definition of terms in research sample

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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About Sowjanya Pedada

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

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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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Scientific Research and Methodology

2.2 conceptual and operational definitions.

Research studies usually include terms that must be carefully and precisely defined, so that others know exactly what has been done and there are no ambiguities. Two types of definitions can be given: conceptual definitions and operational definitions .

Loosely speaking, a conceptual definition explains what to measure or observe (what a word or a term means for your study), and an operational definitions defines exactly how to measure or observe it.

For example, in a study of stress in students during a university semester. A conceptual definition would describe what is meant by ‘stress.’ An operational definition would describe how the ‘stress’ would be measured.

Sometimes the definitions themselves aren’t important, provided a clear definition is given. Sometimes, commonly-accepted definitions exist, so should be used unless there is a good reason to use a different definition (for example, in criminal law, an ‘adult’ in Australia is someone aged 18 or over ).

Sometimes, a commonly-accepted definition does not exist, so the definition being used should be clearly articulated.

Example 2.2 (Operational and conceptual definitions) Players and fans have become more aware of concussions and head injuries in sport. A Conference on concussion in sport developed this conceptual definition ( McCrory et al. 2013 ) :

Concussion is a brain injury and is defined as a complex pathophysiological process affecting the brain, induced by biomechanical forces. Several common features that incorporate clinical, pathologic and biomechanical injury constructs that may be utilised in defining the nature of a concussive head injury include: Concussion may be caused either by a direct blow to the head, face, neck or elsewhere on the body with an “impulsive” force transmitted to the head. Concussion typically results in the rapid onset of short-lived impairment of neurological function that resolves spontaneously. However, in some cases, symptoms and signs may evolve over a number of minutes to hours. Concussion may result in neuropathological changes, but the acute clinical symptoms largely reflect a functional disturbance rather than a structural injury and, as such, no abnormality is seen on standard structural neuroimaging studies. Concussion results in a graded set of clinical symptoms that may or may not involve loss of consciousness. Resolution of the clinical and cognitive symptoms typically follows a sequential course. However, it is important to note that in some cases symptoms may be prolonged.

While this is all helpful… it does not explain how to identify a player with concussion during a game.

Rugby decided on this operational definition ( Raftery et al. 2016 ) :

… a concussion applies with any of the following: The presence, pitch side, of any Criteria Set 1 signs or symptoms (table 1)… [ Note : This table includes symptoms such as ‘convulsion,’ ‘clearly dazed,’ etc.]; An abnormal post game, same day assessment…; An abnormal 36–48 h assessment…; The presence of clinical suspicion by the treating doctor at any time…

Example 2.3 (Operational and conceptual definitions) Consider a study requiring water temperature to be measured.

An operational definition would explain how the temperature is measured: the thermometer type, how the thermometer was positioned, how long was it left in the water, and so on.

Example 2.4 (Operational definitions) Consider a study measuring stress in first-year university students.

Stress cannot be measured directly, but could be assessed using a survey (like the Perceived Stress Scale (PSS) ( Cohen et al. 1983 ) ).

The operational definition of stress is the score on the ten-question PSS. Other means of measuring stress are also possible (such as heart rate or blood pressure).

Meline ( 2006 ) discusses five studies about stuttering, each using a different operational definition:

• Study 1: As diagnosed by speech-language pathologist.
• Study 2: Within-word disfluences greater than 5 per 150 words.
• Study 3: Unnatural hesitation, interjections, restarted or incomplete phrases, etc.
• Study 4: More than 3 stuttered words per minute.
• Study 5: State guidelines for fluency disorders.

A study of snacking in Australia ( Fayet-Moore et al. 2017 ) used this operational definition of ‘snacking’:

…an eating occasion that occurred between meals based on time of day. — Fayet-Moore et al. ( 2017 ) (p. 3)

A study examined the possible relationship between the ‘pace of life’ and the incidence of heart disease ( Levine 1990 ) in 36 US cities. The researchers used four different operational definitions for ‘pace of life’ (remember the article was published in 1990!):

• The walking speed of randomly chosen pedestrians.
• The speed with which bank clerks gave ‘change for two $20 bills or [gave] two $20 bills for change.’
• The talking speed of postal clerks.
• The proportion of men and women wearing a wristwatch.

None of these perfectly measure ‘pace of life,’ of course. Nonetheless, the researchers found that, compared to people on the West Coast,

… people in the Northeast walk faster, make change faster, talk faster and are more likely to wear a watch… — Levine ( 1990 ) (p. 455)

Writing Help

Where to find a research paper definition of terms sample.

When writing your research paper, you want to ensure that attention is given to the minutest of details. A definition of terms may not be deemed necessary for some students, especially those who prefer taking the easier route. However, incorporating a definition of terms can greatly enhance your research paper.

Benefits of a Definition of Terms

• This is a useful place to include technical terms in your topic or your research question.
• You can clarify the definition of a term especially if it has different meanings. Include the definition according to how it will be used throughout your research.
• Makes it easy for someone to consult to revisit the definition of a term instead of searching through the paper to try and locate it.
• Remember your paper is written not only for your professor but also for a general audience. You want to ensure that the general public is able to read your research paper and understand technical terminology and jargons.

This being said, if you have never seen a research paper with a definition of terms, you can find here. Otherwise to find samples of definition of terms, you can consider doing the following:

• Use several different research samples that your professor can provide you. From these samples, pick out the ones that contain a definition of terms.
• Use the internet and plug the terms into your favorite search engines. If you do choose the option of using the Internet, find here useful samples.
• Make use of a handbook for research papers which normally have samples there that you can copy and utilize as a guide.

A Guide For Your Definition of Terms

When you go through the definition of terms samples that you can find here, take note that this is not a place for you to add just any terms. This is a place where you define those terms of a technical nature to the research, a term that you would not want your audience to misinterpret. If this will not add any value to your research paper, then you do not have to include a definition of terms which is optional.

• Biography paper suggestions
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Chapter Twelve: Positing a Thesis Statement and Composing a Title / Defining Key Terms

Defining Key Terms

You are viewing the first edition of this textbook. a second edition is available – please visit the latest edition for updated information..

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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Lesson 21: Definition of Terms

A word or phrase used to describe a thing or to express concept, especially In a particular kind of language or branch of study.

Guidelines in defining terms:

1.     Definition of terms works like a glossary but have a different twist. It is placed on the beginning of the research paper to tell the meaning of the terms used in the said paper.

2.     Only terms, words, or phrases which have special or unique meanings in the study are defined.

3.     There are two types of definition of terms. Conceptual and Operational Terms.

Theoretical Definition are based be taken from encyclopedias, books, magazines and newspaper article, dictionaries, and other publications but the researcher must acknowledge his/her sources.

Conceptual Definition are based on how the researcher may develop his own definition from the characteristics of the term define.

4.     The term should be arranged alphabetically .

5.     When the definition are taken from encyclopedias, books, magazine and newspaper articles, dictionaries and other publications, the researcher must acknowledge his sources .

Definition of terms

Theoretical Definition

Knowledge - the fact or condition of knowing something with familiarity gained through experience or association.

Conceptual Definition

Knowledge - it is a condition of being aware to a certain problem-cyberbullying.

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Chapter 1: Introduction to Research Methods

1.4 Understanding Key Research Concepts and Terms

In this textbook you will be exposed to many terms and concepts associated with research methods, particularly as they relate to the research planning decisions you must make along the way. Figure 1.1 will help you contextualize many of these terms and understand the research process. This general chart begins with two key concepts: ontology and epistemology, advances through other concepts, and concludes with three research methodological approaches: qualitative, quantitative and mixed methods.

Research does not end with making decisions about the type of methods you will use; we could argue that the work is just beginning at this point. Figure 1.3 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 associated with the various methodological approaches you choose. Figure 1.1 is intentioned to provide a general overview of the research concept. 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 three concepts: 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 (Saylor Academy, 2012). In the following sections, we will define these concepts and provide an example of the terms, 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?
• 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: What is the role of City of Vancouver managers in the Emergency Management Department (EMD) 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: What is the effect of the City of Vancouver’s corporate culture in enabling EMD managers 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, given 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; 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 is 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), i.e., 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. She could further state that this hierarchy, which is 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 would likely be no different than those of the old managers, because the management structure and the procedures remain the same. Therefore, she could go back to the new managers and ask them to participate in her research study.

Robert’s supervisor tells him that his research is set up for a subjectivist approach. 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); i.e., the corporate culture at the City continually influences the process of social interaction, and these interactions 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 would be important to get the new managers’ perceptions, he would also need to speak with the previous 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 could 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 the research question of each study guides the decision as to whether the researcher should take a subjective or an objective ontological approach. This decision, in turn, guides their approach to the research study, including whom they should interview.

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 a topic, or perhaps observe them in their natural environment. We could avoid face-to-face interaction altogether by mailing people surveys to complete on their own or by reading people’s opinions in newspaper editorials. Each method of data collection comes with its own set of epistemological assumptions about how to find things out (Saylor Academy, 2012). There are two main subsections of epistemology: positivist and interpretivist philosophies. We will examine these philosophies or paradigms in the following sections.

Research Methods for the Social Sciences: An Introduction Copyright © 2020 by Valerie Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Multidisciplinary Methods for Exploring Organizations

Bias :  a lack of balance and accuracy in the use of research methods. It can appear at any phase of research, from deciding on a sampling frame, sampling, to data collection and analysis.  Bias also arises in the identity of the researcher through assumptions and ideas related to his or her own culture that may influence data collection and analysis.  Bias interfere with the extent to which results are valid and accurate, whether or not the research is reliable, and the potential for results to be representative of, or generalizable to, a wider population.   Click here to access a brief article from the National Institutes of Health on research bias. 

Case Study :  the collection and presentation of in-depth information about a specific individual, group, or community.  Often these data represent the subjective experiences of an individual or group.   Click here to access more information on the case study approach to research.  

Causality :  the relation between cause and effect.  Causality is the agency that links one process or event (the cause) with another process, state, or event (the effect).  The first of these is normally understood to be at least partly responsible for the occurrence of the second, thus the second is dependent upon the first.  Causality is an abstraction based upon experience that is used to show and explain how change happens in the world.  Below is a very useful video explaining causality and how it relates to research.

Cultural Relativism : the idea that cultures are value-neutral.  This means that rather than various cultures being a better or worse ways of organizing behavior, they are simply different.  In anthropology, this idea has been used to make sense out of behaviors and values that seem alien or morally wrong to an outside observer; it has also been used to raise awareness of the potential for bias by an observer.  The concept has been debated in anthropology and has raised concern that it inherently leads to moral relativism.   Most modern anthropologists use the idea of cultural relativism as a way to bracket off one’s own cultural assumptions and biases to the extent possible.  Here is a brief article on cultural relativism by anthropologist Clifford Geertz.

Data :  factual information, collected through systematic methods, that is used as a basis for reasoning and analysis of a phenomenon.

Deductive Reasoning:   a type of reasoning in which conclusions are formulated about particulars from general or universal premises.  Here’s Monty Python’s take on deductive reasoning.

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.  Check out the video under the entry for independent variables  for more information on the difference between dependent and independent variables.

Emic : an approach to the study or description of a language or culture that focuses on its internal elements and logic and their functioning rather than in terms of any existing external scheme.  The term can also refer to the native explanation for a behavior or cultural pattern.  The video below will help you to understand the differences between emic and etic perspectives as they are understood by cultural anthropologists.

Etic : an approach to the study or description of a language or culture that is general, nonstructural, and objective in its perspective.  It is typically explanations for behavior from the perspective of the scientist/researcher observing a culture or language.

Epistemology:   theory of knowledge that questions how we know things, how knowledge is constructed, and what constitutes valid knowledge.  Here is a very detailed definition/discussion of   epistemology from the Stanford online dictionary of philosophy.

Ethnography:  method for studying study groups and/or cultures over an extended period of time using a variety of qualitative (and sometimes quantitative) research techniques. Ethnography employs participant observation, which is intended to allow researchers to understand a group through immersion into its lifestyles.  This allows for a detailed, in-depth, understanding of human experience.  Check out the TEDx video below for a nice discussion of the use of ethnography in business.

Field Studies : research studies carried out in natural settings, rather than in laboratories, classrooms, or other structured environments.

Focus Groups :  small, roundtable discussion groups charged with examining or discussing topics or problems associated with a research project.  In some cases, these may also involve discussion of solutions to identified problems.   Focus groups usually consist of 4-12 participants and are guided by moderators to keep the discussion moving and collect data.  Here is more on focus group research from the Robert Wood Johnson Foundation.

Grounded Theory:  an approach to research in which theories emerge from observing a group rather than being brought to the context of observation. Theories are grounded in the group’s observable experiences and interpretations, but researchers add their own insight into why those experiences exist.  Click here to access the website Grounded Theory Online .

Hypothesis : a tentative explanation or educated guess based on theory or observation that is used to predict a causal relationship between variables.  Click here to review some examples of hypotheses .

Independent Variable:   the conditions or variables of an experiment that are systematically manipulated by the researcher or a variable that is not impacted by the dependent variable, but that itself impacts the dependent variable.  Check out the video below for more information on the difference between dependent and independent variables.

Inductive Reasoning:  a type of reasoning in which a generalized conclusion is formulated generated based on particular instances.  Below is a video on the difference between inductive and deductive approaches to reasoning.

Naturalistic Observation:   observation of behaviors and events in natural settings rather than in experimental contexts that involve manipulation of variables or other types of interference.

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.  Click here for a detailed discussion of logic and ontology from the Stanford online dictionary of philosophy.

Organization :  For the purposes of MMEO, and organization is an institutionalized structure that is formed for a specific purpose.  Examples of organizations are businesses, academic institutions, religious institutions, or government institutions.

Participant observation :  a form of qualitative research that involves participating in the activities of the people being observed as a way of developing an experience-near understanding of their behaviors and ideas.

Phenomenology:  a qualitative research approach that focuses on meaning expressed by individuals through their lived experience of a particular idea, concept, or event.  This link will take you to more information on phenomenology .

Probability :  the likelihood that a phenomenon will occur randomly. As a statistical measure, it is represented as p.

Qualitative research:  a systematic approach to creating knowledge about how people interpret their surroundings, construct meaning, and interpret the meanings they construct. Qualitative research relies upon subtle and complex techniques of observation, recording data, and writing to develop an interpretive framework for analyzing and explaining why people do what they do and think what they think.

Quantitative research:  Quantitative research focuses on identifying objective measurements of phenomena such as human behavior.  In human subjects research it makes use of statistical, mathematical, and numerical analysis of empirical data collected using instruments such as questionnaires or through analyzing and manipulating pre-existing statistical data using computational techniques. Quantitative research uses numerical data to draw general conclusions across groups of people as a way of explaining particular behaviors or phenomena.  This link to a site as USC will give you more details on quantitative research .

Questionnaire :  structured groups of questions used to gather information, attitudes, or opinions.  Questionnaires can be either quantitative, including forced-choice questions, or qualitative, including open-ended questions.

Random Sampling : a process used in research to draw a sample of a population that does not reflect any pattern or order beyond chance.

Reliability : the extent to which a research method yields consistent results.  If the observational or measurement instrument is reliable, then administering it to similar groups should yield similar results. Reliability is a prerequisite for validity. If a data collection approach is unreliable, then cannot produce trustworthy results.

Rigor:   degree to which research methods are carefully designed and carried out.

Sample :  any population researched in a study. In many studies, researchers often try to select a “sample population” that is believed to be representative of the behaviors or other qualities (race, ethnicity, gender) of people for whom results will be generalized.  This video will help you understand different types of sampling and the goals in sampling.

Sampling Error : the degree to which the results from the sample deviate from those that would be obtained from the entire population.  This can be a result of random error in the selection of participants and any corresponding reduction in reliability that arises as a result of that error.

Standard Deviation : a measure  used to quantify how much variation or dispersion there is in a set of data values.  A low standard deviation means that the data points tend to be close to the mean; a high standard deviation means the data points are spread out over a wider range of values and further from the mean.

Statistical Analysis :  application of statistical methods and theory to the collection, presentation, and interpretation of numerical data.

Statistical Significance:  in any experiment or observation that involves using a sample from a population, statistical significance refers to the likelihood that a behavior or set of behaviors is due to chance.  The probability that the null hypothesis can be rejected at a predetermined significance level [0.05 or 0.01].

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.

Triangulation:   a multi-method or pluralistic approach to research that uses a variety of methods to collect data from different viewpoints.  This produces a complex and multi-faceted data set that helps in checking the validity of findings.

Unit of Analysis:   the thing being observed, analyzed, and for which data are collected in the form of variables.

Validity — the degree to which a study accurately represents and assesses the specific phenomenon a researcher wants to measure.  This brief video will help you to understand the difference between validity and reliability in research.

Variable:  any characteristic or trait that can vary from person to person.  Race, gender, education level, hair color, age, political beliefs, religion are all examples of variables.  This link will take you to a website that provides more detail on variables.

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2.6: Defining Terms- Types and Purposes of Definitions

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9 Defining Terms: Types and Purposes of Definitions 21

Clearly defining terms is one way of helping to resolve problems of ambiguity and there are many types of definitions one can use:

• Lexical or dictionary definitions

The OED defines “defines” as…

• Disambiguating definitions

“ When I said…I meant…”

• Stipulative definitions

For the purposes of this class, a “kwijybo” is “a big dumb balding North American ape with no chin and a short temper”

• Precising definitions

A small amount of salt is less than .5 tsp

• Systematic or theoretical

Brother-in-law: husband of my sister (OR brother of my wife!)

The point of using definitions like these is simple: to make sure that you are clear in what you say. If anything can be uncertain, it is best to define it or use other, more precise words.

We will be covering fallacies more later in this course, but there are a few that are very relevant right now, as these are all ones that can be fixed by using a definitional approach. Again, a “fallacy” is drawing an unsupported conclusion by using a common method of reasoning that is usually in error. Being familiar with fallacies makes them very easy to recognize (and avoid yourself, as well as understand how to properly resolve them).

Loaded Question Fallacy

(Also known as complex question, fallacy of presupposition, trick question) The fallacy of asking a question that has a presupposition built in, which implies something (often questionable) but protects the person asking the question from accusations of false claims or even slander.

Example: Have you stopped sleeping in unicorn sheets?

This question is a real ‘catch-22’ since to answer ‘yes’ implies that you used to sleep in unicorn sheets but have now stopped, and to answer ‘no’ means you are still sleeping in them. The question rests on the assumption that you sleep in unicorn sheets , and so either answer to it seems to endorse that idea.

Equivocation

(Also known as doublespeak) A fallacy that occurs when one uses an ambiguous term or phrase in more than one sense, thus rendering the argument misleading. The ambiguity in this fallacy is lexical and not grammatical, meaning the term or phrase that is ambiguous has two distinct meanings. In other words, it happens when one term is assumed to mean the same thing in two different contexts, but actually means two different things. One can often see equivocation in jokes.

Example: Man is the only rational animal, and no woman is a man, so women are not rational.

Example: If you don’t pay your exorcist you can get repossessed.

Example: A feather is light; whatever is light cannot be dark; therefore, a feather cannot be dark.

A fallacy of ambiguity, where the ambiguity in question arises directly from the poor grammatical structure in a sentence. The fallacy occurs when a bad argument relies on the grammatical ambiguity to sound strong and logical.

Example: I’m going to return this car to the dealer I bought this car from. Their ad said “Used 1995 Ford Taurus with air conditioning, cruise, leather, new exhaust and chrome rims.” But the chrome rims aren’t new at all.

There are other kinds of amphiboly fallacies, like those of ambiguous pronoun reference: “I took some pictures of the dogs at the park playing, but they were not good.” Does ‘they’ mean the dogs or the pictures “were not good”? And there is amphiboly when modifiers are misplaced, such as in a famous Groucho Marx joke: “One morning I shot an elephant in my pajamas. How he got into my pajamas I’ll never know.”

Fallacy of the Undistributed Middle

(Also known as undistributed middle term) A formal fallacy that occurs in a categorical syllogism (we’ll look at these later ), when the middle term is undistributed is not distributed at least in one premise. According to the rules of categorical syllogism, the middle term must be distributed at least once for it to be valid.

Example of the form: All X’s are Y’s; All Z’s are Y’s; Therefore, All X’s are Z’s.

Example in words: All ghosts are spooky; all zombies are spooky; therefore all ghosts are

The problem here is that you’re relating the incorrect categories with each other. It is fine to say, “All dogs are mammals, all mammals are animals, so all dogs are animals” but not “All dogs are mammals, all chihuahuas are mammals, so all chihuahuas are dogs” because even though your conclusion is true, the route that led you there is invalid.

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But may use as an adjunct in the research methods course. Concise and accessible but a little too brief to use as a main text

So easy for students to follow!

• The Glossary of Research Terms chapter contains definitions and descriptions of over 1500 research terms. It is the heart of the glossary, features crisp and clear statements as to the meaning of each entry.
• Commonly Used Statistical Terms: A dedicated chapter offers a brief synopsis of commonly used statistical terms, ranging from the alpha coefficient to the Z-test. Questions related to the meaning of a statistical term will likely be found here.
• Commonly Used Acronyms, Symbols, Abbreviations and Terms Found in Research and Evaluation Studies are organized into categories so readers can easily find them: for example, research and evaluation studies, statistics, Internet, and U.S. federal government.
• Core Disciplinary Journals in Selected Social and Behavioral Science chapter lists the most highly ranked journals in over a dozen social and behavioral sciences and professional disciplines, as determined by their impact factors published by the Journal Citation Reports database. URLs are also provided to each journal so that the reader can locate each journal's individual webpage.

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Chapter Two: Commonly Used Acronyms, Symbols, Abbreviations, and Terms Found in

Chapter Three: Commonly Used Statistical Terms

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Any untoward occurrence in a research participant. The occurrence need not have a clear causal relationship with the individual’s participation in the research; an AE can be any unfavorable and unintended sign, symptom, event, or occurrence affecting a participant’s physical, mental, social, financial, legal, or psychological well-being. An unanticipated AE should be reported to the committee as soon as possible after it is identified.

Agreement by an individual not competent to give legally valid informed consent (e.g., a child or cognitively impaired person) to participate in research. An assent is typically paired with permission from a parent or guardian, and together they comprise the informed consent to participate.

An officer of an institution with the authority to speak for and legally commit the institution to adherence to the requirements of the federal regulations regarding the involvement of human subjects in biomedical and behavioral research.

A statement of basic ethical principles governing research involving human subjects issued by the National Commission for the Protection of Human Subjects in 1979. View a summary of the Belmont Report . The Belmont Report principles permeate human subjects research to this day.

An ethical principle discussed in the Belmont Report that entails an obligation to protect persons from harm. The principle of beneficence can be expressed in two general rules: 1) do not harm; and 2) protect from harm by maximizing possible benefits and minimizing possible risks of harm.

A valued or desired outcome associated with a research project. Anticipated benefits may express the probability that subjects and society may benefit from the research procedures. Research may benefit the individual or society as a whole. If research will not benefit individuals, it is required to provide a reasonable likelihood of resulting in benefits to society. UNLV’s human research application requests information about the direct benefits accruing to the research participants and to society. Compensation and incentives given to participants are not considered benefit.

This is a certificate issued by the National Institutes of Health that protects identifiable research information of a sensitive nature from forced disclosure. It is typically requested when the researcher believes his/her research objectives could not be met without this form of protection. 

Persons who have not attained the legal age for consent to treatment or procedures involved in the research, as determined under the applicable law of the jurisdiction in which the research will be conducted [45 CFR 46 46.401(a)]. In Nevada, individuals younger than 18 years of age are considered children for most research situations, and informed consent then consists of the child’s assent and the parent’s permission.(See “Assent.”)

The act of forcing or compelling one to take action against one’s will. Coercion can be overt or perceived, and it can occur when the researcher is in a position of authority or power over the subject (for example, teachers over students or physicians over patients). It can also occur when incentives become so great that the participant will only participate to attain the incentive.

Having either a psychiatric disorder (e.g., psychosis, neurosis, personality or behavior disorders, or dementia) or a developmental disorder (e.g., mental retardation) that affects cognitive or emotional functions to the extent that capacity for judgment and reasoning is significantly diminished. Others, including persons under the influence of or dependent on drugs or alcohol, those suffering from degenerative diseases affecting the brain, terminally ill patients, and persons with severely disabling physical handicaps, may also be compromised in their ability to make decisions in their best interests.

Human subjects research projects conducted by more than one institution. Each institution is responsible for safeguarding the rights and welfare of human subjects. Arrangements for joint review, relying upon one qualified IRB, or similar arrangements are acceptable. (Please contact the ORI-HS staff if this situation occurs; they can assist with the arrangements.)

Payment for participation in research. Compensation should be appropriate for the amount of effort involved, and not excessive and thereby coercive. Compensation is NOT considered a benefit.

Technically, a legal term, used to denote capacity to act on one’s own behalf; the ability to understand information presented, to appreciate the consequences of acting (or not acting) on that information, and to make a choice. (See also: Incompetence, Incapacity)

Pertains to the treatment of information that an individual has disclosed in a relationship of trust and with the expectation that it will not be divulged to others without permission in ways that are inconsistent with the understanding of the original disclosure.

Defined as a set of conditions in which an investigator’s judgment concerning a primary interest (e.g., subject welfare, integrity of research) could be biased by a secondary interest (e.g., personal or financial gain). See information regarding UNLV’s Conflict of Interest/Compensated Outside Services Policy .

See “Informed Consent.”

Subject(s) used for comparison who are not given the treatment under study or who do not have a given condition, background, or risk factor that is the object of study. Control conditions may be concurrent (occurring more or less simultaneously with the condition under study) or historical (preceding the condition under study). When the present condition of subjects is compared with their own condition on a prior regimen or treatment, the study is considered historically controlled.

The other primary scholar or researcher involved in conducting the research. Co-PIs must also meet the UNLV PI eligibility requirements.

Giving subjects previously undisclosed information about the research project following completion of their participation in research.

A code of ethics for clinical research approved by the World Medical Association in 1964 and widely adopted by medical associations in various countries. It was revised most recently in 2008.

Any study that is not truly experimental (e.g., quasi-experimental studies, correlational studies, record reviews, case histories, and observational studies).

A legal status conferred upon persons who have not yet attained the age of legal competency as defined by state law (for such purposes as consenting to medical care), but who are entitled to treatment as if they had by virtue of assuming adult responsibilities such as marriage, procreation, or being self-supporting and not living at home. (See also “Mature Minor.”)

Fair or just; used in the context of selection of subjects to indicate that the benefits and burdens of research are fairly distributed.

The code of federal regulations (45 CFR 46.101(b)) identifies several categories of minimal risk research as exempt from the Federal Policy for the Protection of Research Subjects. This determination must not be made by the PI, but by the IRB or someone appointed by the IRB. For more information, see the U.S. Health and Human Services website, “ Exempt Research and Research That May Undergo Expedited Review .”

The code of federal regulations (45 CFR 46.110 and 21 CFR 56.110) identifies several categories of minimal risk research that may be reviewed through an expedited review process. For more information, see the U.S. Health and Human Services website on “ Guidance on Expedited Review Procedures .”

This act defines the rights of students and parents concerning reviewing, amending, and disclosing educational records and requires written permission to disclose personally identifiable information from a student’s education record, except under certain circumstances such as an order of subpoena. 1

The federal policy that provides regulations for the involvement of human subjects in research. The policy applies to all research involving human subjects conducted, supported, or otherwise subject to regulation by any federal department or agency that takes appropriate administrative action to make the policy applicable to such research. Currently, 16 federal agencies have adopted this policy, commonly referred to as “The Federal Policy,” but also known as the “Common Rule.”

A formal written, binding commitment that is submitted to the Department of Health and Human Services (DHHS) Office of Human Research Protections (OHRP) in which an institution agrees to comply with applicable regulations governing research with human subjects and stipulates the procedures through which compliance will be achieved. UNLV’s assurance number is FWA00002305.

Review of proposed research at a convened meeting at which a majority of the membership of the IRB are present, including at least one member whose primary concerns are in nonscientific areas. For the research to be approved, it must receive the approval of a majority of those members present at the meeting. Generally, studies that undergo full board review are studies involving greater than minimal risk, risky, or novel procedures or vulnerable populations.

An individual who is authorized under applicable state or local law to give permission on behalf of a child for general medical care. In Nevada, under NRS 159.0805, guardians may not give permission for a child to enter into a research study unless a court order has been obtained.

The rule which protects the privacy of individually identifiable health information. The privacy rule provides federal protections for personal health information held by covered entities and gives patients specific rights with respect to that information.

Individuals whose physiological or behavioral characteristics and responses are the object of study in a research project. Under the federal regulations, human subjects are defined as living individual(s) about whom an investigator conducting research obtains: (1) data through intervention or interaction with the individual; or (2) identifiable private information.

Federal regulations define identifiable to mean that the identity of the individual subject is or may readily be ascertained by the investigator or may be associated with the information.

This refers to a person’s mental status and means inability to understand information presented, to appreciate the consequences of acting (or not acting) on that information, and to make a choice. The term is often used as a synonym for incompetence.

A legal term meaning inability to manage one’s own affairs, and often used as a synonym for incapacity.

A person’s voluntary agreement, based upon adequate knowledge and understanding of relevant information, to participate in research or to undergo a diagnostic, therapeutic, or preventive procedure. In giving informed consent, subjects may not waive or appear to waive any of their legal rights, or release or appear to release the investigator, the sponsor, the institution, or agents thereof from liability for negligence.

Institutional research (also called internal research) is the gathering of data from or about UNLV students, faculty, and staff by university offices or organizations, with the sole intent of using the data for internal informational purposes or for required data-collection purposes. This data would not be made generalizable. Examples include surveys to improve university services or procedures; ascertain the opinions, experiences, or preferences of the university community; or to provide necessary information to characterize the university community. This kind of data gathering does not require IRB review unless respondents are queried about sensitive aspects of their own behavior. For debatable projects, investigators should submit an exclusion review form to the ORI-HS.

A specially constituted, federally mandated review body established or designated by an entity to protect the welfare of human subjects recruited to participate in biomedical or behavioral research. UNLV has two IRBs – Social/Behavioral and Biomedical.

The federal regulations define interaction as “communication or interpersonal contact between investigator and subject.”

The federal regulations define intervention as both physical procedures by which data are gathered (for example, venipuncture) and manipulations of the subject or the subject’s environment that are performed for research purposes.

This refers to a researcher conducting the project. Investigators can be principal investigators or co-principal investigators. Students are always listed as student investigators.

A formal agreement between UNLV and another FWA-holding institution that allows the one IRB to serve as the “IRB of Record” for protocols involving collaborative research between UNLV and the other institution.

A term utilized when an institution assumes the IRB responsibilities for a human subject research protocol conducted at another institution. An IRB authorization agreement signed by institutional officials at both institutions is required.

An ethical principle discussed in the Belmont Report requiring fairness in distribution of burdens and benefits; those that bear the burdens of research should also receive the benefits. There must be fair and equitable selection of subjects.

A person authorized either by statute or by court appointment to make decisions on behalf of another person. In human subjects research, an individual or judicial or other body authorized under applicable law to consent on behalf of a prospective subject to the subject’s participation in the procedure(s) involved in the research.

Someone who has not reached adulthood (as defined by state law) but who may be treated as an adult for certain purposes (e.g., consenting to medical care). Note that a mature minor is not necessarily an emancipated minor. (See also “Emancipated Minor.”)

A risk is minimal when the probability and magnitude of harm or discomfort anticipated in the proposed research are not greater, in and of themselves, than those ordinarily encountered in daily life or during the performance of routine physical or psychological examinations or tests. For example, the risk of drawing a small amount of blood from a healthy individual for research purposes is no greater than the risk of doing so as part of routine physical examination. Note: The definition of minimal risk for research involving prisoners differs somewhat from that given for non-institutionalized adults.

Any change to an IRB-approved study protocol, regardless of the level of review it receives initially.

A federally mandated member of an Institutional Review Board who has no ties to the parent institution, its staff, or faculty. This individual is usually from the local community (e.g., business person, attorney, or teacher).

A code of research ethics developed during the trials of Nazi war criminals following World War II and widely adopted as a standard during the 1950s and 1960s for protecting human subjects.

The office within the Department of Health and Human Services that is responsible for implementing DHHS regulations (45CFR46) governing research involving human subjects.

The UNLV office, formerly known as the Office for the Protection of Research Subjects (OPRS), that serves as an administrative hub for the UNLV IRB’s oversight of human subjects research.

The agreement of parent(s) to the participation of their child in research.

The scientist or scholar with primary responsibility for the design and conduct of a research project. See UNLV’s PI Eligibility Policy for those who are eligible for automatic PI status and how to apply for PI status.

An individual involuntarily confined in a penal institution, including persons: 1) sentenced under a criminal or civil statue; 2) detained pending arraignment, trial, or sentencing; and 3) detained in other facilities (e.g., for drug detoxification or treatment of alcoholism) under statutes or commitment procedures providing such alternatives to criminal prosecution or incarceration in a penal institution. Note that this includes adjudicated youth.

Control over the extent, timing, and circumstances of disclosing personal information (physical, behavioral, or intellectual) with others.

Defined by the federal regulations to include information about behavior that occurs in a context in which an individual can reasonably expect that no observation or recording is taking place. It also includes information that has been provided for specific purposes by an individual and which the individual can reasonably expect will not be made public (e.g., a medical record). Private information must be individually identifiable (i.e., the identity of the subject is or may readily be ascertained by the investigator or associated with the information) in order for the acquisition of the information to constitute research involving human subjects.

Studies designed to observe outcomes or events that occur subsequent to the identification of the group of subjects to be studied. Prospective studies need not involve manipulation or intervention but may be purely observational or involve only the collection of data.

Applies to survey research conducted in schools and states that parents have the right to inspect surveys and questionnaires distributed within schools. This amendment also specifies that parental permission must be obtained to have minors participate in surveys that disclose certain types of sensitive information. 1

The formal design or plan of an experiment or research study; specifically, the plan submitted to an IRB for review and to an agency for research support. The protocol includes a description of the research design or methodology to be employed, the eligibility requirements for prospective subjects and controls, the treatment regimen(s), and the proposed methods of analysis that will be performed on the collected data.

A systematic investigation (i.e., the gathering and analysis of information) designed to develop or contribute to generalizable knowledge.

An ethical principle discussed in the Belmont Report requiring that individual autonomy be respected and persons with diminished autonomy be protected.

Research conducted by reviewing records from the past (e.g., birth and death certificates, medical records, school records, or employment records) or by obtaining information about past events elicited through interviews or surveys. Case control studies are an example of this type of research. This requires IRB review, as long as it involves private information about humans.

The probability of harm or injury (physical, psychological, social, or economic) occurring as a result of participation in a research study. Both the probability and magnitude of possible harm may vary from minimal to significant. Risks include immediate risks of study participation as well as risks of long-term effects.

This involves two types of data: 1) data collected by someone other than the principal investigator for a research or non-research purpose, or 2) data that was collected by the principal investigator, but when collected was not intended to be used for human subjects research. For data to be considered secondary data, the data must exist prior to the initiation of the current research study or be “on the shelf” at the time of study initiation. Principal investigators must submit and receive approval for use of secondary human subjects data prior to initiation of the project.

A visit by agency officials, representatives, or consultants to the location of a research activity to assess the adequacy of IRB protection of human subjects or the capability of personnel to conduct the research.

“Participant” is the preferred term since it more correctly portrays the participatory aspects of research. Sometimes “subject” more accurately describes the role.

Free of coercion, duress, or undue inducement or influence. Used in the research context to refer to a subject’s decision to participate (or to continue to participate) in a research activity.

Content Validity in Research: Definition & Examples

Charlotte Nickerson

Research Assistant at Harvard University

Undergraduate at Harvard University

Charlotte Nickerson is a student at Harvard University obsessed with the intersection of mental health, productivity, and design.

Learn about our Editorial Process

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

• Content validity is a type of criterion validity that demonstrates how well a measure covers the construct it is meant to represent.
• It is important for researchers to establish content validity in order to ensure that their study is measuring what it intends to measure.
• There are several ways to establish content validity, including expert opinion, focus groups , and surveys.

What Is Content Validity?

Content Validity is the degree to which elements of an assessment instrument are relevant to a representative of the targeted construct for a particular assessment purpose.

This encompasses aspects such as the appropriateness of the items, tasks, or questions to the specific domain being measured and whether the assessment instrument covers a broad enough range of content to enable conclusions to be drawn about the targeted construct (Rossiter, 2008).

One example of an assessment with high content validity is the Iowa Test of Basic Skills (ITBS). The ITBS is a standardized test that has been used since 1935 to assess the academic achievement of students in grades 3-8.

The test covers a wide range of academic skills, including reading, math, language arts, and social studies. The items on the test are carefully developed and reviewed by a panel of experts to ensure that they are fair and representative of the skills being tested.

As a result, the ITBS has high content validity and is widely used by schools and districts to measure student achievement.

Meanwhile, most driving tests have low content validity.  The questions on the test are often not representative of the skills needed to drive safely. For example, many driving permit tests do not include questions about how to parallel park or how to change lanes.

Meanwhile, driving license tests often do not test drivers in non-ideal conditions, such as rain or snow. As a result, these tests do not provide an accurate measure of a person’s ability to drive safely.

The higher the content validity of an assessment, the more accurately it can measure what it is intended to measure — the target construct (Rossiter, 2008).

Why is content validity important in research?

Content validity is important in research as it provides confidence that an instrument is measuring what it is supposed to be measuring.

This is particularly relevant when developing new measures or adapting existing ones for use with different populations.

It also has implications for the interpretation of results, as findings can only be accurately applied to groups for which the content validity of the measure has been established.

Step-by-step guide: How to measure content validity?

Haynes et al. (1995) emphasized the importance of content validity and gave an overview of ways to assess it.

One of the first ways of measuring content validity was the Delphi method, which was invented by NASA in 1940 as a way of systematically creating technical predictions. 

The method involves a group of experts who make predictions about the future and then reach a consensus about those predictions. Today, the Delphi method is most commonly used in medicine.

In a content validity study using the Delphi method, a panel of experts is asked to rate the items on an assessment instrument on a scale. The expert panel also has the opportunity to add comments about the items.

After all ratings have been collected, the average item rating is calculated. In the second round, the experts receive summarized results of the first round and are able to make further comments and revise their first-round answers.

This back-and-forth continues until some homogeneity criterion — similarity between the results of researchers — is achieved (Koller et al., 2017).

Lawshie (1975) and Lynn (1986) created numerical methods to assess content validity. Both of these methods require the development of a content validity index (CVI). A content validity index is a statistical measure of the degree to which an assessment instrument covers the content domain of interest.

There are two steps in calculating a content validity index:

• Determining the number of items that should be included in the assessment instrument;
• Determining the percentage of items that actually are included in the assessment instrument.

The first step, determining the number of items that should be included in an assessment instrument, can be done using one of two approaches: item sampling or expert consensus.

Item sampling involves selecting a sample of items from a larger set of items that cover the content domain. The number of items in the sample is then used to estimate the total number of items needed to cover the content domain.

This approach has the advantage of being quick and easy, but it can be biased if the sample of items is not representative of the larger set (Koller et al., 2017).

The second approach, expert consensus, involves asking a group of experts how many items should be included in an assessment instrument to adequately cover the content domain. This approach has the advantage of being more objective, but it can be time-consuming and expensive.

Experts are able to assign these items to dimensions of the construct that they intend to measure and assign relevance values to decide whether an item is a strong measure of the construct.

Although various attempts to numerize the process of measuring content validity exist, there is no systematic procedure that could be used as a general guideline for the evaluation of content validity (Newman et al., 2013).

When is content validity used?

Education assessment.

In the context of educational assessment, validity is the extent to which an assessment instrument accurately measures what it is intended to measure. Validity concerns anyone who is making inferences and decisions about a learner based on data.

This can have deep implications for students’ education and future. For instance, a test that poorly measures students’ abilities can lead to placement in a future course that is unsuitable for the student and, ultimately, to the student’s failure (Obilor, 2022).

There are a number of factors that specifically affect the validity of assessments given to students, such as (Obilor, 2018):

• Unclear Direction: If directions do not clearly indicate to the respondent how to respond to the tool’s items, the validity of the tool is reduced.
• Vocabulary: If the vocabulary of the respondent is poor, and he does not understand the items, the validity of the instrument is affected.
• Poorly Constructed Test Items: If items are constructed in such a way that they have different meanings for different respondents, validity is affected.
• Difficulty Level of Items: In an achievement test, too easy or too difficult test items would not discriminate among students, thereby lowering the validity of the test.
• Influence of Extraneous Factors: Extraneous factors like the style of expression, legibility, mechanics of grammar (spelling, punctuation), handwriting, and length of the tool, amongst others, influence the validity of a tool.
• Inappropriate Time Limit: In a speed test, if enough time limit is given, the result will be invalidated as a measure of speed. In a power test, an inappropriate time limit will lower the validity of the test.

There are a few reasons why interviews may lack content validity . First, interviewers may ask different questions or place different emphases on certain topics across different candidates. This can make it difficult to compare candidates on a level playing field.

Second, interviewers may have their own personal biases that come into play when making judgments about candidates.

Finally, the interview format itself may be flawed. For example, many companies ask potential programmers to complete brain teasers — such as calculating the number of plumbers in Chicago or coding tasks that rely heavily on theoretical knowledge of data structures — even if this knowledge would be used rarely or never on the job.

Questionnaires

Questionnaires rely on the respondents’ ability to accurately recall information and report it honestly. Additionally, the way in which questions are worded can influence responses.

To increase content validity when designing a questionnaire, careful consideration must be given to the types of questions that will be asked.

Open-ended questions are typically less biased than closed-ended questions, but they can be more difficult to analyze.

It is also important to avoid leading or loaded questions that might influence respondents’ answers in a particular direction. The wording of questions should be clear and concise to avoid confusion (Koller et al., 2017).

Is content validity internal or external?

Most experts agree that content validity is primarily an internal issue. This means that the concepts and items included in a test should be based on a thorough analysis of the specific content area being measured.

The items should also be representative of the range of difficulty levels within that content area. External factors, such as the opinions of experts or the general public, can influence content validity, but they are not necessarily the primary determinant.

In some cases, such as when developing a test for licensure or certification, external stakeholders may have a strong say in what is included in the test (Koller et al., 2017).

How can content validity be improved?

There are a few ways to increase content validity. One is to create items that are more representative of the targeted construct. Another is to increase the number of items on the assessment so that it covers a greater range of content.

Finally, experts can review the items on the assessment to ensure that they are fair and representative of the skills being tested (Koller et al., 2017).

How do you test the content validity of a questionnaire?

There are a few ways to test the content validity of a questionnaire. One way is to ask experts in the field to review the questions and provide feedback on whether or not they believe the questions are relevant and cover all important topics.

Another way is to administer the questionnaire to a small group of people and then analyze the results to see if there are any patterns or themes emerging from the responses.

Finally, it is also possible to use statistical methods to test for content validity, although this approach is more complex and usually requires access to specialized software (Koller et al., 2017).

How can you tell if an instrument is content-valid?

There are a few ways to tell if an instrument is content-valid. The first of these involves looking at two subsets of content validity: face and construct validity.

Face validity is a measure of whether or not the items on the test appear to measure what they claim to measure. This is highly subjective but convenient to assess.

Another way is to look at the construct validity, which is whether or not the items on the test measure what they are supposed to measure. Finally, you can also look at the criterion-related validity, which is whether or not the items on the test predict future performance.

What is the difference between content and criterion validity?

Content validity is a measure of how well a test covers the content it is supposed to cover.

Criterion validity, meanwhile, is an index of how well a test correlates with an established standard of comparison or a criterion.

For example, if a measure of criminal behavior is criterion valid, then it should be possible to use it to predict whether an individual will be arrested in the future for a criminal violation, is currently breaking the law, and has a previous criminal record (American Psychological Association).

Are content validity and construct validity the same?

Content validity is not the same as construct validity.

Content validity is a method of assessing the degree to which a measure covers the range of content that it purports to measure.

In contrast, construct validity is a method of assessing the degree to which a measure reflects the underlying construct that it purports to measure.

It is important to note that content validity and construct validity are not mutually exclusive; a measure can be both valid and invalid with respect to content and construct.

However, content validity is a necessary but not sufficient condition for construct validity. That is, a measure cannot be construct valid if it does not first have content validity (Koller et al., 2017).

For example, an academic achievement test in math may have content validity if it contains questions from all areas of math a student is expected to have learned before the test, but it may not have construct validity if it does not somehow relate to tests of similar and different constructs.

How many experts are needed for content validity?

There is no definitive answer to this question as it depends on a number of factors, including the nature of the instrument being validated and the purpose of the validation exercise.

However, in general, a minimum of three experts should be used in order to ensure that the content validity of an instrument is adequately established (Koller et al., 2017).

American Psychological Association. (n.D.). Content Validity. American Psychological Association Dictionary.

Haynes, S. N., Richard, D., & Kubany, E. S. (1995). Content validity in psychological assessment: A functional approach to concepts and methods. Psychological assessment , 7 (3), 238.

Koller, I., Levenson, M. R., & Glück, J. (2017). What do you think you are measuring? A mixed-methods procedure for assessing the content validity of test items and theory-based scaling. Frontiers in psychology , 8 , 126.

Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel psychology , 28 (4), 563-575.

Lynn, M. R. (1986). Determination and quantification of content validity. Nursing research .

Obilor, E. I. (2018). Fundamentals of research methods and Statistics in Education and Social Sciences. Port Harcourt: SABCOS Printers & Publishers.

OBILOR, E. I. P., & MIWARI, G. U. P. (2022). Content Validity in Educational Assessment.

Newman, Isadore, Janine Lim, and Fernanda Pineda. “Content validity using a mixed methods approach: Its application and development through the use of a table of specifications methodology.” Journal of Mixed Methods Research 7.3 (2013): 243-260.

Rossiter, J. R. (2008). Content validity of measures of abstract constructs in management and organizational research. British Journal of Management , 19 (4), 380-388.

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What was Trump found guilty of? See the 34 business records the jury decided he falsified

Donald Trump was found guilty of 34 felony counts of falsifying business records after prosecutors successfully convinced a jury he disguised hush money reimbursement as legal expenses. He is the first former president to be convicted of a crime.

Each count is tied to a different business record that prosecutors demonstrated Trump is responsible for changing to conceal or commit another crime .

Those records include 11 checks paid to former lawyer Michael Cohen , 11 invoices from Michael Cohen and 12 entries in Trump's ledgers.

The jury found that Trump authorized a plan to reimburse Cohen for the $130,000 hush money payment issued to Stormy Daniels and spread the payments across 12 months disguised as legal expenses.

Live updates: Former President Donald Trump found guilty on all counts in hush money case

Prep for the polls: See who is running for president and compare where they stand on key issues in our Voter Guide

Breakdown of 34 counts of falsifying business records

Here are the 34 business records Trump was found guilty of falsifying, as described in Judge Juan Merchan 's jury instructions :

• Count 1: Michael Cohen's invoice dated Feb. 14, 2017
• Count 2: Entry in the Detail General Ledger for the Donald J. Trump Revocable Trust dated Feb. 14, 2017
• Count 3: Entry in the Detail General Ledger for the Donald J. Trump Revocable Trust dated Feb. 14, 2017
• Count 4: A Donald J. Trump Revocable Trust Account check and check stub dated Feb. 14, 2017
• Count 5: Michael Cohen's invoice dated March 16, 2017
• Count 6: Entry in the Detail General Ledger for the Donald J. Trump Revocable Trust dated March 17, 2017
• Count 7: A Donald J. Trump Revocable Trust Account check and check stub dated March 17, 2017
• Count 8: Michael Cohen's invoice dated April 13, 2017
• Count 9: Entry in the Detail General Ledger for Donald J. Trump dated June 19, 2017
• Count 10: A Donald J. Trump account check and check stub dated June 19, 2017
• Count 11: Michael Cohen's invoice dated May 22, 2017
• Count 12: Entry in the Detail General Ledger for Donald J. Trump dated May 22, 2017
• Count 13: A Donald J. Trump account check and check stub May 23, 2017
• Count 14: Michael Cohen's invoice dated June 16, 2017
• Count 15: Entry in the Detail General Ledger for Donald J. Trump dated June 19, 2017
• Count 16: A Donald J. Trump account check and check stub dated June 19, 2017
• Count 17: Michael Cohen's invoice dated July 11, 2017
• Count 18: Entry in the Detail General Ledger for Donald J. Trump dated July 11, 2017
• Count 19: A Donald J. Trump account check and check stub dated July 11, 2017
• Count 20: Michael Cohen's invoice dated Aug. 1, 2017
• Count 21: Entry in the Detail General Ledger for Donald J. Trump dated Aug. 1, 2017
• Count 22: A Donald J. Trump account check and check stub dated Aug. 1, 2017
• Count 23: Michael Cohen's invoice dated Sept. 11, 2017
• Count 24: Entry in the Detail General Ledger for Donald J. Trump dated Sept. 11, 2017
• Count 25: A Donald J. Trump account check and check stub dated Sept. 12, 2017
• Count 26: Michael Cohen's invoice dated Oct. 18, 2017
• Count 27: Entry in the Detail General Ledger for Donald J. Trump dated Oct. 18, 2017
• Count 28: A Donald J. Trump account check and check stub dated Oct. 18, 2017
• Count 29: Michael Cohen's invoice dated Nov. 20, 2017
• Count 30: Entry in the Detail General Ledger for Donald J. Trump dated Nov. 20, 2017
• Count 31: A Donald J. Trump account check and check stub dated Nov. 21, 2017
• Count 32: Michael Cohen's invoice dated Dec. 1, 2017
• Count 33: Entry in the Detail General Ledger for Donald J. Trump dated Dec. 1, 2017
• Count 34: A check and check stub dated Dec. 5 2017

Jurors saw copies of these records entered as evidence. Evidence from the entire trial is available on the New York Courts website .

Contributing: Aysha Bagchi

• Patient Care & Health Information
• Diseases & Conditions
• Neurofibromatosis type 1

Neurofibromatosis type 1 (NF1) is a genetic condition that causes changes in skin pigment and tumors on nerve tissue. Skin changes include flat, light brown spots and freckles in the armpits and groin. Tumors can grow anywhere in the nervous system, including the brain, spinal cord and nerves. NF1 is rare. About 1 in 2,500 is affected by NF1.

The tumors often are not cancerous, known as benign tumors. But sometimes they can become cancerous. Symptoms often are mild. But complications can occur and may include trouble with learning, heart and blood vessel conditions, vision loss, and pain.

Treatment focuses on supporting healthy growth and development in children and early management of complications. If NF1 causes large tumors or tumors that press on a nerve, surgery can reduce symptoms. A newer medicine is available to treat tumors in children, and other new treatments are being developed.

Neurofibromatosis type 1 (NF1) usually is diagnosed during childhood. Symptoms are seen at birth or shortly afterward and almost always by age 10. Symptoms tend to be mild to moderate, but they can vary from person to person.

Symptoms include:

• Flat, light brown spots on the skin, known as cafe au lait spots. These harmless spots are common in many people. But having more than six cafe au lait spots suggests NF1. They often are present at birth or appear during the first years of life. After childhood, new spots stop appearing.
• Freckling in the armpits or groin area. Freckling often appears by ages 3 to 5. Freckles are smaller than cafe au lait spots and tend to occur in clusters in skin folds.
• Tiny bumps on the iris of the eye, known as Lisch nodules. These nodules can't easily be seen and don't affect vision.
• Soft, pea-sized bumps on or under the skin called neurofibromas. These benign tumors usually grow in or under the skin but can also grow inside the body. A growth that involves many nerves is called a plexiform neurofibroma. Plexiform neurofibromas, when located on the face, can cause disfigurement. Neurofibromas may increase in number with age.
• Bone changes. Changes in bone development and low bone mineral density can cause bones to form in an irregular way. People with NF1 may have a curved spine, known as scoliosis, or a bowed lower leg.
• Tumor on the nerve that connects the eye to the brain, called an optic pathway glioma. This tumor usually appears by age 3. The tumor rarely appears in late childhood and among teenagers, and almost never in adults.
• Learning disabilities. It's common for children with NF1 to have some trouble with learning. Often there is a specific learning disability, such as trouble with reading or math. Attention-deficit/hyperactivity disorder (ADHD) and speech delay also are common.
• Larger than average head size. Children with NF1 tend to have a larger than average head size due to increased brain volume.
• Short stature. Children who have NF1 often are below average in height.

When to see a doctor

See a healthcare professional if your child has symptoms of neurofibromatosis type 1. The tumors are often not cancerous and are slow growing, but complications can be managed. If your child has a plexiform neurofibroma, a medicine is available to treat it.

Neurofibromatosis type 1 is caused by an altered gene that either is passed down by a parent or occurs at conception.

The NF1 gene is located on chromosome 17. This gene produces a protein called neurofibromin that helps regulate cell growth. When the gene is altered, it causes a loss of neurofibromin. This allows cells to grow without control.

Risk factors

Autosomal dominant inheritance pattern

In an autosomal dominant disorder, the changed gene is a dominant gene. It's located on one of the nonsex chromosomes, called autosomes. Only one changed gene is needed for someone to be affected by this type of condition. A person with an autosomal dominant condition — in this example, the father — has a 50% chance of having an affected child with one changed gene and a 50% chance of having an unaffected child.

The biggest risk factor for neurofibromatosis type 1 (NF1) is a family history. For about half of people who have NF1, the disease was passed down from a parent. People who have NF1 and whose relatives aren't affected are likely to have a new change to a gene.

NF1 has an autosomal dominant inheritance pattern. This means that any child of a parent who is affected by the disease has a 50% chance of having the altered gene.

Complications

Complications of neurofibromatosis type 1 (NF1) vary, even within the same family. Generally, complications occur when tumors affect nerve tissue or press on internal organs.

Complications of NF1 include:

• Neurological symptoms. Trouble with learning and thinking are the most common neurological symptoms associated with NF1. Less common complications include epilepsy and the buildup of excess fluid in the brain.
• Concerns with appearance. Visible signs of NF1 can include widespread cafe au lait spots, many neurofibromas in the facial area or large neurofibromas. In some people this can cause anxiety and emotional distress, even if they're not medically serious.
• Skeletal symptoms. Some children have bones that didn't form as usual. This can cause bowing of the legs and fractures that sometimes don't heal. NF1 can cause curvature of the spine, known as scoliosis, that may need bracing or surgery. NF1 also is associated with lower bone mineral density, which increases the risk of weak bones, known as osteoporosis.
• Changes in vision. Sometimes a tumor called an optic pathway glioma develops on the optic nerve. When this happens, it can affect vision.
• Increase in symptoms during times of hormonal change. Hormonal changes associated with puberty or pregnancy might cause an increase in neurofibromas. Most people who have NF1 have healthy pregnancies but will likely need monitoring by an obstetrician who is familiar with NF1.
• Cardiovascular symptoms. People who have NF1 have an increased risk of high blood pressure and may develop blood vessel conditions.
• Trouble breathing. Rarely, plexiform neurofibromas can put pressure on the airway.
• Cancer. Some people who have NF1 develop cancerous tumors. These usually arise from neurofibromas under the skin or from plexiform neurofibromas. People who have NF1 also have a higher risk of other forms of cancer. They include breast cancer, leukemia, colorectal cancer, brain tumors and some types of soft tissue cancer. Screening for breast cancer should begin earlier, at age 30, for women with NF1 compared to the general population.
• Benign adrenal gland tumor, known as a pheochromocytoma. This noncancerous tumor produces hormones that raise your blood pressure. Surgery often is needed to remove it.

Neurofibromatosis type 1 care at Mayo Clinic

• Ferri FF. Neurofibromatosis. In: Ferri's Clinical Advisor 2024. Elsevier; 2024. https://www.clinicalkey.com. Accessed Feb. 21, 2024.
• Neurofibromatosis. National Institute of Neurological Disorders and Stroke. https://www.ninds.nih.gov/Disorders/Patient-Caregiver-Education/Fact-Sheets/Neurofibromatosis-Fact-Sheet. Accessed Feb. 21, 2024.
• Korf BR, et al. Neurofibromatosis type 1 (NF1): Pathogenesis, clinical features, and diagnosis. https://www.uptodate.com/contents/search. Accessed Feb. 21, 2024.
• Saleh M, et al. Neurofibromatosis type 1 system-based manifestations and treatments: A review. Neurological Sciences. 2023; doi:10.1007/s10072-023-06680-5.
• Neurofibromatosis. American Association of Neurological Surgeons. https://www.aans.org/en/Patients/Neurosurgical-Conditions-and-Treatments/Neurofibromatosis. Accessed Feb. 21, 2024.
• Neurofibromatosis. Merck Manual Professional Version. https://www.merckmanuals.com/professional/pediatrics/neurocutaneous-syndromes/neurofibromatosis. Accessed Feb. 21, 2024.
• Jankovic J, et al., eds. Neurocutaneous syndromes. In: Bradley and Daroff's Neurology in Clinical Practice. 8th ed. Elsevier; 2022. https://www.clinicalkey.com. Accessed Feb. 21, 2024.
• Armstrong AE, et al. Treatment decisions and the use of the MEK inhibitors for children with neurofibromatosis type 1-related plexiform neurofibromas. BMC Cancer. 2023; doi:10.1186/s12885-023-10996-y.
• Zitelli BJ, et al., eds. Neurology. In: Zitelli and Davis' Atlas of Pediatric Physical Diagnoses. 8th ed. Elsevier; 2023. https://www.clinicalkey.com. Accessed Feb. 21, 2024.
• Kellerman RD, et al. Neurofibromatosis (type 1). In: Conn's Current Therapy 2024. Elsevier; 2024. https://www.clinicalkey.com. Accessed Feb. 21, 2024.
• Babovic-Vuksanovic D (expert opinion). Mayo Clinic. March 26, 2024.
• Tamura R. Current understanding of neurofibromatosis type 1, 2 and schwannomatosis. International Journal of Molecular Sciences. 2021; doi:10.3390/ijms22115850.
• Legius E, et al. Revised diagnostic criteria for neurofibromatosis type 1 and Legius syndrome: An international consensus recommendation. Genetics in Medicine. 2021; doi:10.1038/s41436-021-01170-5.
• Find a doctor. Children's Tumor Foundation. https://www.ctf.org/understanding-nf/find-a-doctor/. Accessed Feb. 26, 2024.
• Ami TR. Allscripts EPSi. Mayo Clinic. April 18, 2024.

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

Published on 4.6.2024 in Vol 26 (2024)

eHealth Literacy and the Use of NHS 111 Online Urgent Care Service in England: Cross-Sectional Survey

Authors of this article:

Original Paper

• Joanne Turnbull 1 , PhD   ; 
• Jane Prichard 1 , PhD   ; 
• Jennifer MacLellan 2 , PhD   ; 
• Catherine Pope 2 , PhD  

1 School of Health Sciences, University of Southampton, Southampton, United Kingdom

2 Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom

Corresponding Author:

Joanne Turnbull, PhD

School of Health Sciences

University of Southampton

Highfield Campus

Southampton, SO17 1BJ

United Kingdom

Phone: 44 2380597940

Email: [email protected]

Background: Many health care systems have used digital technologies to support care delivery, a trend amplified by the COVID-19 pandemic. “Digital first” may exacerbate health inequalities due to variations in eHealth literacy. The relationship between eHealth literacy and web-based urgent care service use is unknown.

Objective: This study aims to measure the association between eHealth literacy and the use of NHS (National Health Service) 111 online urgent care service.

Methods: A cross-sectional sequential convenience sample survey was conducted with 2754 adults (October 2020-July 2021) from primary, urgent, or emergency care; third sector organizations; and the NHS 111 online website. The survey included the eHealth Literacy Questionnaire (eHLQ), questions about use, preferences for using NHS 111 online, and sociodemographic characteristics.

Results: Across almost all dimensions of the eHLQ, NHS 111 online users had higher mean digital literacy scores than nonusers ( P <.001). Four eHLQ dimensions were significant predictors of use, and the most highly significant dimensions were eHLQ1 (using technology to process health information) and eHLQ3 (ability to actively engage with digital services), with odds ratios (ORs) of 1.86 (95% CI 1.46-2.38) and 1.51 (95% CI 1.22-1.88), respectively. Respondents reporting a long-term health condition had lower eHLQ scores. People younger than 25 years (OR 3.24, 95% CI 1.87-5.62) and those with formal qualifications (OR 0.74, 95% CI 0.55-0.99) were more likely to use NHS 111 online. Users and nonusers were likely to use NHS 111 online for a range of symptoms, including chest pain symptoms (n=1743, 70.4%) or for illness in children (n=1117, 79%). The users of NHS 111 online were more likely to have also used other health services, particularly the 111 telephone service ( χ 1 2 =138.57; P <.001).

Conclusions: These differences in eHealth literacy scores amplify perennial concerns about digital exclusion and access to care for those impacted by intersecting forms of disadvantage, including long-term illness. Although many appear willing to use NHS 111 online for a range of health scenarios, indicating broad acceptability, not all are able or likely to do this. Despite a policy ambition for NHS 111 online to substitute for other services, it appears to be used alongside other urgent care services and thus may not reduce demand.

Introduction

“Digital first” as the central point of contact is increasingly being pursued in the delivery of a wide range of services, including health care [ 1 ]. The COVID-19 pandemic rapidly accelerated the use of apps, web-based digital technologies, and web-based triage in general practice and urgent care internationally [ 2 ] and in the United Kingdom [ 3 ]. Digital and telephone access are now core to primary [ 4 , 5 ], urgent, and emergency care provision in the UK NHS (National Health Service) [ 6 , 7 ], with a range of telephone and web-based services that triage and manage demand via e-consultation systems [ 6 , 8 ]. These systems typically offer urgent care call handling, web-based triage, and signposting to suitable services (eg, general practice, urgent care centers [UCCs], and emergency departments [EDs]) [ 9 ].

Digital health care offers the potential to improve the quality of patient care and provide timely and more convenient access to services [ 6 ]. They may also empower people to manage and maintain their own health [ 10 ]. Evidence suggests that participants often express high levels of satisfaction with web-based symptom checkers and assessment services [ 7 ]. However, important longstanding concerns that remain are socioeconomic and cultural factors [ 11 ], language difficulties, disability, and wider structural and technical infrastructure obstacles, which act as barriers to using, and benefitting from, digital access to health care [ 12 ]. Studies have shown that people from lower socioeconomic groups are typically less likely to use web-based information seeking [ 13 ] and symptom checkers [ 14 ]. Black or African American and Hispanic adults in the United States have been shown to be less likely to use technology for health-related purposes [ 15 ]. Conversely, younger and more highly educated people were more likely to use web-based triage and symptom checkers [ 7 ].

Accessing health care services via digital technologies predicates that people have sufficient knowledge, skills, resources, and motivation to access and use digital technologies to make decisions about a health problem [ 16 ]. The concept of eHealth literacy, which combines ideas about “health” and “health service” literacy (appreciation of symptoms and signs of illness and awareness of service provision) with digital literacy (ability to use digital technologies such as the web or smartphones) [ 16 , 17 ], has proved useful for examining this “digital divide” [ 11 , 18 - 20 ]. Studies using the eHealth literacy scale (eHEALS) [ 17 ] have demonstrated that lower eHealth literacy is associated with increased age [ 21 , 22 ], lower levels of education [ 23 ], lower socioeconomic status [ 22 ], and the presence of a long-term health condition (LTHC) [ 23 ]. Much of this literature has focused on eHealth literacy in relation to internet use for health information seeking rather than using symptom checkers or web-based triage. A survey using the eHealth Literacy Questionnaire (eHLQ) [ 24 ] reported that users who have digital access to health care services (eg, communicating with health professionals and accessing health-related information) scored higher on most dimensions of the scale [ 25 ]. However, little research has focused on eHealth literacy in the context of web-based urgent care triage and assessment.

NHS 111 Online

The NHS 111 online urgent care service was launched in 2017 across the 4 nations of the United Kingdom and is an exemplar of a policy push to “digital first” that is not unique to the NHS. NHS 111 online was designed to augment the NHS 111 telephone triage and assessment service, which was launched in England in 2011 [ 3 ]. NHS 111 online is freely available 24 hours a day, giving access to web-based assessment and triage (via a smartphone, tablet, or computer) for people with urgent (nonemergency) care needs aged older than 5 years. The NHS 111 telephone and online services are both underpinned by a computer decision support software system. NHS 111 online users follow a tailored algorithm, answering questions about symptoms or health concerns. This results in an outcome that directs users to appropriate services (eg, emergency ambulance, ED, and general practice) or provides self-care advice. Where indicated, a call back from a health care professional may be offered. Facilities for booking arrival at an ED were more recently added [ 26 ]. In a single month (April 2024), 661,987 NHS 111 online sessions were completed: 64,754 (10%) resulted in an ambulance outcome; 73,366 (11%) emergency treatment; 283,808 (43%) primary care; 102,182 (15%), a prescription; 39,622 (6%) dental care; and 46,572 (7%) another service. Only 51,683 (8%) of calls resulted in self-management [ 27 ]. There is some expectation that NHS 111 online may help reduce or ameliorate demand for face-to-face urgent and emergency care services [ 3 ], but there is some evidence to suggest that NHS 111 online had little impact on the number of calls to the NHS 111 telephone service [ 28 ].

There is little research to date about eHealth literacy and the use of web-based triage and assessment urgent care services. Since NHS 111 online is used directly by patients and the public—without a call handler or clinical intermediary—this raises additional concerns about eHealth literacy and equity of access via digital services. It is unclear whether the potential benefits of urgent web-based health services, such as improving access to services and greater empowerment or self-management of own health [ 6 , 10 ], may be hindered by eHealth literacy. This study provides the first large-scale survey that aims to measure eHealth literacy and the help-seeking preferences of users and nonusers of NHS 111 online in the context of web-based urgent care use.

Study Design

A cross-sectional survey was conducted in England between October 2020 and July 2021, including periods when COVID-19 restrictions were in place. The survey included eHLQ, a validated 35-item 7-dimension questionnaire [ 28 ] used to explore individuals’ reported competencies, experiences, and interactions with technologies and services. The eHLQ consists of 7 dimensions: eHLQ1, using technology to process health information (5 items); eHLQ2, understanding of health concepts and language 5 items); eHLQ3, ability to actively engage with digital services (5 items); eHLQ4, feel safe and in control (5 items); eHLQ5, motivated to engage with digital services (5 items); eHLQ6, access to digital services that work (6 items); and eHLQ7, digital services that suit individual needs (4 items). The eHLQ was developed simultaneously in Danish and English using classical and modern test theory [ 28 ]. The instrument has been used in a range of countries and health care settings. Since its development, there have been several translations and cultural adaptations, and research indicates that the instrument is robust across a range of health care contexts [ 29 - 31 ].

The eHLQ is scored using a 4-point ordinal scale, from strongly disagree (1) to strongly agree (4). Each dimension contains between 4 and 6 items, with scores averaged to calculate each dimension. A higher mean score indicates a higher self-reported eHealth literacy score (a scale of 1 to 4). The highest score of 4 indicates individuals’ self-reported positive experiences and self-reported strengths and comfort with using digitized health services. The eHLQ does not include cut-off points or a benchmarking score for high or low eHealth literacy levels.

The survey also included questions about age, gender, educational attainment, employment status, and the presence of an LTHC. Respondents were asked if they had “a long-term condition or chronic disease” (eg, diabetes, chronic obstructive pulmonary disease, arthritis, and hypertension). As such, having an LTHC is defined by the respondents themselves. Educational attainment was aggregated to four levels: (1) no formal qualifications; (2) comprehensive or secondary school education equivalent—International Standard Classification of Education 2011 (ISCED-2011) levels 1 and 2; (3) further (short) education equivalent to ISCED levels 3, 4, and 5; and (4) higher education (medium and long equivalent to ISCED levels 6, 7, and 8) [ 32 ].

Additionally, 10 scenarios describing common presenting conditions or urgent care needs were used to explore preferences for using NHS 111 online. These scenarios were informed by data from our previous research [ 33 ] and developed in consultation with NHS Digital and patient and public representatives. Scenario preferences for using NHS 111 online were rated on a 5-point Likert scale from “very likely” to “very unlikely.” Respondents were also asked if they had ever previously used an urgent and emergency service (NHS 111 online, NHS 111 telephone service, UCC, general practice out-of-hours service, 999 emergency ambulance, and ED).

Survey Sampling and Participants

Nonprobability sequential convenience sampling was a pragmatic choice to access people who had and had not previously used NHS 111 online. The sampling and recruitment strategy meant that it was not possible to calculate a response rate. Respondents were recruited via 24 primary care organizations, 7 urgent or emergency care settings, the NHS 111 online website, and 2 non-NHS third sector (charity) organizations. The small number of respondents from the charity sites (n=5 respondents) have been combined with primary care data in the analysis presented here. Potential respondents (aged 18 years or older) were identified sequentially by administrative or clinical staff at participating sites or organizations (eg, by reception staff at EDs, or general practice surgeries). General practices used an SMS text message mail out of the survey to eligible patients registered at their practice who had agreed to receive practice information via text message. Practices were asked to select a minimum of 100 random patients on their practice list that had consented to SMS mail outs. Some practices chose to sample more patients than 100 to increase recruitment numbers (practices sampled between an additional 1 and 135 patients per practice). EDs and UCCs invited attendees to their services to take part either by providing them with a web-based link to the survey or by offering the opportunity to complete the survey on a computer tablet in the waiting room (assisted, if necessary, by a research nurse). Sequential patients were offered a survey until a minimum of 50 participants had been recruited at each site. Patients in England who completed the NHS 111 online triage were offered a tailored hyperlink to complete the survey. Of 2754 valid responses, 1621 (58.9%) were recruited via primary care and charity settings, 626 (22.7%) through ED and UCC, and 507 (18.4%) via NHS 111 online.

Patient and Public Involvement

A patient and public involvement (PPI) representative was on the project team and the study steering group and contributed to the design of the study and interpretation of the results. Additional PPI representatives (homeless health peer advocates of the charity Groundswell and members of the public from the Deep End Sheffield cluster PPI Group) took part in PPI events throughout the project, contributing to decisions about survey recruitment, helping to develop the scenario questions, and discussing the interpretation of results and how best to present information from the study for public audiences.

Data Analysis

The analysis compared those who had previously used NHS 111 online at least once (users) and those who had not (nonusers). Descriptive categorical data are summarized and presented as frequency counts and percentages. Chi-square analysis was used to compare users and nonusers and whether they had ever used other urgent and emergency services and the likelihood of using NHS 111 online for the 10 health scenarios with previous use or nonuse of NHS 111 online. We created a binary variable of “likely” or “not likely” by removing the small number of neutral responses. Neutral responses accounted for 8%-16% of the data depending on the scenario. While this grouping loses some of the details of responses, it facilitates comparison. Analyses of the difference between users and nonusers were conducted using Bonferroni adjusted α levels of .007 per test (.05/7). Effect sizes are reported due to the large sample size (Φ correlation coefficient).

A secondary analysis was performed to assess the effects of age, gender, education, and use of NHS 111 online on eHealth literacy scores. Continuous data are presented as means (SDs). When comparing a continuous variable between 2 groups, 2-tailed t tests were applied. The mean eHLQ score for each dimension was compared for users and nonusers of NHS 111 online. Analysis of the difference in eHLQ scores was conducted using Bonferroni adjusted α levels of .007 per test (0.05/7). Due to the large sample size, effect sizes are reported (Hedges g ).

Logistic regression was used to extend the univariate analysis outlined above to explore use versus nonuse of NHS 111 online. Logistic regression reports odds ratios (ORs) associated with each predictor value. The “enter” method (where all variables are entered into the model) was chosen so that all the chosen variables were entered into the model in a single step. Education was aggregated into a binary variable in the regression analysis since there was no strong association between eHLQ and education level (except that people with no formal qualifications had lower eHLQ scores compared to people with any level of qualification). The logistic regression model was examined for multicollinearity by examining tolerance, variance inflation factor, and variance of proportions.

We included respondents with incomplete data. Data for each analysis included all available values (case-by-case). In calculating the eHLQ dimensions, where more than 50% of the data were missing, a score was not calculated for that dimension and was excluded from the analysis.

Ethical Considerations

This study involves human participants and ethical approval was granted for the study by the London Stanmore Research Ethics Authority (20/ LO/0294). Participants gave informed consent to participate in the study before taking part.

Characteristics of Users and Nonusers of NHS 111 Online

Of 2754 valid respondents, 1617 (58.7%) had previously used NHS 111 online (“users”) and 1137 (41.3%) had not used NHS 111 online (“nonusers”). In total, 1745 (63.5%) of respondents were female, 1195 (43.6%) were aged between 45 and 64 years, and 1197 (44.2%) reported an LTHC ( Table 1 ). More female participants reported using NHS 111 online, and the proportion of NHS 111 online users declined consistently with each increasing age and increased with the reported level of education. In total, there is a small difference in the proportion of people with a long-term or chronic condition who had used NHS 111 online compared to those who had not, 523 (46.7%) and 674 (42.4%), respectively.

a Sex: significant difference between male and female ( χ 1 2 =5.46; P =.02; Φ=0.05).

b Long-term health condition: significant difference between yes and no ( χ 1 2 =4.94; P= .03; Φ=0.04).

eHealth Literacy

Across almost all dimensions of the eHLQ, as might be expected, NHS 111 online users had higher eHealth literacy ( Table 2 ). Significant differences were observed for all dimensions except eHLQ4 (feel safe and in control) and eHLQ6 (access to digital services that work). Effect size calculations revealed that differences between users and nonusers were largest for the dimensions of eHLQ1 (using technology to process health information), eHLQ3 (ability to actively engage with digital services), and eHLQ5 (motivated to engage with digital services).

Respondents who reported having an LTHC tended to have lower eHLQ scores on some dimensions and yet were also more likely to have used NHS 111 online ( Table 1 ). Further analysis identified that the subset of people with an LTHC who were nonusers of NHS 111 had the lowest eHLQ mean score for each dimension ( Table 3 ). This difference was statistically significant for 5 dimensions when compared to users both with and without an LTHC but was not significant for eHLQ4 (feel safe and in control) and eHLQ6 (access to digital services that work).

a eHLQ: eHealth Literacy Questionnaire.

b Grouped by self-reported long-term health condition (yes or no).

c Italic formatting indicates significant differences between groups ( P <.001).

Use of Other Services In Addition to NHS 111 Online

The use of NHS 111 online is associated with increased previous use of other urgent and emergency services ( Table 4 ; ie, if the respondent had ever used other urgent and emergency services). Notably, NHS 111 online users were likely to have also used the 111 telephone service.

Scenarios Where NHS 111 Online Would Be Considered

There were 2 scenarios for which both users and nonusers reported they were especially likely to use NHS 111 online ( Table 5 ); “young child with a temperature and crying” and “severe chest pain that goes away after a few minutes.” A sizeable proportion of nonusers reported that they might use NHS 111 online for seeking advice about young children (n=1117, 76.2%) or severe chest pain (n=1008, 69.3%). Nearly half of the nonusers also reported that they would be likely to use it for an itchy bite or sting (n=591, 42.5%), pain when urinating (n=696, 50.9%), and a headache for several hours (n=577, 43.3%).

Predicting Who Will Use NHS 111 Online

Logistic regression was used to predict the use (vs nonuse) of NHS 111 online for categorical variables such as age, gender, education, and LTHC and the mean scores for the 7 eHLQ continuous variables ( Table 6 ). In the regression model, the reference group for age is the oldest group (≥75 years). For other variables, being female, no LTHC, and any qualification were the reference groups. Multicollinearity was tested in the model examining tolerance, the inverse of the tolerance, collinearity diagnostics, and the variance of proportions. Multicollinearity of greater than 0.5 occurred between dimensions 1 and 5 (0.63 for dimension 1 and 0.49 for dimension 5). Removing dimension 5 from the model improved the model fit slightly. Dimension 4 did not behave like the other dimension (there was little difference in this dimension between age, education, and LTHC), and so it was also removed from the model, providing a very small improvement in model fit. A total of 2534 respondents were included in the regression analysis, with 220 (8%) missing data either on at least 1 sociodemographic variable or eHLQ mean score. The model included 1055 respondents who had used NHS 111 online.

Age was a predictor of using NHS 111 online; people younger than 25 years (OR 3.24, 95% CI 1.87-5.62) and aged between 25 and 44 years (OR 2.35, 95% CI 1.47-3.75) were most likely to have used NHS 111 online. Although more women reported use of NHS 111 online, gender was not a significant predictor in the regression model. Education level was not a strong predictor of use, although those with formal qualifications were, perhaps unsurprisingly, more likely to report using NHS 111 online (95% CI 0.55-0.99). Respondents reporting LTHC had lower eHLQ scores and a subset of nonusers with an LTHC had the lowest eHLQ scores. Four eHLQ dimensions (eHLQ1, eHLQ2, eHLQ3, and eHLQ6) were significant predictors of NHS 111 online use, and most highly significant were dimensions eHLQ1 (using technology to process health information) and eHLQ3 (the ability to actively engage with digital services), with ORs of 1.86 (95% CI 1.46-2.38) and 1.51 (95% CI 1.22-1.88), respectively.

a LTHC: long-term health condition.

b eHLQ: eHealth Literacy Questionnaire.

Principal Results and Comparison With Prior Work

Our findings are consistent with previous research, which shows that women [ 12 , 14 , 34 ] and younger people are more likely to use digital health services and that people with no formal qualifications are less likely to use NHS 111 online [ 11 , 12 , 35 , 36 ]. To our knowledge, this is the first time the eHLQ has been used to examine eHealth literacy in relation to the use of an urgent web-based health service (NHS 111 online). Despite relying on web-based data collection methods in some of our settings (due to COVID-19), we found clear differences in reported eHealth literacy between users and nonusers of NHS 111 online. This finding suggests that the digital divide may be even greater than our data indicate. Similar significant differences have been reported in other studies of users and nonusers of technologies, for example, in medical outpatients using the eHLQ [ 25 ] and the eHEALS instrument in a population of baby boomers and older adults seeking health information on the internet [ 34 ]. These eHealth literacy differences highlight the potential for digital exclusion and widening of health inequalities and warrant further investigation.

The survey showed that respondents who had an LTHC appeared more likely to use NHS 111 online compared to those without an LTHC. This is consistent with previous research [ 36 ] and might be used to argue that NHS 111 online is meeting a need for this group. However, our findings are more nuanced; respondents who reported having an LTHC tended to have lower eHLQ scores and the subset of respondents with very low eHLQ scores who reported having an LTHC had not used NHS 111 online. This apparent digital exclusion may be a cause for concern and a source of inequitable service provision.

People who currently use NHS 111 online appear to concurrently use a range of other urgent and emergency services. This may suggest that the web-based service is not a substitute for other services and does not seem to offer an alternative but an addition to the 111 telephone service in help seeking for urgent care. It is important to note that the survey question asked if respondents had ever used other urgent and emergency services, so we do not know if multiple services are used within a single episode of care (eg, using NHS 111 online in addition to other services such as NHS 111 telephone services), or whether different services are used at different time points for different reasons. The value and health benefits of NHS 111 online as an additional service are unclear but, given that one of the key functions of the service is to refer to and signpost to other services, it seems unlikely that NHS 111 online will reduce demand for other urgent and emergency care services.

Our survey showed that people would consider using NHS 111 online for a range of symptom presentations. It was worrying that significant numbers reported they might use NHS 111 online for potentially more serious chest pain symptoms. We asked 2 PPI groups to reflect on this finding and they suggested that the now ubiquitous use of internet searching might underlie this, ie, people experiencing a symptom for the first time would “Google it.” The use of NHS 111 online for help seeking about illness in children may be similarly problematic as this service is not intended use children younger than 5 years. The use of NHS 111 online for potentially more serious symptom scenarios or younger children may introduce unnecessary delays in getting help. More targeted information to clarify the scope of NHS 111 online and encourage greater awareness of appropriate use is necessary.

Strengths and Limitations

This large cross-sectional survey is the first to report on the eHealth literacy of people using and not using urgent care triage and assess technology (NHS 111 online). We acknowledge the limitations of eHealth health literacy measures [ 37 , 38 ] and the problem of using self-reports to assess eHealth literacy, but the eHLQ has shown high construct validity, discriminant validity, and scale reliability [ 24 , 29 , 30 ]. The requirement to report the 7 dimensions separately adds analytical complexity compared to other measures, which offer a single digital literacy score (such as the eHEALS instrument) [ 16 ]. Our pragmatic recruitment strategy (designed to capture users and nonusers of NHS 111 online) meant that we were unable to calculate or estimate a response rate. Survey data collection was conducted primarily via the internet resulting inevitably in some bias toward digital literacy in our sample. Some population groups (such as older adults and people with very low educational attainment) may be underrepresented. Recruitment via general practices via text mail excluded those without access to text and those who had not consented to receiving text messages; again, this may disproportionally reduce the responses from some groups (eg, older people). Nonetheless, we have demonstrated differences in reported eHealth literacy and we contend that these are likely to underreport the digital divide, given that people with the lowest literacy and greatest barriers to access to digital technologies were less well represented in the survey.

Our data were collected from across England, including areas of deprivation and high health need. The survey took place during the COVID-19 pandemic 2020-2021 and health services will adjust coming out of the pandemic; however, NHS 111 online remains a core component of urgent care provision and demand management.

Conclusions and Future Research

Our findings about eHealth literacy and use of NHS 111 online may not be surprising; younger and more educated people are more digitally literate and may be expected to be better able to use this urgent care service. However, we have identified important differences in reported eHealth literacy between users and nonusers of NHS 111 online, notably for those with LTHCs. Going forward, the NHS must ensure that “digital first” policies do not entrench or exacerbate health inequalities.

One of the hopes for NHS 111 online was that it would substitute for other services, such as telephone or face-to-face urgent and emergency care [ 3 ]. Our survey shows that NHS 111 online users were more likely to have used other NHS urgent and emergency care services in addition to using NHS 111 online, and they had higher cumulative use across these services compared to nonusers. The implications of this, both in terms of health outcomes and service costs, warrant further investigation.

Our survey also suggests that people who have not previously used NHS 111 online appear likely to consider using it for a wide range of health scenarios. Understanding this reservoir of demand and their eHealth literacy will be important as web-based services continue to develop.

Acknowledgments

This study was funded by the National Institute for Health Research (NIHR) Health and Social Care Delivery Research (HS&DR) Programme (127590) and will be published in full in HS&DR. This paper reports independent research commissioned by the NIHR. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.

Conflicts of Interest

CP is a National Institute for Health Research board member but was not on the board that commissioned this project. None declared for all other authors.

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Abbreviations

Edited by A Mavragani; submitted 28.06.23; peer-reviewed by D Furstrand, M Bardus, S Kujala, L Kayser; comments to author 02.11.23; revised version received 21.11.23; accepted 11.04.24; published 04.06.24.

©Joanne Turnbull, Jane Prichard, Jennifer MacLellan, Catherine Pope. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 04.06.2024.

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