# kb

id: network-based-diffusion-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Network-based_diffusion_analysis-0.md
category: reference
title: Network-based diffusion analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Network-based_diffusion_analysis
date_saved: 2026-05-05T10:02:42.840541+00:00
instance: kb-cron
parent: network-based-diffusion-analysis-20260505-0
summary:
Network-based diffusion analysis (NBDA) is a statistical tool to detect and quantify social transmission of information or a behaviour in social networks (SNA, etc.). NBDA assumes that social transmission of a behavior follows the social network of associations or interactions among individuals, since individuals who spend a lot of time together, or who interact more have more opportunity to learn from each other. Therefore, NBDA infers social transmission if the spread of a novel behavior follows the social network of a population. NBDA thus allows the study of social learning to be linked to animal behavior research that uses social network analysis. NBDA was introduced by Franz & Nunn and further developed by Hoppitt, Boogert, & Laland. An R package for performing Bayesian NBDA, STbayes, was published by Chimento & Hoppitt in 2025. == Implementation == NBDA requires prior knowledge about the underlying social network of a population. In an observational study, the order (or timing) at which individuals in the population acquire a behaviour or information is recorded. NBDA then tests whether the spread of information or behaviour is explained by the previously determined network or not. Because more closely associated individuals are more likely to interact with each


id: open-label-trial-20260505-0
file: data/en.wikipedia.org/wiki/Open-label_trial-0.md
category: reference
title: Open-label trial
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open-label_trial
date_saved: 2026-05-05T10:02:44.071326+00:00
instance: kb-cron
parent: open-label-trial-20260505-0
summary:
An open-label trial, or open trial, is a type of clinical trial in which information is not withheld from trial participants. In particular, both the researchers and participants know which treatment is being administered. This contrasts with a double-blinded trial, where information is withheld both from the researchers and the participants to reduce bias. Open-label trials may be appropriate for comparing two similar treatments to determine which is most effective, such as a comparison of different prescription anticoagulants, or possible relief from symptoms of some disorders when a placebo is given. An open-label trial may still be randomized. Open-label trials may also be uncontrolled (without a placebo group), with all participants receiving the same treatment. This would be a single-arm study design. == References ==


id: parallel-study-20260505-0
file: data/en.wikipedia.org/wiki/Parallel_study-0.md
category: reference
title: Parallel study
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Parallel_study
date_saved: 2026-05-05T10:02:45.211284+00:00
instance: kb-cron
parent: parallel-study-20260505-0
summary:
A parallel study is a type of clinical study where two groups of treatments, A and B, are given so that one group receives only A while another group receives only B. Other names for this type of study include "between patient" and "non-crossover". This is unlike a crossover study where at first one group receives treatment A and later followed by treatment B while the other group receives treatment B followed by treatment A. There are, however, certain characteristics that allow for differentiation between these two types of trials. For example, a parallel study would be more appropriate if any concerns about carryover effects were present. This type of study might also be more beneficial if the disease or disorder being studied has a likely chance of progression during the time in which the study takes place. One significant issue with parallel studies, though, is the concept of intra subject variability, which is defined as variability in response occurring within the same patient. The two treatment groups in a parallel study can either consist of two completely separate treatments (i.e. different drugs), or simply different doses of a common drug. One major aspect of a parallel study is randomization


id: parataxonomy-20260505-0
file: data/en.wikipedia.org/wiki/Parataxonomy-0.md
category: reference
title: Parataxonomy
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Parataxonomy
date_saved: 2026-05-05T10:02:46.406435+00:00
instance: kb-cron
parent: parataxonomy-20260505-0
summary:
Parataxonomy is a system of labor division for use in biodiversity research, in which the rough sorting tasks of specimen collection, field identification, documentation and preservation are conducted by primarily local, less specialized individuals, thereby alleviating the workload for the "alpha" or "master" taxonomist. Parataxonomy may be used to improve taxonomic efficiency by enabling more expert taxonomists to restrict their activity to the tasks that require their specialist knowledge and skills, which has the potential to expedite the rate at which new taxa may be described and existing taxa may be sorted and discussed. Parataxonomists generally work in the field, sorting collected samples into recognizable taxonomic units (RTUs) based on easily recognized features. The process can be used alone for rapid assessment of biodiversity. Some researchers consider reliance on parataxonomist-generated data to be prone to error depending on the sample, the sorter and the group of organisms in question. Therefore, quantitative studies based on parataxonomic processes may be unreliable and is therefore controversial. The concepts of citizen science and parataxonomy are somewhat overlapping, with unclear distinctions between those employed to provide supplemental services to taxonomists and those who do so voluntarily, whether for personal enrichment or the altruistic desire to


id: participatory-action-research-20260505-0
file: data/en.wikipedia.org/wiki/Participatory_action_research-0.md
category: reference
title: Participatory action research
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Participatory_action_research
date_saved: 2026-05-05T10:02:47.553680+00:00
instance: kb-cron
parent: participatory-action-research-20260505-0
summary:
Participatory action research (PAR) is an approach to action research emphasizing participation and action by members of communities affected by that research. It seeks to understand the world by trying to change it, collaboratively and following reflection. PAR emphasizes collective inquiry and experimentation grounded in experience and social history. Within a PAR process, "communities of inquiry and action evolve and address questions and issues that are significant for those who participate as co-researchers". PAR contrasts with mainstream research methods, which emphasize controlled experimentaction, statistical analysis, and reproducibility of findings. PAR practitioners make a concerted effort to integrate three basic aspects of their work: participation (life in society and democracy), action (engagement with experience and history), and research (soundness in thought and the growth of knowledge). "Action unites, organically, with research" and collective processes of self-investigation. The way each component is actually understood and the relative emphasis it receives varies nonetheless from one PAR theory and practice to another. This means that PAR is not a monolithic body of ideas and methods but rather a pluralistic orientation to knowledge making and social change. == Overview == In the UK and North America the work of Kurt Lewin and the Tavistock Institute


id: participatory-action-research-20260505-1
file: data/en.wikipedia.org/wiki/Participatory_action_research-1.md
category: reference
title: Participatory action research
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Participatory_action_research
date_saved: 2026-05-05T10:02:47.553680+00:00
instance: kb-cron
parent: participatory-action-research-20260505-0
summary:
"Do not monopolise your knowledge nor impose arrogantly your techniques, but respect and combine your skills with the knowledge of the researched or grassroots communities, taking them as full partners and co-researchers. Do not trust elitist versions of history and science which respond to dominant interests, but be receptive to counter-narratives and try to recapture them. Do not depend solely on your culture to interpret facts, but recover local values, traits, beliefs, and arts for action by and with the research organisations. Do not impose your own ponderous scientific style for communicating results, but diffuse and share what you have learned together with the people, in a manner that is wholly understandable and even literary and pleasant, for science should not be necessarily a mystery nor a monopoly of experts and intellectuals." PAR can be thought of as a guiding paradigm to influence and democratize the creation of knowledge making, and ground it in real community needs and learning. Knowledge production controlled by elites can sometimes further oppress marginalized populations. PAR can be a way of overcoming the ineffectiveness and elitism of conventional schooling and science, and the negative effects of market forces and industry on the workplace, community life


id: participatory-action-research-20260505-2
file: data/en.wikipedia.org/wiki/Participatory_action_research-2.md
category: reference
title: Participatory action research
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Participatory_action_research
date_saved: 2026-05-05T10:02:47.553680+00:00
instance: kb-cron
parent: participatory-action-research-20260505-0
summary:
=== Organizational life === Action research in the workplace took its initial inspiration from Lewin's work on organizational development (and Dewey's emphasis on learning from experience). Lewin's seminal contribution involves a flexible, scientific approach to planned change that proceeds through a spiral of steps, each of which is composed of 'a circle of planning, action, and fact-finding about the result of the action', towards an organizational 'climate' of democratic leadership and responsible participation that promotes critical self-inquiry and collaborative work. These steps inform Lewin's work with basic skill training groups, T-groups where community leaders and group facilitators use feedback, problem solving, role play and cognitive aids (lectures, handouts, film) to gain insights into themselves, others and groups with a view to 'unfreezing' and changing their mindsets, attitudes and behaviours. Lewin's understanding of action-research coincides with key ideas and practices developed at the influential Tavistock Institute (created in 1947)) in the UK and National Training Laboratories (NTL) in the US. An important offshoot of Tavistock thinking and practise is the sociotechnical systems perspective on workplace dynamics, guided by the idea that greater productivity or efficiency does not hinge on improved technology alone. Improvements in organizational life call instead for the interaction


id: participatory-action-research-20260505-3
file: data/en.wikipedia.org/wiki/Participatory_action_research-3.md
category: reference
title: Participatory action research
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Participatory_action_research
date_saved: 2026-05-05T10:02:47.553680+00:00
instance: kb-cron
parent: participatory-action-research-20260505-0
summary:
=== Community development and sustainable livelihoods === PAR emerged in the postwar years as an important contribution to intervention and self-transformation within groups, organizations and communities. It has left a singular mark on the field of rural and community development, especially in the Global South. Tools and concepts for doing research with people, including "barefoot scientists" and grassroots "organic intellectuals" (see Gramsci), are now promoted and implemented by many international development agencies, researchers, consultants, civil society and local community organizations around the world. This has resulted in countless experiments in diagnostic assessment, scenario planning and project evaluation in areas ranging from fisheries and mining to forestry, plant breeding, agriculture, farming systems research and extension, watershed management, resource mapping, environmental conflict and natural resource management, land rights, appropriate technology, local economic development, communication, tourism, leadership for sustainability, biodiversity and climate change. This prolific literature includes the many insights and methodological creativity of participatory monitoring, participatory rural appraisal (PRA) and participatory learning and action (PLA) and all action-oriented studies of local, indigenous or traditional knowledge. On the whole, PAR applications in these fields are committed to problem solving and adaptation to nature at the household or community level, using friendly methods of


id: participatory-action-research-20260505-4
file: data/en.wikipedia.org/wiki/Participatory_action_research-4.md
category: reference
title: Participatory action research
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Participatory_action_research
date_saved: 2026-05-05T10:02:47.553680+00:00
instance: kb-cron
parent: participatory-action-research-20260505-0
summary:
=== Feminism and gender === Feminist research and women's development theory also contributed to rethinking the role of scholarship in challenging existing regimes of power, using qualitative and interpretive methods that emphasize subjectivity and self-inquiry rather than the quantitative approach of mainstream science. As did most research in the 1970s and 1980s, PAR remained androcentric. In 1987, Patricia Maguire critiqued this male-centered participatory research, arguing that "rarely have feminist and participatory action researchers acknowledged each other with mutually important contributions to the journey." Given that PAR aims to give equitable opportunity for diverse and marginalized voices to be heard, engaging gender minorities is an integral pillar in PAR's tenants. In addition to gender minorities, PAR must consider points of intersecting oppressions individuals may experience. After Maguire published Traveling Companions: Feminism, Teaching, And Action Research, PAR began to extend toward not only feminism, but also Intersectionality through Black Feminist Thought and Critical Race Theory (CRT). Today, applying an intersectional feminist lens to PAR is crucial to recognize the social categories, such as race, class, ability, gender, and sexuality, that construct individuals' power relations and lived experiences. PAR seeks to recognize the deeply complex condition of human living. Therefore, framing PAR's qualitative


id: participatory-action-research-20260505-5
file: data/en.wikipedia.org/wiki/Participatory_action_research-5.md
category: reference
title: Participatory action research
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Participatory_action_research
date_saved: 2026-05-05T10:02:47.553680+00:00
instance: kb-cron
parent: participatory-action-research-20260505-0
summary:
== Ethics == Given the often delicate power balances between researchers and participants in PAR, there have been calls for a code of ethics to guide the relationship between researchers and participants in a variety of PAR fields. Norms in research ethics involving humans include respect for the autonomy of individuals and groups to deliberate about a decision and act on it. This principle is usually expressed through the free, informed and ongoing consent of those participating in research (or those representing them in the case of persons lacking the capacity to decide). Another mainstream principle is the welfare of participants who should not be exposed to any unfavourable balance of benefits and risks with participation in research aimed at the advancement of knowledge, especially those that are serious and probable. Since privacy is a factor that contributes to people's welfare, confidentiality obtained through the collection and use of data that are anonymous (e.g. survey data) or anonymized tends to be the norm. Finally, the principle of justice—equal treatment and concern for fairness and equity—calls for measures of appropriate inclusion and mechanisms to address conflicts of interests. While the choice of appropriate norms of ethical conduct is rarely an either/or


id: participatory-action-research-20260505-6
file: data/en.wikipedia.org/wiki/Participatory_action_research-6.md
category: reference
title: Participatory action research
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Participatory_action_research
date_saved: 2026-05-05T10:02:47.553680+00:00
instance: kb-cron
parent: participatory-action-research-20260505-0
summary:
== Challenges == PAR offers a long history of experimentation with evidence-based and people-based inquiry, a groundbreaking alternative to mainstream positive science. As with positivism, the approach creates many challenges as well as debates on what counts as participation, action and research. Differences in theoretical commitments (Lewinian, Habermasian, Freirean, psychoanalytic, feminist, etc.) and methodological inclinations (quantitative, qualitative, mixed) are numerous and profound. This is not necessarily a problem, given the pluralistic value system built into PAR. Ways to better answer questions pertaining to PAR's relationship with science and social history are nonetheless key to its future. One critical question concerns the problem-solving orientation of engaged inquiry—the rational means-ends focus of most PAR experiments as they affect organizational performance or material livelihoods, for instance. In the clinical perspective of French psychosociology, a pragmatic orientation to inquiry neglects forms of understanding and consciousness that are not strictly instrumental and rational. PAR must pay equal attention to the interconnections of self-awareness, the unconscious and life in society. Another issue, more widely debated, is scale—how to address broad-based systems of power and issues of complexity, especially those of another development on a global scale. How can PAR develop a macro-orientation to democratic dialogue and


id: participatory-design-20260505-0
file: data/en.wikipedia.org/wiki/Participatory_design-0.md
category: reference
title: Participatory design
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Participatory_design
date_saved: 2026-05-05T10:02:48.714500+00:00
instance: kb-cron
parent: participatory-design-20260505-0
summary:
Participatory design (originally co-operative design or design participation, now often co-design and also co-creation) is an approach to design that attempts to involve a variety of stakeholders (e.g. employees, partners, customers, citizens, end users) in the design process to help ensure the result meets their needs and is usable. Participatory design is an approach which is focused on processes and procedures of design and is not a design style. The term is used in a variety of fields, e.g. software design, urban design, architecture, landscape architecture, product design, sustainability, graphic design, industrial design, planning, and health services development, as a way of creating environments that are more satisfactory and appropriate to their inhabitants' and users' practical, cultural, emotional and spiritual needs. It is also one approach to placemaking. Participatory design has been used in many settings and at various scales. For some, this approach has a political dimension of user empowerment and democratization. This inclusion of external parties in the design process does not excuse designers of their responsibilities. In their article "Participatory Design and Prototyping", Wendy Mackay and Michel Beaudouin-Lafon support this point by stating that "[a] common misconception about participatory design is that designers are expected to abdicate


id: participatory-design-20260505-1
file: data/en.wikipedia.org/wiki/Participatory_design-1.md
category: reference
title: Participatory design
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Participatory_design
date_saved: 2026-05-05T10:02:48.714500+00:00
instance: kb-cron
parent: participatory-design-20260505-0
summary:
=== History in Scandinavia === In several Scandinavian countries, during the 1960s and 1970s, participatory design was rooted in work with trade unions; its ancestry also includes action research and sociotechnical design. Research projects on user participation in systems development date back to the 1970s. The so-called "collective resource approach" developed strategies and techniques for workers to influence the design and use of computer applications at the workplace: The Norwegian Iron and Metal Workers Union (NJMF) project took a first move from traditional research to working with people, directly changing the role of the union clubs in the project. The Scandinavian projects developed an action research approach, emphasizing active co-operation between researchers and workers of the organization to help improve the latter's work situation. While researchers got their results, the people whom they worked with were equally entitled to get something out of the project. The approach built on people's own experiences, providing for them resources to be able to act in their current situation. The view of organizations as fundamentally harmonious—according to which conflicts in an organization are regarded as pseudo-conflicts or "problems" dissolved by good analysis and increased communication—was rejected in favor of a view of organizations recognizing


id: participatory-design-20260505-2
file: data/en.wikipedia.org/wiki/Participatory_design-2.md
category: reference
title: Participatory design
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Participatory_design
date_saved: 2026-05-05T10:02:48.714500+00:00
instance: kb-cron
parent: participatory-design-20260505-0
summary:
Public interest design is a design movement, extending to architecture, with the main aim of structuring design around the needs of the community. At the core of its application is participatory design. Through allowing individuals to have a say in the process of design of their own surrounding built environment, design can become proactive and tailored towards addressing wider social issues facing that community. Public interest design is meant to reshape conventional modern architectural practice. Instead of having each construction project solely meet the needs of the individual, public interest design addresses wider social issues at their core. This shift in architectural practice is a structural and systemic one, allowing design to serve communities responsibly. Solutions to social issues can be addressed in a long-term manner through such design, serving the public, and involving it directly in the process through participatory design. The built environment can become the very reason for social and community issues to arise if not executed properly and responsibly. Conventional architectural practice often does cause such problems since only the paying client has a say in the design process. Portland, Oregon City Repair Project was a form of participatory design involving the community co-designing problem areas


id: participatory-design-20260505-3
file: data/en.wikipedia.org/wiki/Participatory_design-3.md
category: reference
title: Participatory design
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Participatory_design
date_saved: 2026-05-05T10:02:48.714500+00:00
instance: kb-cron
parent: participatory-design-20260505-0
summary:
== Research methodology == Increasingly researchers are focusing on co-design as a way of doing research, and therefore are developing parts of its research methodology. For instance, in the field of generative co-design Vandekerckhove et al. have proposed a methodology to assemble a group of stakeholders to participate in generative co-design activities in the early innovation process. They propose first to sample a group of potential stakeholders through snowball sampling, afterwards interview these people and assess their knowledge and inference experience, lastly they propose to assemble a diverse group of stakeholders according to their knowledge and inference experience. Though not completely synonymous, research methods of Participatory Design can be defined under Participatory Research (PR): a term for research designs and frameworks using direct collaboration with those affected by the studied issue. More specifically, Participatory Design has evolved from Community-Based Research and Participatory Action Research (PAR). PAR is a qualitative research methodology involving: "three types of change, including critical consciousness development of researchers and participants, improvement of lives of those participating in research, and transformation of societal 'decolonizing' research methods with the power of healing and social justice". Participatory Action Research (PAR) is a subset of Community-Based Research aimed explicitly at


id: patient-and-public-involvement-20260505-0
file: data/en.wikipedia.org/wiki/Patient_and_public_involvement-0.md
category: reference
title: Patient and public involvement
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Patient_and_public_involvement
date_saved: 2026-05-05T10:02:49.972651+00:00
instance: kb-cron
parent: patient-and-public-involvement-20260505-0
summary:
Public involvement (or public and patient involvement, PPI) in medical research refers to the practice where people with health conditions (patients), carers and members of the public work together with researchers and influence what is researched and how. Involvement is not the same as participation which means taking part in research, for example taking a drug in a clinical trial. == Definition == Public involvement in medical research can be defined as research being carried out "with" or "by" members of the public rather than "to", "about" or "for" them. Through PPI patients, carers and people with lived experience work alongside researchers to influence and contribute to how research is designed and conducted. Members of the public involved in research are frequently referred to as public members or public contributors. === Terminology === Researchers and others use different terms to describe how they interact with the public, and this can vary across organisations and countries. The terms involvement, engagement and participation are sometimes used interchangeably. The National Institute for Health and Care Research (NIHR) uses the term public partnerships to encompass the components of involvement, engagement and participation. It can be summarised as "a term to collectively describe ways in


id: patient-and-public-involvement-20260505-1
file: data/en.wikipedia.org/wiki/Patient_and_public_involvement-1.md
category: reference
title: Patient and public involvement
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Patient_and_public_involvement
date_saved: 2026-05-05T10:02:49.972651+00:00
instance: kb-cron
parent: patient-and-public-involvement-20260505-0
summary:
Identifying and prioritising research. People with lived experience help to identify the right topics to ensure that the research is looking at what matters to them. Commissioning research. Involving members of the public in deciding which research proposals should get funding. Public members can also continue monitoring the research projects that get funded. Designing research. Public members help designing the research to make sure it is feasible, ethical and relevant. This happens usually before applying for funding. Managing research. Public involvement in the steering group or managing committee that oversees the research. Undertaking research. Members of the public help carrying out the research. This can include, for example, gathering evidence, reviewing literature, conducting interviews and focus groups, and analysing the results. Disseminating (sharing) research. Public members help sharing the findings of research. They can be consulted on the ways of dissemination, help summarising the research in plain language, work on accessibility issues. Implementing research. Public members influencing how the result is taken into practice and making sure it leads to action. Public involvement can be short-term and task-based or long-term across a research project or an institutional programme. == Barriers and issues == There are a wide range of challenges


id: patient-and-public-involvement-20260505-2
file: data/en.wikipedia.org/wiki/Patient_and_public_involvement-2.md
category: reference
title: Patient and public involvement
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Patient_and_public_involvement
date_saved: 2026-05-05T10:02:49.972651+00:00
instance: kb-cron
parent: patient-and-public-involvement-20260505-0
summary:
== Diversity and inclusion == In order for research to be relevant for all, the PPI process needs to include members of the public from diverse and inclusive groups. A 2021 survey highlights that the majority of public contributors to NIHR research were predominantly female (57%), 61 years of age and over, white and heterosexual. The Health Research Authority found that people from ethnic and lower socioeconomic groups felt less confident about being treated in a dignified and respectful way in research in comparison to white and higher socioeconomic individuals. Black and minority ethnic (BME) involvement in research has widespread support, however it tends to be limited to certain phases of the research cycle and particular ethnicities. === Frameworks for diversity === The Race Equality Framework (REF) was produced as a self assessment tool aiming to help organisations improve racial equity in health and care research. It was co-produced by the Race Equality Public Action Group (REPAG). Similar frameworks exist for research participants, for example, the INCLUDE ethnicity framework and the National Health Service (NHS) guidance for increasing diversity in research participation. == History == The development of patient and public involvement in research was influenced by grassroots social movements,


id: periscop-20260505-0
file: data/en.wikipedia.org/wiki/PERISCOP-0.md
category: reference
title: PERISCOP
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/PERISCOP
date_saved: 2026-05-05T10:02:51.163872+00:00
instance: kb-cron
parent: periscop-20260505-0
summary:
The PERISCOP is a pressurized recovery device designed for retrieving deep-sea marine life at depths exceeding 2,000 metres. The device was designed by Bruce Shillito and Gerard Hamel at the Universite Pierre et Marie Curie. The name is an acronym for the French phrase Projet d’Enceinte de Remontée Isobare Servant la Capture d’Organismes Profonds ("Enclosure project for isobaric ascent serving to capture deep organisms"). == History == The PERISCOP is a unique pressurized recovery device that contains three chambers – one for capture, one for recovery under exterior pressure, and one for transfer to the laboratory while maintaining pressure. Previous recovery devices used one chamber for all purposes. An arm designed to capture samples by force of suction is attached to the device. During ascent, pressure is maintained within the chamber by use of pressurized water. Upon surfacing, samples can be observed, filmed, and/or photographed through transparent view ports in the device. Due to fluctuations in atmospheric pressure and temperature recorded pressures during ascent and at the surface may ranged from 74%-111% of the natural pressure at sea depth. The device set a record for the deepest live-fish capture under pressure when it captured a Pachycara at 2,300 m. The


id: pooled-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Pooled_analysis-0.md
category: reference
title: Pooled analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Pooled_analysis
date_saved: 2026-05-05T10:02:52.330665+00:00
instance: kb-cron
parent: pooled-analysis-20260505-0
summary:
A pooled analysis is a statistical technique for combining the results of multiple epidemiological studies. It is one of three types of literature reviews frequently used in epidemiology, along with meta-analysis and traditional narrative reviews. Pooled analyses may be either retrospective or prospective. It is often used when the results of individual studies do not allow for a firm conclusion to be drawn. Unlike meta-analyses, pooled analyses can only be conducted if the included studies used the same study design and statistical models, and if their respective populations were homogeneous. If individual-level data from the included studies is available, the result of a pooled analysis can be considered more reliable. == References ==


id: postmortem-studies-20260505-0
file: data/en.wikipedia.org/wiki/Postmortem_studies-0.md
category: reference
title: Postmortem studies
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Postmortem_studies
date_saved: 2026-05-05T10:02:53.502702+00:00
instance: kb-cron
parent: postmortem-studies-20260505-0
summary:
Postmortem studies are a type of neurobiological research, which provides information to researchers and individuals who will have to make medical decisions in the future. Postmortem researchers conduct a longitudinal study of the brain of an individual, who has some sort of phenomenological condition (i.e. cannot speak, trouble moving left side of body, Alzheimer's, etc.) that is examined after death. Researchers look at certain lesions in the brain that could have an influence on cognitive or motor functions. These irregularities, damage, or other cerebral anomalies observed in the brain are attributed to an individual's pathophysiology and their environmental surroundings. Postmortem studies provide a unique opportunity for researchers to study different brain attributes that would be unable to be studied on a living person. Postmortem studies allow researchers to determine causes and cure for certain diseases and functions. It is critical for researchers to develop hypotheses, in order to discover the characteristics that are meaningful to a particular disorder. The results that the researcher discovers from the study will help the researcher trace the location in the brain to specific behaviors. When tissue from a postmortem study is obtained it is imperative that the researcher ensures the quality is adequate to


id: pragmatic-validity-20260505-0
file: data/en.wikipedia.org/wiki/Pragmatic_validity-0.md
category: reference
title: Pragmatic validity
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Pragmatic_validity
date_saved: 2026-05-05T10:02:54.708020+00:00
instance: kb-cron
parent: pragmatic-validity-20260505-0
summary:
Pragmatic validity in research looks to a different paradigms from more traditional, (post)positivistic research approaches. It tries to ameliorate problems associated with the rigour-relevance debate, and is applicable in all kinds of research streams. Simply put, pragmatic validity looks at research from a prescriptive-driven perspective. Solutions to problems that actually occur in the complex and highly multivariate field of practice are developed in a way that, while valid for a specific situation, need to be adjusted according to the context in which they are to be applied. The term "validity" is often seen as a sort catch-all for the question whether the knowledge claims resulting from research are warranted. The confusion might arise from the mingling of the terms 'internal validity' and 'external validity', where the former refers to proof of a causal link between a treatment and effect, and the latter is concerned with generalizability. (In this discussion I maintain the term 'generalizability' rather than external validity mainly to avoid any possible confusion between the two terms.) During this discussion I consider that validity is reflected in the question, "did we measure the right thing?", or, in other words, can the researcher prove that the effect he observed was


id: pragmatic-validity-20260505-1
file: data/en.wikipedia.org/wiki/Pragmatic_validity-1.md
category: reference
title: Pragmatic validity
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Pragmatic_validity
date_saved: 2026-05-05T10:02:54.708020+00:00
instance: kb-cron
parent: pragmatic-validity-20260505-0
summary:
Thus, Cook (1983) actually questions the validity of causal explanations generated in a context-free setting (the goal of positivistic, explanatory research). Causal relationships in pragmatic research are looked at somewhat differently, which is apparent in the wording alone. A statement about a causal relationship in positivistic research is something like the following; if you perform action x to subject y, then z happens. This assumes that the confounding variables have been ruled out, and the statement is always true, regardless of the situation (internally and externally valid). What I want to do now is use the concept of 'technological rules' in order to illustrate how causality is shown in prescriptive. In pragmatic science, the goal is to develop knowledge that can be used to improve a situation. This we can call prescriptive knowledge. Prescriptive knowledge, according to van Aken (2004, 2004b, 2005) can take the form of a technological rule. A technological rule is "... a chunk of general knowledge linking an intervention or artifact with an expected outcome or performance in a certain field of application" (van Aken, 2005: p23). This rule can be formulated much the same way as my earlier example of a causal statement; 'if you


id: qualitative-research-20260505-0
file: data/en.wikipedia.org/wiki/Qualitative_research-0.md
category: reference
title: Qualitative research
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Qualitative_research
date_saved: 2026-05-05T10:02:55.894451+00:00
instance: kb-cron
parent: qualitative-research-20260505-0
summary:
Qualitative research is a type of research that aims to gather and analyse non-numerical (descriptive) data in order to gain an understanding of individuals' social reality, including understanding their attitudes, beliefs, and motivation. This type of research typically involves in-depth interviews, focus groups, or field observations in order to collect data that is rich in detail and context. Qualitative research is often used to explore complex phenomena or to gain insight into people's experiences and perspectives on a particular topic. It is particularly useful when researchers want to understand the meaning that people attach to their experiences or when they want to uncover the underlying reasons for people's behavior. Qualitative methods include ethnography, grounded theory, discourse analysis, and interpretative phenomenological analysis. Qualitative research methods have been used in sociology, anthropology, political science, psychology, communication studies, social work, folklore, educational research, information science and software engineering research. == Background == Qualitative research has been informed by several strands of philosophical thought and examines aspects of human life, including culture, expression, beliefs, morality, life stress, and imagination. Contemporary qualitative research has been influenced by a number of branches of philosophy, for example, positivism, postpositivism, critical theory, and constructivism. The historical transitions or


id: qualitative-research-20260505-1
file: data/en.wikipedia.org/wiki/Qualitative_research-1.md
category: reference
title: Qualitative research
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Qualitative_research
date_saved: 2026-05-05T10:02:55.894451+00:00
instance: kb-cron
parent: qualitative-research-20260505-0
summary:
== Data analysis == Qualitative researchers have a number of analytic strategies available to them. === Coding === In general, coding refers to the act of associating meaningful ideas with the data of interest. In the context of qualitative research, interpretative aspects of the coding process are often explicitly recognized and articulated; coding helps to produce specific words or short phrases believed to be useful abstractions from the data. === Pattern thematic analysis === Data may be sorted into patterns for thematic analyses as the primary basis for organizing and reporting the study findings. === Content analysis === According to Krippendorf, "Content analysis is a research technique for making replicable and valid inference from data to their context" (p. 21). It is applied to documents and written and oral communication. Content analysis is an important building block in the conceptual analysis of qualitative data. It is frequently used in sociology. For example, content analysis has been applied to research on such diverse aspects of human life as changes in perceptions of race over time, the lifestyles of contractors, and even reviews of automobiles. == Multi-method qualitative analysis == === Benefits === This is, for example, illustrated by studies on classroom


id: qualitative-research-20260505-2
file: data/en.wikipedia.org/wiki/Qualitative_research-2.md
category: reference
title: Qualitative research
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Qualitative_research
date_saved: 2026-05-05T10:02:55.894451+00:00
instance: kb-cron
parent: qualitative-research-20260505-0
summary:
=== Limitations of qualitative research === Qualitative research is not without limitations. These limitations include participant reactivity, the potential for a qualitative investigator to over-identify with one or more study participants, "the impracticality of the Glaser-Strauss idea that hypotheses arise from data unsullied by prior expectations," the inadequacy of qualitative research for testing cause-effect hypotheses, and the Baconian character of qualitative research. Participant reactivity refers to the fact that people often behave differently when they know they are being observed. Over-identifying with participants refers to a sympathetic investigator studying a group of people and ascribing, more than is warranted, a virtue or some other characteristic to one or more participants. Compared to qualitative research, experimental research and certain types of nonexperimental research (e.g., prospective studies), although not perfect, are better means for drawing cause-effect conclusions. Glaser and Strauss, influential members of the qualitative research community, pioneered the idea that theoretically important categories and hypotheses can emerge "naturally" from the observations a qualitative researcher collects, provided that the researcher is not guided by preconceptions. The ethologist David Katz wrote "a hungry animal divides the environment into edible and inedible things....Generally speaking, objects change...according to the needs of the animal." Karl Popper


id: qualitative-research-20260505-3
file: data/en.wikipedia.org/wiki/Qualitative_research-3.md
category: reference
title: Qualitative research
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Qualitative_research
date_saved: 2026-05-05T10:02:55.894451+00:00
instance: kb-cron
parent: qualitative-research-20260505-0
summary:
== Further reading == Adler, P. A. & Adler, P. (1987). : context and meaning in social inquiry / edited by Richard Jessor, Anne Colby, and Richard A. Shweder OCLC 46597302 Baškarada, S. (2014) "Qualitative Case Study Guidelines", in The Qualitative Report, 19(40): 1-25. Available from [1] Archived 2015-03-26 at the Wayback Machine Boas, Franz (1943). "Recent anthropology". Science. 98 (2546): 311–314, 334–337. Bibcode:1943Sci....98..334B. doi:10.1126/science.98.2546.334. PMID 17794461. Creswell, J. W. (2003). Research design: Qualitative, quantitative, and mixed method approaches. Thousand Oaks, CA: Sage Publications. Denzin, N. K., & Lincoln, Y. S. (2000). Handbook of qualitative research ( 2nd ed.). Thousand Oaks, CA: Sage Publications. Denzin, N. K., & Lincoln, Y. S. (2011). The SAGE Handbook of qualitative research ( 4th ed.). Los Angeles: Sage Publications. DeWalt, K. M. & DeWalt, B. R. (2002). Participant observation. Walnut Creek, CA: AltaMira Press. Fischer, C.T. (Ed.) (2005). Qualitative research methods for psychologists: Introduction through empirical studies. Academic Press. ISBN 0-12-088470-4. Franklin, M. I. (2012), "Understanding Research: Coping with the Quantitative-Qualitative Divide". London/New York. Routledge Giddens, A. (1990). The consequences of modernity. Stanford, CA: Stanford University Press. Gubrium, J. F. and J. A. Holstein. (2000). "The New Language of Qualitative Method." New York: Oxford


id: randomised-non-comparative-trial-20260505-0
file: data/en.wikipedia.org/wiki/Randomised_non-comparative_trial-0.md
category: reference
title: Randomised non-comparative trial
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Randomised_non-comparative_trial
date_saved: 2026-05-05T10:02:57.097615+00:00
instance: kb-cron
parent: randomised-non-comparative-trial-20260505-0
summary:
A randomised non-comparative trial, RNCT (or also non-comparative randomised trial), is a type of clinical trial where participants are randomised to different conditions (arms), but where the primary analysis involves comparing each arm separately to a historical control, predefined benchmark or something else, with no formal comparison between the two arms. The study design appears to have arisen in oncology, where single-arm studies are not unusual. It promises reduced sample size requirements. An RNCT acts like multiple single-arm designs run concurrently. A review found RNCTs dating back to 2002, and having been used in high-profile oncology studies and also beyond oncology. The design has been criticised by statisticians. It is unclear what benefit randomisation adds compared to running separate single-arm studies. Pavlos Msaouel described the use of the word "randomisation" as merely being "talismanic" in a 2025 article, it leading readers to think the study has the benefits of a randomised controlled trial (RCT). While the point of an RNCT is not to have a comparison between arms, about half of reported RNCTs still reported some sort of comparison. == References ==


id: randomized-controlled-trial-20260505-0
file: data/en.wikipedia.org/wiki/Randomized_controlled_trial-0.md
category: reference
title: Randomized controlled trial
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Randomized_controlled_trial
date_saved: 2026-05-05T10:02:58.342301+00:00
instance: kb-cron
parent: randomized-controlled-trial-20260505-0
summary:
A randomized controlled trial (RCT) is a type of statistical experiment designed to evaluate the efficacy or safety of an intervention by minimizing bias through the random allocation of participants to one or more comparison groups. In this approach, at least one group receives the intervention or process under study (such as a drug, surgical procedure, medical device or diet), while the other groups receive an alternative treatment, a placebo, or standard care. RCTs are a fundamental methodology in modern clinical trials and have been widely considered one of the highest-quality sources of evidence in evidence-based medicine, due to their ability to reduce selection bias and the influence of confounding factors. However, they have also been criticized for failing to reduce bias in some cases. Participants who enroll in RCTs differ from one another in known and unknown ways that can influence study outcomes, and yet cannot be directly controlled. By randomly allocating participants among compared treatments, an RCT enables statistical control over these influences. Provided it is designed well, conducted properly, and enrolls enough participants, an RCT may achieve sufficient control over these confounding factors to deliver a useful comparison of the treatments studied. == Definition and examples ==


id: randomized-controlled-trial-20260505-1
file: data/en.wikipedia.org/wiki/Randomized_controlled_trial-1.md
category: reference
title: Randomized controlled trial
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Randomized_controlled_trial
date_saved: 2026-05-05T10:02:58.342301+00:00
instance: kb-cron
parent: randomized-controlled-trial-20260505-0
summary:
== Ethics == Although subjects almost always provide informed consent for their participation in an RCT, studies since 1982 have documented that RCT subjects may believe that they are certain to receive treatment that is best for them personally; that is, they do not understand the difference between research and treatment. Determining the amount of information required to ensure informed consent can be difficult, and further research is necessary to determine the prevalence of and ways to address therapeutic misconception. Placebo-controlled trials have been deemed unethical in instances where not receiving treatment may lead to harm for the patient, such as an aggravation of symptoms or risk of death. Crossover trials, active-controlled trials, and other approaches have been used to mitigate this issue, though these options may not always be suitable for study, and have received their own criticism. Active-controlled trials in particular may raise ethical considerations regarding clinical equipoise. Although the principle of equipoise ("genuine uncertainty within the expert medical community... about the preferred treatment") is common to clinical trials and has been applied to RCTs, equipoise may be difficult to ascertain, and the ethics of RCTs have special considerations. It has been argued that equipoise itself is insufficient


id: randomized-controlled-trial-20260505-2
file: data/en.wikipedia.org/wiki/Randomized_controlled_trial-2.md
category: reference
title: Randomized controlled trial
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Randomized_controlled_trial
date_saved: 2026-05-05T10:02:58.342301+00:00
instance: kb-cron
parent: randomized-controlled-trial-20260505-0
summary:
"It eliminates bias in treatment assignment," specifically selection bias and confounding. "It facilitates blinding (masking) of the identity of treatments from investigators, participants, and assessors." "It permits the use of probability theory to express the likelihood that any difference in outcome between treatment groups merely indicates chance." There are two processes involved in randomizing patients to different interventions. First is choosing a randomization procedure to generate an unpredictable sequence of allocations; this may be a simple random assignment of patients to any of the groups at equal probabilities, may be "restricted", or may be "adaptive." A second and more practical issue is allocation concealment, which refers to the stringent precautions taken to ensure that the group assignment of patients are not revealed prior to definitively allocating them to their respective groups. Non-random "systematic" methods of group assignment, such as alternating subjects between one group and the other, can cause "limitless contamination possibilities" and can cause a breach of allocation concealment. However empirical evidence that adequate randomization changes outcomes relative to inadequate randomization has been difficult to detect. === Procedures === The treatment allocation is the desired proportion of patients in each treatment arm. An ideal randomization procedure would achieve the


id: randomized-controlled-trial-20260505-3
file: data/en.wikipedia.org/wiki/Randomized_controlled_trial-3.md
category: reference
title: Randomized controlled trial
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Randomized_controlled_trial
date_saved: 2026-05-05T10:02:58.342301+00:00
instance: kb-cron
parent: randomized-controlled-trial-20260505-0
summary:
"Allocation concealment" (defined as "the procedure for protecting the randomization process so that the treatment to be allocated is not known before the patient is entered into the study") is important in RCTs. In practice, clinical investigators in RCTs often find it difficult to maintain impartiality. Stories abound of investigators holding up sealed envelopes to lights or ransacking offices to determine group assignments in order to dictate the assignment of their next patient. Such practices introduce selection bias and confounders (both of which should be minimized by randomization), possibly distorting the results of the study. Adequate allocation concealment should defeat patients and investigators from discovering treatment allocation once a study is underway and after the study has concluded. Treatment related side-effects or adverse events may be specific enough to reveal allocation to investigators or patients thereby introducing bias or influencing any subjective parameters collected by investigators or requested from subjects. Some standard methods of ensuring allocation concealment include sequentially numbered, opaque, sealed envelopes (SNOSE); sequentially numbered containers; pharmacy controlled randomization; and central randomization. It is recommended that allocation concealment methods be included in an RCT's protocol, and that the allocation concealment methods should be reported in detail in a publication


id: randomized-controlled-trial-20260505-4
file: data/en.wikipedia.org/wiki/Randomized_controlled_trial-4.md
category: reference
title: Randomized controlled trial
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Randomized_controlled_trial
date_saved: 2026-05-05T10:02:58.342301+00:00
instance: kb-cron
parent: randomized-controlled-trial-20260505-0
summary:
=== Relative importance and observational studies === Two studies published in The New England Journal of Medicine in 2000 found that observational studies and RCTs overall produced similar results. The authors of the 2000 findings questioned the belief that "observational studies should not be used for defining evidence-based medical care" and that RCTs' results are "evidence of the highest grade." However, a 2001 study published in Journal of the American Medical Association concluded that "discrepancies beyond chance do occur and differences in estimated magnitude of treatment effect are very common" between observational studies and RCTs. According to a 2014 (updated in 2024) Cochrane review, there is little evidence for significant effect differences between observational studies and randomized controlled trials. To evaluate differences it is necessary to consider things other than design, such as heterogeneity, population, intervention or comparator. Two other lines of reasoning question RCTs' contribution to scientific knowledge beyond other types of studies: If study designs are ranked by their potential for new discoveries, then anecdotal evidence would be at the top of the list, followed by observational studies, followed by RCTs. RCTs may be unnecessary for treatments that have dramatic and rapid effects relative to the expected stable


id: randomized-controlled-trial-20260505-5
file: data/en.wikipedia.org/wiki/Randomized_controlled_trial-5.md
category: reference
title: Randomized controlled trial
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Randomized_controlled_trial
date_saved: 2026-05-05T10:02:58.342301+00:00
instance: kb-cron
parent: randomized-controlled-trial-20260505-0
summary:
=== Time and costs === RCTs can be expensive; one study found 28 Phase III RCTs funded by the National Institute of Neurological Disorders and Stroke prior to 2000 with a total cost of US$335 million, for a mean cost of US$12 million per RCT. Nevertheless, the return on investment of RCTs may be high, in that the same study projected that the 28 RCTs produced a "net benefit to society at 10-years" of 46 times the cost of the trials program, based on evaluating a quality-adjusted life year as equal to the prevailing mean per capita gross domestic product. The conduct of an RCT takes several years until being published; thus, data is restricted from the medical community for long years and may be of less relevance at time of publication. It is costly to maintain RCTs for the years or decades that would be ideal for evaluating some interventions. Interventions to prevent events that occur only infrequently (e.g., sudden infant death syndrome) and uncommon adverse outcomes (e.g., a rare side effect of a drug) would require RCTs with extremely large sample sizes and may, therefore, best be assessed by observational studies. Due to the costs of running RCTs,


id: randomized-controlled-trial-20260505-6
file: data/en.wikipedia.org/wiki/Randomized_controlled_trial-6.md
category: reference
title: Randomized controlled trial
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Randomized_controlled_trial
date_saved: 2026-05-05T10:02:58.342301+00:00
instance: kb-cron
parent: randomized-controlled-trial-20260505-0
summary:
=== Economics === RCTs have become a staple of identifying causal inference among microeconomic studies, particularly in development economics. In 1994, Paul Glewwe, eventual Nobel Prize winner, Michael Kremer, and Sylvie Moulin started one of the earliest RCTs in an economic setting by conducting a long run intervention in a school in Kenya, publishing the results fifteen years later. Three years later in 1997, the largest field experiment in a developing context, the PROGRESA program in Mexico, was studied by a multitude of economic researchers. The impact of RCTs on the discipline has only grown, as economists have found this method as a first-best approach to causal inference identification. While not at the forefront, the use of RCTs has helped to bolster the credibility revolution in empirical microeconomics, as well as becoming popularized as a result of the need for more rigorous identification. Despite the shift towards using RCTs in research, there still remains division between economists on its use. John A. List, a proponent of field experiments, particularly RCTs, finds that this method differs significantly from lab experiments, and therefore provide more robust measures for identification. RCTs also offer the advantage of providing true observational data that can be


id: rapid-reviews-20260505-0
file: data/en.wikipedia.org/wiki/Rapid_reviews-0.md
category: reference
title: Rapid reviews
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Rapid_reviews
date_saved: 2026-05-05T10:02:59.617551+00:00
instance: kb-cron
parent: rapid-reviews-20260505-0
summary:
Rapid reviews are a systematic survey of literature on a topic or question of interest. Compared to a systematic review of literature, in a rapid review, several design decisions and practical steps are undertaken to reduce the time it takes to identify, aggregate and answer the question of interest. The Cochrane Rapid Reviews Methods Group proposes that rapid reviews can take different forms, and they define rapid reviews as: "A form of knowledge synthesis that accelerates the process of conducting a traditional systematic review through streamlining or omitting specific methods to produce evidence for stakeholders in a resource-efficient manner". == In medicine and healthcare == Rapid reviews are a form of evidence synthesis, similar to a systematic review, that can be used to inform decision-making and healthcare initiative. The World Health Organization (WHO) considers rapid reviews as a way of generating evidence in a short period using an abbreviated systematic review method. During the COVID-19 pandemic rapid reviews were employed to answer pressing questions under strict time constraints. == In software engineering == For Software Engineering, Rico et al. have recently adapted and extended the rapid review method. Their proposal takes into account the unique requirements of industry-academia collaboration in


id: reporting-bias-20260505-0
file: data/en.wikipedia.org/wiki/Reporting_bias-0.md
category: reference
title: Reporting bias
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Reporting_bias
date_saved: 2026-05-05T10:03:00.766807+00:00
instance: kb-cron
parent: reporting-bias-20260505-0
summary:
In epidemiology, reporting bias is defined as "selective revealing or suppression of information" by subjects (for example about past medical history, smoking, sexual experiences). In artificial intelligence research, the term reporting bias is used to refer to people's tendency to under-report all the information available. In empirical research, authors may be under-reporting unexpected or undesirable experimental results, attributing the results to sampling or measurement error, while being more trusting of expected or desirable results, though these may be subject to the same sources of error. In this context, reporting bias can eventually lead to a status quo where multiple investigators discover and discard the same results, and later experimenters justify their own reporting bias by observing that previous experimenters reported different results. Thus, each incident of reporting bias can make future incidents more likely. == Reporting biases in research == Research can only contribute to knowledge if it is communicated from investigators to the community. The generally accepted primary means of communication is "full" publication of the study methods and results in an article published in a scientific journal. Sometimes, investigators choose to present their findings at a scientific meeting as well, either through an oral or poster presentation. These


id: reporting-bias-20260505-1
file: data/en.wikipedia.org/wiki/Reporting_bias-1.md
category: reference
title: Reporting bias
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Reporting_bias
date_saved: 2026-05-05T10:03:00.766807+00:00
instance: kb-cron
parent: reporting-bias-20260505-0
summary:
The publication or nonpublication of research findings, depend on the nature and direction of the results. Although medical writers have acknowledged the problem of reporting biases for over a century, it was not until the second half of the 20th century that researchers began to investigate the sources and size of the problem of reporting biases. Over the past two decades, evidence has accumulated that failure to publish research studies, including clinical trials testing intervention effectiveness, is pervasive. Almost all failure to publish is due to failure of the investigator to submit; only a small proportion of studies are not published because of rejection by journals. Some reviews have reported that only half or all trials are published. The most direct evidence of publication bias in the medical field comes from follow-up studies of research projects identified at the time of funding or ethics approval. These studies have shown that "positive findings" is the principal factor associated with subsequent publication: researchers say that the reason they do not write up and submit reports of their research for publication is usually because they are "not interested" in the results (editorial rejection by journals is a rare cause of failure to publish).


id: reporting-bias-20260505-2
file: data/en.wikipedia.org/wiki/Reporting_bias-2.md
category: reference
title: Reporting bias
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Reporting_bias
date_saved: 2026-05-05T10:03:00.766807+00:00
instance: kb-cron
parent: reporting-bias-20260505-0
summary:
Bias –also known as spin- in research reporting interventional medical studies could arise from, misleading reporting, misleading interpretation and inadequate extrapolation of the results or conclusions. Research reporting bias can take various forms. Misleading reporting could be attributed to failure to report adverse effects of a certain medical or surgical intervention, selective reporting of results, or inappropriate use of language in the conclusions as overstated conclusions or undue generalizations that are not supported by study's results. Misleading interpretation of research results could be attributed to misinterpretation of both statistically significant and statistically non-significant etcetera. Inadequate extrapolation of research results could be caused by extending the positive outcomes of research to include a broader or different patient population that was not originally investigated. The selective reporting of some outcomes but not others, depending on the nature and direction of the results. A study may be published in full, but pre-specified outcomes omitted or misrepresented. Efficacy outcomes that are statistically significant have a higher chance of being fully published compared to those that are not statistically significant. Research interpretation bias or spin is prevalent across medical publications irrespective of discipline i.e. surgical versus medical and irrespective of journal ranking or study's level-of-evidence


id: research-synthesis-20260505-0
file: data/en.wikipedia.org/wiki/Research_synthesis-0.md
category: reference
title: Research synthesis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Research_synthesis
date_saved: 2026-05-05T10:03:01.946663+00:00
instance: kb-cron
parent: research-synthesis-20260505-0
summary:
Research synthesis or evidence synthesis is the process of combining the results of multiple primary research studies aimed at testing the same conceptual hypothesis. It may be applied to either quantitative or qualitative research. Its general goals are to make the findings from multiple different studies more generalizable and applicable. It aims to generate new knowledge by combining and comparing the results of multiple studies on a given topic. One approach is to use a systematic review method. == Techniques == Meta-analysis is the preferred technique of quantitative research synthesis in many fields, such as medical science. It is a statistical technique that combines measures of effect size from a number of studies to calculate an overall measure. This measure of effect has greater precision (less uncertainty) than the individual studies, but it may be biased by differences between the studies in study design, the type of evidence, or other key characteristics. In qualitative research, methods of synthesis include narrative synthesis and meta-ethnography. Narrative synthesis allows researchers to address a wide range of questions in their review, while meta-ethnography aims to preserve the cultural context in which the original findings of the included studies were generated. The narrative synthesis approach


id: reverse-correlation-technique-20260505-0
file: data/en.wikipedia.org/wiki/Reverse_correlation_technique-0.md
category: reference
title: Reverse correlation technique
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Reverse_correlation_technique
date_saved: 2026-05-05T10:03:03.129594+00:00
instance: kb-cron
parent: reverse-correlation-technique-20260505-0
summary:
The reverse correlation technique is a data driven study method used primarily in psychological and neurophysiological research. This method earned its name from its origins in neurophysiology, where cross-correlations between white noise stimuli and sparsely occurring neuronal spikes could be computed quicker when only computing it for segments preceding the spikes. The term has since been adopted in psychological experiments that usually do not analyze the temporal dimension, but also present noise to human participants. In contrast to the original meaning, the term is here thought to reflect that the standard psychological practice of presenting stimuli of defined categories to the participants is "reversed": Instead, the participant's mental representations of categories are estimated from interactions of the presented noise and the behavioral responses. It is used to create composite pictures of individual and/or group mental representations of various items (e.g. faces, bodies, and the self) that depict characteristics of said items (e.g. trustworthiness and self-body image). This technique is helpful when evaluating the mental representations of those with and without mental illnesses. == Terms == This technique utilizes spike-triggered average to explain what areas of signal and noise in an image are valuable for the given research question. Signal is


id: reverse-correlation-technique-20260505-1
file: data/en.wikipedia.org/wiki/Reverse_correlation_technique-1.md
category: reference
title: Reverse correlation technique
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Reverse_correlation_technique
date_saved: 2026-05-05T10:03:03.129594+00:00
instance: kb-cron
parent: reverse-correlation-technique-20260505-0
summary:
=== Step 1: making stimuli === When designing the stimuli for a reverse correlation study, the two primary factors that one should consider are (1) the base image and (2) the noise that will be used. While not all bases are images per se, the majority are and for this reason the base is typically referred to as a base image. The base image should represent whatever the research question is addressing. For example, if you are interested in peoples’ mental representations of Chinese people, it would not make sense to use a base image of a Spanish or Caucasian person. Again, if you are interested in the mental representations of male vocal patterns, it would make the most sense to use a base vocal pattern that has been produced by a male. Having a base is important because it provides a kind of anchor for participants to work from. When there is no base image, the number of trials that are required increases dramatically, thus making it harder to collect data. While there are studies that have excluded a base image, (e.g. the S study), for more elaborate and nuanced research questions, it is important to have a base


id: reverse-correlation-technique-20260505-2
file: data/en.wikipedia.org/wiki/Reverse_correlation_technique-2.md
category: reference
title: Reverse correlation technique
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Reverse_correlation_technique
date_saved: 2026-05-05T10:03:03.129594+00:00
instance: kb-cron
parent: reverse-correlation-technique-20260505-0
summary:
=== Step 2: data collection === Once the stimuli for the study has been developed, the researcher must make a few decisions before actually collecting the data. The researcher must come to a conclusion on how many stimuli will be presented at a time and how many trials the participants will see. In terms of stimuli presentation, a researcher can choose from either a 2-Image Forced Choice (2IFC) or a 4-Alternative Forced Choice (4AFC). The 2IFC presents two images at once (side by side) and requires participants to choose between the two on a specified category (e.g. which image looks the most like a male). Typically the noise from the left image is the mathematical inverse of the noise from the right image. This method was developed to better answer questions that could not be fully answered by the 4AFC method. As compared to the 2IFC, the 4AFC only shows participants one image per trial and requires them to rate the image on a 4-point scale ((1) Probably X, (2) Possibly X, (3) Possibly Y, (4) Probably Y). For example, here X might represent male and Y might represent female. Typically, during data analysis, only images that are chosen as


id: reverse-correlation-technique-20260505-3
file: data/en.wikipedia.org/wiki/Reverse_correlation_technique-3.md
category: reference
title: Reverse correlation technique
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Reverse_correlation_technique
date_saved: 2026-05-05T10:03:03.129594+00:00
instance: kb-cron
parent: reverse-correlation-technique-20260505-0
summary:
=== Step 4: evaluating classification images and drawing conclusions === After computing classification images for individual participants and/or for the group, the researcher will use these images to draw conclusions about their research questions. However, while not always the case, occasionally after the first set of classification images have been generated, researchers will then take these images and present them to a new sample of participants and ask them to rate the images on a subsequent factor of interest. This process is referred to as a two-phase reverse correlation. For example, if a classification image was computed after participants were asked to choose the image that looked the most like a police officer, the generated classification images could then be presented to a new sample who would evaluate the images on how aggressive the faces look. This process makes it easier to draw conclusions on the data. While this step can ease in drawing conclusions, one must use caution to not collect too many participants in the second phase, because high numbers of participants will make the tiniest of differences appear significant, therefore resulting in a Type 1 Error. While reverse correlation is typically used to create a visual representation


id: secondary-research-20260505-0
file: data/en.wikipedia.org/wiki/Secondary_research-0.md
category: reference
title: Secondary research
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Secondary_research
date_saved: 2026-05-05T10:03:04.325479+00:00
instance: kb-cron
parent: secondary-research-20260505-0
summary:
Secondary research involves the summary, collation and/or synthesis of existing research. Secondary research is contrasted with primary research in that primary research involves the generation of data, whereas secondary research uses primary research sources as a source of data for analysis. A notable marker of primary research is the inclusion of a "methods" section, where the authors describe how the data was generated. Common examples of secondary research include textbooks, encyclopedias, news articles, review articles, and meta analyses. When conducting secondary research, authors may draw data from published academic papers, government documents, statistical databases, and historical records. == Fields == The term is widely used in fields such as history, legal research, market research, and Wikipedia editing. The principal methodology in health secondary research is the systematic review, commonly using meta-analytic statistical techniques. Other methods of synthesis, like realist reviews and meta-narrative reviews, have been developed in the 21st century. Secondary market research includes the reuse by a second party of any data collected from a first party such as telephone interviews or surveys. Secondary market research can be broken up into two categories: information from internal sources such as an agency or company, and information from external sources held


id: single-arm-study-design-20260505-0
file: data/en.wikipedia.org/wiki/Single-arm_study_design-0.md
category: reference
title: Single-arm study design
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Single-arm_study_design
date_saved: 2026-05-05T10:03:05.523833+00:00
instance: kb-cron
parent: single-arm-study-design-20260505-0
summary:
A single-arm study design is a type of clinical or experimental study where all participants receive the same intervention, with no control or placebo group. The term "single-arm" refers to the presence of only one participant group, in contrast to designs such as randomized controlled trials, which include both a treatment arm and control or placebo arm. == Overview == Single-arm trials include one experimental group without the inclusion of a parallel control group. The design is open-label and does not involve randomization or blinding. This type of study design is commonly applied to advance stage cancer, rare diseases, emerging infectious diseases, new treatment methods, and medical devices. While randomized control trials are considered the “gold standard” in clinical research, they are not always feasible due to limitations in the study population, challenges in obtaining evidence, high costs, and ethical considerations. As a result, single arm trials are used to address these concerns. == Applications == Since there is no randomization in single-arm trials, all patients receive the same intervention within a particular study/trial. This design is most commonly used in early-phase clinical trials or in studies of rare or serious diseases, where including a control group may be impractical


