CASRAI Dictionary

Tag: research ethics

  • Good Clinical Practice (GCP) and the ICH Guidelines

    Good Clinical Practice (GCP) is the international ethical and scientific quality standard for designing, conducting, recording and reporting trials that involve human participants. Its purpose is twofold: to protect the rights, safety and wellbeing of participants, and to ensure that trial data are credible and accurate. This explainer describes GCP as a standards framework and is not clinical or regulatory advice.

    Where GCP comes from

    The modern standard is the International Council for Harmonisation guideline ICH E6 (Good Clinical Practice), adopted across the major regulatory regions so that a trial conducted to its standard is recognised internationally. GCP descends ethically from the Declaration of Helsinki and historically from earlier codes that established the primacy of informed consent. Clinical research conducted to GCP can support regulatory submissions in multiple countries without being repeated.

    The principles of GCP

    • Ethics first. The rights, safety and wellbeing of participants take precedence over the interests of science and society.
    • Informed consent. Participation is voluntary and based on clear, comprehensible information.
    • Independent review. An ethics committee or institutional review board approves and oversees the trial.
    • Sound science. The trial rests on a clear protocol and adequate non-clinical and clinical information.
    • Qualified people. Investigators and staff are appropriately trained and qualified.
    • Data quality. Records are accurate, complete and verifiable, supporting reliable reporting and reconstruction of the trial.

    Roles and documentation

    GCP assigns clear responsibilities to sponsors (who initiate and finance a trial), investigators (who conduct it) and monitors (who verify conduct and data). The trial master file holds the essential documents that together allow the conduct of the trial and the quality of the data to be evaluated. Source data must satisfy data-integrity expectations — attributable, legible, contemporaneous, original and accurate — the same ALCOA principles used in manufacturing.

    GCP, reporting and the research record

    GCP governs how a trial is run; reporting standards such as CONSORT govern how it is published, and prospective registration records what it set out to do. Together these standards make a clinical study transparent and reusable. The thread connecting them — documented methods, clear contributor roles and persistent identifiers — is exactly the infrastructure that keeps the regulatory and scholarly records aligned. See our companion explainers on clinical trial phases and Good Manufacturing Practice.

    Frequently asked questions

    What is Good Clinical Practice?

    Good Clinical Practice is the international ethical and scientific standard for conducting clinical trials, designed to protect participants and to ensure that the resulting data are credible and accurate.

    What is ICH E6?

    ICH E6 is the International Council for Harmonisation’s Good Clinical Practice guideline, adopted across major regulatory regions so that trials conducted to its standard are mutually recognised.

    Why does GCP matter for data quality?

    GCP requires accurate, complete and verifiable records, allowing a trial to be reconstructed and its results trusted. These data-integrity expectations mirror those used in regulated manufacturing.

    How does GCP relate to clinical research more broadly?

    GCP is the quality framework within which clinical research involving people is conducted, complementing reporting standards like CONSORT and prospective trial registration to make studies transparent and reusable.

  • What Is Psychology? Scope, Methods and the Scientific Discipline

    Psychology is the scientific study of mind and behaviour, using systematic observation, measurement and experiment to build and test theories. As an empirical discipline it spans the biological, cognitive, developmental, social and individual aspects of how people and animals perceive, think, feel and act. The American Psychological Association (APA) frames it as a science grounded in evidence rather than intuition or anecdote.

    The scope of the discipline

    Psychology sits at the intersection of the natural and social sciences. It draws on biology and neuroscience to understand the brain, on statistics to quantify behaviour, and on social science to study groups and culture. Its defining commitment is methodological: claims about the mind are evaluated against data gathered under controlled, reproducible conditions rather than accepted on authority. That commitment distinguishes scientific psychology from folk or popular psychology, which may offer intuitively appealing explanations that have never been tested. The discipline’s value lies in its willingness to discard attractive ideas when evidence contradicts them, and to quantify uncertainty rather than asserting confident conclusions about complex human behaviour.

    Major subfields

    Subfield Central question
    Cognitive psychology How do attention, memory, language and reasoning work?
    Developmental psychology How do mind and behaviour change across the lifespan?
    Social psychology How do others influence thought, feeling and action?
    Biological psychology How do brain and body underpin behaviour?
    Personality & individual differences How and why do people differ in stable ways?
    Clinical & counselling How are psychological difficulties understood and supported?

    Research methods

    Psychology relies on a toolkit of complementary methods. Experiments manipulate one variable while holding others constant to test cause and effect, ideally with random assignment to conditions. Observational and correlational studies measure variables as they naturally occur, describing associations without claiming causation. Psychometrics is the science of building and evaluating measures—questionnaires, ability tests and rating scales—so that scores are consistent and meaningful. Underpinning all of these is careful attention to reliability and validity, the twin pillars of sound measurement.

    Quantitative and qualitative approaches

    Psychological research is often divided into quantitative and qualitative traditions, and mature programmes frequently combine them. Quantitative work expresses phenomena as numbers and analyses them statistically, prioritising measurement, comparison and generalisation across large samples. Qualitative work—interviews, focus groups, thematic analysis of text—seeks rich, contextual understanding of how people make meaning, and is well suited to generating hypotheses or studying experiences that resist tidy quantification. Neither is inherently superior; the appropriate method depends on the question. A study estimating how common an attitude is needs quantitative survey methods, whereas one exploring why people hold that attitude may begin qualitatively. Mixed-methods designs deliberately pair the two so that numerical breadth and interpretive depth inform each other.

    The scientific method in psychology

    Psychological research follows the general cycle of the scientific method: observe a phenomenon, derive a testable hypothesis, design a study, collect and analyse data, and revise theory in light of results. Because human behaviour is variable, psychologists lean heavily on statistics to separate genuine effects from chance. The discipline has also become more reflective about its own methods following the replication crisis, adopting practices such as preregistration and data sharing to strengthen the reliability of published findings.

    Measurement and assessment

    Much of psychology depends on turning abstract constructs—intelligence, anxiety, conscientiousness—into numbers. This is harder than it looks, and the field has a long tradition of scrutinising its instruments. Popular tools are not automatically trustworthy: assessments such as the Myers-Briggs Type Indicator illustrate how an instrument can be widely used yet fall short on psychometric grounds. Responsible practice means reporting how a measure was validated, a discipline reflected in CASRAI’s work on responsible assessment.

    A short history of the discipline

    Psychology emerged as a distinct experimental science in the late nineteenth century, conventionally dated to Wilhelm Wundt’s establishment of a dedicated laboratory in Leipzig in 1879. Early schools—structuralism, functionalism and later behaviourism—debated whether psychology should study inner experience or only observable behaviour. The mid-twentieth-century cognitive revolution restored the study of mental processes such as memory and attention using rigorous experimental methods, and the subsequent rise of neuroscience linked those processes to brain function. This trajectory matters because it shows the field repeatedly tightening its methods, a self-correcting tendency that continues in today’s reforms.

    Statistics and inference

    Because behaviour varies between people and occasions, psychology cannot rely on single observations. It uses inferential statistics to ask whether a pattern in a sample is likely to hold in the wider population. Two ideas are central: effect size, which expresses how large a difference or relationship is, and statistical power, the probability that a study will detect a real effect if one exists. Underpowered studies—those with samples too small to reliably find the effects they seek—produce unstable, often exaggerated results. Understanding these concepts is essential to reading psychological research critically, and their neglect contributed directly to the field’s reproducibility problems.

    Distinguishing good evidence from popular myth

    A practical skill the discipline cultivates is separating well-supported findings from appealing but shaky claims. Many ideas that circulate as “psychology” in popular media—rigid personality types, single-study effects presented as laws, or memorable graphs taken at face value—rest on weaker foundations than their fame suggests. Sound practice asks how a finding was measured, whether it has replicated, and how large the effect actually is. This is why the field places such weight on reproducibility and on transparent reporting: a claim is only as good as the method behind it.

    Ethics in psychological research

    Because psychology studies people, it is bound by strong ethical standards. Core principles include informed consent, the right to withdraw, minimisation of harm, confidentiality and, where deception is unavoidable, careful debriefing. Institutional ethics committees, often called institutional review boards, review proposals before data collection begins, and professional bodies such as the APA publish detailed ethics codes. These safeguards became more formalised after historical cases in which participants were exposed to undue stress, and they now shape study design from the outset. Such governance is part of the wider research lifecycle that good metadata and clear terminology, recorded in resources like the research dictionary, are designed to support.

    Frequently asked questions

    Is psychology a science?

    Yes. Psychology uses the scientific method—systematic observation, hypothesis testing, controlled experiments and statistical analysis—to study mind and behaviour, and it revises its theories in light of replicable evidence.

    What are the main branches of psychology?

    Major subfields include cognitive, developmental, social, biological, personality and clinical psychology. They share common methods but differ in the questions they ask and the populations and processes they study.

    What methods do psychologists use?

    Psychologists use experiments, observational and correlational studies, and psychometric testing, supported by statistics. Method choice depends on whether the goal is to establish causation, describe associations or measure an attribute reliably.

    Why does measurement matter so much in psychology?

    Because psychological constructs are abstract, conclusions are only as good as the instruments used. Reliable, valid measures are essential, which is why the field scrutinises its tests and encourages transparent reporting for authors.

  • US Tribal data sovereignty: Indigenous data governance in the United States

    Indigenous data sovereignty is often introduced through the well-known frameworks of other countries — Canada’s OCAP® principles, Aotearoa New Zealand’s Te Mana Raraunga — but the United States has its own distinct and rapidly maturing movement, grounded in the unique legal and political standing of tribal nations. The federally recognised tribes in the US are not merely communities or ethnic groups; they are sovereign nations with their own governments, a status affirmed in treaties, statute and case law. That sovereignty has profound implications for research data. When data is collected about a tribe’s people, lands, health or knowledge, the question of who governs it is, for tribal nations, an extension of nationhood itself. This article looks at how Indigenous data governance is taking shape in the United States, drawing on the Indigenous data and CARE domain of the CASRAI Dictionary.

    Sovereignty as the foundation

    The defining feature of the US context is that data sovereignty rests on political sovereignty. Tribal nations possess inherent rights of self-government, and from that flows a claim that is stronger than a request for ethical treatment: the right to govern data about their citizens, territories and affairs as a matter of jurisdiction. This reframes the conversation. Where a researcher might ordinarily think of data governance in terms of consent and confidentiality, working with tribal data means recognising a nation’s authority — its right to set the terms on which research about its people is conducted, and to decide what is collected, how it is used, and whether it is shared. Indigenous data sovereignty in the US is, at root, the application of self-determination to the information age.

    The United States Indigenous Data Sovereignty Network

    A central force in articulating and advancing this agenda is the United States Indigenous Data Sovereignty Network (USIDSN). It serves as a network and resource for tribes, Native organisations and researchers, working to ensure that data for and about Indigenous peoples and nations is used in ways that advance Indigenous self-determination. USIDSN sits within a wider international movement — alongside the Global Indigenous Data Alliance — but its focus is the specific US landscape of tribal nations and the federal and state systems that collect data about them. Its work includes raising awareness, supporting tribes in asserting governance over their data, and informing policy so that statistics and research about Native peoples reflect their priorities rather than being done to them. The National Congress of American Indians (NCAI), the oldest and largest national organisation representing tribal nations, has likewise adopted resolutions supporting Indigenous data sovereignty, lending the movement weight at the highest representative level.

    Governing genomic and biological data

    Nowhere are the stakes higher than in genomic and biological research, where the history of extractive, harmful practice is most acute. The Native BioData Consortium is a notable response: a non-profit research institute led by Indigenous scientists and governed by Native people, established to keep biological samples and genomic data under Indigenous control. Its existence answers a long-standing problem — that biological materials and the data derived from them have too often left Native communities, to be stored, analysed and benefited from elsewhere, with little return for the people they came from. By creating Indigenous-governed infrastructure for biological data, the Consortium demonstrates a constructive form of sovereignty: not merely objecting to misuse, but building the institutions that allow research to proceed on Indigenous terms, with governance and benefit-sharing built in from the start.

    The CARE Principles in the US context

    Underpinning much of this work are the CARE Principles for Indigenous Data Governance — Collective benefit, Authority to control, Responsibility and Ethics. CARE was developed to complement the FAIR principles, which make data Findable, Accessible, Interoperable and Reusable but say nothing about power, history or whose interests data serves. CARE supplies what FAIR omits. In the US setting, Authority to control maps directly onto tribal sovereignty: it affirms the right of Indigenous peoples to govern data about them. Collective benefit insists that data ecosystems be designed so that Indigenous nations derive benefit, not merely outside researchers. Responsibility and Ethics require that those working with Indigenous data do so in ways accountable to, and respectful of, the communities concerned. Applied together, FAIR and CARE allow data to be both well-managed and justly governed.

    Local Contexts and the practical tools

    Principles need instruments, and one of the most influential practical tools is Local Contexts, an initiative that provides Traditional Knowledge (TK) Labels and Biocultural (BC) Labels. These labels attach to data and collections to communicate community-specific provenance, protocols and permissions — signalling, in a machine-readable and human-readable way, that material originates with a particular community and carries expectations about how it may be used. The Labels do not replace law or negotiation, but they make Indigenous interests visible in the metadata layer, travelling with data into archives and databases that would otherwise strip away that context. They are a concrete answer to a hard question: how do you carry a community’s authority and cultural protocols alongside data as it moves through systems designed without those protocols in mind?

    A consistent vocabulary for Indigenous data governance

    For Indigenous governance to be honoured across institutions, funders and repositories, the terms involved — governance status, access conditions, provenance, community protocols — must be described consistently, or a label or condition recognised in one system will be lost in another. That consistency is what the CASRAI Dictionary works towards: a shared vocabulary so that the metadata expressing Indigenous data governance is understood the same way wherever it travels. And because conducting research with and for Indigenous communities is genuine, recognisable contribution, the work involved can be described using the same framework as any other — the CRediT taxonomy and its full set of contribution roles, set within sound research administration. US tribal data sovereignty is not a regional variation on a theme; it is self-determination applied to data, and the institutions Native nations are building show what that looks like in practice.