Pilot Natural Experiment

feasibility natural experiment
preliminary natural experiment
pilot quasi-experiment
exploratory natural experiment

Combination of natural experiment tradition (Dunning, Angrist, Pischke) and pilot study methodology

A pilot natural experiment is a small-scale preliminary study that exploits an existing exogenous event or policy variation to test whether a full natural experiment is viable. It preserves the core logic of natural experiments — using real-world discontinuities to approximate causal inference — while explicitly scoping the work to assess data availability, group comparability, effect detectability, and procedural feasibility before committing resources to a larger study.

Observational data with exogenous variation; administrative records, surveys, secondary data

Dunning, T. (2012). Natural Experiments in the Social Sciences: A Design-Based Approach. Cambridge University Press.

Thabane, L., Ma, J., Chu, R., Cheng, J., Ismaila, A., Rios, L. P., ... & Goldsmith, C. H. (2010). A tutorial on pilot studies: the what, why and how. BMC Medical Research Methodology, 10(1), 1.

natural-experiment pilot-randomized-controlled-trial pilot-field-experiment quasi-experimental-design difference-in-differences regression-discontinuity-design


Pilot pretest-posttest experimental design

Pilot Pretest-Posttest Experimental Design

pilot pretest-posttest study
small-scale pretest-posttest trial
feasibility pretest-posttest design
pilot two-group pretest-posttest

1963 (formal codification by Campbell & Stanley); pilot trials widely used from mid-20th century onward

Donald T. Campbell and Julian C. Stanley (experimental design codification); pilot study concept widely credited to early 20th-century clinical trial practice

A pilot pretest-posttest experimental design is a small-scale, preliminary study that applies a pretest-posttest measurement framework to a limited sample before a full-scale experiment. Its primary goals are to assess the feasibility of procedures, estimate effect sizes for power analysis, identify instrument problems, and uncover logistical barriers — all before committing to the cost and scale of a definitive trial. It combines the internal-validity advantages of repeated measurement with the pragmatic scope of a pilot study.

Quantitative (continuous or ordinal outcome measures collected at pre- and post-intervention time points)

Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. In N. L. Gage (Ed.), Handbook of Research on Teaching (pp. 171–246). Rand McNally.

Leon, A. C., Davis, L. L., & Kraemer, H. C. (2011). The role and interpretation of pilot studies in clinical research. Journal of Psychiatric Research, 45(5), 626–629.

pretest-posttest-control-group-design randomized-controlled-trial quasi-experimental-design one-group-pretest-posttest-design feasibility-study repeated-measures-design


Pilot Randomized Controlled Trial

pilot RCT
feasibility RCT
pilot trial
preliminary RCT

Formalized through clinical trials methodology community

A pilot randomized controlled trial (pilot RCT) is a small-scale, fully randomized experiment conducted before a definitive RCT to test the feasibility of study procedures, estimate key parameters such as recruitment rates and effect-size variability, and identify practical barriers. It uses the same randomization, intervention, and measurement protocol as the planned full trial but on a fraction of the target sample. The goal is not to confirm efficacy but to refine and justify the main trial design.

Continuous, categorical, and ordinal outcome data; recruitment and retention rates

Lancaster, G. A., Dodd, S., & Williamson, P. R. (2004). Design and analysis of pilot studies: recommendations for good practice. Journal of Evaluation in Clinical Practice, 10(2), 307-312.

randomized-controlled-trial feasibility-study adaptive-randomized-controlled-trial cluster-randomized-controlled-trial crossover-randomized-controlled-trial factorial-randomized-controlled-trial


Pilot Single-Subject Experimental Design

pilot SSED
pilot N-of-1 experiment
pilot single-case experimental design
pilot SCED

2000s–2010s (as an explicitly named piloting strategy for SSED)

Derived from single-subject experimental design traditions (Sidman, 1960; Kazdin) and pilot study methodology (Lancaster, Dodd, Williamson, 2004; Thabane et al., 2010)

A pilot single-subject experimental design (pilot SSED) is a small-scale feasibility trial applied to one or very few individuals, combining the repeated-measurement logic of single-subject experimental design with the explicit preparatory aims of a pilot study. It is used to test an intervention protocol, measurement procedures, and design logistics before committing to a full-scale single-case or group experiment.

Repeated behavioral or outcome measurements on one or very few individuals

Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press.


Pilot Solomon Four-Group Design

Pilot Solomon Four-Group Experimental Design

pilot four-group pretest-posttest control design

1949 (Solomon design); pilot usage documented in experimental methodology literature from 1960s onward

Richard L. Solomon (base design); pilot application is a standard methodological practice

The Pilot Solomon Four-Group Design is a small-scale, preliminary implementation of the Solomon four-group experimental design. Its purpose is to test the feasibility and logistics of the full design before committing to a resource-intensive main study. The Solomon four-group design, introduced by Richard L. Solomon in 1949, controls for pretest sensitisation by using four groups — two that receive a pretest and two that do not — crossed with treatment and control conditions. Piloting this design allows researchers to estimate effect sizes, detect procedural problems, and verify that the pretest does not unduly influence posttest scores.

Continuous or categorical outcome measures; pretest and posttest scores

Solomon, R. L. (1949). An extension of control group design. Psychological Bulletin, 46(2), 137–150.

Braver, M. C. W., & Braver, S. L. (1988). Statistical treatment of the Solomon four-group design: A meta-analytic approach. Psychological Bulletin, 104(1), 150–154.

solomon-four-group-design pretest-posttest-control-group-design randomized-controlled-trial pilot-study factorial-design quasi-experimental-design


Pilot-tested API-based data collection

pilot API data collection
pre-tested API harvesting
API data collection pilot study
pilot-validated API scraping

Convergence of survey pilot-testing tradition (Presser et al., 2004) and computational social science API methods (Salganik, 2018)

Pilot-tested API-based data collection is a structured digital data-gathering approach in which a researcher designs an API query or harvesting script and then runs a small-scale trial before executing the full collection. The pilot phase exposes authentication issues, rate-limit constraints, schema inconsistencies, and coverage gaps, enabling targeted refinements that protect the integrity and completeness of the final dataset. It bridges the software-engineering practice of integration testing with the social-science tradition of instrument pre-testing.

Structured digital data retrieved via application programming interfaces (JSON, XML, CSV)

Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press.

Presser, S., Couper, M. P., Lessler, J. T., Martin, E., Martin, J., Rothgeb, J. M., & Singer, E. (2004). Methods for testing and evaluating survey questions. Public Opinion Quarterly, 68(1), 109–130.

api-based-data-collection pilot-tested-survey web-scraping pilot-tested-web-scraping mobile-api-based-data-collection online-api-based-data-collection


Pilot-tested experiment log

pilot-tested lab journal
pilot-tested research logbook
validated experiment diary
pre-tested lab log

19th–20th century (lab notebooks); pilot-testing conventions codified mid-20th century

Scientific research community (laboratory practice); pilot-testing formalized by survey and experimental methodologists

A pilot-tested experiment log is a structured research instrument — a systematic journal of experimental procedures, observations, and results — that has been trialed with a small representative sample before full deployment. The pilot phase identifies ambiguous recording fields, impractical time demands, or inconsistent terminology, enabling targeted revisions that improve the log's reliability and completeness before the main data-collection phase begins.

Instrument-validation + structured data collection

Structured observational and procedural records; qualitative and quantitative entries

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

research-diary field-notes pilot-tested-diary-method longitudinal-research-diary sensor-data-collection document-collection


Pilot-tested field notes

pre-validated field notes
pilot field observation notes
trial-tested observational notes

Field notes: early 20th century; pilot-testing protocols formalised mid-20th century

Ethnographic tradition (Bronislaw Malinowski, Robert Emerson et al.); pilot testing practice generalised across social sciences

Pilot-tested field notes combine the classical ethnographic practice of systematic observational recording with a deliberate pre-validation phase. Before the main data collection begins, the researcher conducts one or more trial observation sessions to test and refine the note-taking protocol — assessing categories, focus areas, and recording conventions — so that the main fieldwork captures relevant data consistently and completely.

Observational text data (written field notes, jottings, descriptive and reflective entries)

Emerson, R. M., Fretz, R. I., & Shaw, L. L. (2011). Writing Ethnographic Fieldnotes (2nd ed.). University of Chicago Press.

Van Maanen, J. (2011). Tales of the Field: On Writing Ethnography (2nd ed.). University of Chicago Press.

field-notes non-participant-observation participant-observation pilot-tested-participant-observation ethnography pilot-tested-diary-method


Pilot-tested In-depth Interview

pretested in-depth interview
pilot in-depth interview
pilot-tested IDI
pilot interview protocol

1990s–2000s (pilot testing of qualitative instruments formalized)

Rooted in interview methodology tradition; pilot testing practice systematized by Kvale and others

A pilot-tested in-depth interview is a qualitative data collection approach in which the interview guide is administered to a small number of participants before the main study, specifically to identify ambiguous questions, refine probes, estimate session duration, and verify that the protocol elicits rich, relevant narratives. The revised guide is then used in the full data collection phase, improving data quality and interview consistency.

Verbal/narrative data from individual interviews

Kvale, S., & Brinkmann, S. (2009). InterViews: Learning the Craft of Qualitative Research Interviewing (2nd ed.). Sage.

van Teijlingen, E., & Hundley, V. (2002). The importance of pilot studies. Nursing Standard, 16(40), 33-36.

in-depth-interview semi-structured-interview pilot-tested-semi-structured-interview pilot-tested-focus-group cognitive-interviewing face-to-face-in-depth-interview


Pilot-tested mobile experience sampling

Pilot-tested Mobile Experience Sampling Method

pilot-tested mobile ESM
pretested mESM
validated mobile experience sampling
mobile ESM with pilot phase

2000s–2010s (mobile ESM); pilot-testing practice codified in 2010s

Reed Larson & Mihaly Csikszentmihalyi (ESM); mobile adaptation developed across 2000s–2010s

Pilot-tested mobile experience sampling (mESM) is a data collection approach that combines smartphone-delivered, real-time self-report prompts — the Experience Sampling Method — with a structured pilot phase to validate the instrument, signal timing, burden level, and response quality before full deployment. The pilot phase is not optional decoration; it is the core quality gate that separates a rigorously validated mESM study from an ad hoc one.

Real-time self-report data collected via smartphone prompts

Larson, R., & Csikszentmihalyi, M. (1983). The experience sampling method. New Directions for Methodology of Social and Behavioral Science, 15, 41–56.

van Berkel, N., Ferreira, D., & Kostakos, V. (2017). The experience sampling method on mobile devices. ACM Computing Surveys, 50(6), 1–40.

mobile-experience-sampling experience-sampling-method pilot-tested-survey ecological-momentary-assessment diary-method sensor-data-collection


Pilot-tested Semi-structured Interview

Pilot-tested Semi-structured Interview Protocol

pilot semi-structured interview
pre-tested qualitative interview
pilot interview protocol
trial semi-structured interview

1970s–1980s (as formalised pilot testing of qualitative instruments)

Standard qualitative methods practice; systematised in social research methodology literature

A pilot-tested semi-structured interview combines the flexibility of semi-structured interviewing — a guide of open-ended questions allowing conversational depth — with a mandatory pre-study pilot phase in which the guide is trialled on a small subset of participants or informants. The pilot reveals ambiguous questions, poor sequencing, and missing topics before the main data collection begins, substantially strengthening the validity and efficiency of the final instrument.

Qualitative data collection technique with pre-validation phase

Audio/video recordings and transcripts from semi-structured interviews

Bryman, A. (2016). Social Research Methods (5th ed.). Oxford University Press.

van Teijlingen, E., & Hundley, V. (2001). The importance of pilot studies. Social Research Update, 35, 1–4.

semi-structured-interview pilot-tested-structured-interview pilot-tested-in-depth-interview cognitive-interviewing face-to-face-semi-structured-interview online-semi-structured-interview


Pilot-tested Sensor Data Collection

sensor pilot study
sensor pre-deployment testing
instrument validation with sensors
sensor calibration pilot

1990s–2000s (formalized with proliferation of digital sensing technologies)

General research methods practice; sensor pilot testing codified through IoT and environmental monitoring literature

Pilot-tested sensor data collection is a structured data gathering approach in which sensor instruments — hardware or software-based devices that measure physical, environmental, physiological, or behavioral signals — are deployed in a small-scale trial before the main study. The pilot phase verifies sensor accuracy, communication reliability, data format consistency, and placement adequacy, allowing researchers to identify and correct technical problems before full-scale data collection begins.

Data collection procedure with pre-deployment validation phase

Quantitative continuous or discrete measurements (physical, environmental, physiological, or behavioral sensor readings)

Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). Sage Publications.

Lajnef, N., Chatti, M., Chakrabartty, S., Rhimi, M., & Bhatt, P. (2015). Health monitoring of civil infrastructures by wireless sensor networks. ISRN Civil Engineering, 2012, 1–14.

sensor-data-collection pilot-tested-field-notes mobile-sensor-data-collection longitudinal-sensor-data-collection remote-sensor-data-collection api-based-data-collection


Pilot-tested Survey

pre-tested survey
survey pre-testing
questionnaire pilot study
survey field test

Systematic practice codified by Jean M. Converse and Stanley Presser

A pilot-tested survey is a structured questionnaire that has been administered to a small, representative sample before the main data-collection phase. The purpose is to detect problems with wording, response options, skip logic, or timing, allowing the researcher to refine the instrument before it reaches the full sample. Pilot testing is not a separate research design; it is a quality-assurance step embedded within survey methodology that substantially reduces measurement error.

Questionnaire response data (quantitative and/or qualitative feedback from pilot participants)

Converse, J. M., & Presser, S. (1986). Survey Questions: Handcrafting the Standardized Questionnaire. Sage.

Presser, S., Couper, M. P., Lessler, J. T., Martin, E., Martin, J., Rothgeb, J. M., & Singer, E. (2004). Methods for testing and evaluating survey questions. Public Opinion Quarterly, 68(1), 109-130.

survey online-survey face-to-face-survey structured-interview longitudinal-survey mobile-survey


Pilot Theoretical Sampling

pilot-phase theoretical sampling
exploratory theoretical sampling
preliminary theoretical sampling

1967 (theoretical sampling origin); compound practice formalized in qualitative methodology literature

Glaser & Strauss (theoretical sampling); pilot study concept is longstanding in research methodology

Pilot theoretical sampling applies the logic of theoretical sampling — selecting participants based on emerging concepts and theory — within a deliberate pilot or preliminary phase of a study. Rather than committing immediately to a full sampling strategy, the researcher conducts a small initial round of data collection and analysis to test whether theoretical sampling is feasible, to refine the sensitizing concepts guiding participant selection, and to identify whether the field is productive before full-scale data collection begins.

Qualitative sampling strategy with pilot phase

Qualitative data (interviews, observations, documents) collected in a preliminary exploratory phase

Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine.

Thabane, L., Ma, J., Chu, R., Cheng, J., Ismaila, A., Rios, L. P., ... & Goldsmith, C. H. (2010). A tutorial on pilot studies: the what, why and how. BMC Medical Research Methodology, 10(1), 1.

theoretical-sampling purposive-sampling pilot-purposive-sampling grounded-theory adaptive-theoretical-sampling snowball-sampling


Pilot Weighted Sampling

pilot phase weighted sampling
weighted pilot sampling
pilot probability proportional sampling
pilot PPS sampling

Mid-20th century (classical weighted sampling ~1934–1977; pilot study integration formalized in survey practice ~1970s–1980s)

Pilot weighted sampling applies weighted (unequal-probability) sampling within a small-scale preliminary study to estimate key design parameters — variance components, design effects, and optimal stratum weights — before committing resources to the full survey. By using differential inclusion probabilities in the pilot, researchers obtain more precise parameter estimates for rarer or more variable subgroups while keeping total pilot cost low. The results directly inform the weighting scheme and sample-size allocation for the main survey.

Probability sampling with differential selection probabilities in a preliminary study phase

Quantitative; numerical population frame with known or estimable auxiliary size measures

Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley.

Groves, R. M., Fowler, F. J., Couper, M. P., Lepkowski, J. M., Singer, E., & Tourangeau, R. (2009). Survey Methodology (2nd ed.). Wiley.


Pinch Analysis

Pinch Analysis for Heat Recovery and Integration

heat integration
pinch point method
process integration

Pinch analysis is a systematic method for identifying the minimum energy requirements and optimal heat recovery opportunities in chemical processes. Developed by Bodo Linnhoff and John Flower in 1978, it graphically identifies the 'pinch point'—the most constrained part of the process where heating and cooling demands nearly balance. By targeting these bottlenecks, engineers can design energy-efficient heat exchanger networks and reduce operating costs dramatically.

Linnhoff, B., & Flower, J. R. (1978). Synthesis of heat exchanger networks: I. Systematic generation of energy optimal networks. AIChE Journal, 24(4), 633-642.

Smith, R. (2005). Chemical Process Design and Integration (2nd ed.). John Wiley & Sons.

Kemp, I. C. (2007). Pinch Analysis and Process Integration: A User Guide on Process Integration for the Efficient Use of Energy (2nd ed.). Butterworth-Heinemann.

peng-robinson-equation-of-state unifac cstr-model pfr-model


Piper Fatigue Scale

The Piper Fatigue Scale is a 22-item multidimensional self-report instrument that evaluates cancer-related fatigue across four conceptually distinct domains: behavioral/severity, affective/meaning, sensory, and cognitive/mood. Developed by Barbara Piper and colleagues in 1989 and revised in 1998, the PFS is grounded in a theoretical model of fatigue mechanisms and is widely used in oncology research and clinical practice to assess treatment-related and disease-related fatigue.

Patient self-report multidimensional fatigue scale

Piper, B. F., Dibble, S. L., Dodd, M. J., Weiss, M. C., Slater, G., & Paul, S. M. (1989). The revised Piper Fatigue Scale: psychometric evaluation in women with breast cancer. Oncol Nurs Forum, 16(6), 751–758.

Piper, B. F., Lindsey, A. M., & Dodd, M. J. (1987). Fatigue mechanisms in cancer patients: developing nursing theory. Oncol Nurs Forum, 14(6), 17–23.

brief-fatigue-inventory cancer-fatigue-scale chalder-fatigue-scale multidimensional-fatigue-inventory edmonton-symptom-assessment


PIPRECIA

PIPRECIA (PIvot Pairwise RElative Criteria Importance Assessment) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Stanujkić, D., Karabašević, D., Zavadskas, E. K., Turskis, Z., Maksimović, M. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Pivot pairwise sequential ratio weighting

Stanujkić, D., Karabašević, D., Zavadskas, E. K., Turskis, Z., Maksimović, M. (2017). An approach to determining customer satisfaction in mobile commerce: A new approach to the SWARA method — PIPRECIA. Transformations in Business & Economics

ahpsort aploco aras aroman artasi cobra cocoso codas


Pitch Detection Algorithm

Pitch Detection and Fundamental Frequency Estimation Algorithm

f0 detection
fundamental frequency tracking
monophonic pitch extraction

Pitch detection (or fundamental frequency estimation) is the task of automatically determining the perceived pitch of a monophonic (single-source) audio signal at each moment in time. Formalized by de Cheveigné and Kawahara (2002) through the YIN algorithm, it is foundational to music and speech processing. Pitch detection enables vocal analysis, music transcription, instrument tuning, and speech analysis. Monophonic pitch is unambiguous; polyphonic pitch detection is fundamentally harder and a distinct problem.

de Cheveigné, A., & Kawahara, H. (2002). YIN, a fundamental frequency estimator for speech and music. The Journal of the Acoustical Society of America, 111(4), 1917-1930.

McLeod, P., & Wyvill, G. (2005). A smarter way to find pitch. In Proceedings of the International Computer Music Conference.

Mauch, M., Cannam, C., Bittner, R., Fazekas, G., Salamon, J., Wade, J., & Benetos, E. (2015). Computer-aided Research on Monophonic Singing. In Frontiers in Psychology.

melody-extraction automatic-music-transcription beat-tracking key-detection-music vocal-separation


Pittsburgh Sleep Quality Index

Pittsburgh Sleep Quality Index - Clinical Sleep Assessment

Sleep quality and sleep disorder screening

The Pittsburgh Sleep Quality Index (PSQI) is a comprehensive self-report questionnaire developed by Buysse and colleagues in 1989 to assess sleep quality and sleep disturbances. The PSQI comprises 19 items aggregated into seven components that evaluate sleep duration, sleep efficiency, sleep disturbances, daytime dysfunction, and use of sleep medications. It is one of the most widely used instruments for both clinical sleep assessment and sleep research.

Multidimensional sleep quality assessment

Buysse, D. J., Reynolds, C. F., Monk, T. H., Berman, S. R., & Kupfer, D. J. (1989). The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Research, 28(2), 193-213.

Carpenter, J. S., & Andrykowski, M. A. (1998). Psychometric evaluation of the Pittsburgh Sleep Quality Index. Journal of Psychosomatic Research, 45(1), 5-13.

Backhaus, J., Junghanns, K., Broocks, A., Riemann, D., & Hohagen, F. (2002). Test-retest reliability and validity of the Pittsburgh Sleep Quality Index in primary insomnia. Journal of Psychosomatic Research, 53(3), 737-740.


PIV

PIV (Proximity Indexed Value) is a ranking multi-criteria decision-making (MCDM) method introduced by Mufazzal, S. Muzakkir, S. M. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Distance from weighted ideal minimization via linear proximity index

Mufazzal, S., Muzakkir, S. M. (2018). A new multi-criterion decision making (MCDM) method based on proximity indexed value for minimizing rank reversals. Computers & Industrial Engineering


Pixel-Based Classification

Per-Pixel Classification
Spectral Classification
Pixel-by-Pixel Classification
Piksel Tabanlı Sınıflandırma

Pixel-based image classification is a fundamental remote-sensing technique that assigns each individual pixel in a satellite or aerial image to a thematic land-cover category based solely on its spectral values across multiple bands. Systematically surveyed and formalized by Lu and Weng (2007), the approach encompasses both supervised methods—where labeled training samples guide the classifier—and unsupervised clustering approaches that discover natural spectral groupings without prior labels.

Supervised/unsupervised spectral image classification

Input: Multispectral or hyperspectral raster imagery

Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823–870.


PL-EDAS

PL-EDAS (Probabilistic Linguistic extension of EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Pang, Q. Wang, H. Xu, Z. in 2016. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Probabilistic Linguistic outranking/ranking — Probabilistic Linguistic Term Set (PLTS: {L_k|p_k})

Pang, Q., Wang, H., Xu, Z. (2016). Probabilistic linguistic term sets in multi-attribute group decision making. Information Sciences

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


PL-MABAC

PL-MABAC (Plithogenic MABAC (with BWM weighting and Rough Number uncertainty)) is a ranking multi-criteria decision-making (MCDM) method introduced by Pamučar, D. Ćirović, G. in 2015. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Plithogenic-rough border approximation area (PL+RN+MABAC)

Pamučar, D., Ćirović, G. (2015). The selection of transport and handling resources in logistics centers using MABAC. Expert Systems with Applications


PL-MARCOS

PL-MARCOS (Probabilistic Linguistic extension of MARCOS) is a ranking multi-criteria decision-making (MCDM) method introduced by Wang, J., Wei, G., Wei, C., Wei, Y. in 2023. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

(). UNCONFIRMED — PL-MARCOS specific seminal not confirmed via systematic literature search. PENDING


PL-MULTIMOORA

PL-MULTIMOORA (Probabilistic Linguistic extension of MULTIMOORA) is a ranking multi-criteria decision-making (MCDM) method introduced by Wu, X. Liao, H. Xu, Z. S. Hafezalkotob, A. Herrera, F. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Probabilistic Linguistic multi-objective ranking — PLTS: {L_k|p_k} with expectation function + Borda aggregation

Wu, X., Liao, H., Xu, Z. S., Hafezalkotob, A., Herrera, F. (2018). Probabilistic Linguistic MULTIMOORA: A Multicriteria Decision Making Method Based on the Probabilistic Linguistic Expectation Function and the Improved Borda Rule. IEEE Transactions on Fuzzy Systems

ahp anp bwm critic entropy merec swara fucom


PL-TODIM

PL-TODIM (Probabilistic Linguistic extension of TODIM) is a ranking multi-criteria decision-making (MCDM) method introduced by Liu, P., Teng, F. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Liu, P., Teng, F. (2017). Probabilistic linguistic TODIM approach for multiple attribute decision-making. Granular Computing


PL-TOPSIS

PL-TOPSIS (Probabilistic Linguistic extension of TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Lu, J., Wei, C. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Lu, J., Wei, C. (2019). TOPSIS Method for Probabilistic Linguistic MAGDM with Entropy Weight and Its Application to Supplier Selection of New Agricultural Machinery Products. Entropy


PL-VIKOR

PL-VIKOR (Probabilistic Linguistic extension of VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Li, P., Liu, J., Wei, C. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Li, P., Liu, J., Wei, C. (2021). An Improved PL-VIKOR Model for Risk Evaluation of Technological Innovation Projects with Probabilistic Linguistic Term Sets. International Journal of Fuzzy Systems


Place Attachment Scale

The Place Attachment Scale (PAS), developed by Williams & Vaske (1992) and refined by Jorgensen & Stedman (2001), measures individuals' emotional and functional bonds to destinations—the extent to which places become integral to identity and sense of belonging. Comprising dimensions of place identity (destination as self-definition), place dependence (destination optimizes one's activities), emotional bonds (love, comfort), and community belonging, the PAS distinguishes deep loyalty-drivers from transactional satisfaction. Essential for understanding repeat visitation, community tourism governance, heritage conservation, and destination branding strategies that cultivate belonging.

Williams, D. R., & Vaske, J. J. (1992). The measurement of place attachment: Valid and reliable instruments for natural environments. Society and Natural Resources, 15(3), 271-280.

Jorgensen, B. S., & Stedman, R. C. (2001). Sense of place as an attitude: Lakeshore property owners' attitudes toward their properties. Journal of Environmental Psychology, 21(3), 233-248.

Ribeiro, M. A., Fesenmaier, D. R., & Khoo-Lattimore, C. (2017). Social media as a co-creation mechanism for tourism experiences. Journal of Travel & Tourism Marketing, 34(9), 1227-1244.

Lewicka, M. (2011). Place attachment: How far have we come in the last 40 years? Journal of Environmental Psychology, 31(3), 207-230.

destination-image-scale tourist-loyalty-scale travel-motivation-scale tourist-satisfaction-scale perceived-value-scale-tourism


Placebo Test in Education Research

Placebo Test for Causal Identification in Education Research

placebo regression
falsification test
placebo check
fake-treatment test

Widely adopted in applied econometrics and education research; codified by Imbens, Wooldridge, Lee, and Lemieux

A placebo test is a falsification check used in quasi-experimental education research to validate a causal design. By applying the same estimator to a time period, group, or outcome where no real effect should exist, researchers verify that their identification strategy is not picking up spurious patterns. A statistically significant placebo estimate signals a flaw in the design, while a null result supports its credibility.

Panel data, repeated cross-sections, administrative records

Imbens, G. W., & Wooldridge, J. M. (2009). Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature, 47(1), 5-86.

Lee, D. S., & Lemieux, T. (2010). Regression Discontinuity Designs in Economics. Journal of Economic Literature, 48(2), 281-355.

difference-in-differences regression-discontinuity-design instrumental-variables event-study-design propensity-score-matching synthetic-control-method


Placebo Tests

Placebo Tests for Causal Inference Validation

Plasebo Testleri — Nedensel Çıkarım Doğrulama

Abadie, Diamond & Hainmueller (synthetic control placebos); Imbens & Lemieux (RDD validity)

Placebo tests are a family of falsification checks that probe the credibility of a causal claim by re-running the analysis on a fake treatment, a false intervention date, or an outcome that should not have been affected. The approach was popularised through the synthetic control work of Abadie, Diamond and Hainmueller (2010) and the regression-discontinuity validity checks of Imbens and Lemieux (2008).

Falsification / robustness test family for causal inference

Re-estimation of the causal effect on a placebo (false) treatment, date, unit, or outcome

Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505.

Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.

regression-discontinuity difference-in-discontinuities sensitivity-analysis-observational dag-identification causal-discovery


Plackett-Burman Design

PB design
PB screening
Plackett-Burman Tarama Deseni

The Plackett-Burman design is a two-level orthogonal screening design introduced by R.L. Plackett and J.P. Burman in 1946 that allows researchers to estimate the main effect of each factor independently using the smallest possible number of experimental runs. Run counts are always multiples of four, making it exceptionally economical for studies with many candidate factors.

main effects partially aliased with two-factor interactions

Plackett, R.L. & Burman, J.P. (1946). The Design of Optimum Multifactorial Experiments. Biometrika, 33(4), 305–325.

Montgomery, D.C. (2017). Design and Analysis of Experiments (9th ed.). Wiley.

two-level-factorial fractional-factorial response-surface-methodology central-composite-design one-way-anova


Plackett-Luce Model

Luce's Choice Axiom Model
Rank-Ordered Logit Model
Exploded Logit Model
Sıralama Tercih Modeli

The Plackett-Luce model is a probabilistic framework for analysing and predicting rank-ordered data. Introduced by Robin Plackett (1975) — building on R. Duncan Luce's earlier axiom of choice (1959) — it models the probability of any complete ranking of items as a sequential selection process, where each item's chance of being chosen at each position is proportional to its latent worth parameter. It is widely used in preference learning, recommender systems, and choice modelling.

Latent utility scores (worth parameters)

Plackett, R. L. (1975). The analysis of permutations. Journal of the Royal Statistical Society: Series C, 24(2), 193–202.


Plagiarism in Academic Research

Plagiarism Detection and Prevention in Academic Research

Text Plagiarism
Idea Plagiarism
Self-Plagiarism

U.S. Office of Research Integrity (ORI) and institutional policies

Plagiarism—the use of others' words, ideas, or methods without attribution—is formally classified as research misconduct by the U.S. Office of Research Integrity and most institutions worldwide. It ranges from verbatim copying of text to paraphrasing without citation to presenting others' ideas as one's own. Unlike accidental omission of a citation (which is corrected via erratum), plagiarism implies intent or gross negligence and triggers investigation, potential retraction, and career consequences. Plagiarism detection tools (e.g., Turnitin, iThenticate) and manual checking by journals now routinely screen manuscripts.

U.S. Office of Research Integrity (2023). Definition of Research Misconduct. Federal Policy on Research Misconduct (42 CFR Part 93). ORI.

Committee on Publication Ethics (2023). Flowcharts and Advice on Plagiarism. COPE.

Weber-Wulff, D. C. (2012). Plagiarism Detectors Are Not Reliable. The Guardian. Online opinion.

duplicate-publication icmje-authorship-criteria cope-guidelines retraction-process


Plant Propagation Success Rate

Quantitative Assessment of Vegetative and Generative Propagation Efficiency

propagation efficiency
rooting success assessment
nursery propagation management

Propagation methods and nursery management

Plant propagation success rate quantifies the efficiency of vegetative (cuttings, layers, division) and generative (seed) propagation methods by measuring germination, rooting, and survival percentages. This method combines environmental monitoring, growth observations, and statistical analysis to optimize propagation protocols and predict nursery output. It is fundamental to nursery operations and plant breeding programs worldwide.

propagation efficiency measurement pipeline

Hartmann, H. T., Kester, D. E., Davies, F. T., & Geneve, R. L. (2011). Plant Propagation: Principles and Practices (8th ed.). Prentice Hall.

Briggs, J. L., & Pliley, A. L. (1997). Laboratory evaluation of seed dormancy-breaking and germination stimulants. HortScience, 32(2), 206–210.

grafting-success-evaluation phenological-stage-monitoring greenhouse-climate-control hydroponic-nutrient-solution


Plant Disease SEIR Model

Susceptible-Exposed-Infectious-Removed Model for Plant Disease Epidemiology

plant SEIR epidemic model
botanical SEIR model
plant disease compartmental model
SEIR phytopathological model

Plant disease epidemiology / Quantitative phytopathology

1963 (Van der Plank); SEIR plant adaptation developed through 1970s–1990s

Multiple contributors (Van der Plank foundational; Kermack-McKendrick SIR adapted to plant pathology)

The Plant Disease SEIR Model is a deterministic compartmental modelling framework adapted from human epidemiology to describe how a pathogen spreads through a host plant population. Rooted in the foundational work of J. E. Van der Plank and the Kermack-McKendrick tradition, it partitions all plants into four states — Susceptible, Exposed (latently infected), Infectious, and Removed — and tracks their transitions over time using a system of ordinary differential equations. It is a core tool in quantitative plant pathology and crop protection research.

Deterministic compartmental epidemic model

Time-series disease incidence or severity data, host population counts, infection rate parameters

Van der Plank, J. E. (1963). Plant Diseases: Epidemics and Control. Academic Press, New York.

Madden, L. V., Hughes, G., & van den Bosch, F. (2007). The Study of Plant Disease Epidemics. American Phytopathological Society Press, St. Paul, MN.

sir-model logistic-growth-model gompertz-disease-progress basic-reproduction-number agent-based-simulation spatial-epidemic-model


Plaque Reduction Neutralization Test

PRNT
plaque neutralization assay
neutralization plaque assay
serum neutralization plaque test

The Plaque Reduction Neutralization Test (PRNT) is a quantitative cell-based serological assay used in veterinary and human virology to measure the ability of antibodies in a serum sample to neutralize a live virus. By counting visible plaques — areas of cell destruction on a monolayer — the method determines the serum titer at which viral infectivity is reduced by 50% or 90%, making it the gold-standard technique for detecting and quantifying neutralizing antibodies against many RNA and DNA viruses.

Quantitative cell-based serological assay

Cell monolayer plaque counts, serum dilution titers

Dulbecco, R., & Vogt, M. (1954). Plaque formation and isolation of pure lines with poliomyelitis viruses. Journal of Experimental Medicine, 99(2), 167–182.

Roehrig, J. T., Hombach, J., & Barrett, A. D. T. (2008). Guidelines for plaque-reduction neutralization testing of human antibodies to dengue viruses. Viral Immunology, 21(2), 123–132.


Plasmonic Resonance

surface plasmon resonance
localized surface plasmon resonance
LSPR
SPR

Plasmonic resonance refers to the collective oscillation of free electrons in metallic nanostructures that interact strongly with light, resulting in dramatic enhancements of electric fields, absorption, and scattering. First discovered by Kretschmann and Raether in 1968, plasmonic resonance is now central to nanophotonics, enabling applications from biosensing to photothermal therapy and advanced optical devices with subwavelength control.

Kretschmann, E., & Raether, H. (1968). Radiative decay of non radiative surface plasmons excited by light. Zeitschrift für Naturforschung A, 23(12), 2135-2136.

Maier, S. A. (2007). Plasmonics: Fundamentals and Applications. Springer.

Halas, N. J., Lal, S., Chang, W. S., Link, S., & Nordlander, P. (2011). Plasmons in strongly coupled metallic nanostructures. Chemical Reviews, 111(6), 3913-3961.


Plastic Hinge Analysis

Plastic Hinge Analysis in Structural Engineering

plastic hinge method
plastic collapse analysis
limit state plastic analysis
yield hinge analysis

Multiple contributors (Kazinczy, Kist, Baker, Horne, Neal)

Plastic hinge analysis is a structural engineering method that determines the load-carrying capacity of a structure by tracking the sequential formation of plastic hinges — localised zones where a cross-section has fully yielded — until a kinematic collapse mechanism is formed. Rooted in plastic theory, it provides a more economical and realistic estimate of ultimate structural capacity than purely elastic approaches, and is widely used in the design and assessment of steel frames, reinforced concrete beams, and other ductile structural systems.

Member cross-section properties, material yield stress, applied loads

Chen, W. F., & Sohal, A. S. (1995). Plastic Design and Second-Order Analysis of Steel Frames. Springer.

Neal, B. G. (1977). The Plastic Methods of Structural Analysis (3rd ed.). Chapman and Hall.

pushover-analysis limit-analysis nonlinear-static-analysis moment-redistribution yield-line-theory finite-element-analysis


PLS-SEM is a variance-based approach to structural equation modeling developed by Herman Wold (1985) that estimates latent variable models by maximizing the variance explained in dependent variables. Unlike covariance-based SEM, PLS-SEM is particularly useful for exploratory research, small to medium samples, complex models with many constructs, and non-normal data.

Component-based structural equation model

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd ed.). Sage Publications.

Wold, H. (1985). Partial least squares. In S. Kotz & N. L. Johnson (Eds.), Encyclopedia of Statistical Sciences (Vol. 6, pp. 581-591). Wiley.

Chin, W. W. (2010). How to write up and report PLS analyses. In V. E. Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of Partial Least Squares: Concepts, Methods and Applications (pp. 655-690). Springer.


Pluralistic Walkthrough

The Pluralistic Walkthrough is a usability inspection method that brings together users, developers, and usability specialists to walk through an interface and voice their reactions and concerns. Developed by Randolph Bias in 1992, this method combines elements of cognitive walkthroughs with user involvement, creating a collaborative evaluation setting that captures diverse perspectives. By including actual users in the evaluation session, the method bridges the gap between expert judgment and real-world user experience, uncovering unexpected insights and building stakeholder consensus around design improvements.

User-centered walkthrough with mixed stakeholders

Bias, R. G. (1994). The pluralistic walkthrough: Coordinating technology and pedagogy in software development. In J. Nielsen & R. L. Mack (Eds.), Usability Inspection Methods (pp. 63–76). John Wiley & Sons.

Bias, R. G. (1992). The pluralistic walkthrough: Coordinating technology and pedagogy. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 407–410).

cognitive-walkthrough heuristic-evaluation think-aloud-protocol contextual-inquiry


POEM

The POEM is a brief, patient-administered severity measure for atopic dermatitis that focuses on frequency of symptoms experienced over the past week. Developed by Charman, Venn, and Williams in 2004, it emphasizes the patient's lived experience rather than clinician observation, making it practical for routine clinical practice and remote monitoring. POEM is increasingly used alongside objective measures in clinical trials and outpatient care.

Charman CR, Venn AJ, Williams HC. The Patient-Oriented Eczema Measure: development and initial validation of a new tool for measuring atopic eczema severity from the patients' perspective. Arch Dermatol. 2004;140(12):1513-1519.

Charman CR, Venn AJ, Williams HC. Measurement of body surface area involvement in atopic eczema: an updated comparison of estimation methods. Br J Dermatol. 2005;152(2):288-294.


Point-Biserial Correlation

rpb
r_pb
point biserial r
item-total correlation
dichotomous-continuous correlation

The point-biserial correlation coefficient (r_pb) measures the strength and direction of the linear association between one naturally dichotomous variable (coded 0/1) and one continuous variable. It is a special case of the Pearson product-moment correlation formally derived by Tate (1954) in the Annals of Mathematical Statistics and is the standard index used in psychometric item analysis, validity studies, and any research context where a binary grouping variable is related to a continuous outcome.

Algebraically equivalent to Pearson r when X is coded 0/1

Tate, R. F. (1954). Correlation between a discrete and a continuous variable. Point-biserial correlation. Annals of Mathematical Statistics, 25(3), 603–607.

Tate, R. F. (1955). The theory of correlation between two continuous variables when one is dichotomized. Biometrika, 42(1–2), 205–216.

Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). McGraw-Hill.

pearson-correlation biserial-correlation spearman-correlation phi-coefficient independent-t-test item-response-theory


Poisson Regression

Poisson and Negative Binomial Regression

count regression
log-linear count model
negative binomial regression
Poisson / Negatif Binom Regresyon

Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)

Poisson regression is a generalized linear model for count outcomes — events tallied as non-negative integers such as hospital admissions, accidents, or article counts. It models the log of the expected count as a linear function of the predictors, and is developed in the standard count-data treatment of Cameron and Trivedi (1998); when the counts are over-dispersed, the closely related negative binomial model (Hilbe, 2011) is preferred.

Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.

Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press.

ols-regression logistic-regression quantile-regression panel-fixed-effects


Polar Codes

Polar Codes with Successive Cancellation Decoding

Polar codes, introduced by Erdal Arikan in 2009, are the first constructive family of codes proven to achieve the Shannon capacity of symmetric binary-input memoryless channels. They use recursive construction and successive cancellation decoding, a simple greedy algorithm with theoretical guarantees. Polar codes were adopted in 5G NR for control channel coding and are studied for future 6G systems. Unlike turbo and LDPC codes (which are empirical), polar codes provide rigorous theoretical foundations.

Arikan, E. (2009). Channel polarization: A method for constructing capacity-achieving codes for symmetric binary-input memoryless channels. IEEE Transactions on Information Theory, 55(7), 3051-3073.

Sasoglu, E., Telatar, I., & Yildirim, E. (2011). Polarization for arbitrary discrete memoryless channels. In Proceedings of the IEEE Information Theory Workshop (ITW), 144-148.

ldpc-codes turbo-code shannon-capacity ofdm mimo


Policy Evaluation Causal Impact Analysis via Bayesian Structural Time-Series

policy causal impact
BSTS policy evaluation
Bayesian policy impact assessment
CIA policy evaluation

Brodersen, Gallusser, Koehler, Remy & Scott (2015); adapted for policy evaluation contexts

Policy Evaluation Causal Impact Analysis applies the Bayesian structural time-series (BSTS) framework of Brodersen et al. (2015) to estimate the causal effect of a policy intervention on aggregate outcomes. By constructing a synthetic counterfactual from pre-policy data and control covariates, it asks: what would have happened had the policy not been enacted? The difference between observed and predicted post-policy outcomes is the estimated policy effect.

Aggregate time-series outcomes with pre- and post-policy periods

Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274.

Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic control methods for comparative case studies: Estimating the effect of California's tobacco control program. Journal of the American Statistical Association, 105(490), 493-505.


Policy Evaluation Coarsened Exact Matching

Coarsened Exact Matching for Policy Evaluation

CEM
Coarsened Exact Matching
CEM policy evaluation
coarsening-based matching

Coarsened Exact Matching (CEM) is a quasi-experimental causal-inference technique that creates balanced treatment and control groups from observational data by temporarily coarsening covariates into bins, exactly matching units within those bins, and then pruning unmatched observations before estimating policy effects. Introduced by Iacus, King, and Porro, CEM belongs to the monotonic imbalance bounding family of matching methods and is especially popular in policy evaluation.

Cross-sectional or panel observational data with treatment and control units

Iacus, S. M., King, G., & Porro, G. (2012). Causal inference without balance checking: Coarsened exact matching. Political Analysis, 20(1), 1-24.

Iacus, S. M., King, G., & Porro, G. (2011). Multivariate matching methods that are monotonic imbalance bounding. Journal of the American Statistical Association, 106(493), 345-361.

propensity-score-matching difference-in-differences inverse-probability-weighting nearest-neighbor-matching entropy-balancing synthetic-control-method


Policy Evaluation Counterfactual Impact Evaluation

Counterfactual Impact Evaluation for Policy Assessment

CIE
policy CIE
counterfactual policy evaluation
impact evaluation

1974 (Rubin potential outcomes); 2010s (EU policy CIE formalisation)

Rubin (potential outcomes framework); European Commission DG Research formalised policy CIE guidelines

Counterfactual Impact Evaluation (CIE) for policy assessment estimates the causal effect of a public policy or programme by comparing observed outcomes of participants against a rigorously constructed counterfactual — what would have happened had the policy not existed. Rooted in the Rubin potential-outcomes framework, CIE is the standard methodology endorsed by the European Commission for evaluating research, innovation, and structural funding programmes.

Panel data, administrative records, survey data

Imbens, G. W., & Rubin, D. B. (2015). Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge University Press.

Cerulli, G. (2014). Econometric Evaluation of Socioeconomic Programs: Theory and Applications. Springer.

counterfactual-impact-evaluation difference-in-differences propensity-score-matching synthetic-control-method regression-discontinuity-design instrumental-variables


Difference-in-Differences for Policy Evaluation

policy DiD
program evaluation DiD
policy impact DiD
DiD policy assessment

Ashenfelter (1978); Heckman, LaLonde & Smith (1999); Imbens & Wooldridge (2009)

Policy Evaluation DiD applies the difference-in-differences estimator specifically to assess the causal impact of government programs, regulations, or policy reforms. It compares outcome changes in a group exposed to the policy against a comparable untreated group, before and after the policy took effect, isolating the net policy effect from pre-existing trends and time-common shocks.

Panel data or repeated cross-sections with a treated and comparison group

Imbens, G. W., & Wooldridge, J. M. (2009). Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature, 47(1), 5-86.

Heckman, J. J., LaLonde, R. J., & Smith, J. A. (1999). The Economics and Econometrics of Active Labor Market Programs. In O. Ashenfelter & D. Card (Eds.), Handbook of Labor Economics, Vol. 3A (pp. 1865-2097). Elsevier.

difference-in-differences dynamic-difference-in-differences staggered-difference-in-differences propensity-score-matching synthetic-control-method regression-discontinuity-design


Doubly Robust Estimation for Policy Evaluation

DR estimation for policy
augmented IPW for policy evaluation
AIPW policy evaluation
doubly robust policy analysis

Robins, Rotnitzky & Zhao (1994); Bang & Robins (2005)

Policy Evaluation Doubly Robust Estimation applies the doubly robust (DR) estimator to assess the causal effect of a public policy or programme. It combines a model of treatment assignment (propensity score) with a model of the outcome, and requires only one of the two models to be correctly specified to produce a consistent estimate of the average treatment effect, making it a resilient tool for programme evaluation.

Observational cross-sectional or panel data with binary treatment and continuous or binary outcome

Bang, H., & Robins, J. M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics, 61(4), 962-973.

Robins, J. M., Rotnitzky, A., & Zhao, L. P. (1994). Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association, 89(427), 846-866.


Policy Evaluation Entropy Balancing

Entropy Balancing for Causal Policy Evaluation

Entropy Balancing
EB Weighting
Maximum-Entropy Reweighting
Hainmueller Balancing

Entropy balancing is a maximum-entropy reweighting method that assigns weights to control-group units so that their weighted covariate moments exactly match those of the treated group. Introduced by Hainmueller (2012), it provides exact balance on specified moments without iterative propensity-score trimming, making it a powerful preprocessing tool for causal policy evaluation in observational studies.

Observational cross-sectional or panel data with binary treatment

Hainmueller, J. (2012). Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies. Political Analysis, 20(1), 25-46.

Zhao, Q., & Cooney, D. (2017). Entropy Balancing is Doubly Robust. Journal of Causal Inference, 5(1).

propensity-score-matching inverse-probability-weighting difference-in-differences covariate-balancing-propensity-score augmented-inverse-probability-weighting synthetic-control-method


Policy Evaluation Event Study Design

Policy Evaluation Event Study Design for Causal Inference

event study
event-study DiD
dynamic DiD
PEESD

Andrews (1993), MacKinlay (1997); formalized for policy evaluation by Freyaldenhoven, Hansen & Shapiro (2019) and Callaway & Sant'Anna (2021)

A policy evaluation event study design is a quasi-experimental approach that estimates causal effects of a policy by plotting treatment-period-by-period coefficients around a common event time. It extends difference-in-differences to visualize both pre-treatment parallel trends and the dynamic post-treatment evolution of the policy effect, and has become the standard credibility check in applied policy research.

Panel data or repeated cross-sections with time-stamped policy adoption

Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.

Freyaldenhoven, S., Hansen, C., & Shapiro, J. M. (2019). Pre-event trends in the panel event-study design. American Economic Review, 109(9), 3307-3338.

difference-in-differences synthetic-control panel-fixed-effects staggered-difference-in-differences interrupted-time-series regression-discontinuity


Policy Evaluation Fuzzy Regression Discontinuity

Fuzzy Regression Discontinuity Design for Policy Evaluation

Fuzzy RDD
Fuzzy RD
Fuzzy Regression Discontinuity
Imperfect Compliance RDD

Fuzzy Regression Discontinuity Design (Fuzzy RDD) estimates the causal effect of a policy when eligibility is determined by crossing a threshold on a continuous score, but actual take-up or compliance is imperfect. Developed formally by Hahn, Todd, and Van der Klaauw (2001), it uses the threshold as an instrumental variable to recover a Local Average Treatment Effect (LATE) among compliers near the cutoff.

Observational cross-sectional or panel data with a continuous running variable and imperfect compliance

Hahn, J., Todd, P., & Van der Klaauw, W. (2001). Identification and estimation of treatment effects with a regression-discontinuity design. Review of Economic Studies, 68(1), 201-209.

Imbens, G. W., & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice. Journal of Econometrics, 142(2), 615-635.


Policy Evaluation Instrumental Variables

Instrumental Variables Estimation for Policy Evaluation

IV policy evaluation
2SLS policy analysis
natural-experiment IV
policy IV estimation

1996 (modern policy-evaluation framing); IV roots 1920s

Angrist, Imbens & Rubin (canonical 1996 JASA framework); foundational IV roots in Wright (1928) and Theil (1953)

Instrumental Variables (IV) estimation for policy evaluation is a quasi-experimental technique that uses an exogenous instrument — a variable that shifts exposure to a policy but is otherwise unrelated to the outcome — to recover the causal effect of a program or intervention from non-experimental data. Popularised in policy research by Angrist, Imbens, and Rubin (1996), it identifies the Local Average Treatment Effect (LATE) among units whose treatment status is changed by the instrument.

Quasi-experimental causal inference / IV regression

Observational or natural-experiment data with a valid instrument; cross-sectional or panel

Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996). Identification of Causal Effects Using Instrumental Variables. Journal of the American Statistical Association, 91(434), 444-455.

Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press.

instrumental-variables two-stage-least-squares difference-in-differences regression-discontinuity-design local-average-treatment-effect propensity-score-matching


Policy Evaluation Interrupted Time Series

Interrupted Time Series Analysis for Policy Evaluation

ITS for policy evaluation
policy ITS
segmented regression for policy
policy impact ITS

1975 (intervention analysis); 2000s–2010s (policy evaluation framing)

Box & Tiao (1975); popularised for policy by Shadish, Cook & Campbell (2002) and Bernal et al. (2017)

Interrupted Time Series (ITS) for policy evaluation uses routinely collected aggregate time-series data to estimate the causal impact of a policy change. A segmented regression model splits the series at a known intervention date, estimating both an immediate level shift and a change in trend attributable to the policy — without requiring a randomised control group.

Longitudinal aggregate or individual-level time-series data with a known policy change date

Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355.

Box, G. E. P., & Tiao, G. C. (1975). Intervention Analysis with Applications to Economic and Environmental Problems. Journal of the American Statistical Association, 70(349), 70-79.


Policy Evaluation Inverse Probability Weighting

Inverse Probability Weighting for Policy Evaluation

inverse probability of treatment weighting

1952 (IPW origin); 2000s (policy evaluation application)

Horvitz & Thompson (1952); extended to causal policy settings by Robins, Hernan & Brumback (2000) and Imbens & Wooldridge (2009)

Policy evaluation inverse probability weighting (IPW) uses estimated propensity scores to reweight observed units so that the weighted sample mimics a randomised experiment. Each unit is weighted by the inverse of its probability of receiving the policy, creating a pseudo-population in which treatment assignment is independent of observed covariates and the average treatment effect (ATE) can be read off directly.

Reweighting estimator for causal policy analysis

Cross-sectional or panel; binary treatment; continuous or binary outcome

Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.


Marginal Structural Model for Policy Evaluation

MSM for policy evaluation
policy MSM
causal MSM
structural policy weighting model

James M. Robins, Miguel A. Hernan, Babette Brumback

A Policy Evaluation Marginal Structural Model (MSM) is a causal inference framework that estimates the population-average effect of a policy by using inverse probability weighting to create a pseudo-population in which treatment assignment is independent of measured confounders, enabling unbiased comparison of potential outcomes under different policy scenarios from observational data.

Observational longitudinal or repeated cross-sectional data with time-varying treatments

Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550–560.

Hernan, M. A., & Robins, J. M. (2020). Causal Inference: What If. Chapman & Hall/CRC.

marginal-structural-model inverse-probability-weighting doubly-robust-estimation propensity-score-weighting difference-in-differences counterfactual-impact-evaluation


Policy Evaluation Matching Estimator

matching estimator
program evaluation matching
treatment effect matching
Abadie-Imbens estimator

Heckman, Ichimura & Todd; Abadie & Imbens

The policy evaluation matching estimator estimates the causal effect of a program or policy on treated units by pairing each participant with one or more non-participants who share similar pre-treatment characteristics. Developed rigorously by Heckman, Ichimura & Todd (1998) and Abadie & Imbens (2006), it avoids parametric outcome models and is the standard non-parametric tool for program and policy evaluation.

Cross-sectional or panel; continuous, binary, or count outcome

Abadie, A., & Imbens, G. W. (2006). Large sample properties of matching estimators for average treatment effects. Econometrica, 74(1), 235-267.

Heckman, J. J., Ichimura, H., & Todd, P. (1998). Matching as an econometric evaluation estimator. Review of Economic Studies, 65(2), 261-294.

propensity-score-matching difference-in-differences instrumental-variables inverse-probability-weighting regression-discontinuity coarsened-exact-matching


Policy Evaluation Panel Event Study

Policy Evaluation Panel Event Study Design

panel event study
event-study DiD
staggered event study
difference-in-differences event study

Callaway & Sant'Anna (2021); Borusyak, Jaravel & Spiess (2024); Sun & Abraham (2021)

A panel event study is a quasi-experimental design that traces how an outcome evolves in periods before and after a policy event, using unit and time fixed effects to identify the causal effect. Widely used in economics and policy research, it tests for anticipation effects, verifies parallel pre-trends, and estimates dynamic treatment effects across post-treatment horizons — making it the standard toolkit for rigorous policy evaluation with observational panel data.

Causal inference / quasi-experimental panel design

Panel data with staggered or simultaneous treatment timing

Borusyak, K., Jaravel, X., & Spiess, J. (2024). Revisiting event study designs: Robust and efficient estimation. Review of Economic Studies, 91(6), 3253-3285.

difference-in-differences panel-fixed-effects synthetic-control propensity-score-matching instrumental-variables regression-discontinuity


Policy Evaluation Placebo Test

placebo test
falsification test
fake treatment test
placebo regression

Bertrand, Duflo & Mullainathan (2004 canonical formalization); Imbens & Wooldridge (2009 textbook treatment)

A policy evaluation placebo test is a falsification check used in quasi-experimental research to validate a causal identification strategy. The researcher applies the same estimation method to a pseudo-treatment — a time period, group, or outcome where the real policy could not have had an effect — and checks that no spurious effect is detected. A null placebo result builds confidence that the main estimate reflects a genuine causal impact rather than bias or confounding.

Panel, repeated cross-sections, or cross-sectional observational data

Imbens, G. W., & Wooldridge, J. M. (2009). Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature, 47(1), 5-86.

Bertrand, M., Duflo, E., & Mullainathan, S. (2004). How Much Should We Trust Differences-in-Differences Estimates? Quarterly Journal of Economics, 119(1), 249-275.

difference-in-differences regression-discontinuity-design instrumental-variables synthetic-control-method permutation-test event-study-design


Policy Evaluation Propensity Score Matching

Propensity Score Matching for Policy Evaluation

propensity matching for program evaluation

Rosenbaum & Rubin (1983); Heckman, Ichimura & Todd (1997) for program/policy evaluation application

Policy evaluation propensity score matching applies the propensity score framework — originally developed by Rosenbaum and Rubin (1983) and operationalized for program evaluation by Heckman et al. (1997) — to estimate the causal effect of a policy intervention. It constructs a credible comparison group from non-participants by matching them to participants on their estimated probability of receiving the treatment, enabling unbiased effect estimation without random assignment.

Observational cross-sectional or panel data with binary treatment indicator

Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.

Heckman, J. J., Ichimura, H., & Todd, P. E. (1997). Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme. Review of Economic Studies, 64(4), 605-654.

propensity-score-matching propensity-score-weighting difference-in-differences inverse-probability-weighting coarsened-exact-matching doubly-robust-estimation


Policy Evaluation Propensity Score Weighting

Propensity Score Weighting for Policy Evaluation

inverse probability weighting for policy

Rosenbaum & Rubin (1983); extended to policy evaluation by Hirano, Imbens & Ridder (2003)

Policy evaluation propensity score weighting applies inverse-probability weighting to observational data to estimate the causal effect of a policy program. By reweighting participants and non-participants so they resemble a target population, it removes selection bias from voluntary or administratively allocated program assignment without requiring randomization.

Observational cross-sectional or panel data with a binary or multi-valued treatment

Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score. Econometrica, 71(4), 1161-1189.

Caliendo, M., & Kopeinig, S. (2008). Some Practical Guidance for the Implementation of Propensity Score Matching. Journal of Economic Surveys, 22(1), 31-72.


Regression Discontinuity Design for Policy Evaluation

regression discontinuity policy analysis

1960; policy evaluation applications widespread from 2000s

Thistlethwaite & Campbell (1960); popularized in policy evaluation by Lee & Lemieux (2010)

Policy Evaluation Regression Discontinuity Design (Policy RDD) exploits a known eligibility threshold in a policy rule to estimate the causal effect of that policy on outcomes. Units just below the cutoff serve as a credible comparison group for units just above it, making RDD one of the most transparent quasi-experimental strategies for assessing what a policy actually achieves.

Observational data with a continuous assignment variable and a known policy threshold

Lee, D. S., & Lemieux, T. (2010). Regression Discontinuity Designs in Economics. Journal of Economic Literature, 48(2), 281-355.

Imbens, G. W., & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice. Journal of Econometrics, 142(2), 615-635.


Policy Evaluation Synthetic Control Method

Synthetic Control Method for Policy Evaluation

Synthetic Control Method
SCM
Synthetic Control
Abadie-Diamond-Hainmueller method

Alberto Abadie & Javier Gardeazabal; extended by Abadie, Diamond & Hainmueller

The Synthetic Control Method (SCM) is a causal inference technique for evaluating the effect of a policy or intervention on a single treated unit — such as a region, country, or firm — by constructing a weighted combination of untreated comparison units that closely mirrors the treated unit before the intervention. Introduced by Abadie and Gardeazabal (2003) and formalized by Abadie, Diamond, and Hainmueller (2010), it provides a data-driven, transparent counterfactual for comparative case studies.

Causal inference / comparative case study

Aggregate panel data (few treated units, many pre-treatment periods)

Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505.

Abadie, A., & Gardeazabal, J. (2003). The Economic Costs of Conflict: A Case Study of the Basque Country. American Economic Review, 93(1), 113-132.

difference-in-differences propensity-score-matching instrumental-variables interrupted-time-series regression-discontinuity-design panel-fixed-effects


Policy Gradient

Policy Gradient Methods (REINFORCE / Actor-Critic)

REINFORCE
actor-critic
policy optimization
politika gradyanı

Ronald Williams (REINFORCE); Sutton et al. (policy gradient theorem)

Policy gradient methods are reinforcement-learning algorithms that optimize a parameterized policy directly by gradient ascent on the expected return, rather than learning action-values and acting greedily. Founded on Ronald Williams' 1992 REINFORCE algorithm and the policy gradient theorem of Sutton and colleagues (2000), they naturally handle stochastic and continuous action spaces and underpin modern actor-critic and deep-RL algorithms.

A parameterized policy directly by gradient ascent on expected return

Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8(3–4), 229–256.

Sutton, R. S., McAllester, D., Singh, S., & Mansour, Y. (2000). Policy gradient methods for reinforcement learning with function approximation. Advances in Neural Information Processing Systems, 12, 1057–1063.

q-learning deep-reinforcement-learning stochastic-gradient-descent convex-optimization


Policy Scenario Agent-Based Modeling

Policy Scenario Agent-Based Modeling — Comparative policy evaluation using agent-based simulation

Policy ABM
Policy Scenario ABM
Scenario-Based ABM
PS-ABM

Axelrod, R. and colleagues in computational social science

Policy Scenario Agent-Based Modeling (PS-ABM) is a simulation method that uses agent-based models to evaluate and compare multiple policy scenarios. Heterogeneous autonomous agents interact under different policy regimes, and emergent system-level outcomes are compared across scenarios to inform evidence-based policy decisions. It is widely used in public health, urban planning, economics, and social policy research.

Agent behavioral rules, environmental parameters, policy levers

Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press.

Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(S3), 7280-7287.

agent-based-modeling scenario-analysis policy-scenario-analysis system-dynamics policy-scenario-system-dynamics monte-carlo-simulation


Policy Scenario Analysis

Policy Scenario Analysis — Structured evaluation of policy interventions across plausible future states

PSA
Policy Scenarios
Policy Impact Scenario Analysis
Counterfactual Policy Analysis

Kahn, H. & Wiener, A. J. (seminal); adapted for policy by RAND Corporation and OECD

Policy Scenario Analysis is a structured method for evaluating how different policy interventions perform across a range of plausible future states. By pairing specific policy levers with alternative scenarios, analysts can assess robustness, trade-offs, and unintended consequences of policy choices before implementation — making it a cornerstone of evidence-based policy design in fields from climate to public health.

Qualitative-quantitative hybrid scenario method

Policy parameters, model outputs, expert elicitation, historical baselines

Swart, R., Raskin, P., Robinson, J. (2004). The problem of the future: sustainability science and scenario analysis. Global Environmental Change, 14(2), 137–146.

Bishop, P., Hines, A., Collins, T. (2007). The current state of scenario development: an overview of techniques. Foresight, 9(1), 5–25.

scenario-analysis monte-carlo-simulation system-dynamics sensitivity-analysis stochastic-scenario-analysis policy-scenario-system-dynamics


Policy Scenario Cellular Automata

Policy Scenario Cellular Automata — Scenario-driven grid-based simulation for policy impact analysis

PSCA
CA Policy Scenario Modeling
Policy-driven CA Simulation
Scenario-based Cellular Automata

Tobler, W. (CA foundations); Clarke, K.C. et al. (policy/urban CA scenarios)

Policy Scenario Cellular Automata (PSCA) combines cellular automata simulation with structured scenario analysis to evaluate how alternative policy decisions reshape spatially distributed systems over time. Each scenario encodes a different set of transition rules or constraints, and the model iterates to reveal divergent spatial outcomes — enabling direct, visual comparison of policy consequences at the local and system level.

Spatial raster grids, categorical land-use or state data, policy parameter sets

Clarke, K. C., Hoppen, S., & Gaydos, L. (1997). A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B: Planning and Design, 24(2), 247–261.

Batty, M. (2005). Cities and Complexity: Understanding Cities with Cellular Automata, Agent-Based Models, and Fractals. MIT Press. ISBN 978-0262025836.

cellular-automata policy-scenario-analysis agent-based-modeling system-dynamics discrete-event-simulation scenario-analysis


Policy Scenario Discrete-Event Simulation — Evaluating policy alternatives through scenario-driven DES models

Policy DES
Scenario-based DES
Policy simulation DES
DES policy analysis

Tocher, K. D. and Gordon, G. (early DES); policy scenario extension emerged through operations research and health policy modeling communities

Policy Scenario Discrete-Event Simulation combines the event-by-event fidelity of Discrete-Event Simulation with systematic policy scenario analysis to evaluate how different interventions, regulations, or resource allocations change system performance. By running multiple well-defined policy scenarios through the same DES model, analysts can compare outcomes — throughput, waiting times, costs — across alternatives before real-world implementation.

Event logs, process times, resource capacities, policy parameters

Law, A. M. (2015). Simulation Modeling and Analysis (5th ed.). McGraw-Hill Education.

Robinson, S. (2014). Simulation: The Practice of Model Development and Use (2nd ed.). Palgrave Macmillan.

discrete-event-simulation scenario-analysis policy-scenario-analysis monte-carlo-simulation system-dynamics policy-scenario-system-dynamics


Policy Scenario Dynamic Programming

Policy Scenario Dynamic Programming — Sequential policy evaluation via Bellman optimality across discrete future states

PSDP
Policy-Scenario DP
Scenario-Based Dynamic Programming
Policy DP

Policy Scenario Dynamic Programming (PSDP) applies Bellman's recursive optimization framework to a set of pre-specified policy scenarios, enabling decision-makers to compare staged, sequential decisions under distinct future conditions. It decomposes a complex, multi-period policy choice into tractable sub-problems solved backward through time, yielding optimal action sequences for each scenario and a structured basis for scenario comparison.

Sequential optimization with scenario branching

Discrete state-action spaces, policy parameters, transition probabilities

Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ.

Puterman, M. L. (1994). Markov Decision Processes: Discrete Stochastic Dynamic Programming. John Wiley & Sons, New York.

dynamic-programming scenario-analysis markov-model stochastic-dynamic-programming policy-scenario-analysis multi-objective-dynamic-programming


Policy Scenario Genetic Algorithm

Policy Scenario Genetic Algorithm — Evolutionary Search over Discrete Policy Alternative Spaces

PSGA
Policy-GA
Policy Optimization Genetic Algorithm
Evolutionary Policy Scenario Search

1975 (GA); 2000s (policy scenario application)

Holland, J. H. (GA foundation); Lempert, Popper & Bankes (policy scenario search)

The Policy Scenario Genetic Algorithm applies evolutionary search to systematically explore large, combinatorial policy alternative spaces under multiple future scenarios. Rather than exhaustively enumerating options, it breeds successive generations of candidate policies, retaining those that perform well across scenario conditions, yielding robust, high-performing policy recommendations.

Evolutionary metaheuristic for policy scenario exploration

Discrete or mixed policy parameter spaces, scenario-conditioned objective functions

Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI.

Lempert, R. J., Popper, S. W., & Bankes, S. C. (2003). Shaping the Next One Hundred Years: New Methods for Quantitative, Long-Term Policy Analysis. RAND Corporation, Santa Monica, CA.


Policy Scenario Goal Programming

Policy Scenario Goal Programming — Goal programming applied within policy scenario frameworks

PSGP
Policy GP
Scenario-based Goal Programming
Multi-scenario Goal Programming

1961 (goal programming); policy scenario application 1980s–present

Charnes, A., Cooper, W. W. (goal programming); policy scenario integration developed in OR/policy literature

Policy Scenario Goal Programming (PSGP) integrates goal programming optimization with policy scenario analysis to evaluate how well competing policy objectives can be achieved under distinct future conditions. Decision-makers define multiple goals and several plausible policy scenarios, then solve a goal programming model for each scenario to identify which policy strategies best satisfy priority targets across the full scenario space.

Optimization under multiple conflicting goals across policy scenarios

Quantitative targets, goal deviations, scenario parameters

Charnes, A., Cooper, W. W. (1961). Management Models and Industrial Applications of Linear Programming. Wiley, New York.

Ignizio, J. P. (1976). Goal Programming and Extensions. Lexington Books, Lexington, MA.

goal-programming scenario-analysis policy-scenario-analysis multi-objective-goal-programming stochastic-goal-programming robust-goal-programming


Policy Scenario Integer Programming

Policy Scenario Integer Programming — Discrete Optimization Across Policy Alternatives

PSIP
scenario-based integer programming
policy-driven IP
scenario integer optimization

1950s–1960s (scenario extension: 1990s onwards)

Operations research community (Dantzig, Gomory, and others)

Policy Scenario Integer Programming (PSIP) solves an integer programming model — where some or all decision variables must take whole-number values — separately under each of several distinct policy scenarios, then compares objective values, feasibility, and solution structures to identify which policy environment leads to the best discrete allocation or assignment outcome.

Discrete combinatorial optimization under scenario uncertainty

Discrete decision variables, policy parameters, scenario-specific constraint sets

Birge, J. R., & Louveaux, F. (2011). Introduction to Stochastic Programming (2nd ed.). Springer.

Williams, H. P. (2013). Model Building in Mathematical Programming (5th ed.). Wiley.


Policy Scenario Microsimulation

Policy Scenario Microsimulation — Individual-level simulation for policy impact analysis across defined scenarios

PSM
Policy Microsimulation
Scenario-Based Microsimulation
Policy Impact Microsimulation

Policy Scenario Microsimulation applies microsimulation methods to evaluate and compare the distributional and aggregate effects of alternative policy scenarios on a synthetic population. By simulating individual-level behaviour under each policy regime, researchers can measure winners and losers, fiscal costs, and equity outcomes before real implementation.

Simulation — individual-level policy scenario analysis

Individual or household-level microdata; demographic, income, and behavioral records

Orcutt, G. H. (1957). A new type of socio-economic system. Review of Economics and Statistics, 39(2), 116–123.

Gupta, A., & Kapur, V. (Eds.) (2000). Microsimulation in Government Policy and Forecasting. North-Holland.

microsimulation scenario-analysis policy-scenario-analysis agent-based-microsimulation monte-carlo-simulation stochastic-microsimulation


Policy Scenario Monte Carlo Simulation

Policy Scenario Monte Carlo Simulation — Probabilistic uncertainty analysis across defined policy scenarios

PS-MCS
Policy MC Simulation
Scenario-Based Monte Carlo
Policy Uncertainty Simulation

Developed within health economics and policy modeling communities; foundational work by Briggs, Claxton, and Sculpher

Policy Scenario Monte Carlo Simulation combines pre-defined discrete policy scenarios with probabilistic Monte Carlo sampling to quantify uncertainty in outcomes across each scenario. Rather than evaluating a single stochastic model, analysts define two or more policy alternatives and run thousands of Monte Carlo iterations within each, producing probability distributions of outcomes that support evidence-based policy comparison.

Quantitative parameters with probability distributions; discrete policy scenario definitions

Briggs, A. H., Claxton, K., & Sculpher, M. J. (2006). Decision Modelling for Health Economic Evaluation. Oxford University Press.

Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley.

monte-carlo-simulation scenario-analysis sensitivity-analysis stochastic-monte-carlo-simulation policy-scenario-analysis stochastic-scenario-analysis


Policy Scenario Multi-Objective Optimization — Scenario-conditioned Pareto-optimal Policy Search

Evolved from multi-objective optimization and policy scenario analysis communities

Policy Scenario Multi-Objective Optimization (PS-MOO) integrates explicit policy scenario construction with multi-objective optimization to identify Pareto-optimal policy options across plausible future states. Decision-makers evaluate trade-offs between competing objectives — such as economic efficiency, equity, and environmental impact — for each distinct policy scenario, then compare Pareto fronts to select robust or scenario-contingent strategies.

Numerical decision variables, policy scenario parameters, multiple objective functions

Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Chichester.

Walker, W. E., Harremoës, P., Rotmans, J., van der Sluijs, J. P., van Asselt, M. B. A., Janssen, P., & Krayer von Krauss, M. P. (2003). Defining uncertainty: a conceptual basis for uncertainty management in model-based decision support. Integrated Assessment, 4(1), 5–17.

multi-objective-optimization scenario-analysis nsga-ii policy-scenario-analysis robust-multi-objective-optimization multi-objective-genetic-algorithm


Policy Scenario Particle Swarm Optimization — PSO-driven search across alternative policy futures

PS-PSO
Policy PSO
Scenario-based PSO
Policy scenario swarm optimization

1995 (PSO); applied to policy scenarios from 2000s onward

Kennedy, J. & Eberhart, R. (PSO); policy scenario framing from planning and operations research literature

Policy Scenario Particle Swarm Optimization integrates Particle Swarm Optimization (PSO) with explicit policy scenario analysis. A swarm of candidate policy solutions is evaluated under multiple defined future scenarios, and PSO's velocity-position update rules guide the swarm toward solutions that perform well—or robustly—across all considered scenarios. It is used in energy, environmental, infrastructure, and public resource planning.

Metaheuristic optimization within policy scenario framework

Continuous or mixed decision variables; scenario-defined objective functions

Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948.

Poli, R., Kennedy, J., Blackwell, T. (2007). Particle swarm optimization: An overview. Swarm Intelligence, 1(1), 33–57.


Policy Scenario Queueing Simulation

Policy Scenario Queueing Simulation — Comparative queueing analysis across alternative service or resource-allocation policies

PSQS
policy queueing analysis
queueing policy comparison
scenario-based queueing model

1909 (queueing theory); scenario application from 1960s–1970s OR literature

Erlang, A. K. (foundation); generalized by operations research community

Policy Scenario Queueing Simulation applies queueing theory and discrete-event simulation to evaluate two or more competing service or resource-allocation policies under realistic demand and capacity conditions. By holding the system structure constant and varying only the policy rules, analysts can directly compare throughput, waiting times, utilization, and equity outcomes before committing to real-world implementation.

Arrival rates, service times, queue disciplines, resource capacities, policy parameters

Kleinrock, L. (1975). Queueing Systems, Volume 1: Theory. Wiley-Interscience, New York.

Law, A. M. (2015). Simulation Modeling and Analysis (5th ed.). McGraw-Hill Education, New York.


Policy Scenario Sensitivity Analysis

Policy Scenario Sensitivity Analysis — Structured examination of how model outputs respond to input variation across defined policy alternatives

PSSA
Policy Sensitivity Analysis
Scenario-Based Sensitivity Analysis
Policy Robustness Analysis

Saltelli, A. et al.; Lempert, R. J. et al.

Policy Scenario Sensitivity Analysis (PSSA) combines structured scenario planning with formal sensitivity analysis to determine which model inputs and policy parameters most strongly drive outcomes across a set of distinct policy alternatives or future states. It is widely used in public health, climate, energy, and economic policy modeling to identify robust interventions that perform well even when key assumptions vary.

Analytical framework combining scenario planning with sensitivity analysis

Model inputs, policy parameters, uncertain variables across scenario sets

Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. John Wiley & Sons, Chichester.

sensitivity-analysis scenario-analysis policy-scenario-analysis monte-carlo-simulation robust-sensitivity-analysis stochastic-sensitivity-analysis


Policy Scenario System Dynamics

Policy Scenario System Dynamics — Scenario-Based Simulation of Policy Interventions Using System Dynamics Models

PSSD
Policy SD Simulation
Scenario-Based System Dynamics
Policy Systems Modeling

Forrester, J. W. (system dynamics); scenario integration formalized by Sterman and others

Policy Scenario System Dynamics combines system dynamics modeling with structured scenario analysis to evaluate how different policy interventions affect complex, feedback-driven systems over time. By running multiple policy scenarios through a calibrated stock-and-flow model, analysts can compare long-run outcomes, identify leverage points, and anticipate unintended consequences before real-world implementation.

Stock-and-flow data, causal loop diagrams, policy levers, time-series outputs

Sterman, J. D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw-Hill.

Forrester, J. W. (1969). Urban Dynamics. MIT Press.

system-dynamics scenario-analysis policy-scenario-analysis agent-based-system-dynamics stochastic-system-dynamics multi-objective-system-dynamics


Political Efficacy Scale

Political Efficacy Scale (Internal and External)

Political Efficacy
Internal Efficacy
External Efficacy

Richard Niemi, Steven Craig, Albert Bandura

Political efficacy measures sense of personal agency and power in the political system, encompassing both internal efficacy (belief in own political competence and understanding) and external efficacy (belief that the political system is responsive to citizen input). Rooted in Bandura's self-efficacy theory (1977) and developed for political contexts by Niemi, Craig, and colleagues (1969 onwards), the measure explains why some citizens feel empowered to engage in politics while others feel powerless. High-efficacy citizens are substantially more likely to participate, contact representatives, and vote; low-efficacy citizens withdraw from politics and are susceptible to anti-democratic appeals.

Niemi, R. G., Craig, S. C., & Mattei, F. (1991). Measuring internal political efficacy in the 1988 National Election Study. American Political Science Review, 85(4), 1407-1413.

Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191-215.

Craig, S. C. (1979). Efficacy, trust, and political behavior: An attempt to resolve a lingering conceptual dilemma. American Political Science Review, 73(2), 571-587.


Political Ideology Scale

Left-Right Political Ideology Self-Placement Scale

Left-Right Scale
Ideology Continuum
Political Spectrum Scale

Hans-Dieter Klingemann & Norberto Bobbio

The Political Ideology Scale measures individual self-placement on a left-right political spectrum, capturing fundamental preferences for government role, economic organization, and social values. The single-item self-placement measure (most common) asks respondents to rate themselves on a 0-10 or 0-100 continuum; multi-item versions assess distinct ideological dimensions (economic policy, social policy, nationalism). The left-right axis remains the dominant organizing principle of political competition globally, predicting party choice, policy preferences, and electoral behavior despite critiques that it oversimplifies multidimensional political space.

Fuchs, D., & Klingemann, H. D. (1990). The left-right schema. In M. Kent Jennings & Jan W. Van Deth (Eds.), Continuities in political action. Berlin: De Gruyter.

Inglehart, R., & Klingemann, H. D. (2000). Genes, culture, democracy, and happiness. In E. Diener & E. M. Suh (Eds.), Culture and subjective well-being. Cambridge, MA: MIT Press.

Bobbio, N. (1996). Left and right: The significance of a political distinction. Chicago: University of Chicago Press.


Political Participation Scale

Political Participation and Civic Engagement Scale (PPCS)

PPCS
Civic Participation Measure
Political Activity Scale

Sidney Verba, Kay Lehman Schlozman, Henry Brady

The Political Participation Scale measures engagement in civic and political activities, encompassing voting, campaign involvement, contacting officials, organizational membership, community volunteering, and protest activity. Developed by Verba, Schlozman, and Brady (1995), the measure captures both conventional participation (voting, contacting representatives) and unconventional participation (protest, civil disobedience). It addresses fundamental questions in political science: Why do some citizens engage while others withdraw? How do structural resources (time, money, education) and psychological factors (efficacy, interest) drive participation?

Verba, S., Schlozman, K. L., & Brady, H. E. (1995). Voice and equality: Civic voluntarism in American politics. Cambridge, MA: Harvard University Press.

Putnam, R. D. (2000). Bowling alone: The collapse and revival of American community. New York: Simon & Schuster.

Van Deth, J. W. (2014). A conceptual map of political participation. Acta Politica, 49(3), 349-367.


Political Tolerance Scale

Democratic Tolerance and Civil Liberties Scale (DTCL)

DTCL
Civil Liberties Scale
Majoritarian Constraint Scale

Samuel Stouffer, James Gibson, John Sullivan

The Political Tolerance Scale measures willingness to permit unpopular groups to exercise civil liberties and political rights, including free speech, assembly, and voting rights even for groups the respondent strongly opposes. Pioneered by Stouffer (1955) measuring tolerance of communists during McCarthyism and extended by Gibson (1989) and Sullivan, Piereson, and Marcus (1982), the scale assesses fundamental democratic commitment—that pluralism and minority rights supersede majoritarian preference. It addresses the paradox: can democracy survive if majorities vote to restrict minority rights? Tolerance is essential for democratic stability, particularly as polarization increases.

Stouffer, S. A. (1955). Communism, conformity, and civil liberties: A cross-section of the nation speaks its mind. Garden City, NY: Doubleday.

Gibson, J. L. (1989). Understanding the tolerant: A latent variable model of support for civil liberties in the Soviet Union. American Journal of Political Science, 33(3), 797-825.

Sullivan, J. L., Piereson, J., & Marcus, G. E. (1982). Political tolerance and American democracy. Chicago: University of Chicago Press.


Political Trust Scale

Comparative Study of Electoral Systems (CSES) Trust Module

The Political Trust Scale measures citizen confidence in government institutions, elected officials, and the political system's responsiveness and fairness. Pioneered by Miller (1974) and operationalized across comparative electoral studies (CSES Module 5), the scale captures both diffuse trust (in the political system generally) and specific trust (in particular institutions such as parliament or the executive). It is central to understanding democratic legitimacy, political engagement, and support for democratic institutions.

Miller, A. H. (1974). Political issues and trust in government: 1964-1970. American Political Science Review, 68(3), 951-972.

Hetherington, M. J. (2005). Why trust matters: Declining political trust and the demise of American democracy. Princeton, NJ: Princeton University Press.

Comparative Study of Electoral Systems (CSES) Module 5 (2016-2021). Political Trust and Legitimacy Scales. CSES Secretariat, University of Michigan.


Pollination Efficiency Assessment

Quantitative Evaluation of Pollinator Activity and Pollen Transfer Success

pollinator monitoring
pollen viability assessment
fruit set efficiency

Reproductive ecology and crop pollination

Pollination efficiency assessment quantifies the effectiveness of pollinator activity and pollen transfer in achieving fruit set. This method integrates field observation of pollinator visits, pollen viability testing, stigma receptivity assessment, and fruit set measurement to diagnose pollination limitations and optimize management. It is essential for crops dependent on animal pollination and for understanding reproductive success in variable environmental conditions.

ecological and physiological measurement pipeline

Westerkamp, C., & Gottsberger, G. (1991). Dipterophagous flowers—Pollination by oil-collecting insects. Botanica Acta, 104(2), 88–100.

Stanley, R. G., & Linskens, H. F. (1998). Pollen: Biology, Biochemistry, Management. Springer-Verlag.

crop-load-management phenological-stage-monitoring pruning-response-analysis fruit-color-analysis


Polygenic Risk Score

Polygenic Risk Score for Disease Prediction and Stratification

A polygenic risk score (PRS) is a summary measure that aggregates the effects of many genetic variants across the genome to predict an individual's genetic predisposition to disease or other complex traits. Developed initially by Purcell and colleagues in 2007, PRS methods combine genome-wide association study (GWAS) results with an individual's genotype to generate a personalized risk estimate. PRS approaches have transformed precision medicine by enabling risk stratification and early intervention in populations at high genetic risk.

Purcell, S. M., Wray, N. R., Stone, J. L., Visscher, P. M., O'Donovan, M. C., Sullivan, P. F., & Sklar, P. (2007). Common polygenic variation contributes to risk of schizophrenia. Nature, 460(7256), 748–752.

Evans, D. M., Visscher, P. M., & Wray, N. R. (2009). Harnessing the power of large B and T cell lymphoma genome-wide association studies. Nature Reviews Genetics, 10(7), 431–442.

Khera, A. V., Chaffin, M., Wade, K. H., Zaharieva, S., King, C., Arvanitis, M., & Aherwar, D. (2018). Polygenic prediction of weight and obesity trajectories. PLoS Genetics, 15(7), e1007616.

qtl-mapping ld-block-analysis transmission-disequilibrium-test f-statistics


Polynomial Regression

polynomial least squares
curvilinear regression
Polinom Regresyonu

Montgomery, Peck & Vining (textbook treatment); classical least squares

Polynomial regression is a regression method that models non-linear relationships by including squared and higher-degree terms of an explanatory variable, and it is a core tool of response surface analysis. As developed in Montgomery, Peck and Vining's Introduction to Linear Regression Analysis (2012), it remains linear in its parameters even though the fitted curve bends.

Linear regression in transformed predictors

Ordinary least squares on polynomial-expanded design

Montgomery, D. C., Peck, E. A. & Vining, G. G. (2012). Introduction to Linear Regression Analysis. Wiley.

ols-regression ridge-regression lasso-regression spline-regression response-surface-methodology