words in research article

50 Useful Academic Words & Phrases for Research

Like all good writing, writing an academic paper takes a certain level of skill to express your ideas and arguments in a way that is natural and that meets a level of academic sophistication. The terms, expressions, and phrases you use in your research paper must be of an appropriate level to be submitted to academic journals.

Therefore, authors need to know which verbs , nouns , and phrases to apply to create a paper that is not only easy to understand, but which conveys an understanding of academic conventions. Using the correct terminology and usage shows journal editors and fellow researchers that you are a competent writer and thinker, while using non-academic language might make them question your writing ability, as well as your critical reasoning skills.

What are academic words and phrases?

One way to understand what constitutes good academic writing is to read a lot of published research to find patterns of usage in different contexts. However, it may take an author countless hours of reading and might not be the most helpful advice when faced with an upcoming deadline on a manuscript draft.

Briefly, “academic” language includes terms, phrases, expressions, transitions, and sometimes symbols and abbreviations that help the pieces of an academic text fit together. When writing an academic text–whether it is a book report, annotated bibliography, research paper, research poster, lab report, research proposal, thesis, or manuscript for publication–authors must follow academic writing conventions. You can often find handy academic writing tips and guidelines by consulting the style manual of the text you are writing (i.e., APA Style , MLA Style , or Chicago Style ).

However, sometimes it can be helpful to have a list of academic words and expressions like the ones in this article to use as a “cheat sheet” for substituting the better term in a given context.

How to Choose the Best Academic Terms

You can think of writing “academically” as writing in a way that conveys one’s meaning effectively but concisely. For instance, while the term “take a look at” is a perfectly fine way to express an action in everyday English, a term like “analyze” would certainly be more suitable in most academic contexts. It takes up fewer words on the page and is used much more often in published academic papers.

You can use one handy guideline when choosing the most academic term: When faced with a choice between two different terms, use the Latinate version of the term. Here is a brief list of common verbs versus their academic counterparts:

Although this can be a useful tip to help academic authors, it can be difficult to memorize dozens of Latinate verbs. Using an AI paraphrasing tool or proofreading tool can help you instantly find more appropriate academic terms, so consider using such revision tools while you draft to improve your writing.

Top 50 Words and Phrases for Different Sections in a Research Paper

The “Latinate verb rule” is just one tool in your arsenal of academic writing, and there are many more out there. But to make the process of finding academic language a bit easier for you, we have compiled a list of 50 vital academic words and phrases, divided into specific categories and use cases, each with an explanation and contextual example.

Best Words and Phrases to use in an Introduction section

1. historically.

An adverb used to indicate a time perspective, especially when describing the background of a given topic.

2. In recent years

A temporal marker emphasizing recent developments, often used at the very beginning of your Introduction section.

3. It is widely acknowledged that

A “form phrase” indicating a broad consensus among researchers and/or the general public. Often used in the literature review section to build upon a foundation of established scientific knowledge.

4. There has been growing interest in

Highlights increasing attention to a topic and tells the reader why your study might be important to this field of research.

5. Preliminary observations indicate

Shares early insights or findings while hedging on making any definitive conclusions. Modal verbs like may , might , and could are often used with this expression.

6. This study aims to

Describes the goal of the research and is a form phrase very often used in the research objective or even the hypothesis of a research paper .

7. Despite its significance

Highlights the importance of a matter that might be overlooked. It is also frequently used in the rationale of the study section to show how your study’s aim and scope build on previous studies.

8. While numerous studies have focused on

Indicates the existing body of work on a topic while pointing to the shortcomings of certain aspects of that research. Helps focus the reader on the question, “What is missing from our knowledge of this topic?” This is often used alongside the statement of the problem in research papers.

9. The purpose of this research is

A form phrase that directly states the aim of the study.

10. The question arises (about/whether)

Poses a query or research problem statement for the reader to acknowledge.

Best Words and Phrases for Clarifying Information

11. in other words.

Introduces a synopsis or the rephrasing of a statement for clarity. This is often used in the Discussion section statement to explain the implications of the study .

12. That is to say

Provides clarification, similar to “in other words.”

13. To put it simply

Simplifies a complex idea, often for a more general readership.

14. To clarify

Specifically indicates to the reader a direct elaboration of a previous point.

15. More specifically

Narrows down a general statement from a broader one. Often used in the Discussion section to clarify the meaning of a specific result.

16. To elaborate

Expands on a point made previously.

17. In detail

Indicates a deeper dive into information.

Points out specifics. Similar meaning to “specifically” or “especially.”

19. This means that

Explains implications and/or interprets the meaning of the Results section .

20. Moreover

Expands a prior point to a broader one that shows the greater context or wider argument.

Best Words and Phrases for Giving Examples

21. for instance.

Provides a specific case that fits into the point being made.

22. As an illustration

Demonstrates a point in full or in part.

23. To illustrate

Shows a clear picture of the point being made.

24. For example

Presents a particular instance. Same meaning as “for instance.”

25. Such as

Lists specifics that comprise a broader category or assertion being made.

26. Including

Offers examples as part of a larger list.

27. Notably

Adverb highlighting an important example. Similar meaning to “especially.”

28. Especially

Adverb that emphasizes a significant instance.

29. In particular

Draws attention to a specific point.

30. To name a few

Indicates examples than previously mentioned are about to be named.

Best Words and Phrases for Comparing and Contrasting

31. however.

Introduces a contrasting idea.

32. On the other hand

Highlights an alternative view or fact.

33. Conversely

Indicates an opposing or reversed idea to the one just mentioned.

34. Similarly

Shows likeness or parallels between two ideas, objects, or situations.

35. Likewise

Indicates agreement with a previous point.

36. In contrast

Draws a distinction between two points.

37. Nevertheless

Introduces a contrasting point, despite what has been said.

38. Whereas

Compares two distinct entities or ideas.

Indicates a contrast between two points.

Signals an unexpected contrast.

Best Words and Phrases to use in a Conclusion section

41. in conclusion.

Signifies the beginning of the closing argument.

42. To sum up

Offers a brief summary.

43. In summary

Signals a concise recap.

44. Ultimately

Reflects the final or main point.

45. Overall

Gives a general concluding statement.

Indicates a resulting conclusion.

Demonstrates a logical conclusion.

48. Therefore

Connects a cause and its effect.

49. It can be concluded that

Clearly states a conclusion derived from the data.

50. Taking everything into consideration

Reflects on all the discussed points before concluding.

Edit Your Research Terms and Phrases Before Submission

Using these phrases in the proper places in your research papers can enhance the clarity, flow, and persuasiveness of your writing, especially in the Introduction section and Discussion section, which together make up the majority of your paper’s text in most academic domains.

However, it's vital to ensure each phrase is contextually appropriate to avoid redundancy or misinterpretation. As mentioned at the top of this article, the best way to do this is to 1) use an AI text editor , free AI paraphrasing tool or AI proofreading tool while you draft to enhance your writing, and 2) consult a professional proofreading service like Wordvice, which has human editors well versed in the terminology and conventions of the specific subject area of your academic documents.

For more detailed information on using AI tools to write a research paper and the best AI tools for research , check out the Wordvice AI Blog .

• Link to facebook
• Link to linkedin
• Link to twitter
• Link to youtube
• Writing Tips

How to Pick the Best Keywords for a Journal Article

3-minute read

• 27th October 2019

Once you’ve written an academic journal article, you may need to pick some keywords before submitting it for publication. These will help people find your work, so read our guide on how to pick keywords for a journal article.

Why Do I Need to Pick Keywords for My Article?

Academic publishers usually ask authors to pick a few keywords whenever they submit a paper. These ‘keywords’ are terms relevant to your article that people can search for on a  journal database .

Along with your title and  abstract , these keywords will impact how many people find, read, and cite your paper. It is therefore vital to give this issue a little thought. But how should you pick keywords for a journal article?

5 Tips on How to Select Keywords

To find the best keywords for a journal article, follow these tips.

1. Use the Publisher’s Guidelines

Check whether the journal’s publisher has any guidelines on how to select keywords. At the very least, they should provide advice on how many keywords are required (usually five to eight). These guidelines are often part of the author instructions, along with advice on writing style and formatting.

2. Focus on the Main Topic of Your Research

Use the main topic of your paper to guide keyword selection. For instance, if your paper is about the medical usage of nanotechnology, your keywords would include terms like “nanomedicine” and “nanopharmaceutics.” It can also help to consider what your target reader is likely to search for in a database.

3. Don’t Duplicate Words from Your Title

The   title of your paper is important partly because it will register on search engines. And since you can only pick a few keywords for your article, you should avoid duplicating any terminology already used in the paper’s title.

Find this useful?

Subscribe to our newsletter and get writing tips from our editors straight to your inbox.

4. Be Specific

Try to be as specific as possible. This may include using “key phrases” of two or three words, since single-word terms can be too ambiguous.

For instance, whereas “nanotechnology” would bring up every paper related to nanotechnology in any way, searching for a key phrase like “cancer nanotechnology” would only find papers specifically about cancer AND nanotechnology. Using specific keywords will therefore help readers with a specific interest in your subject area to find your paper.

5. Test Your Keywords

Finally, once you have selected some keywords, enter them into a relevant journal database. If they bring up articles on topics similar to your own, then you’ve selected good keywords. If not, you may need to try again.

In addition, if you’re struggling to come up with keywords that work, you can search for some articles that cover a similar subject. You can then check their keywords and use these to guide your own choices.

Summary: How to Select Keywords for a Journal Article

In summary, when selecting keywords for a journal article you should always:

• Follow the publisher’s guidelines for selecting keywords.
• Focus on terms related to the main topic of your research.
• Avoid duplicating words used in your title.
• Be specific and use multi-word “key phrases” where possible.
• Test your keywords on a relevant journal database.

Good luck! And let us know if you need help  proofreading your article .

Share this article:

Post A New Comment

Got content that needs a quick turnaround? Let us polish your work. Explore our editorial business services.

4-minute read

The Benefits of Using an Online Proofreading Service

Proofreading is important to ensure your writing is clear and concise for your readers. Whether...

2-minute read

6 Online AI Presentation Maker Tools

Creating presentations can be time-consuming and frustrating. Trying to construct a visually appealing and informative...

What Is Market Research?

No matter your industry, conducting market research helps you keep up to date with shifting...

8 Press Release Distribution Services for Your Business

In a world where you need to stand out, press releases are key to being...

How to Get a Patent

In the United States, the US Patent and Trademarks Office issues patents. In the United...

The 5 Best Ecommerce Website Design Tools 

A visually appealing and user-friendly website is essential for success in today’s competitive ecommerce landscape....

Make sure your writing is the best it can be with our expert English proofreading and editing.

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

Glossary of research terms.

• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• 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

This glossary is intended to assist you in understanding commonly used terms and concepts when reading, interpreting, and evaluating scholarly research. Also included are common words and phrases defined within the context of how they apply to research in the social and behavioral sciences.

• Acculturation -- refers to the process of adapting to another culture, particularly in reference to blending in with the majority population [e.g., an immigrant adopting American customs]. However, acculturation also implies that both cultures add something to one another, but still remain distinct groups unto themselves.
• Accuracy -- a term used in survey research to refer to the match between the target population and the sample.
• Affective Measures -- procedures or devices used to obtain quantified descriptions of an individual's feelings, emotional states, or dispositions.
• Aggregate -- a total created from smaller units. For instance, the population of a county is an aggregate of the populations of the cities, rural areas, etc. that comprise the county. As a verb, it refers to total data from smaller units into a large unit.
• Anonymity -- a research condition in which no one, including the researcher, knows the identities of research participants.
• Baseline -- a control measurement carried out before an experimental treatment.
• Behaviorism -- school of psychological thought concerned with the observable, tangible, objective facts of behavior, rather than with subjective phenomena such as thoughts, emotions, or impulses. Contemporary behaviorism also emphasizes the study of mental states such as feelings and fantasies to the extent that they can be directly observed and measured.
• Beliefs -- ideas, doctrines, tenets, etc. that are accepted as true on grounds which are not immediately susceptible to rigorous proof.
• Benchmarking -- systematically measuring and comparing the operations and outcomes of organizations, systems, processes, etc., against agreed upon "best-in-class" frames of reference.
• Bias -- a loss of balance and accuracy in the use of research methods. It can appear in research via the sampling frame, random sampling, or non-response. It can also occur at other stages in research, such as while interviewing, in the design of questions, or in the way data are analyzed and presented. Bias means that the research findings will not be representative of, or generalizable to, a wider population.
• Case Study -- the collection and presentation of detailed information about a particular participant or small group, frequently including data derived from the subjects themselves.
• Causal Hypothesis -- a statement hypothesizing that the independent variable affects the dependent variable in some way.
• Causal Relationship -- the relationship established that shows that an independent variable, and nothing else, causes a change in a dependent variable. It also establishes how much of a change is shown in the dependent variable.
• Causality -- the relation between cause and effect.
• Central Tendency -- any way of describing or characterizing typical, average, or common values in some distribution.
• Chi-square Analysis -- a common non-parametric statistical test which compares an expected proportion or ratio to an actual proportion or ratio.
• Claim -- a statement, similar to a hypothesis, which is made in response to the research question and that is affirmed with evidence based on research.
• Classification -- ordering of related phenomena into categories, groups, or systems according to characteristics or attributes.
• Cluster Analysis -- a method of statistical analysis where data that share a common trait are grouped together. The data is collected in a way that allows the data collector to group data according to certain characteristics.
• Cohort Analysis -- group by group analytic treatment of individuals having a statistical factor in common to each group. Group members share a particular characteristic [e.g., born in a given year] or a common experience [e.g., entering a college at a given time].
• Confidentiality -- a research condition in which no one except the researcher(s) knows the identities of the participants in a study. It refers to the treatment of information that a participant has disclosed to the researcher in a relationship of trust and with the expectation that it will not be revealed to others in ways that violate the original consent agreement, unless permission is granted by the participant.
• Confirmability Objectivity -- the findings of the study could be confirmed by another person conducting the same study.
• Construct -- refers to any of the following: something that exists theoretically but is not directly observable; a concept developed [constructed] for describing relations among phenomena or for other research purposes; or, a theoretical definition in which concepts are defined in terms of other concepts. For example, intelligence cannot be directly observed or measured; it is a construct.
• Construct Validity -- seeks an agreement between a theoretical concept and a specific measuring device, such as observation.
• Constructivism -- the idea that reality is socially constructed. It is the view that reality cannot be understood outside of the way humans interact and that the idea that knowledge is constructed, not discovered. Constructivists believe that learning is more active and self-directed than either behaviorism or cognitive theory would postulate.
• Content Analysis -- the systematic, objective, and quantitative description of the manifest or latent content of print or nonprint communications.
• Context Sensitivity -- awareness by a qualitative researcher of factors such as values and beliefs that influence cultural behaviors.
• Control Group -- the group in an experimental design that receives either no treatment or a different treatment from the experimental group. This group can thus be compared to the experimental group.
• Controlled Experiment -- an experimental design with two or more randomly selected groups [an experimental group and control group] in which the researcher controls or introduces the independent variable and measures the dependent variable at least two times [pre- and post-test measurements].
• Correlation -- a common statistical analysis, usually abbreviated as r, that measures the degree of relationship between pairs of interval variables in a sample. The range of correlation is from -1.00 to zero to +1.00. Also, a non-cause and effect relationship between two variables.
• Covariate -- a product of the correlation of two related variables times their standard deviations. Used in true experiments to measure the difference of treatment between them.
• Credibility -- a researcher's ability to demonstrate that the object of a study is accurately identified and described based on the way in which the study was conducted.
• Critical Theory -- an evaluative approach to social science research, associated with Germany's neo-Marxist “Frankfurt School,” that aims to criticize as well as analyze society, opposing the political orthodoxy of modern communism. Its goal is to promote human emancipatory forces and to expose ideas and systems that impede them.
• Data -- factual information [as measurements or statistics] used as a basis for reasoning, discussion, or calculation.
• Data Mining -- the process of analyzing data from different perspectives and summarizing it into useful information, often to discover patterns and/or systematic relationships among variables.
• Data Quality -- this is the degree to which the collected data [results of measurement or observation] meet the standards of quality to be considered valid [trustworthy] and  reliable [dependable].
• Deductive -- a form of reasoning in which conclusions are formulated about particulars from general or universal premises.
• Dependability -- being able to account for changes in the design of the study and the changing conditions surrounding what was studied.
• Dependent Variable -- a variable that varies due, at least in part, to the impact of the independent variable. In other words, its value “depends” on the value of the independent variable. For example, in the variables “gender” and “academic major,” academic major is the dependent variable, meaning that your major cannot determine whether you are male or female, but your gender might indirectly lead you to favor one major over another.
• Deviation -- the distance between the mean and a particular data point in a given distribution.
• Discourse Community -- a community of scholars and researchers in a given field who respond to and communicate to each other through published articles in the community's journals and presentations at conventions. All members of the discourse community adhere to certain conventions for the presentation of their theories and research.
• Discrete Variable -- a variable that is measured solely in whole units, such as, gender and number of siblings.
• Distribution -- the range of values of a particular variable.
• Effect Size -- the amount of change in a dependent variable that can be attributed to manipulations of the independent variable. A large effect size exists when the value of the dependent variable is strongly influenced by the independent variable. It is the mean difference on a variable between experimental and control groups divided by the standard deviation on that variable of the pooled groups or of the control group alone.
• Emancipatory Research -- research is conducted on and with people from marginalized groups or communities. It is led by a researcher or research team who is either an indigenous or external insider; is interpreted within intellectual frameworks of that group; and, is conducted largely for the purpose of empowering members of that community and improving services for them. It also engages members of the community as co-constructors or validators of knowledge.
• Empirical Research -- the process of developing systematized knowledge gained from observations that are formulated to support insights and generalizations about the phenomena being researched.
• Epistemology -- concerns knowledge construction; asks what constitutes knowledge and how knowledge is validated.
• Ethnography -- method to study groups and/or cultures over a period of time. The goal of this type of research is to comprehend the particular group/culture through immersion into the culture or group. Research is completed through various methods but, since the researcher is immersed within the group for an extended period of time, more detailed information is usually collected during the research.
• Expectancy Effect -- any unconscious or conscious cues that convey to the participant in a study how the researcher wants them to respond. Expecting someone to behave in a particular way has been shown to promote the expected behavior. Expectancy effects can be minimized by using standardized interactions with subjects, automated data-gathering methods, and double blind protocols.
• External Validity -- the extent to which the results of a study are generalizable or transferable.
• Factor Analysis -- a statistical test that explores relationships among data. The test explores which variables in a data set are most related to each other. In a carefully constructed survey, for example, factor analysis can yield information on patterns of responses, not simply data on a single response. Larger tendencies may then be interpreted, indicating behavior trends rather than simply responses to specific questions.
• Field Studies -- academic or other investigative studies undertaken in a natural setting, rather than in laboratories, classrooms, or other structured environments.
• Focus Groups -- small, roundtable discussion groups charged with examining specific topics or problems, including possible options or solutions. Focus groups usually consist of 4-12 participants, guided by moderators to keep the discussion flowing and to collect and report the results.
• Framework -- the structure and support that may be used as both the launching point and the on-going guidelines for investigating a research problem.
• Generalizability -- the extent to which research findings and conclusions conducted on a specific study to groups or situations can be applied to the population at large.
• Grey Literature -- research produced by organizations outside of commercial and academic publishing that publish materials, such as, working papers, research reports, and briefing papers.
• Grounded Theory -- practice of developing other theories that emerge from observing a group. Theories are grounded in the group's observable experiences, but researchers add their own insight into why those experiences exist.
• Group Behavior -- behaviors of a group as a whole, as well as the behavior of an individual as influenced by his or her membership in a group.
• Hypothesis -- a tentative explanation based on theory to predict a causal relationship between variables.
• Independent Variable -- the conditions of an experiment that are systematically manipulated by the researcher. A variable that is not impacted by the dependent variable, and that itself impacts the dependent variable. In the earlier example of "gender" and "academic major," (see Dependent Variable) gender is the independent variable.
• Individualism -- a theory or policy having primary regard for the liberty, rights, or independent actions of individuals.
• Inductive -- a form of reasoning in which a generalized conclusion is formulated from particular instances.
• Inductive Analysis -- a form of analysis based on inductive reasoning; a researcher using inductive analysis starts with answers, but formulates questions throughout the research process.
• Insiderness -- a concept in qualitative research that refers to the degree to which a researcher has access to and an understanding of persons, places, or things within a group or community based on being a member of that group or community.
• Internal Consistency -- the extent to which all questions or items assess the same characteristic, skill, or quality.
• Internal Validity -- the rigor with which the study was conducted [e.g., the study's design, the care taken to conduct measurements, and decisions concerning what was and was not measured]. It is also the extent to which the designers of a study have taken into account alternative explanations for any causal relationships they explore. In studies that do not explore causal relationships, only the first of these definitions should be considered when assessing internal validity.
• Life History -- a record of an event/events in a respondent's life told [written down, but increasingly audio or video recorded] by the respondent from his/her own perspective in his/her own words. A life history is different from a "research story" in that it covers a longer time span, perhaps a complete life, or a significant period in a life.
• Margin of Error -- the permittable or acceptable deviation from the target or a specific value. The allowance for slight error or miscalculation or changing circumstances in a study.
• Measurement -- process of obtaining a numerical description of the extent to which persons, organizations, or things possess specified characteristics.
• Meta-Analysis -- an analysis combining the results of several studies that address a set of related hypotheses.
• Methodology -- a theory or analysis of how research does and should proceed.
• Methods -- systematic approaches to the conduct of an operation or process. It includes steps of procedure, application of techniques, systems of reasoning or analysis, and the modes of inquiry employed by a discipline.
• Mixed-Methods -- a research approach that uses two or more methods from both the quantitative and qualitative research categories. It is also referred to as blended methods, combined methods, or methodological triangulation.
• Modeling -- the creation of a physical or computer analogy to understand a particular phenomenon. Modeling helps in estimating the relative magnitude of various factors involved in a phenomenon. A successful model can be shown to account for unexpected behavior that has been observed, to predict certain behaviors, which can then be tested experimentally, and to demonstrate that a given theory cannot account for certain phenomenon.
• Models -- representations of objects, principles, processes, or ideas often used for imitation or emulation.
• Naturalistic Observation -- observation of behaviors and events in natural settings without experimental manipulation or other forms of interference.
• Norm -- the norm in statistics is the average or usual performance. For example, students usually complete their high school graduation requirements when they are 18 years old. Even though some students graduate when they are younger or older, the norm is that any given student will graduate when he or she is 18 years old.
• Null Hypothesis -- the proposition, to be tested statistically, that the experimental intervention has "no effect," meaning that the treatment and control groups will not differ as a result of the intervention. Investigators usually hope that the data will demonstrate some effect from the intervention, thus allowing the investigator to reject the null hypothesis.
• Ontology -- a discipline of philosophy that explores the science of what is, the kinds and structures of objects, properties, events, processes, and relations in every area of reality.
• Panel Study -- a longitudinal study in which a group of individuals is interviewed at intervals over a period of time.
• Participant -- individuals whose physiological and/or behavioral characteristics and responses are the object of study in a research project.
• Peer-Review -- the process in which the author of a book, article, or other type of publication submits his or her work to experts in the field for critical evaluation, usually prior to publication. This is standard procedure in publishing scholarly research.
• Phenomenology -- a qualitative research approach concerned with understanding certain group behaviors from that group's point of view.
• Philosophy -- critical examination of the grounds for fundamental beliefs and analysis of the basic concepts, doctrines, or practices that express such beliefs.
• Phonology -- the study of the ways in which speech sounds form systems and patterns in language.
• Policy -- governing principles that serve as guidelines or rules for decision making and action in a given area.
• Policy Analysis -- systematic study of the nature, rationale, cost, impact, effectiveness, implications, etc., of existing or alternative policies, using the theories and methodologies of relevant social science disciplines.
• Population -- the target group under investigation. The population is the entire set under consideration. Samples are drawn from populations.
• Position Papers -- statements of official or organizational viewpoints, often recommending a particular course of action or response to a situation.
• Positivism -- a doctrine in the philosophy of science, positivism argues that science can only deal with observable entities known directly to experience. The positivist aims to construct general laws, or theories, which express relationships between phenomena. Observation and experiment is used to show whether the phenomena fit the theory.
• Predictive Measurement -- use of tests, inventories, or other measures to determine or estimate future events, conditions, outcomes, or trends.
• Principal Investigator -- the scientist or scholar with primary responsibility for the design and conduct of a research project.
• Probability -- the chance that a phenomenon will occur randomly. As a statistical measure, it is shown as p [the "p" factor].
• Questionnaire -- structured sets of questions on specified subjects that are used to gather information, attitudes, or opinions.
• Random Sampling -- a process used in research to draw a sample of a population strictly by chance, yielding no discernible pattern beyond chance. Random sampling can be accomplished by first numbering the population, then selecting the sample according to a table of random numbers or using a random-number computer generator. The sample is said to be random because there is no regular or discernible pattern or order. Random sample selection is used under the assumption that sufficiently large samples assigned randomly will exhibit a distribution comparable to that of the population from which the sample is drawn. The random assignment of participants increases the probability that differences observed between participant groups are the result of the experimental intervention.
• Reliability -- the degree to which a measure yields consistent results. If the measuring instrument [e.g., survey] is reliable, then administering it to similar groups would yield similar results. Reliability is a prerequisite for validity. An unreliable indicator cannot produce trustworthy results.
• Representative Sample -- sample in which the participants closely match the characteristics of the population, and thus, all segments of the population are represented in the sample. A representative sample allows results to be generalized from the sample to the population.
• Rigor -- degree to which research methods are scrupulously and meticulously carried out in order to recognize important influences occurring in an experimental study.
• Sample -- the population researched in a particular study. Usually, attempts are made to select a "sample population" that is considered representative of groups of people to whom results will be generalized or transferred. In studies that use inferential statistics to analyze results or which are designed to be generalizable, sample size is critical, generally the larger the number in the sample, the higher the likelihood of a representative distribution of the population.
• Sampling Error -- the degree to which the results from the sample deviate from those that would be obtained from the entire population, because of random error in the selection of respondent and the corresponding reduction in reliability.
• Saturation -- a situation in which data analysis begins to reveal repetition and redundancy and when new data tend to confirm existing findings rather than expand upon them.
• Semantics -- the relationship between symbols and meaning in a linguistic system. Also, the cuing system that connects what is written in the text to what is stored in the reader's prior knowledge.
• Social Theories -- theories about the structure, organization, and functioning of human societies.
• Sociolinguistics -- the study of language in society and, more specifically, the study of language varieties, their functions, and their speakers.
• Standard Deviation -- a measure of variation that indicates the typical distance between the scores of a distribution and the mean; it is determined by taking the square root of the average of the squared deviations in a given distribution. It can be used to indicate the proportion of data within certain ranges of scale values when the distribution conforms closely to the normal curve.
• Statistical Analysis -- application of statistical processes and theory to the compilation, presentation, discussion, and interpretation of numerical data.
• Statistical Bias -- characteristics of an experimental or sampling design, or the mathematical treatment of data, that systematically affects the results of a study so as to produce incorrect, unjustified, or inappropriate inferences or conclusions.
• Statistical Significance -- the probability that the difference between the outcomes of the control and experimental group are great enough that it is unlikely due solely to chance. The probability that the null hypothesis can be rejected at a predetermined significance level [0.05 or 0.01].
• Statistical Tests -- researchers use statistical tests to make quantitative decisions about whether a study's data indicate a significant effect from the intervention and allow the researcher to reject the null hypothesis. That is, statistical tests show whether the differences between the outcomes of the control and experimental groups are great enough to be statistically significant. If differences are found to be statistically significant, it means that the probability [likelihood] that these differences occurred solely due to chance is relatively low. Most researchers agree that a significance value of .05 or less [i.e., there is a 95% probability that the differences are real] sufficiently determines significance.
• Subcultures -- ethnic, regional, economic, or social groups exhibiting characteristic patterns of behavior sufficient to distinguish them from the larger society to which they belong.
• Testing -- the act of gathering and processing information about individuals' ability, skill, understanding, or knowledge under controlled conditions.
• Theory -- a general explanation about a specific behavior or set of events that is based on known principles and serves to organize related events in a meaningful way. A theory is not as specific as a hypothesis.
• Treatment -- the stimulus given to a dependent variable.
• Trend Samples -- method of sampling different groups of people at different points in time from the same population.
• Triangulation -- a multi-method or pluralistic approach, using different methods in order to focus on the research topic from different viewpoints and to produce a multi-faceted set of data. Also used to check the validity of findings from any one method.
• Unit of Analysis -- the basic observable entity or phenomenon being analyzed by a study and for which data are collected in the form of variables.
• Validity -- the degree to which a study accurately reflects or assesses the specific concept that the researcher is attempting to measure. A method can be reliable, consistently measuring the same thing, but not valid.
• Variable -- any characteristic or trait that can vary from one person to another [race, gender, academic major] or for one person over time [age, political beliefs].
• Weighted Scores -- scores in which the components are modified by different multipliers to reflect their relative importance.
• White Paper -- an authoritative report that often states the position or philosophy about a social, political, or other subject, or a general explanation of an architecture, framework, or product technology written by a group of researchers. A white paper seeks to contain unbiased information and analysis regarding a business or policy problem that the researchers may be facing.

Elliot, Mark, Fairweather, Ian, Olsen, Wendy Kay, and Pampaka, Maria. A Dictionary of Social Research Methods. Oxford, UK: Oxford University Press, 2016; Free Social Science Dictionary. Socialsciencedictionary.com [2008]. Glossary. Institutional Review Board. Colorado College; Glossary of Key Terms. Writing@CSU. Colorado State University; Glossary A-Z. Education.com; Glossary of Research Terms. Research Mindedness Virtual Learning Resource. Centre for Human Servive Technology. University of Southampton; Miller, Robert L. and Brewer, John D. The A-Z of Social Research: A Dictionary of Key Social Science Research Concepts London: SAGE, 2003; Jupp, Victor. The SAGE Dictionary of Social and Cultural Research Methods . London: Sage, 2006.

• << Previous: Independent and Dependent Variables
• Next: 1. Choosing a Research Problem >>
• Last Updated: Apr 1, 2024 9:56 AM
• URL: https://libguides.usc.edu/writingguide

Evaluating Information

• Understanding Primary and Secondary Sources
• Exploring and Evaluating Popular, Trade, and Scholarly Sources

Reading a Scholarly Article

Common components of original research articles, while you read, reading strategies, reading for citations, further reading, learning objectives.

This page was created to help you:

Identify the different parts of a scholarly article

Efficiently analyze and evaluate scholarly articles for usefulness

This page will focus on reading scholarly articles — published reports on original research in the social sciences, humanities, and STEM fields. Reading and understanding this type of article can be challenging. This guide will help you develop these skills, which can be learned and improved upon with practice.

We will go over:

There are many different types of articles that may be found in scholarly journals and other academic publications. For more, see:

• Types of Information Sources

Reading a scholarly article isn’t like reading a novel, website, or newspaper article. It’s likely you won’t read and absorb it from beginning to end, all at once.

Instead, think of scholarly reading as inquiry, i.e., asking a series of questions as you do your research or read for class. Your reading should be guided by your class topic or your own research question or thesis.

For example, as you read, you might ask yourself:

• What questions does it help to answer, or what topics does it address?
• Are these relevant or useful to me?
• Does the article offer a helpful framework for understanding my topic or question (theoretical framework)?
• Do the authors use interesting or innovative methods to conduct their research that might be relevant to me?
• Does the article contain references I might consult for further information?

In Practice

Scanning and skimming are essential when reading scholarly articles, especially at the beginning stages of your research or when you have a lot of material in front of you.

Many scholarly articles are organized to help you scan and skim efficiently. The next time you need to read an article, practice scanning the following sections (where available) and skim their contents:

• The abstract: This summary provides a birds’ eye view of the article contents.
• The introduction:  What is the topic(s) of the research article? What is its main idea or question?
• The list of keywords or descriptors
• Methods: How did the author(s) go about answering their question/collecting their data?
• Section headings:  Stop and skim those sections you may find relevant.
• Figures:  Offer lots of information in quick visual format.
• The conclusion:  What are the findings and/or conclusions of this article?

Mark Up Your Text

Read with purpose.

• Scanning and skimming with a pen in hand can help to focus your reading.
• Use color for quick reference. Try highlighters or some sticky notes. Use different colors to represent different topics.
• Write in the margins, putting down thoughts and questions about the content as you read.
• Use digital markup features available in eBook platforms or third-party solutions, like Adobe Reader or Hypothes.is.

Categorize Information

Create your own informal system of organization. It doesn’t have to be complicated — start basic, and be sure it works for you.

• Jot down a few of your own keywords for each article. These keywords may correspond with important topics being addressed in class or in your research paper.  
• Write keywords on print copies or use the built-in note taking features in reference management tools like Zotero and EndNote.  
• Your keywords and system of organization may grow more complex the deeper you get into your reading.

Highlight words, terms, phrases, acronyms, etc. that are unfamiliar to you. You can highlight on the text or make a list in a notetaking program.

• Decide if the term is essential to your understanding of the article or if you can look it up later and keep scanning.

You may scan an article and discover that it isn’t what you thought it was about. Before you close the tab or delete that PDF, consider scanning the article one more time, specifically to look for citations that might be more on-target for your topic.  

You don’t need to look at every citation in the bibliography — you can look to the literature review to identify the core references that relate to your topic. Literature reviews are typically organized by subtopic within a research question or thesis. Find the paragraph or two that are closely aligned with your topic, make note of the author names, then locate those citations in the bibliography or footnote.

See the Find Articles page for what to do next:

• Find Articles

See the Citation Searching page for more on following a citation trail:

• Citation Searching
• Taking notes effectively. [blog post] Raul Pacheco-Vega, PhD
• How to read an academic paper. [video] UBCiSchool. 2013
• How to (seriously) read a scientific paper. (2016, March 21). Science | AAAS.
• How to read a paper. S. Keshav. 2007. SIGCOMM Comput. Commun. Rev. 37, 3 (July 2007), 83–84.

This guide was designed to help you:

• << Previous: Exploring and Evaluating Popular, Trade, and Scholarly Sources
• Last Updated: Feb 16, 2024 3:55 PM
• URL: https://libguides.brown.edu/evaluate

Brown University Library  |  Providence, RI 02912  |  (401) 863-2165  |  Contact  |  Comments  |  Library Feedback  |  Site Map

Library Intranet

WRITING A SCIENTIFIC RESEARCH ARTICLE | Format for the paper | Edit your paper! | Useful books | FORMAT FOR THE PAPER Scientific research articles provide a method for scientists to communicate with other scientists about the results of their research. A standard format is used for these articles, in which the author presents the research in an orderly, logical manner. This doesn't necessarily reflect the order in which you did or thought about the work.  This format is: | Title | Authors | Introduction | Materials and Methods | Results (with Tables and Figures ) | Discussion | Acknowledgments | Literature Cited | TITLE Make your title specific enough to describe the contents of the paper, but not so technical that only specialists will understand. The title should be appropriate for the intended audience. The title usually describes the subject matter of the article: Effect of Smoking on Academic Performance" Sometimes a title that summarizes the results is more effective: Students Who Smoke Get Lower Grades" AUTHORS 1. The person who did the work and wrote the paper is generally listed as the first author of a research paper. 2. For published articles, other people who made substantial contributions to the work are also listed as authors. Ask your mentor's permission before including his/her name as co-author. ABSTRACT 1. An abstract, or summary, is published together with a research article, giving the reader a "preview" of what's to come. Such abstracts may also be published separately in bibliographical sources, such as Biologic al Abstracts. They allow other scientists to quickly scan the large scientific literature, and decide which articles they want to read in depth. The abstract should be a little less technical than the article itself; you don't want to dissuade your potent ial audience from reading your paper. 2. Your abstract should be one paragraph, of 100-250 words, which summarizes the purpose, methods, results and conclusions of the paper. 3. It is not easy to include all this information in just a few words. Start by writing a summary that includes whatever you think is important, and then gradually prune it down to size by removing unnecessary words, while still retaini ng the necessary concepts. 3. Don't use abbreviations or citations in the abstract. It should be able to stand alone without any footnotes. INTRODUCTION What question did you ask in your experiment? Why is it interesting? The introduction summarizes the relevant literature so that the reader will understand why you were interested in the question you asked. One to fo ur paragraphs should be enough. End with a sentence explaining the specific question you asked in this experiment. MATERIALS AND METHODS 1. How did you answer this question? There should be enough information here to allow another scientist to repeat your experiment. Look at other papers that have been published in your field to get some idea of what is included in this section. 2. If you had a complicated protocol, it may helpful to include a diagram, table or flowchart to explain the methods you used. 3. Do not put results in this section. You may, however, include preliminary results that were used to design the main experiment that you are reporting on. ("In a preliminary study, I observed the owls for one week, and found that 73 % of their locomotor activity occurred during the night, and so I conducted all subsequent experiments between 11 pm and 6 am.") 4. Mention relevant ethical considerations. If you used human subjects, did they consent to participate. If you used animals, what measures did you take to minimize pain? RESULTS 1. This is where you present the results you've gotten. Use graphs and tables if appropriate, but also summarize your main findings in the text. Do NOT discuss the results or speculate as to why something happened; t hat goes in th e Discussion. 2. You don't necessarily have to include all the data you've gotten during the semester. This isn't a diary. 3. Use appropriate methods of showing data. Don't try to manipulate the data to make it look like you did more than you actually did. "The drug cured 1/3 of the infected mice, another 1/3 were not affected, and the third mouse got away." TABLES AND GRAPHS 1. If you present your data in a table or graph, include a title describing what's in the table ("Enzyme activity at various temperatures", not "My results".) For graphs, you should also label the x and y axes. 2. Don't use a table or graph just to be "fancy". If you can summarize the information in one sentence, then a table or graph is not necessary. DISCUSSION 1. Highlight the most significant results, but don't just repeat what you've written in the Results section. How do these results relate to the original question? Do the data support your hypothesis? Are your results consistent with what other investigators have reported? If your results were unexpected, try to explain why. Is there another way to interpret your results? What further research would be necessary to answer the questions raised by your results? How do y our results fit into the big picture? 2. End with a one-sentence summary of your conclusion, emphasizing why it is relevant. ACKNOWLEDGMENTS This section is optional. You can thank those who either helped with the experiments, or made other important contributions, such as discussing the protocol, commenting on the manuscript, or buying you pizza. REFERENCES (LITERATURE CITED) There are several possible ways to organize this section. Here is one commonly used way: 1. In the text, cite the literature in the appropriate places: Scarlet (1990) thought that the gene was present only in yeast, but it has since been identified in the platypus (Indigo and Mauve, 1994) and wombat (Magenta, et al., 1995). 2. In the References section list citations in alphabetical order. Indigo, A. C., and Mauve, B. E. 1994. Queer place for qwerty: gene isolation from the platypus. Science 275, 1213-1214. Magenta, S. T., Sepia, X., and Turquoise, U. 1995. Wombat genetics. In: Widiculous Wombats, Violet, Q., ed. New York: Columbia University Press. p 123-145. Scarlet, S.L. 1990. Isolation of qwerty gene from S. cerevisae. Journal of Unusual Results 36, 26-31.   EDIT YOUR PAPER!!! "In my writing, I average about ten pages a day. Unfortunately, they're all the same page." Michael Alley, The Craft of Scientific Writing A major part of any writing assignment consists of re-writing. Write accurately Scientific writing must be accurate. Although writing instructors may tell you not to use the same word twice in a sentence, it's okay for scientific writing, which must be accurate. (A student who tried not to repeat the word "hamster" produced this confusing sentence: "When I put the hamster in a cage with the other animals, the little mammals began to play.") Make sure you say what you mean. Instead of: The rats were injected with the drug. (sounds like a syringe was filled with drug and ground-up rats and both were injected together) Write: I injected the drug into the rat.

• Be careful with commonly confused words:

Temperature has an effect on the reaction. Temperature affects the reaction.

I used solutions in various concentrations. (The solutions were 5 mg/ml, 10 mg/ml, and 15 mg/ml) I used solutions in varying concentrations. (The concentrations I used changed; sometimes they were 5 mg/ml, other times they were 15 mg/ml.)

 Less food (can't count numbers of food) Fewer animals (can count numbers of animals)

A large amount of food (can't count them) A large number of animals (can count them)

The erythrocytes, which are in the blood, contain hemoglobin. The erythrocytes that are in the blood contain hemoglobin. (Wrong. This sentence implies that there are erythrocytes elsewhere that don't contain hemoglobin.)

Write clearly

1. Write at a level that's appropriate for your audience.

"Like a pigeon, something to admire as long as it isn't over your head." Anonymous

 2. Use the active voice. It's clearer and more concise than the passive voice.

 Instead of: An increased appetite was manifested by the rats and an increase in body weight was measured. Write: The rats ate more and gained weight.

 3. Use the first person.

 Instead of: It is thought Write: I think

 Instead of: The samples were analyzed Write: I analyzed the samples

 4. Avoid dangling participles.

 "After incubating at 30 degrees C, we examined the petri plates." (You must've been pretty warm in there.)

  Write succinctly

 1. Use verbs instead of abstract nouns

 Instead of: take into consideration Write: consider

 2. Use strong verbs instead of "to be"

 Instead of: The enzyme was found to be the active agent in catalyzing... Write: The enzyme catalyzed...

 3. Use short words.

Instead of: Write: possess have sufficient enough utilize use demonstrate show assistance help terminate end

4. Use concise terms.

 Instead of: Write: prior to before due to the fact that because in a considerable number of cases often the vast majority of most during the time that when in close proximity to near it has long been known that I'm too lazy to look up the reference

5. Use short sentences. A sentence made of more than 40 words should probably be rewritten as two sentences.

 "The conjunction 'and' commonly serves to indicate that the writer's mind still functions even when no signs of the phenomenon are noticeable." Rudolf Virchow, 1928

Check your grammar, spelling and punctuation

1. Use a spellchecker, but be aware that they don't catch all mistakes.

 "When we consider the animal as a hole,..." Student's paper

 2. Your spellchecker may not recognize scientific terms. For the correct spelling, try Biotech's Life Science Dictionary or one of the technical dictionaries on the reference shelf in the Biology or Health Sciences libraries.

 3. Don't, use, unnecessary, commas.

 4. Proofread carefully to see if you any words out.

USEFUL BOOKS

Victoria E. McMillan, Writing Papers in the Biological Sciences , Bedford Books, Boston, 1997 The best. On sale for about $18 at Labyrinth Books, 112th Street. On reserve in Biology Library

Jan A. Pechenik, A Short Guide to Writing About Biology , Boston: Little, Brown, 1987

Harrison W. Ambrose, III & Katharine Peckham Ambrose, A Handbook of Biological Investigation , 4th edition, Hunter Textbooks Inc, Winston-Salem, 1987 Particularly useful if you need to use statistics to analyze your data. Copy on Reference shelf in Biology Library.

Robert S. Day, How to Write and Publish a Scientific Paper , 4th edition, Oryx Press, Phoenix, 1994. Earlier editions also good. A bit more advanced, intended for those writing papers for publication. Fun to read. Several copies available in Columbia libraries.

William Strunk, Jr. and E. B. White, The Elements of Style , 3rd ed. Macmillan, New York, 1987. Several copies available in Columbia libraries.  Strunk's first edition is available on-line.

• school Campus Bookshelves
• menu_book Bookshelves
• perm_media Learning Objects
• login Login
• how_to_reg Request Instructor Account
• hub Instructor Commons
• Download Page (PDF)
• Download Full Book (PDF)
• Periodic Table
• Physics Constants
• Scientific Calculator
• Reference & Cite
• Tools expand_more
• Readability

selected template will load here

This action is not available.

4.5: Word Choice and Research

• Last updated
• Save as PDF
• Page ID 170514

Diction and Research

When conducting research for any college assignment, you need to eventually put the information you collect forward in words that are comprehensible and engaging, but before then, you probably need to entrench yourself in the library’s databases. Remember that topic comes from the Greek word for place , so as a writer you are like a guide, offering vistas and perspectives of different places within your overarching topic to your reader that open up new modes of thinking, new juxtapositions and opinions. How you choose to form those words into a specific structure is determined by the writing task or tasks, the prompt , that you receive from your instructor.

When you start to do research for any paper, you need to generate keywords. Keywords are words that you use to find research that relates, or is relevant, to your topic. In the following video, observe how the researcher begins with three words that indicate a topic that they may we writing about for an argumentative paper. You can assume they began with the following research question:

What are the effects of regular fast-food consumption on long-term health?

So, how do you find the right words (the diction) to help you research this topic?

How To Generate Keywords for Research

• Step 1: Pull out the most important words from your research question(s).

In this case, we would pull out the words “effects,” “fast-food,” and “health.”

• Step 2: Generate synonyms. Use a synonym generator if you need (a thesaurus), like this one from Merriam-Webster.
• Step 3: Sort through the terms to ensure they mean what you want. Use a dictionary to find denotations and connotations that will help you in your paper-writing processes.

For example, aftermath is a word that intones the results of some event, usually cataclysmic or tragic. Longevity is not a direct synonym for health; it deals with the idea of living a long life ( long -evity). Although we may look for articles that discuss how consuming fast-food regularly affects longevity, the word has a different shade of meaning than the word health .

• Step 4: Input the keywords and begin researching! Note how most articles you find will have related terms. I think of them like librarians do when they archive new materials: like hashtags you use to generate hype about a social media post. Even Wikipedia articles provide linked concepts, artworks, and terms for most subjects—just be aware that an encyclopedia entry is not considered a scholarly, refereed, peer-reviewed research article. You can mine these resources, including the ones you like, or the ones that have more relevance to your research question(s), to find more keywords and then more resources that you can use to quote, summarize, or paraphrase and use as support in your essays, compositions, and papers.

Activity: Generate Keywords \(\PageIndex{1}\)

Directions:

• Look at the following research questions and brainstorm some (3-4) keywords or phrases you can take from each one. Try to choose the most important, or meaningful, words from each research question.
• Then, generate two synonyms or related words or phrases to use as keywords when searching for resources.
• Research question: Should jails be more rehabilitative than punitive for those with mental illnesses?
• Keywords: jails, rehabilitative, punitive, mental illness
• Synonymous or related words: prisons, confinements, rehabilitate, reform, punish, criminal, incriminate, mental health,

You can even make a chart if it helps you:

Research Question: What are the effects of the Philippines being colonized by the Spanish?

Keywords: ____________________________________________________________________

Research Question: Should people be allowed to implant microchips in their own bodies?

Research Question: Should the Reid-Hillview County Airport be closed due to the lead content within the fuel in airplanes?

Research Question: Does social media negatively impact teens?

Research Question: Can playing video games help dementia patients?

"Search Keywords Tutorial." YouTube , uploaded by Ray W. Howard Library at Shoreline Community College, 23 May 2013, youtu.be/x9diL8-ZpAk.

• SpringerLink shop

Title, Abstract and Keywords

The importance of titles.

The title of your manuscript is usually the first introduction readers (and reviewers) have to your work. Therefore, you must select a title that grabs attention, accurately describes the contents of your manuscript, and makes people want to read further.

An effective title should:

• Convey the  main topics  of the study
• Highlight the  importance  of the research
• Be  concise
• Attract  readers

Writing a good title for your manuscript can be challenging. First, list the topics covered by the manuscript. Try to put all of the topics together in the title using as few words as possible. A title that is too long will seem clumsy, annoy readers, and probably not meet journal requirements.

Does Vaccinating Children and Adolescents with Inactivated Influenza Virus Inhibit the Spread of Influenza in Unimmunized Residents of Rural Communities?

This title has too many unnecessary words.

Influenza Vaccination of Children: A Randomized Trial

This title doesn’t give enough information about what makes the manuscript interesting.

Effect of Child Influenza Vaccination on Infection Rates in Rural Communities: A Randomized Trial This is an effective title. It is short, easy to understand, and conveys the important aspects of the research.

Think about why your research will be of interest to other scientists. This should be related to the reason you decided to study the topic. If your title makes this clear, it will likely attract more readers to your manuscript. TIP: Write down a few possible titles, and then select the best to refine further. Ask your colleagues their opinion. Spending the time needed to do this will result in a better title.

Abstract and Keywords

The Abstract is:

• A  summary  of the content of the journal manuscript
• A time-saving  shortcut  for busy researchers
• A guide to the most important parts of your manuscript’s written content

Many readers will only read the Abstract of your manuscript. Therefore, it has to be able to  stand alone . In most cases the abstract is the only part of your article that appears in indexing databases such as Web of Science or PubMed and so will be the most accessed part of your article; making a good impression will encourage researchers to read your full paper.

A well written abstract can also help speed up the peer-review process. During peer review, referees are usually only sent the abstract when invited to review the paper. Therefore, the abstract needs to contain enough information about the paper to allow referees to make a judgement as to whether they have enough expertise to review the paper and be engaging enough for them to want to review it.

Your Abstract should answer these questions about your manuscript:

• What was done?
• Why did you do it?
• What did you find?
• Why are these findings useful and important?

Answering these questions lets readers know the most important points about your study, and helps them decide whether they want to read the rest of the paper. Make sure you follow the proper journal manuscript formatting guidelines when preparing your abstract.

TIP: Journals often set a maximum word count for Abstracts, often 250 words, and no citations. This is to ensure that the full Abstract appears in indexing services.

Keywords  are a tool to help indexers and search engines find relevant papers. If database search engines can find your journal manuscript, readers will be able to find it too. This will increase the number of people reading your manuscript, and likely lead to more citations.

However, to be effective, Keywords must be chosen carefully. They should:

• Represent  the content of your manuscript
• Be  specific  to your field or sub-field

Manuscript title:  Direct observation of nonlinear optics in an isolated carbon nanotube

Poor keywords:  molecule, optics, lasers, energy lifetime

Better keywords:  single-molecule interaction, Kerr effect, carbon nanotubes, energy level structure

Manuscript title:  Region-specific neuronal degeneration after okadaic acid administration Poor keywords:  neuron, brain, OA (an abbreviation), regional-specific neuronal degeneration, signaling

Better keywords:  neurodegenerative diseases; CA1 region, hippocampal; okadaic acid; neurotoxins; MAP kinase signaling system; cell death

Manuscript title:  Increases in levels of sediment transport at former glacial-interglacial transitions

Poor keywords:  climate change, erosion, plant effects Better keywords:  quaternary climate change, soil erosion, bioturbation

Back │ Next

• Affiliate Program

• UNITED STATES
• 台灣 (TAIWAN)
• TÜRKIYE (TURKEY)
• Academic Editing Services
• - Research Paper
• - Journal Manuscript
• - Dissertation
• - College & University Assignments
• Admissions Editing Services
• - Application Essay
• - Personal Statement
• - Recommendation Letter
• - Cover Letter
• - CV/Resume
• Business Editing Services
• - Business Documents
• - Report & Brochure
• - Website & Blog
• Writer Editing Services
• - Script & Screenplay
• Our Editors
• Client Reviews
• Editing & Proofreading Prices
• Wordvice Points
• Partner Discount
• Plagiarism Checker
• APA Citation Generator
• MLA Citation Generator
• Chicago Citation Generator
• Vancouver Citation Generator
• - APA Style
• - MLA Style
• - Chicago Style
• - Vancouver Style
• Writing & Editing Guide
• Academic Resources
• Admissions Resources

Effective Transition Words for Research Papers

What are transition words in academic writing?

A transition is a change from one idea to another idea in writing or speaking and can be achieved using transition terms or phrases. These transitions are usually placed at the beginning of sentences, independent clauses, and paragraphs and thus establish a specific relationship between ideas or groups of ideas. Transitions are used to enhance cohesion in your paper and make its logical development clearer to readers.

Types of Transition Words

Transitions accomplish many different objectives. We can divide all transitions into four basic categories:

• Additive transitions  signal to the reader that you are adding or referencing information
• Adversative transitions  indicate conflict or disagreement between pieces of information
• Causal transitions  point to consequences and show cause-and-effect relationships
• Sequential transitions  clarify the order and sequence of information and the overall structure of the paper

Additive Transitions

These terms signal that new information is being added (between both sentences and paragraphs), introduce or highlight information, refer to something that was just mentioned, add a similar situation, or identify certain information as important.

Adversative Transitions

These terms and phrases distinguish facts, arguments, and other information, whether by contrasting and showing differences; by conceding points or making counterarguments; by dismissing the importance of a fact or argument; or replacing and suggesting alternatives.

Causal Transitions

These terms and phrases signal the reasons, conditions, purposes, circumstances, and cause-and-effect relationships. These transitions often come after an important point in the research paper has been established or to explore hypothetical relationships or circumstances.

Sequential Transitions

These transition terms and phrases organize your paper by numerical sequence; by showing continuation in thought or action; by referring to previously-mentioned information; by indicating digressions; and, finally, by concluding and summing up your paper. Sequential transitions are essential to creating structure and helping the reader understand the logical development through your paper’s methods, results, and analysis.

How to Choose Transitions in Academic Writing

Transitions are commonplace elements in writing, but they are also powerful tools that can be abused or misapplied if one isn’t careful. Here are some ways to ensure you are using transitions effectively.

• Check for overused, awkward, or absent transitions during the paper editing process. Don’t spend too much time trying to find the “perfect” transition while writing the paper.
• When you find a suitable place where a transition could connect ideas, establish relationships, and make it easier for the reader to understand your point, use the list to find a suitable transition term or phrase.
• Similarly, if you have repeated some terms again and again, find a substitute transition from the list and use that instead. This will help vary your writing and enhance the communication of ideas.
• Read the beginning of each paragraph. Did you include a transition? If not, look at the information in that paragraph and the preceding paragraph and ask yourself: “How does this information connect?” Then locate the best transition from the list.
• Check the structure of your paper—are your ideas clearly laid out in order? You should be able to locate sequence terms such as “first,” “second,” “following this,” “another,” “in addition,” “finally,” “in conclusion,” etc. These terms will help outline your paper for the reader.

For more helpful information on academic writing and the journal publication process, visit Wordvice’s  Academic Resources  Page. And be sure to check out Wordvice’s professional English editing services if you are looking for  paper editing and proofreading  after composing your academic document.

 Wordvice Tools

• Wordvice APA Citation Generator
• Wordvice MLA Citation Generator
• Wordvice Chicago Citation Generator
• Wordvice Vancouver Citation Generator
• Wordvice Plagiarism Checker
• Editing & Proofreading Guide

Wordvice Resources

• How to Write the Best Journal Submissions Cover Letter
• 100+ Strong Verbs That Will Make Your Research Writing Amazing
• How to Write an Abstract
• Which Tense to Use in Your Abstract
• Active and Passive Voice in Research Papers
• Common Phrases Used in Academic Writing

Other Resources Around the Web

• MSU Writing Center.  Transition Words.
• UW-Madison Writing Center.  Transition Words and Phrases.

English dominates scientific research – here’s how we can fix it, and why it matters

Científica del Instituto de Lengua, Literatura y Antropología (ILLA), del Centro de Ciencias Humanas y Sociales (CCHS) del Consejo Superior de Investigaciones Científicas (CSIC), Centro de Ciencias Humanas y Sociales (CCHS - CSIC)

Disclosure statement

Elea Giménez Toledo does not receive a salary, nor does she own shares, nor does she receive funding from any company or organisation that might benefit from this article. She is a commissioner of the SEGIB, which implies only unpaid scientific advice to this institution, as part of the scientific activity carried out at the CSIC.

Consejo Superior de Investigaciones Científicas provides funding as a founding partner of The Conversation ES.

View all partners

It is often remarked that Spanish should be more widely spoken or understood in the scientific community given its number of speakers around the world, a figure the Instituto Cervantes places at almost 600 million .

However, millions of speakers do not necessarily grant a language strength in academia. This has to be cultivated on a scientific, political and cultural level, with sustained efforts from many institutions and specialists.

The scientific community should communicate in as many languages as possible

By some estimates, as much as 98% of the world’s scientific research is published in English , while only around 18% of the world’s population speaks it. This makes it essential to publish in other languages if we are to bring scientific research to society at large.

The value of multilingualism in science has been highlighted by numerous high profile organisations, with public declarations and statements on the matter from the European Charter for Researchers , the Helsinki Initiative on Multiligualism , the Unesco Recommendation on Open Science , the OPERAS Multiligualism White Paper , the Latin American Forum on Research Assessment , the COARA Agreement on Reforming Research Assessment , and the Declaration of the 5th Meeting of Minsters and Scientific Authorities of Ibero-American Countries . These organisations all agree on one thing: all languages have value in scientific communication .

As the last of these declarations points out, locally, regionally and nationally relevant research is constantly being published in languages other than English. This research has an economic, social and cultural impact on its surrounding environment, as when scientific knowledge is disseminated it filters through to non-academic professionals, thus creating a broader culture of knowledge sharing.

Greater diversity also enables fluid dialogue among academics who share the same language, or who speak and understand multiple languages. In Ibero-America, for example, Spanish and Portuguese can often be mutually understood by non-native speakers, allowing them to share the scientific stage. The same happens in Spain with the majority of its co-official languages .

Read more: Non-native English speaking scientists work much harder just to keep up, global research reveals

No hierarchies, no categories

Too often, scientific research in any language other than English is automatically seen as second tier, with little consideration for the quality of the work itself.

This harmful prejudice ignores the work of those involved, especially in the humanities and social sciences. It also profoundly undermines the global academic community’s ability to share knowledge with society.

By defending and preserving multilingualism, the scientific community brings research closer to those who need it. Failing to pursue this aim means that academia cannot develop or expand its audience. We have to work carefully, systematically and consistently in every language available to us.

Read more: Prestigious journals make it hard for scientists who don't speak English to get published. And we all lose out

The logistics of strengthening linguistic diversity in science

Making a language stronger in academia is a complex process. It does not happen spontaneously, and requires careful coordination and planning. Efforts have to come from public and private institutions, the media, and other cultural outlets, as well as from politicians, science diplomacy , and researchers themselves.

Many of these elements have to work in harmony, as demonstrated by the Spanish National Research Council’s work in ES CIENCIA , a project which seeks to unite scientific and and political efforts.

Academic publishing and AI models: a new challenge

The global academic environment is changing as a result the digital transition and new models of open access. Research into publishers of scientific content in other languages will be essential to understanding this shift. One thing is clear though: making scientific content produced in a particular language visible and searchable online is crucial to ensuring its strength.

In the case of academic books, the transition to open access has barely begun , especially in the commercial publishing sector, which releases around 80% of scientific books in Spain. As with online publishing, a clear understanding will make it possible to design policies and models that account for the different ways of disseminating scientific research, including those that communicate locally and in other languages. Greater linguistic diversity in book publishing can also allow us to properly recognise the work done by publishers in sharing research among non-English speakers.

Read more: Removing author fees can help open access journals make research available to everyone

Making publications, datasets, and other non-linguistic research results easy to find is another vital element, which requires both scientific and technical support. The same applies to expanding the corpus of scientific literature in Spanish and other languages, especially since this feeds into generative artificial intelligence models.

If linguistically diverse scientific content is not incorporated into AI systems, they will spread information that is incomplete, biased or misleading: a recent Spanish government report on the state of Spanish and co-official languages points out that 90% of the text currently fed into AI is written in English.

Deep study of terminology is essential

Research into terminology is of the utmost importance in preventing the use of improvised, imprecise language or unintelligible jargon. It can also bring huge benefits for the quality of both human and machine translations, specialised language teaching, and the indexing and organisation of large volumes of documents.

Terminology work in Spanish is being carried out today thanks to the processing of large language corpuses by AI and researchers in the TeresIA project, a joint effort coordinated by the Spanish National Research Council. However, 15 years of ups and downs were needed to to get such a project off the ground in Spanish.

The Basque Country, Catalonia and Galicia, on the other hand, have worked intensively and systematically on their respective languages. They have not only tackled terminology as a public language policy issue, but have also been committed to established terminology projects for a long time.

Multiligualism is a global issue

This need for broader diversity also applies to Ibero-America as a whole, where efforts are being coordinated to promote Spanish and Portuguese in academia, notably by the Ibero-American General Secretariat and the Mexican National Council of Humanities, Sciences and Technologies .

While this is sorely needed, we cannot promote the region’s two most widely spoken languages and also ignore its diversity of indigenous and co-official languages. These are also involved in the production of knowledge, and are a vehicle for the transfer of scientific information, as demonstrated by efforts in Spain.

Each country has its own unique role to play in promoting greater linguistic diversity in scientific communication. If this can be achieved, the strength of Iberian languages – and all languages, for that matter – in academia will not be at the mercy of well intentioned but sporadic efforts. It will, instead, be the result of the scientific community’s commitment to a culture of knowledge sharing.

This article was originally published in Spanish

• Scientific publishing
• Multilingualism
• The Conversation Europe

Lecturer (Hindi-Urdu)

Initiative Tech Lead, Digital Products COE

Director, Defence and Security

Opportunities with the new CIEHF

School of Social Sciences – Public Policy and International Relations opportunities

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Published: 01 April 2024

Physics words with surprising origins

Nature Reviews Physics ( 2024 ) Cite this article

12 Altmetric

Metrics details

Many everyday English words have a double meaning, being used as physics jargon. This month, we share some of our favourite stories of how physics terms came to be.

As the old joke goes, “Gravity. It isn’t just a good idea. It’s the law.” To state the obvious, the punchline works because ‘law’ in everyday English and ‘law’ in physics jargon are two words that are spelt the same and pronounced the same, but their meanings are distinct. This month, we ponder some of the more amusing physics usage of standard English words, and the stories behind how those words came to have two meanings.

Often, physics terminology is based in whimsy. When physicists talk about charm, they are usually referring to a type of quark. The name, according to Sheldon Glashow, refers to the pleasing symmetry that charm quarks bring to the subnuclear world; Glashow, John Iliopoulos and Luciano Maiani also pointed out that a charm is “a magical device to avert evil”, which is fitting for charm quarks that prevent quark theory from predicting decays that aren’t backed up by experiments 1 .

Other times, the choice of an everyday word is something of an in-joke. For example, in particle physics, a barn is 10 −28 m 2 and is a unit for the cross-sectional area of a reaction, approximately equal to the cross-section of a uranium nucleus. The term originated in the Manhattan Project, where it was natural to want to work in units where a uranium nucleus has a cross-section close to unity. A Los Alamos National Laboratory report from 1944 (ref. 2 ) claims that scientists considered naming the unit after a person but the obvious choices weren’t suitable — ‘Bethe’ would be confused with the Greek letter beta, and ‘Oppenheimer’ was too long. In the end, they arrived on ‘barn’ because, in the context of nuclear cross-sections, 10 −28 m 2 may as well have been as big as a barn (pictured).

Another physics term that started as a joke is the penguin diagram, a class of Feynman diagram. The diagrams themselves were initially studied by Mikhail Shifman, Arkady Vainshtein, and Valentin Zakharov, but as Shifman recounts, “they did not look like penguins at all. Later on they were made to look like penguins and called penguins by John Ellis” (ref. 3 ). Ellis had been playing darts against Melissa Franklin, and had been bet that if he lost (which he did), he had to include the word penguin in his next paper. He managed to fulfil his side of the bet after realizing that some of the diagrams looked a bit like penguins 3 .

There are also physics terms that have a different meaning to the same word in other sciences. Sometimes, the overlap is simply a matter of two words coming from the same origin, such as the nucleus of an atom and the nucleus of a biological cell both being named after the Latin for ‘nut’. Other times, the overlap is deliberate. Plasma is a state of matter, as well as a component of blood. In fact, medical scientists were already using the word plasma, derived from the Greek verb ‘plassein’ meaning ‘to mould’, when Irving Langmuir started experimenting with electrical discharges in the 1920s. Langmuir chose the name in a deliberate reference to blood plasma: blood plasma carries around blood cells, and the plasma Langmuir was studying carries around particles 4 .

As editors, part of our job is to help our authors choose language that will be as clear as possible for as many readers as possible. Sometimes we ask authors to add an explanation of a term they consider standard, to avoid confusing readers who come from other areas of physics. Other times, we let context do the work of making the meaning clear — editing is as much an art as a science. But we always enjoy seeing the ways that language is used.

Glashow, S. The hunting of the quark. New York Times (18 July 1976).

Holloway, M. G. & Baker, C. P. How the barn was born. Phys. Today https://doi.org/10.1063/1.3070918 (1972).

Article   Google Scholar  

Shifman, M. ITEP Lectures on Particle Physics and Field Theory (World Scientific, 1999).

Arnoux, R. Why “plasma”? ITER Newsline (29 April 2013); https://www.iter.org/newsline/266/1571 .

Download references

Rights and permissions

Reprints and permissions

About this article

Cite this article.

Physics words with surprising origins. Nat Rev Phys (2024). https://doi.org/10.1038/s42254-024-00717-0

Download citation

Published : 01 April 2024

DOI : https://doi.org/10.1038/s42254-024-00717-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

• International edition
• Australia edition
• Europe edition

Dogs can understand the meaning of nouns, new research finds

Study confirms our canine companions can grasp more than simple commands – or at least for items they care about

Dogs understand what certain words stand for, according to researchers who monitored the brain activity of willing pooches while they were shown balls, slippers, leashes and other highlights of the domestic canine world.

The finding suggests that the dog brain can reach beyond commands such as “sit” and “fetch”, and the frenzy-inducing “walkies”, to grasp the essence of nouns, or at least those that refer to items the animals care about.

“I think the capacity is there in all dogs,” said Marianna Boros, who helped arrange the experiments at Eötvös Loránd University in Hungary. “This changes our understanding of language evolution and our sense of what is uniquely human.”

Scientists have long been fascinated by whether dogs can truly learn the meanings of words and have built up some evidence to back the suspicion. A survey in 2022 found that dog owners believed their furry companions responded to between 15 and 215 words.

More direct evidence for canine cognitive prowess came in 2011 when psychologists in South Carolina reported that after three years of intensive training, a border collie called Chaser had learned the names of more than 1,000 objects , including 800 cloth toys, 116 balls and 26 Frisbees.

However, studies have said little about what is happening in the canine brain when it processes words. To delve into the mystery, Boros and her colleagues invited 18 dog owners to bring their pets to the laboratory along with five objects the animals knew well. These included balls, slippers, Frisbees, rubber toys, leads and other items.

At the lab, the owners were instructed to say words for objects before showing their dog either the correct item or a different one. For example, an owner might say “Look, here’s the ball”, but hold up a Frisbee instead. The experiments were repeated multiple times with matching and non-matching objects.

During the tests, researchers monitored the dogs’ brain activity through non-invasive electroencephalography, or EEG. The traces revealed different patterns of activity when the objects matched or clashed with the words their owner said. The difference in the traces was more pronounced for words that owners believed their dogs knew best.

Similar blips in EEG recordings were seen when humans performed the tests and were interpreted as people understanding a word well enough to form a mental representation that was either confirmed or confounded by the object they were subsequently shown.

Writing in Current Biology , the scientists say the results “provide the first neural evidence for object word knowledge in a non-human animal”.

Boros emphasised she was not claiming dogs understood words as well as humans. It will take further work to understand, for example, whether dogs can generalise in the way humans learn to as infants, and grasp that the word “ball” need not refer to one specific, heavily chewed spongy sphere.

The study raises the question of why, if dogs understand certain nouns, more of them don’t show it. One possibility is that a dog knows what a word refers to but is not bothered about acting on it. “My dog only cares about his ball,” said Boros. “If I bring him another toy, he doesn’t care about it at all.”

Dr Holly Root-Gutteridge, a postdoctoral researcher at the University of Lincoln who was not involved in the study, called the work “fascinating”.

“It’s particularly interesting because I think it’s unlikely this started during domestication, so it may be widespread throughout mammals,” she said. “That’s highly exciting in itself as it shines new light on language evolution.

“It might be that the dogs don’t really care enough about the game of ‘fetch this particular thing’ to play along with the way we’ve been training and testing them so far. Your dog may understand what you’re saying but choose not to act.”

• Animal behaviour

Most viewed

• How We're Funded
• Staff Directory
• Board of Directors

Advancing Equity and Innovation in Research Publishing: Time for a New Era in the Open Access Movement?

Recommended.

Today marks a significant milestone as the Bill & Melinda Gates Foundation (BMGF) announces a new Open Access policy , representing a departure from traditional practices. This policy will cease support for individual article publishing fees, known as APCs, and mandate the use of preprints while advocating for their review. This blog looks at the rationale behind this change, exploring the persistent challenges in research publishing and the potential of preprint servers as a solution. It also examines the implications for researchers and research users, highlighting the benefits and drawbacks of this new approach. Finally, it offers recommendations for research funders and researchers to embrace this shift towards equity and innovation in research publishing.

The new BMGF open access policy

For a decade, the BMGF has championed transparency, access, and equity in scholarly publishing by advocating for a more open research ecosystem. Yet major challenges with research publishing persist, and as such, the foundation has decided to make a significant departure from traditional practices.

At its core, the BMGF Open Access policy will:

• End the foundation’s support for individual article publishing fees
• Require preprints and advocate for their review

The approach aligns with a strategy to reform global research publishing known as Plan U . At its core, Plan U aims to separate the slow and sometimes idiosyncratic process of quality assessment (peer review) from the publishing of research.

Why is this change needed?

Two key challenges persist in research publishing: timeliness and openness. Journal publications often take months or in some cases, more than a year to be published , and the majority of research remains behind paywalls . Despite decades of initiatives, recent increases in Open Access publication have been achieved through costly APCs, effectively excluding those unable to afford publishing fees. Moreover, even if research funders are willing to overpay and accept a system that locks out those who can’t, at the current rate of change it will take 70 years to see the big five publishers flip their journals to fully Open Access. Urgent global problems do not work on this timescale.

This is not just a problem for researchers. Globally $1 trillion per year is spent on research ; investment which supports the pursuit of all kinds of human social and economic progress. Concerns about the inability of our current publishing systems to adequately share the results of this investment should be a higher political priority .

Why the focus on “preprint servers”?

Preprint servers offer a no-frills, low-cost digital publishing platform where authors can upload their work before formal peer review. Many scientific disciplines have used preprint servers for decades and their use has grown rapidly in recent years, partly due to the  COVID-19 pandemic, when immediate access to information became paramount. Publishing in this way offers immediate, full, open access to research results, though it does not typically include a quality assessment step.

While peer review remains important for ensuring quality, there are many issues with the current model and there is much innovation in approaches to peer review already underway. Focusing publication requirements on preprint servers creates space for innovation in peer review or other quality assessment and curation models. It will be important for funders to monitor research quality and invest in mechanisms for ensuring research integrity.

Today, print copies of research papers are increasingly rare, begging a change to the label of “preprint server”. Nevertheless, low-cost digital publishing platforms must be key to major reform of research publishing and funders are also beginning to view preprints as valid indicators of research .

Implications for researchers and research users

Researchers will be able to quickly and easily share the results of their work, without the traditional gatekeepers. However, in the current prestige economy, some may still feel pressure to publish in traditional journals to meet career or funding requirements. Additionally, research funders that decline to pay APCs but require open publication may feel disadvantaged compared to peers supported by funders willing to pay APCs for publication in top-tier journals. This could also result in more paywalled journal articles forcing those who struggle with access to articles to rely on other methods. However, pursuing Open Access through pay-to-publish without controlling costs to research funders, as major recent initiatives effectively did, does not solve any of these issues.

The major benefits of preprint servers to research users are quick and cost-free access to new research. However, the lack of quality assessment in pre-prints is a potential drawback. Innovation in open quality assessment will be needed to address this challenge and complement the shift towards preprint-centric Open Access policies.

Recommendations

Political leaders:.

• Recognise that systems for sharing the results of significant research investment are dysfunctional and consider whether efforts to drive innovation in the sector are worth backing .

Research funders:

• Consider requiring preprints and ceasing to pay for APCs to promote equitable publishing practices.
• Invest this funding into models that benefit the whole ecosystem and not individual funded researchers.
• Support innovative initiatives that facilitate peer review and curation separately from traditional publication.
• Value research output based on its merits, rather than the perceived prestige of the publishing platform.

Researchers:

• Consider alternative Open Access publishing models to promote equitable access to research.
• Peer review for journals whose values match your own—where you give your labour and work for free matters.
• Advocate for more research funders to adopt preprint-centric Open Access policies.
• Value peer-reviewed research on its merits, rather than the perceived prestige of the publishing platform.

As we navigate this new era in the Open Access movement, a willingness to embrace change and new ways of working among research funders, researchers, and research users will be crucial to advancing equity and innovation in research publishing. Let us take bold action to try something different and adapt as needed. Let us embrace this opportunity to create a more inclusive and impactful research ecosystem for the benefit of all.

Thanks to Ashley Farley and Javier Guzman for feedback on an earlier draft.

CGD blog posts reflect the views of the authors, drawing on prior research and experience in their areas of expertise. CGD is a nonpartisan, independent organization and does not take institutional positions.

View the discussion thread.

More Reading

Ideas to action: independent research for global prosperity

© 2024 Center for Global Development | Privacy Notice and Cookie Policy

Sign up to get weekly development updates:

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• HHS Author Manuscripts

Defining and Assessing Wisdom: A Review of the Literature

Katherine j. bangen.

1 Department of Psychiatry, University of California, San Diego, 9500 Gilman Drive 0603, La Jolla, CA, 92093-0603

Thomas W. Meeks

Dilip v. jeste.

2 Department of Neurosciences, University of California, San Diego

3 Sam and Rose Stein Institute for Research on Aging, University of California, San Diego, 9500 Gilman Drive 0664 La Jolla, CA, 92093

With increasing longevity and a growing focus on successful aging, there has been a recent growth of research designed to operationalize and assess wisdom. We aimed to ( 1 ) investigate the degree of overlap among empirical definitions of wisdom, ( 2 ) identify the most commonly cited wisdom subcomponents, ( 3 ) examine the psychometric properties of existing assessment instruments, and ( 4 ) investigate whether certain assessment procedures work particularly well in tapping the essence of subcomponents of the various empirical definitions. We searched PsychINFO-indexed articles published through May 2012 and their bibliographies. Studies were included if they were published in a peer-reviewed journal and ( 1 ) proposed a definition of wisdom or ( 2 ) discussed the development or validation of an instrument designed to assess wisdom. Thirty-one articles met inclusion criteria. Despite variability among the 24 reviewed definitions, there was significant overlap. Commonly cited subcomponents of wisdom included knowledge of life, prosocial values, self-understanding, acknowledgement of uncertainty, emotional homeostasis, tolerance, openness, spirituality, and sense of humor. Published reports describing the psychometric properties of nine instruments varied in comprehensiveness but most measures were examined for selected types of reliability and validity, which were generally acceptable. Given limitations of self-report procedures, an approach integrating multiple indices (e.g., self-report and performance-based measures) may better capture wisdom. Significant progress in the empirical study of wisdom has occurred over the past four decades; however, much needs to be done. Future studies with larger, more diverse samples are needed to determine the generalizability, usefulness, and clinical applicability of these definitions and assessment instruments. Such work will have relevance for the fields of geriatrics, psychiatry, psychology, sociology, education, and public health, among others.

INTRODUCTION

Over 700 years ago, Thomas Aquinas decreed, “of all the pursuits open to men, the search for wisdom is most perfect, more sublime, more profitable, and more full of joy.” Despite deep historical roots in philosophy and religion ( 1 , 2 ), empirical studies of wisdom in psychology and gerontology did not begin until the 1970s. The long delay may be related in part to early gerontology’s emphasis on a deficit model, which characterizes the normative course of aging as a series of losses. Furthermore, psychology and neuroscience have generally tended to focus on elemental components or processes that can be relatively easily operationalized and measured. Given the complex nature of wisdom, there are challenges in defining, operationalizing, and assessing this construct.

Folk psychology suggests that individuals become wiser with increasing age, although published results from empirical studies have been inconsistent, with some demonstrating no age-related differences in wisdom ( 3 ) whereas others report increases in wisdom with age ( 4 - 6 ). Evidence suggesting that wisdom is related to better physical health and improved quality of life among older adults ( 7 , 8 ) in combination with the widespread belief that wisdom increases with age, the global trend of increasing longevity, and the growing interest in successful aging ( 9 - 11 ) have likely contributed to the notable increase in wisdom research over the past several decades ( 12 ).

The word ‘wise’ is used in everyday language and the intended or perceived meaning may differ somewhat depending on the context, however, its scientific usage should be precise ( 13 ). While there are somewhat different perspectives regarding the essential subcomponents of wisdom, in order for valid empirical research to grow, general agreement on the main characteristics of and the optimal method/s to assess this complex construct is important. Such consistency would be useful in comparing and integrating findings across studies, which is currently difficult given a lack of consensus regarding how to operationalize and measure wisdom. Despite the growth of scientific research and several excellent books on wisdom, to our knowledge, this paper is the first to summarize articles published in peer-reviewed journals that describe the development of definitions of wisdom and instruments designed to assess wisdom. Unlike previously published review articles ( 13 , 14 ), we restricted our search to include only those articles that were published in peer-reviewed journals so as to focus on those definitions and instruments that were developed through empirical methods.

In reviewing the literature, we aimed to (1) investigate the degree of overlap among empirical definitions of wisdom, (2) identify the most commonly cited wisdom subcomponents, (3) examine the psychometric properties of existing assessment instruments, and (4) investigate whether certain assessment procedures work particularly well in tapping the essence of subcomponents of the various empirical definitions. Summarizing the current literature and addressing these questions will inform future empirical research on wisdom to facilitate further elucidation of its conceptualization, assessment, and application to clinical interventions.

DESIGN AND METHODS

To identify articles for review, we surveyed the PsychINFO online database through May 2012 with the following criteria: (1) included the term wisdom in the title, (2) published in English, and (3) published in a peer-reviewed journal. This search yielded 571 articles of potential interest, of which 105 were deemed relevant (i.e., involved the study of the construct of wisdom) based upon a review of the abstract. References cited in these 105 articles were also reviewed. To be included in this review, articles either (1) proposed a definition of wisdom or (2) discussed the development, validation, and/or psychometric properties of an instrument designed to assess wisdom. Thirty-one articles met these criteria. At least two authors examined each journal article and then extracted information related to the proposed definition and/or assessment instrument.

Given our focus on empirically based definitions and measures of wisdom published in peer-reviewed journals, we did not include those published in books ( 15 - 18 ). In addition, as we are interested in the construct of wisdom, we did not include work by researchers who have focused on subcomponents of this construct rather than wisdom per se . Such work includes Happé et al.’s work on theory of mind among older adults ( 4 ); Levenson and colleagues’ research on self-transcendence ( 19 ); conceptualizations emphasizing dialectical thinking and viewing wisdom as post-formal reasoning thereby extending beyond Piaget’s stages of cognitive development ( 20 ); and Kitchener and colleagues’ ( 21 ) work on the reflective judgment theory.

Table 1 summarizes the key theories and definitions published since the early 1980s when the empirical study of wisdom expanded. A distinction that often has been made among the modern conceptualizations of wisdom involves whether researchers adopt an implicit versus explicit theoretical approach ( 22 ). Implicit theories of wisdom highlight lay conceptions or common sense approaches and examine how wisdom is described in everyday language and how individuals are characterized as wise. In contrast, explicit theories are based on constructions of expert theorists and focus on behavioral manifestations of wisdom ( 13 , 23 ). However, some definitions may be difficult to classify based on such distinctions because they represent a hybrid approach or are not entirely consistent with one of these categories. Given this, theories are organized below in terms of chronology, an approach that highlights the historical development of the study of wisdom.

Definitions of Wisdom: Common Subcomponents

To integrate conceptually similar dimensions of wisdom, we have summarized the various definitions based on the inclusion of nine specific subcomponents identified through a conceptual review of the literature. For a subcomponent to be used in summarizing it had to be included in at least three of the definitions. The subcomponents overlap with but are more comprehensive than those identified in an earlier literature review by Meeks and Jeste ( 12 ). The frequency of inclusion of each of these nine characteristics in the reviewed definitions, which was determined based on consensus among the authors, is included in the last row of Table 1 . As demonstrated in Table 1 , there is significant overlap among the various empirical definitions of wisdom. The most commonly included subcomponents, which appeared in more than half of the definitions are (1) social decision making and pragmatic knowledge of life, which relates to social reasoning, ability to give good advice, life knowledge, and life skills; (2) prosocial attitudes and behaviors, which include empathy, compassion, warmth, altruism, and a sense of fairness; (3) reflection and self-understanding, which relates to introspection, insight, intuition, and self-knowledge and awareness; (4) acknowledgement of and coping effectively with uncertainty ; and (5) emotional homeostasis , which relates to affect regulation and self-control. Finally, subcomponents included in fewer than half of the reviewed definitions include (1) value relativism and tolerance, which involves a nonjudgmental stance and acceptance of other value systems; (2) openness to new experience ; (3) spirituality ; and (4) sense of humor .

The reviewed definitions have been developed using a variety of methods including theoretical approaches involving review, synthesis, and/or expansion of existing theories of wisdom or related constructs ( 7 , 12 , 24 , 25 ); prototypical studies involving methods requiring participants to provide or rate wisdom-related characteristics followed by researchers analyzing those responses to reveal underlying subcomponents of wisdom ( 26 , 27 ); interview-based methods asking participants to identify/nominate wise individuals and/or describe instances in which they themselves were wise ( 28 ); and consensus of international experts using the Delphi method ( 29 ).

ASSESSMENT INSTRUMENTS

Table 2 summarizes instruments developed to assess wisdom in terms of the format, sample(s) used during development or validation, reliability, validity, wisdom subcomponents that the measure was designed to assess, strengths, and limitations. Given that the reviewed instruments are in the form of either interview-based measures, questionnaires, or a hybrid of these two approaches, instruments are organized according to these three categories.

Measures Designed to Assess Wisdom

To summarize the various instruments, as demonstrated in Table 2 , of the nine instruments, three are interview-based, five use a questionnaire format, and one involves a hybrid of these two formats. Interview-based measures are scored by trained raters whereas the questionnaires ask participants to respond using Likert-type scales and range in length from 13 to 79 items. With the exceptions of the measures associated with the Berlin Wisdom Paradigm and the Wise Thinking and Acting Questionnaire ( 30 ), which were developed in Germany and Greece, respectively, all measures were developed in the United States. Sample sizes used in development and validation studies ranged from 60 participants ( 3 ) to 2,715 participants ( 31 ). In terms of reliability, seven of the nine measures assessed internal consistency with Cronbach’s alpha (α) values ranging from .60 ( 30 ) to .96 ( 32 ). The four measures involving an interview component assessed inter-rater reliability with Cronbach’s α values ranging from .51 to .99 ( 33 , 34 ). Three measures reported test-retest reliability data which was calculated across different time intervals ranging from two weeks ( 35 ) to 12 months ( 23 ) and with correlation coefficients ranging from .65 to .94 ( 23 ). Regarding validity, eight of the nine scales assessed convergent validity; five examined discriminant validity; five investigated construct validity using factor analyses; and one each assessed content validity, predictive validity, and concurrent validity. Finally, one measure attempted to validate their coding scheme.

Given that each measure was developed by researchers who had also proposed an empirical definition and the two were often developed in tandem, measures were assumed to generally be designed to assess the particular subcomponents proposed in the associated definition. Each measures has strengths and limitations. Measures with significant strengths include those associated with the Berlin wisdom paradigm, given their foundation from a large body of empirical work across many samples; the Three-Dimensional Wisdom Scale (3D-WS), in view of its rigorous development and good psychometric properties; the Wisdom Development Scale (WDS) and Self-Assessed Wisdom Scale (SAWS) given demonstrations of several types of validity across multiple samples; and the social reasoning measure developed by Grossmann and colleagues ( 5 ), in light of its use of naturalistic materials and structured interview format.

Through our review of the literature, we aimed to (1) investigate the degree of overlap among empirical definitions of wisdom, (2) identify the most commonly cited wisdom subcomponents, (3) examine the psychometric properties of existing assessment instruments, and (4) investigate whether certain assessment procedures work particularly well in tapping the essence of subcomponents of the various empirical definitions. To summarize our findings, despite some variability, there is a significant degree of overlap among definitions. Further, the most commonly cited subcomponents, which appeared in at least half of the reviewed definitions, relate to social decision making/knowledge of life, prosocial values, reflection, and acknowledgement of uncertainty. Additional subcomponents included in fewer than half of the definitions relate to emotional homeostasis, value relativism/tolerance, openness to new experience, spirituality, and sense of humor.

Standardized assessment measures have generally involved an interview-based or questionnaire format or a hybrid of the two. Published reports examining the reliability and validity of these scales vary in terms of degree of comprehensiveness and detail. However, all of the instruments have been evaluated for inter-item or internal consistency with several measures also having been examined for additional forms of reliability. There was variability in terms of what type(s) of validity was/were assessed (e.g., convergent, divergent, etc.). Nonetheless, a majority of measures evidenced acceptable psychometric properties.

Most reviewed measures are based on self-report interviews or questionnaires and although each of the subcomponents of wisdom listed above could hypothetically be assessed with either of these formats, some assessment procedures may work particularly well in tapping the essence of subcomponents of the definitions. In particular, given that a key subcomponent of wisdom involves acknowledgement of uncertainty and limits, including limits of one’s own knowledge, a wiser individuals would theoretically always score lower than a less wise person on measures asking her to reflect on her own level of aspects of wisdom (e.g., knowledge, self-reflection, or emotional homeostasis) ( 29 ). Therefore, self-report measures may not best capture wisdom. Alternative assessment procedures include having an informant report on an individual’s level of wisdom. However, the informant may not know the person very well and may have her own biases. Examining an individual’s behavior over long periods of time would be the optimal method for assessing wisdom, however, this is neither practical nor feasible. Further, certain subcomponents, including self-reflection and spirituality are difficult to observe. Taken together, wisdom may be best assessed from a variety of sources involving integrating self-report, informant-based, and performance-based measures.

There are limitations to the current review. Despite our best efforts, we might have missed a few relevant articles on this topic. In addition, summarizing the reviewed theories had some inherent challenges. In particular, many authors discuss similar concepts but use different language to describe them; the definitions of characteristics are not always provided by the authors; domains of wisdom are sometimes explicitly stated and at other times implied, which requires a degree of interpretation; and if a domain/characteristic is not mentioned in a particular theory, it is unclear whether it was assessed and subsequently excluded or never considered. Furthermore, given that each measure was developed by researchers who had also proposed an empirical definition and the two were often developed in tandem, we assume that measures were generally designed to assess the particular subcomponents proposed in the associated definition, however, this may not always be the case.

Although debate and diverse perspectives are useful, general agreement on the main characteristics of wisdom will facilitate the advancement of empirical research on this construct as well as comparison and integration of findings across studies. Although there is still no consensus definition of wisdom, there has been recent progress as evidenced by the significant overlap among empirical definitions as well as the recent expert panel conducted by Jeste and colleagues ( 29 ). Notably, most theorists believe that wisdom is multi-dimensional ( 5 , 7 , 12 , 23 , 29 , 36 , 37 ). Most conceptualizations involve integration and can be considered holistic in the sense that individual subcomponents are necessary but not sufficient for the development of wisdom. For instance, possessing knowledge and good decision-making abilities but lacking prosocial values, can only make an individual smart, but not wise ( 37 ). Wisdom is thought to be a complex, multidimensional characteristic with the whole being greater than the sum of its parts. An individual should holistically integrate several or all subcomponents of wisdom to a high degree in order to be wise. The relative weighting of the various subcomponents is unclear and may vary depending on the context or culture (e.g., subcomponents such as spirituality or sense of humor may be more or less important depending on the context). However, behavior or action is an essential part of wisdom. An individual may think wisely, but unless she acts wisely, she does not truly embody wisdom.

A subcomponent of wisdom that was cited by nearly all definitions relates to prosocial values and behavior, suggesting that wisdom is a useful construct and serves a common good ( 18 , 23 ). Implicit in this conceptualization is that wisdom is not simply a conglomeration of personality traits but it serves a purpose and is actively exhibited through behavior and social interaction. Given that an important component of wisdom involves promoting the well-being of others, taken together with evidence suggesting that wisdom is related to better physical health, improved quality of life, and better quality of relationships among older adults ( 7 , 8 ), suggests that wisdom is useful for both individuals and society at large.

Despite significant progress in the development of assessment instruments, all existing measures have limitations. Several of these weaknesses are not specific to instruments designed to assess wisdom and may be relevant to interview-based measures or questionnaires in general (e.g., time consuming nature of transcribing and rating qualitative interviews, susceptibility of self-report measures to response bias, concerns about ecological validity). However, there are potential problems that may be more relevant for measures designed to assess wisdom compared to those assessing other constructs (e.g., as mentioned above, there is difficulties using self-report to assess one’s wisdom given that a key component of wisdom relates to recognizing one’s own limitations). Nonetheless, existing measures with significant strengths include those associated with the Berlin wisdom paradigm, given their foundation from a large body of empirical work across many samples; the 3D-WS, in view of its rigorous development and good psychometric properties; the WDS and SAWS given demonstrations of several types of validity across multiple samples; and the social reasoning measure developed by Grossmann and colleagues ( 5 ), in light of its use of naturalistic materials and structured interview format.

There are several potential areas of wisdom-related research that merit further focused investigation and are outlined below.

1. Establishing the generalizability of definitions and measures of wisdom

In light of the relatively small and homogenous samples included in most empirical studies (i.e., mostly Caucasian and highly educated participants) as well as documented cross-cultural differences in beliefs about wisdom ( 38 ), demonstrating the applicability of definitions and measures across larger, more diverse samples in terms of culture and socioeconomic and educational background is a key to demonstrating their generalizability and broader relevance. Notably, the vast majority of reviewed definitions and instruments were developed by researchers based in North America or Europe. However, one definition was developed by a researcher based in Taiwan ( 25 ) and the definition derived based on the Delphi method involved an international group of experts ( 29 ). Nonetheless, these definitions and measures would generally benefit from additional investigations involving larger and more diverse samples.

2. Constructing standardized multimodal measures of wisdom characterized by good psychometric properties and feasibility and assessing the usefulness of these measures

Recently published constructive commentaries debating different approaches ( 39 , 40 ) represent an important step in achieving a better understanding of how to measure wisdom. However, given that wisdom is a multidimensional construct along with the weaknesses of individual measurement techniques (e.g., social desirability biases associated with self-report measures), it may be best assessed from a variety of sources. For instance, a combination of quantitative data and qualitative semi-structured interviews using both hypothetical situations and situations from an individual’s own life would be helpful as would integrating self-report, informant-based, and performance-based measures. Instruments should focus on measuring observable behavior and strike a balance between being comprehensive and brief. It may be impossible to develop an ideal scale that would be appropriate for all individuals and all contexts. It may be that different scales are useful for different purposes (e.g., assessing wisdom in young adults versus older adults).

In addition, whether these measures assess a useful concept of wisdom should be assessed. Studies investigating the benefits of wisdom have demonstrated that wisdom is more strongly associated with life satisfaction than physical health, socioeconomic status, social involvement, physical environment, and age ( 7 ). Other studies using social reasoning vignettes have implied that wisdom is useful given that wise behavior is defined, in part, as prosocial behavior that serves the common good ( 5 ). Taken together, evidence suggests that wisdom is a useful construct. However, future systematic investigations of the usefulness of wisdom as assessed by these instruments are important for further determining the ecological validity of these measures as well as determining which assessment methods (e.g., performance-based measures involving social reasoning) may be more likely to advance the understanding and application of this construct.

3. Developing interventions designed to promote wisdom

Despite increasing research focus, wisdom has received little clinical attention. Although it is generally thought that wisdom is not likely to be enhanced by medication, it is thought that it can be cultivated ( 29 ). To our knowledge, only one psychotherapy technique has explicitly targeted increased wisdom as a therapy goal. So called wisdom therapy uses modified versions of the Berlin Wisdom Project’s research protocol to facilitate the client’s abilities to consider challenging life events from multiple perspectives with the aim of enhancing subcomponents of wisdom including flexible thinking and acceptance of uncertainty ( 41 ). Additional interventions that may promote wisdom or its components include mindfulness and acceptance based psychotherapies, which emphasize aspects of wisdom including nonjudgmental awareness and emotional regulation ( 42 ); volunteer programs through which older adults mentor and tutor school-aged children facilitating the activation of wisdom among older adults as well as intergenerational transmission of wisdom ( 43 ); and cognitive rehabilitation techniques designed to improve executive functioning and cognitive flexibility which may help older adults improve their abilities related to accepting multiple viewpoints and acknowledging uncertainty. Finally, additional research on the developmental process of wisdom (as opposed to wisdom as an outcome) may inform how to best facilitate the growth of wisdom ( 44 ). Interventions promoting wisdom may be relevant not only to older adults but also for to the study of disorders and conditions, such as antisocial personality disorder and frontotemporal dementia, that affect commonly proposed subcomponents of wisdom (e.g., prosocial attitudes and behaviors, emotional homeostasis).

In conclusion, throughout history and across cultures, wisdom has been considered an optimal outcome of human development ( 45 ). Evidence suggests that wisdom is related to better physical health and improved quality of life among older adults ( 7 , 8 ), suggesting that wisdom is a useful construct and may have important implications for individuals, the healthcare system, and society at large. There has been a considerable growth in empirical research on wisdom over the past three to four decades ( 12 ). As a result, excellent empirically based contributions aimed at defining and measuring wisdom have been made. However, there is still much work to be done and the field is ripe for continued growth. Further elucidation of wisdom and investigation of wisdom across diverse samples as well as the development of theoretically and psychometrically valid multimodal assessment instruments are important steps in the promotion of the rigorous scientific study of this complex construct. Such work has relevance for the fields of geriatrics, psychiatry, psychology, sociology, education, and public health, among others, and would facilitate the development of wisdom-based interventions.

Acknowledgments

This work was supported by the National Institutes of Health grants T32 MH 19934-17, P30 MH080002, and NCRS UL1RR031980, and the Sam and Rose Stein Institute for Research on Aging, University of California, San Diego.

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Conflicts of Interest and Source of Funding: Dr. Jeste currently receives funding from National Institutes of Health grants and the Sam and Rose Stein Institute for Research on Aging, University of California, San Diego. No disclosures to report.

• Student Opportunities

About Hoover

Located on the campus of Stanford University and in Washington, DC, the Hoover Institution is the nation’s preeminent research center dedicated to generating policy ideas that promote economic prosperity, national security, and democratic governance. 

• The Hoover Story
• Hoover Timeline & History
• Mission Statement
• Vision of the Institution Today
• Key Focus Areas
• About our Fellows
• Research Programs
• Annual Reports
• Hoover in DC
• Fellowship Opportunities
• Visit Hoover
• David and Joan Traitel Building & Rental Information
• Newsletter Subscriptions
• Connect With Us

Hoover scholars form the Institution’s core and create breakthrough ideas aligned with our mission and ideals. What sets Hoover apart from all other policy organizations is its status as a center of scholarly excellence, its locus as a forum of scholarly discussion of public policy, and its ability to bring the conclusions of this scholarship to a public audience.

• Scott Atlas
• Thomas Sargent
• Stephen Kotkin
• Michael McConnell
• Morris P. Fiorina
• John F. Cogan
• China's Global Sharp Power Project
• Economic Policy Group
• History Working Group
• Hoover Education Success Initiative
• National Security Task Force
• National Security, Technology & Law Working Group
• Middle East and the Islamic World Working Group
• Military History/Contemporary Conflict Working Group
• Renewing Indigenous Economies Project
• State & Local Governance
• Strengthening US-India Relations
• Technology, Economics, and Governance Working Group
• Taiwan in the Indo-Pacific Region

Books by Hoover Fellows

Economics Working Papers

Hoover Education Success Initiative | The Papers

• Hoover Fellows Program
• National Fellows Program
• Student Fellowship Program
• Veteran Fellowship Program
• Congressional Fellowship Program
• Media Fellowship Program
• Silas Palmer Fellowship
• Economic Fellowship Program

Throughout our over one-hundred-year history, our work has directly led to policies that have produced greater freedom, democracy, and opportunity in the United States and the world.

• Determining America’s Role in the World
• Answering Challenges to Advanced Economies
• Empowering State and Local Governance
• Revitalizing History
• Confronting and Competing with China
• Revitalizing American Institutions
• Reforming K-12 Education
• Understanding Public Opinion
• Understanding the Effects of Technology on Economics and Governance
• Energy & Environment
• Health Care
• Immigration
• International Affairs
• Key Countries / Regions
• Law & Policy
• Politics & Public Opinion
• Science & Technology
• Security & Defense
• State & Local
• Books by Fellows
• Published Works by Fellows
• Working Papers
• Congressional Testimony
• Hoover Press
• PERIODICALS
• The Caravan
• China's Global Sharp Power
• Economic Policy
• History Lab
• Hoover Education
• Global Policy & Strategy
• National Security, Technology & Law
• Middle East and the Islamic World
• Military History & Contemporary Conflict
• Renewing Indigenous Economies
• State and Local Governance
• Technology, Economics, and Governance

Hoover scholars offer analysis of current policy challenges and provide solutions on how America can advance freedom, peace, and prosperity.

• China Global Sharp Power Weekly Alert
• Email newsletters
• Hoover Daily Report
• Subscription to Email Alerts
• Periodicals
• California on Your Mind
• Defining Ideas
• Hoover Digest
• Video Series
• Uncommon Knowledge
• Battlegrounds
• GoodFellows
• Hoover Events
• Capital Conversations
• Hoover Book Club
• AUDIO PODCASTS
• Matters of Policy & Politics
• Economics, Applied
• Free Speech Unmuted
• Secrets of Statecraft
• Pacific Century
• Libertarian
• Library & Archives

Support Hoover

Learn more about joining the community of supporters and scholars working together to advance Hoover’s mission and values.

What is MyHoover?

MyHoover delivers a personalized experience at  Hoover.org . In a few easy steps, create an account and receive the most recent analysis from Hoover fellows tailored to your specific policy interests.

Watch this video for an overview of MyHoover.

Log In to MyHoover

Forgot Password

Don't have an account? Sign up

Have questions? Contact us

• Support the Mission of the Hoover Institution
• Subscribe to the Hoover Daily Report
• Follow Hoover on Social Media

Make a Gift

Your gift helps advance ideas that promote a free society.

• About Hoover Institution
• Meet Our Fellows
• Focus Areas
• Research Teams
• Library & Archives

Library & archives

Events, news & press.

Policing Language In The Intelligence Community: The IC DEIA’s Assault On “Problematic Terminology” In Counterterrorism Analysis

“Our Words Matter.” So begins a recent article in The Dive , a quarterly newsletter produced by the Intelligence Community Diversity, Equity, Inclusion, and Accessibility (IC DEIA) Office within the Office of the Director of National Intelligence (ODNI).

“Our Words Matter.”

So begins a recent article in The Dive , a quarterly newsletter produced by the Intelligence Community Diversity, Equity, Inclusion, and Accessibility (IC DEIA) Office within the Office of the Director of National Intelligence (ODNI). The Dive is an internal publication, marked as unclassified but not for public release. Earlier this month, however, the ODNI, which oversees the operations of the nation’s 18 intelligence agencies, released a redacted version of the newsletter’s winter issue as part of a Freedom of Information Act records request. The theme of this issue was “the importance of words,” and the lead article was devoted to the “dilemma of problematic terminology” in counterterrorism.

The article, titled “Words Matter: Changing Terminology Related to Counterterrorism,” highlights the IC DEIA’s efforts to influence the sorts of terms used by intelligence professionals in the context of counterterrorism. The goal, in short, is to “disentangl[e] Islam from words and phrases used to discuss terrorism and extremist violence.” Words such as “Salafi-Jihadist,” “Jihadist,” “Islamic-Extremist,” “Sunni/Shia-Extremism,” and “Radical Islamists” are held to be “offensive” and “problematic” on the grounds that they “incorrectly suggest that Islamic beliefs somehow condone the actions and rhetoric espoused by these foreign terrorist organizations.” Above the article is a graphic depicting a group of analysts using the words “Islamic extremism,” “radical Islam,” “Salafi Jihadist,” and “Sunni-Shia Extremism,” with a caption stating that “[a]lthough some words may be considered common, they are in fact offensive and inaccurate.”

If these terms are all offensive and accurate, then what does the IC DEIA propose instead?

“We recommend identifying individuals and groups based on the foreign terrorist organization they are a part of and the region where they operate …. Overall, it is encouraged to identify them for who they are—international terrorism extremists, or violent extremists—and explicitly state that they manipulate and distort Islam to wrongly justify violence. In cases where none of these substitute phrases are amenable, we recommend a word that many Islamic scholars, public leaders, and academics use to accurately identify extremists: Khawarij … We ask that employees spread awareness about the concern of the terminology we are seeing and utilize the substitute terms recommended to ensure we are inclusive, accurate, and sensitive.”

Several objections arise to this well-intended bit of terminological advice. Intelligence analysts covering terrorism are in the business of understanding the behavior, motives, and intentions of the terrorist groups they study. It is not their duty to “disentangle” them from Islam or to denounce them as inherently un-Islamic. To begin from the premise that groups such as al-Qaida and the Islamic State have nothing to do with Islam or are manifestly distorting and manipulating every aspect of the faith is to take a position with analytical import. It may be “sensitive” and “inclusive” in some sense, but it is not necessarily “accurate.”

How, and in what ways, these groups relate to and draw on the Islamic tradition is important for the intelligence community to understand and consider. It tells us about their motivations and intentions and the possible bases of their support. As it happens, al-Qaida and the Islamic State belong to a distinct movement within the larger Sunni Islamist frame known in Arabic as “Jihadi Salafism” ( al-salafiyya al-jihadiyya ) or “the jihadi current” ( al-tayyar al-jihadi ). These are not terms imposed upon them by the national security establishment but ones that they themselves use in their own discourse and internal debates. In assessing their status and future, it is important to understand that the jihadis form an ideological movement going back at least to the 1970s with a distinct doctrinal apparatus drawing on the Salafi tradition in Sunni Islam, a tradition informed above all by the theological writings of Ibn Taymiyya (d. 1328) and his more militant heirs in the Wahhabi movement that took form in 18 th -century Arabia. They are also motivated by a unique approach to jihad that sees armed struggle against the perceived impious regimes of the Islamic world as an individual duty binding on all able-bodied Muslims.

None of this should be taken to imply that Jihadi Salafism is representative of mainstream Islamic beliefs and practices. It is not. But to deny intelligence analysts access to terms like “Jihadi Salafi” and “jihadi” is only to deny them access to these groups’ own frame of reference. One cannot possibly hope to understand what motivates them, what they aim to achieve, or the limits of their appeal if one is denied entry into their own worldview. Words like “international terrorism extremists” and “violent extremists” tell us little about the actors they are intended to describe. They are fine as neutral descriptors but are inadequate to the task of nuanced analysis.

Even less helpful is the suggestion of Khawarij—an Arabic plural more commonly anglicized as Kharijites. The Kharijites were a seventh-century Muslim sect remembered in the Islamic heresiographical tradition as extremist schismatics who eagerly fought and excommunicated fellow Muslims. They were also deeply pious. The Kharijite label has long been used as a derogatory term for versions of Islam seen as extreme by the mainstream. The Wahhabis, for instance, were branded by their enemies as Kharijites. It did not stop them from conquering most of the Arabian Peninsula by the early nineteenth century.

To call modern-day Islamic extremists Khawarij is thus to enter into a world of intra-Islamic polemic. This may have some utility in the realm of public diplomacy (though that is certainly debatable) but in the realm of analysis it is mostly counterproductive. The term is misleading in giving the impression that the jihadis are followers of a seventh-century schismatic sect when in fact they identify with the theological and legal heritage of Sunni Islam. The jihadis do not see themselves as Kharijites and in fact present themselves, pursuant to the Salafi tradition, as occupying a theological middle ground between the unbounded zealotry of the Kharijites and the laxity of the Murji’ites (another early Muslim sect). Referring to them as Kharijites misses the way that they understand and portray themselves.

Another problem with the Kharijite label is that the term is used by the jihadis in their internal debates. Within the jihadi current there is considerable ideological disagreement, particularly as regards takfir , or the practice of declaring someone to be an unbeliever. Followers of the Islamic State are more inclined to pronounce takfir on perceived heretics, including the Shia, than are followers of al-Qaida. This disagreement goes some way in explaining the ideological cleavage between the two groups. They are not the same, and in fact al-Qaida has sought to brand the Islamic State as Khawarij, the very term the IC DEIA recommends the intelligence community use for both groups. Why it is helpful to get drawn into internal Islamic polemic is hardly self-evident.

Since 9/11, American politicians have struggled with the question of how to identify and classify the enemy in what soon became known as the “war on terror.” In general, our politicians have shied away from emphasizing the Islamic dimension of the threat, in some cases arguing that the jihadis do not represent Islam at all. As President George Bush said after 9/11, “The face of terror is not the true faith of Islam,” and as President Barack Obama said about the Islamic State in 2015, “ISIL does not speak for Islam. They are thugs and killers.” Such comments might make sense in the context of public diplomacy. The question is whether they belong in the context of intelligence, where dispassionate analysis, unencumbered and unconstrained by political or other bias, is supposed to rule the day. Clearly, the IC DEIA, with its language initiative, believes they do. And according to The Dive , “our initiative is gaining traction across the USG and has been positively received by so many, including executive leaders.”

If true, this is a concerning development to say the least. If there is any place in the U.S. government where one ought to be free to assess terrorist groups on their own terms and without restrictions imposed by biased parties, it is surely the intelligence community. The IC DEIA, by seeking to constrain the terminology that analysts are allowed to use, only makes their job more difficult if not impossible.

Restricting the use of terms such as “Jihadi Salafi” and “jihadi” does not make the problem go away, it only obscures it. By narrowing the range of language, to borrow from Orwell, you inevitably “narrow the range of thought.” It is in that sense, above all, that our words matter.

View the discussion thread.

Join the Hoover Institution’s community of supporters in ideas advancing freedom.

Research on the Relationship Between Environmental and Economic Coupling Systems in Bohai Bay Area Based on a Vector Autoregression (VAR) Model

• Published: 01 April 2024
• Volume 23 , pages 557–566, ( 2024 )

Cite this article

• Huimin Cao 1 ,
• Ping Wang 2 ,
• Surong Zhang 1 ,
• Dongpo Xu 1 &
• Weijun Tian 1  

This study analyzed the impact of land-based contaminants and tertiary industrial structure on economic development in the selected Bohai Bay area, China. Based on panel data spanning 2011–2020, a vector autoregressive (VAR) model is used to analyze and forecast the short-run and long-run relationships between three industrial structures, pollutant discharge, and economic development. The results showed that the environmental index had a long-term cointegration relationship with the industrial structure economic index. Per capital chemical oxygen demand (PCOD) and per capita ammonia nitrogen (PNH 3 N) had a positive impact on delta per capita GDP (dPGDP), while per capita solid waste (PSW), the secondary industry rate (SIR) and delta tertiary industry (dTIR) had a negative impact on dPGDP. The VAR model under this coupling system had stability and credibility. The impulse response results showed that the short-term effect of the coupling system on dPGDP was basically consistent with the Granger causality test results. In addition, variance decomposition was used in this study to predict the long-term impact of the coupling system in the next ten periods ( i.e. , ten years). It was found that dTIR had a great impact on dPGDP, with a contribution rate as high as 74.35% in the tenth period, followed by the contribution rate of PCOD up to 3.94%, while the long-term contribution rates of PSW, SIR and PNH 3 N were all less than 1%. The results show that the government should support the development of the tertiary industry to maintain the vitality of economic development and prevent environmental deterioration.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Altinoz, B., and Aslan, A., 2021. New insight to tourism-environment nexus in Mediterranean countries: Evidence from panel vector autoregression approach. Environment Development and Sustainability , 24 (10): 12263–12275.

Article   Google Scholar  

Asif, M., Khan, K. B., Anser, M. K., Nassani, A. A., Abro, M. M. Q., and Zaman, K., 2020. Dynamic interaction between financial development and natural resources: Evaluating the ‘resource curse’ hypothesis. Resources Policy , 65 : 101566.

Bai, Y., Shi, K., Yu, H., Shang, N., Hao, W., Wang, C., et al. , 2022. Source apportionment of polycyclic aromatic hydrocarbons (PAHs) in a sediment core from Lake Dagze Co., Tibetan Plateau, China: Comparison of three receptor models. Journal of Environmental Sciences , 121 : 224–233.

Article   CAS   Google Scholar  

Chen, L., 2019. Research on the ocean news in Bohai Bay Party newspaper (2016–2018). Master thesis. Hebei University.

Deng, Q., and Yan, X., 2020. Information-sharing mechanism of marine environmental management in China. Journal of Coastal Research , 106 : 544–548.

Feng, J., Wang, H., and Li, H., 2007. Variation trend and pollution risk of the COD in Bohai Bay. Journal of Tianjin University , 11 : 1377–1381.

Google Scholar  

Haldar, A., and Sethi, N., 2022. Environmental effects of information and communication technology–Exploring the roles of renewable energy, innovation, trade and financial development. Renewable & Sustainable Energy Reviews , 153 : 111–754.

Khan, I., Tan, D., Hassan, S., and Bilal, T., 2022. Role of alternative and nuclear energy in stimulating environmental sustainability: Impact of government expenditures. Environmental Science and Pollution Research , 29 : 37894–37905.

Khezri, M., Heshmati, A., and Khodaei, M., 2022. Environmental implications of economic complexity and its role in determining how renewable energies affect CO (2) emissions. Applied Energy , 306 (B): 117948.

Li, S., Jiang, H., Xu, Z., and Zhang, Q., 2022. Backgrounds as a potentially important component of riverine nitrate loads. Science of the Total Environment , 838 (2): 155999.

Pedrotti, M., Lombard, F., Baudena, A., Galgani, F., Elineau, A., Petit, S., et al. , 2022. An integrative assessment of the plastic debris load in the Mediterranean Sea. Science of the Total Environment , 838 (1): 155958.

Sezen, J., 2020. Analysis of relation between sectoral breakdown of the operations with environment impact assessment (EIA) report and economic growth in Turkey. Journal of Environmental Protection and Ecology , 21 (2): 747–756.

Shen, Z., 2020. Analysis on the similarity of industrial structure of three provinces and two cities around Bohai Sea. Journal of North China University of Science and Technology (Social Science Edition) , 20 (3): 54–60.

Shi, L., 2022. Local government dual target management and environmental pollution: An empirical study based on Chinese urban data. The Theory and Practice of Finance and Economics , 43 (1): 104–113.

Song, T., 2012. Bohai reginal economic development and internal disparity analysis. Master thesis. East China University of Science and Technology.

Wang, X., Li, K., Liang, S., Zhang, L., and Wang, X., 2014. Industrial structure adjustment of Qingdao under the comprehensive constraint of economy and environment. China Environmental Science , 34 (10): 2714–2720.

Wang, Y., Lindblom, E., Zhu, Y. J., Mathews, R. E., Malmaeus, M., and Lei, K., 2021. Environmental management in the Bohai and Baltic Seas from a source-to-sea perspective: Challenges and opportunities. Water International , 46 (2): 157–175.

Wu, Y., Qi, D., Yao, H., Ren, J. Y, Hu, J. R., Lyu, Y. T., et al. , 2022. Antibiotic resistome and its driving factors in an urban river in northern China. Science of the Total Environment , 838 (3): 156536.

Yang, J., Tan, Y., and Xue, D., 2019. The impacts of globalization on city environments in developing countries: A case study of 21 cities in Guangdong Province, China. Journal of Cleaner Production , 240 : 118273.

Zhang, C., and Tan, T., 2020. Analysis of the impact of energy consumption on economic growth in Guangdong Province. 2020 6th International Conference on Energy, Environment and Materials Science , 585 : 012015.

Zhang, F., Wang, Y., and Liao, X. Y., 2022. Recognition method for the health risks of potentially toxic elements in a headwater catchment. Science of the Total Environment , 839 : 156287.

Zhang, J., and Yang, J., 2020. Research on the behavior subject of ‘Bay Chief System’ in China. Research of Administrative Science , 12 : 32–38.

CAS   Google Scholar  

Zhang, N., Guan, Y., Jiang, Y. Q., Zhang, X. Y., Ding, D., Wang, S. X., et al. , 2022. Regional demarcation of synergistic control for PM2.5 and ozone pollution in China based on long-term and massive data mining. Science of the Total Environment , 838 (2): 155975.

Zheng, F., Zhou, X., Rahat, B., and Rubbaniy, G., 2021. Carbon neutrality target for leading exporting countries: On the role of economic complexity index and renewable energy electricity. Journal of Environmental Management , 299 : 113558.

Download references

Acknowledgements

This work was supported by the research funds for Coupling Research on Industrial Upgrade and Environmental Management in the Bohai Rim–Technique, methodology, and Environmental Economic Policies (No. 42076221).

Author information

Authors and affiliations.

Key Laboratory of Marine Environment and Ecology, Ministry of Education, Ocean University of China, Qingdao, 266100, China

Huimin Cao, Surong Zhang, Dongpo Xu & Weijun Tian

Business School, Qingdao University, Qingdao, 266100, China

You can also search for this author in PubMed   Google Scholar

Corresponding authors

Correspondence to Ping Wang or Weijun Tian .

Rights and permissions

Reprints and permissions

About this article

Cao, H., Wang, P., Zhang, S. et al. Research on the Relationship Between Environmental and Economic Coupling Systems in Bohai Bay Area Based on a Vector Autoregression (VAR) Model. J. Ocean Univ. China 23 , 557–566 (2024). https://doi.org/10.1007/s11802-024-5510-7

Download citation

Received : 10 August 2022

Revised : 26 October 2022

Accepted : 02 April 2023

Published : 01 April 2024

Issue Date : April 2024

DOI : https://doi.org/10.1007/s11802-024-5510-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Bohai Bay area
• environmental pollution
• industrial structure
• cointegration theory
• impulse response
• Find a journal
• Publish with us
• Track your research