key terms definition research

Chapter Twelve: Positing a Thesis Statement and Composing a Title / Defining Key Terms

Defining Key Terms

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Earlier in this course, we discussed how to conduct a library search using key terms. Here we discuss how to present key terms. Place yourself in your audience’s position and try to anticipate their need for information. Is your audience composed mostly of novices or professionals? If they are novices, you will need to provide more definition and context for your key concepts and terms.

Because disciplinary knowledge is filled with specialized terms, an ordinary dictionary is of limited value. Disciplines like psychology, cultural studies, and history use terms in ways that are often different from the way we communicate in daily life. Some disciplines have their own dictionaries of key terms. Others may have terms scattered throughout glossaries in important primary texts and textbooks.

Key terms are the “means of exchange” in disciplines. You gain entry into the discussion by demonstrating how well you know and understand them. Some disciplinary keywords can be tricky because they mean one thing in ordinary speech but can mean something different in the discipline. For instance, in ordinary speech, we use the word  shadow  to refer to a darker area produced by an object or person between a light source and a surface. In Jungian psychology,  shadow  refers to the unconscious or unknown aspects of a personality. Sometimes there is debate within a discipline about what key terms mean or how they should be used.

To avoid confusion, define all key terms in your paper before you begin a discussion about them. Even if you think your audience knows the definition of key terms, readers want to see how  you  understand the terms before you move ahead. If a definition is contested—meaning different writers define the term in different ways—make sure you acknowledge these differences and explain why you favor one definition over the others. Cite your sources when presenting key terms and concepts.

Key Takeaways

Strategies for Conducting Literary Research Copyright © 2021 by Barry Mauer & John Venecek is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Confusion to Clarity: Definition of Terms in a Research Paper

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

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

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

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

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

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

Purpose Of Definition Of Terms In A Research Paper

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

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

Benefits Of Having A Definition Of Terms In A Research Paper

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

Clarity And Comprehension

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

Consistency And Precision

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

Common Understanding

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

Avoiding Misinterpretation

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

Accessibility For Diverse Audiences

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

Types Of Definitions

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

Lexical Definitions

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

Operational Definitions

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

Conceptual Definitions

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

Descriptive Definitions

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

Theoretical Definitions

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

Also read: Understanding What is Theoretical Framework

Types Of Terms

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

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

Technical Term

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

Legal Terms

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

Definitional Term

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

Career Privacy Term

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

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

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

Steps To Writing Definitions Of Terms

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

Step 1: Identify Key Terms

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

Step 2: Conduct Research

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

Step 3: Craft Concise Definitions

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

Step 4: Use Your Own Words

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

Step 5: Provide Examples Or Explanations

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

Step 6: Order And Format

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

Step 7: Revise And Refine

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

Step 8: Include Proper Citations

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

Step 9: Incorporate The Section Into Your Paper

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

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

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

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

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Getting Started: Library Research Strategy

• Choosing Your Topic
• Gathering Background Information
• Defining Key Terms
• Crafting a Research Question
• Gathering Relevant Information
• Evaluating Sources This link opens in a new window
• Formulating a Thesis Statement
• Avoiding Plagiarism This link opens in a new window
• Citation Styles This link opens in a new window

If you have chosen a topic, you may break the topic down into a few main concepts and then list and/or define key terms related to that concept. If you have performed some background searching, you can include some of the words that were used to describe your topic.

For example, if your topic deals with the relationship between teenage smoking and advertising in the United States, the following key terms may apply:

smoking -- tobacco -- nicotine -- cigarettes

teenage -- adolescents -- children -- teens -- youth

advertising -- marketing -- media -- commercials -- TV -- billboards

When listing the key terms or concepts of your topic, be sure to consider synonyms for these terms as well. Since research is an iterative process, you will also find additional key terms to utilize through the resources you encounter throughout your research process.

• << Previous: Gathering Background Information
• Next: Crafting a Research Question >>
• Last Updated: Mar 11, 2024 4:57 PM
• URL: https://libguides.chapman.edu/strategy

• 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
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

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.

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Academic Phrasebank

Defining terms.

• GENERAL LANGUAGE FUNCTIONS
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• Compare and contrast
• Describing trends
• Describing quantities
• Explaining causality
• Giving examples
• Signalling transition
• Writing about the past

In academic work students are often expected to give definitions of key words and phrases in order to demonstrate to their tutors that they understand these terms clearly. More generally, however, academic writers define terms so that their readers understand exactly what is meant when certain key terms are used. When important words are not clearly understood misinterpretation may result. In fact, many disagreements (academic, legal, diplomatic, personal) arise as a result of different interpretations of the same term. In academic writing, teachers and their students often have to explore these differing interpretations before moving on to study a topic.

Introductory phrases

The term ‘X’ was first used by … The term ‘X’ can be traced back to … Previous studies mostly defined X as … The term ‘X’ was introduced by Smith in her … Historically, the term ‘X’ has been used to describe … It is necessary here to clarify exactly what is meant by … This shows a need to be explicit about exactly what is meant by the word ‘X’.

Simple three-part definitions

General meanings or application of meanings

X can broadly be defined as … X can be loosely described as … X can be defined as … It encompasses … In the literature, the term tends to be used to refer to … In broad terms, X can be defined as any stimulus that is … Whereas X refers to the operations of …, Y refers to the … The broad use of the term ‘X’ is sometimes equated with … The term ‘disease’ refers to a biological event characterised by … Defined as …, X is now considered a worldwide problem and is associated with …

Indicating varying definitions

The definition of X has evolved. There are multiple definitions of X. Several definitions of X have been proposed. In the field of X, various definitions of X are found. The term ‘X’ embodies a multitude of concepts which … This term has two overlapping, even slightly confusing meanings. Widely varying definitions of X have emerged (Smith and Jones, 1999). Despite its common usage, X is used in different disciplines to mean different things. Since the definition of X varies among researchers, it is important to clarify how the term is …

Indicating difficulties in defining a term

X is a contested term. X is a rather nebulous term … X is challenging to define because … A precise definition of X has proved elusive. A generally accepted definition of X is lacking. Unfortunately, X remains a poorly defined term. There is no agreed definition on what constitutes … There is little consensus about what X actually means. There is a degree of uncertainty around the terminology in … These terms are often used interchangeably and without precision. Numerous terms are used to describe X, the most common of which are …. The definition of X varies in the literature and there is terminological confusion. Smith (2001) identified four abilities that might be subsumed under the term ‘X’: a) … ‘X’ is a term frequently used in the literature, but to date there is no consensus about … X is a commonly-used notion in psychology and yet it is a concept difficult to define precisely. Although differences of opinion still exist, there appears to be some agreement that X refers to …

Specifying terms that are used in an essay or thesis

The term ‘X’ is used here to refer to … In the present study, X is defined as … The term ‘X’ will be used solely when referring to … In this essay, the term ‘X’ will be used in its broadest sense to refer to all … In this paper, the term that will be used to describe this phenomenon is ‘X’. In this dissertation, the terms ‘X’ and ‘Y’ are used interchangeably to mean … Throughout this thesis, the term ‘X’ is used to refer to informal systems as well as … While a variety of definitions of the term ‘X’ have been suggested, this paper will use the definition first suggested by Smith (1968) who saw it as …

Referring to people’s definitions: author prominent

For Smith (2001), X means … Smith (2001) uses the term ‘X’ to refer to … Smith (1954) was apparently the first to use the term … In 1987, psychologist John Smith popularized the term ‘X’ to describe … According to a definition provided by Smith (2001:23), X is ‘the maximally … This definition is close to those of Smith (2012) and Jones (2013) who define X as … Smith, has shown that, as late as 1920, Jones was using the term ‘X’ to refer to particular … One of the first people to define nursing was Florence Nightingale (1860), who wrote: ‘… …’ Chomsky writes that a grammar is a ‘device of some sort for producing the ….’ (1957, p.11). Aristotle defines the imagination as ‘the movement which results upon an actual sensation.’ Smith  et al . (2002) have provided a new definition of health: ‘health is a state of being with …

Referring to people’s definitions: author non-prominent

X is defined by Smith (2003: 119) as ‘… …’ The term ‘X’ is used by Smith (2001) to refer to … X is, for Smith (2012), the situation which occurs when … A further definition of X is given by Smith (1982) who describes … The term ‘X’ is used by Aristotle in four overlapping senses. First, it is the underlying … X is the degree to which an assessment process or device measures … (Smith  et al ., 1986).

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Introduction to Research Methods

3 Understanding Key Research Concepts and Terms

Many terms and concepts are associated with research methods, particularly as it relates to the research planning decisions you must make along the way.  Throughout this textbook, you will be exposed to many of these terms and concepts.  Figure 1.1 is a general chart that will help you contextualize many of these terms and also understand the research process.  As you can see, Figure 1.1 begins with two key concepts: ontology and epistemology, advances through other concepts and concludes with three research methodological approaches: qualitative, quantitative and mixed methods.

However, it is important to note that research does not end with making decisions about the type of methods you will use. In fact, we could argue that the work is just beginning at this point. As such, Figure 1.1 does not represent an all-encompassing list of concepts and terms related to research methods. Keep in mind that each strategy has its own data collection and analysis approaches, which are associated with the various methodological approaches you choose.  Figure 1.1 is meant to provide a general overview of the lay of the research land. You may want to keep this figure handy as you read through the various chapters.

Ontology & epistemology

Thinking about what you know and how you know what you know involves questions of ontology and epistemology. Perhaps you have heard these concepts before in a philosophy class?  These concepts are relevant to the work of sociologists as well. As sociologists (those who undertake socially-focused research), we want to understand some aspect of our social world.  Usually, we are not starting with zero knowledge.  In fact, we usually start with some understanding of: 1) what is; 2) what can be known about what is; and, 3) what the best mechanism happens to be for learning about what is (Schmitz, 2012). In the following sections, we will define these terms and provide an example of ontology and epistemology

Ontology is a Greek word that means the study, theory, or science of being.  Ontology is concerned with the what is or the nature of reality (Saunders, Lewis, & Thornhill, 2009).  It can involve some very large and difficult to answer questions, such as: What is the purpose of life? What, if anything, exists beyond our universe?  Ontology also asks: What categories does it belong to? Is there such a thing as objective reality?  What does the verb “to be” mean?  

Ontology is comprised of two aspects: objectivism and subjectivism. Objectivism means that social entities exist externally to the social actors who are concerned with their existence. Subjectivism means that social phenomena are created from the perceptions and actions of the social actors who are concerned with their existence (Saunders, et al., 2009).  Figure 1.2 provides an example of a similar research project to be undertaken by two different students.  While the projects being proposed by the students are similar, they each have different research questions.  Read the scenario and then answer the questions that follow.

Subjectivist and objectivist approaches (adapted from Saunders et al., 2009)

Ana is an Emergency & Security Management Studies (ESMS) student at a local college. She is just beginning her capstone research project and she plans to do research at the City of Vancouver. Her research question is as follows: What is the role of City of Vancouver managers, working in the emergency management department, in enabling positive community relationships? She will be collecting data related to the roles and duties of managers in enabling positive community relationships.

Robert is also an ESMS student at the same college. He too will be undertaking his research at the City of Vancouver. His research question is as follows: What is the effect of the City of Vancouver’s corporate culture in enabling managers, working in the emergency management department, to develop a positive relationship with the local community? He will be collecting data related to perceptions of corporate culture and its effect on enabling positive community-emergency management department relationships.

Before the students begin collecting data, they learn that six months ago, the long-time emergency department manager and assistance manager both retired. They have been replaced by two senior staff managers who have Bachelor’s degrees in Emergency Services Management. These new managers are considered more up-to-date and knowledgeable on emergency services management, give their specialized academic training and practical on-the-job work experience in this department. The new managers have, essentially, the same job duties and operate under the same procedures as the managers they replaced. When Ana and Robert approach the managers to ask them to participate in their separate studies, the new managers state that they are just new on the job and probably cannot answer the research questions and they decline to participate. Ana and Robert are worried that they will need to start all over again with a new research project. They return to their supervisors to get their opinions on what they should do.

Before reading about their supervisors’ responses, answer the following questions:

• Is Ana’s research question indicative of an objectivist or a subjectivist approach?
• Is Robert’s research question indicative of an objectivist or a subjectivist approach?
• Given your answer in question 1, which managers could Ana interview (new, old, or both) for her research study? Why?
• Given your answer in question 2, which managers could Robert interview (new, old, or both) for his research study? Why?

Ana’s supervisor tells her that her research question set up for an objectivist approach. Her supervisor tells her that in her study the social entity (the City) exists in reality external to the social actors (the managers). In other words, there is a formal management structure at the City that has largely remained unchanged since the old managers left and the new ones started. The procedures remain the same regardless of whoever occupies those positions. As such, Ana using an objectivist approach, could state that the new managers have job descriptions which describe their duties and that they are a part of a formal structure with a hierarchy of people reporting to them and to whom they report to. She could further state that this hierarchy, which unique to this organization, also resembles hierarchies found in other similar organizations. As such, she can argue that the new managers will be able to speak about the role they play in enabling positive community relationships. Their answers are likely to be no different than the old managers, because the management structure and the procedures remain the same. Therefore, she can go back to the new managers and ask them to participate in her research study.

Robert’s supervisor tells him that his research sets up for a subjectivist approach because in his study the social phenomena (the effect of corporate culture on the relationship with the community) is created from the perceptions and consequent actions of the social actors (the managers). In other words, there is a continual process of social interaction, that is influenced by the corporate culture at the City, and it is these interactions that influence perceptions of the relationship with the community. The relationship is in a constant state of revision. As such, Robert, using a subjectivist approach, could state that the new managers may have had few interactions with the community members to date and therefore may not be fully cognizant of how the corporate culture affects the department’s relationship with the community. While it will be important to get the new mangers’ perceptions, he will also need to speak with the precious managers to get their perceptions from the time they were employed in their positions. This is because the community-department relationship is in a state of constant revision, which is influenced by the various managers perceptions of the corporate culture and its effect on their ability to form positive community relationships. Therefore, he can go back to the current managers and ask them to participate in his study and also ask that the department please contact the previous managers to see if they would be willing to participate in his study.

As you can see from the previous examples, it is the research question of each study that served to guide the decision as to whether the researcher should take a subjective or an objective ontological approach. This decision, in turn, guided their approach to the research study, including to whom they should interview in order to answer their respective interview questions.  We will be speaking a lot more about research questions in the upcoming chapters.

• Epistemology

Epistemology has to do with knowledge. Rather than dealing with questions about what is , epistemology deals with questions of how we know what is.  In sociology, there are many ways to uncover knowledge. We might interview people to understand public opinion about some topic, or perhaps we’ll observe them in their natural environment. We could avoid face-to-face interaction altogether by mailing people surveys for them to complete on their own or by reading what people have to say about their opinions in newspaper editorials. These methods are all ways that sociologists gain knowledge. Each method of data collection comes with its own set of epistemological assumptions about how to find things out (Schmitz, 2012). There are two main subsections of epistemology: positivist and interpretivist philosophies. We will examine these philosophies or paradigms in the following sections.

Long Descriptions

Figure 1.1 long description: The research process.

• Interpretivism
• Post-modernism
• Social constructivism
• Non-experiment
• Quasi-experiment
• Quantitative
• Qualitative
• Mixed methods
• Unobtrusive methods

[Return to Figure 1.1]

An Introduction to Research Methods in Sociology Copyright © 2019 by Valerie A. Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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• What is a Glossary? | Definition, Templates, & Examples

What Is a Glossary? | Definition, Templates, & Examples

Published on May 24, 2022 by Tegan George . Revised on July 18, 2023.

A glossary is a collection of words pertaining to a specific topic. In your thesis or dissertation , it’s a list of all terms you used that may not immediately be obvious to your reader.

Your glossary only needs to include terms that your reader may not be familiar with, and it’s intended to enhance their understanding of your work. Glossaries are not mandatory, but if you use a lot of technical or field-specific terms, it may improve readability to add one.

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Glossaries and definitions often fall into the category of common knowledge , meaning that they don’t necessarily have to be cited.

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In addition to the glossary, you can also include a list of tables and figures and a list of abbreviations in your thesis or dissertation if you choose.

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A glossary is a collection of words pertaining to a specific topic. In your thesis or dissertation, it’s a list of all terms you used that may not immediately be obvious to your reader. In contrast, dictionaries are more general collections of words.

A glossary or “glossary of terms” is a collection of words pertaining to a specific topic. In your thesis or dissertation, it’s a list of all terms you used that may not immediately be obvious to your reader. Your glossary only needs to include terms that your reader may not be familiar with, and is intended to enhance their understanding of your work.

Glossaries are not mandatory, but if you use a lot of technical or field-specific terms, it may improve readability to add one to your thesis or dissertation. Your educational institution may also require them, so be sure to check their specific guidelines.

A glossary is a collection of words pertaining to a specific topic. In your thesis or dissertation, it’s a list of all terms you used that may not immediately be obvious to your reader. In contrast, an index is a list of the contents of your work organized by page number.

Definitional terms often fall into the category of common knowledge , meaning that they don’t necessarily have to be cited. This guidance can apply to your thesis or dissertation glossary as well.

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Research Tips and Tricks

• Narrowing Topics
• Research Questions and Key Terms
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Creating Key Terms

What do you even search for once you've got your topic and research question solidified, or at least started? Well, you take the most important words in your research statement/question and use them as key terms. Use those key terms in conjunction with each other (see the section on "Combining Key Terms" for advice about how to do so). Also, use synonyms of your key terms. 

Example Key Terms

Research question:.

• What effect would paying college athletes have on collegiate sports?
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Research Methods Key Term Glossary

Last updated 22 Mar 2021

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This key term glossary provides brief definitions for the core terms and concepts covered in Research Methods for A Level Psychology.

Don't forget to also make full use of our research methods study notes and revision quizzes to support your studies and exam revision.

The researcher’s area of interest – what they are looking at (e.g. to investigate helping behaviour).

A graph that shows the data in the form of categories (e.g. behaviours observed) that the researcher wishes to compare.

Behavioural categories

Key behaviours or, collections of behaviour, that the researcher conducting the observation will pay attention to and record

In-depth investigation of a single person, group or event, where data are gathered from a variety of sources and by using several different methods (e.g. observations & interviews).

Closed questions

Questions where there are fixed choices of responses e.g. yes/no. They generate quantitative data

Co-variables

The variables investigated in a correlation

Concurrent validity

Comparing a new test with another test of the same thing to see if they produce similar results. If they do then the new test has concurrent validity

Confidentiality

Unless agreed beforehand, participants have the right to expect that all data collected during a research study will remain confidential and anonymous.

Confounding variable

An extraneous variable that varies systematically with the IV so we cannot be sure of the true source of the change to the DV

Content analysis

Technique used to analyse qualitative data which involves coding the written data into categories – converting qualitative data into quantitative data.

Control group

A group that is treated normally and gives us a measure of how people behave when they are not exposed to the experimental treatment (e.g. allowed to sleep normally).

Controlled observation

An observation study where the researchers control some variables - often takes place in laboratory setting

Correlational analysis

A mathematical technique where the researcher looks to see whether scores for two covariables are related

Counterbalancing

A way of trying to control for order effects in a repeated measures design, e.g. half the participants do condition A followed by B and the other half do B followed by A

Covert observation

Also known as an undisclosed observation as the participants do not know their behaviour is being observed

Critical value

The value that a test statistic must reach in order for the hypothesis to be accepted.

After completing the research, the true aim is revealed to the participant. Aim of debriefing = to return the person to the state s/he was in before they took part.

Involves misleading participants about the purpose of s study.

Demand characteristics

Occur when participants try to make sense of the research situation they are in and try to guess the purpose of the research or try to present themselves in a good way.

Dependent variable

The variable that is measured to tell you the outcome.

Descriptive statistics

Analysis of data that helps describe, show or summarize data in a meaningful way

Directional hypothesis

A one-tailed hypothesis that states the direction of the difference or relationship (e.g. boys are more helpful than girls).

Dispersion measure

A dispersion measure shows how a set of data is spread out, examples are the range and the standard deviation

Double blind control

Participants are not told the true purpose of the research and the experimenter is also blind to at least some aspects of the research design.

Ecological validity

The extent to which the findings of a research study are able to be generalized to real-life settings

Ethical guidelines

These are provided by the BPS - they are the ‘rules’ by which all psychologists should operate, including those carrying out research.

Ethical issues

There are 3 main ethical issues that occur in psychological research – deception, lack of informed consent and lack of protection of participants.

Evaluation apprehension

Participants’ behaviour is distorted as they fear being judged by observers

Event sampling

A target behaviour is identified and the observer records it every time it occurs

Experimental group

The group that received the experimental treatment (e.g. sleep deprivation)

External validity

Whether it is possible to generalise the results beyond the experimental setting.

Extraneous variable

Variables that if not controlled may affect the DV and provide a false impression than an IV has produced changes when it hasn’t.

Face validity

Simple way of assessing whether a test measures what it claims to measure which is concerned with face value – e.g. does an IQ test look like it tests intelligence.

Field experiment

An experiment that takes place in a natural setting where the experimenter manipulates the IV and measures the DV

A graph that is used for continuous data (e.g. test scores). There should be no space between the bars, because the data is continuous.

This is a formal statement or prediction of what the researcher expects to find. It needs to be testable.

Independent groups design

An experimental design where each participants only takes part in one condition of the IV

Independent variable

The variable that the experimenter manipulates (changes).

Inferential statistics

Inferential statistics are ways of analyzing data using statistical tests that allow the researcher to make conclusions about whether a hypothesis was supported by the results.

Informed consent

Psychologists should ensure that all participants are helped to understand fully all aspects of the research before they agree (give consent) to take part

Inter-observer reliability

The extent to which two or more observers are observing and recording behaviour in the same way

Internal validity

In relation to experiments, whether the results were due to the manipulation of the IV rather than other factors such as extraneous variables or demand characteristics.

Interval level data

Data measured in fixed units with equal distance between points on the scale

Investigator effects

These result from the effects of a researcher’s behaviour and characteristics on an investigation.

Laboratory experiment

An experiment that takes place in a controlled environment where the experimenter manipulates the IV and measures the DV

Matched pairs design

An experimental design where pairs of participants are matched on important characteristics and one member allocated to each condition of the IV

Measure of central tendency calculated by adding all the scores in a set of data together and dividing by the total number of scores

Measures of central tendency

A measurement of data that indicates where the middle of the information lies e.g. mean, median or mode

Measure of central tendency calculated by arranging scores in a set of data from lowest to highest and finding the middle score

Meta-analysis

A technique where rather than conducting new research with participants, the researchers examine the results of several studies that have already been conducted

Measure of central tendency which is the most frequently occurring score in a set of data

Natural experiment

An experiment where the change in the IV already exists rather than being manipulated by the experimenter

Naturalistic observation

An observation study conducted in the environment where the behaviour would normally occur

Negative correlation

A relationship exists between two covariables where as one increases, the other decreases

Nominal level data

Frequency count data that consists of the number of participants falling into categories. (e.g. 7 people passed their driving test first time, 6 didn’t).

Non-directional hypothesis

A two-tailed hypothesis that does not predict the direction of the difference or relationship (e.g. girls and boys are different in terms of helpfulness).

Normal distribution

An arrangement of a data that is symmetrical and forms a bell shaped pattern where the mean, median and mode all fall in the centre at the highest peak

Observed value

The value that you have obtained from conducting your statistical test

Observer bias

Occurs when the observers know the aims of the study study or the hypotheses and allow this knowledge to influence their observations

Open questions

Questions where there is no fixed response and participants can give any answer they like. They generate qualitative data.

Operationalising variables

This means clearly describing the variables (IV and DV) in terms of how they will be manipulated (IV) or measured (DV).

Opportunity sample

A sampling technique where participants are chosen because they are easily available

Order effects

Order effects can occur in a repeated measures design and refers to how the positioning of tasks influences the outcome e.g. practice effect or boredom effect on second task

Ordinal level data

Data that is capable of being out into rank order (e.g. places in a beauty contest, or ratings for attractiveness).

Overt observation

Also known as a disclosed observation as the participants given their permission for their behaviour to be observed

Participant observation

Observation study where the researcher actually joins the group or takes part in the situation they are observing.

Peer review

Before going to publication, a research report is sent other psychologists who are knowledgeable in the research topic for them to review the study, and check for any problems

Pilot study

A small scale study conducted to ensure the method will work according to plan. If it doesn’t then amendments can be made.

Positive correlation

A relationship exists between two covariables where as one increases, so does the other

Presumptive consent

Asking a group of people from the same target population as the sample whether they would agree to take part in such a study, if yes then presume the sample would

Primary data

Information that the researcher has collected him/herself for a specific purpose e.g. data from an experiment or observation

Prior general consent

Before participants are recruited they are asked whether they are prepared to take part in research where they might be deceived about the true purpose

Probability

How likely something is to happen – can be expressed as a number (0.5) or a percentage (50% change of tossing coin and getting a head)

Protection of participants

Participants should be protected from physical or mental health, including stress - risk of harm must be no greater than that to which they are exposed in everyday life

Qualitative data

Descriptive information that is expressed in words

Quantitative data

Information that can be measured and written down with numbers.

Quasi experiment

An experiment often conducted in controlled conditions where the IV simply exists so there can be no random allocation to the conditions

Questionnaire

A set of written questions that participants fill in themselves

Random sampling

A sampling technique where everyone in the target population has an equal chance of being selected

Randomisation

Refers to the practice of using chance methods (e.g. flipping a coin' to allocate participants to the conditions of an investigation

The distance between the lowest and the highest value in a set of scores.

A measure of dispersion which involves subtracting the lowest score from the highest score in a set of data

Reliability

Whether something is consistent. In the case of a study, whether it is replicable.

Repeated measures design

An experimental design where each participants takes part in both/all conditions of the IV

Representative sample

A sample that that closely matched the target population as a whole in terms of key variables and characteristics

Retrospective consent

Once the true nature of the research has been revealed, participants should be given the right to withdraw their data if they are not happy.

Right to withdraw

Participants should be aware that they can leave the study at any time, even if they have been paid to take part.

A group of people that are drawn from the target population to take part in a research investigation

Scattergram

Used to plot correlations where each pair of values is plotted against each other to see if there is a relationship between them.

Secondary data

Information that someone else has collected e.g. the work of other psychologists or government statistics

Semi-structured interview

Interview that has some pre-determined questions, but the interviewer can develop others in response to answers given by the participant

A statistical test used to analyse the direction of differences of scores between the same or matched pairs of subjects under two experimental conditions

Significance

If the result of a statistical test is significant it is highly unlikely to have occurred by chance

Single-blind control

Participants are not told the true purpose of the research

Skewed distribution

An arrangement of data that is not symmetrical as data is clustered ro one end of the distribution

Social desirability bias

Participants’ behaviour is distorted as they modify this in order to be seen in a positive light.

Standard deviation

A measure of the average spread of scores around the mean. The greater the standard deviation the more spread out the scores are. .

Standardised instructions

The instructions given to each participant are kept identical – to help prevent experimenter bias.

Standardised procedures

In every step of the research all the participants are treated in exactly the same way and so all have the same experience.

Stratified sample

A sampling technique where groups of participants are selected in proportion to their frequency in the target population

Structured interview

Interview where the questions are fixed and the interviewer reads them out and records the responses

Structured observation

An observation study using predetermined coding scheme to record the participants' behaviour

Systematic sample

A sampling technique where every nth person in a list of the target population is selected

Target population

The group that the researchers draws the sample from and wants to be able to generalise the findings to

Temporal validity

Refers to how likely it is that the time period when a study was conducted has influenced the findings and whether they can be generalised to other periods in time

Test-retest reliability

Involves presenting the same participants with the same test or questionnaire on two separate occasions and seeing whether there is a positive correlation between the two

Thematic analysis

A method for analysing qualitative data which involves identifying, analysing and reporting patterns within the data

Time sampling

A way of sampling the behaviour that is being observed by recording what happens in a series of fixed time intervals.

Type 1 error

Is a false positive. It is where you accept the alternative/experimental hypothesis when it is false

Type 2 error

Is a false negative. It is where you accept the null hypothesis when it is false

Unstructured interview

Also know as a clinical interview, there are no fixed questions just general aims and it is more like a conversation

Unstructured observation

Observation where there is no checklist so every behaviour seen is written down in an much detail as possible

Whether something is true – measures what it sets out to measure.

Volunteer sample

A sampling technique where participants put themselves forward to take part in research, often by answering an advertisement

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14 Key Terms for Psychological Research

Introduction to Psychology & Neuroscience Copyright © 2020 by Edited by Leanne Stevens is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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Research Methods – Key Terms for A Level Sociology

Last Updated on January 9, 2019 by Karl Thompson

Definitions of core concepts covered as part of the research methods component of AS and A Level Sociology. Organised in alphabetical order – so effectively this is a research methods A-Z. If this is too much for you, then have a look at my ‘ top ten research methods concepts ‘ first!

For more information about research methods in general please see my main page of links to posts on research methods in sociology !

Anthropology – the study of humans, past and present. Historically, anthropologists mostly studied traditional (e.g. tribal) cultures using participant observation as its main method, however, more recently anthropologists have increasingly focused much a greater array of aspects of culture within modern and post-modern societies using a more diverse range of methods. One of the key aims of anthropology is to explore and explain the enormous diversity as well as the commonalities within and between human cultures.

Attrition r ate – the percentage of respondents who drop out of a research study during the course of that study. This can often be a problem with longitudinal research.

Bias – where someone’s personal, subjective feelings or thoughts affect one’s judgement.

Case study – researching a single case or example of something using multiple methods, for example researching one school or factor

Closed Questions – Questions which have a limited range of answers attached to them – such as Yes/ No or Likerhert Scale answers.

Dependent and independent variables – a dependent variable is the object under study in an experiment, the independent variables are what the researcher varies to see how they effect the dependent variable.

Ethics/ ethical factors – ethics means taking into consideration how the research impacts on those involved with the research process. Ethical research should gain informed consent, ensure confidentiality, be legal and ensure that respondents and those related to them are not subjected to harm. Ultimately research should aim to do more good than harm to society.

Field d iary – A notebook in which a researcher records observation during the research process. One of the key tools of Participant Observation.

Formal content analysis – a quantitative approach to analysing mass media content which involves developing a system of classification to analyse the key features of media sources and then simply counting how many times these features occur in a given text.

Hawthorne e ffect – where respondents alter their behaviour because they know they are being observed. This is one of the biggest disadvantages of overt laboratory and field experiments.

Independent variable – see dependent variable.

Interviews – a method of gathering information by asking questions orally, either face to face or by telephone. Interviews can be individual or group and there are three main types of interview – structured, unstructured and semi-structured.

The more structured the interview, the more rigid the interview schedule will be. Before conducting an interview it is usual for the researcher to know something about the topic area and the respondents themselves, and so they will have at least some idea of the questions they are likely to ask: even if they are doing ‘unstructured interviews’ an interviewer will have some kind of interview schedule, even if it is just a list of broad topic areas to discuss, or an opening question.

Life documents – written or audio-visual sources created by individuals which record details of that person’s experiences and social actions. They are predominantly qualitative and may offer insights into people’s subjective states. They can be historical or contemporary and can take a wide variety of forms.

Multistage sampling – w ith multistage sampling, a researcher selects a sample by using combinations of different sampling methods. For example, in Stage one , a researcher might use systematic sampling, and in Stage two , he might use random sampling to select a subset for the final sample.

Objective knowledge – knowledge which is free of the biases, opinions and values of the researcher, it reflects what is really ‘out there’ in the social world.

Open-ended question – questions for which there are no set answers. Open questions allow individuals to write their own answers or dictate them to interviewers. For example ‘have you enjoyed studying Sociology this year?’

Overt research – see covert research.

Personal documents – first-hand accounts of social events and personal experiences, which generally include the writer’s feelings and attitudes about the events they think are personally significant. Examples of personal documents are letters, diaries, photo albums and autobiographies.

Practical factors – include such things as the amount of time the research will take, how much it will cost, whether you can achieve funding, opportunities for research including ease of access to respondents, and the personal skills and characteristics of the researcher.

Public documents – are produced by organisations such as government departments and their agencies as well as businesses and charities and include OFSTED and other official government enquiries. These reports are a matter of public record and should be available for anyone who wishes to see them.

Quota sampling – In this method researchers will be told to ensure the sample fits with certain quotas, for example they might be told to find 90 participants, with 30 of them being unemployed. The researcher might then find these 30 by going to a job centre. The problem of representativeness is again a problem with the quota sampling method.

Reliability – i f research is reliable, it means if someone else repeats the same research with the same population then they should achieve the same results.

R epresentative ness thus depends on who is being studied. If one’s research aim is to look at the experiences of all white male AS Sociology students studying sociology, then one’s sample should consist of all white, male sociology students. If one wishes to study sociology students in general, one will need to have a proportionate amount of AS/ A2 students as well as a range of genders and ethnicities in order to reflect the wider student body.

S ampling f rame – a list from which a sample will be drawn.

Semi- s tructured interviews – those in which res earchers have a pre-determined list of questions to ask respondents , but are free to ask further, differentiated questions based on the responses given.

Social surveys are written in advance by the researcher and tend to to be pre-coded and have a limited number of closed-questions and they tend to focus on relatively simple topics. A good example is the UK National Census. Social surveys can be administered (carried out) in a number of different ways – they might be self-completion (completed by the respondents themselves) or they might take the form of a structured interview on the high street, as is the case with some market research.

Structured or formal interviews – those in which the interviewer asks the interviewee the same questions in the same way to different respondents. This will typically involve reading out questions from a pre-written and pre-coded structured questionnaire.

Target p opulation – all people who could potentially be studied as part of the research.

Thematic a nalysis – involves trying to understand the intentions which lie behind the production of mass media documents by subjecting a particular area of reportage to detailed investigation.

Transcription – the process of writing down (or typing up) what respondents say in an interview. In order to be able to transcribe effectively interviews will need to be recorded.

Validity – r esearch is valid if it provides a true picture of what is really ‘out there’ in the world.

Verstehen – a German word meaning to ‘understand in a deep way’ – in order to achieve ‘Verstehen’ a researcher aims to understand another person’s experience by putting themselves in the other person’s shoes.

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Data Point: A data point is one particular number or item from a data set.

Data Set: A data set is simply a group of numbers. In formal mathematics, data sets are distinguished from each other by using brackets. A more formal mathematical definition allows a data set to contain other things besides numbers (such as letters, items, or even concepts and ideas). The following data set contains only the numbers 2, 5, and 7.

Distribution: A distribution is simply how the data points are clustered. Are they spread apart evenly, or do most of them cluster in the middle and fall off towards the edge like a bell-shaped curve? Two data sets may have the same mean or median, but having different distributions gives them radically different properties.

Mean: The mean (or arithmetic mean) is what most people are referring to when the say average. It is simply the total sum of all the numbers in a data set, divided by the number of different data points.

Median: The middle data point in a data set.

Mode: The most common data point in a data set. This is the value that occurs with greatest frequency.

Population: A population is all of the members contained within a group. In statistics, the population is the group you want your results to generalize about. For example, if you are studying a particular species of fish. such as a Yellow Fin Tuna, then your population is all Yellow Fin Tuna. Your population would not be all fish, nor would your population be all the different species of tuna.

Sample: A sample is all of the units or members that you have studied, drawn from a larger population. In our tuna example, researchers may have found 50 particular yellow fin tuna to study. The sample therefore would consist of 50 yellow fin tuna. As a researcher, you hope that your sample is as representative of your population as possible. The closer the sample represents the population, the stronger and more accurate an inference drawn from the sample will be. This is why you want a large sample to study from.

T-test: A t-test is a common statistical test used to compare two groups, typically two groups' means (the difference of two means divided by a measure of variability). A t-test takes into account the number of units in the sample.

Glossary of Key Terms in Educational Research

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Science and the scientific method: Definitions and examples

Here's a look at the foundation of doing science — the scientific method.

The scientific method

Hypothesis, theory and law, a brief history of science, additional resources, bibliography.

Science is a systematic and logical approach to discovering how things in the universe work. It is also the body of knowledge accumulated through the discoveries about all the things in the universe. 

The word "science" is derived from the Latin word "scientia," which means knowledge based on demonstrable and reproducible data, according to the Merriam-Webster dictionary . True to this definition, science aims for measurable results through testing and analysis, a process known as the scientific method. Science is based on fact, not opinion or preferences. The process of science is designed to challenge ideas through research. One important aspect of the scientific process is that it focuses only on the natural world, according to the University of California, Berkeley . Anything that is considered supernatural, or beyond physical reality, does not fit into the definition of science.

When conducting research, scientists use the scientific method to collect measurable, empirical evidence in an experiment related to a hypothesis (often in the form of an if/then statement) that is designed to support or contradict a scientific theory .

"As a field biologist, my favorite part of the scientific method is being in the field collecting the data," Jaime Tanner, a professor of biology at Marlboro College, told Live Science. "But what really makes that fun is knowing that you are trying to answer an interesting question. So the first step in identifying questions and generating possible answers (hypotheses) is also very important and is a creative process. Then once you collect the data you analyze it to see if your hypothesis is supported or not."

The steps of the scientific method go something like this, according to Highline College :

• Make an observation or observations.
• Form a hypothesis — a tentative description of what's been observed, and make predictions based on that hypothesis.
• Test the hypothesis and predictions in an experiment that can be reproduced.
• Analyze the data and draw conclusions; accept or reject the hypothesis or modify the hypothesis if necessary.
• Reproduce the experiment until there are no discrepancies between observations and theory. "Replication of methods and results is my favorite step in the scientific method," Moshe Pritsker, a former post-doctoral researcher at Harvard Medical School and CEO of JoVE, told Live Science. "The reproducibility of published experiments is the foundation of science. No reproducibility — no science."

Some key underpinnings to the scientific method:

• The hypothesis must be testable and falsifiable, according to North Carolina State University . Falsifiable means that there must be a possible negative answer to the hypothesis.
• Research must involve deductive reasoning and inductive reasoning . Deductive reasoning is the process of using true premises to reach a logical true conclusion while inductive reasoning uses observations to infer an explanation for those observations.
• An experiment should include a dependent variable (which does not change) and an independent variable (which does change), according to the University of California, Santa Barbara .
• An experiment should include an experimental group and a control group. The control group is what the experimental group is compared against, according to Britannica .

The process of generating and testing a hypothesis forms the backbone of the scientific method. When an idea has been confirmed over many experiments, it can be called a scientific theory. While a theory provides an explanation for a phenomenon, a scientific law provides a description of a phenomenon, according to The University of Waikato . One example would be the law of conservation of energy, which is the first law of thermodynamics that says that energy can neither be created nor destroyed. 

A law describes an observed phenomenon, but it doesn't explain why the phenomenon exists or what causes it. "In science, laws are a starting place," said Peter Coppinger, an associate professor of biology and biomedical engineering at the Rose-Hulman Institute of Technology. "From there, scientists can then ask the questions, 'Why and how?'"

Laws are generally considered to be without exception, though some laws have been modified over time after further testing found discrepancies. For instance, Newton's laws of motion describe everything we've observed in the macroscopic world, but they break down at the subatomic level.

This does not mean theories are not meaningful. For a hypothesis to become a theory, scientists must conduct rigorous testing, typically across multiple disciplines by separate groups of scientists. Saying something is "just a theory" confuses the scientific definition of "theory" with the layperson's definition. To most people a theory is a hunch. In science, a theory is the framework for observations and facts, Tanner told Live Science.

The earliest evidence of science can be found as far back as records exist. Early tablets contain numerals and information about the solar system , which were derived by using careful observation, prediction and testing of those predictions. Science became decidedly more "scientific" over time, however.

1200s: Robert Grosseteste developed the framework for the proper methods of modern scientific experimentation, according to the Stanford Encyclopedia of Philosophy. His works included the principle that an inquiry must be based on measurable evidence that is confirmed through testing.

1400s: Leonardo da Vinci began his notebooks in pursuit of evidence that the human body is microcosmic. The artist, scientist and mathematician also gathered information about optics and hydrodynamics.

1500s: Nicolaus Copernicus advanced the understanding of the solar system with his discovery of heliocentrism. This is a model in which Earth and the other planets revolve around the sun, which is the center of the solar system.

1600s: Johannes Kepler built upon those observations with his laws of planetary motion. Galileo Galilei improved on a new invention, the telescope, and used it to study the sun and planets. The 1600s also saw advancements in the study of physics as Isaac Newton developed his laws of motion.

1700s: Benjamin Franklin discovered that lightning is electrical. He also contributed to the study of oceanography and meteorology. The understanding of chemistry also evolved during this century as Antoine Lavoisier, dubbed the father of modern chemistry , developed the law of conservation of mass.

1800s: Milestones included Alessandro Volta's discoveries regarding electrochemical series, which led to the invention of the battery. John Dalton also introduced atomic theory, which stated that all matter is composed of atoms that combine to form molecules. The basis of modern study of genetics advanced as Gregor Mendel unveiled his laws of inheritance. Later in the century, Wilhelm Conrad Röntgen discovered X-rays , while George Ohm's law provided the basis for understanding how to harness electrical charges.

1900s: The discoveries of Albert Einstein , who is best known for his theory of relativity, dominated the beginning of the 20th century. Einstein's theory of relativity is actually two separate theories. His special theory of relativity, which he outlined in a 1905 paper, " The Electrodynamics of Moving Bodies ," concluded that time must change according to the speed of a moving object relative to the frame of reference of an observer. His second theory of general relativity, which he published as " The Foundation of the General Theory of Relativity ," advanced the idea that matter causes space to curve.

In 1952, Jonas Salk developed the polio vaccine , which reduced the incidence of polio in the United States by nearly 90%, according to Britannica . The following year, James D. Watson and Francis Crick discovered the structure of DNA , which is a double helix formed by base pairs attached to a sugar-phosphate backbone, according to the National Human Genome Research Institute .

2000s: The 21st century saw the first draft of the human genome completed, leading to a greater understanding of DNA. This advanced the study of genetics, its role in human biology and its use as a predictor of diseases and other disorders, according to the National Human Genome Research Institute .

• This video from City University of New York delves into the basics of what defines science.
• Learn about what makes science science in this book excerpt from Washington State University .
• This resource from the University of Michigan — Flint explains how to design your own scientific study.

Merriam-Webster Dictionary, Scientia. 2022. https://www.merriam-webster.com/dictionary/scientia

University of California, Berkeley, "Understanding Science: An Overview." 2022. ​​ https://undsci.berkeley.edu/article/0_0_0/intro_01  

Highline College, "Scientific method." July 12, 2015. https://people.highline.edu/iglozman/classes/astronotes/scimeth.htm  

North Carolina State University, "Science Scripts." https://projects.ncsu.edu/project/bio183de/Black/science/science_scripts.html  

University of California, Santa Barbara. "What is an Independent variable?" October 31,2017. http://scienceline.ucsb.edu/getkey.php?key=6045  

Encyclopedia Britannica, "Control group." May 14, 2020. https://www.britannica.com/science/control-group  

The University of Waikato, "Scientific Hypothesis, Theories and Laws." https://sci.waikato.ac.nz/evolution/Theories.shtml  

Stanford Encyclopedia of Philosophy, Robert Grosseteste. May 3, 2019. https://plato.stanford.edu/entries/grosseteste/  

Encyclopedia Britannica, "Jonas Salk." October 21, 2021. https://www.britannica.com/ biography /Jonas-Salk

National Human Genome Research Institute, "​Phosphate Backbone." https://www.genome.gov/genetics-glossary/Phosphate-Backbone  

National Human Genome Research Institute, "What is the Human Genome Project?" https://www.genome.gov/human-genome-project/What  

‌ Live Science contributor Ashley Hamer updated this article on Jan. 16, 2022.

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Complementary, Alternative, or Integrative Health: What’s In a Name?

We’ve all seen the words “complementary,” “alternative,” and “integrative,” but what do they really mean?

This fact sheet looks into these terms to help you understand them better and gives you a brief picture of the mission and role of the National Center for Complementary and Integrative Health (NCCIH) in this area of research. The terms “complementary,” “alternative,” and “integrative” are continually evolving, along with the field, but the descriptions of these terms below are how we at the National Institutes of Health currently define them.

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} Complementary Versus Alternative

According to a 2012 national survey, many Americans—more than 30 percent of adults and about 12 percent of children—use health care approaches that are not typically part of conventional medical care or that may have origins outside of usual Western practice. When describing these approaches, people often use “alternative” and “complementary” interchangeably, but the two terms refer to different concepts:

• If a non-mainstream approach is used  together with  conventional medicine, it’s considered “complementary.”
• If a non-mainstream approach is used  in place of  conventional medicine, it’s considered “alternative.”

Most people who use non-mainstream approaches also use conventional health care.

In addition to the terms complementary and alternative, you may also hear the term “functional medicine.” This term sometimes refers to a concept similar to integrative health (described below), but it may also refer to an approach that more closely resembles  naturopathy  (a medical system that has evolved from a combination of traditional practices and health care approaches popular in Europe during the 19th century).

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Integrative health brings conventional and complementary approaches together in a coordinated way. Integrative health also emphasizes multimodal interventions, which are two or more interventions such as conventional health care approaches (like medication, physical rehabilitation, psychotherapy), and complementary health approaches (like acupuncture, yoga, and probiotics) in various combinations, with an emphasis on treating the whole person rather than, for example, one organ system. Integrative health aims for well-coordinated care among different providers and institutions by bringing conventional and complementary approaches together to care for the whole person.

The use of integrative approaches to health and wellness has grown within care settings across the United States. Researchers are currently exploring the potential benefits of integrative health in a variety of situations, including pain management for military personnel and veterans, relief of symptoms in cancer patients and survivors, and programs to promote healthy behaviors.

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Whole person health refers to helping individuals, families, communities, and populations improve and restore their health in multiple interconnected domains—biological, behavioral, social, environmental—rather than just treating disease. Research on whole person health includes expanding the understanding of the connections between these various aspects of health, including connections between organs and body systems.

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• An NCCIH-funded study is developing an innovative, collaborative treatment model involving chiropractors, primary care providers, and mental health providers for veterans with spine pain and related mental health conditions.
• Other NCCIH-funded studies are testing the effects of adding mindfulness meditation, self-hypnosis, or other complementary approaches to pain management programs for veterans. The goal is to help patients feel and function better and reduce their need for pain medicines that can have serious side effects.
• For more information on pain management for military personnel and veterans, see NCCIH’s  Complementary Health Practices for U.S. Military, Veterans, and Families  webpage.

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• Massage therapy may lead to short-term improvements in pain and mood in patients with advanced cancer.
• Yoga may relieve the persistent fatigue that some women experience after breast cancer treatment, according to the results of a preliminary study.
• Tai chi or qigong have shown promise for managing symptoms such as fatigue, sleep difficulty, and depression in cancer survivors.
• For more information, see  NCCIH’s fact sheet on cancer .

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• Preliminary research suggests that yoga and meditation-based therapies may help smokers quit.
• In a study funded by the National Cancer Institute, complementary health practitioners (chiropractors, acupuncturists, and massage therapists) were successfully trained to provide evidence-based smoking cessation interventions to their patients.
• An NCCIH-funded study is testing whether a mindfulness-based program that involves the whole family can improve weight loss and eating behavior in adolescents who are overweight.
• For more information, see the NCCIH  Quitting Smoking  and  Weight Control  webpages.

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Complementary approaches can be classified by their primary therapeutic input (how the therapy is taken in or delivered), which may be:

• Nutritional (e.g., special diets, dietary supplements, herbs, and probiotics)
• Psychological (e.g., mindfulness)
• Physical (e.g., massage, spinal manipulation)
• Combinations such as psychological and physical (e.g., yoga, tai chi, acupuncture, dance or art therapies) or psychological and nutritional (e.g., mindful eating)

Nutritional approaches include what NCCIH previously categorized as natural products, whereas psychological and/or physical approaches include what was referred to as mind and body practices.

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These approaches include a variety of products, such as  herbs   (also known as botanicals),  vitamins and minerals , and  probiotics . They are widely marketed, readily available to consumers, and often sold as  dietary supplements .

According to the 2012 National Health Interview Survey (NHIS), which included a comprehensive survey on the use of complementary health approaches by Americans, 17.7 percent of American adults had used a dietary supplement other than vitamins and minerals in the past year. These products were the most popular complementary health approach in the survey. (See chart.) The most commonly used nonvitamin, nonmineral dietary supplement was fish oil.

Researchers have done large, rigorous studies on a few dietary supplements, but the results often showed that the products didn’t work for the conditions studied. Research on others is in progress. While there are indications that some may be helpful, more needs to be learned about the effects of these products in the human body, and about their  safety  and potential  interactions with medicines  and other natural products.

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Complementary physical and/or psychological approaches include tai chi , yoga , acupuncture , massage therapy , spinal manipulation , art therapy, music therapy, dance, mindfulness-based stress reduction, and many others. These approaches are often administered or taught by a trained practitioner or teacher. The 2012 NHIS showed that yoga, chiropractic and osteopathic manipulation , and meditation are among the most popular complementary health approaches used by adults. According to the 2017 NHIS , the popularity of yoga has grown dramatically in recent years, from 9.5 percent of U.S. adults practicing yoga in 2012 to 14.3 percent in 2017. The 2017 NHIS also showed that the use of meditation increased more than threefold from 4.1 percent in 2012 to 14.2 percent in 2017.

Other psychological and physical approaches include relaxation techniques   (such as breathing exercises and guided imagery),  qigong ,  hypnotherapy , Feldenkrais method, Alexander technique, Pilates, Rolfing Structural Integration, and Trager psychophysical integration.

Research findings suggest that several psychological and physical approaches, alone or in combination, are helpful for a variety of conditions. A few examples include the following:

• Acupuncture  may help ease types of pain that are often chronic, such as low-back pain, neck pain, and osteoarthritis/knee pain. Acupuncture may also help reduce the frequency of tension headaches and prevent migraine headaches.
• Meditation  may help reduce blood pressure, symptoms of anxiety and depression, and symptoms of irritable bowel syndrome and flare-ups in people with ulcerative colitis. Meditation may also benefit people with insomnia.
• Tai chi  appears to help improve balance and stability, reduce back pain and pain from knee osteoarthritis, and improve quality of life in people with heart disease, cancer, and other chronic illnesses.
• Yoga  may benefit people’s general wellness by relieving stress, supporting good health habits, and improving mental/emotional health, sleep, and balance. Yoga may also help with low-back pain and neck pain, anxiety or depressive symptoms associated with difficult life situations, quitting smoking, and quality of life for people with chronic diseases.

The amount of research on psychological and physical approaches varies widely depending on the practice. For example, researchers have done many studies on acupuncture, yoga, spinal manipulation, and meditation, but there have been fewer studies on some other approaches.

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Some complementary approaches may not neatly fit into either of these groups—for example, the practices of traditional healers, Ayurvedic medicine , traditional Chinese medicine , homeopathy , naturopathy , and functional medicine.

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NCCIH is the Federal Government’s lead agency for scientific research on complementary and integrative health approaches.

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The mission of NCCIH is to determine, through rigorous scientific investigation, the fundamental science, usefulness, and safety of complementary and integrative health approaches and their roles in improving health and health care.

NCCIH’s vision is that scientific evidence informs decision making by the public, by health care professionals, and by health policymakers regarding the integrated use of complementary health approaches in a whole person health framework.

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Nccih strategic plan.

NCCIH’s current strategic plan, Strategic Plan FY 2021 – 2025: Mapping a Pathway to Research on Whole Person Health , presents a series of goals and objectives to guide us in determining priorities for future research on complementary health approaches. 

NCCIH Clearinghouse

The NCCIH Clearinghouse provides information on NCCIH and complementary and integrative health approaches, including publications and searches of Federal databases of scientific and medical literature. The Clearinghouse does not provide medical advice, treatment recommendations, or referrals to practitioners.

Toll-free in the U.S.: 1-888-644-6226

Telecommunications relay service (TRS): 7-1-1

Website: https://www.nccih.nih.gov

Email: [email protected] (link sends email)

This publication is not copyrighted and is in the public domain. Duplication is encouraged.

NCCIH has provided this material for your information. It is not intended to substitute for the medical expertise and advice of your health care provider(s). We encourage you to discuss any decisions about treatment or care with your health care provider. The mention of any product, service, or therapy is not an endorsement by NCCIH.

Related Topics

NCCIH Strategic Plan FY 2021–⁠2025 Mapping a Pathway to Research on Whole Person Health

Related Fact Sheets

Are You Considering a Complementary Health Approach?

Be an Informed Consumer

Glossary of Key Research Terms

This glossary provides definitions of many of the terms used in the guides to conducting qualitative and quantitative research. The definitions were developed by members of the research methods seminar (E600) taught by Mike Palmquist in the 1990s and 2000s.

Citation Information

Members of the Research Methods Seminar (E600) taught by Mike Palmquist in the 1990s and 2000s. (1994-2024). Glossary of Key Terms. The WAC Clearinghouse. Colorado State University. Available at https://wac.colostate.edu/repository/writing/guides/.

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What the data says about gun deaths in the U.S.

More Americans died of gun-related injuries in 2021 than in any other year on record, according to the latest available statistics from the Centers for Disease Control and Prevention (CDC). That included record numbers of both gun murders and gun suicides. Despite the increase in such fatalities, the rate of gun deaths – a statistic that accounts for the nation’s growing population – remained below the levels of earlier decades.

Here’s a closer look at gun deaths in the United States, based on a Pew Research Center analysis of data from the CDC, the FBI and other sources. You can also read key public opinion findings about U.S. gun violence and gun policy .

This Pew Research Center analysis examines the changing number and rate of gun deaths in the United States. It is based primarily on data from the Centers for Disease Control and Prevention (CDC) and the Federal Bureau of Investigation (FBI). The CDC’s statistics are based on information contained in official death certificates, while the FBI’s figures are based on information voluntarily submitted by thousands of police departments around the country.

For the number and rate of gun deaths over time, we relied on mortality statistics in the CDC’s WONDER database covering four distinct time periods:  1968 to 1978 ,  1979 to 1998 ,  1999 to 2020 , and 2021 . While these statistics are mostly comparable for the full 1968-2021 period, gun murders and suicides between 1968 and 1978 are classified by the CDC as involving firearms  and  explosives; those between 1979 and 2021 are classified as involving firearms only. Similarly, gun deaths involving law enforcement between 1968 and 1978 exclude those caused by “operations of war”; those between 1979 and 2021 include that category, which refers to gun deaths among military personnel or civilians  due to war or civil insurrection in the U.S . All CDC gun death estimates in this analysis are adjusted to account for age differences over time and across states.

The FBI’s statistics about the types of firearms used in gun murders in 2020 come from the bureau’s  Crime Data Explorer website . Specifically, they are drawn from the expanded homicide tables of the agency’s  2020 Crime in the United States report . The FBI’s statistics include murders and non-negligent manslaughters involving firearms.

How many people die from gun-related injuries in the U.S. each year?

In 2021, the most recent year for which complete data is available, 48,830 people died from gun-related injuries in the U.S., according to the CDC. That figure includes gun murders and gun suicides, along with three less common types of gun-related deaths tracked by the CDC: those that were accidental, those that involved law enforcement and those whose circumstances could not be determined. The total excludes deaths in which gunshot injuries played a contributing, but not principal, role. (CDC fatality statistics are based on information contained in official death certificates, which identify a single cause of death.)

What share of U.S. gun deaths are murders and what share are suicides?

Though they tend to get less public attention than gun-related murders, suicides have long accounted for the majority of U.S. gun deaths . In 2021, 54% of all gun-related deaths in the U.S. were suicides (26,328), while 43% were murders (20,958), according to the CDC. The remaining gun deaths that year were accidental (549), involved law enforcement (537) or had undetermined circumstances (458).

What share of all murders and suicides in the U.S. involve a gun?

About eight-in-ten U.S. murders in 2021 – 20,958 out of 26,031, or 81% – involved a firearm. That marked the highest percentage since at least 1968, the earliest year for which the CDC has online records. More than half of all suicides in 2021 – 26,328 out of 48,183, or 55% – also involved a gun, the highest percentage since 2001.

How has the number of U.S. gun deaths changed over time?

The record 48,830 total gun deaths in 2021 reflect a 23% increase since 2019, before the onset of the coronavirus pandemic .

Gun murders, in particular, have climbed sharply during the pandemic, increasing 45% between 2019 and 2021, while the number of gun suicides rose 10% during that span.

The overall increase in U.S. gun deaths since the beginning of the pandemic includes an especially stark rise in such fatalities among children and teens under the age of 18. Gun deaths among children and teens rose 50% in just two years , from 1,732 in 2019 to 2,590 in 2021.

How has the rate of U.S. gun deaths changed over time?

While 2021 saw the highest total number of gun deaths in the U.S., this statistic does not take into account the nation’s growing population. On a per capita basis, there were 14.6 gun deaths per 100,000 people in 2021 – the highest rate since the early 1990s, but still well below the peak of 16.3 gun deaths per 100,000 people in 1974.

The gun murder rate in the U.S. remains below its peak level despite rising sharply during the pandemic. There were 6.7 gun murders per 100,000 people in 2021, below the 7.2 recorded in 1974.

The gun suicide rate, on the other hand, is now on par with its historical peak. There were 7.5 gun suicides per 100,000 people in 2021, statistically similar to the 7.7 measured in 1977. (One caveat when considering the 1970s figures: In the CDC’s database, gun murders and gun suicides between 1968 and 1978 are classified as those caused by firearms and explosives. In subsequent years, they are classified as deaths involving firearms only.)

Which states have the highest and lowest gun death rates in the U.S.?

The rate of gun fatalities varies widely from state to state. In 2021, the states with the highest total rates of gun-related deaths – counting murders, suicides and all other categories tracked by the CDC – included Mississippi (33.9 per 100,000 people), Louisiana (29.1), New Mexico (27.8), Alabama (26.4) and Wyoming (26.1). The states with the lowest total rates included Massachusetts (3.4), Hawaii (4.8), New Jersey (5.2), New York (5.4) and Rhode Island (5.6).

The results are somewhat different when looking at gun murder and gun suicide rates separately. The places with the highest gun murder rates in 2021 included the District of Columbia (22.3 per 100,000 people), Mississippi (21.2), Louisiana (18.4), Alabama (13.9) and New Mexico (11.7). Those with the lowest gun murder rates included Massachusetts (1.5), Idaho (1.5), Hawaii (1.6), Utah (2.1) and Iowa (2.2). Rate estimates are not available for Maine, New Hampshire, Vermont or Wyoming.

The states with the highest gun suicide rates in 2021 included Wyoming (22.8 per 100,000 people), Montana (21.1), Alaska (19.9), New Mexico (13.9) and Oklahoma (13.7). The states with the lowest gun suicide rates were Massachusetts (1.7), New Jersey (1.9), New York (2.0), Hawaii (2.8) and Connecticut (2.9). Rate estimates are not available for the District of Columbia.

How does the gun death rate in the U.S. compare with other countries?

The gun death rate in the U.S. is much higher than in most other nations, particularly developed nations. But it is still far below the rates in several Latin American countries, according to a 2018 study of 195 countries and territories by researchers at the Institute for Health Metrics and Evaluation at the University of Washington.

The U.S. gun death rate was 10.6 per 100,000 people in 2016, the most recent year in the study, which used a somewhat different methodology from the CDC. That was far higher than in countries such as Canada (2.1 per 100,000) and Australia (1.0), as well as European nations such as France (2.7), Germany (0.9) and Spain (0.6). But the rate in the U.S. was much lower than in El Salvador (39.2 per 100,000 people), Venezuela (38.7), Guatemala (32.3), Colombia (25.9) and Honduras (22.5), the study found. Overall, the U.S. ranked 20th in its gun fatality rate that year .

How many people are killed in mass shootings in the U.S. every year?

This is a difficult question to answer because there is no single, agreed-upon definition of the term “mass shooting.” Definitions can vary depending on factors including the number of victims and the circumstances of the shooting.

The FBI collects data on “active shooter incidents,” which it defines as “one or more individuals actively engaged in killing or attempting to kill people in a populated area.” Using the FBI’s definition, 103 people – excluding the shooters – died in such incidents in 2021 .

The Gun Violence Archive, an online database of gun violence incidents in the U.S., defines mass shootings as incidents in which four or more people are shot, even if no one was killed (again excluding the shooters). Using this definition, 706 people died in these incidents in 2021 .

Regardless of the definition being used, fatalities in mass shooting incidents in the U.S. account for a small fraction of all gun murders that occur nationwide each year.

How has the number of mass shootings in the U.S. changed over time?

The same definitional issue that makes it challenging to calculate mass shooting fatalities comes into play when trying to determine the frequency of U.S. mass shootings over time. The unpredictability of these incidents also complicates matters: As Rand Corp. noted in a research brief , “Chance variability in the annual number of mass shooting incidents makes it challenging to discern a clear trend, and trend estimates will be sensitive to outliers and to the time frame chosen for analysis.”

The FBI found an increase in active shooter incidents between 2000 and 2021. There were three such incidents in 2000. By 2021, that figure had increased to 61.

Which types of firearms are most commonly used in gun murders in the U.S.?

In 2020, the most recent year for which the FBI has published data, handguns were involved in 59% of the 13,620 U.S. gun murders and non-negligent manslaughters for which data is available. Rifles – the category that includes guns sometimes referred to as “assault weapons” – were involved in 3% of firearm murders. Shotguns were involved in 1%. The remainder of gun homicides and non-negligent manslaughters (36%) involved other kinds of firearms or those classified as “type not stated.”

It’s important to note that the FBI’s statistics do not capture the details on all gun murders in the U.S. each year. The FBI’s data is based on information voluntarily submitted by police departments around the country, and not all agencies participate or provide complete information each year.

Note: This is an update of a post originally published on Aug. 16, 2019.

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Climate change refers to long-term shifts in temperatures and weather patterns. Such shifts can be natural, due to changes in the sun’s activity or large volcanic eruptions. But since the 1800s, human activities have been the main driver of climate change , primarily due to the burning of fossil fuels like coal, oil and gas.

Burning fossil fuels generates greenhouse gas emissions that act like a blanket wrapped around the Earth, trapping the sun’s heat and raising temperatures.

The main greenhouse gases that are causing climate change include carbon dioxide and methane. These come from using gasoline for driving a car or coal for heating a building, for example. Clearing land and cutting down forests can also release carbon dioxide. Agriculture, oil and gas operations are major sources of methane emissions. Energy, industry, transport, buildings, agriculture and land use are among the main sectors  causing greenhouse gases.

Humans are responsible for global warming

Climate scientists have showed that humans are responsible for virtually all global heating over the last 200 years. Human activities like the ones mentioned above are causing greenhouse gases that are warming the world faster than at any time in at least the last two thousand years.

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People are experiencing climate change in diverse ways

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Every increase in global warming matters

In a series of UN reports , thousands of scientists and government reviewers agreed that limiting global temperature rise to no more than 1.5°C would help us avoid the worst climate impacts and maintain a livable climate. Yet policies currently in place point to a 3°C temperature rise by the end of the century.

The emissions that cause climate change come from every part of the world and affect everyone, but some countries produce much more than others .The seven biggest emitters alone (China, the United States of America, India, the European Union, Indonesia, the Russian Federation, and Brazil) accounted for about half of all global greenhouse gas emissions in 2020.

Everyone must take climate action, but people and countries creating more of the problem have a greater responsibility to act first.

We face a huge challenge but already know many solutions

Many climate change solutions can deliver economic benefits while improving our lives and protecting the environment. We also have global frameworks and agreements to guide progress, such as the Sustainable Development Goals , the UN Framework Convention on Climate Change and the Paris Agreement . Three broad categories of action are: cutting emissions, adapting to climate impacts and financing required adjustments.

Switching energy systems from fossil fuels to renewables like solar or wind will reduce the emissions driving climate change. But we have to act now. While a growing number of countries is committing to net zero emissions by 2050, emissions must be cut in half by 2030 to keep warming below 1.5°C. Achieving this means huge declines in the use of coal, oil and gas: over two-thirds of today’s proven reserves of fossil fuels need to be kept in the ground by 2050 in order to prevent catastrophic levels of climate change.

Adapting to climate consequences protects people, homes, businesses, livelihoods, infrastructure and natural ecosystems. It covers current impacts and those likely in the future. Adaptation will be required everywhere, but must be prioritized now for the most vulnerable people with the fewest resources to cope with climate hazards. The rate of return can be high. Early warning systems for disasters, for instance, save lives and property, and can deliver benefits up to 10 times the initial cost.

We can pay the bill now, or pay dearly in the future

Climate action requires significant financial investments by governments and businesses. But climate inaction is vastly more expensive. One critical step is for industrialized countries to fulfil their commitment to provide $100 billion a year to developing countries so they can adapt and move towards greener economies.

To get familiar with some of the more technical terms used in connection with climate change, consult the Climate Dictionary .

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What Is Big Data?

Sherry Tiao | Senior Manager, AI & Analytics, Oracle | March 11, 2024

In This Article

Big Data Defined

The three “vs” of big data, the value—and truth—of big data, the history of big data, big data use cases, big data challenges, how big data works, big data best practices.

What exactly is big data?

The definition of big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three “Vs.”

Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before.

Two more Vs have emerged over the past few years: value and veracity . Data has intrinsic value. But it’s of no use until that value is discovered. Equally important: How truthful is your data—and how much can you rely on it?

Today, big data has become capital. Think of some of the world’s biggest tech companies. A large part of the value they offer comes from their data, which they’re constantly analyzing to produce more efficiency and develop new products.

Recent technological breakthroughs have exponentially reduced the cost of data storage and compute, making it easier and less expensive to store more data than ever before. With an increased volume of big data now cheaper and more accessible, you can make more accurate and precise business decisions.

Finding value in big data isn’t only about analyzing it (which is a whole other benefit). It’s an entire discovery process that requires insightful analysts, business users, and executives who ask the right questions, recognize patterns, make informed assumptions, and predict behavior.

But how did we get here?

Although the concept of big data itself is relatively new, the origins of large data sets go back to the 1960s and ‘70s when the world of data was just getting started with the first data centers and the development of the relational database.

Around 2005, people began to realize just how much data users generated through Facebook, YouTube, and other online services. Hadoop (an open source framework created specifically to store and analyze big data sets) was developed that same year. NoSQL also began to gain popularity during this time.

The development of open source frameworks, such as Hadoop (and more recently, Spark) was essential for the growth of big data because they make big data easier to work with and cheaper to store. In the years since then, the volume of big data has skyrocketed. Users are still generating huge amounts of data—but it’s not just humans who are doing it.

With the advent of the Internet of Things (IoT), more objects and devices are connected to the internet, gathering data on customer usage patterns and product performance. The emergence of machine learning has produced still more data.

While big data has come far, its usefulness is only just beginning. Cloud computing has expanded big data possibilities even further. The cloud offers truly elastic scalability, where developers can simply spin up ad hoc clusters to test a subset of data. And graph databases are becoming increasingly important as well, with their ability to display massive amounts of data in a way that makes analytics fast and comprehensive.

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Big Data Benefits

• Big data makes it possible for you to gain more complete answers because you have more information.
• More complete answers mean more confidence in the data—which means a completely different approach to tackling problems.

Big data can help you address a range of business activities, including customer experience and analytics. Here are just a few.

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While big data holds a lot of promise, it is not without its challenges.

First, big data is…big. Although new technologies have been developed for data storage, data volumes are doubling in size about every two years. Organizations still struggle to keep pace with their data and find ways to effectively store it.

But it’s not enough to just store the data. Data must be used to be valuable and that depends on curation. Clean data, or data that’s relevant to the client and organized in a way that enables meaningful analysis, requires a lot of work. Data scientists spend 50 to 80 percent of their time curating and preparing data before it can actually be used.

Finally, big data technology is changing at a rapid pace. A few years ago, Apache Hadoop was the popular technology used to handle big data. Then Apache Spark was introduced in 2014. Today, a combination of the two frameworks appears to be the best approach. Keeping up with big data technology is an ongoing challenge.

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Big data gives you new insights that open up new opportunities and business models. Getting started involves three key actions:

1.  Integrate Big data brings together data from many disparate sources and applications. Traditional data integration mechanisms, such as extract, transform, and load (ETL) generally aren’t up to the task. It requires new strategies and technologies to analyze big data sets at terabyte, or even petabyte, scale.

During integration, you need to bring in the data, process it, and make sure it’s formatted and available in a form that your business analysts can get started with.

2.  Manage Big data requires storage. Your storage solution can be in the cloud, on premises, or both. You can store your data in any form you want and bring your desired processing requirements and necessary process engines to those data sets on an on-demand basis. Many people choose their storage solution according to where their data is currently residing. The cloud is gradually gaining popularity because it supports your current compute requirements and enables you to spin up resources as needed.

3.  Analyze Your investment in big data pays off when you analyze and act on your data. Get new clarity with a visual analysis of your varied data sets. Explore the data further to make new discoveries. Share your findings with others. Build data models with machine learning and artificial intelligence. Put your data to work.

To help you on your big data journey, we’ve put together some key best practices for you to keep in mind. Here are our guidelines for building a successful big data foundation.

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