app use frequency, session duration, and speech performance metrics are necessary for monitoring therapeutic progress and adapting the intervention as needed. Our research team used proactive measures to ensure consistent participation. For instance, reduced adherence to the app triggered alerts, which prompted our team to reconnect with the participants to understand and address their concerns. While participants could withdraw from the study at any time, the research team reserved the right to exclude those who required immediate medical attention for reasons that were not limited to the study parameters.

We recruited 129 patients with acute to early subacute cerebral infarction between January 18, 2022, and May 31, 2022. These patients were screened based on the eligibility criteria. Of these, 81 patients exhibited symptoms of dysarthria. During the screening process, 14 patients were excluded due to coexisting aphasia, 10 due to psychological problems or medication, 11 due to dementia or cognitive dysfunction, 3 due to inability to use or access smartphone technology, and 3 due to visual or hearing impairment. Finally, 40 participants were enrolled.

The 40 participants were randomized into 2 study groups, as shown in Figure 3 . We excluded 7 participants who could not complete the study for personal reasons: 5 in the intervention group and 2 in the control group. Additionally, 1 participant in the control group was excluded because of another speech disorder, apraxia. The final analysis included 32 participants (16 each in the treatment and control groups).

Table 2 presents the baseline characteristics of the participants. Chi-square and independent 2-tailed t tests revealed no significant differences between the 2 study groups. Among the 32 participants, 25 were male and 7 were female, with a mean age of 65.25 (SD 12.97; treatment group: mean 60.44, SD 11.94 and control group: mean 70.06, SD 12.47) years. All the participants were in the acute and early subacute phases of poststroke dysarthria. The treatment group participants were observed for an average of 7.06 (SD 3.66) days after stroke. In contrast, the control group participants were assessed on an average of 7.88 (SD 6.45) days after stroke.

a N/A: not applicable.

b U-TAP2: Urimal Test of Articulation and Phonology 2.

c NIHSS: National Institute of Health Stroke Scale.

d PHQ-9: Patient Health Questionnaire-9.

e GAD-7: Generalized Anxiety Disorder 7-Item Scale.

f mCSES: Modified Computer Self-Efficacy Scale.

Primary Outcome

During the baseline assessment, none of the participants were rated as 0=completely understandable or 6=completely unintelligible. Of the total 32 participants, 16 had a rating of 1, indicating slight difficulties in speech intelligibility. Another 8 participants had a rating of 2, demonstrating mild dysarthria. A range of speech intelligibility issues was observed: 5 participants had a rating of 3, which indicated moderate dysarthria; and 2 participants had a rating of 4, which suggested more severe difficulties. Only 1 participant had a rating of 5, which indicated they were close to being unintelligible.

Repeated measures ANOVA was conducted to assess the impact of time, group, and time-by-group interactions on speech intelligibility. The results revealed a significant effect of time ( F 1,30 =34.35; P <.001). This finding indicated that there were significant changes in speech intelligibility between baseline and 4 weeks after the intervention. The mean speech intelligibility score in the intervention group improved from 1.56 (SD 0.89) at baseline to 0.69 (SD 1.09) after intervention. Additionally, a significant group effect was observed ( F 1,30 =6.18; P =.02). This analysis suggested significant differences in speech intelligibility between the treatment and control groups. Furthermore, the interaction effect between time and group was also significant ( F 1,30 =6.91; P =.01), which indicates that the changes in speech intelligibility over time varied significantly between the groups.

The intervention group demonstrated notable improvements in secondary outcomes compared with the control group after intervention ( Table 3 ).

a U-TAP2: Urimal Test of Articulation and Phonology 2.

b NIHSS: National Institute of Health Stroke Scale.

c PHQ-9: Patient Health Questionnaire-9.

d GAD-7: Generalized Anxiety Disorder 7-Item Scale.

e mCSES: Modified Computer Self-Efficacy Scale.

First, the percentage of correct consonants measured by the U-TAP2 showed a significant time effect ( F 1,30 =5.57; P =.03) compared to the change between baseline and 4 weeks after the intervention. However, the group effect ( F 1,30 =3.52; P =.07) and time-by-group interaction ( F 1,30 =4.13; P =.05) were not statistically significant.

Second, significant findings emerged from the assessment of the severity of poststroke dysarthria. The time effect was significant ( F 1,30 =2.21; P ≤.001). This highlights a notable improvement in the severity over 4 weeks. Furthermore, a significant group effect ( F 1,30 =5.52; P =.03) indicated differences in severity between the treatment and control groups. Most importantly, the significant time-by-group interaction ( F 1,30 =5.29; P =.03) suggests that the groups experienced different trajectories of severity over time.

Third, no significant benefits were observed for depression or anxiety. For depression, as measured by the Patient Health Questionnaire-9, there was no significant time effect ( F 1,30 =1.42; P =.24), and the time-by-group interaction was also not significant ( F 1,30 =0.66; P =.42). However, a significant group effect was observed ( F 1,30 =8.33; P =.007). In terms of anxiety levels, as assessed by the Generalized Anxiety Disorder 7-Item Scale, no significant effects were found for time ( F 1,30 =2.09; P =.16; group: F 1,30 =2.15; P =.15; or time-by-group interaction: F 1,30 =0.13; P =.91).

Finally, a significant time effect was noted for the overall quality of life measured by the EQ-5D-3L ( F 1,30 =13.25; P ≤.001). No significant effects were observed for group ( F 1,30 =3.64; P =.07) or time-by-group interactions ( F 1,30 =0.76; P =.79). In addition, the EQ-VAS scores showed a significant time effect ( F 1,30 =7.74; P =.009) and group effect ( F 1,30 =6.06; P =.02). However, there was no significant time-by-group interaction ( F 1,30 =0.15; P =.70).

Feasibility

We met our recruitment goal by successfully enrolling 40 participants during the study period. The final assessment completion rate was 80%. Regarding adherence, 64% (n=20) of participants in the intervention group consistently used the smartphone-based speech therapy app throughout the designated period. More than 51% (n=16) of the participants completed the prescribed sessions.

System usability was considered excellent, as measured by the mean SUS score of 80.78 (SD 16.27). Concerning self-efficacy, measured by the mCSES, the intervention group had a substantial group effect ( F 1,30 =10.81; P =.003), but there were no significant changes over time ( F 1,30 =2.99; P =.09) or in the time-by-group interaction ( F 1,30 =0.97; P =.33). No significant adverse events were observed during the study period.

Principal Findings

Despite its significant impact on communication and psychosocial well-being, poststroke dysarthria remains underresearched. In particular, there is a lack of evidence on poststroke dysarthria interventions, highlighting the urgent need for more comprehensive research [ 53 ]. Understanding the prognosis of speech therapy in the critical initial months after stroke is vital because early intervention can hasten recovery [ 9 ]. Unfortunately, there is a knowledge gap in the evidence regarding poststroke dysarthria during the acute and early subacute phases [ 54 ]. Our trial findings provide evidence of the efficacy of smartphone-based speech therapy in the treatment of poststroke dysarthria.

In this study, participants experienced significant improvements in speech intelligibility and articulation after 4 weeks of using the smartphone-based speech therapy app compared to those receiving standard stroke care. This intervention was effective in several ways. It showed the potential for reducing the severity of dysarthria. It also helped alleviate depression and improve the quality of life of the participants. Consistent with prior studies, these results underscore the reliability of smartphone-based interventions [ 55 , 56 ].

The efficacy of traditional behavioral speech therapy has been proven in the chronic phase; however, studies on patients with acute and early subacute strokes are limited. Prior studies have shown encouraging results for behavioral speech therapy such as breathing exercises, nonspeech oro-motor exercises, and Lee Silverman Voice Treatment for the chronic poststroke phase [ 57 ]. One study used the Lee Silverman Voice Treatment that focuses on high phonatory effort and reading exercises [ 58 ]. This study showed promising results in a small group of 4 individuals who have survived a stroke with dysarthria for 9 months. According to another study, repetitive speech therapy had a positive effect on patients with stroke for at least 6 months [ 59 ].

Our study expands traditional behavioral speech therapy into a digital format using a smartphone-based app [ 58 - 61 ]. This approach overcomes the limitations of traditional methods by offering more accessible, engaging, and cost-effective speech therapy that enables self-management [ 62 - 65 ]. Patients can perform various speech exercises at home. Home-based treatment reduces the need for frequent clinical visits and reduces expenses [ 66 , 67 ]. Moreover, the app provides uninterrupted therapy sessions, even during the COVID-19 pandemic. This serves as a reliable alternative to clinical treatment [ 68 ].

Patients with poststroke dysarthria also commonly experience adverse psychological effects [ 6 , 7 ]. Previous studies focusing on speech therapy in participants with poststroke aphasia have demonstrated improvements in depression [ 69 ], anxiety [ 70 ], and quality of life [ 71 ]. However, specific evidence for poststroke dysarthria remains limited. Although we observed a significant decrease in depressive symptoms, no significant changes in anxiety levels were observed. Notably, the EQ-5D-3L and EQ-VAS scores indicated a substantial improvement in quality of life over time and a positive effect of the intervention. However, the lack of significant group differences in these scores suggests that improvements in quality of life were not solely attributable to the intervention. This divergence in findings highlights the complexity of assessing the full effect of speech therapy interventions on psychological well-being. Due to the significant impact of psychological well-being deterioration in patients with poststroke dysarthria, cognitive behavioral therapy should also be considered as a potential treatment [ 72 ]. Since this study is primarily focused on speech intelligibility, it may not have fully captured the broader impact of speech therapy on psychological well-being. Given these findings, there is a clear need for further research with larger sample sizes to provide a better understanding of the benefits of speech therapy interventions on the psychological well-being of patients with poststroke dysarthria. This can help develop effective treatment strategies, specifically in the areas of speech and psychological well-being.

Meanwhile, the average SUS score of 80.78 (SD 16.27) signifies excellent usability, which indicates that the participants found the app user-friendly and efficient. Participants also noted increased self-efficacy in app use compared with before treatment. These results suggest that the app helped overcome apprehensions about using the technology, particularly among older users. This increased system feasibility is a promising sign of active participation in therapy.

However, the treatment adherence was lower than expected. Notably, measuring adherence was challenging because of variable internet connectivity among the participants. Due to low-specification phones or unstable home internet connections, many participants, especially older users, experienced frequent internet disconnections. These challenges hindered the proper storage of log data, which may have led to inaccuracies in adherence measurements. Our app includes features, such as progress graphs and feedback, to address adherence-related issues and encourage self-monitoring [ 12 ]. Although these features are standard in health apps and are crucial for self-therapy, they have limited long-term effectiveness [ 73 , 74 ]. This limitation is particularly relevant for older adults who are unfamiliar with digital devices [ 75 , 76 ]. Given these challenges, future research should focus on improving adherence to therapy and making it more accessible to diverse patient groups. Including more subjects and a broader range of variables could enhance our understanding of how digital interventions can be most effectively used in poststroke care. Regarding home therapies, various factors, such as the patient’s social context and home environment, can affect the treatment effectiveness. For example, providing an admin system to monitor and control patient performance data is recommended. This would allow clinicians or family caregivers to remotely track adherence and performance and address potential issues arising from the lack of face-to-face interactions. This could help older adults maintain adherence and maximize the therapeutic effects of treatment [ 77 ].

Limitations

This study has several limitations. First, even as a pilot trial, this study included a small number of participants. Additionally, there was a gender imbalance with a significantly higher number of male participants. Future studies should aim for larger sample sizes and consider recruitment from multiple centers to improve the feasibility and generalizability.

Second, this study focused only on patients with poststroke dysarthria in the acute and early subacute stages. However, dysarthria affects patients in both the acute and chronic stages of stroke. To validate the effectiveness of the intervention across diverse patient profiles, future research should include a broader range of patients with stroke and consider the onset period and severity of dysarthria. Additionally, this study only recruited patients in the acute and early subacute stages of joint impairment after stroke, which may have resulted in the exclusion of patients with severe joint impairment. These selection criteria may have influenced the observed effects of smartphone-based speech therapy. In future studies, it would be beneficial to include participants with varying degrees of dysarthria to understand better the efficacy of this therapy across a spectrum of severity. A more detailed analysis, which may include secondary assessments, can be carried out to evaluate the therapy’s efficacy in addressing speech impairments of varying severity. This approach would enable a deeper understanding of the therapy’s applicability to a broader range of dysarthria cases after stroke.

Third, regarding the measurement of consonant accuracy using U-TAP2 at the word level, we recognize that this approach has limitations, particularly in adult poststroke dysarthria. While U-TAP2 is extensively used to assess articulatory precision in Korean children with developmental articulation disorders, its application is limited [ 40 ]. When measuring speech intelligibility in adults with poststroke dysarthria, particularly in continuous speech, U-TAP2 may not fully capture all the complexities. This tool needs to be equipped to grasp the full range of speech intelligibility challenges this adult population faces. Specifically, this method may overlook critical aspects of speech, such as rhythm, prosody, and coarticulation effects, which are essential for understanding overall speech severity. The choice of U-TAP2 was influenced by the absence of standardized assessment tools for adult poststroke dysarthria in the Korean clinical environment. However, we acknowledge that future research should explore more comprehensive tools like the Frenchay Dysarthria Assessment to analyze the various influencing factors of dysarthria more thoroughly [ 78 ].

Finally, the smartphone-based speech therapy app used in this study was developed in Korean. Future research should aim to create multilingual versions of the app. Studying multilingual versions would enable researchers to assess their effectiveness across different nationalities and broaden their reach.

Conclusions

This study emphasized the importance of digital speech therapy in the treatment of poststroke dysarthria. Smartphone apps designed for speech therapy can be used alongside traditional speech therapies and have shown promising results in improving speech outcomes and the overall quality of life. Our findings provide encouraging evidence for the integration of these apps into existing treatment plans. However, more extensive and comprehensive studies are needed to fully understand the impact of digital speech therapy and optimize its use in treating poststroke dysarthria.

Acknowledgments

This research was supported by the Technology Development Program (S3301230) and funded by the Ministry of SMEs and Startups (Korea). We sincerely thank all the participants for their valuable time and commitment to this study. This research was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute, funded by the Ministry of Health and Welfare, Republic of Korea (grant RS-2023-00262087 to TJS). This research was supported by the Institute of Information & Communications Technology Planning & Evaluation grant funded by the Korean government (MSIT 2022-0-00621) for the development of artificial intelligence technology that provides dialog-based multimodal explainability. This paper presents the original work of the authors and is not under consideration for publication elsewhere.

Authors' Contributions

All authors contributed significantly to this study. YK developed app content conceived, designed the study, analyzed and interpreted the data, and drafted and revised the paper. MK contributed to the app design and development and paper revision. As the principal investigator, TJS recruited participants and critically reviewed the paper. JK and TJS secured the funding for this study. All authors have reviewed and approved the final version of the paper for submission.

Conflicts of Interest

None declared.

CONSORT-EHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile Health Applications and Online Telehealth) V 1.6.1 checklist.

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Abbreviations

Edited by G Eysenbach; submitted 16.01.24; peer-reviewed by SJ Lee; comments to author 13.02.24; revised version received 21.02.24; accepted 20.03.24; published 25.04.24.

©Yuyoung Kim, Minjung Kim, Jinwoo Kim, Tae-Jin Song. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 25.04.2024.

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Published: 17 April 2024

The economic commitment of climate change

Maximilian Kotz ORCID: orcid.org/0000-0003-2564-5043 1 , 2 ,
Anders Levermann ORCID: orcid.org/0000-0003-4432-4704 1 , 2 &
Leonie Wenz ORCID: orcid.org/0000-0002-8500-1568 1 , 3

Nature volume 628 , pages 551–557 ( 2024 ) Cite this article

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Environmental economics
Environmental health
Interdisciplinary studies
Projection and prediction

Global projections of macroeconomic climate-change damages typically consider impacts from average annual and national temperatures over long time horizons 1 , 2 , 3 , 4 , 5 , 6 . Here we use recent empirical findings from more than 1,600 regions worldwide over the past 40 years to project sub-national damages from temperature and precipitation, including daily variability and extremes 7 , 8 . Using an empirical approach that provides a robust lower bound on the persistence of impacts on economic growth, we find that the world economy is committed to an income reduction of 19% within the next 26 years independent of future emission choices (relative to a baseline without climate impacts, likely range of 11–29% accounting for physical climate and empirical uncertainty). These damages already outweigh the mitigation costs required to limit global warming to 2 °C by sixfold over this near-term time frame and thereafter diverge strongly dependent on emission choices. Committed damages arise predominantly through changes in average temperature, but accounting for further climatic components raises estimates by approximately 50% and leads to stronger regional heterogeneity. Committed losses are projected for all regions except those at very high latitudes, at which reductions in temperature variability bring benefits. The largest losses are committed at lower latitudes in regions with lower cumulative historical emissions and lower present-day income.

Climate damage projections beyond annual temperature

Investment incentive reduced by climate damages can be restored by optimal policy

Climate economics support for the UN climate targets

Projections of the macroeconomic damage caused by future climate change are crucial to informing public and policy debates about adaptation, mitigation and climate justice. On the one hand, adaptation against climate impacts must be justified and planned on the basis of an understanding of their future magnitude and spatial distribution 9 . This is also of importance in the context of climate justice 10 , as well as to key societal actors, including governments, central banks and private businesses, which increasingly require the inclusion of climate risks in their macroeconomic forecasts to aid adaptive decision-making 11 , 12 . On the other hand, climate mitigation policy such as the Paris Climate Agreement is often evaluated by balancing the costs of its implementation against the benefits of avoiding projected physical damages. This evaluation occurs both formally through cost–benefit analyses 1 , 4 , 5 , 6 , as well as informally through public perception of mitigation and damage costs 13 .

Projections of future damages meet challenges when informing these debates, in particular the human biases relating to uncertainty and remoteness that are raised by long-term perspectives 14 . Here we aim to overcome such challenges by assessing the extent of economic damages from climate change to which the world is already committed by historical emissions and socio-economic inertia (the range of future emission scenarios that are considered socio-economically plausible 15 ). Such a focus on the near term limits the large uncertainties about diverging future emission trajectories, the resulting long-term climate response and the validity of applying historically observed climate–economic relations over long timescales during which socio-technical conditions may change considerably. As such, this focus aims to simplify the communication and maximize the credibility of projected economic damages from future climate change.

In projecting the future economic damages from climate change, we make use of recent advances in climate econometrics that provide evidence for impacts on sub-national economic growth from numerous components of the distribution of daily temperature and precipitation 3 , 7 , 8 . Using fixed-effects panel regression models to control for potential confounders, these studies exploit within-region variation in local temperature and precipitation in a panel of more than 1,600 regions worldwide, comprising climate and income data over the past 40 years, to identify the plausibly causal effects of changes in several climate variables on economic productivity 16 , 17 . Specifically, macroeconomic impacts have been identified from changing daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall that occur in addition to those already identified from changing average temperature 2 , 3 , 18 . Moreover, regional heterogeneity in these effects based on the prevailing local climatic conditions has been found using interactions terms. The selection of these climate variables follows micro-level evidence for mechanisms related to the impacts of average temperatures on labour and agricultural productivity 2 , of temperature variability on agricultural productivity and health 7 , as well as of precipitation on agricultural productivity, labour outcomes and flood damages 8 (see Extended Data Table 1 for an overview, including more detailed references). References 7 , 8 contain a more detailed motivation for the use of these particular climate variables and provide extensive empirical tests about the robustness and nature of their effects on economic output, which are summarized in Methods . By accounting for these extra climatic variables at the sub-national level, we aim for a more comprehensive description of climate impacts with greater detail across both time and space.

Constraining the persistence of impacts

A key determinant and source of discrepancy in estimates of the magnitude of future climate damages is the extent to which the impact of a climate variable on economic growth rates persists. The two extreme cases in which these impacts persist indefinitely or only instantaneously are commonly referred to as growth or level effects 19 , 20 (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for mathematical definitions). Recent work shows that future damages from climate change depend strongly on whether growth or level effects are assumed 20 . Following refs. 2 , 18 , we provide constraints on this persistence by using distributed lag models to test the significance of delayed effects separately for each climate variable. Notably, and in contrast to refs. 2 , 18 , we use climate variables in their first-differenced form following ref. 3 , implying a dependence of the growth rate on a change in climate variables. This choice means that a baseline specification without any lags constitutes a model prior of purely level effects, in which a permanent change in the climate has only an instantaneous effect on the growth rate 3 , 19 , 21 . By including lags, one can then test whether any effects may persist further. This is in contrast to the specification used by refs. 2 , 18 , in which climate variables are used without taking the first difference, implying a dependence of the growth rate on the level of climate variables. In this alternative case, the baseline specification without any lags constitutes a model prior of pure growth effects, in which a change in climate has an infinitely persistent effect on the growth rate. Consequently, including further lags in this alternative case tests whether the initial growth impact is recovered 18 , 19 , 21 . Both of these specifications suffer from the limiting possibility that, if too few lags are included, one might falsely accept the model prior. The limitations of including a very large number of lags, including loss of data and increasing statistical uncertainty with an increasing number of parameters, mean that such a possibility is likely. By choosing a specification in which the model prior is one of level effects, our approach is therefore conservative by design, avoiding assumptions of infinite persistence of climate impacts on growth and instead providing a lower bound on this persistence based on what is observable empirically (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for further exposition of this framework). The conservative nature of such a choice is probably the reason that ref. 19 finds much greater consistency between the impacts projected by models that use the first difference of climate variables, as opposed to their levels.

We begin our empirical analysis of the persistence of climate impacts on growth using ten lags of the first-differenced climate variables in fixed-effects distributed lag models. We detect substantial effects on economic growth at time lags of up to approximately 8–10 years for the temperature terms and up to approximately 4 years for the precipitation terms (Extended Data Fig. 1 and Extended Data Table 2 ). Furthermore, evaluation by means of information criteria indicates that the inclusion of all five climate variables and the use of these numbers of lags provide a preferable trade-off between best-fitting the data and including further terms that could cause overfitting, in comparison with model specifications excluding climate variables or including more or fewer lags (Extended Data Fig. 3 , Supplementary Methods Section 1 and Supplementary Table 1 ). We therefore remove statistically insignificant terms at later lags (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). Further tests using Monte Carlo simulations demonstrate that the empirical models are robust to autocorrelation in the lagged climate variables (Supplementary Methods Section 2 and Supplementary Figs. 4 and 5 ), that information criteria provide an effective indicator for lag selection (Supplementary Methods Section 2 and Supplementary Fig. 6 ), that the results are robust to concerns of imperfect multicollinearity between climate variables and that including several climate variables is actually necessary to isolate their separate effects (Supplementary Methods Section 3 and Supplementary Fig. 7 ). We provide a further robustness check using a restricted distributed lag model to limit oscillations in the lagged parameter estimates that may result from autocorrelation, finding that it provides similar estimates of cumulative marginal effects to the unrestricted model (Supplementary Methods Section 4 and Supplementary Figs. 8 and 9 ). Finally, to explicitly account for any outstanding uncertainty arising from the precise choice of the number of lags, we include empirical models with marginally different numbers of lags in the error-sampling procedure of our projection of future damages. On the basis of the lag-selection procedure (the significance of lagged terms in Extended Data Fig. 1 and Extended Data Table 2 , as well as information criteria in Extended Data Fig. 3 ), we sample from models with eight to ten lags for temperature and four for precipitation (models shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). In summary, this empirical approach to constrain the persistence of climate impacts on economic growth rates is conservative by design in avoiding assumptions of infinite persistence, but nevertheless provides a lower bound on the extent of impact persistence that is robust to the numerous tests outlined above.

Committed damages until mid-century

We combine these empirical economic response functions (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) with an ensemble of 21 climate models (see Supplementary Table 5 ) from the Coupled Model Intercomparison Project Phase 6 (CMIP-6) 22 to project the macroeconomic damages from these components of physical climate change (see Methods for further details). Bias-adjusted climate models that provide a highly accurate reproduction of observed climatological patterns with limited uncertainty (Supplementary Table 6 ) are used to avoid introducing biases in the projections. Following a well-developed literature 2 , 3 , 19 , these projections do not aim to provide a prediction of future economic growth. Instead, they are a projection of the exogenous impact of future climate conditions on the economy relative to the baselines specified by socio-economic projections, based on the plausibly causal relationships inferred by the empirical models and assuming ceteris paribus. Other exogenous factors relevant for the prediction of economic output are purposefully assumed constant.

A Monte Carlo procedure that samples from climate model projections, empirical models with different numbers of lags and model parameter estimates (obtained by 1,000 block-bootstrap resamples of each of the regressions in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) is used to estimate the combined uncertainty from these sources. Given these uncertainty distributions, we find that projected global damages are statistically indistinguishable across the two most extreme emission scenarios until 2049 (at the 5% significance level; Fig. 1 ). As such, the climate damages occurring before this time constitute those to which the world is already committed owing to the combination of past emissions and the range of future emission scenarios that are considered socio-economically plausible 15 . These committed damages comprise a permanent income reduction of 19% on average globally (population-weighted average) in comparison with a baseline without climate-change impacts (with a likely range of 11–29%, following the likelihood classification adopted by the Intergovernmental Panel on Climate Change (IPCC); see caption of Fig. 1 ). Even though levels of income per capita generally still increase relative to those of today, this constitutes a permanent income reduction for most regions, including North America and Europe (each with median income reductions of approximately 11%) and with South Asia and Africa being the most strongly affected (each with median income reductions of approximately 22%; Fig. 1 ). Under a middle-of-the road scenario of future income development (SSP2, in which SSP stands for Shared Socio-economic Pathway), this corresponds to global annual damages in 2049 of 38 trillion in 2005 international dollars (likely range of 19–59 trillion 2005 international dollars). Compared with empirical specifications that assume pure growth or pure level effects, our preferred specification that provides a robust lower bound on the extent of climate impact persistence produces damages between these two extreme assumptions (Extended Data Fig. 3 ).

Estimates of the projected reduction in income per capita from changes in all climate variables based on empirical models of climate impacts on economic output with a robust lower bound on their persistence (Extended Data Fig. 1 ) under a low-emission scenario compatible with the 2 °C warming target and a high-emission scenario (SSP2-RCP2.6 and SSP5-RCP8.5, respectively) are shown in purple and orange, respectively. Shading represents the 34% and 10% confidence intervals reflecting the likely and very likely ranges, respectively (following the likelihood classification adopted by the IPCC), having estimated uncertainty from a Monte Carlo procedure, which samples the uncertainty from the choice of physical climate models, empirical models with different numbers of lags and bootstrapped estimates of the regression parameters shown in Supplementary Figs. 1 – 3 . Vertical dashed lines show the time at which the climate damages of the two emission scenarios diverge at the 5% and 1% significance levels based on the distribution of differences between emission scenarios arising from the uncertainty sampling discussed above. Note that uncertainty in the difference of the two scenarios is smaller than the combined uncertainty of the two respective scenarios because samples of the uncertainty (climate model and empirical model choice, as well as model parameter bootstrap) are consistent across the two emission scenarios, hence the divergence of damages occurs while the uncertainty bounds of the two separate damage scenarios still overlap. Estimates of global mitigation costs from the three IAMs that provide results for the SSP2 baseline and SSP2-RCP2.6 scenario are shown in light green in the top panel, with the median of these estimates shown in bold.

Damages already outweigh mitigation costs

We compare the damages to which the world is committed over the next 25 years to estimates of the mitigation costs required to achieve the Paris Climate Agreement. Taking estimates of mitigation costs from the three integrated assessment models (IAMs) in the IPCC AR6 database 23 that provide results under comparable scenarios (SSP2 baseline and SSP2-RCP2.6, in which RCP stands for Representative Concentration Pathway), we find that the median committed climate damages are larger than the median mitigation costs in 2050 (six trillion in 2005 international dollars) by a factor of approximately six (note that estimates of mitigation costs are only provided every 10 years by the IAMs and so a comparison in 2049 is not possible). This comparison simply aims to compare the magnitude of future damages against mitigation costs, rather than to conduct a formal cost–benefit analysis of transitioning from one emission path to another. Formal cost–benefit analyses typically find that the net benefits of mitigation only emerge after 2050 (ref. 5 ), which may lead some to conclude that physical damages from climate change are simply not large enough to outweigh mitigation costs until the second half of the century. Our simple comparison of their magnitudes makes clear that damages are actually already considerably larger than mitigation costs and the delayed emergence of net mitigation benefits results primarily from the fact that damages across different emission paths are indistinguishable until mid-century (Fig. 1 ).

Although these near-term damages constitute those to which the world is already committed, we note that damage estimates diverge strongly across emission scenarios after 2049, conveying the clear benefits of mitigation from a purely economic point of view that have been emphasized in previous studies 4 , 24 . As well as the uncertainties assessed in Fig. 1 , these conclusions are robust to structural choices, such as the timescale with which changes in the moderating variables of the empirical models are estimated (Supplementary Figs. 10 and 11 ), as well as the order in which one accounts for the intertemporal and international components of currency comparison (Supplementary Fig. 12 ; see Methods for further details).

Damages from variability and extremes

Committed damages primarily arise through changes in average temperature (Fig. 2 ). This reflects the fact that projected changes in average temperature are larger than those in other climate variables when expressed as a function of their historical interannual variability (Extended Data Fig. 4 ). Because the historical variability is that on which the empirical models are estimated, larger projected changes in comparison with this variability probably lead to larger future impacts in a purely statistical sense. From a mechanistic perspective, one may plausibly interpret this result as implying that future changes in average temperature are the most unprecedented from the perspective of the historical fluctuations to which the economy is accustomed and therefore will cause the most damage. This insight may prove useful in terms of guiding adaptation measures to the sources of greatest damage.

Estimates of the median projected reduction in sub-national income per capita across emission scenarios (SSP2-RCP2.6 and SSP2-RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ). a , Impacts arising from all climate variables. b – f , Impacts arising separately from changes in annual mean temperature ( b ), daily temperature variability ( c ), total annual precipitation ( d ), the annual number of wet days (>1 mm) ( e ) and extreme daily rainfall ( f ) (see Methods for further definitions). Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Nevertheless, future damages based on empirical models that consider changes in annual average temperature only and exclude the other climate variables constitute income reductions of only 13% in 2049 (Extended Data Fig. 5a , likely range 5–21%). This suggests that accounting for the other components of the distribution of temperature and precipitation raises net damages by nearly 50%. This increase arises through the further damages that these climatic components cause, but also because their inclusion reveals a stronger negative economic response to average temperatures (Extended Data Fig. 5b ). The latter finding is consistent with our Monte Carlo simulations, which suggest that the magnitude of the effect of average temperature on economic growth is underestimated unless accounting for the impacts of other correlated climate variables (Supplementary Fig. 7 ).

In terms of the relative contributions of the different climatic components to overall damages, we find that accounting for daily temperature variability causes the largest increase in overall damages relative to empirical frameworks that only consider changes in annual average temperature (4.9 percentage points, likely range 2.4–8.7 percentage points, equivalent to approximately 10 trillion international dollars). Accounting for precipitation causes smaller increases in overall damages, which are—nevertheless—equivalent to approximately 1.2 trillion international dollars: 0.01 percentage points (−0.37–0.33 percentage points), 0.34 percentage points (0.07–0.90 percentage points) and 0.36 percentage points (0.13–0.65 percentage points) from total annual precipitation, the number of wet days and extreme daily precipitation, respectively. Moreover, climate models seem to underestimate future changes in temperature variability 25 and extreme precipitation 26 , 27 in response to anthropogenic forcing as compared with that observed historically, suggesting that the true impacts from these variables may be larger.

The distribution of committed damages

The spatial distribution of committed damages (Fig. 2a ) reflects a complex interplay between the patterns of future change in several climatic components and those of historical economic vulnerability to changes in those variables. Damages resulting from increasing annual mean temperature (Fig. 2b ) are negative almost everywhere globally, and larger at lower latitudes in regions in which temperatures are already higher and economic vulnerability to temperature increases is greatest (see the response heterogeneity to mean temperature embodied in Extended Data Fig. 1a ). This occurs despite the amplified warming projected at higher latitudes 28 , suggesting that regional heterogeneity in economic vulnerability to temperature changes outweighs heterogeneity in the magnitude of future warming (Supplementary Fig. 13a ). Economic damages owing to daily temperature variability (Fig. 2c ) exhibit a strong latitudinal polarisation, primarily reflecting the physical response of daily variability to greenhouse forcing in which increases in variability across lower latitudes (and Europe) contrast decreases at high latitudes 25 (Supplementary Fig. 13b ). These two temperature terms are the dominant determinants of the pattern of overall damages (Fig. 2a ), which exhibits a strong polarity with damages across most of the globe except at the highest northern latitudes. Future changes in total annual precipitation mainly bring economic benefits except in regions of drying, such as the Mediterranean and central South America (Fig. 2d and Supplementary Fig. 13c ), but these benefits are opposed by changes in the number of wet days, which produce damages with a similar pattern of opposite sign (Fig. 2e and Supplementary Fig. 13d ). By contrast, changes in extreme daily rainfall produce damages in all regions, reflecting the intensification of daily rainfall extremes over global land areas 29 , 30 (Fig. 2f and Supplementary Fig. 13e ).

The spatial distribution of committed damages implies considerable injustice along two dimensions: culpability for the historical emissions that have caused climate change and pre-existing levels of socio-economic welfare. Spearman’s rank correlations indicate that committed damages are significantly larger in countries with smaller historical cumulative emissions, as well as in regions with lower current income per capita (Fig. 3 ). This implies that those countries that will suffer the most from the damages already committed are those that are least responsible for climate change and which also have the least resources to adapt to it.

Estimates of the median projected change in national income per capita across emission scenarios (RCP2.6 and RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ) are plotted against cumulative national emissions per capita in 2020 (from the Global Carbon Project) and coloured by national income per capita in 2020 (from the World Bank) in a and vice versa in b . In each panel, the size of each scatter point is weighted by the national population in 2020 (from the World Bank). Inset numbers indicate the Spearman’s rank correlation ρ and P -values for a hypothesis test whose null hypothesis is of no correlation, as well as the Spearman’s rank correlation weighted by national population.

To further quantify this heterogeneity, we assess the difference in committed damages between the upper and lower quartiles of regions when ranked by present income levels and historical cumulative emissions (using a population weighting to both define the quartiles and estimate the group averages). On average, the quartile of countries with lower income are committed to an income loss that is 8.9 percentage points (or 61%) greater than the upper quartile (Extended Data Fig. 6 ), with a likely range of 3.8–14.7 percentage points across the uncertainty sampling of our damage projections (following the likelihood classification adopted by the IPCC). Similarly, the quartile of countries with lower historical cumulative emissions are committed to an income loss that is 6.9 percentage points (or 40%) greater than the upper quartile, with a likely range of 0.27–12 percentage points. These patterns reemphasize the prevalence of injustice in climate impacts 31 , 32 , 33 in the context of the damages to which the world is already committed by historical emissions and socio-economic inertia.

Contextualizing the magnitude of damages

The magnitude of projected economic damages exceeds previous literature estimates 2 , 3 , arising from several developments made on previous approaches. Our estimates are larger than those of ref. 2 (see first row of Extended Data Table 3 ), primarily because of the facts that sub-national estimates typically show a steeper temperature response (see also refs. 3 , 34 ) and that accounting for other climatic components raises damage estimates (Extended Data Fig. 5 ). However, we note that our empirical approach using first-differenced climate variables is conservative compared with that of ref. 2 in regard to the persistence of climate impacts on growth (see introduction and Methods section ‘Empirical model specification: fixed-effects distributed lag models’), an important determinant of the magnitude of long-term damages 19 , 21 . Using a similar empirical specification to ref. 2 , which assumes infinite persistence while maintaining the rest of our approach (sub-national data and further climate variables), produces considerably larger damages (purple curve of Extended Data Fig. 3 ). Compared with studies that do take the first difference of climate variables 3 , 35 , our estimates are also larger (see second and third rows of Extended Data Table 3 ). The inclusion of further climate variables (Extended Data Fig. 5 ) and a sufficient number of lags to more adequately capture the extent of impact persistence (Extended Data Figs. 1 and 2 ) are the main sources of this difference, as is the use of specifications that capture nonlinearities in the temperature response when compared with ref. 35 . In summary, our estimates develop on previous studies by incorporating the latest data and empirical insights 7 , 8 , as well as in providing a robust empirical lower bound on the persistence of impacts on economic growth, which constitutes a middle ground between the extremes of the growth-versus-levels debate 19 , 21 (Extended Data Fig. 3 ).

Compared with the fraction of variance explained by the empirical models historically (<5%), the projection of reductions in income of 19% may seem large. This arises owing to the fact that projected changes in climatic conditions are much larger than those that were experienced historically, particularly for changes in average temperature (Extended Data Fig. 4 ). As such, any assessment of future climate-change impacts necessarily requires an extrapolation outside the range of the historical data on which the empirical impact models were evaluated. Nevertheless, these models constitute the most state-of-the-art methods for inference of plausibly causal climate impacts based on observed data. Moreover, we take explicit steps to limit out-of-sample extrapolation by capping the moderating variables of the interaction terms at the 95th percentile of the historical distribution (see Methods ). This avoids extrapolating the marginal effects outside what was observed historically. Given the nonlinear response of economic output to annual mean temperature (Extended Data Fig. 1 and Extended Data Table 2 ), this is a conservative choice that limits the magnitude of damages that we project. Furthermore, back-of-the-envelope calculations indicate that the projected damages are consistent with the magnitude and patterns of historical economic development (see Supplementary Discussion Section 5 ).

Missing impacts and spatial spillovers

Despite assessing several climatic components from which economic impacts have recently been identified 3 , 7 , 8 , this assessment of aggregate climate damages should not be considered comprehensive. Important channels such as impacts from heatwaves 31 , sea-level rise 36 , tropical cyclones 37 and tipping points 38 , 39 , as well as non-market damages such as those to ecosystems 40 and human health 41 , are not considered in these estimates. Sea-level rise is unlikely to be feasibly incorporated into empirical assessments such as this because historical sea-level variability is mostly small. Non-market damages are inherently intractable within our estimates of impacts on aggregate monetary output and estimates of these impacts could arguably be considered as extra to those identified here. Recent empirical work suggests that accounting for these channels would probably raise estimates of these committed damages, with larger damages continuing to arise in the global south 31 , 36 , 37 , 38 , 39 , 40 , 41 , 42 .

Moreover, our main empirical analysis does not explicitly evaluate the potential for impacts in local regions to produce effects that ‘spill over’ into other regions. Such effects may further mitigate or amplify the impacts we estimate, for example, if companies relocate production from one affected region to another or if impacts propagate along supply chains. The current literature indicates that trade plays a substantial role in propagating spillover effects 43 , 44 , making their assessment at the sub-national level challenging without available data on sub-national trade dependencies. Studies accounting for only spatially adjacent neighbours indicate that negative impacts in one region induce further negative impacts in neighbouring regions 45 , 46 , 47 , 48 , suggesting that our projected damages are probably conservative by excluding these effects. In Supplementary Fig. 14 , we assess spillovers from neighbouring regions using a spatial-lag model. For simplicity, this analysis excludes temporal lags, focusing only on contemporaneous effects. The results show that accounting for spatial spillovers can amplify the overall magnitude, and also the heterogeneity, of impacts. Consistent with previous literature, this indicates that the overall magnitude (Fig. 1 ) and heterogeneity (Fig. 3 ) of damages that we project in our main specification may be conservative without explicitly accounting for spillovers. We note that further analysis that addresses both spatially and trade-connected spillovers, while also accounting for delayed impacts using temporal lags, would be necessary to adequately address this question fully. These approaches offer fruitful avenues for further research but are beyond the scope of this manuscript, which primarily aims to explore the impacts of different climate conditions and their persistence.

Policy implications

We find that the economic damages resulting from climate change until 2049 are those to which the world economy is already committed and that these greatly outweigh the costs required to mitigate emissions in line with the 2 °C target of the Paris Climate Agreement (Fig. 1 ). This assessment is complementary to formal analyses of the net costs and benefits associated with moving from one emission path to another, which typically find that net benefits of mitigation only emerge in the second half of the century 5 . Our simple comparison of the magnitude of damages and mitigation costs makes clear that this is primarily because damages are indistinguishable across emissions scenarios—that is, committed—until mid-century (Fig. 1 ) and that they are actually already much larger than mitigation costs. For simplicity, and owing to the availability of data, we compare damages to mitigation costs at the global level. Regional estimates of mitigation costs may shed further light on the national incentives for mitigation to which our results already hint, of relevance for international climate policy. Although these damages are committed from a mitigation perspective, adaptation may provide an opportunity to reduce them. Moreover, the strong divergence of damages after mid-century reemphasizes the clear benefits of mitigation from a purely economic perspective, as highlighted in previous studies 1 , 4 , 6 , 24 .

Historical climate data

Historical daily 2-m temperature and precipitation totals (in mm) are obtained for the period 1979–2019 from the W5E5 database. The W5E5 dataset comes from ERA-5, a state-of-the-art reanalysis of historical observations, but has been bias-adjusted by applying version 2.0 of the WATCH Forcing Data to ERA-5 reanalysis data and precipitation data from version 2.3 of the Global Precipitation Climatology Project to better reflect ground-based measurements 49 , 50 , 51 . We obtain these data on a 0.5° × 0.5° grid from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) database. Notably, these historical data have been used to bias-adjust future climate projections from CMIP-6 (see the following section), ensuring consistency between the distribution of historical daily weather on which our empirical models were estimated and the climate projections used to estimate future damages. These data are publicly available from the ISIMIP database. See refs. 7 , 8 for robustness tests of the empirical models to the choice of climate data reanalysis products.

Future climate data

Daily 2-m temperature and precipitation totals (in mm) are taken from 21 climate models participating in CMIP-6 under a high (RCP8.5) and a low (RCP2.6) greenhouse gas emission scenario from 2015 to 2100. The data have been bias-adjusted and statistically downscaled to a common half-degree grid to reflect the historical distribution of daily temperature and precipitation of the W5E5 dataset using the trend-preserving method developed by the ISIMIP 50 , 52 . As such, the climate model data reproduce observed climatological patterns exceptionally well (Supplementary Table 5 ). Gridded data are publicly available from the ISIMIP database.

Historical economic data

Historical economic data come from the DOSE database of sub-national economic output 53 . We use a recent revision to the DOSE dataset that provides data across 83 countries, 1,660 sub-national regions with varying temporal coverage from 1960 to 2019. Sub-national units constitute the first administrative division below national, for example, states for the USA and provinces for China. Data come from measures of gross regional product per capita (GRPpc) or income per capita in local currencies, reflecting the values reported in national statistical agencies, yearbooks and, in some cases, academic literature. We follow previous literature 3 , 7 , 8 , 54 and assess real sub-national output per capita by first converting values from local currencies to US dollars to account for diverging national inflationary tendencies and then account for US inflation using a US deflator. Alternatively, one might first account for national inflation and then convert between currencies. Supplementary Fig. 12 demonstrates that our conclusions are consistent when accounting for price changes in the reversed order, although the magnitude of estimated damages varies. See the documentation of the DOSE dataset for further discussion of these choices. Conversions between currencies are conducted using exchange rates from the FRED database of the Federal Reserve Bank of St. Louis 55 and the national deflators from the World Bank 56 .

Future socio-economic data

Baseline gridded gross domestic product (GDP) and population data for the period 2015–2100 are taken from the middle-of-the-road scenario SSP2 (ref. 15 ). Population data have been downscaled to a half-degree grid by the ISIMIP following the methodologies of refs. 57 , 58 , which we then aggregate to the sub-national level of our economic data using the spatial aggregation procedure described below. Because current methodologies for downscaling the GDP of the SSPs use downscaled population to do so, per-capita estimates of GDP with a realistic distribution at the sub-national level are not readily available for the SSPs. We therefore use national-level GDP per capita (GDPpc) projections for all sub-national regions of a given country, assuming homogeneity within countries in terms of baseline GDPpc. Here we use projections that have been updated to account for the impact of the COVID-19 pandemic on the trajectory of future income, while remaining consistent with the long-term development of the SSPs 59 . The choice of baseline SSP alters the magnitude of projected climate damages in monetary terms, but when assessed in terms of percentage change from the baseline, the choice of socio-economic scenario is inconsequential. Gridded SSP population data and national-level GDPpc data are publicly available from the ISIMIP database. Sub-national estimates as used in this study are available in the code and data replication files.

Climate variables

Following recent literature 3 , 7 , 8 , we calculate an array of climate variables for which substantial impacts on macroeconomic output have been identified empirically, supported by further evidence at the micro level for plausible underlying mechanisms. See refs. 7 , 8 for an extensive motivation for the use of these particular climate variables and for detailed empirical tests on the nature and robustness of their effects on economic output. To summarize, these studies have found evidence for independent impacts on economic growth rates from annual average temperature, daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall. Assessments of daily temperature variability were motivated by evidence of impacts on agricultural output and human health, as well as macroeconomic literature on the impacts of volatility on growth when manifest in different dimensions, such as government spending, exchange rates and even output itself 7 . Assessments of precipitation impacts were motivated by evidence of impacts on agricultural productivity, metropolitan labour outcomes and conflict, as well as damages caused by flash flooding 8 . See Extended Data Table 1 for detailed references to empirical studies of these physical mechanisms. Marked impacts of daily temperature variability, total annual precipitation, the number of wet days and extreme daily rainfall on macroeconomic output were identified robustly across different climate datasets, spatial aggregation schemes, specifications of regional time trends and error-clustering approaches. They were also found to be robust to the consideration of temperature extremes 7 , 8 . Furthermore, these climate variables were identified as having independent effects on economic output 7 , 8 , which we further explain here using Monte Carlo simulations to demonstrate the robustness of the results to concerns of imperfect multicollinearity between climate variables (Supplementary Methods Section 2 ), as well as by using information criteria (Supplementary Table 1 ) to demonstrate that including several lagged climate variables provides a preferable trade-off between optimally describing the data and limiting the possibility of overfitting.

We calculate these variables from the distribution of daily, d , temperature, T x , d , and precipitation, P x , d , at the grid-cell, x , level for both the historical and future climate data. As well as annual mean temperature, ${\bar{T}}_{x,y}$ , and annual total precipitation, P x , y , we calculate annual, y , measures of daily temperature variability, ${\widetilde{T}}_{x,y}$ :

the number of wet days, Pwd x , y :

and extreme daily rainfall:

in which T x , d , m , y is the grid-cell-specific daily temperature in month m and year y , ${\bar{T}}_{x,m,{y}}$ is the year and grid-cell-specific monthly, m , mean temperature, D m and D y the number of days in a given month m or year y , respectively, H the Heaviside step function, 1 mm the threshold used to define wet days and P 99.9 x is the 99.9th percentile of historical (1979–2019) daily precipitation at the grid-cell level. Units of the climate measures are degrees Celsius for annual mean temperature and daily temperature variability, millimetres for total annual precipitation and extreme daily precipitation, and simply the number of days for the annual number of wet days.

We also calculated weighted standard deviations of monthly rainfall totals as also used in ref. 8 but do not include them in our projections as we find that, when accounting for delayed effects, their effect becomes statistically indistinct and is better captured by changes in total annual rainfall.

Spatial aggregation

We aggregate grid-cell-level historical and future climate measures, as well as grid-cell-level future GDPpc and population, to the level of the first administrative unit below national level of the GADM database, using an area-weighting algorithm that estimates the portion of each grid cell falling within an administrative boundary. We use this as our baseline specification following previous findings that the effect of area or population weighting at the sub-national level is negligible 7 , 8 .

Empirical model specification: fixed-effects distributed lag models

Following a wide range of climate econometric literature 16 , 60 , we use panel regression models with a selection of fixed effects and time trends to isolate plausibly exogenous variation with which to maximize confidence in a causal interpretation of the effects of climate on economic growth rates. The use of region fixed effects, μ r , accounts for unobserved time-invariant differences between regions, such as prevailing climatic norms and growth rates owing to historical and geopolitical factors. The use of yearly fixed effects, η y , accounts for regionally invariant annual shocks to the global climate or economy such as the El Niño–Southern Oscillation or global recessions. In our baseline specification, we also include region-specific linear time trends, k r y , to exclude the possibility of spurious correlations resulting from common slow-moving trends in climate and growth.

The persistence of climate impacts on economic growth rates is a key determinant of the long-term magnitude of damages. Methods for inferring the extent of persistence in impacts on growth rates have typically used lagged climate variables to evaluate the presence of delayed effects or catch-up dynamics 2 , 18 . For example, consider starting from a model in which a climate condition, C r , y , (for example, annual mean temperature) affects the growth rate, Δlgrp r , y (the first difference of the logarithm of gross regional product) of region r in year y :

which we refer to as a ‘pure growth effects’ model in the main text. Typically, further lags are included,

and the cumulative effect of all lagged terms is evaluated to assess the extent to which climate impacts on growth rates persist. Following ref. 18 , in the case that,

the implication is that impacts on the growth rate persist up to NL years after the initial shock (possibly to a weaker or a stronger extent), whereas if

then the initial impact on the growth rate is recovered after NL years and the effect is only one on the level of output. However, we note that such approaches are limited by the fact that, when including an insufficient number of lags to detect a recovery of the growth rates, one may find equation ( 6 ) to be satisfied and incorrectly assume that a change in climatic conditions affects the growth rate indefinitely. In practice, given a limited record of historical data, including too few lags to confidently conclude in an infinitely persistent impact on the growth rate is likely, particularly over the long timescales over which future climate damages are often projected 2 , 24 . To avoid this issue, we instead begin our analysis with a model for which the level of output, lgrp r , y , depends on the level of a climate variable, C r , y :

Given the non-stationarity of the level of output, we follow the literature 19 and estimate such an equation in first-differenced form as,

which we refer to as a model of ‘pure level effects’ in the main text. This model constitutes a baseline specification in which a permanent change in the climate variable produces an instantaneous impact on the growth rate and a permanent effect only on the level of output. By including lagged variables in this specification,

we are able to test whether the impacts on the growth rate persist any further than instantaneously by evaluating whether α L > 0 are statistically significantly different from zero. Even though this framework is also limited by the possibility of including too few lags, the choice of a baseline model specification in which impacts on the growth rate do not persist means that, in the case of including too few lags, the framework reverts to the baseline specification of level effects. As such, this framework is conservative with respect to the persistence of impacts and the magnitude of future damages. It naturally avoids assumptions of infinite persistence and we are able to interpret any persistence that we identify with equation ( 9 ) as a lower bound on the extent of climate impact persistence on growth rates. See the main text for further discussion of this specification choice, in particular about its conservative nature compared with previous literature estimates, such as refs. 2 , 18 .

We allow the response to climatic changes to vary across regions, using interactions of the climate variables with historical average (1979–2019) climatic conditions reflecting heterogenous effects identified in previous work 7 , 8 . Following this previous work, the moderating variables of these interaction terms constitute the historical average of either the variable itself or of the seasonal temperature difference, ${\hat{T}}_{r}$ , or annual mean temperature, ${\bar{T}}_{r}$ , in the case of daily temperature variability 7 and extreme daily rainfall, respectively 8 .

The resulting regression equation with N and M lagged variables, respectively, reads:

in which Δlgrp r , y is the annual, regional GRPpc growth rate, measured as the first difference of the logarithm of real GRPpc, following previous work 2 , 3 , 7 , 8 , 18 , 19 . Fixed-effects regressions were run using the fixest package in R (ref. 61 ).

Estimates of the coefficients of interest α i , L are shown in Extended Data Fig. 1 for N = M = 10 lags and for our preferred choice of the number of lags in Supplementary Figs. 1 – 3 . In Extended Data Fig. 1 , errors are shown clustered at the regional level, but for the construction of damage projections, we block-bootstrap the regressions by region 1,000 times to provide a range of parameter estimates with which to sample the projection uncertainty (following refs. 2 , 31 ).

Spatial-lag model

In Supplementary Fig. 14 , we present the results from a spatial-lag model that explores the potential for climate impacts to ‘spill over’ into spatially neighbouring regions. We measure the distance between centroids of each pair of sub-national regions and construct spatial lags that take the average of the first-differenced climate variables and their interaction terms over neighbouring regions that are at distances of 0–500, 500–1,000, 1,000–1,500 and 1,500–2000 km (spatial lags, ‘SL’, 1 to 4). For simplicity, we then assess a spatial-lag model without temporal lags to assess spatial spillovers of contemporaneous climate impacts. This model takes the form:

in which SL indicates the spatial lag of each climate variable and interaction term. In Supplementary Fig. 14 , we plot the cumulative marginal effect of each climate variable at different baseline climate conditions by summing the coefficients for each climate variable and interaction term, for example, for average temperature impacts as:

These cumulative marginal effects can be regarded as the overall spatially dependent impact to an individual region given a one-unit shock to a climate variable in that region and all neighbouring regions at a given value of the moderating variable of the interaction term.

Constructing projections of economic damage from future climate change

We construct projections of future climate damages by applying the coefficients estimated in equation ( 10 ) and shown in Supplementary Tables 2 – 4 (when including only lags with statistically significant effects in specifications that limit overfitting; see Supplementary Methods Section 1 ) to projections of future climate change from the CMIP-6 models. Year-on-year changes in each primary climate variable of interest are calculated to reflect the year-to-year variations used in the empirical models. 30-year moving averages of the moderating variables of the interaction terms are calculated to reflect the long-term average of climatic conditions that were used for the moderating variables in the empirical models. By using moving averages in the projections, we account for the changing vulnerability to climate shocks based on the evolving long-term conditions (Supplementary Figs. 10 and 11 show that the results are robust to the precise choice of the window of this moving average). Although these climate variables are not differenced, the fact that the bias-adjusted climate models reproduce observed climatological patterns across regions for these moderating variables very accurately (Supplementary Table 6 ) with limited spread across models (<3%) precludes the possibility that any considerable bias or uncertainty is introduced by this methodological choice. However, we impose caps on these moderating variables at the 95th percentile at which they were observed in the historical data to prevent extrapolation of the marginal effects outside the range in which the regressions were estimated. This is a conservative choice that limits the magnitude of our damage projections.

Time series of primary climate variables and moderating climate variables are then combined with estimates of the empirical model parameters to evaluate the regression coefficients in equation ( 10 ), producing a time series of annual GRPpc growth-rate reductions for a given emission scenario, climate model and set of empirical model parameters. The resulting time series of growth-rate impacts reflects those occurring owing to future climate change. By contrast, a future scenario with no climate change would be one in which climate variables do not change (other than with random year-to-year fluctuations) and hence the time-averaged evaluation of equation ( 10 ) would be zero. Our approach therefore implicitly compares the future climate-change scenario to this no-climate-change baseline scenario.

The time series of growth-rate impacts owing to future climate change in region r and year y , δ r , y , are then added to the future baseline growth rates, π r , y (in log-diff form), obtained from the SSP2 scenario to yield trajectories of damaged GRPpc growth rates, ρ r , y . These trajectories are aggregated over time to estimate the future trajectory of GRPpc with future climate impacts:

in which GRPpc r , y =2020 is the initial log level of GRPpc. We begin damage estimates in 2020 to reflect the damages occurring since the end of the period for which we estimate the empirical models (1979–2019) and to match the timing of mitigation-cost estimates from most IAMs (see below).

For each emission scenario, this procedure is repeated 1,000 times while randomly sampling from the selection of climate models, the selection of empirical models with different numbers of lags (shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) and bootstrapped estimates of the regression parameters. The result is an ensemble of future GRPpc trajectories that reflect uncertainty from both physical climate change and the structural and sampling uncertainty of the empirical models.

Estimates of mitigation costs

We obtain IPCC estimates of the aggregate costs of emission mitigation from the AR6 Scenario Explorer and Database hosted by IIASA 23 . Specifically, we search the AR6 Scenarios Database World v1.1 for IAMs that provided estimates of global GDP and population under both a SSP2 baseline and a SSP2-RCP2.6 scenario to maintain consistency with the socio-economic and emission scenarios of the climate damage projections. We find five IAMs that provide data for these scenarios, namely, MESSAGE-GLOBIOM 1.0, REMIND-MAgPIE 1.5, AIM/GCE 2.0, GCAM 4.2 and WITCH-GLOBIOM 3.1. Of these five IAMs, we use the results only from the first three that passed the IPCC vetting procedure for reproducing historical emission and climate trajectories. We then estimate global mitigation costs as the percentage difference in global per capita GDP between the SSP2 baseline and the SSP2-RCP2.6 emission scenario. In the case of one of these IAMs, estimates of mitigation costs begin in 2020, whereas in the case of two others, mitigation costs begin in 2010. The mitigation cost estimates before 2020 in these two IAMs are mostly negligible, and our choice to begin comparison with damage estimates in 2020 is conservative with respect to the relative weight of climate damages compared with mitigation costs for these two IAMs.

Data availability

Data on economic production and ERA-5 climate data are publicly available at https://doi.org/10.5281/zenodo.4681306 (ref. 62 ) and https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 , respectively. Data on mitigation costs are publicly available at https://data.ene.iiasa.ac.at/ar6/#/downloads . Processed climate and economic data, as well as all other necessary data for reproduction of the results, are available at the public repository https://doi.org/10.5281/zenodo.10562951 (ref. 63 ).

Code availability

All code necessary for reproduction of the results is available at the public repository https://doi.org/10.5281/zenodo.10562951 (ref. 63 ).

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Acknowledgements

We gratefully acknowledge financing from the Volkswagen Foundation and the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH on behalf of the Government of the Federal Republic of Germany and Federal Ministry for Economic Cooperation and Development (BMZ).

Open access funding provided by Potsdam-Institut für Klimafolgenforschung (PIK) e.V.

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Leonie Wenz

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All authors contributed to the design of the analysis. M.K. conducted the analysis and produced the figures. All authors contributed to the interpretation and presentation of the results. M.K. and L.W. wrote the manuscript.

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Correspondence to Leonie Wenz .

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Nature thanks Xin-Zhong Liang, Chad Thackeray and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended data fig. 1 constraining the persistence of historical climate impacts on economic growth rates..

The results of a panel-based fixed-effects distributed lag model for the effects of annual mean temperature ( a ), daily temperature variability ( b ), total annual precipitation ( c ), the number of wet days ( d ) and extreme daily precipitation ( e ) on sub-national economic growth rates. Point estimates show the effects of a 1 °C or one standard deviation increase (for temperature and precipitation variables, respectively) at the lower quartile, median and upper quartile of the relevant moderating variable (green, orange and purple, respectively) at different lagged periods after the initial shock (note that these are not cumulative effects). Climate variables are used in their first-differenced form (see main text for discussion) and the moderating climate variables are the annual mean temperature, seasonal temperature difference, total annual precipitation, number of wet days and annual mean temperature, respectively, in panels a – e (see Methods for further discussion). Error bars show the 95% confidence intervals having clustered standard errors by region. The within-region R 2 , Bayesian and Akaike information criteria for the model are shown at the top of the figure. This figure shows results with ten lags for each variable to demonstrate the observed levels of persistence, but our preferred specifications remove later lags based on the statistical significance of terms shown above and the information criteria shown in Extended Data Fig. 2 . The resulting models without later lags are shown in Supplementary Figs. 1 – 3 .

Extended Data Fig. 2 Incremental lag-selection procedure using information criteria and within-region R 2 .

Starting from a panel-based fixed-effects distributed lag model estimating the effects of climate on economic growth using the real historical data (as in equation ( 4 )) with ten lags for all climate variables (as shown in Extended Data Fig. 1 ), lags are incrementally removed for one climate variable at a time. The resulting Bayesian and Akaike information criteria are shown in a – e and f – j , respectively, and the within-region R 2 and number of observations in k – o and p – t , respectively. Different rows show the results when removing lags from different climate variables, ordered from top to bottom as annual mean temperature, daily temperature variability, total annual precipitation, the number of wet days and extreme annual precipitation. Information criteria show minima at approximately four lags for precipitation variables and ten to eight for temperature variables, indicating that including these numbers of lags does not lead to overfitting. See Supplementary Table 1 for an assessment using information criteria to determine whether including further climate variables causes overfitting.

Extended Data Fig. 3 Damages in our preferred specification that provides a robust lower bound on the persistence of climate impacts on economic growth versus damages in specifications of pure growth or pure level effects.

Estimates of future damages as shown in Fig. 1 but under the emission scenario RCP8.5 for three separate empirical specifications: in orange our preferred specification, which provides an empirical lower bound on the persistence of climate impacts on economic growth rates while avoiding assumptions of infinite persistence (see main text for further discussion); in purple a specification of ‘pure growth effects’ in which the first difference of climate variables is not taken and no lagged climate variables are included (the baseline specification of ref. 2 ); and in pink a specification of ‘pure level effects’ in which the first difference of climate variables is taken but no lagged terms are included.

Extended Data Fig. 4 Climate changes in different variables as a function of historical interannual variability.

Changes in each climate variable of interest from 1979–2019 to 2035–2065 under the high-emission scenario SSP5-RCP8.5, expressed as a percentage of the historical variability of each measure. Historical variability is estimated as the standard deviation of each detrended climate variable over the period 1979–2019 during which the empirical models were identified (detrending is appropriate because of the inclusion of region-specific linear time trends in the empirical models). See Supplementary Fig. 13 for changes expressed in standard units. Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Extended Data Fig. 5 Contribution of different climate variables to overall committed damages.

a , Climate damages in 2049 when using empirical models that account for all climate variables, changes in annual mean temperature only or changes in both annual mean temperature and one other climate variable (daily temperature variability, total annual precipitation, the number of wet days and extreme daily precipitation, respectively). b , The cumulative marginal effects of an increase in annual mean temperature of 1 °C, at different baseline temperatures, estimated from empirical models including all climate variables or annual mean temperature only. Estimates and uncertainty bars represent the median and 95% confidence intervals obtained from 1,000 block-bootstrap resamples from each of three different empirical models using eight, nine or ten lags of temperature terms.

Extended Data Fig. 6 The difference in committed damages between the upper and lower quartiles of countries when ranked by GDP and cumulative historical emissions.

Quartiles are defined using a population weighting, as are the average committed damages across each quartile group. The violin plots indicate the distribution of differences between quartiles across the two extreme emission scenarios (RCP2.6 and RCP8.5) and the uncertainty sampling procedure outlined in Methods , which accounts for uncertainty arising from the choice of lags in the empirical models, uncertainty in the empirical model parameter estimates, as well as the climate model projections. Bars indicate the median, as well as the 10th and 90th percentiles and upper and lower sixths of the distribution reflecting the very likely and likely ranges following the likelihood classification adopted by the IPCC.

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Kotz, M., Levermann, A. & Wenz, L. The economic commitment of climate change. Nature 628 , 551–557 (2024). https://doi.org/10.1038/s41586-024-07219-0

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The U.S. labor market can affect ‘people who are not even here,’ research finds

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A recently published paper co-authored by Brian Cadena finds deep connections between the U.S. and Mexican economies

That the job market in Phoenix can affect a child’s education in Mexico may strain credulity, but it’s nevertheless true, according to a recent paper co-authored by Brian Cadena , a University of Colorado Boulder associate professor of economics.

People from specific regions in Mexico tend to migrate to specific regions in the United States, and when U.S. work dries up in some areas, those migrants tend to return to Mexico, Cadena and his co-authors, María Esther Caballero of American University and Brian K. Kovak of Carnegie Mellon, found.

Their paper, published in the Journal of International Economics in November, explores the U.S. labor market’s influence on the lives of people in Mexico by comparing how neighboring Mexican counties, or “municipios,” fared during the Great Recession.

Brian Cadena, a CU Boulder associate professor of economics, and his research colleagues explore the U.S. labor market’s influence on the lives of people in Mexico by comparing how neighboring Mexican counties fared during the Great Recession.

To perform their analysis, Cadena, Caballero and Kovak drew upon data from the Matrícula Consular de Alta Seguridad (MCAS), a governmental organization that issues identity cards to Mexican migrants.

Unlike either the U.S. or Mexican census, MCAS provides in-depth, granular information on migrant workers, specifying the municipios they leave and where in the United States they settle.

MCAS is a treasure trove, says Cadena. But it wasn’t long ago that researchers didn’t know how to use it. Cadena, Caballero and Kovak changed that with another paper they published in 2018, which validated the MCAS data and thereby opened up a whole range of potential research.

“This identity-card data really allowed us to drill down and make tight comparisons between municipios,” says Cadena.

The strength of networks

A key finding that emerged from the MCAS data is that people from the same municipio often move to the same cities and states in the United States. “People follow their networks,” says Cadena. And these networks are so strong that migrants from nearby municipios often end up hundreds of miles apart in the States.

Migrants from the municipio of Dolores Hidalgo, for example, tend to move to Texas, while those from nearby Jaral del Progreso generally relocate to Chicago, California and the Southwest. Same region in Mexico, different time zones in the United States.

The close proximity of the municipios is important for the kind of research Cadena, Caballero and Kovak are doing, Cadena explains, because it cuts down on confounding variables. Neighboring municipios experience the same weather, suffer the same droughts, follow the same or similar laws, etc., which means differences in their economic outcomes are likely due to something they don’t share—the job market in the cities and states where their migrants moved.

To unearth these differences, Cadena, Caballero and Kovak measured the job-market losses in the U.S. regions linked to each municipio and then compared the economic outcomes in the municipios connected to harder-hit regions to those connected to softer-hit regions.

As it happens, labor demand in Texas survived the Great Recession relatively unscathed, so the municipios of the migrants who ventured there remained stable. The American Southwest, however, suffered some major blows, and so the municipios connected to that region exhibited several changes.

(Un)expected observations

Some of those changes were unsurprising, says Cadena.

“One of the things we’re finding is how connected these two economies are," says CU Boulder researcher Brian Cadena of the United States and Mexico. On the one hand, the stark differences in what someone can earn and what the labor market looks like in one country as opposed to the other suggests that we have made the separation between those countries real and meaningful. On the other hand, we are certainly not islands.”

“When work dried up, more immigrants returned to Mexico, and fewer new immigrants came from that source community.” This then led to a fall in remittances, or money transfers from migrant workers to their families back in Mexico.

Yet Cadena, Caballero and Kovak also observed some changes they didn’t expect. One was that more women joined the Mexican workforce.

“This is called the added worker effect,” says Cadena. “When the primary earner of a household”—in this case, the migrant laborer—“loses their job, it’s a common reaction by the household to say, ‘Let’s send someone else to work.’”

Another unexpected change was a drop in school retention. “We found some suggestive evidence that a loss of jobs in the United States reduced investment in schooling in Mexico. We saw more schooling dropout, especially at transition ages, when kids move from one level of schooling to the next,” says Cadena.

Blurred lines and better choices

What do these findings suggest about the perceived separation between these two countries and their economies?

It makes that separation “a little fuzzier,” says Cadena.

“One of the things we’re finding is how connected these two economies are. On the one hand, the stark differences in what someone can earn and what the labor market looks like in one country as opposed to the other suggests that we have made the separation between those countries real and meaningful. On the other hand, we are certainly not islands.”

Realizing this, Cadena believes, could inform policymaking, specifically regarding immigration.

“When we’re thinking about immigration policy—when we’re thinking about all these things that affect the low-wage labor market—we are making policy that has a real and noticeable effect on the lives of people who are not even here,” he says.

“I’m not a politician, but I think that a more holistic sense of all the impacts of the choices we make as a country could help us make better choices.”

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100+ Research Vocabulary Words & Phrases

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2. Study Language Patterns of Similarly Published Works

3. Use More Active and Precise Verbs

A. Describing the scope of a current project or prior research

Research Paper – Structure, Examples and Writing Guide

Research Paper

Structure of Research Paper

Introduction

Literature Review

How to Write Research Paper

Research Paper Example

Applications of Research Paper

When to Write Research Paper

Purpose of Research Paper

Characteristics of Research Paper

Advantages of Research Paper

Limitations of Research Paper

About the author

Muhammad Hassan

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Regions & Countries

What It Means To Be Asian in America

Table of Contents

Publications from the Being Asian in America project

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