CONDORCET

CONDORCET (Condorcet Method — Pairwise majority winner from ranked ballots) is a aggregationoperator multi-criteria decision-making (MCDM) method introduced by Marquis de Condorcet in 1900. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Pairwise majority rule — winner beats every other alternative in pairwise contest

Marquis de Condorcet (1900). Essai sur l'application de l'analyse à la probabilité des décisions rendues à la pluralité des voix (1785). Imprimerie Royale, Paris (original 1785)


Conference Paper and Proceedings

Conference Paper and Proceedings (Peer-Reviewed Conference Presentations and Publication)

conference abstract
conference proceedings
conference presentation
poster presentation

Academic conferences (20th century formalization)

A conference paper is original research presented at an academic conference, typically via oral presentation or poster. Conference papers are published in proceedings (collection of papers from a conference) and indexed in databases (Scopus, Web of Science). Unlike journal articles requiring 12–24 months for publication, conference papers are disseminated rapidly (often within weeks or months), making them valuable for communicating cutting-edge findings and early-stage research. Peer review rigor varies: some conferences employ rigorous peer review; others are less selective. Conference papers often precede or parallel journal publication, facilitating scholarly dialogue and networking.

International Committee of Medical Journal Editors (2023). Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals. ICMJE.

Conference Series International. Guidelines for Conference Paper Submission and Presentation. https://www.conferenceseries.com

Scopus Coverage of Conference Papers. https://www.elsevier.com/products/scopus

original-research-article peer-review-process poster-presentation-design academic-networking


Confidence Interval

Confidence Interval Estimation and Interpretation in Statistical Inference

CI
95% CI
credible interval
interval estimate

A confidence interval (CI) is a range of values, calculated from sample data, that likely contains the true population parameter. Introduced by Jerzy Neyman in 1937, it provides an interval estimate rather than a single point estimate, incorporating both the observed value and the uncertainty around it. The standard 95% confidence interval is a robust, intuitive alternative to p-values for communicating research results.

Neyman, J. (1937). Outline of a Theory of Statistical Estimation Based on the Classical Theory of Probability. Philosophical Transactions of the Royal Society, 236, 333–380.

Altman, D. G., Machin, D., Bryant, T. N., & Gardner, M. J. (1989). Statistics with Confidence. British Medical Journal.

Cumming, G. (2014). The New Statistics: Why and How. Psychological Science, 25(1), 7–29.

p-value-significance effect-size statistical-power null-hypothesis


Configuration Interaction

CI
configuration interaction method
CI calculations

Configuration Interaction (CI) is a post-Hartree-Fock quantum chemistry method that improves upon mean-field molecular orbital theory by treating electron correlation through a linear combination of electronic configurations. Introduced by Roothaan in 1960, CI corrects for the fundamental limitation of single-determinant theory by allowing the wavefunction to be a superposition of excited-state Slater determinants, systematically accounting for electron-electron interactions.

Roothaan, C. C. J. (1960). New developments in molecular orbital theory. Reviews of Modern Physics, 32(2), 179-185.

Szabo, A., & Ostlund, N. S. (1996). Modern Quantum Chemistry: Introduction to Advanced Electronic Structure Theory. Dover Publications.


Confirmatory Factor Analysis for Scales

Confirmatory Factor Analysis Method for Scale Validation and Structural Testing

CFA
Confirmatory factor analysis
Path analysis
Structural equation modeling

Confirmatory Factor Analysis (CFA) is a statistical method for testing whether a hypothesized factorial structure fits empirical data. Developed by Karl G. Jöreskog in 1969, CFA is the standard approach for validating psychometric scales by evaluating whether items load onto theoretically specified latent factors as expected. Unlike exploratory factor analysis, CFA requires a priori specification of the factor structure and provides goodness-of-fit indices to assess model adequacy.

Confirmatory factor analysis methodology

Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183-202.

Hoyle, R. H. (Ed.). (2012). Handbook of Structural Equation Modeling. New York: Guilford Press.

Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling (4th ed.). New York: Guilford Press.

likert-scale-construction factor-analysis-scale content-validity-ratio floor-ceiling-effect


Confirmatory factor analysis

CFA
confirmatory FA
measurement model
restricted factor analysis

Confirmatory factor analysis tests a researcher-specified factor structure against observed data. Unlike exploratory approaches, the researcher decides in advance which indicators load on which latent factor, and the model is evaluated by how closely the implied covariance matrix reproduces the sample covariance matrix. CFA is central to scale validation, construct validity assessment, and measurement invariance testing.

Hypothesis-testing latent variable model

Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202.

Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). Guilford Press.

exploratory-factor-analysis structural-equation-modeling measurement-invariance cronbachs-alpha mcdonalds-omega convergent-validity


Confirmatory Research

Confirmatory Quantitative Research Design

hypothesis-testing research
deductive research
theory-testing research
confirmatory study

1934 (Popper); widely adopted in social sciences from 1960s onward

Karl Popper (falsificationism); formalized in behavioral sciences by Paul Meehl and others

Confirmatory research is a deductive quantitative design in which the researcher specifies hypotheses derived from existing theory before data collection, then tests whether the data support or refute those hypotheses. Unlike exploratory approaches that generate ideas from data, confirmatory research begins with an established theoretical framework, pre-registers predictions, and applies statistical tests to evaluate those predictions against empirical evidence. It is the backbone of hypothesis-driven social, behavioral, and health science inquiry.

Numerical data from surveys, experiments, secondary datasets

Popper, K. R. (1959). The Logic of Scientific Discovery. Hutchinson.

Kline, R. B. (2013). Beyond Significance Testing: Statistics Reform in the Behavioral Sciences (2nd ed.). American Psychological Association.

hypothesis-testing-research model-testing-research exploratory-quantitative-research correlational-research explanatory-research cross-sectional-research


Conflict of Interest in Research

Managing Financial and Non-Financial Conflicts of Interest in Research

Multiple (NIH, ICMJE, institutional COI policies)

A conflict of interest (COI) in research exists when a researcher has financial, professional, or personal interests that might bias their research judgment or outcomes. Conflicts are inherent in research communities—researchers often have legitimate stakes in their research's success—but unmanaged conflicts compromise research integrity and public trust. Managing COI requires transparent disclosure, institutional oversight, and proactive mitigation strategies to minimize bias risk while allowing legitimate research to proceed.

International Committee of Medical Journal Editors. (2023). Defining the Role of Authors and Contributors. ICMJE Recommendations for Manuscript Authorship.

U.S. Department of Health and Human Services. (2013). Physician Payments Sunshine Act Reporting. Code of Federal Regulations Title 42, Section 1320a-7h.

National Institutes of Health. (2019). Financial Conflict of Interest Requirements. NIH Grant Conditions and Regulations.


Conflict Tactics Scale

intimate partner violence and conflict assessment

The Conflict Tactics Scale is the most widely used instrument for measuring how intimate partners handle disagreements and conflict, including tactics ranging from negotiation and psychological aggression to physical violence and sexual coercion. Developed by Murray Straus in 1979 and substantially revised in 1996 (CTS2), it is used extensively in family research, intimate partner violence assessment, and couple therapy evaluation. The CTS2 measures five dimensions of conflict behavior: negotiation (reasoning and discussion), psychological aggression (insults, threats), physical assault (pushing, hitting, weapon use), injury (physical consequences of violence), and sexual coercion.

Straus, M. A. (1996). Measuring intrafamily conflict and violence: The Conflict Tactics Scale. Journal of Family Issues, 41(1), 75-88.

Straus, M. A., & Douglas, E. M. (2004). A short form of the Revised Conflict Tactics Scale, and typologies for violence and control. Violence and Victims, 19(5), 507-520.

Hamby, S. L., Poindexter, V. C., & Reaney, S. (2006). Adaptations for diverse populations of the Conflict Tactics Scale: A review. Aggression and Violent Behavior, 11(4), 375-385.


Conformal Prediction (Time Series)

Conformal Prediction for Time-Series Forecasting

Konformal Tahmin (Conformal Prediction — Zaman Serisi)

Angelopoulos & Bates (tutorial); Xu & Xie (time-series EnbPI)

Conformal prediction is a distribution-free wrapper that turns any point forecaster — ARIMA, a neural network, or a machine-learning model — into valid prediction intervals using only its residuals. The time-series form was popularised by Xu & Xie (2021) and the modern tutorial treatment by Angelopoulos & Bates (2023).

Distribution-free prediction interval wrapper

Nonconformity (residual) quantile calibration

Angelopoulos, A. N. & Bates, S. (2023). Conformal Prediction: A Gentle Introduction. Foundations and Trends in Machine Learning, 16(4), 494-591.

Xu, C. & Xie, Y. (2021). Conformal Prediction Interval for Dynamic Time-Series. International Conference on Machine Learning (ICML).

arima quantile-regression ols-regression gradient-boosting lstm-forecast


Conformal Prediction

Conformal Prediction (Distribution-Free Prediction Sets)

Conformal Inference
Conformal Risk Control
Inductive Conformal Prediction
Uyumsal Tahmin

Conformal Prediction is a distribution-free framework for constructing statistically valid prediction sets (for classification) or prediction intervals (for regression) around the output of any pre-trained machine learning model. Introduced by Vovk, Gammerman, and Shafer in their 2005 monograph, it provides a finite-sample marginal coverage guarantee — the true label falls inside the prediction set with at least 1-alpha probability — without requiring parametric assumptions about the data distribution.

Distribution-free uncertainty quantification framework

Marginal validity at user-specified level 1-alpha

Requires exchangeable (i.i.d.) calibration data

Vovk, V., Gammerman, A., & Shafer, G. (2005). Algorithmic Learning in a Random World. Springer.


Confusion Matrix

The confusion matrix is a table that displays the counts of true positives, true negatives, false positives, and false negatives. It provides a complete picture of where a classifier makes correct and incorrect predictions, enabling calculation of all other classification metrics.

Everitt, B. S., & Hothorn, T. (2005). A Handbook of Statistical Analyses Using R. Chapman and Hall/CRC.

Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.

accuracy precision recall specificity matthews-correlation-coefficient


Conjoint Analysis

Conjoint Analysis (Choice-Based and Adaptive Variants)

Birleşik Analiz (Conjoint Analysis — CBC, ACA)

Conjoint analysis is a preference-measurement technique that decomposes overall product evaluations into the separate utility values — called part-worths — that respondents assign to each attribute level. Formalised by Green and Srinivasan in their seminal 1978 Journal of Consumer Research paper, the method has become the dominant tool in marketing research and product design for quantifying what buyers truly trade off when they choose between options.

CBC (Choice-Based), ACA (Adaptive), Full-Profile

Output: Part-worth utilities, relative attribute importance

Green, P.E. & Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5(2), 103–123.

Orme, B.K. (2020). Getting Started with Conjoint Analysis: Strategies for Product Design and Pricing Research (3rd ed.). Research Publishers.

discrete-choice-simulation factorial-design fractional-factorial multinomial-logit randomized-controlled-trial response-surface-methodology


Conjugate Gradient Method

Conjugate Gradient Method for Linear Systems

The Conjugate Gradient (CG) Method is an iterative algorithm for solving large sparse symmetric positive-definite linear systems Ax = b, developed by Hestenes and Stiefel in 1952. It is one of the most widely used iterative solvers in scientific computing because it converges in at most n iterations for an n × n matrix and typically requires far fewer.

Hestenes, M. R., & Stiefel, E. (1952). Methods of conjugate gradients for solving linear systems. Journal of Research of the National Bureau of Standards, 49(6), 409–436.

Saad, Y. (2003). Iterative Methods for Sparse Linear Systems (2nd ed.). SIAM.

Nocedal, J., & Wright, S. J. (2006). Numerical Optimization (2nd ed.). Springer.


Conjugate Prior Analysis

conjugate priors
conjugate Bayesian updating
closed-form posterior analysis
Beta-Binomial model
Normal-Normal model
natural conjugate analysis

Raiffa & Schlaifer (1961); DeGroot (1970)

Conjugate prior analysis is a class of Bayesian inference methods in which the prior distribution and the likelihood belong to a matched family — called a conjugate pair — so that the posterior distribution has exactly the same functional form as the prior and can be derived in closed form. Introduced systematically by Raiffa and Schlaifer (1961) and consolidated by DeGroot (1970), conjugate analysis is the pedagogic backbone of introductory Bayesian statistics and a practical tool whenever analytical tractability is required.

posterior inference / parameter estimation

depends on conjugate pair (binary, count, continuous)

posterior distribution / updated hyperparameters / credible intervals

Beta-Binomial, Normal-Normal, Gamma-Poisson, Dirichlet-Multinomial

Raiffa, H. & Schlaifer, R. (1961). Applied Statistical Decision Theory. Harvard University Press.

DeGroot, M. H. (1970). Optimal Statistical Decisions. McGraw-Hill.

Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.

bayesian-regression mcmc hierarchical-bayes empirical-bayes variational-bayes


CNS

nature connection and affinity assessment

The Connectedness to Nature Scale (CNS) measures the degree to which individuals feel emotionally and cognitively connected to nature as part of their sense of self. Developed by Mayer and Frantz (2004), the CNS operationalizes the construct of nature connection—the felt sense of kinship, interdependence, and belonging with the natural world. The scale is widely employed in environmental psychology research, health outcome studies examining nature exposure effects, and intervention evaluations designed to strengthen human-nature relationships.

Mayer, F. S., & Frantz, C. M. (2004). The connectedness to nature scale: A measure of individuals' feeling of dependence on nature. Journal of Environmental Psychology, 24(4), 503–515.


Conners Rating Scales

The Conners Rating Scales-Revised (CRS-R), developed by Keith Conners and updated in 2008, is the most widely used rating scale instrument for identifying and assessing attention-deficit/hyperactivity disorder (ADHD) and behavioral problems in children and adolescents aged 6–18 years. Available in parent, teacher, and self-report versions, the CRS-R provides comprehensive assessment of ADHD symptoms and comorbid behavioral concerns across multiple informants and settings.

Multi-informant ADHD and behavior rating scale

Conners, C. K. (2008). Conners Rating Scales-Revised (CRS-R): Technical Manual. Multi-Health Systems.

Conners, C. K. (1997). Conners Rating Scales-Revised: Technical Manual. Multi-Health Systems Inc.

vanderbilt-adhd-scale cbcl-child-behavior strengths-difficulties-questionnaire achenbach-youth-self-report


Conover-Iman Test

Conover-Iman Post-Hoc Multiple Comparison Test

Conover-Iman post-hoc test
Conover post-hoc test
Conover-Iman Post-Hoc Testi

The Conover-Iman test is a rank-based post-hoc procedure, introduced by Conover and Iman in 1979, that identifies which pairs of groups differ after a significant Kruskal-Wallis or Friedman test. It builds a t-style statistic on the pooled ranks and is generally more powerful than the comparable Dunn test.

Nonparametric post-hoc multiple comparison

Conover, W. J. & Iman, R. L. (1979). On Multiple-Comparisons Procedures. Technical Report LA-7677-MS, Los Alamos Scientific Laboratory.

Hollander, M., Wolfe, D. A. & Chicken, E. (2014). Nonparametric Statistical Methods (3rd ed.). Wiley.

dunn-test kruskal-wallis-test friedman-test mann-whitney-u-test nemenyi-test


CONSENSUS-REACHING

CONSENSUS-REACHING (Consensus Reaching — Iterative aggregation of expert opinions toward group consensus) is a ranking multi-criteria decision-making (MCDM) method introduced by Herrera-Viedma, E., Herrera, F., Chiclana, F. in 2002. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Group decision — iterative feedback-driven consensus convergence

Herrera-Viedma, E., Herrera, F., Chiclana, F. (2002). A consensus model for multiperson decision making with different preference structures. IEEE Transactions on Systems, Man and Cybernetics — Part A


Consensus Sleep Diary

Sleep Diary
Consensus Sleep Diary for Insomnia

Sleep tracking; daily prospective monitoring

The Consensus Sleep Diary is a standardized daily self-report instrument for prospective monitoring of sleep and wakefulness patterns. Developed by Carney and colleagues in 2012 through an international consensus process involving sleep medicine researchers and clinicians, it represents a unified approach to sleep tracking across clinical and research settings. The Consensus Sleep Diary records time in bed, sleep onset time, number and duration of nighttime awakenings, sleep quality, and other sleep-relevant variables, providing detailed information about sleep patterns that polysomnography cannot capture (daytime napping, sleep-wake schedule, weekly variation).

Carney, C. E., Buysse, D. J., Ancoli-Israel, S., et al. (2012). The consensus sleep diary: standardizing prospective sleep self-monitoring. Sleep, 35(2), 287-302.


CONSORT Reporting Checklist

Consolidated Standards of Reporting Trials Checklist

The CONSORT (Consolidated Standards of Reporting Trials) Statement is a 25-item evidence-based checklist and flow diagram developed to standardize reporting of parallel-group randomized controlled trials. First published in 1996 and updated in 2010 (CONSORT 2010), it is endorsed by over 600 journals including The Lancet, JAMA, and BMJ, and is mandatory or strongly recommended for RCT manuscript submission across clinical research.

Schulz, K. F., Altman, D. G., & Moher, D. (2010). CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials. The Lancet, 375(9721), 1657–1668.

cochrane-risk-of-bias casp-rct-checklist prisma-checklist strobe-checklist


Conspiracy Mentality Questionnaire

Conspiracy Mentality Questionnaire (CMQ)

CMQ
Conspiracy Ideation Scale
Generic Conspiracy Belief

The Conspiracy Mentality Questionnaire measures individual differences in generic conspiracy thinking—the tendency to attribute significant events to hidden, coordinated group actions by powerful actors rather than to incompetence, chance, or transparent public causes. Developed by Bruder et al. (2013), the five-item CMQ assesses a stable dispositional trait that predicts belief in diverse conspiracy theories (JFK assassination, 9/11 truthers, anti-vaccine narratives, QAnon) and distrust of institutions. It captures conspiracy mentality as a generalised political attitude distinct from specific beliefs.

Bruder, M., Haffke, P., Neave, N., Nouripanah, N., & Imhoff, R. (2013). Measuring individual differences in generic beliefs in conspiracy: Conspiracy Mentality Questionnaire. Frontiers in Psychology, 4, 225.

Imhoff, R., & Bruder, M. (2014). Speaking (un-)truth to power: Conspiracy mentality as a generalised political attitude. European Journal of Personality, 28(1), 25-43.

Swami, V., Coles, R., Stieger, S., Pietschnig, J., Furnham, A., Rehim, S., & Voracek, M. (2011). Conspiracist ideation in Britain and Austria: Validating a scale for the assessment of belief in conspiracy theories. Social Psychiatry and Psychiatric Epidemiology, 46(12), 1189-1198.


Constant Comparative Method

CCM
constant comparison
constant comparative analysis
comparative constant analysis

The Constant Comparative Method (CCM) is a systematic qualitative analysis procedure in which every newly coded incident is immediately compared with all previously coded incidents in the same category. Introduced by Glaser and Strauss in their 1967 grounded theory framework, CCM drives theory development by cycling continuously between data collection and analysis, progressively refining categories until theoretical saturation is reached. Though closely associated with grounded theory, the method has been widely adopted as a stand-alone analytic strategy across qualitative traditions.

Interviews, field notes, documents, observations (text data)

Theoretical sampling until saturation (commonly 15–40 participants or units)

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

Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic Inquiry. Sage.

grounded-theory thematic-analysis content-analysis case-study phenomenology narrative-analysis


Constituency Parsing

Constituency Parsing (Phrase-Structure Parsing)

Kurucu Öbek Ayrıştırma (Constituency Parsing)

Michael Collins (statistical models, 2003)

Constituency parsing is a natural-language-processing task that represents a sentence as a tree of recursively nested phrase-structure constituents — for example S → NP + VP. Building on the head-driven statistical parsing models introduced by Collins (2003) and the later neural parsers of Kitaev and colleagues (2019), it exposes the hierarchical syntactic skeleton of a sentence for grammatical pattern extraction and grammar research.

Output: Phrase-structure (constituency) parse tree, e.g. S → NP + VP

Grammar-based (CFG) / neural self-attention parsers

Collins, M. (2003). Head-Driven Statistical Models for Natural Language Parsing. Computational Linguistics, 29(4), 589-637.

Kitaev, N., Cao, S. & Klein, D. (2019). Multilingual Constituency Parsing with Self-Attention and Pre-Training. Proceedings of ACL.


Constraint Programming

Constraint Satisfaction Programming
Constraint-Based Optimization
Kısıt Programlama
CSP Optimization

Constraint Programming (CP) is a declarative optimization paradigm in which a problem is formulated as a set of variables, finite domains, and constraints, and a solver systematically searches for assignments that satisfy all constraints. Formalized comprehensively by Rossi, van Beek, and Walsh in their 2006 Handbook of Constraint Programming, CP unifies propagation-based pruning with intelligent backtracking search to tackle combinatorial problems across scheduling, planning, and configuration domains.

NP-complete in general; polynomial for tractable subclasses

Declarative constraint propagation with search

Rossi, F., van Beek, P., & Walsh, T. (Eds.). (2006). Handbook of Constraint Programming. Elsevier.


Construct Validity

construct validation
factorial validity
nomological validity evidence
validity of interpretation

Construct validity is the degree to which a test or scale actually measures the theoretical construct it is intended to measure. Introduced by Cronbach and Meehl in 1955, it is the central validity concern in psychological and educational measurement, evaluated by accumulating multiple lines of empirical and logical evidence rather than by any single statistical test.

Scores from psychological / educational instruments

Cronbach, L. J. & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302.

Messick, S. (1989). Validity. In R. L. Linn (Ed.), Educational Measurement (3rd ed., pp. 13–103). American Council on Education / Macmillan.

convergent-validity discriminant-validity content-validity confirmatory-factor-analysis nomological-validity exploratory-factor-analysis


Constructed Wetland Design

Ecological Wastewater Treatment via Constructed Wetlands

CW design
treatment wetlands
natural treatment systems
artificial wetlands

Constructed wetland design is an environmental engineering approach that harnesses natural biological and chemical processes—microorganism metabolism, plant uptake, soil sorption, sedimentation—to treat wastewater, stormwater, and agricultural runoff. Developed systematically in the 1970s by German researchers Seidel and Kickuth, constructed wetlands operate with minimal energy input and create amenity and biodiversity co-benefits alongside treatment. The design process integrates hydrology, biogeochemistry, and landscape planning to optimize contaminant removal.

Kadlec, R. H., & Wallace, S. D. (2009). Treatment Wetlands (2nd ed.). CRC Press.

Tanner, C. C. (2000). Design Manual: Wastewater Treatment Using Free Water Surface Constructed Wetlands. New Zealand Water and Wastes Association.

García, J., Rousseau, D. P. L., Morató, J., Lesage, E., Matamoros, V., & Bayona, J. M. (2010). Contaminant Removal Processes in Subsurface-Flow Constructed Wetlands: A Review. Critical Reviews in Environmental Science and Technology, 40(7), 561-661.

stormwater-management wastewater-treatment-design activated-sludge-model green-infrastructure-design environmental-impact-assessment


Constructivist Grounded Theory

Constructivist Grounded Theory (Charmaz)

CGT
constructivist GT
Charmaz grounded theory
interpretive grounded theory

2000s (Charmaz 2000–2006; classic GT roots 1967)

Kathy Charmaz (building on Glaser & Strauss, 1967)

Constructivist Grounded Theory (CGT) is a qualitative methodology developed by Kathy Charmaz that systematically builds mid-range theory from empirical data through iterative coding, memo-writing, and theoretical sampling. Unlike the original objectivist version by Glaser and Strauss, CGT treats both data and theory as co-constructed between researcher and participants, acknowledging the researcher's interpretive perspective as an integral part of the analytic process rather than a source of bias to be eliminated.

Interviews, focus groups, observations, documents, field notes

15–30 participants (theoretical sampling until saturation)

Charmaz, K. (2006). Constructing Grounded Theory: A Practical Guide Through Qualitative Analysis. Sage.

Charmaz, K. (2014). Constructing Grounded Theory (2nd ed.). Sage.

grounded-theory phenomenology ethnography case-study narrative-analysis thematic-analysis


Consumer Involvement Scale

Product Involvement Scale
Personal Involvement Inventory

The Consumer Involvement Scale (CIS), developed by Zaichkowsky (1985), measures the degree to which a consumer feels personally invested in a product, brand, or purchase decision. Originally a 20-item instrument operationalizing the concept of 'personal relevance,' the CIS was refined to 10 items in 1994 (Revised Personal Involvement Inventory, PII), maintaining measurement of consumer involvement across multiple semantic dimensions. Involvement captures both the personal importance of a product category and the perceived risk of making a poor choice. The scale is fundamental in consumer behavior research for understanding motivation, information processing, and purchase decision intensity.

Uni-dimensional consumer involvement scale

Zaichkowsky, J. L. (1985). Measuring the Involvement Construct. Journal of Consumer Research, 12(3), 341-352.

Zaichkowsky, J. L. (1994). The Personal Involvement Inventory: Reduction, Revision, and Application to Advertising. Journal of Advertising, 23(4), 59-70.


Contact Angle Goniometry

Contact Angle Goniometry Surface Characterization

sessile drop method
contact angle measurement
wettability analysis

Contact angle goniometry is a technique for measuring the wettability of a solid surface by determining the angle at which a liquid droplet meets the surface. Rooted in Thomas Young's thermodynamic analysis from 1805, the method uses optical measurement of droplet profile to quantify surface energy and hydrophilicity. It is indispensable in biomaterials characterization, helping researchers assess whether a scaffold or implant surface will promote or inhibit cell adhesion, protein adsorption, and biointegration.

Young, T. (1805). An essay on the cohesion of fluids. Philosophical Transactions of the Royal Society, 95, 65-87.

Owens, D. K., & Wendt, R. C. (1969). Estimation of surface free energy of polymers. Journal of Applied Polymer Science, 13(8), 1741-1747.

Good, R. J. (1979). Surface free energy of solids and liquids: thermodynamics, measurement, and applicability. Journal of Colloid and Interface Science, 52(2), 308-313.

electrospinning dynamic-mechanical-analysis swelling-and-degradation decellularization


Darrin S. Thordarson, Adam S. Radomsky, and colleagues

The Vancouver Obsessional Compulsive Inventory (VOCI) is a 55-item self-report questionnaire designed to assess the frequency and distress associated with obsessive-compulsive symptoms across multiple domains. Developed by Thordarson and colleagues in 2004, the VOCI measures six subscales: Contamination Obsessions, Checking Compulsions, Obsessions, Hoarding, Just Right Compulsions, and Doubt/Responsibility. It is a comprehensive tool for assessing the full spectrum of OCD presentations and monitoring treatment response.

Thordarson, D. S., Radomsky, A. S., Rachman, S., Shafran, R., Sawchuk, C. N., & Ralph Hakstian, A. (2004). The Vancouver Obsessional Compulsive Inventory (VOCI). Behaviour Research and Therapy, 42(11), 1289–1314.


Qualitative Content Analysis

Content Analysis
Categorical Content Analysis

Klaus Krippendorff; refined by Margrit Schreier

Qualitative Content Analysis (QCA) is a systematic, inductive method for analyzing textual or visual data by identifying and categorizing meaning units into content categories. Developed and formalized by Klaus Krippendorff (1980), QCA can be purely qualitative (inductive, exploratory) or combined with quantitative counting; it analyzes manifest content (explicit, surface meanings) and latent content (underlying, interpretive meanings).

Krippendorff, K. (1980). Content analysis: An introduction to its methodology. Sage Publications.

Schreier, M. (2012). Qualitative content analysis in practice. Sage Publications.

Hsieh, H. F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Journal of Nursing Research, 13(4), 203–215.

thematic-analysis discourse-analysis manifest-content latent-content coding-scheme


Content Analysis

İçerik Analizi
systematic content coding
quantitative content analysis

Systematised through Krippendorff's methodology work; 4th edition 2018

Klaus Krippendorff (systematic formulation); roots in early 20th-century communications research

Content analysis is a systematic research technique for reducing text, visual, or media material into coded categories so that patterns can be counted, compared, and interpreted. Formalised by Klaus Krippendorff in his widely cited methodology textbook (latest edition 2018), the method sits at the boundary of qualitative and quantitative inquiry: it imposes structured, replicable coding on inherently meaning-laden material.

Qualitative / mixed-method research technique

Output: Coded categories with frequencies or themes

Krippendorff, K. (2018). Content Analysis: An Introduction to Its Methodology (4th ed.). Sage.

thematic-analysis discourse-analysis grounded-theory sentiment-analysis text-classification


Content Validity Ratio

Lawshe's Content Validity Ratio Method for Expert Panel Assessment

CVR
Content validity index
Expert judgment content validity
Lawshe CVR

The Content Validity Ratio (CVR) is a quantitative method developed by Charles Lawshe in 1975 for evaluating the extent to which items in a measurement instrument are relevant and representative of a target construct. The method aggregates expert panel judgments into a single validity coefficient for each item, enabling researchers to identify and retain only those items deemed essential by domain experts. CVR provides objective support for content validity claims during scale development.

Expert panel content validity assessment

Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563-575.

Tristán-López, A. (2008). Modification of the content validity ratio. Revista Educación y Pedagogía, 20(48), 11-18.

Polit, D. F., & Beck, C. T. (2006). The content validity index: are you sure? Research in Nursing & Health, 29(5), 489-497.

likert-scale-construction factor-analysis-scale confirmatory-factor-analysis-scale floor-ceiling-effect


Content Validity

content-related validity
logical validity
face validity
content validation

C. H. Lawshe (quantitative framework); earlier qualitative traditions in educational measurement

Content validity is evidence that a measurement instrument adequately samples the full domain of the construct it is intended to measure. It is established through systematic expert review and quantified with indices such as Lawshe's Content Validity Ratio (CVR) and Lynn's Content Validity Index (CVI), making it the foundational validity step in scale development.

Validity evidence / expert judgement procedure

Expert panel ratings (ordinal / categorical)

Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563–575.

Lynn, M. R. (1986). Determination and quantification of content validity. Nursing Research, 35(6), 382–385.

construct-validity convergent-validity discriminant-validity nomological-validity scale-development exploratory-factor-analysis


Contextual Inquiry

CI
Contextual Design
Work-centered Design

Contextual Inquiry is a field research method for understanding users by observing and interviewing them in their real work environment. Developed by Hugh Beyer and Karen Holtzblatt at Applied Research and Technology, this method combines ethnographic observation with targeted questioning to capture not just what users say they do, but what they actually do—including workarounds, informal practices, and priorities often invisible in lab settings. Contextual Inquiry uncovers the context, constraints, and real-world complexity of user tasks, providing rich insights for user-centered design.

In-situ user research method capturing real work practices

Beyer, H., & Holtzblatt, K. (1998). Contextual Design: Defining Customer-Centered Systems. Morgan Kaufmann.

Holtzblatt, K., & Jones, S. (1993). Contextual inquiry: A participatory technique for system design. In D. Schuler & A. Namioka (Eds.), Participatory Design (pp. 177–210). Lawrence Erlbaum Associates.

think-aloud-protocol pluralistic-walkthrough card-sorting retrospective-think-aloud


Contingent Valuation

CVM
Willingness-to-Pay Survey
WTP Elicitation

Contingent Valuation (CVM), developed by Robert Davis in the 1960s, is a survey-based method for estimating the economic value of non-market environmental goods and services—such as wilderness preservation, air quality, or species protection—by directly asking people their willingness to pay (WTP) for specified improvements or willingness to accept (WTA) compensation for losses. It provides a valuation where market prices do not exist.

Mitchell, R. C., & Carson, R. T. (1989). Using Surveys to Value Public Goods: The Contingent Valuation Method. Resources for the Future.

Arrow, K., Solow, R., Portney, P. R., Leamer, E. E., Radner, R., & Schuman, H. (1993). Report of the NOAA Panel on Contingent Valuation. Federal Register, 58(10), 4601–4614.

Bateman, I. J., Carson, R. T., Day, B., Hanemann, M., Hanley, N., Hett, T., & Loomes, G. (2002). Economic Valuation with Stated Preference Techniques: A Manual. Edward Elgar.


Contour Analysis

Contour analysis is the process of detecting and analyzing the boundaries of objects in images by identifying connected edges and extracting shape information. The Suzuki-Abe algorithm provides an efficient method for finding contours in binary images, enabling shape-based object classification and segmentation.

Suzuki, S., & Abe, K. (1985). Topological structural analysis of digitized binary images by border following. Computer Vision, Graphics, and Image Processing, 30(1), 32–46.

Hu, M. K. (1962). Visual pattern recognition by moment invariants. IRE Transactions on Information Theory, 8(2), 179–187.

canny-edge-detection watershed-segmentation image-morphology blob-detection harris-corner-detection


Contrast Analysis

Kontrast Analizi — Planlanmış Karşılaştırmalar

Rosenthal, Rosnow & Rubin (modern formalization)

Planned contrast analysis is a parametric hypothesis-testing method that evaluates specific, theoretically motivated comparisons among group means — comparisons that the researcher specifies before data collection, not in response to observed patterns. Formalized comprehensively by Rosenthal, Rosnow, and Rubin (2000), the approach assigns a set of contrast coefficients to the groups being compared, with the constraint that the coefficients sum to zero, and then tests whether the resulting weighted combination of means differs significantly from zero.

Rosenthal, R., Rosnow, R. L. & Rubin, D. B. (2000). Contrasts and Effect Sizes in Behavioral Research: A Correlational Approach. Cambridge University Press.

one-way-anova tukey-hsd bonferroni-correction post-hoc-tests factorial-anova


Contrast Ratio Measurement

Color Contrast Evaluation
Luminance Contrast Analysis

Contrast Ratio Measurement is a systematic method for quantifying the perceptual difference between two colors, typically foreground and background text or elements. Grounded in color science and accessibility standards, this pipeline calculates luminance-based contrast to ensure visual content is readable for all viewers, including those with low vision or color blindness.

World Wide Web Consortium (2018). Web Content Accessibility Guidelines (WCAG) 2.1. https://www.w3.org/WAI/WCAG21/quickref/

Poynton, C. (2012). Gamma FAQ: Frequently Asked Questions about Gamma. http://poynton.ca/GammaFAQ.html

Legge, G. E., Pelli, D. G., Rubin, G. S., & Schleske, M. M. (2007). Psychophysics of Reading—I. Normal Vision. Vision Research, 25(2), 239–252.

typography-legibility-test color-harmony-analysis visual-saliency-map color-palette-extraction image-aesthetics-assessment


Visual Contrastive Learning

Visual Contrastive Self-Supervised Learning (SimCLR / MoCo / BYOL)

Karşıtlık Öğrenmesi — Görsel (SimCLR / MoCo / BYOL)

self-supervised visual representation learning

Chen, T. et al. (SimCLR); He, K. et al. (MoCo)

Visual contrastive learning is a self-supervised deep-learning approach — popularised by frameworks such as SimCLR (Chen et al., 2020) and MoCo (He et al., 2020) — that learns rich image representations without labels by pulling different augmentations of the same image together and pushing different images apart. It turns a large pool of unlabelled images into a useful feature extractor.

Self-supervised deep representation learning

Visual representation learning / classification

Chen, T., Kornblith, S., Norouzi, M. & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. ICML.

He, K., Fan, H., Wu, Y., Xie, S. & Girshick, R. (2020). Momentum Contrast for Unsupervised Visual Representation Learning. CVPR.

mixture-of-experts graph-attention-network longformer-bigbird random-forest xgboost


Contrastive Learning for NLP

Contrastive Learning for Natural Language Processing

Karşıtlık Öğrenmesi — NLP (Contrastive Learning)

Gao, Yao & Chen (SimCSE, 2021); Khosla et al. (Supervised Contrastive, 2020)

Contrastive learning for NLP is a representation-learning technique — popularised by SimCSE (Gao et al., 2021) and Supervised Contrastive Learning (Khosla et al., 2020) — that trains a text encoder by pulling embeddings of similar text pairs together while pushing embeddings of dissimilar pairs apart. The result is a dense, high-quality embedding space that can be learned with no labels at all, or with minimal supervision, making it especially valuable when annotated data are scarce.

Self-supervised / supervised representation learning

Input: Text pairs (positive and, optionally, negative examples)

Output: Dense sentence embeddings in a shared vector space

Gao, T., Yao, X., & Chen, D. (2021). SimCSE: Simple Contrastive Learning of Sentence Embeddings. Proceedings of EMNLP 2021.

Khosla, P., et al. (2020). Supervised Contrastive Learning. Advances in Neural Information Processing Systems (NeurIPS) 33.

bert-embeddings sentence-transformers text-classification semantic-similarity self-supervised-learning


Control chart

Statistical Control Chart (Shewhart Chart)

Shewhart chart
process-behavior chart
SPC chart
quality control chart

A control chart is a time-series graph with statistically derived upper and lower control limits that separates the natural, random variation of a process (common cause) from unusual, assignable variation (special cause). Invented by Walter Shewhart at Bell Labs in 1924, control charts remain the foundational tool of Statistical Process Control and are used across manufacturing, healthcare, software, and service industries to monitor whether a process remains stable and predictable over time.

Statistical monitoring and control technique

Time-ordered continuous or attribute process measurements

Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. Van Nostrand.

Montgomery, D. C. (2009). Introduction to Statistical Quality Control (6th ed.). Wiley.

statistical-process-control process-capability-analysis failure-mode-and-effects-analysis six-sigma-dmaic design-of-experiments quality-function-deployment


Control Group Experimental Design

controlled experiment
true experimental design
randomized controlled design
treatment-control design

1935 (Fisher); 1963 (Campbell & Stanley codification)

Ronald A. Fisher; systematised by Donald T. Campbell & Julian C. Stanley

Control group experimental design is a fundamental experimental structure in which participants are assigned to at least two groups — a treatment group that receives the intervention and a control group that does not — so that the effect of the intervention can be isolated by comparing outcomes across groups. Randomisation of assignment strengthens causal inference by balancing known and unknown confounders.

Quantitative outcome measures (continuous or categorical)

Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally.

Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.

pretest-posttest-experimental-design randomized-controlled-trial solomon-four-group-design factorial-experiment quasi-experimental-design between-subjects-design


Control Preferences Scale

Desired Role in Decision Making
Decision Role Preference

The Control Preferences Scale (CPS) is a five-item measure that assesses a patient's preferred role in healthcare decision making, ranging from a passive (physician-directed) to active (patient-directed) or shared approach. Developed by Lois Degner and colleagues in 1997, the CPS measures the degree of control patients wish to exercise in treatment decisions: whether they prefer to leave decisions to the clinician, collaborate with the clinician, or make the final decision themselves. The scale is widely used to understand patient preferences for decision-making involvement and to evaluate the alignment between preferred and actual roles.

Degner, L. F., Sloan, J. A., & Venkatesh, P. (1997). The Control Preferences Scale. Canadian Journal of Nursing Research, 29(3), 21-43.

Brace, C., Keating, N. L., Hemminki, K., et al. (2006). Informed decision making and cancer screening: the role of the physician. American Journal of Medical Genetics Part A, 140A(20), 2256-2264.

collaboste-scale decisional-conflict-scale patient-enablement-instrument trust-in-physician-scale


Controlled Atmosphere Storage

Manipulation of Gas Composition for Extended Shelf Life and Quality Retention

CA storage
modified atmosphere packaging
O2 and CO2 management

Controlled atmosphere (CA) storage extends fruit shelf life beyond cold storage alone by actively regulating oxygen (O₂) and carbon dioxide (CO₂) concentrations during storage. By reducing respiration and ethylene production rates, CA storage can maintain fruit quality for months. This advanced technique is expensive but economically justified for premium fruit destined for long-distance export or extended market windows.

Kader, A. A. (2002). Postharvest Technology of Horticultural Crops (3rd ed.). University of California Agricultural and Natural Resources Publication.

Beaudry, R. M. (2000). Responses of horticultural commodities to low oxygen: Limits to the expanded use of controlled atmospheres. Journal of the American Society for Horticultural Science, 125(6), 698–705.

cold-storage-protocol postharvest-storage-simulation ripeness-index brix-measurement


Convergent Cross Mapping

CCM
Cross-Convergent Mapping
Empirical Dynamic Modelling Causality
Yakınsak Çapraz Haritalama

Convergent Cross Mapping (CCM) is a nonlinear, state-space method for detecting causality between time-series variables embedded in a shared dynamical system. Introduced by George Sugihara and colleagues in their landmark 2012 Science paper, CCM exploits Takens' embedding theorem: if variable X causally influences Y, the historical record of Y contains enough information to recover the states of X. Causality is confirmed when cross-map skill improves—converges—as the time-series library grows longer.

O(L·E) per library size L and embedding dimension E

Sugihara, G., et al. (2012). Detecting causality in complex ecosystems. Science, 338(6106), 496–500.


Convergent Validity

convergent construct validity
convergence validity
AVE-based convergent validity

Convergent validity is the degree to which multiple indicators that are theoretically expected to measure the same construct actually correlate with one another. It is one of the two complementary forms of construct validity identified by Campbell and Fiske (1959) and is now routinely assessed via factor loadings and the Average Variance Extracted (AVE) statistic in SEM-based scale validation.

Validity evidence / construct validation

Ordinal or continuous item responses; factor loadings from CFA

Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81–105.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

discriminant-validity construct-validity confirmatory-factor-analysis cronbachs-alpha mcdonalds-omega nomological-validity


Conversation Analysis

CA
talk-in-interaction
sequential analysis
interactional analysis

Late 1960s–1974 (foundational lectures 1964–1972; landmark article 1974)

Conversation Analysis (CA) is a qualitative research method that examines the fine-grained sequential structure of naturally occurring talk and social interaction. Developed by sociologists Harvey Sacks, Emanuel Schegloff, and Gail Jefferson in the 1960s and 1970s, CA investigates how participants in a conversation accomplish social actions — such as invitations, refusals, or diagnoses — through the precise moment-by-moment organisation of their talk, including turn-taking, sequence structure, repair, and recipient design.

Audio or video recordings of naturally occurring talk and interaction

Varies widely; 10–100+ interaction episodes or 2–20 hours of recorded talk

Sacks, H., Schegloff, E. A., & Jefferson, G. (1974). A simplest systematics for the organization of turn-taking for conversation. Language, 50(4), 696–735.

Sidnell, J. (2010). Conversation Analysis: An Introduction. Wiley-Blackwell.

discourse-analysis ethnography narrative-analysis phenomenology thematic-analysis content-analysis


Convex Optimization

Convex Programming
Disciplined Convex Programming
Dışbükey Optimizasyon
Convex Mathematical Programming

Convex optimization is a subfield of mathematical optimization that studies the problem of minimizing convex functions over convex sets. Formalized and popularized by Stephen Boyd and Lieven Vandenberghe in their landmark 2004 textbook, the framework unifies a wide family of problems — including linear programming, quadratic programming, semidefinite programming, and second-order cone programming — under a single theoretical roof. Its defining property is that any locally optimal solution is also globally optimal, making it tractable and reliable for engineering, statistics, machine learning, and operations research.

Boyd, S., & Vandenberghe, L. (2004). Convex Optimization. Cambridge University Press.


COOK-SEIFORD

COOK-SEIFORD (Cook & Seiford (1978) — distance-based ranking aggregation) is a aggregationoperator multi-criteria decision-making (MCDM) method introduced by Orakçı, E. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Orakçı, E. (2024). Çok Kriterli Karar Verme Problemleri için Toplulaştırma Teknikleri. Özgür Yayınları

borda condorcet copeland dodgson topsis vikor ahp


Coordination Compound Synthesis

Coordination Compound Synthesis and Characterization

complex synthesis
coordination complex
metal complex synthesis

Coordination compound synthesis is the methodology for preparing metal-ligand complexes, ranging from simple aqueous solutions of metal ions to sophisticated organometallic catalysts and biological metalloproteins. Developed systematically from the 1960s onward by pioneers like Geoffrey Wilkinson and others, coordination chemistry enables creation of compounds with tailored properties for catalysis, materials science, and medicine.

Wilkinson, G., Gillard, R. D., & McCleverty, J. A. (1966). Comprehensive Coordination Chemistry (1st ed.). Pergamon Press.

Constable, E. C. (2013). Metals and Ligand Reactivity. VCH.


CAT

CAT
COPD Assessment Test
COPD Assessment Tool

The COPD Assessment Test (CAT) is a simple, rapid, patient-centered measure of COPD symptom burden and functional impact. Developed by Paul Jones and colleagues in 2009, this 8-item questionnaire captures how COPD affects cough, sputum, chest tightness, breathing difficulty, activity limitation, confidence, sleep, and energy. It is used worldwide in clinical practice to guide disease management decisions and as a primary outcome measure in COPD clinical trials.

Self-report symptom and impact questionnaire

Jones, P. W., Harding, G., Berry, P., Wiklund, I., Chen, W. H., & Kline Leidy, N. (2009). Development and first validation of the COPD Assessment Test. European Respiratory Journal, 34(3), 648-654.

Dodd, J. W., Hogg, L., Nolan, J., Jeffries, C., Grant, E., Lord, V. M., ... & Hopkinson, N. S. (2012). The COPD assessment test (CAT): Response to pulmonary rehabilitation. A multicentre, prospective study. Thorax, 66(5), 425-429.

Tinkelman, D., White, B., & Murray, S. (2009). Tracking COPD patient outcomes with COPD Assessment Test (CAT): A validation study. Journal of COPD F, 6(1), 1-6.

eortc-qlq-c30 asthma-control-test chronic-heart-failure-questionnaire diabetes-quality-of-life


COPE Guidelines for Publication Ethics

Committee on Publication Ethics Guidelines and Resources

COPE
Publication Ethics Flowcharts
Editorial Guidelines

The Committee on Publication Ethics (COPE), founded in 1997, is an international organization of journal editors and publishers that promotes and advances research integrity and publication ethics. COPE provides practical guidance through flowcharts, position statements, and ethical guidelines addressing misconduct (fabrication, falsification, plagiarism), authorship disputes, conflicts of interest, and corrections. COPE's resources are freely available and adopted by thousands of journals globally. COPE is not a regulator; it advises editors and researchers on handling ethical issues and promotes a culture of integrity in publishing.

Committee on Publication Ethics (2023). COPE Guidelines. COPE Website.

Committee on Publication Ethics (2022). COPE Handbook of Authorship, Authorship Disputes, and Conflicts of Interest. COPE.

Oreskes, N. (2012). Merchants of Doubt: How a Handful of Scientists Obscured the Truth on Issues from Tobacco Smoke to Global Warming. Bloomsbury Press.

icmje-authorship-criteria plagiarism-in-research duplicate-publication retraction-process


COPELAND

COPELAND (Copeland Method — Pairwise majority voting with net win-loss score) is a aggregationoperator multi-criteria decision-making (MCDM) method introduced by Copeland, A. H. in 1951. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Pairwise majority rule (Condorcet-based aggregation)

Copeland, A. H. (1951). A 'reasonable' social welfare function. Mimeograph, University of Michigan Seminar on Applications of Mathematics to Social Sciences


Copenhagen Burnout Inventory

Tage Søren Kristensen, Margrethe Borritz, Ebbe Villadsen, Karl B. Christensen

The Copenhagen Burnout Inventory (CBI) is a multidimensional burnout assessment tool designed to measure exhaustion and disengagement in occupational settings. Developed by Kristensen and colleagues in 2005, the CBI distinguishes among personal, work-related, and client-related burnout, making it particularly valuable for healthcare, education, and social service professions.

Kristensen, T. S., Borritz, M., Villadsen, E., & Christensen, K. B. (2005). The Copenhagen Burnout Inventory: a new tool for the assessment of burnout. Work & Stress, 19(3), 192-207.

oldenburg-burnout-inventory effort-reward-imbalance-scale areas-of-worklife-scale recovery-experience-questionnaire maslach-burnout-inventory


COPM

Law, M., Baptiste, S., Carswell, A., McColl, M., Polatajko, H., & Pollock, N.

The COPM is a client-centered, semi-structured assessment tool designed to measure change in occupational performance over time. Developed by Law and colleagues (1990) at McMaster University in Canada, it has become a cornerstone of occupational therapy practice, focusing on identifying and evaluating performance issues that matter most to each individual client.

Client self-report (semi-structured interview)

Law, M., Baptiste, S., Carswell, A., McColl, M. A., Polatajko, H., & Pollock, N. (1990). Canadian Occupational Performance Measure. CAOT Publications.

Law, M., Steinwender, S., & Leclair, L. (1998). Occupation, health and well-being. Canadian Journal of Occupational Therapy, 65(2), 81-91.

wolf-motor-function-test upper-extremity-functional-scale occupational-self-assessment nine-hole-peg-test


COPRAS

COPRAS (Complex Proportional Assessment) is a ranking multi-criteria decision-making (MCDM) method introduced by Zavadskas, E. K., Kaklauskas, A. in 1996. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Proportional assessment (benefit/cost split)

Zavadskas, E. K., Kaklauskas, A. (1996). Determination of an Efficient Contractor by Using the New Method of Multicriteria Assessment. International Symposium for The Organization and Management of Construction. Shaping Theory and Practice. Vol. 2: Managing the Construction Project and Managing Risk. CIB W 65

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


Copula CDO Model

The copula CDO model (Li 2000) uses Gaussian copulas to price collateralized debt obligations (CDOs) by modeling joint default probabilities across a portfolio of bonds. The model became the industry standard for CDO pricing but was heavily criticized post-2008 for underestimating tail risk and correlation breakdowns during crises.

Li, D. X. (2000). On default correlation: A copula function approach. Journal of Fixed Income, 9(4), 43-54.

Schonbucher, P. J. (2003). Credit Derivatives Pricing Models: Models, Pricing and Implementation. John Wiley & Sons.


Copula Models

Copula Models (Gaussian, t, Clayton, Gumbel, Frank)

Kopula Modelleri (Gaussian, t, Clayton, Gumbel, Frank)

Sklar (1959); dependence-concept treatment by Joe (1997)

Copula models are a family of functions that describe the dependence structure between variables separately from their individual (marginal) distributions. The foundation is Sklar's theorem (1959), which shows that any multivariate distribution can be split into its marginals plus a copula; Joe (1997) developed the modern catalogue of dependence concepts. They are central to portfolio risk and credit modelling.

Inference Functions for Margins (IFM); maximum likelihood

Sklar, A. (1959). Fonctions de répartition à n dimensions et leurs marges. Publications de l'Institut Statistique de l'Université de Paris, 8, 229-231.

Joe, H. (1997). Multivariate Models and Dependence Concepts. Chapman & Hall.

pearson-correlation johansen-cointegration extreme-value-theory value-at-risk garch


Copy Number Variation Analysis

CNV analysis
copy number variant detection
CNV calling
somatic copy number alteration analysis

Pinkel et al. (array CGH); Redon et al. (genome-wide CNV map)

Copy number variation (CNV) analysis is a genomic pipeline for detecting regions where individuals carry fewer or more copies of a DNA segment than the reference genome. CNVs span kilobases to megabases and are a major class of structural variation implicated in cancer, neurodevelopmental disorders, and population diversity. The pipeline typically processes SNP array intensities or read-depth signals from whole-genome sequencing, applies segmentation algorithms, calls gain and loss events, and annotates them against gene and clinical databases.

Genomic structural variant detection pipeline

Whole-genome sequencing (WGS), SNP array, array CGH

Redon, R., Ishikawa, S., Fitch, K. R., et al. (2006). Global variation in copy number in the human genome. Nature, 444(7118), 444–454.

Olshen, A. B., Venkatraman, E. S., Lucito, R., & Wigler, M. (2004). Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics, 5(4), 557–572.


Core Self-Evaluations Scale

The Core Self-Evaluations Scale (CSES) measures fundamental assessments people make about their own worth, competence, and ability to meet life demands. Developed by Judge and colleagues starting in 1997, the 12-item scale captures a broad personality dimension encompassing self-esteem, self-efficacy, locus of control, and emotional stability. Core self-evaluations predict job satisfaction, life satisfaction, engagement, and performance across occupations.

Judge, T. A., Locke, E. A., & Durham, C. C. (1997). The dispositional causes of job satisfaction: A core evaluations approach. Research in Organizational Behavior, 19, 151–188.

Judge, T. A., Erez, A., Bono, J. E., & Thoresen, C. J. (2005). Core self-evaluations and job and life satisfaction: The role of self-concordance and goal attainment. Journal of Applied Psychology, 90(2), 257–268.

Judge, T. A., Bono, J. E., Erez, A., & Locke, E. A. (2005). Core self-evaluations and job and life satisfaction: A response to bottom line concerns. Journal of Organizational Behavior, 26(5), 491–511.

psychological-capital-questionnaire proactive-personality-scale organizational-commitment-questionnaire job-descriptive-index leader-member-exchange-scale


Coreference Resolution

Eşgönderim Çözümleme (Coreference Resolution)

Hobbs (1978); Lee et al. (2017, neural end-to-end)

Coreference resolution is a natural-language-processing task that detects when different expressions in a text refer to the same entity — for example a name, a later pronoun, and a descriptive phrase all pointing at one person. Rooted in early linguistic work by Hobbs (1978) and advanced by the end-to-end neural model of Lee et al. (2017), it improves the quality of information extraction and text understanding.

Output: Clusters of mentions that refer to the same entity

Lee, K. et al. (2017). End-to-end Neural Coreference Resolution. EMNLP.

Hobbs, J.R. (1978). Resolving Pronoun References. Lingua, 44(4), 311-338.

named-entity-recognition semantic-role-labeling question-answering sentiment-analysis


Corporate Governance Questionnaire

Corporate Governance Assessment and Maturity Questionnaire

Jensen and Meckling (foundational); Cadbury Committee (operational framework)

Corporate Governance encompasses the system of rules, practices, and processes by which a company is directed and controlled. Jensen and Meckling's (1976) agency theory formalized the principal-agent problem—how to ensure management (agents) acts in shareholders' (principals') interests despite information asymmetry and incentive misalignment. The Cadbury Report (1992) operationalized this into practical governance frameworks emphasizing board independence, audit committees, and transparency. This questionnaire assesses organizational governance maturity across multiple dimensions: board structure and independence, internal controls and risk management, audit and compliance, stakeholder engagement, and transparency. Strong governance reduces agency costs, improves decision quality, and protects against fraud and misconduct.

Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305–360.

The Committee on the Financial Aspects of Corporate Governance (1992). Report of the Committee on the Financial Aspects of Corporate Governance (Cadbury Report). London: The Financial Reporting Council.

Brown, L. D., & Caylor, M. L. (2009). Corporate governance and firm operating performance. Review of Quantitative Finance and Accounting, 32(2), 129–144.

strategic-orientation-scale organizational-resilience-scale knowledge-management-scale dynamic-capabilities-scale balanced-scorecard-measure


CSR Scale

Corporate Social Responsibility Scale (CSR Scale)

Multiple scholars; Turker formalized measurement

The Corporate Social Responsibility (CSR) Scale is a 19-item instrument measuring organizational commitment to social and environmental responsibilities across multiple stakeholder dimensions. Formalized by Turker in 2009, the CSR Scale assesses employee perception of organizational CSR practices toward society, employees, customers, and the environment.

Carroll, A. B. (1979). A three-dimensional model of corporate performance. Academy of Management Review, 4(4), 497-505.

Turker, D. (2009). Measuring corporate social responsibility: A scale development study. Journal of Business Ethics, 85(4), 411-427.


Corpus Linguistics

Corpus Linguistics is the study of language based on large, representative collections of texts (corpora) processed by computer. Pioneered by John Sinclair and others, the method uses statistical analysis, concordancing, and computational tools to examine patterns of actual language use. Corpus linguistics has transformed our understanding of English and other languages, revealing frequency patterns, collocation preferences, and register variation that were previously hidden. It serves theoretical linguistics, applied language teaching, and natural language processing.

Sinclair, J. M. (1991). Corpus, Concordance, Collocation. Oxford: Oxford University Press.

McEnery, T., & Hardie, A. (2012). Corpus Linguistics: Method, Theory and Practice. Cambridge: Cambridge University Press.

Biber, D., Conrad, S., & Reppen, R. (2006). Corpus Linguistics: Investigating Language Structure and Use. Cambridge: Cambridge University Press.


Correlation vs Causation

Understanding the Distinction Between Correlation and Causation in Research

correlation and causation
causal inference
spurious correlation
confounding

Multiple sources (Bradford Hill, Judea Pearl, Donald Rubin)

Correlation measures the strength and direction of association between two variables; causation implies that changes in one variable directly produce changes in another. A strong correlation (e.g., r = 0.9) does not prove causation. Classic examples abound: shoe size and reading ability are correlated in children (confounded by age), but shoe size does not cause reading ability. Understanding when correlation implies causation requires evaluating study design, confounding variables, temporal precedence, and mechanism. Randomized experiments offer the strongest causal evidence; observational studies must carefully control for confounders.

Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press.

Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66(5), 688–701.

Hill, A. B. (1965). The Environment and Disease: Association or Causation? Proceedings of the Royal Society of Medicine, 58(5), 295–300.

p-value-significance null-hypothesis effect-size multiple-comparisons-problem


Correspondence Analysis

CA
Simple Correspondence Analysis
Reciprocal Averaging
Karşılıklı Uyum Analizi

Correspondence Analysis (CA) is an exploratory multivariate technique for visualizing the association structure of a two-way contingency table. Developed systematically by Jean-Paul Benzécri in France during the 1960s–1970s and brought to an English-language audience by Michael Greenacre in 1984, CA decomposes the chi-square statistic of a cross-tabulation to produce a low-dimensional joint display — called a biplot — in which rows and columns are represented as points whose proximities reflect their associations.

Exploratory multivariate technique for categorical data

Input: Two-way contingency table (cross-tabulation)

Output: Low-dimensional biplot of row and column profiles

Greenacre, M. J. (1984). Theory and Applications of Correspondence Analysis. Academic Press.


Cosine Distance

cosine similarity distance
angular distance

Cosine distance measures the angular distance between two non-zero vectors in a multi-dimensional space. Originally developed by Gerard Salton for information retrieval in 1975, it captures dissimilarity by computing one minus the cosine similarity, ranging from 0 (identical direction) to 1 (opposite direction). It is widely used in text analysis, document comparison, and decision-making contexts where direction matters more than magnitude.

Spearman, C. (1904). The proof and measurement of association between two things. American Journal of Psychology, 15(1), 72-101.

Salton, G., & McGill, M. J. (1975). Introduction to Modern Information Retrieval. McGraw-Hill.


COSMIN Checklist

Patient-reported outcome measure (PROM) quality assessment

COSMIN (COnsensus-based Standards for the selection of health Measurement INstruments) is a systematic framework and 10-item checklist developed by Mokkink et al. (2010) to evaluate the methodological quality of studies that assess the measurement properties of patient-reported outcome measures (PROMs), questionnaires, and clinical scales. COSMIN guides the development, validation, and selection of health measurement instruments across clinical research and practice.

Mokkink, L. B., Terwee, C. B., Patrick, D. L., Alonso, J., Stratford, P. W., Knol, D. L., ... & de Vet, H. C. (2010). The COSMIN checklist for assessing the methodological quality of studies on measurement properties of health status measurement instruments: an international Delphi study. Quality of Life Research, 19(4), 539–549.

grade-evidence-profiling casp-rct-checklist cochrane-risk-of-bias prisma-checklist


Cosmological Perturbation Theory

Cosmological Perturbation Theory and Structure Growth

structure formation theory
linear perturbations
growth of density fluctuations

Cosmological perturbation theory describes how small density fluctuations in the early universe grow into galaxies, clusters, and large-scale structure under gravity. Originating from James Jeans's 1902 stability analysis and extended by Lifshitz, Bardeen, and others, this theory is the foundation of structure formation cosmology. It explains how quantum fluctuations in the early universe—amplified by inflation—seeded the growth of all cosmic structures.

Theoretical framework and computational method

Jeans, J. H. (1902). The stability of a spherical nebula. Philosophical Transactions of the Royal Society A, 199, 1-53.

Lifshitz, E. M. (1946). On the gravitational stability of the expanding universe. Journal of Physics USSR, 10, 116.

Bardeen, J. M., Bond, J. R., Kaiser, N., & Szalay, A. S. (1986). The statistics of peaks of Gaussian random fields. The Astrophysical Journal, 304, 15-61.


Cost-Benefit Analysis

CBA
economic appraisal
benefit-cost ratio

Boardman, Greenberg, and colleagues (welfare economics)

Cost-benefit analysis compares the total monetary value of benefits produced by a program against its total monetary costs, reporting net present value (NPV) or benefit-cost ratio (BCR). Rooted in welfare economics and used extensively in public policy (transportation, environmental, education, health), CBA answers the question: 'Is this program worth doing from a societal perspective?' Unlike cost-effectiveness analysis, CBA monetizes both costs and benefits, enabling comparison across disparate program types.

Boardman, A. E., Greenberg, D. H., Vining, A. R., & Weimer, D. L. (2018). Cost-Benefit Analysis: Concepts and Practice (5th ed.). Cambridge: Cambridge University Press.

Layard, R., & Glaister, S. (Eds.). (2003). Cost-Benefit Analysis (2nd ed.). Cambridge: Cambridge University Press.

Drummond, M. F., Sculpher, M. J., Claxton, K., Stoddart, G. L., & Torrance, G. W. (2015). Methods for the Economic Evaluation of Health Care Programmes (4th ed.). Oxford: Oxford University Press.

cost-effectiveness-analysis quality-adjusted-life-year markov-model-health-economics decision-analytic-modeling budget-impact-analysis


Cost-Effectiveness Analysis in HTA

Cost-Effectiveness Analysis for Healthcare Technology Evaluation and Reimbursement

CEA
Cost-Effectiveness Analysis Healthcare

Cost-Effectiveness Analysis (CEA) is an economic evaluation method that compares the cost and health benefits of alternative treatments to determine whether an intervention provides good value for money. Within Health Technology Assessment, CEA is the primary tool for recommending reimbursement and coverage decisions.

Gold, M. R., Siegel, J. E., Russell, L. B., & Weinstein, M. C. (Eds.). (1996). Cost-Effectiveness in Health and Medicine. Oxford University Press.

Drummond, M. F., Sculpher, M. J., Claxton, K., Stoddart, G. L., & Torrance, G. W. (2015). Methods for the Economic Evaluation of Health Care Programmes (4th ed.). Oxford University Press.

Shiroiwa, T., Sung-Jae, I., Fukuda, T., & Sanon, M. (2016). International survey on QALYs and cost-effectiveness thresholds. Health Policy, 120(5), 504–514.

health-technology-assessment dea-hospital-efficiency balanced-scorecard-healthcare clinical-audit staffing-ratio-analysis


Cost-Effectiveness Analysis

CEA
ICER
Incremental Cost-Effectiveness Ratio

Drummond & Stoddart (Health Economics Research Group, McMaster University)

Cost-effectiveness analysis compares the incremental cost per unit of health benefit gained by one intervention relative to a comparator (standard care or best alternative). Developed rigorously in the 1980s by Drummond, Stoddart, and colleagues, CEA is now the standard framework for technology appraisal globally. NICE, HAS, CADTH, and other health technology assessment bodies use CEA to decide which treatments warrant public funding and at what price.

Gold, M. R., Siegel, J. E., Russell, L. B., & Weinstein, M. C. (Eds.). (1996). Cost-Effectiveness in Health and Medicine. New York: Oxford University Press.

National Institute for Health and Care Excellence (NICE). (2022). Guide to the Methods of Technology Appraisal. London: NICE.

quality-adjusted-life-year disability-adjusted-life-year cost-benefit-analysis markov-model-health-economics decision-analytic-modeling


Cost-Volume-Profit Analysis

Cost-Volume-Profit (CVP) Analysis Framework for Managerial Decision Making

Break-Even Analysis
CVP Analysis
Contribution Margin Analysis

Cost-Behavior Analysis and Profitability Planning

Managerial accounting theorists and practitioners

Cost-Volume-Profit (CVP) Analysis is a foundational managerial accounting method that examines the relationships among costs, sales volume, and profit. By analyzing how changes in production volume, selling price, and cost structure affect profitability, managers can make informed decisions about pricing, production, and strategic direction. CVP analysis provides insight into break-even points and the profit generated at various activity levels.

Managerial accounting and decision analysis framework

Garrison, R. H., Noreen, E. W., & Brewer, P. C. (2015). Managerial accounting (15th ed.). McGraw-Hill Education.

Horngren, C. T., Datar, S. M., & Rajan, M. V. (2015). Cost accounting: A managerial emphasis (15th ed.). Pearson Education.


COSY

Correlation Spectroscopy (COSY) is a two-dimensional NMR technique that correlates proton chemical shifts through scalar coupling (J-coupling), revealing which protons are magnetically coupled and hence bonded through multiple bonds. Developed by Aue, Bartholdi, and Ernst in 1976, COSY became one of the most important tools in structural elucidation, enabling chemists to map out proton connectivity patterns and deduce molecular topology without isotopic labeling.

Aue, W. P., Bartholdi, E., & Ernst, R. R. (1976). Two-dimensional spectroscopy. Application to nuclear magnetic resonance. The Journal of Chemical Physics, 64(5), 2229-2246.

Bax, A., & Davis, D. G. (1985). MLEV-17-based two-dimensional homonuclear magnetization transfer spectroscopy. Journal of Magnetic Resonance, 65(2), 355-360.


Coulometry

coulometric titration
electrochemical coulometry
amperes titration

Coulometry is an electrochemical analytical method that determines the concentration of an analyte by measuring the total electric charge (in coulombs) required to oxidize or reduce the analyte completely at an electrode. Developed by James J. Lingane in the 1940s, coulometry is highly accurate because it is based on fundamental constants (Faraday's law) and does not require external standards or calibration curves. This method is particularly valuable for trace analysis, water determination, and analysis of reactive species.

Lingane, J. J. (1974). Electroanalytical Chemistry (2nd ed.). Interscience Publishers.

Skoog, D. A., West, D. M., Holler, F. J., & Crouch, S. R. (2014). Fundamentals of Analytical Chemistry (9th ed.). Cengage Learning.

Simonsen, K. B., Larsen, K. L., Frandsen, H., & Andersen, J. E. T. (2012). Coulometric titration for determination of peroxide compounds. Journal of Pharmaceutical and Biomedical Analysis, 71, 22–28.

voltammetry potentiometric-titration ion-chromatography uv-vis-spectrophotometry atomic-absorption-spectroscopy


Counter-Movement Jump

Counter-Movement Jump and Lower Body Power Assessment

The counter-movement jump (CMJ) is a simple, field-friendly test of lower-body explosive power in which the athlete stands on a force plate, descends into a shallow squat (counter-movement phase), and explosively extends to jump as high as possible. Pioneered by Bosco and Komi (1983), the CMJ captures the integrated function of strength, rate of force development, and elastic energy utilization. Jump height (measured via flight time from force plate or motion capture) and peak power are reported. The CMJ is among the most widely used tests in sports science, athlete monitoring, and research due to simplicity, objectivity, and relevance to explosive power in nearly all sports.

Bosco, C., Luhtanen, P., & Komi, P. V. (1983). A simple method for measurement of mechanical power in jumping. European Journal of Applied Physiology, 50(2), 273-282.

Linthorne, N. P. (2001). Analysis of standing vertical jumps using a force platform. American Journal of Physics, 69(11), 1198-1204.

Cormie, P., McBride, J. M., & McCaulley, G. O. (2010). Validation of power measurement techniques in dynamic lower body resistance exercises. Journal of Applied Biomechanics, 23(2), 103-118.


Counterfactual Explanations

Algorithmic Recourse
Contrastive Explanations
What-If Explanations
Karşıolgusal Açıklamalar

Sandra Wachter, Brent Mittelstadt & Chris Russell

Counterfactual explanations, introduced by Wachter, Mittelstadt, and Russell in 2017, answer the question: 'What is the smallest change to the input that would have produced a different model output?' Rather than explaining why a model made a decision, they describe what would need to change for that decision to be reversed, making them particularly valuable for high-stakes applications such as credit scoring, medical diagnosis, and hiring decisions under frameworks like the EU GDPR.

Individual prediction (local explanation)

Output: Minimal feature change to flip model decision

Wachter, S., Mittelstadt, B., & Russell, C. (2017). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harvard Journal of Law & Technology, 31, 841–887.


Counterfactual Impact Evaluation in Education Research

CIE in education
counterfactual program evaluation
causal impact evaluation
education policy impact evaluation

Blundell & Costa Dias; formalized for EU education policy by the European Commission Joint Research Centre

Counterfactual impact evaluation (CIE) is the systematic application of causal inference designs — such as difference-in-differences, regression discontinuity, matching, and instrumental variables — to measure the genuine effect of education programs, policies, or interventions by constructing a credible counterfactual: what would have happened to participants had they not been treated.

Quasi-experimental causal inference framework

Panel data, repeated cross-sections, or administrative records on education participants

Blundell, R., & Costa Dias, M. (2002). Alternative approaches to evaluation in empirical microeconomics. Portuguese Economic Journal, 1(2), 91-115.

Cerulli, G. (2015). Econometric Evaluation of Socio-Economic Programs: Theory and Applications. Springer.

difference-in-differences propensity-score-matching regression-discontinuity-design instrumental-variables synthetic-control-method interrupted-time-series


Counterfactual Impact Evaluation

CIE
counterfactual evaluation
counterfactual policy evaluation
impact evaluation

Heckman, Imbens, Rubin, and the program evaluation literature

Counterfactual Impact Evaluation is a family of causal methods that estimates the effect of an intervention by comparing what actually happened to participants with what would have happened had the intervention not taken place. Formalised in the Rubin Causal Model and extended by Heckman, Imbens and others, CIE underlies most modern program and policy evaluation practice.

Observational or quasi-experimental panel or cross-sectional data

Heckman, J. J., & Vytlacil, E. J. (2007). Econometric evaluation of social programs, Part I: Causal models, structural models and econometric policy evaluation. Handbook of Econometrics, 6B, 4779-4874.

Imbens, G. W., & Wooldridge, J. M. (2009). Recent developments in the econometrics of program evaluation. Journal of Economic Literature, 47(1), 5-86.

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


Coupled Cluster CCSD

Coupled Cluster Singles and Doubles (CCSD(T))

Coupled Cluster theory, particularly CCSD (Singles and Doubles) and CCSD(T) with perturbative triples, is one of the most accurate methods for molecular electronic structure. Developed by Jiri Cizek in 1966, CC theory treats the ground state wave function as an exponential of excitation operators applied to the Hartree-Fock reference, enabling systematic treatment of electron correlation with guaranteed size consistency.

Cizek, J. (1966). On the correlation problem in atomic and molecular systems. Journal of Chemical Physics, 45, 4256–4266.

Raghavachari, K., Trucks, G. W., Pople, J. A., Head-Gordon, M. (1989). A fifth-order perturbation comparison of electron correlation theories. Chemical Physics Letters, 157, 479–483.

Szabo, A., Ostlund, N. S. (2012). Modern Quantum Chemistry. Dover Publications.


Cournot Competition

Quantity Competition
Cournot Equilibrium
Cournot-Nash Equilibrium

Cournot Competition models oligopolistic markets where firms choose quantities simultaneously, not prices. Originally formulated by Augustin-Louis Cournot in 1838, the model assumes each firm's profit depends on the total market quantity produced. The resulting Cournot-Nash Equilibrium captures the strategic interaction where each firm maximizes profit given competitors' quantities, leading to prices between monopoly and perfect competition levels.

Cournot, A. A. (1838). Recherches sur les principes mathématiques de la théorie des richesses. L. Hachette.

Vives, X. (1999). Oligopoly Pricing: Old Ideas and New Tools. MIT Press.

stackelberg-competition nash-equilibrium bayesian-nash-equilibrium evolutionary-game-theory


Course Experience Questionnaire

The Course Experience Questionnaire (CEQ) is an institutional assessment tool measuring students' perceptions of their learning environment and educational experience in a course. Developed by Wilson, Lizzio, and Ramsden (1997), it assesses dimensions including good teaching, clear goals, appropriate workload, appropriate assessment, appropriate feedback, and learning community. The CEQ provides universities with evaluative data to guide curriculum development and educational improvement.

Wilson, K. L., Lizzio, A., & Ramsden, P. (1997). The development, validation and application of the Course Experience Questionnaire. Studies in Higher Education, 22(1), 33-53.

Pike, G. R. (2003). Membership in a learning community and college learning outcomes. New Directions for Institutional Research, 17, 5-24.

study-process-questionnaire teaching-effectiveness-scale student-satisfaction-survey student-engagement-scale


COVID-19 Anxiety Scale

The COVID-19 Anxiety Scale (CAS) is a brief, self-administered instrument designed to assess anxiety symptoms specifically related to the COVID-19 pandemic. Developed by Lipp and colleagues in 2020, it captures worry about infection, social isolation, and pandemic-related uncertainties. The scale is widely used in epidemiological surveys and clinical research to identify individuals experiencing pandemic-related anxiety requiring intervention.

Lipp, A., Fazio, S., & Cohen, S. (2020). COVID-19 anxiety in the United States. International Journal of Mental Health and Addiction, 20(3), 1234–1248.


COVID-19 Mental Health Impact Scale

The COVID-19 Mental Health Impact Scale (CMHIS) is a brief, multidimensional instrument assessing anxiety, depression, and stress symptoms triggered by the COVID-19 pandemic. Developed by Wang and colleagues in 2020 during the initial pandemic wave in China, it captures the spectrum of psychological distress across multiple symptom domains. The CMHIS has been widely adopted in pandemic surveillance and mental health research across 30+ countries, providing rapid assessment of population mental health burden.

Wang, C., Pan, R., Wan, X., Tan, Y., Xu, L., Ho, C. S., & Ho, R. C. (2020). Immediate psychological responses and associated factors during the initial stage of the 2019 coronavirus disease (COVID-19) pandemic among the general population in China. International Journal of Environmental Research and Public Health, 17(5), 1729.


Cox proportional hazards

Cox Proportional Hazards Regression Model

Cox regression
Cox PH model
proportional hazards model
CPH

The Cox proportional hazards model is a semi-parametric regression method that estimates the effect of one or more covariates on the hazard — the instantaneous rate of an event such as death, relapse, or failure — while making no assumption about the shape of the baseline hazard function. Introduced by David Cox in 1972, it is the dominant tool for multivariable survival analysis in clinical and epidemiological research.

Time-to-event (survival) data with one or more covariates

Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.

Collett, D. (2015). Modelling Survival Data in Medical Research (3rd ed.). CRC Press.

survival-analysis kaplan-meier-analysis competing-risks-analysis cohort-study logistic-regression accelerated-failure-time


Cox Regression

cox ph model
proportional hazards model
cox ph regression
Cox Orantılı Tehlikeler Regresyonu

Cox proportional hazards regression, introduced by D. R. Cox in 1972, is a semi-parametric model that estimates how one or more covariates affect the hazard — the instantaneous rate of experiencing an event — while leaving the baseline hazard function unspecified. It is the standard multivariable method in survival analysis and produces hazard ratios that quantify the relative risk associated with each predictor.

Hazard Ratio (HR) with 95% confidence interval

n ≥ 50; at least 10 events per predictor variable (EPV ≥ 10)

Cox, D. R. (1972). Regression Models and Life-Tables. Journal of the Royal Statistical Society: Series B, 34(2), 187–202.

Therneau, T. M. & Grambsch, P. M. (2000). Modeling Survival Data: Extending the Cox Model. Springer.

kaplan-meier log-rank-test fine-gray-model nelson-aalen weibull-aft


CP Violation Measurement

Charge-Parity Violation Experimental Measurement

CP asymmetry
matter-antimatter asymmetry
T-symmetry violation

Charge-Parity (CP) violation measurement is the experimental study of asymmetries between particle and antiparticle processes, a fundamental probe of physics beyond the Standard Model. By comparing decay rates and asymmetries in kaons, B mesons, and neutrinos, physicists constrain new sources of CP violation and address the cosmological matter-antimatter imbalance.

Christenson, J. H., et al. (1964). Evidence for the 2π decay of the K₂⁰ meson. Physical Review Letters, 13(4), 138.

Aubert, B., et al. (BaBar Collaboration). (2001). Observation of CP violation in the B meson system. Physical Review Letters, 87(9), 091801.

Kobayashi, M., & Maskawa, T. (1973). CP-violation in the renormalizable theory of weak interaction. Progress of Theoretical Physics, 49(2), 652–657.


CRADIS

CRADIS (Compromise Ranking of Alternatives from Distance to Ideal Solution) is a ranking multi-criteria decision-making (MCDM) method introduced by Puška, A., Stević, Ž., Pamučar, D. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Distance from ideal and anti-ideal (geometric mean compromise)

Puška, A., Stević, Ž., Pamučar, D. (2021). Evaluation and selection of healthcare waste incinerators using extended sustainability criteria and multi-criteria analysis methods. Environment, Development and Sustainability

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


Whiteneck, Charlifue, Gerhart, Overholser, Richardson

The Craig Handicap Assessment and Reporting Technique (CHART) is a comprehensive interview-based measure designed to quantify how much a disabling condition restricts participation in six key social roles: physical independence, mobility, occupation, social integration, economic self-sufficiency, and cognitive independence. Developed by Whiteneck and colleagues at the Craig Hospital (now national leader in spinal cord injury care), CHART has become the gold-standard outcome measure for long-term spinal cord injury and traumatic brain injury follow-up, extensively used in international outcomes research.

Whiteneck, G. G., Charlifue, S. W., Gerhart, K. A., Overholser, J. D., & Richardson, G. N. (1992). Quantifying handicap: a new measure of long-term rehabilitation outcomes. Archives of Physical Medicine and Rehabilitation, 73(6), 519–526.

Charlifue, S., Post, M. W., & Biering-Sørensen, F. (2012). International Spinal Cord Injury Community Survey: does the use of different outcome measures lead to different conclusions about quality of life after spinal cord injury? Spinal Cord, 50(6), 457–463.

community-integration-questionnaire impact-participation-autonomy assessment-life-habits participation-scale whodas-2


Cramer's V

cramers v
cramer v
phi coefficient (r×c)
Cramer's V (İlişki Kuvveti)

Cramer's V is a nonparametric effect-size statistic that measures the strength of association between two categorical variables on a scale from 0 to 1. Introduced by the Swedish mathematician Harald Cramér in his 1946 work Mathematical Methods of Statistics, it generalises the phi coefficient to tables of any size, making it the standard companion statistic to the chi-square test.

V < 0.10 weak; 0.10–0.30 moderate; ≥ 0.30 strong

Cramér, H. (1946). Mathematical Methods of Statistics. Princeton University Press.

chi-square-test fishers-exact-test phi-coefficient goodman-kruskal-lambda logistic-regression


Crank-Nicolson Pricing

The Crank-Nicolson method is a widely-used implicit finite difference scheme for solving PDEs in option pricing. It provides second-order accuracy in both space and time, unconditional stability, and can efficiently price derivatives with early exercise features (American options) or complex boundary conditions.

Crank, J., & Nicolson, P. (1947). A practical method for numerical evaluation of solutions of partial differential equations of the heat-conduction type. Mathematical Proceedings of the Cambridge Philosophical Society, 43(1), 50-67.

Fornberg, B. (1996). A Practical Guide to Pseudospectral Methods. Cambridge University Press.


QSU-Brief

The QSU-Brief is a 10-item self-report instrument that rapidly assesses the intensity of craving for cigarettes and the intention to smoke. Developed by Cox, Tiffany, and Christen in 1996, it is a brief version of the longer Questionnaire on Smoking Urges (QSU) and is widely used in smoking cessation treatment and research settings to measure one of the strongest predictors of smoking relapse. The QSU-Brief is also applicable, with adaptation, to other addictive substances.

Cox, L. S., Tiffany, S. T., & Christen, A. G. (1996). Evaluation of the brief Questionnaire of Smoking Urges (QSU-brief) in laboratory and clinical settings. Nicotine & Tobacco Research, 2(1), 7–16.

dudit brief-addiction-monitor alcohol-urge-questionnaire treatment-motivation-questionnaire


Credibility Theory

Bühlmann Credibility
Experience Rating
Linear Credibility Estimator
Güvenilirlik Teorisi

Credibility Theory is an actuarial framework for estimating the pure premium of an individual risk by blending its own observed loss experience with the collective (portfolio) mean. Introduced by Hans Bühlmann in 1967, the method derives the optimal linear combination—the credibility-weighted premium—that minimises mean squared error. It extends classical experience rating to a rigorous statistical footing rooted in Bayesian and linear estimation principles.

Weighted linear blend of individual and collective experience

Bühlmann, H. (1967). Experience rating and credibility. ASTIN Bulletin, 4(3), 199–207.


Credit Risk Models

Structural and Portfolio Credit Risk Models (Merton, KMV, CreditMetrics)

Kredi Risk Modelleri (Merton, KMV, CreditMetrics)

Robert C. Merton (structural model); J.P. Morgan / Gupton et al. (CreditMetrics)

Credit risk models estimate the probability that a borrower defaults and the resulting distribution of credit losses. The structural approach was introduced by Robert C. Merton in 1974, treating a firm's equity as a call option on its assets, and was later extended into the KMV distance-to-default framework and the CreditMetrics rating-transition portfolio model published by J.P. Morgan in 1997.

Structural and portfolio credit risk model

Default probability via distance-to-default; portfolio loss via rating-transition simulation

Probability of default and credit loss distribution

Merton, R. C. (1974). On the Pricing of Corporate Debt: The Risk Structure of Interest Rates. The Journal of Finance, 29(2), 449-470.

Gupton, G. M., Finger, C. C., & Bhatia, M. (1997). CreditMetrics Technical Document. J.P. Morgan, New York.

interest-rate-models backtesting-var liquidity-risk-models event-study-finance logistic-regression


Credit Scoring

Credit Scorecard
Application Scoring
Behavioural Scoring
Kredi Skorlama

Credit scoring is a statistical technique that estimates the probability that a borrower will default on a financial obligation. Using Weight of Evidence (WoE) binning, Information Value (IV) variable selection, and logistic regression, it converts raw applicant data into a single integer score. Formalized by Hand and Henley (1997) and elaborated by Thomas, Edelman, and Crook, the scorecard framework has become the regulatory standard for retail credit risk assessment in banking, lending, and insurance.

Output: Probability of default + integer scorecard points

Hand, D. J., & Henley, W. E. (1997). Statistical classification methods in consumer credit scoring: a review. Journal of the Royal Statistical Society: Series A, 160(3), 523–541.


Credit Valuation Adjustment

Credit Valuation Adjustment (CVA) is the market price of counterparty credit risk embedded in over-the-counter (OTC) derivatives. CVA measures the loss from counterparty default, accounting for both the probability of default and the exposure at that time. It has become a key component of derivative valuation and risk management since the 2008 financial crisis.

Gregory, J. (2009). Counterparty Credit Risk: The New Challenge for Global Financial Markets. John Wiley & Sons.

Pykhtin, M., & Zhu, S. (2007). A guide to modeling counterparty credit risk. GARP Risk Review, 1, 16-33.


Crime Linkage Analysis

Crime Linkage Analysis and Serial Crime Attribution

case linkage
offender linking
serial crime attribution

Crime linkage analysis is a forensic method that determines whether a series of crimes were committed by the same offender based on behavioral and modus operandi (MO) similarities. Developed systematically by Craig Bennell and colleagues in the early 2000s, crime linkage applies statistical and similarity-matching techniques to establish offender attribution. The method is essential in serial crime investigation, where establishing linkage enables consolidation of investigation resources, geographic profiling, and offender-focused surveillance.

Crime science and offender profiling method

Bennell, C., Canter, D. V., & Alison, L. J. (2002). Linking commercial burglaries by modus operandi: Tests using regression and ROC analysis. Science and Justice, 42(3), 153-164.

Brants, L., de Ridder, H., & de Ridder, A. (2009). Offender linking in serial homicide. Forensic Science International, 171(2-3), 97-103.

Tonkin, M., Santtila, P., Bull, R., & Bond, J. W. (2012). A systematic review of decision support tools for case linkage. Forensic Science International, 221(1-3), 1-13.


CRISPR Screen Analysis

CRISPR Screening Data Analysis and Hit Identification

CRISPR pooled screen
genetic screen analysis

CRISPR screen analysis processes data from pooled genetic screens using CRISPR-Cas9 to identify genes required for cell growth, survival, or phenotype in specific conditions. Developed by Zhang, Sanjana, and others, this computational pipeline transforms sequencing readouts of guide RNA abundances into ranked lists of functional genes.

Shalem, O., Sanjana, N. E., Hartenian, E., Shi, X., Scott, D. A., Mikkelsen, T. S., ... & Zhang, F. (2014). Genome-scale CRISPR-Cas9 knockout screening in human cells. Science, 343(6166), 84-87.

Hart, T., Chandrashekhar, M., Aregger, M., Steinhart, Z., Brown, K. R., MacLeod, G., ... & Moffat, J. (2015). High-resolution CRISPR screens reveal fitness genes and pathways. Molecular Systems Biology, 11(8), 820.

King, J. B., Palmer, A. C., & Sorger, P. K. (2020). Application of a genetic algorithm designed for flexible objective optimization in pharmaceutical research and development. Cancer Research, 79(13 Supplement), 3435.


CRITERIA-REMOVAL

CRITERIA-REMOVAL (Criteria Removal — Sensitivity analysis by sequential criterion exclusion) is a ranking multi-criteria decision-making (MCDM) method introduced by Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. in 2004. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Robustness diagnostic — criterion removal sensitivity

Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. (2004). Sensitivity Analysis in Practice. Wiley, Chichester


CRITIC-M

Criteria Importance Through Intercriteria Correlation - Modified (CRITIC-M)

Based on Diakoulaki et al.'s CRITIC; modified variants developed later

CRITIC-M (Criteria Importance Through Intercriteria Correlation - Modified) is an objective weight derivation method that extends the classical CRITIC approach. It assigns weights to criteria based on two intrinsic properties of the decision matrix: variance (how much a criterion differentiates alternatives) and correlation (how much a criterion conflicts with or supplements others). Modified variants adjust the formulation to improve robustness or interpretability.

Objective weight derivation via correlation and variance

Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The CRITIC method. Computers & Operations Research, 22(7), 763-770.

Jahan, A., Mustapha, F., Sapuan, S. M., Ismail, M. Y., & Badruddin, I. A. (2012). A comprehensive VIKOR method for material selection. Materials & Design, 32(3), 1215-1221.

critic entropy-method variance-based-weighting merec-g swara-ii