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Vulnerable Groups
Vulnerable groups involve human samples considered particularly susceptible to coercion or undue influence in a research setting. A vulnerable group includes persons who may be incapable of understanding what it means to participate in research and/or who may not understand what constitutes informed consent. Individuals considered vulnerable may, for various reasons, have a diminished capacity to anticipate, cope with, resist, and/or recover from the impact of a natural or man-made hazard. Vulnerable groups may also consist of individuals who are unable to care for themselves and/or may have an increased chance of suicide, self-harm, or the likelihood of harming others.
Researchers’ assignment of vulnerable group status is both dynamic and relative, because the nature of those groupings is culturally dependent and those labeled as vulnerable are perceived as being in danger, at risk, under threat, susceptible to problems, helpless, and/or in need of protection or support. A situation that makes one person vulnerable may not make another person vulnerable. Being identified as a vulnerable group participant may also overlap with being identified as a victim or a troubled or troublesome individual.
Groups considered vulnerable can vary across academic disciplines, based on the frequency of practitioners’ or researchers’ interactions with those characteristics being studied. However, a nonexhaustive list of groups considered vulnerable across many human-research fields include children, the elderly, single parents, people with disabilities, ethnic minorities, those who are mentally disabled, asylum seekers and refugees, prisoners, pregnant women and fetuses, addicts, individuals with little social support, patients with an acute illness or chronic pain, victims of intimate and other forms of violence, and those who are homeless, economically disadvantaged, poor, illiterate, or unemployed. Some scholars argue that even students used as study participants are vulnerable if their research participation in a specific study (without reasonable alternatives) is mandatory for class credit.
This entry discusses the types of vulnerable groups, provides examples of inappropriate handling of vulnerable groups for research, and presents guidelines for protecting vulnerable groups during research studies.
Identifying Vulnerable Groups
Clearly, there are many different types of vulnerability that contribute to vulnerable group status. One type is the innate/personal, defined by characteristics unique to an individual person that may be sensitive. For example, innate/personal vulnerabilities may include psychological issues, such as anxiety disorders, Alzheimer’s disease, and autism, or issues affecting self-esteem, such as obesity, illiteracy, or nonstandard appearances. Another type of vulnerability includes structural/contextual/environmental factors, or circumstances that lead to a group status assigned by a culture or society. Being homeless, using particular drugs, being a (legal or illegal) sex worker, or living in a war-torn country are examples of external vulnerabilities determined by the structural norms or environmental factors in a given culture.
Vulnerability may depend not only on how a cultural label is employed but also on how the participants themselves perceive their vulnerable status. Emic vulnerabilities are those for which participants possess particular self-awareness. They are aware that their group status or identity is a stigmatized (or otherwise sensitive) one. Emic vulnerabilities may be based on any number of internal or external contributing factors (e.g., mental illness, intimate violence). Etic vulnerabilities, conversely, are those for which the participants themselves may not be aware of their vulnerable status or may not personally identify with the group label that others consider sensitive. In research contexts, etic vulnerabilities are often based on researchers’ identification of a particular demographic variable or group status that has been associated with health problems or social risks in previous research. For example, being a member of a denigrated caste or living below the poverty level are assumed to be stigmatizing situations and, thus, labeled as vulnerable. Ultimately, vulnerability is shaped by a number of intersecting influences such as individual perceptions, situations, and social, historical, political, and cultural factors.
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- Creating and Conducting Research
- Creation of Research Project
- Authoring: Telling a Research Story
- Body Image and Eating Disorders
- Hypothesis Formulation
- Methodology, Selection of
- Program Assessment
- Research Ideas, Sources of
- Research Project, Planning of
- Research Question Formulation
- Research Topic, Definition of
- Research, Inspiration for
- Social Media: Blogs, Microblogs, and Twitter
- Testability
- Ethics
- Acknowledging the Contribution of Others
- Activism and Social Justice
- Anonymous Source of Data
- Authorship Bias
- Authorship Credit
- Confidentiality and Anonymity of Participants
- Conflict of Interest in Research
- Controversial Experiments
- Copyright Issues in Research
- Cultural Sensitivity in Research
- Data Security
- Debriefing of Participants
- Deception in Research
- Ethical Issues, International Research
- Ethics Codes and Guidelines
- Fraudulent and Misleading Data
- Funding Research
- Health Care Disparities
- Human Subjects, Treatment of
- Informed Consent
- Institutional Review Board
- Organizational Ethics
- Peer Review
- Plagiarism
- Plagiarism, Self-
- Privacy of Information
- Privacy of Participants
- Public Behavior, Recording of
- Reliability, Unitizing
- Research Ethics and Social Values
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- McNemar Test
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- Experimental Design Issues
- p value
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- Blocking Variable
- Causality
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- Counterbalancing
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- Data
- Degrees of Freedom
- Delayed Measurement
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- Factor, Nested
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- False Negative
- False Positive
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- Laboratory Experiments
- Latin Square Design
- Longitudinal Design
- Manipulation Check
- Measures of Variability
- Median Split of Sample
- Mixed Level Design
- Multitrial Design
- Null Hypothesis
- One-Group Pretest–Posttest Design
- Orthogonality
- Overidentified Model
- Pilot Study
- Population/Sample
- Power Curves
- Quantitative Research, Purpose of
- Quantitative Research, Steps for
- Quasi-Experimental Design
- Random Assignment
- Replication
- Research Proposal
- Rigor
- Sampling Theory
- Sampling, Determining Size
- Solomon Four-Group Design
- Stimulus Pre-test
- Two-Group Pretest–Posttest Design
- Two-Group Random Assignment Pretest–Posttest Design
- Variables, Control
- Variables, Dependent
- Variables, Independent
- Variables, Latent
- Variables, Marker
- Variables, Mediating Types
- Variables, Moderating Types
- Within-Subjects Design
- Linear Approaches to Statistics
- Analysis of Residuals
- Bivariate Statistics
- Bootstrapping
- Confidence Interval
- Conjoint Analysis
- Contrast Analysis
- Correlation, Pearson
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- Covariance/Variance Matrix
- Covariate
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- Discriminant Analysis
- Kendall’s Tau
- Kruskal-Wallis Test
- Linear Regression
- Linear Versus Nonlinear Relationships
- Multicollinearity
- Multiple Regression
- Multiple Regression: Block Analysis
- Multiple Regression: Covariates in Multiple Regression
- Multiple Regression: Multiple R
- Multiple Regression: Standardized Regression Coefficient
- Partial Correlation
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- Semi-Partial r
- Simple Bivariate Correlation
- Statistical Measurement Issues
- Z score
- Categorization
- Cluster Analysis
- Data Transformation
- Errors of Measurement
- Errors of Measurement: Attenuation
- Errors of Measurement: Ceiling and Floor Effects
- Errors of Measurement: Dichotomization of a Continuous Variable
- Errors of Measurement: Range Restriction
- Errors of Measurement: Regression Toward the Mean
- Frequency Distributions
- Heterogeneity of Variance
- Heteroskedasticity
- Homogeneity of Variance
- Hypothesis Testing, Logic of
- Intraclass Correlation
- Mean, Arithmetic
- Mean, Geometric
- Mean, Harmonic
- Measures of Central Tendency
- Median
- Mode
- Mortality in Sample
- Normal Curve Distribution
- Relationships Between Variables
- Sampling, Probability
- Sensitivity Analysis
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- Simple Descriptive Statistics
- Skewness
- Standard Deviation and Variance
- Standard Error
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- Statistical Power Analysis
- Type I error
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- Univariate Statistics
- Variables, Categorical
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- Variables, Defining
- Variables, Interaction of
- Statistical Models
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- Cross Validation
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- Margin of Error
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- Meta-Analysis: Literature Search Issues
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- Multivariate Analysis of Variance (MANOVA)
- Multivariate Statistics
- Odds Ratio
- Ordinary Least Squares
- Path Analysis
- Probit Analysis
- Quasi-F
- Sobel Test
- Structural Equation Modeling
- Time-Series Analysis
- Analysis of Variance Approaches
- Understanding the Scope of Communication Research
- Areas of Inquiry
- Acculturation
- African American Communication and Culture
- Agenda Setting
- Applied Communication
- Argumentation Theory
- Asian/Pacific American Communication Studies
- Bad News, Communication of
- Basic Course in Communication
- Business Communication
- Communication and Aging Research
- Communication and Culture
- Communication and Evolution
- Communication and Future Studies
- Communication and Human Biology
- Communication and Technology
- Communication Apprehension
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- Communication Competence
- Communication Education
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- Communication History
- Communication Privacy Management Theory
- Communication Skills
- Communication Theory
- Conflict, Mediation, and Negotiation
- Corporate Communication
- Crisis Communication
- Cross-Cultural Communication
- Cultural Studies and Communication
- Cyberchondria
- Dark Side of Communication
- Debate and Forensics
- Development of Communication in Children
- Diaspora
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- Dime Dating
- Disability and Communication
- Distance Learning
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- Empathic Listening
- English as a Second Language
- Environmental Communication
- Family Communication
- Feminist Communication Studies
- Film Studies
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- Freedom of Expression
- Game Studies
- Gender and Communication
- GeoMedia
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- GLBT Social Media
- Group Communication
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- Passing
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- Structure of Research Community
- Areas of Inquiry
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