Search across guide titles, topics, keywords, and content throughout the Researcher's Field Guide.
Showing 226–250 of 342 results
Research participation is voluntary when people can make a meaningful choice about whether to join and remain in a study without coercion, undue influence, or fear of negative consequences. A signed consent form alone does not establish that the choice was free.
Read Guide →Friends, relatives, and colleagues are not automatically excluded from research participation. Personal relationships can, however, introduce loyalty, obligation, awkwardness, privacy concerns, and subtle influence that researchers need to address before treating familiar people as convenient participants.
Read Guide →Exclusion criteria are ethically justified when they have a defensible scientific, safety, methodological, or ethical purpose. Researchers should be able to explain why each important restriction is necessary rather than treating familiar or convenient exclusions as self-justifying.
Read Guide →Unexpected participant distress requires an immediate participant-centered response rather than simply finishing the research procedure. Researchers should pause, assess immediate needs and safety, follow the approved distress protocol, and determine whether further research participation remains appropriate.
Read Guide →Researchers asking about traumatic or highly sensitive experiences should prepare participant support before data collection begins. That preparation may include trained staff, privacy safeguards, verified referral pathways, distress procedures, and clear escalation plans appropriate to the study's actual risks.
Read Guide →Research ethics and data protection often address the same participant data, but they ask different questions. Understanding the distinction helps researchers avoid assuming that ethics approval automatically means their data practices are legally compliant.
Read Guide →A research data breach can involve unauthorized disclosure, loss, alteration, destruction, or loss of access to personal data. Researchers should report suspected breaches immediately so the responsible organization can contain, assess, document, and notify as required.
Read Guide →Deception does not create permission to expose participants to whatever risks a scientifically valuable study requires. Some major frameworks restrict alterations to consent to minimal-risk research, while professional standards may impose additional limits on particular harms.
Read Guide →Archived research data may be scientifically valuable long after the original study ends, but depositing data in an archive does not erase researchers' ethical responsibilities. Secondary users need to understand the data's provenance, authorization, restrictions, privacy risks, and governance before reuse.
Read Guide →Respectful community engagement begins early enough for community perspectives to influence the research itself. It requires more than informing leaders about a finished protocol or obtaining permission immediately before recruitment.
Read Guide →Fieldwork and data collection can expose researchers to risks ranging from physical danger and harassment to emotional distress, legal problems, and digital threats. The relevant risks depend on what researchers do, where they do it, and whom or what they encounter.
Read Guide →Working alone with a participant is not automatically unsafe or unethical. The decision should depend on the participant, setting, activity, researcher, available safeguards, and consequences if something goes wrong.
Read Guide →Financial conflicts involve monetary or economic interests, while nonfinancial conflicts can arise from professional, personal, academic, or intellectual interests. Both may matter when they intersect with a researcher’s responsibilities.
Read Guide →Commercial survey panels can provide useful research data, but their reliability depends on recruitment, respondent verification, sampling, and quality-control procedures. Learn how to evaluate panel providers, recognize limitations of opt-in samples, and avoid treating demographic quotas or large sample sizes as guarantees of representativeness.
Read Guide →Researchers may sometimes have legitimate reasons to restrict methodological details that could enable serious harm. However, withholding information can undermine reproducibility and scientific scrutiny. Learn when restrictions may be justified, which alternatives can preserve accountability, and how to distinguish responsible disclosure from inadequate methodological reporting.
Read Guide →AI can assist with many demanding research tasks, but some responsibilities should remain under meaningful human control. Researchers should not completely delegate accountable scholarly judgment, ethical responsibility, authoritative verification, or final interpretation to AI.
Read Guide →No general-purpose generative AI should be assumed to have access to every published research paper. Scholarly literature is distributed across publishers, databases, repositories, and access systems, while model training and retrieval tools provide only particular forms of coverage.
Read Guide →An AI hallucination occurs when a generative AI system produces information that is false, unsupported, inconsistent, or unfaithful to the available evidence while presenting it as a plausible response. For researchers, the practical consequence is simple: plausible AI-generated content should not be treated as verified evidence.
Read Guide →Generative AI can invent methodological details or incorrectly describe methods used in real studies. Researchers should verify designs, samples, instruments, procedures, and analyses directly against the original study or authoritative methodological source.
Read Guide →Generative AI can invent journal names, article titles, author combinations, and other bibliographic details. More deceptively, it can combine real scholarly entities into a publication that never existed.
Read Guide →AI models can sometimes detect and correct their own mistakes, particularly under structured verification procedures, but self-verification remains unreliable as a general guarantee of correctness. For consequential research work, use self-review to identify possible problems and external evidence to resolve them.
Read Guide →Researchers should generally prefer verifiable citations when AI is used for source-dependent work. Citations make claims auditable, but only if the sources are real, identifiable, relevant, and actually support what the AI says.
Read Guide →A research ethics committee may restrict or require changes to AI use when it affects participants, consent, privacy, confidentiality, risk, or an approved research protocol.
Read Guide →Generative AI can be useful for producing, expanding, and recombining possible research ideas. Its strongest role is as an ideation partner, not as an authority on whether an idea is novel, important, feasible, or supported by the literature.
Read Guide →Generative AI can help researchers notice possible problems, tensions, contradictions, and unexplained phenomena worth investigating. It cannot establish from plausible language alone that a research problem genuinely exists or deserves study.
Read Guide →