Embedded Case Study

embedded single-case design
multiple-unit case study
nested case study
embedded unit analysis

1984–1995 (Yin's foundational editions; Stake 1995)

Robert K. Yin (systematic case study design); Robert E. Stake (naturalistic tradition)

An embedded case study is a case study design in which one or more units of analysis are nested within a single overarching case. Rather than treating the case as a single, holistic entity, the researcher deliberately examines multiple sub-units — such as departments within an organisation, classrooms within a school, or programmes within a hospital — to build a richer, more nuanced understanding of the phenomenon under study. Formalised by Robert K. Yin, the design is contrasted with the holistic single-case study and with multi-case (multiple-case) designs.

Interviews, documents, observations, artefacts (multiple data sources within each unit)

1 case with 2–6 embedded units of analysis

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

Stake, R. E. (1995). The Art of Case Study Research. Sage.

case-study ethnography grounded-theory phenomenology action-research mixed-methods


Embedded Explanatory Sequential Mixed Methods Design

embedded mixed methods with explanatory sequence

The embedded explanatory sequential mixed methods design combines two structural logics: the explanatory sequential framework (a dominant quantitative phase followed by a qualitative follow-up) and the embedded design principle (one method nested within the other to serve a supporting role). Quantitative data are collected and analyzed first to identify patterns or outcomes; qualitative data are then gathered — embedded within or alongside the QUAN phase — to explain, interpret, or contextualize those findings. The result is a study in which numerical results drive the inquiry and qualitative voices provide the explanatory depth.

Quantitative (primary) and qualitative (embedded/supplementary) data

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

Creswell, J. W., & Plano Clark, V. L. (2011). Designing and Conducting Mixed Methods Research (2nd ed.). Sage.


Embedded Exploratory Sequential Mixed Methods Design

embedded-exploratory design
nested exploratory sequential design
qual-first embedded design
QUAN(qual) exploratory sequential

Creswell & Plano Clark (embedded and exploratory sequential variants codified)

An embedded exploratory sequential mixed methods design opens with a qualitative phase that explores an understudied phenomenon, then embeds a secondary quantitative strand within or alongside that primary qualitative work. The qualitative findings guide what is measured quantitatively, while the embedded quantitative data provide additional scope or precision without displacing the qualitative logic driving the study.

Qualitative data (primary exploratory strand) and quantitative data (embedded secondary strand)

Creswell, J. W. (2015). A Concise Introduction to Mixed Methods Research. Sage.


Embedded Intervention Mixed Methods

Embedded Intervention Mixed Methods Design

mixed methods intervention design with embedded strand

Creswell & Plano Clark (embedded design framework); intervention application developed in health, education, and program evaluation research

Embedded intervention mixed methods is a research design in which a qualitative strand is nested within a primary quantitative intervention (experimental or quasi-experimental) study. The quantitative component tests whether an intervention works; the embedded qualitative component illuminates how and why it works — or does not work — for participants. The two strands run concurrently or sequentially within a single overarching study framework, with integration occurring during interpretation.

Quantitative outcome data (primary) and qualitative data (secondary, embedded strand)

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

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


Embedded mixed methods meta-inference

embedded MMR meta-inference
meta-inference in embedded design
integrated meta-inference (embedded)
EMMD meta-inference

Abbas Tashakkori & Charles Teddlie (meta-inference concept); John W. Creswell & Vicki L. Plano Clark (embedded design framework)

Embedded mixed methods meta-inference is the process of drawing a single, overarching conclusion by integrating the inferences from a dominant (primary) strand and an embedded (secondary) strand within an embedded mixed methods design. The embedded strand — typically qualitative nested inside a quantitative study, or vice versa — answers a supplemental question, and meta-inference synthesises both strands into one coherent interpretive claim that neither strand could produce alone.

Combined quantitative and qualitative data (one strand nested within the other)

Tashakkori, A., & Teddlie, C. (Eds.). (2003). Handbook of Mixed Methods in Social and Behavioral Research. Sage.


Embedded Multilevel Mixed Methods

Embedded Multilevel Mixed Methods Design

embedded multilevel design
nested multilevel mixed methods
multilevel embedded MMR
embedded hierarchical mixed methods

Creswell & Plano Clark; Teddlie & Tashakkori (mixed methods typology literature)

Embedded multilevel mixed methods design nests a secondary qualitative (or quantitative) strand within a primary study that spans hierarchically organized levels — such as students within classrooms, employees within organizations, or patients within clinics. The dominant strand addresses the research question at the structural level while the embedded component enriches understanding at a different level of the hierarchy, producing complementary insights that neither strand could yield alone.

Quantitative and qualitative data collected across multiple levels (e.g., students nested within classrooms, patients nested within clinics)

Teddlie, C., & Tashakkori, A. (2009). Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioral Sciences. Sage.


Embedded Multiphase Mixed Methods

Embedded Multiphase Mixed Methods Design

embedded multi-phase mixed methods
nested multiphase design
multiphase embedded MMR
embedded phased mixed design

Creswell & Plano Clark (embedded design); Nastasi et al. (multiphase)

Embedded multiphase mixed methods is a research design in which a secondary data strand (qualitative or quantitative) is nested within a primary, dominant strand across two or more sequential study phases. Each phase builds on the prior one, while the embedded strand enhances understanding of specific sub-questions that the dominant strand alone cannot answer. This design is suited to complex, longitudinal, or program-evaluation research problems requiring sustained inquiry across stages.

Quantitative and qualitative data collected across multiple phases

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


Embedded Pragmatic Mixed Methods

embedded pragmatic MMR
pragmatic nested mixed methods
embedded pragmatist MMD
nested pragmatic mixed design

Creswell & Plano Clark (embedded structure); Morgan & Tashakkori (pragmatic paradigm integration)

Embedded pragmatic mixed methods is a mixed methods design in which one data strand (typically qualitative) is nested within a larger, dominant strand (typically quantitative), and the entire study is guided by a pragmatist philosophical stance — selecting methods for what works best to answer the research question rather than adhering to a fixed paradigmatic commitment. The nested strand enriches or contextualises the dominant strand without standing on its own.

Quantitative data (primary) and qualitative data (nested/secondary), or vice versa

Morgan, D. L. (2014). Pragmatism as a paradigm for social research. Qualitative Inquiry, 20(8), 1045–1053.


Embedded Qualitative-Priority Mixed Design

Embedded Qualitative-Priority Mixed Methods Design

qual-dominant embedded design
qualitative-primary embedded MMR
embedded QUAL+quan design
nested qualitative-priority design

2007 (first edition of Designing and Conducting Mixed Methods Research)

The embedded qualitative-priority mixed design nests a secondary quantitative strand within a dominant qualitative inquiry. The qualitative strand drives the research logic, framing the questions, guiding data collection, and anchoring interpretation, while the quantitative component plays a supporting role — typically measuring outcomes, tracking context variables, or confirming patterns emerging from the qualitative core. The result is a rich, theoretically grounded account that remains rooted in participants' meanings while gaining empirical precision where needed.

Qualitative data (primary) + quantitative data (secondary/supplementary)

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

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


Embedded Quantitative-Priority Mixed Design

Embedded Quantitative-Priority Mixed Methods Design

quantitative-dominant embedded mixed methods

Creswell & Plano Clark (embedded structure); Morse & Niehaus (priority notation)

The embedded quantitative-priority mixed design is a mixed methods research structure in which a dominant quantitative study (survey, experiment, or longitudinal assessment) provides the primary basis for conclusions, while a qualitative component is embedded within that quantitative framework to address a question the numbers alone cannot answer. Priority and resources lie with the quantitative strand; the qualitative strand enriches, contextualizes, or explains a specific aspect of the larger quantitative investigation.

Primarily quantitative (surveys, experiments, tests); qualitative data (interviews, observations) embedded within

Morse, J. M., & Niehaus, L. (2009). Mixed Method Design: Principles and Procedures. Left Coast Press.


Embedded Transformative Mixed Methods

Embedded Transformative Mixed Methods Design

embedded mixed methods with transformative framework

John W. Creswell & Vicki L. Plano Clark (embedded design); Donna M. Mertens (transformative framework)

Embedded transformative mixed methods is a research design that nests one type of data (quantitative or qualitative) inside a dominant dataset of the other type, with both strands guided by an overarching transformative framework — such as feminist, disability rights, or social justice theory. The design serves research questions where statistical breadth and in-depth qualitative insight must both speak directly to equity, empowerment, or systemic change goals.

Quantitative and qualitative data (one embedded within the other)

Mertens, D. M. (2010). Transformative Mixed Methods Research. Qualitative Inquiry, 16(6), 469–474.

embedded-mixed-methods transformative-mixed-methods sequential-explanatory-mixed-methods convergent-mixed-methods participatory-action-research critical-ethnography


Embryo Transfer Success Evaluation

Embryo Transfer Success Evaluation and Outcome Assessment

ET success rate
embryo transfer outcomes
recipient pregnancy assessment

Assisted reproductive technology and evaluation

Reproductive Physiologists and Embryologists

Embryo transfer (ET) success evaluation is the systematic assessment of pregnancy establishment and calving outcomes following embryo implantation. Developed by reproductive physiologists in the 1970s-1980s, the method measures conception rates, pregnancy retention, calving rates, and calf viability to quantify the efficacy of ET programs and identify factors affecting outcomes. Success assessment is critical for optimizing ET protocols and managing investment in breeding programs.

Bousquet, D., Thibault, C., & Gervais, D. (1985). Evaluation of the fertility of bovine embryos. Theriogenology, 24(1), 1-14.

Looney, C. R., Dimmick, M. A., Bomalaski, M. D., Hasler, J. F., Akagi, L. A., & Denniston, D. J. (2003). Commercial aspects and outcomes of embryo transfer. Veterinary Clinics of North America: Food Animal Practice, 19(2), 381-405.

Hasler, J. F., Henderson, W. B., Hurtgen, P. J., Jin, Z. Q., McCauley, A. D., Mower, S. A., ... & Trimmer, B. (2001). Production, freezing, and transfer of bovine IVF embryos and subsequent calving results. Theriogenology, 35(1), 131-142.


Emerging Pattern Mining

EP Mining
Contrast Pattern Mining
Differential Pattern Mining
Yükselen Örüntü Madenciliği

Emerging Pattern Mining (EPM) is a contrast-based data mining technique that identifies itemsets whose support increases significantly — or jumps from zero — when moving from one dataset (or class) to another. Introduced by Dong and Li in 1999, it is primarily used in classification, anomaly detection, and trend analysis tasks where discovering discriminative patterns between two populations or time periods is the central objective.

Output: Itemsets with high growth rate between classes

Exponential worst-case; border-based algorithms reduce practical cost

Dong, G., & Li, J. (1999). Efficient mining of emerging patterns: Discovering trends and differences. ACM SIGKDD, 43–52.


Emergy Analysis

Embodied Energy Analysis
Environmental Accounting (Odum)
Emergy Accounting
Emerji Analizi

Emergy Analysis, developed by systems ecologist Howard T. Odum and formally presented in his 1996 book, is a biophysical accounting method that converts all inputs to a system — energy, materials, labor, and services — into a common unit of solar energy equivalents called solar emjoules (sej). By tracing how much prior environmental work was required to produce each input, it enables researchers, engineers, and policymakers to compare fundamentally different resource types on a single thermodynamic basis.

Odum, H. T. (1996). Environmental Accounting: Emergy and Environmental Decision Making. John Wiley & Sons.


EMG Envelope

EMG linear envelope
RMS envelope
Activation envelope

Electromyography (EMG) envelope analysis extracts the amplitude modulation of muscle electrical activity to quantify muscle activation over time. By filtering and demodulating the raw EMG signal, practitioners obtain a smoothed activation profile that reflects when and how intensely a muscle is contracting during movement or fatigue.

Phinyomark, A., Quaine, F., Charbonnier, S., & Serviere, C. (2012). Robust EMG feature extraction in the whitespace. IEEE Transactions on Biomedical Engineering, 59(5), 1505-1517.

Merletti, R., & Parker, P. A. (1999). Electromyography: Physiology, Engineering and Noninvasive Applications. Wiley-IEEE Press.


Emotion Detection

emotion recognition
emotion classification
Duygu/His Tespiti (Emotion Detection)

Emotion detection is a natural-language-processing task that classifies the basic and complex emotions expressed in text — fear, joy, anger, sadness, surprise, and disgust — within a recognised emotion framework such as Ekman's basic-emotions model or Plutchik's wheel. It builds on Paul Ekman's 1992 argument for a small set of universal basic emotions, going beyond a simple positive/negative split to attach a specific emotion label to each piece of text.

Ekman (six basic emotions) / Plutchik (wheel of emotions)

fear, joy, anger, sadness, surprise, disgust

Output: Emotion label per document

Ekman, P. (1992). An Argument for Basic Emotions. Cognition & Emotion, 6(3-4), 169-200.

Mohammad, S.M. & Turney, P.D. (2013). Crowdsourcing a Word–Emotion Association Lexicon. Computational Intelligence, 29(3), 436-465.


Emotion Dysregulation Scale

Jennifer S. Silk, Laurence Steinberg, Amanda S. Morris

The EDS is a brief self-report measure of emotion dysregulation—difficulty managing and controlling emotional responses. Developed by Silk, Steinberg, and Morris in 2003 in longitudinal adolescent research, it captures emotional lability, emotional negativity, and emotional undercontrol linked to psychopathology and behavioral problems. The EDS is particularly valuable for adolescent assessment where emotion regulation capacity is still developing.

Silk, J. S., Steinberg, L., & Morris, A. S. (2003). Adolescents' emotion regulation in daily life: Links to depressive symptoms and problem behaviors. Child Development, 74(6), 1869–1883.


The Emotion Regulation Questionnaire for Children and Adolescents (ERQ-CA) is a 10-item self-report measure of emotion regulation strategies in children and adolescents ages 10–18 years. Based on Gross's process model of emotion regulation, the ERQ-CA assesses two key strategies: Cognitive Reappraisal (reinterpreting emotional situations to reduce emotional impact) and Expressive Suppression (inhibiting emotional responses). It is widely used in developmental psychology and clinical research to understand emotion management abilities and links to mental health outcomes.

Gross, J. J., & John, O. P. (1998). Mapping the domain of expressivity: Multimethod evidence for a hierarchical model. Journal of Personality and Social Psychology, 74(1), 170–191.

Gee, D. G., Gabard-Durnam, L., Telzer, E. H., Humphreys, K. L., Goff, B., Shapiro, M., . . . Tottenham, N. (2014). Maternal buffering of human amygdala-prefrontal circuitry during childhood but not during adolescence. Psychological Science, 25(11), 2067–2078.


Emotion Regulation Questionnaire

The ERQ is a 10-item self-report measure assessing two primary emotion regulation strategies: cognitive reappraisal and expressive suppression. Developed by Gross and John in 2003, it has become a foundational instrument in emotion regulation research, widely used across clinical, developmental, and social psychology.

Gross, J. J., & John, O. P. (2003). Individual differences in two emotion regulation processes: Implications for affect, relationships, and well-being. Journal of Personality and Social Psychology, 85(2), 348–362.

difficulties-emotion-regulation affective-lability-scale emotion-dysregulation-scale adult-adhd-self-report-scale


Emotional Exhaustion Scale

Emotional Exhaustion Subscale - Maslach Burnout Inventory (MBI)

The Emotional Exhaustion subscale is one of three core dimensions of the Maslach Burnout Inventory (MBI), developed by Maslach and Jackson in 1981. Emotional exhaustion represents the first stage of burnout, characterized by feeling emotionally drained, fatigued, and depleted as a result of work. The nine-item subscale measures the frequency of exhaustion, energy depletion, and tiredness. It is the strongest dimension of burnout, most closely predicting negative outcomes such as intent to leave, absenteeism, and health problems.

Maslach, C., & Jackson, S. E. (1981). The measurement of experienced burnout. Journal of Organizational Behavior, 2(2), 99-113.

Maslach, C., Jackson, S. E., & Leiter, M. P. (2016). Maslach Burnout Inventory Manual (4th edn). Menlo Park, CA: Mind Garden.

job-demands-resources-scale perceived-stress-scale job-satisfaction-survey work-ability-index organizational-commitment-scale


Empirical Bayes

EB
empirical Bayes estimation
marginal likelihood estimation
James-Stein shrinkage
parametric empirical Bayes
nonparametric empirical Bayes

Herbert Robbins (1956); Bradley Efron & Carl Morris (1973)

Empirical Bayes (EB) is an estimation strategy, introduced by Herbert Robbins in 1956 and developed into practical shrinkage estimators by Bradley Efron and Carl Morris in 1973, in which the hyperparameters of the prior distribution are estimated from the observed data via the marginal likelihood rather than specified in advance. The resulting posterior retains a Bayesian structure but substitutes data-driven hyperparameters for subjective ones, bridging frequentist shrinkage and full Bayesian inference.

hyperparameter estimation / shrinkage / small-area estimation

continuous (counts, proportions in extensions)

marginal likelihood (EB-ML) or method-of-moments

shrunken point estimates / posterior means / posterior intervals

Robbins, H. (1956). An empirical Bayes approach to statistics. In J. Neyman (Ed.), Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1 (pp. 157–164). University of California Press.

Efron, B., & Morris, C. (1973). Stein's estimation rule and its competitors — An empirical Bayes approach. Journal of the American Statistical Association, 68(341), 117–130.

Carlin, B. P., & Louis, T. A. (2000). Bayes and Empirical Bayes Methods for Data Analysis (2nd ed.). Chapman & Hall/CRC.

Efron, B., & Hastie, T. (2016). Computer Age Statistical Inference: Algorithms, Evidence, and Data Science. Cambridge University Press.

hierarchical-bayes bayesian-regression mcmc mixed-effects-model ridge-regression james-stein-estimator


Empirical Mode Decomposition

EMD
Intrinsic Mode Decomposition
Adaptive Signal Decomposition
Ampirik Mod Ayrıştırma

Empirical Mode Decomposition (EMD) is a fully data-driven, adaptive method for decomposing nonlinear and non-stationary time series into a finite set of oscillatory components called Intrinsic Mode Functions (IMFs), plus a monotonic residue. Introduced by Norden E. Huang and colleagues at NASA in 1998, EMD requires no predefined basis functions and derives all components directly from the signal itself, making it fundamentally different from Fourier or wavelet transforms.

Adaptive data-driven decomposition algorithm

Output: Intrinsic Mode Functions (IMFs) + residue

Huang, N. E., et al. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A, 454(1971), 903–995.


Empirical Orthogonal Teleconnection

Empirical Orthogonal Function (EOF) and Teleconnection Analysis

EOF analysis
Empirical orthogonal function
Teleconnection patterns
PCA meteorology

Empirical orthogonal function (EOF) analysis is a statistical technique that identifies dominant spatial patterns and temporal variability in atmospheric or oceanic data. When applied to geographically distant locations, EOF analysis reveals teleconnection patterns—coherent patterns of variability that link weather systems across ocean basins and continents.

Data analysis and pattern identification

Wallace, J. M., & Gutzler, D. S. (1981). Teleconnections in the geopotential height field during the Northern Hemisphere winter. Monthly Weather Review, 109(4), 784-812.

Preisendorfer, R. W. (1988). Principal Component Analysis in Meteorology and Oceanography. Elsevier.


Empirical Wavelet Transform

The empirical wavelet transform (EWT) is a data-driven wavelet decomposition method that automatically defines wavelet bases adapted to the frequency content of the signal. Introduced by Jérémie Gilles (2013), it overcomes a key limitation of classical wavelets—which use fixed, predefined bases—by constructing custom wavelets from the signal's own spectrum. This adaptive approach is particularly effective for analyzing non-stationary signals with complex, multi-component structures.

Gilles, J. (2013). Empirical wavelet transform. IEEE Transactions on Signal Processing, 61(16), 3999–4010.

Gilles, J. (2015). Empirical wavelet transform for multiscale analysis of signals. IEEE Signal Processing Magazine, 32(6), 125–130.

Dragomiretskiy, K., & Zosso, D. (2014). Variational mode decomposition. IEEE Transactions on Signal Processing, 62(3), 531–544.

discrete-wavelet-transform continuous-wavelet-transform empirical-mode-decomposition variational-mode-decomposition


Employee Engagement Survey

Wilmar B. Schaufeli and Arnold B. Bakker

The Employee Engagement Survey, grounded in Schaufeli and Bakker's Utrecht Work Engagement Scale (UWES), is a 17-item instrument measuring occupational engagement across three dimensions: vigor, dedication, and absorption. Originally developed in 2002, the EES assesses the positive psychological state of work engagement, complementing burnout assessment.

Schaufeli, W. B., Salanova, M., González-Romá, V., & Bakker, A. B. (2002). The measurement of engagement and burnout: A two sample confirmatory factor analytic approach. Journal of Happiness Studies, 3(1), 71-92.

Bakker, A. B., & Demerouti, E. (2008). Towards a model of work engagement. Career Development International, 13(3), 209-223.

organizational-trust-scale organizational-citizenship-behavior work-life-balance-scale knowledge-sharing-scale


Employee Wellbeing Scale

The Employee Wellbeing Scale (EWS) measures workers' subjective wellbeing across five dimensions: vitality (energy and physical health), motivation (engagement with work), self-perception (confidence and self-worth), social connection (relationships and belonging), and general life satisfaction. Developed by Page and Vella-Brodrick, the EWS captures holistic employee wellbeing—the balance of psychological, social, and physical health that enables people to thrive at work and in life. The scale is used for occupational health surveillance, evaluation of workplace wellness interventions, and organizational culture assessment.

Page, K. M., & Vella-Brodrick, D. A. (2009). The 'What', 'Why' and 'How' of employee wellbeing: A new model. Soc Indic Res, 90(3), 519–531.

Vella-Brodrick, D. A., & Page, K. M. (2009). Development and validation of an Employee Wellbeing Scale. Soc Indic Res, 88(1), 59–79.


Empowerment Scale

The Empowerment Scale, developed by Elaine Salisbury Rogers and colleagues in 1997, is a 28-item self-report instrument assessing personal empowerment in individuals with serious mental illness. Empowerment reflects the individual's sense of agency, self-efficacy, and power to make meaningful life choices and participate in community. The scale captures three dimensions: self-efficacy/self-esteem, power/powerlessness, and community activism and autonomy. The Empowerment Scale is widely used in recovery-oriented mental health services to assess and monitor personal agency and control.

Rogers, E. S., Chamberlin, J., Ellison, M. L., & Crean, T. (1997). A consumer-constructed scale to measure empowerment among users of mental health services. Psychiatric Services, 48(8), 1042-1047.

recovery-assessment-scale mental-health-recovery-measure social-inclusion-scale recovery-oriented-practices-index


Endemic Compartmental Models

Endemic & Vaccination Compartmental Models (SIS, SIRS, SIRV)

SIS Model
SIRS Model
SIRV Model
Endemic Disease Models

Endemic compartmental models extend the classical SIR framework to capture diseases that persist indefinitely in a population rather than burning out after a single epidemic wave. The SIS model allows recovered individuals to return to susceptibility immediately; SIRS introduces temporary immunity before loss; SIRV adds a vaccinated compartment. Together these models are foundational tools for studying diseases such as influenza, gonorrhea, and seasonal pathogens where reinfection or waning immunity is epidemiologically central.

Hethcote, H. W. (2000). The mathematics of infectious diseases. SIAM Review, 42(4), 599–653.


Endometriosis Health Profile-30

The Endometriosis Health Profile-30 (EHP-30) is a disease-specific quality-of-life questionnaire comprising 30 core items measuring the multidimensional impact of endometriosis on women's health and well-being. Developed by Jones and colleagues in 2001, the EHP-30 assesses five core domains: pain, emotional well-being, social support, sexual relations, and work/study impacts. Optional modular items address specific endometriosis-related concerns (infertility, medical side effects, treatment). It is the gold-standard endometriosis-specific outcome measure used in clinical trials and endometriosis research.

Jones, G. L., Kennedy, S. H., Barnard, A., Wong, J., & Jenkinson, C. (2001). Development of an endometriosis quality-of-life instrument: The Endometriosis Health Profile-30. Obstetrics & Gynecology, 98(2), 258-264.

Jones, G. L., Kennedy, S. H., Jenkinson, C., & Endometriosis Consortium (2002). Evaluating the responsiveness of the Endometriosis Health Profile Questionnaire: comparing responses from women with endometriosis to gynaecological controls. Human Reproduction, 17(9), 2464-2468.


Energy-Dispersive X-ray Spectroscopy

Energy-Dispersive X-ray Spectroscopy (EDS) is an analytical technique that identifies and quantifies chemical elements in microvolumes of samples by analyzing characteristic X-rays emitted during electron bombardment. Rooted in Moseley's discovery of characteristic X-ray lines in 1913 and developed as a practical microanalytical tool by the 1970s, EDS is integrated into scanning electron microscopes (SEM) and transmission electron microscopes (TEM) for spatially-resolved elemental analysis. It is indispensable in materials characterization for phase identification, compositional mapping, and alloy development.

Goldstein, J. I., Newbury, D. E., Michael, J. R., & Ritchie, R. O. (2017). Scanning Electron Microscopy and X-ray Microanalysis (3rd ed.). Springer.

Reed, S. J. B. (1993). Electron Microprobe Analysis (2nd ed.). Cambridge University Press.

Williams, D. B., & Carter, C. B. (2009). Transmission Electron Microscopy: A Textbook for Materials Science (2nd ed.). Springer.


Energy Storage Dispatch Optimization

Energy Storage System Dispatch and Optimization

battery dispatch
storage scheduling
energy arbitrage optimization

Power system operation and renewable integration

Utilities and storage technology developers

Energy storage dispatch optimization determines when to charge and discharge battery systems to maximize revenue, minimize grid stress, or support renewable integration. With falling battery costs and increasing variable renewable generation, storage dispatch has become critical for balancing supply and demand in modern power systems.

Dunn, B., Kamath, H., & Tarascon, J. M. (2021). Electrical energy storage for the grid: A battery of possibilities. Science, 334(6058), 928-935.

Ramasamy, V., Desai, A. S., & Margolis, R. M. (2015). Co-optimization of Solar PV and Battery Storage: A Review of Co-optimization Models and Frameworks. NREL/TP-6A40-64781.

Strbac, G. (2002). Demand side management: Benefits and challenges. Energy Policy, 36(12), 4419-4426.

load-forecasting power-flow-analysis reactive-power-compensation smart-grid-state-estimation


Engle-Granger Cointegration Test

Engle-Granger Two-Step Cointegration Test

EG cointegration test
Engle-Granger two-step method
residual-based cointegration test
EG test

The Engle-Granger two-step method tests whether two or more non-stationary I(1) time series share a common stochastic trend — that is, whether a linear combination of them is stationary. If cointegration is confirmed, an error-correction model (ECM) can be estimated to capture both short-run dynamics and long-run equilibrium adjustment.

Time series (integrated, typically I(1))

Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276.

Hamilton, J. D. (1994). Time Series Analysis. Princeton University Press.

johansen-cointegration-test vector-error-correction-model augmented-dickey-fuller-unit-root-test phillips-perron-unit-root-test granger-causality-test arima-model


Ensemble Active Learning

Ensemble-Based Active Learning (Query by Committee and Variants)

Query by Committee
QBC active learning
committee-based active learning
ensemble query strategy

Ensemble Active Learning combines a committee of diverse models with an active learning loop to select the most informative unlabeled examples for labeling. Rooted in the Query by Committee framework introduced by Seung et al. (1992), it uses disagreement among committee members as a signal for uncertainty, reducing the number of labeled examples needed to achieve strong predictive performance.

Labeled and unlabeled tabular or structured data

Seung, H. S., Opper, M., & Sompolinsky, H. (1992). Query by committee. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory (COLT 1992), pp. 287–294. ACM.

Settles, B. (2009). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison.

active-learning semi-supervised-learning boosting random-forest voting-ensemble query-by-committee


Ensemble Apriori Algorithm

Ensemble Apriori Algorithm (Ensemble-Based Frequent Pattern and Association Rule Mining)

Ensemble Apriori
Ensemble Association Rule Mining
EAR mining
Distributed Apriori Ensemble

1994 (Apriori base); ensemble extensions 2000s–2010s

Agrawal, R. & Srikant, R. (Apriori base); ensemble extension by multiple researchers

The Ensemble Apriori Algorithm applies ensemble principles to the classic Apriori frequent-pattern miner by running multiple Apriori instances on different data partitions or parameter settings and merging their rule sets. This approach improves coverage, reduces sensitivity to the minimum-support threshold, and scales association rule mining to larger transactional datasets.

Transactional / binary / categorical data

Agrawal, R. & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), 1215, 487–499.

apriori-algorithm fp-growth random-forest association-rule-learning bagging boosting


Ensemble Association Rules

Ensemble ARM
aggregated association rules
combined frequent-pattern mining
multi-run association rule learning

Various (applied ensemble philosophy from Breiman and others to association rule mining)

Ensemble Association Rules applies ensemble learning principles to association rule mining: multiple rule sets are discovered from different data subsamples or with varied parameters, then merged and weighted to produce a more stable and complete set of co-occurrence patterns. The approach reduces sensitivity to support and confidence threshold choices and improves robustness on noisy transactional data.

Ensemble meta-learning over association rule learners

Transactional / binary / categorical tabular data

Domingos, P. (1999). MetaCost: A general method for making classifiers cost-sensitive. Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 155–164.

Rymon, R. (1992). Search through systematic set enumeration. Proceedings of the 3rd International Conference on Principles of Knowledge Representation and Reasoning, 539–550. — foundational work on systematic enumeration used in ensemble aggregation of frequent itemsets.

association-rules apriori-algorithm voting-ensemble bagging boosting fp-growth


Ensemble Autoencoder Anomaly Detection

Ensemble Autoencoder Anomaly Detection (Multiple Autoencoder Aggregation for Outlier Scoring)

AE ensemble unsupervised anomaly detection

Ensemble Autoencoder Anomaly Detection trains multiple autoencoder neural networks on normal-class data and aggregates their reconstruction errors to produce a robust anomaly score. By combining diverse autoencoders rather than relying on one, the method stabilises outlier rankings and reduces sensitivity to random initialisation or suboptimal architecture choices.

Continuous numeric tabular data (multivariate)

Chen, J., Sathe, S., Aggarwal, C., & Turaga, D. (2017). Outlier Detection with Autoencoder Ensembles. In Proceedings of the 2017 SIAM International Conference on Data Mining (SDM), pp. 90–98. SIAM.

Aggarwal, C. C. (2017). Outlier Analysis (2nd ed., Ch. 3 & 9). Springer.


Ensemble Decision Tree

Ensemble Decision Tree (Combined Decision Tree Classifiers and Regressors)

multiple classifier system (decision trees)

Ensemble Decision Tree methods train multiple decision trees and combine their outputs to produce predictions that are more accurate and stable than any single tree. Covering strategies such as bagging, random subspacing, and voting, they are among the most effective off-the-shelf techniques for tabular classification and regression tasks.

Ensemble (multiple decision trees combined)

Tabular (continuous, categorical, mixed)

Dietterich, T. G. (2000). Ensemble methods in machine learning. In Multiple Classifier Systems, Lecture Notes in Computer Science, vol. 1857, pp. 1–15. Springer, Berlin, Heidelberg.

Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.

random-forest extra-trees decision-tree boosting bagging voting-ensemble


Ensemble Federated Learning

Ensemble Federated Learning (Federated Ensemble Aggregation)

federated ensemble learning
EFL
federated model ensembling
federated multi-model aggregation

McMahan et al. (FedAvg) extended by subsequent ensemble work

Ensemble Federated Learning combines the privacy-preserving distribution of federated learning with ensemble aggregation: each participating client trains its own local model on private data, and the server aggregates predictions — or model parameters — from all clients using ensemble strategies such as voting, averaging, or stacking, instead of simple parameter averaging alone.

Ensemble meta-strategy over federated clients

Distributed, privacy-sensitive tabular, image, or text data across decentralized nodes

McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 54, 1273–1282.

Chen, Y., Qin, X., Wang, J., Yu, C., & Gao, W. (2021). FedHealth: A federated transfer learning framework for wearable healthcare. IEEE Intelligent Systems, 35(4), 83–93.

federated-learning voting-ensemble stacking-ensemble bagging boosting transfer-learning


Ensemble Few-shot learning

ensemble few-shot classification
multi-model few-shot learning
few-shot ensemble
cooperative few-shot ensemble

Ensemble Few-Shot Learning combines multiple few-shot models — such as prototypical networks or embedding learners — to classify new classes from only one to a handful of labeled examples. By enforcing diversity among base learners and aggregating their predictions, the ensemble consistently outperforms any single few-shot model in accuracy and robustness, especially under severe label scarcity.

Few labeled examples per class (typically 1–20 shots), image or tabular features

Dvornik, N., Schmid, C., & Mairal, J. (2019). Diversity with Cooperation: Ensemble Methods for Few-Shot Classification. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 3716–3725.

Wang, Y., Yao, Q., Kwok, J. T., & Ni, L. M. (2020). Generalizing from a Few Examples: A Survey on Few-Shot Learning. ACM Computing Surveys, 53(3), 1–34.

few-shot-learning transfer-learning voting-ensemble meta-learning semi-supervised-few-shot-learning boosting


Ensemble Gaussian Mixture Model

E-GMM
GMM ensemble
mixture model ensemble
ensemble GMM

Combination of GMM (Dempster et al., 1977) and ensemble learning (Dietterich, 2000)

Ensemble Gaussian Mixture Model (E-GMM) combines multiple independently fitted Gaussian Mixture Models to improve density estimation, clustering stability, and anomaly detection. By averaging or aggregating the probabilistic outputs of several GMMs — each trained on a different data subset or random initialization — the ensemble reduces sensitivity to local optima and random seed choice, yielding more robust and reliable results than any single GMM.

Ensemble of probabilistic generative models

Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 9: Mixture Models and EM). Springer.

Dietterich, T. G. (2000). Ensemble methods in machine learning. Multiple Classifier Systems, Lecture Notes in Computer Science, 1857, 1–15.

gaussian-mixture-model random-forest bagging boosting expectation-maximization k-means-clustering


Ensemble Gaussian Process

Ensemble of Gaussian Processes (Committee / Distributed GP)

Gaussian Process ensemble
GP committee machine
distributed GP
mixture of GPs

Tresp, V. (committee formulation); Deisenroth, M. P. & Ng, J. W. (distributed formulation)

Ensemble Gaussian Process trains multiple independent GP experts on data subsets or overlapping regions, then combines their posterior predictions — means and variances — into a single probabilistic forecast. This approach retains the calibrated uncertainty estimates of standard GPs while overcoming their O(n³) cubic cost bottleneck, making probabilistic regression practical on datasets with thousands to millions of observations.

Ensemble of probabilistic surrogate models

Continuous numerical features; small-to-medium datasets per expert

Tresp, V. (2000). A Bayesian Committee Machine. Neural Computation, 12(11), 2719–2741.

Deisenroth, M. P., & Ng, J. W. (2015). Distributed Gaussian Processes. Proceedings of the 32nd International Conference on Machine Learning (ICML), PMLR 37, 1481–1490.

gaussian-process random-forest bayesian-gaussian-process gaussian-mixture-model support-vector-machine voting-ensemble


Ensemble Gradient Boosting

Gradient Boosting Machine (Ensemble of Additive Decision Trees)

Gradient Boosting Machine
GBM
Gradient Tree Boosting
Stochastic Gradient Boosting

Gradient Boosting is an ensemble method introduced by Jerome Friedman in 2001 that builds a strong predictive model by sequentially adding shallow decision trees, each correcting the errors of the previous ensemble. By framing the problem as gradient descent in function space, it achieves state-of-the-art accuracy on classification, regression, and ranking tasks across tabular data.

Ensemble (sequential boosting of decision trees)

Tabular (continuous, categorical, mixed features)

Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.

Friedman, J. H. (2002). Stochastic gradient boosting. Computational Statistics and Data Analysis, 38(4), 367–378.

random-forest xgboost decision-tree adaboost lightgbm catboost


Ensemble HDBSCAN

Ensemble Hierarchical Density-Based Spatial Clustering of Applications with Noise

HDBSCAN ensemble clustering
consensus HDBSCAN
multi-run HDBSCAN
cluster ensemble HDBSCAN

Vega-Pons, S. & Ruiz-Shulcloper, J. (ensemble clustering framework); McInnes, L. et al. (HDBSCAN base)

Ensemble HDBSCAN runs HDBSCAN multiple times under different hyperparameter settings or data subsamples and combines the resulting partitions into a single stable consensus clustering. Because HDBSCAN is sensitive to its minimum cluster size and minimum samples parameters, pooling multiple runs greatly reduces sensitivity to any single configuration and yields more reproducible cluster assignments on noisy, high-dimensional data.

Continuous, mixed, or high-dimensional unlabeled features

McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205.

Vega-Pons, S., & Ruiz-Shulcloper, J. (2011). A survey of clustering ensemble methods. International Journal of Pattern Recognition and Artificial Intelligence, 25(03), 337–372.

hdbscan k-means gaussian-mixture-model ensemble-k-means ensemble-dbscan semi-supervised-hdbscan


Ensemble Isolation Forest

Ensemble Isolation Forest (Meta-Ensemble Anomaly Detection)

ensemble anomaly detection with isolation trees

2008 (base); ensemble variants 2010s–present

Liu, F. T., Ting, K. M., Zhou, Z.-H. (base IF); ensemble extensions by multiple researchers

Ensemble Isolation Forest trains multiple Isolation Forest models — each with different random seeds, subsampling ratios, or contamination parameters — and combines their anomaly scores to produce a more stable, robust anomaly ranking. By averaging or aggregating across several independent isolation forests, the method reduces the variance inherent in any single forest and yields more reliable outlier detection on complex or high-dimensional data.

Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), pp. 413–422. IEEE.

isolation-forest autoencoder-anomaly-detection one-class-svm gaussian-mixture-model random-forest voting-ensemble


Ensemble K-means

Ensemble K-means Clustering (Consensus Clustering)

consensus K-means
K-means ensemble clustering
cluster ensemble with K-means
EKM

Ensemble K-means runs K-means clustering many times under varied initializations, random seeds, or feature subsets, then aggregates the resulting partitions into a single consensus assignment. This approach reduces K-means' well-known sensitivity to initialization and produces more stable, reproducible clusters than any single run.

Ensemble clustering (consensus aggregation of K-means partitions)

Strehl, A. & Ghosh, J. (2002). Cluster ensembles — a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research, 3, 583–617.

Monti, S., Tamayo, P., Mesirov, J. & Golub, T. (2003). Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Machine Learning, 52, 91–118.

k-means ensemble-dbscan gaussian-mixture-model ensemble-gaussian-mixture-model semi-supervised-k-means k-nearest-neighbors


Ensemble K-nearest neighbors

Ensemble K-Nearest Neighbors (Aggregated KNN)

Ensemble KNN
KNN ensemble
aggregated k-nearest neighbors
combined KNN

Domeniconi, C. & Yan, B. (key formalization)

Ensemble K-Nearest Neighbors combines multiple KNN models — each trained with a different value of k, distance metric, feature subset, or data bootstrap — and aggregates their predictions by majority vote (classification) or averaging (regression). The approach reduces the high variance inherent in any single KNN model and produces more stable, accurate predictions on tabular data.

Ensemble (aggregated KNN classifiers/regressors)

Domeniconi, C., & Yan, B. (2004). Nearest neighbor ensemble. In Proceedings of the 17th International Conference on Pattern Recognition (ICPR), Vol. 1, pp. 228–231. IEEE.

Zhou, Z.-H. (2012). Ensemble Methods: Foundations and Algorithms. Chapman and Hall/CRC.

k-nearest-neighbors random-forest voting-ensemble bagging ensemble-decision-tree ensemble-support-vector-machine


Ensemble Kalman Filter

Ensemble Kalman Filter (Data Assimilation)

EnKF
Monte Carlo Kalman Filter
Stochastic Ensemble Filter
Topluluk Kalman Filtresi

The Ensemble Kalman Filter (EnKF) is a sequential Monte Carlo data assimilation algorithm introduced by Geir Evensen in 1994. It extends the classical Kalman filter to high-dimensional, nonlinear dynamical systems by representing the forecast error covariance through a finite ensemble of model realizations rather than propagating a full covariance matrix. Each ensemble member evolves through the nonlinear model, and observations are assimilated by computing a sample-based Kalman gain, making the method computationally tractable for large geophysical models.

Sequential Monte Carlo data assimilation filter

Geophysics, numerical weather prediction, engineering

O(N·m) per update cycle, N = ensemble size, m = observation dimension

Evensen, G. (1994). Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research, 99(C5), 10143–10162.


Ensemble Linear Regression

Ensemble of Linear Regression Models (Bagged and Stacked Linear Regression)

bagged linear regression
aggregated linear regression
stacked linear models
bootstrap-aggregated OLS

Ensemble Linear Regression combines multiple ordinary least-squares models — each fitted on a different bootstrap sample or feature subset — and averages their predictions. The technique, grounded in Breiman's bagging framework (1996), reduces variance and improves predictive stability compared with a single linear regression fit, while retaining the interpretability of linear assumptions.

Continuous features; continuous target (regression)

Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed., Ch. 8). Springer.

linear-regression-ml regularized-linear-regression random-forest bagging voting-ensemble ridge-regression


Ensemble Logistic Regression

Ensemble Logistic Regression (Combined Logistic Classifier Ensemble)

logistic regression ensemble
bagged logistic regression
aggregated logistic regression
logistic ensemble classifier

Breiman, L. (bagging); broader ensemble literature

Ensemble Logistic Regression trains multiple logistic regression classifiers on varied subsets or perturbations of the training data and combines their probability estimates by averaging or voting. The approach preserves logistic regression's probabilistic interpretability while reducing variance and improving predictive stability through aggregation.

Ensemble of logistic regression classifiers

Tabular data; binary or multiclass labels

Polikar, R. (2006). Ensemble based systems in decision making. IEEE Circuits and Systems Magazine, 6(3), 21–45.

logistic-regression-ml voting-ensemble boosting random-forest stacking-ensemble semi-supervised-logistic-regression


Ensemble Metric Learning

Ensemble Metric Learning (Combined Distance Metric Ensembles)

EML
ensemble distance metric learning
multiple metric fusion
combined metric learning

Multiple contributors (Weinberger, Saul, et al.)

Ensemble Metric Learning trains multiple distance metric learners — each on a different data view, feature subspace, or with a different objective — and combines the resulting metrics to produce a single, more robust similarity function. Combining diverse metrics reduces the variance of any individual metric and improves performance in tasks such as nearest-neighbor classification, retrieval, and few-shot learning.

Labeled or partially labeled tabular or embedding data

Wang, J., Kalousis, A., & Woznica, A. (2012). Parametric local metric learning for nearest neighbor classification. Advances in Neural Information Processing Systems, 25.

metric-learning few-shot-learning k-nearest-neighbors voting-ensemble random-forest transfer-learning


Ensemble Naive Bayes

Bagged Naive Bayes
Boosted Naive Bayes
Naive Bayes ensemble
NB ensemble

Various (Dietterich, T.G.; Webb, G.I.; others)

Ensemble Naive Bayes trains multiple Naive Bayes classifiers — each exposed to a different view of the data through bagging, feature subsets, or boosting — and combines their probabilistic predictions by voting or probability averaging. The approach retains the speed and interpretability of individual Naive Bayes models while reducing variance and improving accuracy through ensemble aggregation.

Categorical, binary, continuous (Gaussian NB), text (Multinomial NB)

Dietterich, T. G. (2000). Ensemble Methods in Machine Learning. In J. Kittler & F. Roli (Eds.), Multiple Classifier Systems (MCS 2000), Lecture Notes in Computer Science, vol. 1857, pp. 1–15. Springer.

Lowd, D. & Domingos, P. (2005). Naive Bayes Models for Probability Estimation. In Proceedings of the 22nd International Conference on Machine Learning (ICML 2005), pp. 529–536. ACM.

naive-bayes voting-ensemble boosting random-forest bagging semi-supervised-naive-bayes


Ensemble One-class SVM

Ensemble of One-Class Support Vector Machines

Ensemble OC-SVM
multiple one-class SVM
OC-SVM ensemble
one-class SVM committee

Tax, D. M. J. & Duin, R. P. W. (ensemble OC classifiers); Scholkopf et al. (OC-SVM base)

Ensemble One-Class SVM combines multiple one-class support vector machine models — each trained on a different random subset of the data or features — and aggregates their anomaly scores. By pooling several OC-SVM boundary estimates, the ensemble reduces the sensitivity to kernel choice and data sampling that afflicts a single one-class SVM, producing a more stable and accurate novelty or outlier detector.

Continuous, mixed (unlabeled or one-class labeled)

Scholkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471.

Tax, D. M. J., & Duin, R. P. W. (2001). Combining one-class classifiers. In Multiple Classifier Systems (MCS 2001), Lecture Notes in Computer Science, vol 2096. Springer, Berlin, Heidelberg.

one-class-svm support-vector-machine isolation-forest autoencoder-anomaly-detection voting-ensemble gaussian-mixture-model


Ensemble Online Learning

Ensemble Online Learning (Online Ensemble Methods)

online ensemble methods
streaming ensemble learning
incremental ensemble learning
adaptive ensemble learning

Ensemble Online Learning combines multiple base learners that are trained incrementally on a stream of data, updating each model one observation at a time. By aggregating the predictions of diverse online learners, the ensemble achieves accuracy and robustness that surpass any single incremental model, while adapting continuously to changing data distributions.

Oza, N. C., & Russell, S. (2001). Online bagging and boosting. In Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS 2001), pp. 229–236.

online-learning boosting voting-ensemble semi-supervised-learning random-forest active-learning


Ensemble Self-supervised Learning

Ensemble Self-supervised Learning (Combining Multiple Self-supervised Models or Objectives)

ensemble SSL
multi-view self-supervised ensemble
self-supervised ensemble learning
SSL ensemble

Multiple contributors (Grill et al., Caron et al., Chen et al.)

Ensemble Self-supervised Learning combines multiple self-supervised models, objectives, or augmentation views into a unified framework to produce more robust and generalizable representations from unlabeled data. By aggregating diverse self-supervised signals, the ensemble reduces the risk of representation collapse and outperforms single-objective SSL approaches on downstream tasks.

Ensemble of self-supervised models or objectives

Unlabeled images, text, audio, or multimodal data

Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P. H., Buchatskaya, E., Doersch, C., Ávila Pires, B., Guo, Z., Gheshlaghi Azar, M., Piot, B., Kavukcuoglu, K., Munos, R., & Valko, M. (2020). Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning. Advances in Neural Information Processing Systems, 33, 21271–21284.

Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021). Emerging Properties in Self-Supervised Vision Transformers. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 9650–9660.

random-forest self-supervised-learning contrastive-learning knowledge-distillation semi-supervised-learning transfer-learning


Ensemble Semi-supervised Learning

Ensemble Semi-supervised Learning (Combining Ensemble Methods with Semi-supervised Paradigms)

semi-supervised ensemble
SSL ensemble
ensemble-based SSL
co-training ensemble

Blum & Mitchell (co-training); Zhou & Li (tri-training)

Ensemble semi-supervised learning combines multiple base learners with the semi-supervised paradigm, exploiting both a small labeled set and a large pool of unlabeled data. By letting diverse classifiers teach each other through pseudo-labeling or co-training, the ensemble improves generalization far beyond what either approach alone could achieve with limited labels.

Ensemble + semi-supervised hybrid paradigm

Mixed labeled and unlabeled tabular, text, or image data

Zhou, Z.-H., & Li, M. (2005). Tri-training: Exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering, 17(11), 1529–1541.

Blum, A., & Mitchell, T. (1998). Combining labeled and unlabeled data with co-training. Proceedings of the 11th Annual Conference on Computational Learning Theory (COLT 1998), pp. 92–100. ACM.

semi-supervised-learning boosting bagging voting-ensemble self-supervised-learning transfer-learning


Ensemble Support Vector Machine

Ensemble Support Vector Machine (Aggregated SVM Ensemble)

Ensemble SVM
SVM ensemble
bagged SVM
SVM committee machine

Ensemble Support Vector Machine combines multiple independently trained SVM classifiers or regressors — each fitted on a different data partition, bootstrap sample, or feature subset — and aggregates their outputs via voting, averaging, or stacking. The approach mitigates the high computational cost and sensitivity to kernel hyperparameters inherent in a single large-scale SVM, while improving generalisation on complex or high-dimensional datasets.

Ensemble of SVMs (bagging, voting, or stacking)

Numerical, categorical (encoded), and high-dimensional feature vectors

Kim, H.-C., Pang, S., Je, H.-M., Kim, D., & Bang, S. Y. (2002). Constructing support vector machine ensemble. Pattern Recognition, 36(12), 2757–2767.

Dietterich, T. G. (2000). Ensemble methods in machine learning. In Multiple Classifier Systems (MCS 2000), Lecture Notes in Computer Science, vol. 1857, pp. 1–15. Springer.

support-vector-machine voting-ensemble random-forest bagging boosting stacking-ensemble


Ensemble Transfer Learning

Ensemble Transfer Learning (Aggregation of Multiple Pre-trained Models)

transfer ensemble
multi-model transfer learning
ensemble of fine-tuned models
ETL

Various (consolidated in deep learning era, 2010s)

Ensemble Transfer Learning combines multiple models that were each pre-trained on a large source domain and then fine-tuned on a target task. By aggregating the predictions of several independently fine-tuned models, it achieves higher accuracy and robustness than any single transferred model alone, especially when the target dataset is small.

Ensemble of pre-trained / fine-tuned models

Images, text, tabular, audio, or any domain with available pre-trained models

Ganaie, M. A., Hu, M., Malik, A. K., Tanveer, M., & Suganthan, P. N. (2022). Ensemble deep learning: A review. Engineering Applications of Artificial Intelligence, 115, 105151.

transfer-learning random-forest boosting voting-ensemble semi-supervised-transfer-learning few-shot-learning


Entity Linking

Entity Linking (Named Entity Disambiguation)

named entity disambiguation
entity disambiguation
entity resolution to knowledge base
Varlık Bağlama (Entity Linking)

Entity linking is a natural-language-processing task that matches ambiguous entity mentions in text — people, places, organisations — to the correct record in a knowledge base such as Wikidata, DBpedia, or a domain dictionary. Surveyed and shaped by Milne and Witten (2008) and later neural approaches reviewed by Sevgili and colleagues (2022), it grounds free text into structured, unambiguous references used in knowledge-graph building and multi-source text analysis.

Wikidata / DBpedia / domain dictionaries

Input: Text with recognised named entities (people, places, organisations)

Output: Each mention linked to a unique knowledge-base record

Milne, D. & Witten, I.H. (2008). Learning to Link with Wikipedia. CIKM (Proceedings of the 17th ACM Conference on Information and Knowledge Management).

Sevgili, O., Shelmanov, A., Arkhipov, M., Panchenko, A. & Biemann, C. (2022). Neural Entity Linking: A Survey of Models Based on Deep Learning. ACM Computing Surveys.

named-entity-recognition knowledge-graph-construction relation-extraction scientific-text-mining


Entity-Relationship Modeling

Entity-Relationship (ER) modeling is a conceptual approach to database design that represents real-world entities, their attributes, and the relationships between them. Introduced by Peter P.-S. Chen in 1976, ER modeling provides a high-level graphical notation (ER diagrams) that bridges the gap between informal requirements and formal database schemas.

Chen, P. P.-S. (1976). The entity-relationship model: Toward a unified view of data. ACM Transactions on Database Systems, 1(1), 9-36.

Chen, P. P.-S. (1977). English sentence structure and entity-relationship diagrams. New York, NY: Elsevier North-Holland.

Teorey, T. J., Yang, D., & Fry, J. P. (1989). A logical design methodology for relational databases using the extended entity-relationship model. ACM Computing Surveys, 18(2), 197-222.

database-normalization schema-design conceptual-modeling data-dictionary requirement-analysis


Entrepreneurial Intention Questionnaire

The Entrepreneurial Intention Questionnaire (EIQ) is a 6-item self-report instrument designed to measure an individual's intention to start a new business. Developed by Liñán and Chen in 2009, it is grounded in the Theory of Planned Behavior and has become widely used across entrepreneurship research and education. The scale captures readiness and commitment to pursuing entrepreneurial ventures.

Liñán, F., & Chen, Y. W. (2009). Development and cross-cultural validation of a specific instrument to measure entrepreneurial intentions. Journal of International Entrepreneurship, 7(3), 238–259.

Lortie, J., & Côté, R. R. (2002). The role of personality in novice entrepreneurial performance. Frontiers of Entrepreneurship Research, 22, 434–449.

Schlaegel, C., & Koenig, M. (2014). Determinants of entrepreneurial intention: a meta-analytic test and integration of competing models. Entrepreneurship Theory and Practice, 38(2), 291–332.

perceived-organizational-support psychological-capital-questionnaire core-self-evaluations-scale career-adapt-abilities-scale proactive-personality-scale


Entrepreneurial Orientation Scale

The Entrepreneurial Orientation (EO) Scale, developed by Danny Miller (1983), measures the extent to which an organization exhibits strategic postures characteristic of entrepreneurship. It assesses three core dimensions—innovativeness, risk-taking, and proactiveness—that distinguish entrepreneurial from conservative firms. This framework has become foundational in strategic management research and organizational behavior.

Miller, D. (1983). The correlates of entrepreneurship in three types of firms. Management Science, 29(7), 770–791.

Covin, J. G., & Slevin, D. P. (1989). Strategic management of small firms in hostile and benign environments. Strategic Management Journal, 10(1), 75–87.

Lumpkin, G. T., & Dess, G. G. (1996). Clarifying the entrepreneurial orientation construct and linking it to performance. Academy of Management Review, 21(1), 135–172.

absorptive-capacity-scale dynamic-capabilities-scale strategic-orientation-scale innovation-ambidexterity-scale market-sensing-capability-scale


Entropy Balancing

EB
entropy reweighting
covariate balancing via entropy
Hainmueller balancing

Entropy balancing is a preprocessing method for causal inference that assigns weights to control-group units so that the reweighted control sample matches the treatment group exactly on a chosen set of covariate moments (means, variances, skewness). Introduced by Hainmueller (2012), it replaces trial-and-error propensity-score trimming with a constrained maximum-entropy optimisation that achieves balance in a single step.

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

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., & Coey, D. (2017). Entropy balancing is doubly robust. Journal of Causal Inference, 5(1). (Working paper version widely cited; see also Zhao & Coey 2018, Stanford GSB Research Paper.)

propensity-score-weighting inverse-probability-weighting coarsened-exact-matching propensity-score-matching doubly-robust-estimation matching-estimator


ENTROPY

ENTROPY (Shannon Entropy Weighting Method) is a weight objective multi-criteria decision-making (MCDM) method introduced by Shannon, C. E. in 1948. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Information-theoretic objective weighting (Shannon entropy)

Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal

ahpsort aploco aras aroman artasi cobra cocoso codas


Environmental Attitudes Scale

Riley E. Dunlap, Kent D. Van Liere, Angela G. Mertig, and Robert E. Jones

The Environmental Attitudes Scale, most commonly operationalized as the New Ecological Paradigm (NEP) scale developed by Dunlap and colleagues in 2000, is a self-report measure assessing individual endorsement of an ecologically sustainable worldview. The scale measures beliefs about human-nature relationships, including anthropocentrism versus ecocentrism and concern about environmental degradation. It has become a standard tool for measuring pro-environmental attitudes in social and environmental psychology research.

Dunlap, R. E., Van Liere, K. D., Mertig, A. G., & Jones, R. E. (2000). New Ecological Paradigm scale: Measuring endorsement of an ecologically sustainable worldview. Journal of Social Issues, 56(3), 425–442.

cultural-values-scale collectivism-individualism-scale social-dominance-orientation-scale modern-racism-scale


ECS

environmental attitudes and worry assessment

The Environmental Concern Scale (ECS) measures the degree to which individuals worry about and feel affected by environmental problems, pollution, and ecological degradation. Originally developed by Weigel and Weigel (1978), the ECS focuses on emotional and affective responses to environmental issues—anxiety, worry, and perceived personal threat from pollution—rather than abstract values or beliefs. The scale is widely used in public opinion research, conservation communication effectiveness studies, and assessing emotional responses to environmental threats like climate change and air pollution.

Weigel, R. H., & Weigel, J. (1978). Environmental concern: The development of a measure. Environment and Behavior, 10(1), 3–15.

Dunlap, R. E., & Jones, R. E. (1992). Environmental concern: Conceptual and measurement issues. In R. E. Dunlap & Y. Michelson (Eds.), Handbook of environmental sociology. Greenwood Press.


EIS

self-identity and environmental values integration

The Environmental Identity Scale (EIS) measures the degree to which individuals incorporate environmental values and ecological concerns into their sense of self—how central environmental stewardship is to personal identity and self-concept. Developed by Clayton (2003) from identity theory and social psychology, the EIS captures environmental identity as a psychological construct distinct from attitudes, values, or behaviors alone. High EIS scores indicate that individuals view themselves as 'environmental people' for whom conservation and sustainability are integral to who they are. The scale is foundational for research on sustainable behavior motivation, examining why environmental values persist and translate into behavior for some individuals but not others, and evaluating whether environmental interventions shift identity and thus self-motivated behavior change.

Self-report identity and self-concept scale

Clayton, S. D. (2003). Environmental identity: A conceptual and an operational definition. In S. D. Clayton & S. Opotow (Eds.), Identity and the natural environment: The psychological significance of nature (pp. 45–65). MIT Press.

Clayton, S. D. (2012). The Oxford handbook of environmental and conservation psychology. Oxford University Press.

Sparks, P., & Shepherd, R. (1992). Self-identity and the theory of planned behaviour: Assessing the role of identification with 'green consumers.' Social Psychology Quarterly, 55(4), 388–399.

connectedness-to-nature-scale new-ecological-paradigm pro-environmental-behavior-scale sustainable-consumption-scale


Environmental Impact Assessment

Systematic Evaluation of Project Environmental Consequences

EIA
impact assessment
environmental screening
cumulative effects assessment

U.S. National Environmental Policy Act (NEPA)

Environmental Impact Assessment (EIA) is a systematic, structured process to identify, predict, and evaluate the environmental and social consequences of proposed development projects (infrastructure, extraction, manufacturing) before implementation. Mandated by law in most jurisdictions since the 1970s (NEPA in USA, EU Directive 2011/92/EU), EIA integrates scientific analysis of air quality, water resources, biodiversity, noise, and socioeconomic effects with stakeholder consultation and decision-making frameworks to inform project approval, design modification, or rejection.

systematic assessment and decision-support pipeline

Glasson, J., Therivel, R., & Chadwick, A. (2005). Introduction to Environmental Impact Assessment (3rd ed.). Routledge.

United Nations Environment Programme. (2002). EIA Training Resource Manual (2nd ed.). UNEP.

International Finance Corporation. (2012). Performance Standards on Environmental and Social Sustainability. IFC.

noise-mapping air-dispersion-modeling soil-remediation groundwater-contamination-model


EORTC QLQ-BR23

European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire—Breast Cancer Module

cancer-specific quality of life, breast cancer

Sprangers, M. A., et al. (EORTC Quality of Life Group)

The EORTC QLQ-BR23 is a 23-item breast-cancer-specific module designed to complement the 30-item EORTC QLQ-C30 core questionnaire, assessing functional and symptom domains unique to breast cancer. Validated by Sprangers et al. in 1996, it measures body image, sexual function, breast symptoms, and arm symptoms, making it a standard instrument in European and international breast cancer clinical trials and quality-of-life research.

Sprangers, M. A., Groenvold, M., Arraras, J. I., Franklin, J., te Velde, A., Muller, M., et al. (1996). The European Organization for Research and Treatment of Cancer breast cancer-specific quality-of-life questionnaire module: first results from a three-country field study. J Clin Oncol, 14(10), 2756–2768.

Aaronson, N. K., Ahmedzai, S., Bergman, B., Bullinger, M., Cull, A., Duez, N. J., et al. (1993). The European Organization for Research and Treatment of Cancer QLQ-C30: a quality-of-life instrument for use in international clinical trials in oncology. J Natl Cancer Inst, 85(5), 365–376.


EORTC QLQ-C15-PAL

European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire—Cancer Palliative (15 items)

cancer-specific quality of life, palliative care

Groenvold, M., et al. (EORTC Quality of Life Group)

The EORTC QLQ-C15-PAL is a 15-item quality-of-life instrument specifically designed for advanced cancer patients receiving palliative care. Developed by Groenvold et al. in 2006, it is a shortened version of the QLQ-C30, retaining core QoL domains while reducing respondent burden—critical in palliative settings where fatigue and functional impairment limit questionnaire completion. It measures physical, emotional, and functional well-being alongside key palliative symptoms.

Groenvold, M., Petersen, M. A., Aaronson, N. K., Arraras, J. I., Blazeby, J. M., Bottomley, A., et al. (2006). The development of the EORTC QLQ-C15-PAL: a shortened quality-of-life questionnaire for cancer patients in palliative care. Eur J Cancer, 42(1), 55–64.


EORTC QLQ-C30

European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire-C30

QLQ-C30
EORTC QLQ-C30
Cancer Quality of Life Scale

The EORTC QLQ-C30 is the most widely used international instrument for assessing quality of life in cancer patients. Developed by the European Organisation for Research and Treatment of Cancer in 1993, it measures physical, emotional, cognitive, and social functioning alongside cancer-specific symptoms and financial impact, making it the standard outcome measure in oncology clinical trials and patient care.

Self-report quality of life questionnaire

Aaronson, N. K., Ahmedzai, S., Bergman, B., Bullinger, M., Cull, A., Duez, N. J., ... & Takeda, F. (1993). The European Organisation for Research and Treatment of Cancer QLQ-C30: A quality-of-life instrument for use in international clinical trials in oncology. Journal of the National Cancer Institute, 85(5), 365-376.

Fayers, P. M., & Bottomley, A. (2001). Quality of life research within the EORTC-the EORTC QLQ-C30. European Journal of Cancer, 38(4), 427-431.

Cella, D. (1998). The Functional Assessment of Cancer Therapy Scale: Development and validation. Journal of Clin Oncol, 11(3), 570-579.

dlqi pdq-39 copd-assessment-test fibromyalgia-impact-questionnaire asthma-control-test


EORTC QLQ-CX24

European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire—Cervical Cancer Module

cancer-specific quality of life, cervical cancer

Greimel, E. R., et al. (EORTC Quality of Life Group)

The EORTC QLQ-CX24 is a 24-item cervical-cancer-specific module designed to complement the EORTC QLQ-C30 core questionnaire. Developed by Greimel et al. in 2006, it measures sexual/vaginal function, body image, lymphedema, neuropathy, and gastrointestinal symptoms specific to cervical cancer and its treatments. It is the standard cervical cancer-specific QoL instrument in international oncology research.

Greimel, E. R., Kuljanic Vlasic, K., Zwahlen, D., Rand, K., Heitz, U., Havsteen, H., et al. (2006). The European Organization for Research and Treatment of Cancer (EORTC) Quality-of-Life questionnaire cervical cancer module: EORTC QLQ-CX24. Acta Oncol, 45(3), 344–351.


EORTC QLQ-LC13

European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire—Lung Cancer Module

cancer-specific quality of life, lung cancer

Bergman, B., et al. (EORTC Quality of Life Group)

The EORTC QLQ-LC13 is a 13-item lung-cancer-specific module designed to complement the 30-item EORTC QLQ-C30 core questionnaire. Developed and validated by Bergman et al. in 1994, it measures lung-specific symptoms (dyspnea, cough, hemoptysis, chest pain) and treatment toxicities (sore mouth, dysphagia). It is the standard lung cancer-specific QoL instrument in European and international trials.

Bergman, B., Aaronson, N. K., Ahmedzai, S., Kaasa, S., & Sullivan, M. (1994). The EORTC QLQ-LC13: a modular supplement to the EORTC core quality of life questionnaire (QLQ-C30) for use in lung cancer clinical trials. Eur J Cancer, 30A(5), 635–642.


Epigenome-wide association study in educational research

Epigenome-Wide Association Study Applied to Educational Outcomes

EWAS of educational attainment
educational EWAS
epigenetic association study
EWAS

EWAS framework ~2011; educational applications ~2017–present

Rakyan, Down, Balding, and Beck (framework); applied to educational outcomes by Marioni, McCartney, and collaborators

An epigenome-wide association study (EWAS) applied to educational research scans DNA methylation levels at hundreds of thousands of CpG sites across the genome to identify loci whose methylation is statistically associated with educational attainment, cognitive ability, or related learning outcomes. By linking blood- or saliva-derived methylation profiles with school records or psychometric scores, EWAS offers a molecular window into how biological and environmental exposures may shape educationally relevant traits across the lifespan.

Observational epigenomic association design

DNA methylation array data (e.g., Illumina 450K or EPIC array) linked to educational attainment or cognitive performance measures

Rakyan, V. K., Down, T. A., Balding, D. J., & Beck, S. (2011). Epigenome-wide association studies for common human diseases. Nature Reviews Genetics, 12(8), 529–541.

Sugden, K., Hannon, E. J., Arseneault, L., Belsky, D. W., Broadbent, J. M., Corcoran, D. L., … Caspi, A. (2020). Patterns of reliability: Assessing the reproducibility of responses across participants in epigenome-wide association studies. Genome Biology, 21(1), 1–17.

genome-wide-association-study dna-methylation-analysis mendelian-randomization polygenic-score transcriptome-wide-association-study mediation-analysis


Epigenome-wide association study

EWAS
methylome-wide association study
epigenetic association study
DNA methylation association study

2008–2011 (term and framework established c. 2011)

Rakyan, Down, Balding & Beck (conceptual framework); Illumina arrays enabled large-scale application

An epigenome-wide association study (EWAS) is a hypothesis-free, genome-scale method that systematically tests whether epigenetic marks — predominantly CpG-site DNA methylation — differ between individuals with and without a trait, disease, or exposure. By scanning hundreds of thousands of genomic positions simultaneously, EWAS identifies loci where the epigenome is reproducibly associated with a phenotype, offering a layer of biological regulation that classical GWAS does not capture.

Population-scale epigenomic association study

DNA methylation array data (e.g., Illumina 450K, EPIC) or bisulfite sequencing data

Pidsley, R., Zotenko, E., Peters, T. J., Lawrence, M. G., Risbridger, G. P., Molloy, P., Van Djik, S., Muhlhausler, B., Stirzaker, C., & Clark, S. J. (2016). Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biology, 17(1), 208.

genome-wide-association-study dna-methylation-analysis chip-seq-peak-calling eqtl-analysis pathway-enrichment-analysis copy-number-variation-analysis


EPOC

afterburn effect
recovery oxygen uptake
post-exercise metabolic elevation
APMR

Excess post-exercise oxygen consumption (EPOC), commonly called the 'afterburn effect', is the elevated rate of oxygen uptake and metabolic activity that persists after exercise ends. First systematically studied by Brehm and Gutin (1986), EPOC reflects the energy cost of restoring homeostasis after physical exertion. During recovery, the body must replenish phosphate stores, clear lactate, restore oxygen debt to muscles, increase body temperature, and return cardiovascular and respiratory function to baseline. This lingering metabolic elevation results in continued calorie burning long after exercise stops, a phenomenon of significant interest in sports science and fitness.

Brehm, B. A., & Gutin, B. (1986). Recovery energy expenditure for steady state exercise in runners and non-runners. Medicine and Science in Sports and Exercise, 18(4), 441-446.

Excess, H. E., Elevation, M., & Oxygen, O. (1992). Biochemical and physiological basis of exercise-induced metabolic elevation. Medicine and Science in Sports and Exercise, 24(12), 1343-1355.

Laforgia, J., Withers, R. T., & Gore, C. J. (2006). Effects of exercise intensity and duration on the excess post-exercise oxygen consumption. Journal of Sports Sciences, 24(12), 1247-1264.

vo2-max respiratory-exchange-ratio session-rpe critical-power heart-rate-recovery


Epoch of Reionization 21-cm

21-cm Observations of the Epoch of Reionization

EoR 21-cm
Hydrogen Line Observations
21-cm Signal Mapping

The 21-centimeter line observation of neutral hydrogen is a powerful technique for studying the Epoch of Reionization, when the first stars and galaxies ionized the intergalactic medium about 13 billion years ago. Proposed by Scott and Rees in 1990, this method probes the universe's transition from the dark ages to the cosmic dawn through the characteristic hyperfine line emission of hydrogen.

Scott, D., & Rees, M. J. (1990). The 21-cm signature of the ionization of the intergalactic medium. Monthly Notices of the Royal Astronomical Society, 247, 510-516.

Furlanetto, S. R., Oh, S. P., & Briggs, F. H. (2006). Cosmology at low frequencies: the 21 cm transition and the high-redshift universe. Physics Reports, 433(4), 181-301.

Bowman, J. D., Rogers, A. E., Monsalve, R. A., et al. (2018). An absorption profile centred at 78 megahertz in the sky-averaged spectrum. Nature Astronomy, 2(4), 301-306.


Epsilon-Based Measure DEA

Epsilon-Based Measure Data Envelopment Analysis (EBM-DEA)

Epsilon-Based Measure DEA (EBM-DEA) is a non-parametric efficiency analysis method that evaluates how efficiently organizational units convert inputs into outputs. Unlike simple ratio-based methods, EBM accounts for slacks (unused inputs, unmet outputs) proportionally in both input and output dimensions. It produces a single efficiency score between 0 and 1, with 1 indicating best-practice efficiency.

Non-parametric efficiency analysis with slack treatment

Tone, K. (2010). Variations of data envelopment analysis: Models and comparisons. International Journal of Data Envelopment Analysis and Operations Research, 1(1), 1-17.

Tone, K. (2011). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130(3), 498-509.

dea crs-dea vrs-dea slack-based-measure superefficiency-dea


Epworth Sleepiness Scale

Epworth Sleepiness Scale - Daytime Somnolence Assessment

Daytime somnolence and sleepiness assessment

The Epworth Sleepiness Scale (ESS) is a brief, validated self-report instrument developed by Johns in 1991 to quantify the level of daytime somnolence or excessive daytime sleepiness. The ESS comprises eight items asking patients to rate the likelihood of dozing off in various everyday situations. It is the most commonly used standardized measure of daytime sleepiness in clinical practice and research.

Johns, M. W. (1991). A new method for measuring daytime sleepiness: the Epworth Sleepiness Scale. Sleep, 14(6), 540-545.

Johns, M. W. (1992). Reliability and factor analysis of the Epworth Sleepiness Scale. Sleep, 15(4), 376-381.

Bloch, K. V., Peixoto, S. V., & Sichieri, R. (2002). Prevalence of overweight and obesity in Rio de Janeiro, Brazil: weight as a protective factor. Journal of Public Health, 36(2), 206-213.


EuroQol Five-Dimension Health Status Questionnaire

The EQ-5D is a standardized, preference-based health utility measure developed by the EuroQol Group in 1990. It combines a descriptive health profile (five dimensions, three or five response levels) with a visual analog scale to quantify overall health status. The instrument has become essential for health economics, clinical trials, and cost-effectiveness analysis worldwide.

Generic preference-based health utility measure

Rabin, R., & de Charro, F. (2001). EQ-5D: a measure of health status from the EuroQol Group. Annals of Medicine, 33(5), 337–343.

EuroQol Group. (1990). EuroQol—a new facility for the measurement of health-related quality of life. Health Policy, 16(3), 199–208.

Herdman, M., Gudex, C., Lloyd, A., et al. (2011). Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L). Quality of Life Research, 20(10), 1727–1736.


eQTL Analysis

Expression Quantitative Trait Loci Analysis

eQTL mapping
expression QTL analysis
transcriptomic QTL analysis
eQTL study

2001 (term coined); widely adopted after 2005

eQTL analysis identifies genomic loci (variants, typically SNPs) whose genotype statistically associates with variation in the expression level of one or more genes. By jointly profiling DNA-level variation and RNA-level expression in the same individuals, eQTL studies decode the regulatory grammar of the genome — revealing which variants control how much a gene is transcribed, in which tissues, and under what conditions.

Genotype data (SNP arrays or WGS) paired with gene expression data (microarray or RNA-seq)

Jansen, R. C., & Nap, J.-P. (2001). Genetical genomics: the added value from segregation. Trends in Genetics, 17(7), 388–391.

GTEx Consortium (2020). The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science, 369(6509), 1318–1330.

genome-wide-association-study rna-seq-differential-expression single-cell-rna-seq-analysis pathway-enrichment-analysis gene-set-enrichment-analysis copy-number-variation-analysis


Equal-weight case-focused mixed methods

Equal-Weight Case-Focused Mixed Methods Design

QUAN+QUAL case study
equal-priority case mixed methods
balanced case-focused mixed methods
equal-status case study mixed methods

2000s–2010s (mixed methods typology formalized ~2007–2011)

Creswell & Plano Clark; Yin (case study tradition)

Equal-weight case-focused mixed methods is a research design that investigates a bounded case — a person, program, organization, or event — using qualitative and quantitative strands that are treated as equally important. Neither strand is subordinate; both contribute with the same priority to the final interpretation of the case. Data are collected and analyzed separately, then integrated at the interpretation stage to produce a richer, more complete understanding of the case than either approach could yield alone.

Qualitative (interviews, observations, documents) and quantitative (surveys, measures) collected for a bounded case

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

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


Equal-weight concurrent embedded mixed methods design

Equal-Weight Concurrent Embedded Mixed Methods Design

equal-priority concurrent embedded design

The equal-weight concurrent embedded mixed methods design collects quantitative and qualitative data simultaneously, with one strand nested inside the other, while assigning both strands equivalent analytic priority. Unlike the standard embedded design where one dominant strand drives the study and the other plays a supporting role, the equal-weight variant treats both strands as co-equal contributors to understanding the research problem, demanding rigorous analysis of both before integration.

Quantitative data (surveys, scales, experiments) and qualitative data (interviews, observations, documents) collected simultaneously

Creswell, J. W., & Plano Clark, V. L. (2011). Designing and Conducting Mixed Methods Research (2nd ed.). Sage.


Equal-weight concurrent triangulation mixed methods design

Equal-Weight Concurrent Triangulation Mixed Methods Design

equal-status concurrent triangulation
balanced concurrent triangulation
QUAN+QUAL concurrent triangulation
equal-priority triangulation design

The equal-weight concurrent triangulation mixed methods design collects quantitative and qualitative data simultaneously, assigning equal priority to both strands, then compares or merges the results to examine convergence, divergence, or complementarity. No single strand dominates: neither the numeric nor the textual evidence is treated as a check on the other — both stand as full and equivalent sources of insight about the same phenomenon.

Concurrent quantitative data (surveys, tests, instruments) and qualitative data (interviews, observations, documents)

Creswell, J. W. (2003). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (2nd ed.). Sage.


Equal-weight explanatory sequential mixed methods design

Equal-Weight Explanatory Sequential Mixed Methods Design

equal-priority explanatory sequential design

1991–2003 (weighting dimension formalised; equal-weight variant explicit in Creswell & Plano Clark 2007 onward)

Creswell & Plano Clark (priority/weighting framework); Morse (1991) introduced strand weighting notation

The equal-weight explanatory sequential mixed methods design collects and analyzes quantitative data first, then uses qualitative data to explain or elaborate on the quantitative findings, assigning equal analytic priority to both strands. Unlike the standard explanatory sequential design — where quantitative data typically holds dominance — this variant treats the qualitative follow-up as equally essential to the study's conclusions, not merely supplementary.

Quantitative data (Phase 1) followed by qualitative data (Phase 2); equal analytic weight assigned to both strands

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

Morse, J. M. (1991). Approaches to qualitative-quantitative methodological triangulation. Nursing Research, 40(2), 120–123.


Equal-weight exploratory sequential mixed methods design

Equal-Weight Exploratory Sequential Mixed Methods Design

QUAL→QUAN equal-priority design
equal-status exploratory sequential MMR
balanced exploratory sequential design
QUAL+QUAN exploratory sequence

2003–2009 (formalized in mixed methods typology literature)

Creswell & Plano Clark (strand-priority typology); Teddlie & Tashakkori (equal-status framing)

The equal-weight exploratory sequential mixed methods design is a two-phase research strategy in which an initial qualitative strand explores a phenomenon in depth, and its findings directly inform the construction of a subsequent quantitative strand. Unlike the qualitative-priority variant, both strands carry equal analytic importance: neither serves merely as a supplement to the other. The design is particularly powerful when theory or validated instruments are lacking and researchers must build measurement tools grounded in participants' own frameworks before testing them at scale.

Qualitative data (Phase 1) followed by quantitative data (Phase 2); both strands carry equal analytic weight

Teddlie, C., & Tashakkori, A. (2009). Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioral Sciences. Sage.


Equal-weight intervention mixed methods

Equal-Weight Intervention Mixed Methods Design

equal-priority intervention MMR
balanced intervention mixed methods
QUAL=QUAN intervention design
equal-status intervention mixed design

Creswell & Plano Clark (weighting framework); intervention design tradition in mixed methods

Equal-weight intervention mixed methods is a research design in which both quantitative and qualitative strands are assigned equal priority and are embedded within or alongside an intervention, program, or experiment. The design evaluates not only whether an intervention works (QUAN outcomes) but also how and why it works or fails (QUAL processes), with neither strand treated as secondary. It is particularly suited to program evaluation, clinical trials with process components, and educational or social interventions.

Both quantitative (surveys, scales, outcome measures) and qualitative (interviews, observations) data collected within an intervention or program evaluation context

Mertens, D. M. (2003). Mixed methods and the politics of human research: The transformative-emancipatory perspective. In A. Tashakkori & C. Teddlie (Eds.), Handbook of Mixed Methods in Social and Behavioral Research (pp. 135–164). SAGE Publications.


Equal-weight multilevel mixed methods

Equal-Weight Multilevel Mixed Methods Design

QUAN+QUAL multilevel design
equal-status multilevel mixed methods
balanced multilevel mixed methods
equal-priority multilevel mixed methods

Tashakkori & Teddlie; Creswell & Plano Clark

Equal-weight multilevel mixed methods is a mixed methods design in which quantitative and qualitative data strands are collected at two or more distinct levels of a social system — such as students, classrooms, and schools — and both strands carry equal analytic priority. The QUAN+QUAL notation (where '+' signals equal weight) is applied across each level, and integration occurs both within and between levels to build a comprehensive, multi-perspectival understanding.

Quantitative data (surveys, tests, administrative records) and qualitative data (interviews, observations, documents) collected at multiple levels of a social system

Creswell, J. W., & Plano Clark, V. L. (2017). Designing and Conducting Mixed Methods Research (3rd ed.). Sage.

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


Equal-Weight Multiphase Mixed Methods Design

QUAL+QUAN multiphase design
balanced multiphase mixed methods
equal-priority multiphase design
multiphase equal-weighting design

Creswell & Plano Clark; equal-weighting notation formalized by Morse

Equal-weight multiphase mixed methods design is a rigorous research framework in which quantitative and qualitative strands are assigned equal priority and implemented across three or more sequential or iterative phases. Each phase informs the next, and neither strand is treated as subordinate. The design is especially suited to large-scale, longitudinal, or program-evaluation projects that require both breadth and depth over time.

Quantitative and qualitative data, collected across multiple sequential or iterative phases

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

Morse, J. M. (2003). Principles of mixed methods and multimethod research design. In A. Tashakkori & C. Teddlie (Eds.), Handbook of Mixed Methods in Social and Behavioral Research (pp. 189–208). Sage Publications.


Equal-weight pragmatic mixed methods

Equal-Weight Pragmatic Mixed Methods Design

equal-status pragmatic MMR
QUAL=QUAN pragmatic design
balanced pragmatic mixed methods
equal-priority pragmatic design

Creswell, Plano Clark, Johnson, Onwuegbuzie (mixed methods methodology scholars)

Equal-weight pragmatic mixed methods is a research design in which quantitative and qualitative strands are assigned the same methodological priority (QUAL = QUAN) and conducted from a pragmatist philosophical stance. Rather than privileging one paradigm, the researcher selects and combines methods that best answer the research question — treating practical utility as the primary criterion for all design decisions.

Quantitative data (surveys, instruments, tests) and qualitative data (interviews, observations, documents), gathered and weighted equally

Johnson, R. B., Onwuegbuzie, A. J., & Turner, L. A. (2007). Toward a definition of mixed methods research. Journal of Mixed Methods Research, 1(2), 112–133.


Equal-weight transformative mixed methods design

Equal-Weight Transformative Mixed Methods Design

transformative equal-status mixed design

Donna M. Mertens; John W. Creswell & Vicki L. Plano Clark

The equal-weight transformative mixed methods design combines quantitative and qualitative strands at equal priority (QUAN + QUAL) within an overarching transformative theoretical framework — such as feminist, critical race, disability rights, or social justice theory. Both data types carry equivalent evidential weight, and the entire study is structured to challenge inequity, give voice to marginalized groups, and generate findings oriented toward advocacy and social change.

Quantitative data (surveys, scales, instruments) and qualitative data (interviews, focus groups, documents) collected at equal weight

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


EQUATOR Network Reporting Guidelines

Enhancing QUAlity and Transparency Of health Research Network and Standards

EQUATOR
reporting guidelines
PRISMA
CONSORT
STROBE

EQUATOR Network (founded 2006); hosted by University of Oxford

EQUATOR (Enhancing QUAlity and Transparency Of health Research) is a global network that develops, endorses, and promotes reporting guidelines for health and life sciences research. Founded in 2006 and hosted by the University of Oxford, EQUATOR maintains a library of 500+ guidelines covering study designs (randomized trials, observational studies, systematic reviews, case reports, qualitative research, etc.). Major guidelines include CONSORT (randomized controlled trials), STROBE (observational studies), PRISMA (systematic reviews and meta-analyses), and CARE (case reports). These guidelines specify which items must be reported and how to report them, reducing inconsistency and enabling readers to assess study validity. Many journals now require adherence to relevant EQUATOR guidelines.

Moher, D., Altman, D. G., Schulz, K. F., Simera, I., & Wager, E. (2012). Guidelines for reporting health research: A user's manual. British Medical Journal, 345, e5997.

EQUATOR Network (2023). Enhancing the QUAlity and Transparency Of health Research. Retrieved from https://www.equator-network.org/

Page, M. J., McKenzie, J. E., Bossuyt, P. M., et al. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. British Medical Journal, 372, n71.

imrad-structure statistical-reporting-standards scientific-writing-clarity journal-submission-process


Equine Gait Analysis

Equine Gait Analysis and Lameness Assessment

lameness evaluation
motion analysis
stride assessment

Equine Veterinary Medicine Research Community

Equine Gait Analysis is a systematic evaluation of a horse's movement patterns at walk, trot, and canter to detect lameness, asymmetry, and biomechanical dysfunction. Combining visual observation with increasingly sophisticated instrumental techniques (force plate analysis, kinematics, accelerometry), gait analysis is essential for diagnosing the causes of lameness, monitoring recovery from injury, and evaluating the safety and suitability of sport horses.

Observational and Instrumental Evaluation

Keegan, K. G., Dent, E. V., Balev, S. F., Bunch, P., Ellington, J. K., French, H., & Bennett, D. K. (2011). Equine movement: from structure to function. Equine Veterinary Journal, 43(3), 285-291.

Dyson, S. J., & Buchanan, L. R. (2002). Lameness and poor performance in sport horses: evaluation with high-speed digital cinematography--an overview. Equine Veterinary Journal Supplement, 34, 366-373.

Weishaupt, M. A., Wiestner, T., Hogg, H. P., Jordan, P., & Auer, J. A. (2005). Gait analysis of naturally infected horses with epizootic lymphangitis. Equine Veterinary Journal, 37(3), 235-240.


Equivalence Test (TOST)

Two One-Sided Tests Procedure for Equivalence

Eşdeğerlik Testi (TOST — Two One-Sided Tests)

The equivalence test using the Two One-Sided Tests (TOST) procedure is a parametric hypothesis test designed to demonstrate that the difference between two group means falls within a pre-specified equivalence region ±Δ. Introduced by Schuirmann (1987) in the context of pharmaceutical bioequivalence, TOST reverses the logic of classical null-hypothesis testing: instead of trying to detect a difference, it provides positive evidence of similarity.

Schuirmann, D.J. (1987). A Comparison of the Two One-Sided Tests Procedure and the Power Approach for Assessing the Equivalence of Average Bioavailability. Journal of Pharmacokinetics and Biopharmaceutics, 15(6), 657–680.

Lakens, D. (2017). Equivalence Tests: A Practical Primer for t Tests, Correlations, and Meta-Analyses. Social Psychological and Personality Science, 8(4), 355–362.

independent-t-test paired-t-test welch-t-test mann-whitney-u one-way-anova