Participatory Action Research

PAR
community-based participatory research
collaborative action research
participatory inquiry

1940s (Lewin); PAR as distinct tradition formalised ~1970s–1980s

Kurt Lewin (action research foundations, 1940s); systematised for participatory contexts by Orlando Fals Borda, Paulo Freire, and William Foote Whyte

Participatory Action Research (PAR) is a qualitative, community-centred methodology in which researchers and community members collaborate as co-investigators to identify a shared problem, take deliberate action, observe outcomes, and reflect critically on results — cycling iteratively until meaningful change is achieved. Unlike conventional research that studies people from the outside, PAR treats participants as active agents who co-own the research process, the knowledge produced, and the practical interventions that follow.

Interviews, focus groups, observations, community documents, participant-generated artefacts

5–50 community co-researchers (varies widely by project scope)

Kemmis, S., McTaggart, R., & Nixon, R. (2014). The Action Research Planner: Doing Critical Participatory Action Research. Springer.

Reason, P., & Bradbury, H. (Eds.). (2006). Handbook of Action Research: Concise Paperback Edition. Sage.

action-research ethnography case-study grounded-theory focus-group mixed-methods


Participatory Biographical Research

collaborative biography
participatory life history
co-constructed biographical inquiry
PBR

Intersection of biographical methods tradition (Denzin, Chamberlayne) and participatory action research (Lewin, Reason & Bradbury)

Participatory Biographical Research (PBR) combines the in-depth life-story tradition of biographical methods with the collaborative ethos of participatory inquiry. Participants are not merely sources of data; they are active co-researchers who help design questions, interpret their own narratives, and validate emerging findings. The result is a richly layered account of individual lives that is jointly owned by both researcher and participant.

Co-constructed biographical narratives, interviews, field notes, reflective journals

Chamberlayne, P., Bornat, J., & Wengraf, T. (Eds.). (2000). The Turn to Biographical Methods in Social Science: Comparative Issues and Examples. Routledge.

McIntyre, A. (2008). Participatory Action Research. Sage Publications.

biographical-research life-history-research participatory-action-research narrative-inquiry oral-history participatory-narrative-research


Participatory Case Study

collaborative case study
participatory case research
co-constructed case study
PCS

Synthesised from Robert K. Yin (case study) and Peter Reason / William Foote Whyte (participatory research)

Participatory Case Study is a qualitative design that embeds participatory principles within a bounded case study framework. Participants are not merely research subjects but active collaborators who co-define the research questions, co-generate data, contribute to analysis, and validate the findings. The approach is appropriate when deep understanding of a specific, bounded context is needed and when the community or group under study has both the capacity and the right to shape the knowledge produced about their own situation.

Interviews, observations, documents, artefacts co-generated with participants

Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage Publications.

Reason, P., & Bradbury, H. (Eds.). (2008). The SAGE Handbook of Action Research: Participative Inquiry and Practice (2nd ed.). Sage Publications.

case-study participatory-action-research participatory-narrative-research participatory-ethnography multiple-case-study collaborative-inquiry


Participatory Concurrent Embedded Mixed Methods Design

participatory embedded concurrent design

Creswell & Plano Clark (embedded design); Mertens, Tashakkori & Teddlie (participatory frameworks)

Participatory concurrent embedded mixed methods is a research design that combines a participatory or community-based action research framework with an embedded concurrent data structure — simultaneously collecting dominant and supplementary data strands from community stakeholders who are active co-investigators rather than passive subjects. The embedded strand (typically qualitative) is nested within the dominant strand (typically quantitative) and both are gathered at the same time while community members guide priorities, instruments, and meaning-making throughout.

Simultaneous quantitative and qualitative data from community participants

Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications.

Tashakkori, A., & Teddlie, C. (Eds.). (2010). SAGE Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.). SAGE Publications.


Participatory Concurrent Triangulation Mixed Methods Design

Creswell & Plano Clark (concurrent triangulation); Mertens and Israel & Schulz (participatory lens)

Participatory concurrent triangulation mixed methods is a research design that embeds a participatory worldview — prioritising community involvement, co-ownership, and social change — within a concurrent triangulation structure, in which quantitative and qualitative data are collected at the same time, analysed independently, and then merged to compare or validate findings. The design is used when researchers need both statistical breadth and lived-experience depth, and when the community affected by the research must be meaningfully involved throughout.

Quantitative data (surveys, measures) and qualitative data (interviews, observations) collected concurrently

Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.

Mertens, D. M. (2009). Transformative Research and Evaluation. Guilford Press.


Participatory Content Analysis

PCA
community-based content analysis
collaborative content analysis
participatory textual analysis

1990s–2000s (formalized in community-based and health research contexts)

Developed at the intersection of participatory action research (Kurt Lewin, 1940s) and qualitative content analysis traditions

Participatory Content Analysis (PCA) is a qualitative method that integrates community members or stakeholders directly into the content analysis process. Rather than treating participants solely as data sources, PCA positions them as co-analysts who help develop coding categories, interpret textual data, and validate findings. This approach is widely used in health communication, education research, and community-based studies where insider knowledge and cultural context are essential to accurate interpretation.

Texts, documents, media, transcripts co-analyzed with community members

Leavy, P. (Ed.). (2014). The Oxford Handbook of Qualitative Research. Oxford University Press.

Hsieh, H.-F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15(9), 1277–1288.

content-analysis participatory-action-research thematic-analysis community-based-participatory-research narrative-analysis focus-group


Participatory Conversation Analysis

PCA
collaborative conversation analysis
practitioner-involved CA
participatory CA

2000s–2010s (building on CA foundations from the 1960s–1970s)

Developed from Harvey Sacks, Emanuel Schegloff, and Gail Jefferson's Conversation Analysis tradition; participatory variant emerged in applied and practitioner research contexts in the 2000s–2010s

Participatory Conversation Analysis (PCA) extends classical Conversation Analysis by actively involving the people whose talk is being studied in the analytical process. Rather than treating analysis as the researcher's exclusive domain, PCA invites practitioners, community members, or research participants to co-review recordings or transcripts of their own interaction, contribute insider meanings, and collaboratively refine the interpretation of interactional patterns. The approach is widely used in education, healthcare communication, and professional learning contexts.

Audio or video recordings of naturally occurring interaction, accompanied by Jefferson transcripts; participants co-review and co-interpret their own talk

Lee, E., & Howes, C. (2020). Conversation analysis as a creative research methodology. Early Child Development and Care, 190(2), 1–14.

conversation-analysis discourse-analysis participatory-action-research ethnomethodology focus-groups narrative-analysis


collaborative critical discourse analysis

1990s–2000s (emerged as integrated approach)

Draws on Ruth Wodak, Norman Fairclough (CDA) and Kurt Lewin, Orlando Fals Borda (participatory action research)

Participatory Critical Discourse Analysis (PCDA) integrates the ideology-exposing tools of Critical Discourse Analysis with the community-centred ethics of participatory action research. Researchers and community members jointly collect and analyse texts and talk to reveal how language constructs, legitimises, or contests unequal power relations — and then use those insights to drive concrete social change.

Qualitative research design and analysis approach

Texts, transcripts, documents, naturally occurring talk, community-produced materials

Wodak, R., & Meyer, M. (Eds.). (2001). Methods of Critical Discourse Analysis. Sage.

Brydon-Miller, M., Greenwood, D., & Maguire, P. (2003). Why action research? Action Research, 1(1), 9–28.

critical-discourse-analysis participatory-action-research discourse-analysis thematic-analysis narrative-analysis ethnography


Participatory Design-Based Research

participatory educational design research

Early 2000s (building on DBR foundations from 1992)

Ann Brown, Allan Collins; participatory extension developed by Penuel, Roschelle, and collaborators

Participatory design-based research (PDBR) is an iterative educational research methodology in which practitioners — teachers, students, or community members — serve as genuine co-designers of interventions alongside researchers. Rooted in design-based research (DBR), PDBR adds explicit mechanisms for shared ownership, distributed decision-making, and practitioner voice across all design cycles, making it especially suited to developing contextually responsive educational solutions.

Iterative collaborative design methodology

Observation, interviews, design artefacts, field notes, survey data

Penuel, W. R., Roschelle, J., & Shechtman, N. (2007). Designing formative assessment software with teachers: An analysis of the co-design process. Research and Practice in Technology Enhanced Learning, 2(1), 51–74.

Barab, S., & Squire, K. (2004). Design-based research: Putting a stake in the ground. Journal of the Learning Sciences, 13(1), 1–14.

design-based-research participatory-action-research educational-action-research lesson-study program-evaluation classroom-observation


Participatory Digital Ethnography

PDE
collaborative digital ethnography
participatory online ethnography
participatory virtual ethnography

Sarah Pink and colleagues; building on Christine Hine's virtual ethnography and Kemmis & McTaggart's participatory action research traditions

Participatory Digital Ethnography (PDE) is a qualitative research design that combines the immersive observation of digital ethnography with the collaborative, co-inquiry stance of participatory action research. Researchers work alongside community members within digital environments — social media platforms, online forums, gaming worlds, or hybrid digital-physical spaces — co-producing knowledge rather than studying participants from a detached observer position.

Digital artifacts, online interactions, co-produced media, field notes from digital spaces

Pink, S., Horst, H., Postill, J., Hjorth, L., Lewis, T., & Tacchi, J. (2016). Digital Ethnography: Principles and Practice. Sage.

Garcia, A. C., Standlee, A. I., Bechkoff, J., & Cui, Y. (2009). Ethnographic Approaches to the Internet and Computer-Mediated Communication. Journal of Contemporary Ethnography, 38(1), 52–84.

digital-ethnography netnography participatory-action-research virtual-ethnography online-focus-groups collaborative-inquiry


Participatory Discourse Analysis

participatory critical discourse analysis

1990s–2000s (consolidated as a named approach)

Developed at the intersection of participatory action research (Kurt Lewin, 1940s) and discourse analysis (Foucault, Fairclough, van Dijk, 1980s–1990s)

Participatory Discourse Analysis (PDA) integrates the collaborative ethos of participatory action research with the language-focused lens of discourse analysis. Community members or research participants are not merely sources of data — they are co-analysts who help collect, interpret, and act on discourse. PDA is used to uncover how language constructs power relations, identities, and social practices within marginalized or under-researched communities, and to translate those findings into concrete change.

Qualitative research design and analytic approach

Texts, talk, interviews, documents, community-generated data

Mohanty, S. P. (2004). The epistemic status of cultural identity: On beloved and the postcolonial condition. In P. Moya & M. Hames-Garcia (Eds.), Reclaiming Identity: Realist Theory and the Predicament of Postmodernism. University of California Press.

Jones, R. H., Chik, A., & Hafner, C. A. (Eds.). (2015). Discourse and Digital Practices: Doing Discourse Analysis in the Digital Age. Routledge.

discourse-analysis critical-discourse-analysis participatory-action-research thematic-analysis narrative-analysis ethnography


Participatory Document analysis

PDA
collaborative document analysis
participatory archival analysis
community-based document analysis

1940s–2000s (synthesis of participatory tradition and systematic document analysis)

Rooted in participatory action research (Kurt Lewin, 1940s); document analysis formalized by Glenn Bowen (2009)

Participatory Document Analysis is a qualitative research approach that systematically examines existing documents — such as policy records, reports, correspondence, and community archives — while actively involving community members or stakeholders as co-researchers in the selection, interpretation, and meaning-making processes. It merges the rigor of established document analysis techniques with the democratic ethos of participatory action research, ensuring that those most affected by the documents have voice in shaping what those documents mean.

Textual documents (policy papers, reports, correspondence, archival materials, community records)

Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40.

Cornwall, A., & Jewkes, R. (1995). What is participatory research? Social Science & Medicine, 41(12), 1667–1676. Reprinted and discussed in: Cornwall, A. (Ed.). (2011). The Participation Reader. Zed Books.

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Participatory Ethnography

collaborative ethnography
participatory fieldwork
engaged ethnography
community-based ethnography

1990s–2000s (collaborative turn); classical roots early 20th century

Rooted in classical ethnography (Malinowski, Boas); collaborative turn formalised by Luke Eric Lassiter and others in the 1990s–2000s

Participatory ethnography is a qualitative research design in which community members are not merely subjects of study but active collaborators throughout the research process — from problem formulation and data collection to analysis and writing. Building on classical ethnographic fieldwork, it shifts the researcher–participant relationship toward genuine partnership, producing knowledge that is accountable to the communities from which it emerges.

Field observations, interviews, participant-produced documents, co-authored texts

Lassiter, L. E. (2005). The Chicago Guide to Collaborative Ethnography. University of Chicago Press.

Sanday, P. R. (1979). The ethnographic paradigm(s). Administrative Science Quarterly, 24(4), 527–538.

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Participatory Explanatory Sequential Mixed Methods Design

community-based explanatory sequential design

transformative explanatory sequential mixed methods

participatory two-phase sequential design

Creswell & Plano Clark (explanatory sequential structure); Mertens (transformative/participatory lens)

The participatory explanatory sequential mixed methods design combines the two-phase QUAN-to-QUAL structure of the explanatory sequential design with a participatory or transformative worldview. Community members and stakeholders are involved as collaborators — not merely subjects — across all stages, from formulating research questions to interpreting results. Quantitative data are collected and analyzed first; findings that need deeper explanation then drive a second, qualitative phase conducted with and by the community.

Quantitative data (Phase 1) followed by qualitative data (Phase 2); community-sourced

Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications.

Mertens, D. M. (2009). Transformative Research and Evaluation. Guilford Press.


Participatory Exploratory Sequential Mixed Methods Design

community-based exploratory sequential design

2007–2011 (participatory variant codified in Creswell & Plano Clark's typology expansions)

John W. Creswell & Vicki L. Plano Clark (exploratory sequential base); Donna M. Mertens (participatory/transformative lens)

Participatory exploratory sequential mixed methods is a two-phase design in which an initial qualitative phase — conducted with and by community members — generates findings that are used to build or refine a quantitative instrument or intervention, which is then tested in a second phase. The participatory lens ensures that affected communities co-own the research agenda, the data, and the interpretation throughout both phases.

Qualitative data (Phase 1: interviews, focus groups, observation); quantitative data (Phase 2: surveys, instruments built from Phase 1)


Participatory Hermeneutic Phenomenology

Participatory Hermeneutic Phenomenological Research

Martin Heidegger (hermeneutic base); Max van Manen (pedagogical application); Peter Reason & colleagues (participatory integration)

Participatory Hermeneutic Phenomenology combines the interpretive, text-oriented tradition of hermeneutic phenomenology — rooted in Heidegger and developed by van Manen — with a participatory ethos in which research participants are treated as active co-inquirers rather than passive informants. The approach seeks to understand the meaning of lived experience through a collaborative hermeneutic circle where researcher and participants jointly interpret experience, text, and context across iterative cycles of dialogue.

Co-constructed narratives, collaborative interviews, participant reflections, field texts

van Manen, M. (1990). Researching Lived Experience: Human Science for an Action Sensitive Pedagogy. State University of New York Press.

Reason, P., & Bradbury, H. (Eds.). (2001). Handbook of Action Research: Participative Inquiry and Practice. Sage.

hermeneutic-phenomenology phenomenology participatory-action-research interpretive-phenomenological-analysis participatory-ethnography narrative-inquiry


Participatory Institutional Ethnography

Dorothy E. Smith (IE); participatory variant developed by Janet Rankin, Marie Campbell, and others in health and social sciences

Participatory Institutional Ethnography (PIE) combines Dorothy Smith's institutional ethnography with participatory research principles, positioning community members or service users as co-researchers who investigate how institutional relations, ruling texts, and organizational practices shape and often constrain their everyday lives. The approach aims both to produce knowledge about institutional coordination and to generate actionable change through collaborative inquiry.

Interviews, observations, institutional texts and documents, field notes

Smith, D. E. (2005). Institutional Ethnography: A Sociology for People. AltaMira Press.

Rankin, J., & Campbell, M. (2006). Managing to Nurse: Inside Canada's Health Care Reform. University of Toronto Press.

institutional-ethnography participatory-action-research ethnography critical-ethnography participatory-ethnography grounded-theory


Participatory IPA
P-IPA
participatory phenomenological inquiry
collaborative IPA

Jonathan A. Smith (IPA foundation); adapted by participatory researchers in disability, health, and education studies

Participatory Interpretive Phenomenological Analysis (Participatory IPA) merges the interpretive, meaning-focused rigour of IPA with participatory research principles, engaging participants as active co-researchers in the design, data collection, and analytic phases. The approach is especially valued in studies involving marginalised or vulnerable groups — such as people with cognitive impairments, chronic illness, or lived experience of social exclusion — where standard interview protocols may silence rather than amplify participant voice.

In-depth interviews, co-constructed narratives, participant-generated materials (text, visual)

Smith, J. A., Flowers, P., & Larkin, M. (2009). Interpretive Phenomenological Analysis: Theory, Method and Research. Sage.

McKeown, J., Clarke, A., Ingleton, C., & Repper, J. (2010). Actively involving people with dementia in qualitative research. Journal of Clinical Nursing, 19(13-14), 1935–1943.

interpretive-phenomenological-analysis participatory-action-research hermeneutic-phenomenology participatory-phenomenology narrative-inquiry reflexive-thematic-analysis


Participatory Intervention Mixed Methods Design

participatory mixed methods intervention

community-based intervention mixed methods

1990s–2000s (formalized as mixed methods variant ~2000–2010)

Donna Mertens; John Creswell & Vicki Plano Clark (mixed methods traditions); community-based participatory research scholars

Participatory Intervention Mixed Methods (PIMM) is a research design that embeds community members as co-investigators in the planning and delivery of an intervention, while collecting and integrating both quantitative outcome data and qualitative experiential data. The design bridges participatory action research traditions with the rigor of mixed methods, enabling researchers to simultaneously measure whether an intervention works and understand how and why it works from participants' own perspectives.

Quantitative outcome data (surveys, scales, tests) and qualitative data (interviews, focus groups, field notes)

Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.

participatory-action-research convergent-mixed-methods embedded-mixed-methods community-based-participatory-research action-research explanatory-sequential-mixed-methods


Participatory Lesson Study

PLS
collaborative lesson study
inclusive lesson study
community lesson study

2000s–2010s (core lesson study from late 19th-century Japan)

Broader participatory framing developed by Pete Dudley and collaborators, building on Japanese jugyokenkyu tradition

Participatory Lesson Study is an iterative, team-based professional development approach in which teachers — and often students, parents, or community members — jointly plan, observe, and critically reflect on live lessons to improve learning for a specific group of students. It extends the Japanese lesson study tradition by explicitly broadening participation beyond the teaching team to include diverse stakeholders, foregrounding equity, inclusion, and community perspectives in the inquiry cycle.

Observation notes, lesson plans, student work samples, reflective discussions (qualitative)

Dudley, P. (Ed.). (2014). Lesson Study: Professional Learning for Our Time. Routledge.

Fernandez, C., & Yoshida, M. (2004). Lesson Study: A Japanese Approach to Improving Mathematics Teaching and Learning. Lawrence Erlbaum Associates.

lesson-study educational-action-research participatory-action-research classroom-observation design-based-research participatory-classroom-observation


Participatory Mixed Methods Matrix

Participatory Mixed Methods Matrix Design

participatory mixed methods joint display

John W. Creswell & Vicki L. Plano Clark (participatory mixed methods design); Michael D. Fetters et al. (integration matrix/joint display)

The Participatory Mixed Methods Matrix is a research design that embeds a joint-display integration matrix within a participatory research framework. Community members or other stakeholders co-design the study, co-collect quantitative and qualitative data strands, and then jointly interpret the matrix where both strands are displayed side by side. The approach operationalises the participatory principle — those affected by a problem share authorship of its investigation — while using the rigour of mixed methods integration.

Quantitative (surveys, scales, counts) and qualitative (interviews, focus groups, observations) collected in parallel or sequentially

Fetters, M. D., Curry, L. A., & Creswell, J. W. (2013). Achieving integration in mixed methods designs — principles and practices. Health Services Research, 48(6 Pt 2), 2134–2156.

mixed-methods-research participatory-action-research community-based-participatory-research convergent-parallel-design explanatory-sequential-design exploratory-sequential-design


Participatory Mixed Methods Meta-Inference

integrated meta-inference in participatory research

Abbas Tashakkori & Charles Teddlie (meta-inference concept); extended to participatory contexts by Sweetman, Badiee & Creswell

Participatory mixed methods meta-inference is the process by which researchers and community co-investigators draw a unified, integrated conclusion — the meta-inference — from separately analysed qualitative and quantitative strands within a participatory mixed methods study. Grounded in the meta-inference framework of Tashakkori and Teddlie and extended into participatory and transformative research contexts, it treats the final synthesis of evidence not merely as a methodological step but as a collaborative, community-accountable act of knowledge production.

Integrative inference procedure within participatory mixed methods

Integrated qualitative and quantitative data co-produced with community participants

Tashakkori, A., & Teddlie, C. (Eds.). (2010). SAGE Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.). SAGE Publications.

Sweetman, D., Badiee, M., & Creswell, J. W. (2010). Use of the transformative framework in mixed methods studies. Qualitative Inquiry, 16(6), 441–454.

participatory-action-research community-based-participatory-research mixed-methods-research transformative-mixed-methods convergent-parallel-design interpretive-integration


Participatory Multilevel Mixed Methods

Participatory Multilevel Mixed Methods Design

participatory mixed-methods multilevel design

community-based multilevel mixed methods

Bonnie K. Nastasi and colleagues; extended by John W. Creswell and Vicki L. Plano Clark

Participatory multilevel mixed methods is a research design that combines the collaborative ethos of participatory research with the analytical depth of multilevel data collection and the complementary power of mixed quantitative and qualitative methods. It is widely applied in community health, education, and social intervention research where phenomena operate simultaneously at individual, group, organizational, and community levels, and where local stakeholders must co-own the inquiry.

Quantitative and qualitative data collected at multiple ecological or social levels with community stakeholder involvement

Nastasi, B. K., Hitchcock, J., Sarkar, S., Burkholder, G., Varjas, K., & Jayasena, A. (2007). Mixed methods in intervention research: Theory to adaptation. Journal of Mixed Methods Research, 1(2), 164–182.

Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage Publications.

mixed-methods participatory-action-research multilevel-analysis community-based-participatory-research convergent-mixed-methods transformative-mixed-methods


Participatory Multiphase Mixed Methods

Participatory Multiphase Mixed Methods Research Design

community-engaged multiphase mixed methods

Synthesised from Creswell & Plano Clark (multiphase design) and Mertens / Israel et al. (participatory principles)

Participatory multiphase mixed methods is a research design that integrates participatory action research principles into a multiphase mixed methods framework. Community members or stakeholders are active co-investigators across multiple sequential or concurrent phases, each combining quantitative and qualitative strands, so that findings from earlier phases directly shape the design of later ones. The result is a longitudinal, iterative programme of inquiry aligned with social justice and practical impact goals.

Quantitative and qualitative data collected across multiple sequential or parallel phases with community co-investigators

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Participatory Multiple case study

Participatory Multiple Case Study Research

participatory multi-case study
collaborative multiple case study
PMCS
participatory comparative case research

1980s–1990s (convergence of case study methodology and participatory research traditions)

Robert K. Yin (multiple case study logic); Kurt Lewin and subsequent PAR scholars (participatory framework)

Participatory multiple case study research integrates the structured logic of multiple case study design — examining two or more bounded cases to build analytic generalisations — with the collaborative ethics of participatory research, where community members or practitioners co-design the inquiry, co-collect data, and co-interpret findings. The approach combines Yin's replication logic across cases with the emancipatory and co-ownership principles that define participatory action research traditions.

Co-produced qualitative data — interviews, focus groups, observations, documents, artefacts — gathered collaboratively with community or organisational participants

Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.

Baum, F., MacDougall, C., & Smith, D. (2006). Participatory action research. Journal of Epidemiology and Community Health, 60(10), 854–857.

multiple-case-study participatory-action-research participatory-single-case-study comparative-multiple-case-study participatory-narrative-research participatory-grounded-theory


Participatory Narrative Research

PNR
participatory narrative inquiry
community narrative research
collaborative narrative research

2000s (Kurtz's PNI framework developed ~2005–2014)

Cynthia Kurtz (systematic PNI framework); rooted in Clandinin & Connelly's narrative inquiry tradition

Participatory Narrative Research (PNR), often operationalized as Participatory Narrative Inquiry (PNI), is a qualitative research design in which community members or stakeholders collect, share, and collectively interpret their own stories to understand complex social phenomena. Unlike researcher-driven narrative approaches, PNR places participants at the center of data collection, analysis, and sense-making, generating actionable insights grounded in lived community experience.

Participatory qualitative research design

Community-generated stories, narrative text, structured story forms

Clandinin, D. J., & Connelly, F. M. (2000). Narrative inquiry: Experience and story in qualitative research. Jossey-Bass.

Kurtz, C. F. (2014). Working with Stories in Your Community or Organization: Participatory Narrative Inquiry (3rd ed.). Kurtz-Fernhout Publishing.

narrative-inquiry community-based-participatory-research action-research oral-history ethnography thematic-analysis


Participatory Netnography

participatory online ethnography
active netnography
engaged netnography
participant-observer netnography

1997 (netnography); participatory variant codified c. 2010–2020

Robert V. Kozinets (netnography foundation); participatory stance elaborated in Kozinets 2010/2020

Participatory Netnography is a qualitative research approach in which the researcher becomes an active, contributing member of an online community in order to study it from within. Building on Kozinets' netnography framework, it extends the purely observational stance to active participation — the researcher posts, replies, and engages authentically — generating richer, context-embedded data about online social life, consumer culture, or community practices than passive observation alone can provide.

Qualitative online ethnographic approach

Online community texts, interactions, field notes, researcher participation records

Kozinets, R. V. (2020). Netnography: The Essential Guide to Qualitative Social Media Research (3rd ed.). Sage.

Kozinets, R. V. (2002). The field behind the screen: Using netnography for marketing research in online communities. Journal of Marketing Research, 39(1), 61–72.

ethnography netnography virtual-ethnography digital-ethnography online-focus-groups participant-observation


Participatory Oral History Method

community oral history
collaborative oral history
participatory oral history
community-based oral history

1970s–1990s (community oral history movement formalized)

Influenced by Alessandro Portelli, Sherna Berger Gluck, Paul Thompson, and development-oriented oral historians

Participatory oral history method is a qualitative research approach in which community members are not merely interview subjects but active co-investigators who help shape the research questions, conduct or co-conduct interviews, analyze narratives, and govern how the resulting record is used. Rooted in both the oral history tradition and participatory action research, it foregrounds community ownership, reciprocity, and the democratic production of historical knowledge from marginalized or underrepresented voices.

Audio/video recorded life history interviews, community narratives, co-produced transcripts

Slim, H., & Thompson, P. (1993). Listening for a Change: Oral Testimony and Community Development. Panos Institute.

Portelli, A. (1997). The Battle of Valle Giulia: Oral History and the Art of Dialogue. University of Wisconsin Press.

oral-history-method participatory-action-research community-based-participatory-research ethnography narrative-analysis focus-group


Participatory Oral History

community oral history
collaborative oral history
participatory memory research
POH

1970s–1990s (formalized participatory dimension by 1990)

Michael Frisch (shared authority concept); broader roots in Alessandro Portelli and oral history movement

Participatory oral history is a qualitative research design in which community members act as co-researchers alongside academic investigators to collect, interpret, and share first-person accounts of lived experience and collective memory. Drawing on Michael Frisch's concept of 'shared authority,' it repositions research participants as active agents in the knowledge-production process rather than passive informants, making it especially powerful for documenting marginalized voices and community-held histories that would otherwise remain invisible in official archives.

Qualitative participatory research design

Audio/video-recorded interviews, community-produced narratives, archival documents

Frisch, M. (1990). A Shared Authority: Essays on the Craft and Meaning of Oral and Public History. State University of New York Press.

Perks, R., & Thomson, A. (Eds.). (2016). The Oral History Reader (3rd ed.). Routledge.

oral-history participatory-action-research narrative-inquiry community-based-participatory-research life-history-research ethnography


Participatory Phenomenology

collaborative phenomenology
participatory phenomenological inquiry
co-operative phenomenology
participatory lifeworld research

1990s (converging streams: van Manen 1990; Heron & Reason 1997)

John Heron and Peter Reason (participatory inquiry); Max van Manen (lifeworld phenomenology)

Participatory phenomenology combines the depth of phenomenological inquiry — attending to the lived structure of experience — with the democratic ethos of participatory research, in which those being studied become active co-researchers. Rather than treating participants as data sources, the approach positions them as collaborative investigators of their own experiential world, producing knowledge that is both phenomenologically rich and collectively validated.

Co-produced narratives, collaborative interviews, reflective journals, focus group transcripts

Heron, J. (1996). Co-operative Inquiry: Research into the Human Condition. Sage.

van Manen, M. (1990). Researching Lived Experience: Human Science for an Action Sensitive Pedagogy. State University of New York Press.

phenomenology participatory-action-research hermeneutic-phenomenology interpretive-phenomenological-analysis co-operative-inquiry community-based-participatory-research


Participatory Program Evaluation

participatory evaluation
collaborative evaluation
PE
stakeholder-involved evaluation

1992 (formal articulation); roots in participatory action research of the 1970s–1980s

J. Bradley Cousins & Lorna Earl (formalization); Michael Q. Patton (utilization-focused lineage)

Participatory program evaluation is an applied evaluation approach in which program stakeholders — staff, participants, funders, or community members — are actively involved as co-evaluators rather than passive subjects. By engaging those closest to the program in designing questions, collecting data, and interpreting findings, the approach aims to increase both the quality of the evaluation and the likelihood that findings will be understood, owned, and acted upon.

Mixed (surveys, interviews, focus groups, documents, observations)

Cousins, J. B., & Earl, L. M. (1992). The case for participatory evaluation. Educational Evaluation and Policy Analysis, 14(4), 397–418.

Patton, M. Q. (2008). Utilization-Focused Evaluation (4th ed.). Sage Publications.

program-evaluation action-research educational-action-research empowerment-evaluation collaborative-inquiry utilization-focused-evaluation


Participatory Qualitative content analysis

community-based qualitative content analysis

collaborative qualitative content analysis

Emerged from integration of participatory action research (Lewin, 1946; Reason & Bradbury, 2001) with qualitative content analysis (Mayring, 2000; Schreier, 2012)

Participatory Qualitative Content Analysis (PQCA) integrates the systematic text-analytic procedures of qualitative content analysis with the collaborative, power-sharing ethos of participatory research. Community members or stakeholders join the research team as co-analysts — helping to define the coding frame, interpret categories, and validate findings — rather than serving merely as data sources. The result is analysis that is both methodologically rigorous and grounded in the perspectives of those most affected by the research topic.

Text, documents, community-generated materials, interview transcripts

Schreier, M. (2012). Qualitative Content Analysis in Practice. Sage.

Reason, P., & Bradbury, H. (Eds.). (2001). Handbook of Action Research: Participative Inquiry and Practice. Sage.

qualitative-content-analysis participatory-action-research participatory-thematic-analysis community-based-participatory-research reflexive-thematic-analysis critical-qualitative-content-analysis


Participatory Qualitative-Priority Mixed Design

Participatory Qualitative-Priority Mixed Methods Design

qual-dominant participatory mixed methods

community-based qualitative-priority mixed design

Creswell & Plano Clark; Donna Mertens (transformative/participatory framing)

Participatory qualitative-priority mixed design combines a participatory research worldview with a qualitative-dominant mixed methods structure. The qualitative strand carries the primary explanatory weight — capturing lived experience, meaning, and community voice — while a smaller quantitative strand supplements and contextualises the findings. Community members or stakeholders are active co-researchers throughout, shaping questions, data collection, analysis, and action planning.

Primarily qualitative (interviews, focus groups, observation) supplemented by quantitative data

Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). SAGE Publications.

Mertens, D. M. (2009). Transformative Research and Evaluation. Guilford Press.

participatory-action-research community-based-participatory-research exploratory-sequential-mixed-methods convergent-parallel-mixed-methods transformative-mixed-methods phenomenology


Participatory Quantitative-Priority Mixed Design

Participatory Quantitative-Priority Mixed Methods Design

QUAN-priority participatory mixed methods

community-based quantitative-priority mixed design

participatory QUAN-dominant mixed methods

Creswell & Plano Clark (core typology); Mertens (participatory-transformative lens)

Participatory quantitative-priority mixed design combines a community-engaged, participatory research framework with a mixed methods structure in which the quantitative strand carries primary weight. Stakeholders and community members co-shape research questions, instruments, and interpretation, while quantitative data provide the dominant evidence base and qualitative data serve a complementary, explanatory, or contextualizing role. This design is particularly suited to applied, evaluative, and social-justice-oriented inquiry where both statistical rigor and community voice are required.

Quantitative data (primary) + qualitative data (secondary); community-generated data


Participatory Reflexive thematic analysis

collaborative reflexive thematic analysis

2006 (reflexive TA); participatory integration developed through 2010s–2020s

Virginia Braun and Victoria Clarke (reflexive thematic analysis); participatory application developed within participatory action research traditions

Participatory Reflexive Thematic Analysis (Participatory RTA) integrates Braun and Clarke's reflexive thematic analysis framework with participatory research principles, actively involving participants as co-analysts in generating, reviewing, or refining themes from qualitative data. The approach is simultaneously a method of analysis and a form of member engagement, ensuring that the themes produced are grounded in participants' own meaning-making rather than imposed solely by the researcher.

Transcripts, field notes, participant-generated text, group discussions

Braun, V., & Clarke, V. (2021). Thematic Analysis: A Practical Guide. Sage.

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.

reflexive-thematic-analysis participatory-action-research participatory-thematic-analysis thematic-analysis participatory-content-analysis collaborative-qualitative-research


Participatory Semiotic Analysis

PSA
community semiotic analysis
collaborative semiotic inquiry
participatory social semiotics

Draws on Peirce, Saussure, Barthes (semiotics) and Lewin, Fals Borda (participatory research); integrated form developed in social semiotics and PAR literature

Participatory Semiotic Analysis (PSA) is a qualitative method that invites community members or research participants to actively co-analyze the signs, symbols, images, and texts that shape their social world. Combining the interpretive rigour of semiotic theory with the democratic ethos of participatory action research, PSA treats participants not as passive informants but as co-analysts who bring insider knowledge to the decoding of culturally embedded meanings.

Qualitative participatory analysis approach

Texts, images, symbols, artefacts, multimodal materials co-analyzed with participants

Kress, G., & van Leeuwen, T. (2006). Reading Images: The Grammar of Visual Design (2nd ed.). Routledge.

Cornwall, A., & Jewkes, R. (1995). What is participatory research? Social Science & Medicine, 41(12), 1667–1676.

semiotic-analysis participatory-action-research participatory-visual-analysis social-semiotics multimodal-discourse-analysis community-based-participatory-research


Participatory Single Case Study

Participatory Single Case Study Research

participatory case study
collaborative single case study
community-engaged case study
PSCS

Emerged as a distinct variant in the 1990s–2000s

Draws on Robert K. Yin (case study methodology) and Kurt Lewin / Orlando Fals-Borda (participatory research tradition)

A participatory single case study is a qualitative design that examines one bounded case in depth while actively involving community members, practitioners, or participants as co-researchers throughout the inquiry. It blends Yin's case study rigor — triangulated evidence, thick description of context — with participatory action research values of collaboration, equity, and action. The result is both a rich, contextual understanding of the case and a knowledge-building process that serves the people within it.

Qualitative case study design with participatory orientation

Interviews, field observations, documents, participant-generated artifacts, co-produced data

Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage.

Baum, F., MacDougall, C., & Smith, D. (2006). Participatory action research. Journal of Epidemiology and Community Health, 60(10), 854–857.

case-study participatory-action-research ethnography narrative-analysis phenomenology community-based-participatory-research


Participatory Straussian Grounded Theory

community-engaged Straussian grounded theory

participatory systematic grounded theory

Anselm Strauss & Juliet Corbin (Straussian GT); integrated with participatory research principles by practitioner-scholars in health and social sciences from the 1990s onward

Participatory Straussian grounded theory combines Strauss and Corbin's systematic, structured version of grounded theory with participatory research principles that give community members an active role in data generation, coding, and theory development. The result is a rigorously structured yet co-constructed theory about a social process, grounded in both the analytic procedures of axial coding and the lived authority of participants.

Interviews, focus groups, field notes, participant-generated documents

Strauss, A., & Corbin, J. (1990). Basics of Qualitative Research: Grounded Theory Procedures and Techniques. Sage.


Participatory Transformative Mixed Methods Design

social-justice participatory mixed methods

Donna M. Mertens (transformative paradigm); John W. Creswell & Vicki L. Plano Clark (mixed methods framework)

Participatory transformative mixed methods is a research design that embeds both a participatory action framework and a transformative paradigm within mixed methods inquiry. Both quantitative and qualitative data are collected in active collaboration with marginalized or underserved communities, with the explicit aim of producing findings that challenge inequity, empower participants, and drive social change. Donna Mertens' transformative paradigm and the mixed methods tradition of Creswell and Plano Clark jointly inform the approach.

Qualitative and quantitative data co-produced with community stakeholders

Mertens, D. M. (2010). Research and Evaluation in Education and Psychology: Integrating Diversity with Quantitative, Qualitative, and Mixed Methods (3rd ed.). Sage.

Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.


Participatory Visual analysis

PVA
participatory visual methods
collaborative visual inquiry
community-based visual analysis

1990s (formalized participatory visual methods); Freire roots 1970s

Wang & Burris (photovoice tradition); broader roots in participatory action research (Fals-Borda, Freire)

Participatory Visual Analysis (PVA) is a qualitative research approach in which community members or research participants actively produce and interpret visual materials — photographs, drawings, videos, or maps — as a means of documenting their own experiences, surfacing knowledge, and informing action. Rather than the researcher imposing an analytical gaze on pre-existing images, participants are co-investigators who create visual data and participate in its interpretation, making the method both epistemologically democratic and particularly powerful for accessing marginalized or hard-to-articulate perspectives.

Qualitative participatory research approach

Participant-generated images, photographs, drawings, videos, community maps, and associated narratives

Wang, C., & Burris, M. A. (1997). Photovoice: Concept, methodology, and use for participatory needs assessment. Health Education and Behavior, 24(3), 369–387.

Rose, G. (2016). Visual Methodologies: An Introduction to Researching with Visual Materials (4th ed.). Sage.

visual-analysis participatory-action-research thematic-analysis ethnography narrative-inquiry photovoice


Particle Filter with Measurement Error

Sequential Monte Carlo Particle Filter with Explicit Measurement Error

SMC with measurement noise
bootstrap filter with observation error
nonlinear particle filter
particle filter state-space model

A particle filter with explicit measurement error is a Sequential Monte Carlo algorithm that tracks the hidden state of a nonlinear, non-Gaussian dynamic system while formally modelling noise in the observations. A population of weighted random samples (particles) represents the posterior state distribution at each time step, and an observation likelihood function quantifies how much each particle is consistent with the noisy measurement received.

Time-series / sequential observations with noise

Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F – Radar and Signal Processing, 140(2), 107–113.

Doucet, A., de Freitas, N., & Gordon, N. (Eds.). (2001). Sequential Monte Carlo Methods in Practice. Springer.

kalman-filter extended-kalman-filter unscented-kalman-filter sequential-monte-carlo hidden-markov-model bayesian-state-space-model


Particle Filter with Missing Data

Sequential Monte Carlo Particle Filter for State-Space Models with Missing Observations

bootstrap filter with missing observations

Doucet, Godsill, Andrieu (2000); Gordon, Salmond & Smith (1993)

A particle filter adapted for state-space models in which some observations are absent. The algorithm tracks a hidden state over time using a cloud of weighted random samples (particles); when a time step has no observed value, the weight-update step is simply skipped, so the particles propagate forward using only the transition model until new data arrives.

Sequential / time-series data with intermittent missing observations

Doucet, A., de Freitas, N. & Gordon, N. J. (Eds.) (2001). Sequential Monte Carlo Methods in Practice. Springer, New York.

Doucet, A., Godsill, S. & Andrieu, C. (2000). On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 10(3), 197-208.

sequential-monte-carlo kalman-filter-with-missing-data particle-filter bayesian-inference-with-missing-data mcmc-with-missing-data dynamic-particle-filter


Particle Filter

Particle Filter (Sequential Monte Carlo)

SMC
sequential Monte Carlo
bootstrap filter
condensation algorithm
SIR filter
sequential importance resampling

The particle filter, introduced by Gordon, Salmond, and Smith in 1993, is a sequential Monte Carlo algorithm that approximates the Bayesian filtering distribution for nonlinear and non-Gaussian state-space models. Rather than tracking a single best estimate, it maintains a cloud of N weighted random samples — particles — that collectively represent the full posterior distribution of a hidden state at each point in time as new observations arrive.

continuous latent states; discrete or continuous observations

weighted particle approximation of posterior p(x_t | y_{1:t})

Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F (Radar and Signal Processing), 140(2), 107–113.

Doucet, A., Godsill, S. J., & Andrieu, C. (2000). On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 10(3), 197–208.

Doucet, A., de Freitas, N., & Gordon, N. (Eds.). (2001). Sequential Monte Carlo Methods in Practice. Springer-Verlag.

kalman-filter mcmc hidden-markov-model bayesian-regression state-space-model


Particle-in-Cell Beam Simulation

Particle-in-Cell Method for Beam Dynamics

PIC simulation
plasma simulation
beam dynamics

The Particle-in-Cell (PIC) method is a powerful computational technique for simulating the dynamics of charged particle beams and plasmas in complex electromagnetic field configurations. By tracking individual macroparticles and self-consistently solving Maxwell's equations on a grid, PIC enables study of collective effects and nonlinear phenomena in beam and accelerator physics.

Birdsall, C. K., & Langdon, A. B. (1991). Plasma Physics via Computer Simulation. Taylor & Francis.

Boeuf, J. P., & Pitchford, L. C. (2003). Three-dimensional model of the coupling of external circuit and plasma in a coaxial geometry. Journal of Applied Physics, 93(8), 4948–4958.

Vay, J. L. (2008). Noninvariance of space-charge dominated beam dynamics in the Lorentz and energy-conserving moment rest frames. Physics of Plasmas, 15(5), 056701.


Particle Swarm Optimization

PSO
swarm intelligence optimization
Parçacık Sürü Optimizasyonu (PSO)

Particle Swarm Optimization (PSO) is a population-based metaheuristic algorithm introduced by Kennedy and Eberhart in 1995, inspired by the collective movement of bird flocks and fish schools. Each candidate solution — called a particle — moves through the search space by updating its velocity and position based on its own best experience and the best experience of the entire swarm, enabling fast convergence across continuous optimization problems.

Population-based metaheuristic / swarm intelligence

Inertia weight (w), cognitive coefficient (c1), social coefficient (c2)

Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.

Shi, Y. & Eberhart, R. (1998). A Modified Particle Swarm Optimizer. IEEE Congress on Evolutionary Computation (CEC).

genetic-algorithm simulated-annealing differential-evolution ant-colony-optimization grey-wolf-optimizer bayesian-optimization


Partisan Identity Scale

Partisan Identity and Party Attachment Scale (PAS)

PAS
Party Identification
Partisan Strength

The Partisan Identity Scale measures strength and direction of psychological attachment to a political party, encompassing both party preference and emotional party identification. Foundational since Campbell et al.'s American Voter (1960), the measure distinguishes party affiliation (which party one is registered with) from party identification (psychological identity with a party as a social group). Partisan identity is among the strongest predictors of voting behavior, political attitudes, and interpretation of political information, functioning as a 'perceptual filter' through which voters process news.

Campbell, A., Converse, P. E., Miller, W. E., & Stokes, D. E. (1960). The American voter. New York: John Wiley & Sons.

Carsey, T. M., & Layman, G. C. (2006). Changing sides or changing minds? Party identification and policy preferences in the American electorate. American Journal of Political Science, 50(2), 464-477.

Greene, S. (2004). Social identity and the psychological attachment to parties. In D. M. Sears, L. Huddy, & R. Jervis (Eds.), Oxford handbook of political psychology. Oxford: Oxford University Press.


PASI

The PASI is the gold-standard clinician-administered measure of psoriasis severity and extent. Developed by Fredriksson and Pettersson in 1978, it evaluates four cardinal clinical signs (erythema, induration, desquamation) across four body regions, each weighted by anatomical importance. PASI is the most widely used endpoint in psoriasis clinical trials and is endorsed by regulatory agencies (FDA, EMA) and international dermatology societies.

Fredriksson T, Pettersson U. Severe psoriasis—oral therapy with a new retinoid. Dermatologica. 1978;157(4):238-244.

Feldman SR, Krueger GG. Psoriasis assessment tools in clinical trials. Ann Rheum Dis. 2005;64(Suppl 2):ii65-ii68.


Passive Social Media Use Scale

Passive Social Media Use Scale (PSMUSES)

Jae-Won Hur (and related work by Verduyn, Valkenburg, and others)

The Passive Social Media Use Scale measures the extent to which individuals engage in passive consumption—scrolling, lurking, and observing others' content—versus active participation like posting, commenting, and messaging. Developed to distinguish between active (interactive) and passive (consumptive) social media behaviors, this scale recognizes that passive use patterns are associated with distinct psychological outcomes including reduced wellbeing and increased social comparison.

Hur, J.-W. (2018). The impact of using social media on reducing social isolation. The Internet and Higher Education, 38, 21–28.

fear-of-missing-out-scale social-comparison-scale-online social-media-disorder-scale online-disinhibition-scale


Patch-Clamp

patch clamp
whole-cell recording
ion channel assay

Patch-clamp electrophysiology is a technique for measuring ionic currents through ion channels in cell membranes, developed by Neher and Sakmann in 1976. It enables direct observation of single-channel and whole-cell currents at millisecond resolution, making it essential for characterizing drug effects on ion channels and cardiac safety assessment.

Neher, E., & Sakmann, B. (1976). Single-channel currents recorded from membrane of denervated frog muscle fibres. Nature, 260(5554), 799-802.

Hamill, O. P., Marty, A., Neher, E., Sakmann, B., & Sigworth, F. J. (1981). Improved patch-clamp techniques for high-resolution current recording from cells and cell-free membrane patches. Pflugers Archiv, 391(2), 85-100.


PatchTST

PatchTST — Yama Tabanlı Zaman Serisi Transformer

PatchTST is a patch-based Transformer architecture for time series forecasting, introduced by Nie and colleagues in 2023, that cuts each series into overlapping patches treated as tokens and processes channels independently. It balances computational efficiency with strong accuracy on long-horizon forecasting.

Nie, Y., Nguyen, N. H., Sinthong, P. & Kalagnanam, J. (2023). A Time Series is Worth 64 Words: Long-term Forecasting with Transformers. ICLR.

Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L. & Jin, R. (2022). FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting. ICML.


Path Analysis

PA
path coefficient analysis
observed-variable SEM
causal path modeling

Path analysis tests a researcher-specified causal diagram among observed variables by decomposing their intercorrelations into direct effects, indirect (mediated) effects, and spurious associations. Developed by Sewall Wright in 1921, it is the observed-variable special case of structural equation modeling and remains a standard tool for theory-driven multivariate causal inference.

Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20(7), 557–585.

Kline, R. B. (2023). Principles and Practice of Structural Equation Modeling (5th ed.). Guilford Press.

structural-equation-modeling confirmatory-factor-analysis mediation-analysis moderation-analysis multiple-regression moderated-mediation


Path Integral Monte Carlo

Path Integral Monte Carlo (PIMC) is a computational method for calculating thermodynamic and structural properties of quantum systems using Feynman's path integral formulation. Developed rigorously by David Ceperley and colleagues in the 1990s, PIMC treats quantum particles as classical polymers in a higher-dimensional space, enabling efficient Monte Carlo sampling of quantum statistics.

Feynman, R. P. (1948). Space-time approach to non-relativistic quantum mechanics. Reviews of Modern Physics, 20, 367–387.

Ceperley, D. M. (1995). Path integrals in the theory of condensed helium. Reviews of Modern Physics, 67, 279–355.

Trofimov, D., et al. (2020). Practical path integral Monte Carlo. Annual Review of Computational Physics, 2, 165–190.


Pathway Enrichment Analysis

PEA
overrepresentation analysis
ORA
functional enrichment analysis

Mootha et al. (2003); systematised by Subramanian et al. (2005)

Pathway enrichment analysis (PEA) is a statistical approach that takes a list of genes or proteins of interest — typically derived from a differential expression or proteomics experiment — and identifies which pre-defined biological pathways or functional gene sets are represented more often than expected by chance. By mapping individual molecular changes onto curated pathway knowledge bases such as KEGG, Gene Ontology, or Reactome, PEA translates long gene lists into interpretable biological processes, making it a central tool in the post-analysis of high-throughput omics experiments.

Statistical functional annotation method

Gene lists or ranked gene/protein lists with pathway/gene-set databases (GO, KEGG, Reactome)

Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550.

Alexa, A., Rahnenführer, J., & Lengauer, T. (2006). Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics, 22(13), 1600–1607.


Patient Activation Measure

Judith H. Hibbard, Jacqueline Stockard, and colleagues

The Patient Activation Measure (PAM) is a 13-item self-report questionnaire developed by Hibbard and colleagues (2004) to assess the degree to which patients understand their role in managing their health, have confidence in their ability to engage in self-care, and take action to manage their health and prevent disease. PAM conceptualizes patient activation as a developmental process moving through four sequential levels: Level 1 (Passive) – the patient is disengaged, lacks understanding of their role, and is unwilling to take action; Level 2 (Aware) – the patient understands their role and importance of health behaviors but lacks confidence or is uncertain about ability; Level 3 (Taking Action) – the patient is taking steps to engage in self-management but may be inconsistent or uncertain how to maintain behavior; Level 4 (Maintaining Behavior) – the patient actively maintains self-management behaviors and prevents relapse. The PAM is widely used in primary care, chronic disease management, health insurance population health programs, and health services research to identify patients at risk of poor outcomes and to evaluate interventions targeting patient engagement.

Hibbard, J. H., Stockard, J., Mahoney, E. R., & Tusler, M. (2004). Development of the Patient Activation Measure (PAM): conceptualizing and measuring activation in patients and consumers. Health Services Research, 39(4), 1005-1026.


Patient Dignity Inventory

Chochinov, Hassard, McClement, and colleagues (University of Manitoba)

The Patient Dignity Inventory (PDI) is a 25-item self-report measure assessing dignity-related distress in patients with advanced cancer and life-limiting illness. Developed by Chochinov and colleagues at the University of Manitoba in 2008, the PDI operationalizes 'dignity' as a multidimensional construct encompassing illness-related functional decline, psychosocial concerns (fear, hopelessness, suicidality), body image distress, existential meaning, and social connection—dimensions often overlooked by symptom-focused assessment. The PDI enables clinicians to identify and address dignity threats systematically, preventing the existential despair that can accompany terminal illness even when physical symptoms are well-controlled.

Chochinov, H. M., Hassard, T., McClement, S., Hack, T., Kristjanson, L. J., Harlos, M., Speca, M., & Tool, T. (2008). The Patient Dignity Inventory: a novel way of measuring dignity-related distress in palliative care. Journal of Pain and Symptom Management, 36(6), 559–571.

Chochinov, H. M., Hack, T., McClement, S., Kristjanson, L., & Harlos, M. (2002). Dignity in the terminally ill: A developing empirical model. Social Science & Medicine, 54(3), 433–443.

spiritual-wellbeing-scale mcgill-quality-of-life good-death-inventory palliative-performance-scale support-team-assessment-schedule


Patient Enablement Instrument

The Patient Enablement Instrument (PEI) is a brief, validated six-item questionnaire that measures the degree to which a clinical consultation leaves the patient feeling more capable of understanding and managing their health condition. Developed by Howie and colleagues in 1998, the PEI assesses whether the consultation helped the patient understand their problem, cope with their illness, and manage their health more effectively. The scale captures the empowering effect of good clinical practice and is widely used in general practice research, quality improvement, and studies evaluating patient-centered and collaborative consultation styles.

Howie, J. G., Heaney, D. J., Maxwell, M., & Zwanenberg, D. (1998). A comparison of a Patient Enablement Instrument (PEI) against two other consultations outcome measures. British Journal of General Practice, 48(427), 1211-1216.

Hogg, W., Huston, P., Martin, C., & Christoffel, D. (2013). Promoting functional independence and well-being in older adults. Canadian Family Physician, 59(8), 847-854.

collaboste-scale decisional-conflict-scale care-transitions-measure trust-in-physician-scale


Patient Engagement Scale

Judith H. Hibbard, Janice Stockard, Ellen R. Mahoney, Martin Tusler

The Patient Engagement Scale measures the degree to which patients take active responsibility for managing their health and healthcare. Developed by Hibbard and colleagues (2004), the Patient Activation Measure (PAM) operationalizes engagement as a progression from awareness of health issues through confident self-management, capturing the psychological, behavioural, and confidence dimensions essential for patient participation in shared decision-making and chronic disease management.

Hibbard, J. H., Stockard, J., Mahoney, E. R., & Tusler, M. (2004). Development of the Patient Activation Measure (PAM): Conceptualizing and measuring activation in patients and consumers. Health Services Research, 39(4), 1005–1026.


Patient Fall Risk Assessment

Comprehensive Patient Fall Risk Assessment Protocol

Fall Risk Screening
Fall Prevention Assessment
PFRA

Multiple researchers (Oliver, Hendrich, and colleagues)

Patient Fall Risk Assessment is a systematic clinical evaluation process used to identify hospitalized or institutionalized patients at increased risk of falling. Falls are a major cause of injury and mortality in healthcare settings, particularly among older adults. The assessment considers intrinsic patient factors (e.g., age, medical conditions, medications) and extrinsic environmental factors (e.g., lighting, equipment, flooring) to guide preventive interventions.

Hendrich, A. L., Bender, P. S., & Nyhuis, A. (2003). Validation of the Hendrich II Fall Risk Model: a large concurrent case/control study of hospitalized patients. Applied Nursing Research, 16(3), 159-171.

Oliver, D., Daly, F., Martin, F. C., & McMurdo, M. E. (2004). Risk factors and risk assessment tools for falls in hospital in-patients: a systematic review. Age and Ageing, 33(2), 122-130.

braden-scale morse-fall-scale care-dependency-scale early-warning-score


Patient Flow Simulation

Discrete Event Simulation for Healthcare Patient Flow Analysis

Healthcare DES
Patient Movement Simulation

Operations research and management science

Discrete Event Simulation (DES) is a computational technique that models the movement of patients through healthcare facilities by simulating individual patient journeys and interactions with resources (staff, beds, equipment). DES allows realistic representation of complex, stochastic healthcare processes and supports 'what-if' analysis without disrupting live operations.

Pidd, M. (1992). Computer Simulation in Management Science (3rd ed.). John Wiley & Sons.

Sokolowski, J. A., & Banks, C. M. (2009). Modeling and Simulation Fundamentals: Theoretical Underpinnings and Practical Domains. John Wiley & Sons.

Gunal, M. M., & Pidd, M. (2010). Discrete event simulation for performance modelling in health care: A review of the literature. Journal of Simulation, 4(1), 42–51.

queuing-theory-healthcare hospital-bed-occupancy-model lean-healthcare six-sigma-healthcare staffing-ratio-analysis


Patient Global Impression of Change

Patient Global Impression of Change (PGIC)

Patient Global Impression of Change Scale

The Patient Global Impression of Change is a single-item, seven-point rating scale asking patients to report their overall impression of change since treatment initiation. Originally published by William Guy in the ECDEU Assessment Manual in 1976, the PGIC has become a standard co-primary endpoint in clinical trials assessing treatment efficacy. The scale is endorsed by the FDA as a patient-reported outcome measure for demonstrating clinical benefit. Despite its simplicity, the PGIC captures patients' holistic perception of improvement—integrating symptom reduction, functional recovery, and subjective well-being.

Guy, W. (1976). ECDEU Assessment Manual for Psychopharmacology. Rockville, MD: National Institute of Mental Health, US Department of Health, Education, and Welfare.

Farnik, M., & Pierzchala, W. (2012). Instruments to assess fatigue in neurology. Neurological Sciences, 33(5), 1015–1020.

FDA. (2009). Guidance for Industry on Patient-Reported Outcome Measures: Use in Medical Product Development to Support Labeling Claims. Center for Drug Evaluation and Research (CDER).

phq-9 quick-inventory-depressive sheehan-disability-scale clinical-global-impressions-scale


Patient Health Questionnaire-2

Patient Health Questionnaire-2 - Depression Screening

Kurt Kroenke, Robert L. Spitzer, and Janet B. Williams

The Patient Health Questionnaire-2 (PHQ-2) is an ultra-brief, validated two-item screening instrument developed by Kroenke and colleagues in 2003 to identify major depression in primary care and medical populations. The PHQ-2 assesses the two cardinal symptoms of depression (depressed mood and anhedonia) over the past two weeks using a 0-3 frequency scale. It is the shortest validated depression screening tool enabling routine use in time-constrained clinical settings.

Two-item depression screening instrument

Kroenke, K., Spitzer, R. L., & Williams, J. B. (2003). The Patient Health Questionnaire-2: validity of a two-item depression screener. Medical Care, 41(11), 1284-1292.

Spitzer, R. L., Kroenke, K., & Williams, J. B. (1999). Validation and utility of a self-report version of PRIME-MD: the PHQ primary care study. JAMA, 282(18), 1737-1744.

Li, C., Friedman, B., Conwell, Y., & Fiscella, K. (2007). Validity of the Patient Health Questionnaire 2 (PHQ-2) in identifying major depression in older people. Journal of the American Geriatrics Society, 55(4), 596-602.


The Patient-Provider Cultural Sensitivity Scale (PPCSS) is a measure designed to assess the degree to which healthcare providers demonstrate cultural sensitivity and respect in clinical encounters. The instrument evaluates provider behaviors and attitudes that honor patients' cultural identities, values, and preferences, including active listening, non-judgmental communication, incorporation of cultural health beliefs into care planning, and shared decision-making. The PPCSS can be completed by both patients (rating provider behavior) and providers (self-rating), making it a valuable tool for evaluating patient-centered, culturally responsive care.

Dogba, M. J., & Foley, R. (2008). Patient preferences for involvement in healthcare decision-making: A comparison of interprofessional joint decision-making and traditional medical approaches. Health Expectations, 11(1), 68–76.


PRCS

PRCS Clinician Communication
Communication Quality Scale

Marianne Haskard Zolnierek, Roxane Dimateo

The Patient-Reported Communication Scale (PRCS) is a brief, validated instrument that measures patients' perceptions of clinician communication quality in healthcare encounters. Developed through meta-analytic research by Haskard Zolnierek and DiMatteo, the PRCS assesses key dimensions of effective patient-clinician communication: clarity of explanations, listening, showing respect and empathy, and addressing patient concerns. The scale is used to evaluate clinician communication competence, identify training needs, and correlate communication quality with patient adherence, satisfaction, and health outcomes.

Haskard Zolnierek, K. B., & DiMatteo, M. R. (2009). Physician communication and patient adherence to treatment: a meta-analysis. Medical Care, 47(8), 826-834.

Street, R. L., Jr., Makoul, G., Arora, N. K., & Epstein, R. M. (2009). How does communication heal? Pathways linking clinician–patient communication to health outcomes. Patient Education and Counseling, 74(3), 295-301.

collaboste-scale trust-in-physician-scale patient-enablement-instrument care-transitions-measure


Patient Reported Experience Measure Framework

Patient Reported Experience Measure (PREM) Framework

NHS England Quality Improvement and Health Quality Improvement Partnership, based on internationally recognized patient experience methodology

The Patient Reported Experience Measure (PREM) framework is a methodological approach for systematically collecting, analyzing, and acting on patient feedback about healthcare experiences. Unlike HCAHPS, which is a specific, standardized survey, PREM is a flexible framework that can be adapted to different care settings, patient populations, and organizational contexts. PREM encompasses structured patient surveys, interviews, focus groups, and real-time feedback mechanisms, all aimed at capturing patient-centered perspectives on care quality, communication, responsiveness, and dignity. PREMs are used alongside Patient Reported Outcome Measures (PROMs, which assess health status changes) to provide a complete picture of care from the patient perspective.

NHS England National Archives and Health Quality Improvement Partnership. (2019). Patient Reported Experience Measures (PREMs): A Resource for Commissioners. National Health Service, United Kingdom.

Black, N., Barron, D., & Jenkinson, C. (2013). How to use patient experience surveys in practice. Journal of Health Services Research and Policy, 16(3), 173–177.

Gibbons, E., & Fitzpatrick, R. (2014). Recent developments in measuring outcomes in routine clinical practice. Journal of Health Services Research & Policy, 19(3), 177–182.

hospital-consumer-assessment patient-safety-climate-scale safety-attitudes-questionnaire nurse-work-environment-scale


Patient Safety Climate in Healthcare Organizations

Colla, J. B., Bracken, A. C., Kinney, L. M., and colleagues

The Patient Safety Climate Scale (PSCS) is a focused, brief assessment tool designed to measure staff perceptions of the safety climate within a specific healthcare unit or department. Unlike broader safety culture instruments, the PSCS concentrates on the immediate work environment—how safety is prioritized at the team and unit level, whether staff feel supported in reporting concerns, and whether leadership demonstrates commitment to preventing harm. The PSCS has been used in hospitals, ambulatory centers, and long-term care facilities to rapidly assess readiness for safety initiatives or to track improvements following targeted interventions.

Blegen, M. A., Gearhart, S., O'Brien, R., Sehgal, N. L., & Alldredge, B. K. (2004). AHRQ's Hospital Survey on Patient Safety Culture: Psychometric analyses. Journal of Patient Safety, 5(3), 139–144.

Colla, J. B., Bracken, A. C., Kinney, L. M., & Weeks, W. B. (2005). Measuring patient safety climate: a review of surveys. Quality & Safety in Health Care, 14(5), 364–366.

Merlo, J., Gerdtham, U. G., Lynch, J., Beckman, A., & Lithman, T. (2009). Inverse care law in an area with universal health coverage. Scandinavian Journal of Public Health, 34(3), 270–276.

safety-attitudes-questionnaire hospital-survey-patient-safety nurse-work-environment-scale healthcare-teamwork-scale


PSCS

Patient Safety Competence Self-Evaluation Scale

Safety Competence Scale
Patient Safety Awareness
Safety Culture Assessment

Lachman et al.; adapted from Reason's error theory

The PSCS is a self-report instrument measuring healthcare students' and professionals' self-perceived competence in patient safety practices, safety awareness, and safety culture engagement. Developed by Lachman and informed by James Reason's theoretical framework of human error and systems thinking, the PSCS evaluates the degree to which individuals understand safety principles, recognize hazards, report incidents, collaborate on safety issues, and contribute to a culture of safety. The scale is used in healthcare education and quality improvement to assess baseline safety competence, evaluate safety training effectiveness, and identify gaps in safety culture understanding.

Reason, J. (2000). Human error: Models and management. BMJ 320(7237): 768–770.

Lachman, V. D. (2012). Patient and nurse safety: Culture of safety. Medsurg Nurs 21(6): 379–382.


Patient Satisfaction with CAM Scale

Patient Satisfaction with Complementary and Alternative Medicine Scale

Margolis, S. A.; Glassman, S.; Wicks, R.

The PSCS is a patient-report instrument measuring satisfaction with complementary and alternative medicine services, including acupuncture, herbal medicine, massage, and other modalities. Developed by Margolis and colleagues in 1998, it captures dimensions of satisfaction specific to CAM practice—practitioner communication, efficacy expectations, cost concerns, and interpersonal warmth.

Margolis, S. A., Glassman, S., & Wicks, R. (1998). Measuring satisfaction of acupuncture and Chinese medicine patients using a newly developed patient satisfaction scale. Alternative Therapies in Health and Medicine, 4(4), 54–60.

Garrow, D., & Egede, L. E. (2006). Association between complementary and alternative medicine use, preventive care practices, and health care utilization. Archives of Internal Medicine, 166(7), 713–718.

cam-use-questionnaire attitudes-cam-scale integrative-medicine-attitudes patient-satisfaction-cam


Patient Satisfaction Questionnaire

Patient Satisfaction Questionnaire - General

Patient satisfaction and experience measurement

The Patient Satisfaction Questionnaire (PSQ) is a psychometrically validated self-report instrument developed by Ware and colleagues beginning in 1983 to measure patient satisfaction with medical care. The PSQ-18, a shortened version, comprises 18 items assessing general dimensions of healthcare satisfaction including accessibility, provider interaction, and trust.

Ware, J. E., Snyder, M. K., Wright, W. R., & Davies, A. R. (1983). Defining and measuring patient satisfaction with medical care. Evaluation and Program Planning, 6(3-4), 247-263.

Marshall, G. N., Hays, R. D., Sherbourne, C. D., & Wells, K. B. (1993). The structure of patient satisfaction with outpatient medical care. Medical Care, 31(12), 1174-1187.

Ware, J. E., Hays, R. D., & Marshall, G. N. (1996). Validation of a brief instrument measuring patient satisfaction with medical care. Medical Care, 34(2), 120-131.


Patient-Specific Functional Scale

Paul W. Stratford, Gill Westaway, Colin Gill, Jill M. Binkley

The Patient-Specific Functional Scale (PSFS) is a unique, individualized outcome instrument that captures patient-identified functional limitations and tracks change in those specific activities. Developed by Stratford and colleagues in 1995 and published in Physiotherapy Canada, the PSFS revolutionized patient-centered assessment by allowing each patient to identify and rate the three to five activities most important to them, rather than answering predetermined questions. This approach ensures relevance and maximizes the instrument's sensitivity to clinically meaningful change in patient-valued outcomes.

Stratford PW, Gill C, Westaway MD, Binkley JM. Assessing disability and change on individual patients: a report of a patient-specific measure. Physiother Can. 1995;47(4):258-263.

global-rating-of-change-scale lower-extremity-functional-scale ikdc-subjective-knee-form faos


Patient-Therapist Agreement Scale

The Patient-Therapist Agreement Scale (PTAS) measures the degree to which client and therapist agree on therapy goals, treatment focus, and expected treatment duration—a core component of the therapeutic alliance. Developed by Nash and colleagues in their foundational study of psychotherapy preparation, the PTAS operationalizes the principle that shared understanding of 'what we're working on and how long it will take' predicts engagement and outcome. It is used primarily in research and training to assess goal alignment and identify mismatches that may undermine treatment.

Nash, E. H., Hoehn-Saric, R., Battle, C. C., Stone, A. R., Imber, S. D., & Frank, J. D. (1965). Systemic preparation of patients for psychotherapy: Effects on therapy behavior and outcome. Journal of Psychiatric Research, 2(4), 267–281.

Gelso, C. J., & Carter, J. A. (1985). The relationship in counseling and psychotherapy: Components, consequences, and theoretical antecedents. The Counseling Psychologist, 13(2), 155–243.

working-alliance-inventory session-rating-scale therapeutic-alliance-scale outcome-rating-scale


Pavement ME Design

MEPDG
Pavement design
Fatigue and rutting

AASHTO (American Association of State Highway and Transportation Officials)

The Mechanistic-Empirical Pavement Design Guide (MEPDG or Pavement ME) is a modern method for designing asphalt pavements that predicts performance (rutting, cracking) using mechanistic stress analysis combined with empirical distress models. Developed by AASHTO in 2008 as a successor to the 1993 AASHTO Empirical Guide, this approach provides better accuracy and enables climate-based, site-specific design.

Performance-prediction model for asphalt pavement design

AASHTO (2008). Mechanistic-Empirical Pavement Design Guide: A Manual of Practice. American Association of State Highway and Transportation Officials.

Wang, D., Refsdal, G., & Creighton, A. (2010). Calibration of Pavement ME pavement performance equations at the project level. Transportation Research Record, 2153, 12-20.

American Institute of Asphalt Pavement Association (2015). Asphalt Pavement Design Guide. Report TIS-20(R18).


PCA-WEIGHT

PCA-WEIGHT (PCA Weighting — Principal Component Analysis based objective weighting) is a weight objective multi-criteria decision-making (MCDM) method introduced by Pearson, K. in 1901. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Weight_Objective (PCA variance explained, eigenvector-based)

Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. Philosophical Magazine

ahpsort aploco aras aroman artasi cobra cocoso codas


Principal Component Analysis

Temel Bileşenler Analizi (PCA)
PCA
principal components analysis
Karhunen-Loève transform

Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)

Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.

Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.

kmeans-clustering factor-analysis lasso-regression linear-regression hierarchical-clustering


PTSD Checklist for DSM-5

Frank W. Weathers, Brett T. Litz, and Terence M. Keane

The PTSD Checklist for DSM-5 (PCL-5) is a 20-item self-report measure of posttraumatic stress disorder (PTSD) symptoms aligned with DSM-5 diagnostic criteria. Developed by Weathers, Litz, and Keane, it is the gold standard screening and outcome measure for PTSD in military, veteran, and civilian trauma populations. The PCL-5 assesses four symptom clusters: re-experiencing, avoidance, negative alterations in cognition and mood, and hyperarousal.

Blevins, C. A., Weathers, F. W., Davis, M. T., Witte, T. K., & Domino, J. L. (2015). The Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5): Development and initial psychometric evaluation. Journal of Traumatic Stress, 28(6), 489-498.

Weathers, F. W., Litz, B. T., Keane, T. M., Palmieri, P. A., Marx, B. P., & Schnurr, P. P. (2013). The PTSD Checklist for DSM-5 (PCL-5). National Center for PTSD.


PTSD Checklist Military Version

PTSD Checklist for DSM-IV (Military Version)

The PCL-M is a 17-item self-report inventory measuring PTSD symptom severity in military personnel. Developed by Weathers and colleagues in 1993, it directly corresponds to DSM-IV diagnostic criteria. It is widely used in military, veteran, and trauma-exposed populations for screening and monitoring treatment response.

Weathers, F. W., Litz, B. T., Herman, D. S., Huska, J. A., & Keane, T. M. (1993). The PTSD Checklist (PCL): Reliability and diagnostic utility. Journal of Traumatic Stress, 6(4), 1-6.

Ruggiero, K. J., Ben, K. L., & Scotti, J. R. (2003). Knowledge, correct conditional probability, and choice of denominators in assessing children's understanding of AIDS transmission. Journal of Pediatric Psychology, 28(1), 45-52.

Blanchard, E. B., Jones-Alexander, J., Buckley, T. C., & Forneris, C. A. (1996). Psychometric properties of the PTSD Checklist (PCL). Behaviour Research and Therapy, 34(8), 669-673.

combat-exposure-scale moral-injury-events-scale peritraumatic-distress-inventory deployment-risk-resilience


Polycystic Ovary Syndrome Questionnaire

The Polycystic Ovary Syndrome Questionnaire (PCOSQ) is a 26-item self-report instrument developed to measure quality of life in women with polycystic ovary syndrome (PCOS). Created by Cronin and colleagues in 1998, the PCOSQ is disease-specific, assessing five dimensions uniquely relevant to PCOS: emotional effects, body hair, weight, infertility, and menstrual problems. It enables clinicians and researchers to evaluate the multidimensional impact of PCOS and monitor outcomes of lifestyle, hormonal, and reproductive interventions.

Cronin, L., Guyatt, G., Griffith, L., Wong, E., Azziz, R., Young, D., & Kjerulff, K. (1998). Development of a health-related quality-of-life questionnaire (PCOSQ) for women with polycystic ovary syndrome (PCOS). The Journal of Clinical Epidemiology, 51(11), 1235-1246.


PDF Fitting

Parton Distribution Function (PDF) fitting is the process of determining the probability distributions of quarks and gluons inside hadrons using high-energy collision data. PDFs are fundamental inputs to all hadron collider phenomenology, essential for predicting cross-sections, designing triggers, and interpreting new physics searches at the Large Hadron Collider.

Bjorken, J. D. (1969). Asymptotic sum rules at infinite momentum. Physical Review, 179(5), 1547.

Alekhin, S., et al. (2014). PDF4LHC recommendations for LHC Run II. The European Physical Journal C, 75(7), 304.

Bailey, S., et al. (2020). Parton distributions for the LHC Run II. The European Physical Journal C, 76(7), 391.


PDQ-39
Parkinson's Disease Questionnaire
PDQ

The PDQ-39 is the most widely used patient-reported outcome measure for Parkinson's disease quality of life. Developed by Crispin Jenkinson and colleagues in 1997, this 39-item self-report questionnaire comprehensively assesses how Parkinson's symptoms affect daily functioning, emotional well-being, stigma, social support, and cognitive-communication abilities. It is the recommended instrument in major Parkinson's disease clinical trials and forms a core component of outcome measurement in movement disorders.

Self-report quality of life questionnaire

Jenkinson, C., Fitzpatrick, R., Peto, V., Greenhall, R., & Hyman, N. (1997). The Parkinson's Disease Questionnaire (PDQ-39): Development and validation of a Parkinson's disease summary index score. Age and Ageing, 26(5), 353-357.

Peto, V., Jenkinson, C., Fitzpatrick, R., & Greenhall, R. (1995). The development and validation of a short measure of functioning and well-being for individuals with Parkinson's disease. Quality of Life Research, 4(3), 241-248.

Martínez-Martín, P., Rodríguez-Blázquez, C., Alvarez, M., Arakaki, T., Bergareche, A., Chade, A., ... & Grupo Centros Colaboradores de la Sociedad Española de Neurología para la validación de escalas en neurología. (2009). Expanded and independent validation of the Movement Disorder Society-sponsored unified Parkinson's disease rating scale (MDS-UPDRS). Journal of Parkinson's Disease, 3(3), 271-283.

eortc-qlq-c30 dlqi fibromyalgia-impact-questionnaire chronic-heart-failure-questionnaire


Pearson Correlation

Pearson Product-Moment Correlation Coefficient

pearson r
product-moment correlation
bivariate correlation
Pearson Korelasyon Analizi

The Pearson product-moment correlation coefficient (r) is a parametric measure of the direction and strength of the linear association between two continuous variables. Introduced by Karl Pearson in 1895, it remains the most widely used bivariate correlation statistic in the social, health, and natural sciences. The coefficient ranges from −1 (perfect negative linear relationship) to +1 (perfect positive), with 0 indicating no linear association.

Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.

Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed.). SAGE.

spearman-correlation simple-linear-regression kendall-tau partial-correlation multiple-regression


PedMIDAS

PedMIDAS: Pediatric Migraine Disability Assessment Scale

headache/migraine pediatric functional disability

The PedMIDAS is a brief 6-item parent-report (with child input for older youth) instrument developed by Hershey et al. in 2001 to quantify migraine-related functional disability in children and adolescents. Rather than measuring pain intensity or headache frequency, the PedMIDAS focuses on the ultimate impact of migraine on daily life: how many days of school, sports/play, and family activities does the child miss or participate in only with significant limitation? This outcome-focused approach makes it particularly useful for assessing the real-world burden of migraine in children and evaluating treatment effectiveness.

Parent-report and/or child self-report of missed activities

Hershey, A. D., Powers, S. W., Vockell, A. L., LeCates, S., & Ellinwood, E. H. (2001). PedMIDAS: Development of a questionnaire to assess disabilities in migrainous children. Headache, 41(6), 556-563.

Powers, S. W., Patton, S. R., Hommel, K. A., & Hershey, A. D. (2004). Quality of life in pediatric migraine: clinical impact and comparison of generic and headache-specific instruments. Headache, 43(5), 499-508.

paqlq pedsql-diabetes child-health-questionnaire qolce


PedsQL Pediatric Quality of Life

PedsQL
PedsQL 4.0
PedsQL Generic Core Scales

The Pediatric Quality of Life Inventory (PedsQL), developed by James Varni in 2001, is a validated, evidence-based patient-reported outcome measure assessing health-related quality of life in children and adolescents aged 2–18 years. Available in generic core scales and disease-specific modules, it measures physical, emotional, social, and school functioning, making it ideal for clinical research, outcome measurement, and population health surveillance.

Pediatric health-related quality of life inventory

Varni, J. W., Seid, M., & Rode, C. A. (2001). The PedsQL: Measurement model for the pediatric quality of life inventory. Journal of Clinical Child & Adolescent Psychology, 30(2), 130-141.

Varni, J. W., Seid, M., & Kurtin, P. S. (2007). PedsQL 4.0: Reliability and validity of the Pediatric Quality of Life Inventory version 4.0 generic core scales. Medical Care, 39(8), 800-812.

bayley-scales ages-stages-questionnaire cbcl-child-behavior school-engagement-scale


Pediatric Sleep Questionnaire

The Pediatric Sleep Questionnaire (PSQ) is a 22–24 item parent-report screening tool for sleep-disordered breathing and associated daytime dysfunction in children ages 2–18 years. Developed by Ronald Chervin at the University of Michigan in 2000, the PSQ measures three domains: symptoms of obstructive sleep apnea (snoring, witnessed apneas, gasping), daytime sleepiness and behavioral consequences, and sleep behavior problems (parasomnias, restlessness). It is widely used in pediatric primary care, ENT, and sleep medicine settings to identify children at risk for clinically significant sleep disorders.

Chervin, R. D., Hedger, K., Dillon, J. E., & Pituch, K. J. (2000). Pediatric sleep questionnaire (PSQ): Validity and reliability of scales for sleep-disordered breathing, snoring, sleepiness, and sleep behavior. Sleep Medicine, 1(1), 21–32.

Chervin, R. D., Weatherly, R. A., Garetz, S. L., Ruzicka, D. L., Giordani, B. J., Hodges, E. K., . . . Dillon, J. E. (2007). Pediatric sleep questionnaire: Prediction of sleep apnea and outcomes. Archives of Otolaryngology–Head & Neck Surgery, 133(3), 216–222.


Pedogenesis Modeling

Pedogenesis Modeling: Quantitative Simulation of Soil Formation Processes

soil formation modeling
soil genesis simulation
pedogenic process modeling
quantitative pedology

Quantitative pedology and soil systems modeling

1941 (Jenny's factorial model); process-based numerical models from 1990s onward

Hans Jenny (foundational framework); later extended by multiple contributors including Simonson, Hoosbeek, and Bryant

Pedogenesis modeling is a quantitative method used in agronomy and soil science to simulate the processes by which soils form and evolve over time. Rooted in Hans Jenny's 1941 factorial framework — soil as a function of climate, organisms, relief, parent material, and time — modern approaches translate these conceptual drivers into coupled numerical process equations, allowing researchers to reconstruct past soil states and project future soil properties under changing land use or climate scenarios.

Quantitative process-based simulation model

Soil profile data, climate records, topographic data, parent material properties, vegetation/land-use data

Minasny, B., Finke, P., Stockmann, U., Vanwalleghem, T., & McBratney, A. B. (2015). Resolving the integral connection between pedogenesis and landscape evolution. Earth-Science Reviews, 150, 102–120.

Jenny, H. (1941). Factors of Soil Formation: A System of Quantitative Pedology. McGraw-Hill, New York.

soil-water-balance watershed-hydrology-model land-use-change-analysis digital-soil-mapping erosion-model carbon-sequestration-model


PedsQL Cancer Module

Pediatric Quality of Life Inventory—Cancer Module

cancer-specific pediatric quality of life

The PedsQL Cancer Module is a 31-item disease-specific instrument developed by Varni et al. in 2002 to measure quality of life in children and adolescents with cancer aged 2–18 years. It captures treatment burden (nausea, vomiting, pain, hair loss), cancer-related worry, cognitive concerns, and emotional and social impacts of diagnosis and treatment. Used alongside the PedsQL Generic Core Scales, it provides comprehensive assessment of both cancer-specific and general health-related quality of life during active treatment, survivorship, and end-of-life care.

Varni, J. W., Burwinkle, T. M., Katz, E. R., Meeske, K., & Dickinson, R. P. (2002). The PedsQL in pediatric cancer: Reliability and validity of the Pediatric Quality of Life Inventory Generic Core Scales, multidimensional fatigue scale, and cancer module. Cancer, 94(7), 2090-2106.

Varni, J. W., Seid, M., & Rode, C. A. (2000). The PedsQL: Measurement model for the Pediatric Quality of Life Inventory. Medical Care, 37(2), 126-139.

pedsql-diabetes paqlq qolce pedsql-cardiac pedsql-sickle-cell


PedsQL Cardiac Module

Pediatric Quality of Life Inventory—Cardiac Module

cardiac-specific pediatric quality of life

The PedsQL Cardiac Module is a disease-specific instrument developed by Varni et al. in the mid-2000s to measure quality of life in children and adolescents with cardiac disease aged 2–18 years. Measuring across domains including cardiac symptom impact, activity limitations, and cardiac-related worry, it captures how congenital heart disease, acquired heart disease, and cardiac treatment affect daily functioning and well-being. Used alongside the PedsQL Generic Core Scales, it provides comprehensive assessment of cardiac-specific and general health-related quality of life.

Varni, J. W., Limbers, C. A., & Burwinkle, T. M. (2007). The PedsQL as a pediatric patient-reported outcome: Reliability and validity of the PedsQL Generic Core Scales and PedsQL 4.0 Multidimensional Fatigue Scale in a National US Sample of Children Reported Health. Journal of Clinical Psychology in Medical Settings, 14(3), 206-214.

Uzark, K., Jones, K., Slusher, J., Limbers, C. A., Burwinkle, T. M., & Varni, J. W. (2012). Quality of life in children and adolescents with heart disease. Congenital Heart Disease, 7(1), 17-25.

pedsql-diabetes pedsql-cancer paqlq pedsql-sickle-cell


PedsQL Diabetes Module

Pediatric Quality of Life Inventory—Diabetes Module

The PedsQL Diabetes Module is a 28-item disease-specific instrument developed by Varni et al. in 2003 to measure quality of life in children and adolescents with type 1 and type 2 diabetes. It captures the impact of diabetes management, glucose monitoring, and disease-related worry on daily functioning. The module is paired with the PedsQL Generic Core Scales, enabling both disease-specific and general health-related quality of life assessment.

Varni, J. W., Burwinkle, T. M., Jacobs, J. R., Gottschalk, M., Kaufman, F., & Jones, K. L. (2003). The PedsQL in type 1 and type 2 diabetes: Reliability and validity of the Pediatric Quality of Life Inventory Generic Core Scales and type 1 Diabetes Module. Diabetes Care, 26(3), 631-637.

paqlq qolce pedsql-cancer pedsql-cardiac pedsql-sickle-cell