Descriptive Phenomenology

Descriptive Phenomenological Method (Giorgi)

Giorgi method
empirical phenomenology
scientific phenomenology
Husserlian descriptive phenomenology

1970s–1985 (systematised by Giorgi; refined 2009)

Amedeo Giorgi (adapting Edmund Husserl's transcendental phenomenology)

Descriptive Phenomenology, systematised by Amedeo Giorgi at Duquesne University, is a rigorous qualitative method for uncovering the general psychological structure of a lived experience. Drawing directly on Husserl's transcendental phenomenology, Giorgi's four-step procedure — epoché, whole reading, meaning-unit discrimination, and transformation into disciplinary language — produces a stable, replicable description of what makes an experience essentially what it is, without theoretical interpretation or causal explanation.

Verbatim interview transcripts; written first-person accounts

Giorgi, A. (2009). The Descriptive Phenomenological Method in Psychology: A Modified Husserlian Approach. Duquesne University Press.

Giorgi, A. (Ed.). (1985). Phenomenology and Psychological Research. Duquesne University Press.

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


Descriptive Research

descriptive study
descriptive survey design
observational descriptive research
non-experimental descriptive research

Late 19th century; formalized in social/behavioral sciences ~1960s–1980s

Francis Galton, Karl Pearson (early empirical tradition); formalized in social science by Fred Kerlinger

Descriptive research is a non-experimental quantitative design that systematically documents the characteristics, frequencies, or distributions of variables in a defined population at a given point in time. It answers 'what is' questions — who, what, when, where, and how much — without manipulating variables or drawing causal conclusions. It is one of the most widely used research designs across the social, behavioral, health, and education sciences.

Non-experimental quantitative research design

Surveys, structured observations, census or archival records, questionnaires

Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). Sage.

Kerlinger, F. N. (1986). Foundations of Behavioral Research (3rd ed.). Holt, Rinehart and Winston.

survey-research cross-sectional-research correlational-research exploratory-quantitative-research observational-quantitative-research longitudinal-research


Descriptive Statistics

summary statistics
exploratory data summary
Betimsel İstatistik

Descriptive statistics is a set of procedures that numerically and visually summarises the essential characteristics of a dataset: central tendency (mean, median, mode), spread (standard deviation, interquartile range), shape (skewness, kurtosis), and frequency distributions. Systematised for applied data analysis by John W. Tukey in his 1977 work on Exploratory Data Analysis, descriptive statistics serves as the indispensable first step before any inferential or modelling procedure.

continuous, categorical, ordinal, binary, count

Tukey, J.W. (1977). Exploratory Data Analysis. Addison-Wesley.

shapiro-wilk-test kolmogorov-smirnov independent-t-test pearson-correlation one-way-anova chi-square


Design-Based Concurrent Embedded Mixed Methods Design

design experiment embedded mixed methods

Creswell & Plano Clark (concurrent embedded framework); Design-Based Research Collective (DBR framework)

Design-based concurrent embedded mixed methods design merges the iterative intervention logic of Design-Based Research (DBR) with the concurrent embedded mixed methods structure, in which one data type (typically qualitative) is nested within a dominant dataset (typically quantitative) and both are collected simultaneously within each design cycle. This approach is especially suited to educational intervention and applied research contexts where a product, curriculum, or tool is being developed, tested, and refined through repeated cycles of implementation and analysis.

Quantitative and qualitative data collected simultaneously within iterative design cycles

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

Nolen, A. L., & VanderPutten, J. (2007). Action research in education: Addressing gaps in ethical principles and practices. Educational Researcher, 36(7), 401–407.


Design-based Explanatory Sequential Mixed Methods Design

Design-Based Explanatory Sequential Mixed Methods Design

DBR explanatory sequential design
design-based explanatory mixed methods
design research explanatory sequential
DBEMM explanatory sequential

2000s–2010s (synthesis of explanatory sequential and design-based research traditions)

Creswell & Plano Clark (explanatory sequential); Brown, Collins & Duguid, and Reeves (design-based research)

This design embeds an explanatory sequential mixed methods structure — quantitative data collection followed by qualitative follow-up — within iterative design-based research (DBR) cycles. The quantitative phase establishes what is happening with a designed intervention or learning environment; the qualitative follow-up explains why. Results then feed directly back into redesign, making the method especially powerful in educational and instructional technology research where both statistical patterns and contextual understanding are needed to refine innovations.

Quantitative data (Phase 1) followed by qualitative data (Phase 2), embedded within iterative design-test cycles

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

McKenney, S., & Reeves, T. C. (2018). Conducting Educational Design Research (2nd ed.). Routledge.


Design-Based Intervention Mixed Methods

Design-Based Intervention Mixed Methods Design

DBR intervention mixed methods
design-based intervention study
design experiment with mixed methods
intervention design-based mixed methods

2003–2010s (convergence of DBR and mixed methods traditions)

Design-Based Research Collective; Creswell & Plano Clark (mixed methods framework)

Design-based intervention mixed methods is a research design that embeds both quantitative and qualitative data collection within iterative intervention cycles drawn from design-based research (DBR). The approach systematically tests and refines a practical intervention — typically an educational program, curriculum, or organizational solution — while using qualitative data to explain why and how the intervention works, and quantitative data to assess its measurable impact. Iteration between design, testing, and revision is the hallmark of this approach.

Quantitative outcome data and qualitative process/contextual data collected during iterative intervention cycles

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

The Design-Based Research Collective. (2003). Design-based research: An emerging paradigm for educational inquiry. Educational Researcher, 32(1), 5–8.


Design-based Mixed Methods Matrix

Design-based Mixed Methods Matrix Framework

mixed methods design matrix
MM design typology matrix
mixed methods design framework
DBMM matrix

John W. Creswell & Vicki L. Plano Clark; Jennifer C. Greene

The design-based mixed methods matrix is a systematic framework for selecting and structuring mixed methods research designs. It organises key design decisions — purpose, timing of data strands, point of integration, and weighting of quantitative versus qualitative components — into a coherent matrix that guides researchers toward a defensible, transparent design. The framework draws on the typology traditions of Creswell and Plano Clark and Greene's purposes-based approach.

Mixed methods design classification framework

Quantitative and qualitative data (combined)

Greene, J. C. (2007). Mixed Methods in Social Inquiry. Jossey-Bass.

mixed-methods-research convergent-parallel-design sequential-explanatory-design sequential-exploratory-design embedded-mixed-methods design-based-research


Design-Based Mixed Methods Meta-Inference

mixed methods meta-inference
MMR meta-inference
integrated meta-inference
design-based meta-inference

Design-based mixed methods meta-inference is the overarching conclusion drawn by explicitly integrating the separate quantitative and qualitative inferences from a mixed methods study, with the integration logic anchored to the a priori research design. Rather than treating quantitative and qualitative results as parallel outputs, the approach requires the researcher to specify — at the design stage — how and why the two strands will be combined, and then to construct a unified meta-inference that is consistent with that design rationale.

Combined quantitative and qualitative data strands

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

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

mixed-methods-research sequential-explanatory-design sequential-exploratory-design convergent-parallel-design triangulation meta-synthesis


Design-based Multilevel Mixed Methods

Design-based Multilevel Mixed Methods Research

DB-MLMM
multilevel design-based mixed methods
design-based multilevel research
DBR multilevel mixed design

Synthesized from Design-Based Research Collective (2003) and Creswell & Plano Clark multilevel mixed methods typology

Design-based multilevel mixed methods combines the iterative, context-sensitive logic of design-based research (DBR) with the analytical power of multilevel data structures and the explanatory depth of mixed methods research. It is used predominantly in educational and organizational research where participants are nested within settings (e.g., students within classrooms within schools) and where a designed intervention must be tested, refined, and understood at multiple organizational levels simultaneously.

Nested quantitative data (e.g., students-in-classrooms) combined with qualitative data (interviews, observations)

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

design-based-research multilevel-modeling mixed-methods-research design-based-intervention-mixed-methods hierarchical-linear-modeling explanatory-sequential-mixed-methods


Design-based Pragmatic Mixed Methods

Design-Based Pragmatic Mixed Methods Research

educational design research with mixed methods

Synthesised from Design-Based Research Collective (2003) and pragmatist mixed methods scholars (Creswell, Tashakkori, Teddlie)

Design-based pragmatic mixed methods combines the iterative, intervention-focused logic of design-based research (DBR) with the philosophical pragmatism that underpins mixed methods inquiry. Researchers design, test, and refine an educational or organisational intervention across multiple cycles while simultaneously collecting quantitative outcome data and qualitative process data. The pragmatist worldview licenses the integration of both data strands in service of a practical research question: does this design work, for whom, and why?

Quantitative measures (tests, surveys, logs) and qualitative data (interviews, observations, artefacts) collected iteratively

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

design-based-research mixed-methods-research pragmatic-research action-research convergent-mixed-methods explanatory-sequential-mixed-methods


Design-based qualitative-priority mixed methods design

Design-Based Qualitative-Priority Mixed Methods Research Design

QUAL-priority mixed methods design
qualitative-dominant mixed methods
design-based QUAL-dominant design
qualitative-priority design-based MMR

1991–2011 (Morse 1991 priority notation; Creswell & Plano Clark 2007–2011 design taxonomy)

Janice Morse (priority notation); John W. Creswell & Vicki L. Plano Clark (design typology)

A design-based qualitative-priority mixed methods design places qualitative inquiry at the centre of the research, using quantitative data in a supporting, secondary role. The qualitative strand drives the research questions, sampling logic, and interpretive conclusions, while quantitative data — collected concurrently or sequentially — provide supplementary breadth, frequency estimates, or contextual triangulation. This approach is codified in the priority-notation system (QUAL + quan) developed by Morse and elaborated in Creswell and Plano Clark's mixed methods design taxonomy.

Qualitative data primary (interviews, observations, documents); quantitative data secondary (surveys, scales, counts)

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

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

exploratory-sequential-mixed-methods explanatory-sequential-mixed-methods convergent-parallel-mixed-methods grounded-theory phenomenology embedded-mixed-methods


Design-based quantitative-priority mixed methods design

Design-Based Quantitative-Priority Mixed Methods Research Design

quantitative-dominant design-based mixed methods

2000s–2010s (emerging with DBR maturation and mixed methods codification)

Synthesized from Design-Based Research (Ann Brown, Allan Collins) and Mixed Methods frameworks (Creswell, Plano Clark)

Design-based quantitative-priority mixed methods research integrates a design-based research (DBR) framework — which involves iterative cycles of design, implementation, and refinement in naturalistic settings — with a mixed methods approach where quantitative data collection and analysis carry the primary evidentiary weight. Qualitative data are gathered in a supporting role to illuminate, explain, or refine quantitative findings across iterative design cycles.

Quantitative (primary) and qualitative (secondary) data

design-based-research explanatory-sequential-design embedded-mixed-methods-design convergent-parallel-design experimental-design action-research


Design-based Research

DBR
design research
design experiment
educational design research

Ann L. Brown and Allan Collins (independently, 1992)

Design-based research (DBR) is an iterative, interventionist methodology that simultaneously designs educational interventions and builds theory about how and why those interventions work in authentic, complex settings. Originating in Ann Brown's 1992 classroom experiments and Allan Collins's parallel work, DBR treats the learning environment as both the object of study and the site of theory generation, cycling through design, enactment, analysis, and redesign until both practical improvement and theoretical insight are achieved.

Interventionist qualitative-quantitative mixed methodology

Observational data, interviews, artifacts, test scores, field notes

Brown, A. L. (1992). Design experiments: Theoretical and methodological challenges in creating complex interventions in classroom settings. Journal of the Learning Sciences, 2(2), 141–178.

Design-Based Research Collective. (2003). Design-based research: An emerging paradigm for educational inquiry. Educational Researcher, 32(1), 5–8.

action-research educational-action-research lesson-study program-evaluation participatory-action-research case-study


Design-based Transformative Mixed Methods Design

Design-Based Transformative Mixed Methods Research Design

transformative design-based mixed methods

social justice design-based mixed methods

Donna M. Mertens (transformative framework); anchored in design-based research tradition (Brown, Collins, Edelson)

Design-Based Transformative Mixed Methods Design integrates Donna Mertens' transformative paradigm — which foregrounds social justice, equity, and the perspectives of marginalized groups — with the iterative intervention cycles of design-based research. It systematically combines quantitative and qualitative data across successive design-test-refine cycles to both understand and actively improve conditions for underrepresented communities. The approach is prominent in education, public health, and community-engaged research where changing unjust structures is an explicit goal alongside knowledge generation.

Quantitative and qualitative data collected iteratively in designed intervention cycles

Mertens, D. M. (2007). Transformative paradigm: Mixed methods and social justice. Journal of Mixed Methods Research, 1(3), 212–225.

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

transformative-mixed-methods design-based-research concurrent-triangulation-design exploratory-sequential-design explanatory-sequential-design participatory-action-research


Design for Manufacturing and Assembly

Design for Manufacturing and Assembly (DFMA) Methodology

Design for Manufacturing and Assembly (DFMA) is a systematic methodology for creating products that are inherently easier and less expensive to manufacture and assemble. Developed by Boothroyd, Dewhurst, and Knight, DFMA evaluates design choices based on their impact on production cost, quality, and speed, guiding designers toward solutions that balance performance, manufacturability, and economics.

Systematic approach to cost-effective product design

Boothroyd, G., Dewhurst, P., & Knight, W. A. (1994). Product Design for Manufacturing and Assembly (1st ed.). Marcel Dekker.

Ulrich, K. T., & Eppinger, S. D. (2003). Product Design and Development (3rd ed.). McGraw-Hill.

Swift, K. G., & Booker, J. D. (2005). Process Selection: From Design to Manufacture (2nd ed.). Butterworth-Heinemann.

cnc-tool-path-generation additive-manufacturing-slicing tolerance-stack-up modal-analysis


Design of experiments

DOE
experimental design
factorial experimentation
planned experimentation

Design of Experiments (DOE) is a systematic framework for planning, conducting, and analyzing controlled experiments to determine how multiple input factors simultaneously affect one or more responses. Introduced by Ronald A. Fisher in 1935, DOE allows researchers and engineers to identify causal relationships, quantify factor effects, and find optimal settings efficiently — using far fewer runs than one-factor-at-a-time approaches. It is foundational in engineering, manufacturing, agriculture, and applied sciences.

Continuous and categorical experimental response data

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

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

full-factorial-design fractional-factorial-design response-surface-methodology taguchi-method central-composite-design analysis-of-variance


Destination Image Scale

The Destination Image Scale (DIS) measures how potential or actual visitors perceive and emotionally evaluate a tourism destination. Developed by Echtner & Ritchie (1991) and extended by Baloglu & Brinberg (1997), it captures both rational beliefs about destination attributes (attractions, climate, value, safety) and affective emotional responses (excitement, pleasantness, arousal). Destination image is a primary driver of visitation intention and repeat patronage, making the DIS essential for destination marketing strategy and competitive positioning.

Self-report questionnaire / Semantic differential scale

Baloglu, S., & Brinberg, D. (1997). Affective images of tourism destinations. Journal of Travel Research, 35(4), 11-15.

Echtner, C. M., & Ritchie, J. R. B. (1991). The meaning and measurement of destination image. Journal of Tourism Studies, 2(2), 2-12.

Hunt, J. D. (1975). Image as a factor in tourism development. Journal of Travel Research, 13(3), 1-7.

Ritchie, J. R. B., & Zins, M. (1978). Culture as determinant of the attractiveness of a tourism region. Annals of Tourism Research, 5(2), 252-267.

tourist-satisfaction-scale tourist-loyalty-scale travel-motivation-scale place-attachment-scale perceived-value-scale-tourism


Detached Eddy Simulation

Detached Eddy Simulation (DES) is a hybrid turbulence modeling approach introduced by Spalart in 1997 that combines the computational efficiency of RANS in attached boundary layers with the accuracy of LES in separated wake regions. By automatically switching between RANS and LES based on local grid spacing and turbulence length scales, DES provides superior predictions for flows with large separations, shear layers, and vortex shedding at a cost between pure RANS and pure LES. DES has become the standard method for complex aerospace applications involving separation and transient phenomena.

Spalart, P. R., Jou, W. H., Strelets, M., & Allmaras, S. R. (1997). Comments on the feasibility of LES for wings, and on a hybrid RANS/LES approach. Advances in DNS/LES, 1, 4-8.

Spalart, P. R., Deck, S., Shur, M. L., Squires, K. D., Strelets, M. Y., & Travin, A. (2006). A new version of detached-eddy simulation, resistant to ambiguous grid densities. Theoretical and Computational Fluid Dynamics, 20(3), 181-195.

Gritskevich, M. S., Garbaruk, A. V., Shur, M. L., & Spalart, P. R. (2012). Development of DDES and IDDES formulations for the k-ω SST turbulence model. Flow, Turbulence and Combustion, 88(3), 431-449.

large-eddy-simulation reynolds-averaged-navier-stokes direct-numerical-simulation boundary-layer-theory smoothed-particle-hydrodynamics


Deterministic Agent-Based Modeling

D-ABM
Deterministic ABM
Rule-Based Agent Simulation
Fixed-Rule Agent-Based Model

Deterministic Agent-Based Modeling (D-ABM) is a computational simulation approach in which autonomous agents follow fully specified, non-random behavioral rules within a structured environment. Every run with identical initial conditions produces identical outcomes, making the model fully reproducible and transparent for analysis of emergent system behavior without stochastic noise.

Computational simulation — deterministic rule-based agents

Agent attributes, interaction rules, initial conditions

Epstein, J. M., & Axtell, R. (1996). Growing Artificial Societies: Social Science from the Bottom Up. MIT Press.

Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(Suppl 3), 7280-7287.

agent-based-modeling stochastic-agent-based-modeling deterministic-system-dynamics cellular-automata deterministic-discrete-event-simulation multi-agent-systems


Deterministic Cellular Automata

Deterministic Cellular Automata — Rule-based discrete dynamical simulation on a grid

Deterministic CA
Classical Cellular Automata
Rule-based CA
Finite Automata Grid Model

Deterministic Cellular Automata (DCA) is a simulation method that models the evolution of complex systems through a regular grid of cells, each holding a discrete state, updated synchronously at each time step according to a fixed, deterministic rule applied to the cell and its neighbors. The outcome is fully reproducible given the same initial conditions and rule set.

State-indexed grid data; binary or multi-state discrete variables

von Neumann, J. (1966). Theory of Self-Reproducing Automata. University of Illinois Press, Urbana, IL. (Edited and completed by A. W. Burks.)

Wolfram, S. (1983). Statistical mechanics of cellular automata. Reviews of Modern Physics, 55(3), 601–644.

stochastic-cellular-automata agent-based-modeling discrete-event-simulation system-dynamics markov-model monte-carlo-simulation


Deterministic Discrete-Event Simulation

Deterministic Discrete-Event Simulation — Event-driven modeling with fixed, non-random inputs

Banks, J.; Carson, J. S.; Nelson, B. L. (codified); roots in 1960s simulation pioneers (Tocher, Conway)

Deterministic Discrete-Event Simulation (Deterministic DES) models a system as a sequence of events occurring at precise, pre-specified times using fixed input parameters. Unlike stochastic DES, no probability distributions are sampled; every arrival, service time, and resource availability is known in advance, making runs fully reproducible and producing a single definitive output trajectory.

Simulation — deterministic event-driven model

Structured process data: fixed service times, arrival schedules, resource capacities

Banks, J., Carson, J. S., Nelson, B. L., and Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Prentice Hall.

discrete-event-simulation stochastic-discrete-event-simulation deterministic-system-dynamics deterministic-markov-model queueing-simulation process-simulation


Deterministic Dynamic Programming

Deterministic Dynamic Programming — Exact sequential optimization under known parameters

DDP
Deterministic DP
Classical Dynamic Programming
Bellman Dynamic Programming

Deterministic Dynamic Programming (DDP) is a mathematical optimization technique that decomposes a multi-stage decision problem into a sequence of simpler subproblems, solving them exactly when all system parameters — transition functions, costs, and rewards — are known with certainty. It guarantees a globally optimal policy via Bellman's principle of optimality.

Deterministic state-transition systems; discrete or continuous state/action spaces

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

Bertsekas, D. P. (2017). Dynamic Programming and Optimal Control (4th ed., Vol. 1). Athena Scientific, Belmont, MA.

stochastic-dynamic-programming multi-objective-dynamic-programming mixed-integer-programming deterministic-linear-programming deterministic-integer-programming markov-model


Deterministic Genetic Algorithm

Deterministic Genetic Algorithm — Evolutionary optimization with deterministic selection and operators

DGA
Deterministic EA
Deterministic Evolutionary Algorithm
Deterministic Selection GA

A Deterministic Genetic Algorithm (DGA) applies the structural framework of evolutionary computation — population, selection, crossover, and replacement — using entirely deterministic operators and fixed decision rules instead of stochastic sampling. By eliminating randomness, the algorithm becomes fully reproducible: running it twice on the same problem yields identical solutions, making it tractable for rigorous benchmarking, reproducibility studies, and systems where stochasticity is undesirable.

Continuous or discrete decision variables, objective function evaluations

Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA.

Mahfoud, S. W. (1995). Niching methods for genetic algorithms. IlliGAL Report No. 95001, University of Illinois at Urbana-Champaign.


Deterministic Integer Programming

DIP
Integer Programming
IP
Integer Linear Programming

Deterministic Integer Programming (DIP) is a mathematical optimization approach that finds the best solution to problems where some or all decision variables must take integer values, given fully known (deterministic) objective and constraint data. It is the classical, non-stochastic form of integer programming, foundational to operations research and combinatorial optimization since the late 1950s.

Deterministic numerical parameters (objective coefficients, constraint coefficients, RHS values)

Gomory, R. E. (1958). Outline of an algorithm for integer solutions to linear programs. Bulletin of the American Mathematical Society, 64(5), 275-278.

Wolsey, L. A. (1998). Integer Programming. Wiley-Interscience, New York.

mixed-integer-programming linear-programming stochastic-integer-programming dynamic-programming multi-objective-integer-programming branch-and-bound


Deterministic Linear Programming

Deterministic Linear Programming — Classical LP with Certain Parameters

Classical LP
Deterministic LP
DLP
Linear Optimization

Deterministic Linear Programming (DLP) is the classical form of linear programming in which all objective function coefficients, constraint coefficients, and right-hand-side values are known with certainty. It finds the optimal allocation of resources to maximize or minimize a linear objective subject to linear constraints, providing an exact, reproducible solution under fixed, certain data.

Continuous numerical parameters (objective coefficients, constraint coefficients, right-hand-side values)

Dantzig, G. B. (1963). Linear Programming and Extensions. Princeton University Press, Princeton, NJ.

stochastic-linear-programming mixed-integer-programming deterministic-dynamic-programming multi-objective-linear-programming deterministic-goal-programming robust-linear-programming


Deterministic Markov Model

Deterministic Markov Model — Fixed-parameter Markov chain for cohort-level state transitions

DMM
Deterministic Markov Chain
Cohort Markov Model
Fixed-Parameter Markov Model

A Deterministic Markov Model is a cohort-level state-transition model in which all transition probabilities, state utilities, and costs are assigned single fixed values and the model is solved analytically in a single pass. Widely used in health technology assessment, policy analysis, and operations research, it traces a hypothetical cohort through mutually exclusive health or system states over discrete time cycles, accumulating expected outcomes such as quality-adjusted life years (QALYs) or costs.

Cohort state-transition model with fixed transition probabilities

Transition probability matrices, state rewards, cohort sizes

Sonnenberg, F. A., & Beck, J. R. (1993). Markov models in medical decision making: a practical guide. Medical Decision Making, 13(4), 322–338.

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

markov-model stochastic-markov-model monte-carlo-simulation discrete-event-simulation sensitivity-analysis scenario-analysis


Deterministic Microsimulation

Deterministic Microsimulation — Rule-based individual-level simulation without random draws

Arithmetic Microsimulation
Static Tax-Benefit Microsimulation
Deterministic Policy Simulation
Rule-based Microsimulation

Deterministic Microsimulation applies a fixed set of policy rules or behavioral equations to each individual or household record in a microdata file, computing exact outcomes without any random sampling. It is the standard engine behind tax-benefit calculators and demographic projection models used by governments worldwide.

Individual-level deterministic rule application

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

Bourguignon, F., & Spadaro, A. (2006). Microsimulation as a tool for evaluating redistribution policies. Journal of Economic Inequality, 4(1), 77–106.

monte-carlo-simulation stochastic-microsimulation markov-model discrete-event-simulation system-dynamics scenario-analysis


Deterministic Mixed-Integer Programming

Deterministic Mixed-Integer Programming (Deterministic MIP)

Deterministic MIP
Deterministic MILP/MIQP
Classical Mixed-Integer Programming
Deterministic MIP Optimization

Gomory, R. E.; Dantzig, G. B.; Land, A. H.; Doig, A. G.

Deterministic Mixed-Integer Programming (MIP) is a mathematical optimization framework that finds the provably optimal solution to problems involving both continuous and integer decision variables under fully known, fixed coefficients and constraints. It is the foundational workhorse of operations research when all data are treated as certain.

Mathematical programming / combinatorial optimization

Deterministic numerical parameters (costs, capacities, demands); continuous and integer decision variables

Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. John Wiley & Sons, New York.


Deterministic Multi-Objective Optimization — Classical Pareto-based and scalarization approaches without stochastic components

Deterministic MOO
Classical Multi-Objective Optimization
Non-Stochastic MOO
Deterministic Pareto Optimization

Kuhn, H. W., Tucker, A. W. (Pareto optimality formalized); Miettinen, K. (systematic deterministic framework)

Deterministic Multi-Objective Optimization (Deterministic MOO) is a family of classical optimization approaches that simultaneously minimize or maximize multiple conflicting objective functions over a deterministic feasible set. It produces a Pareto front — the set of non-dominated solutions — from which a decision-maker selects the preferred trade-off. Unlike stochastic variants, all objective evaluations and constraints are fixed and noise-free.

Optimization framework — deterministic Pareto and scalarization methods

Continuous or integer decision variables; deterministic objective functions and constraints

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

Miettinen, K. (1999). Nonlinear Multiobjective Optimization. Springer, Boston.

multi-objective-optimization stochastic-multi-objective-optimization nsga-ii multi-objective-linear-programming pareto-analysis weighted-sum-method


DPSO
Deterministic PSO
PSO without stochastic components
Fully Deterministic PSO

1995 (PSO); deterministic formulation circa 2002

Kennedy, J., Eberhart, R. (PSO); deterministic variant formalized in convergence analysis literature

Deterministic Particle Swarm Optimization (DPSO) removes the stochastic random coefficients from classical PSO, replacing them with fixed cognitive and social acceleration parameters. Particles move through the search space following fully predictable trajectories, enabling reproducible convergence analysis and guaranteed termination behavior in continuous and combinatorial optimization problems.

Swarm intelligence metaheuristic — deterministic variant

Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95 — International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE.

Clerc, M., Kennedy, J. (2002). The particle swarm — explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6(1), 58–73.


Deterministic Scenario Analysis

Deterministic Scenario Analysis — Fixed-parameter scenario exploration for planning and decision support

DSA
Fixed-Input Scenario Analysis
Classical Scenario Analysis
Deterministic What-If Analysis

Kahn, H., Wiener, A. J. (RAND Corporation / Hudson Institute)

Deterministic Scenario Analysis (DSA) is a structured planning method in which analysts construct a finite set of internally consistent future scenarios, each defined by fixed, precisely specified parameter values rather than probability distributions. By running a model or calculation under each scenario's fixed inputs, decision-makers can map how outcomes diverge across plausible futures and stress-test strategies without requiring full probabilistic characterization of uncertainty.

Exploratory planning and decision-support framework

Quantitative inputs (fixed numeric parameters), qualitative narrative drivers

Kahn, H., Wiener, A. J. (1967). The Year 2000: A Framework for Speculation on the Next Thirty-Three Years. Macmillan, New York.

Schoemaker, P. J. H. (1993). Multiple scenario development: Its conceptual and behavioral foundation. Strategic Management Journal, 14(3), 193–213.

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


Deterministic Sensitivity Analysis

Deterministic Sensitivity Analysis — Systematic Parameter Variation for Model Robustness

DSA
One-Way Sensitivity Analysis
Tornado Diagram Analysis
Parametric Sensitivity Analysis

Saltelli, A. et al.; widely formalized across operations research and health economics

Deterministic Sensitivity Analysis (DSA) tests how model outputs change when individual or combined input parameters are varied across plausible ranges, one at a time or in structured combinations, without invoking probabilistic sampling. It is the standard approach in economic modeling, decision trees, and mathematical programming to identify which parameters drive conclusions and to demonstrate model robustness to regulators, reviewers, and stakeholders.

Parameter variation / robustness testing

Quantitative model outputs and input parameter ranges

Saltelli, A., Tarantola, S., Campolongo, F., & Ratto, M. (2004). Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. John Wiley & Sons, Chichester.

Briggs, A., Sculpher, M., & Buxton, M. (1994). Uncertainty in the economic evaluation of health care technologies: the role of sensitivity analysis. Health Economics, 3(2), 95–104.

monte-carlo-simulation scenario-analysis stochastic-sensitivity-analysis probabilistic-sensitivity-analysis tornado-diagram one-way-sensitivity-analysis


Deterministic Simulated Annealing

Deterministic Simulated Annealing — Annealing-schedule optimization without stochastic acceptance

DSA
Deterministic Annealing
Greedy Annealing
Temperature-Scheduled Descent

Deterministic Simulated Annealing (DSA) is an optimization metaheuristic that adopts the cooling-schedule structure of classical simulated annealing but replaces the probabilistic Metropolis acceptance criterion with a strictly deterministic rule: only improving moves are accepted. This yields a reproducible, greedy-descent procedure guided by an annealing temperature schedule.

Deterministic metaheuristic — annealing schedule without probabilistic acceptance

Continuous or discrete optimization problem instances

Rose, K., Gurewitz, E., Fox, G. C. (1990). A deterministic annealing approach to clustering. Pattern Recognition Letters, 11(9), 589-594.

Kirkpatrick, S., Gelatt, C. D., Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680.

simulated-annealing stochastic-simulated-annealing tabu-search deterministic-tabu-search local-search gradient-descent


Deterministic System Dynamics

Deterministic System Dynamics — Feedback-loop simulation with fixed differential equations

Deterministic SD
Classical System Dynamics
Continuous Simulation SD
Forrester System Dynamics

Deterministic System Dynamics is the classical form of System Dynamics introduced by Jay Forrester in 1961, using fixed (non-probabilistic) ordinary differential equations to simulate stock-and-flow structures and feedback loops over time. All model parameters and relationships are specified as single-valued constants or deterministic functions, yielding a single trajectory for each simulation run. It is widely used in policy analysis, business strategy, ecology, and public health modeling.

Continuous time-series rates, stocks, and flows; quantitative aggregate data

Forrester, J. W. (1961). Industrial Dynamics. MIT Press, Cambridge, MA.

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

system-dynamics stochastic-system-dynamics discrete-event-simulation agent-based-modeling scenario-analysis sensitivity-analysis


DETR (Detection Transformer)

End-to-End Object Detection with Transformers

DETR (Detection Transformer) is an end-to-end framework for object detection introduced by Carion et al. in 2020 that reformulates detection as a direct set prediction problem using transformers. Unlike traditional approaches that use hand-crafted post-processing like non-maximum suppression, DETR treats object detection as a sequence-to-sequence problem where the transformer predicts all objects at once.

Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020). End-to-end object detection with transformers. In European Conference on Computer Vision (pp. 213-229). Springer, Cham.

swin-transformer vision-mamba segment-anything-model masked-autoencoders


Deviant Case Sampling

extreme case sampling
outlier sampling
negative case sampling
deviant-case selection

Deviant case sampling is a purposive qualitative sampling strategy in which the researcher intentionally selects cases that are unusual, exceptional, or markedly different from the norm — outliers, extreme successes, or conspicuous failures. The goal is not statistical representation but deep learning from cases that illuminate the boundaries of a phenomenon, challenge prevailing assumptions, or reveal processes that typical cases obscure.

Qualitative data (interviews, documents, observations)

Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). Sage Publications.

Flyvbjerg, B. (2006). Five misunderstandings about case-study research. Qualitative Inquiry, 12(2), 219-245.

purposive-sampling maximum-variation-sampling typical-case-sampling theoretical-sampling snowball-sampling case-study


DEXA

Dual X-ray absorptiometry
DXA
bone densitometry

Dual-Energy X-ray Absorptiometry (DEXA or DXA) is a non-invasive imaging technique that quantifies bone mineral density (BMD) by measuring the attenuation of X-rays at two different energies as they pass through bone and soft tissue. First developed by Wahner and colleagues in 1987, DEXA has become the gold standard for osteoporosis screening and fracture risk assessment. It is recommended by the World Health Organization for diagnosing osteoporosis and monitoring treatment response.

Kanis, J. A. (1994). Assessment of fracture risk and its application to screening for postmenopausal osteoporosis. World Health Organization Technical Report Series, 843, 1-129.

Genant, H. K., Engelke, K., Fuerst, T., et al. (1996). Noninvasive assessment of bone mineral and structure: state of the art. Journal of Bone and Mineral Research, 11(6), 707-730.

Blake, G. M., Fogelman, I. (2016). The role of DXA bone density scans in the diagnosis and treatment of osteoporosis. Postgraduate Medical Journal, 83(980), 509-517.

ct-iterative-reconstruction quantitative-susceptibility-mapping oct-angiography pet-kinetic-modeling radiomics


DF-GLS Test

Elliott-Rothenberg-Stock test
ERS unit-root test
GLS-detrended Dickey-Fuller test
DF-GLS birim kök testi

The DF-GLS test, introduced by Elliott, Rothenberg, and Stock (1996), is a modified augmented Dickey-Fuller procedure that applies generalized least squares (GLS) detrending before the standard unit-root regression. By removing deterministic components under a local alternative rather than the null hypothesis, the test achieves near-optimal power for detecting stationarity in time series, making it the preferred unit-root test in applied econometrics when a trend or intercept is present.

One-sided t-test on GLS-detrended series

Elliott, G., Rothenberg, T. J., & Stock, J. H. (1996). Efficient tests for an autoregressive unit root. Econometrica, 64(4), 813–836.


DHF-COPRAS

DHF-COPRAS (Dual Hesitant Fuzzy extension of COPRAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Rani, P., Mishra, A. R., Krishankumar, R., Mardani, A., Cavallaro, F., Ravichandran, K. S., Balasubramanian, K. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Dual Hesitant outranking/ranking — Dual Hesitant Fuzzy Element (DHFE: h(x) membership set, g(x) non-membership set)

Rani, P., Mishra, A. R., Krishankumar, R., Mardani, A., Cavallaro, F., Ravichandran, K. S., Balasubramanian, K. (2020). Hesitant Fuzzy SWARA-Complex Proportional Assessment Approach for Sustainable Supplier Selection (HF-SWARA-COPRAS). Symmetry

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


DHF-EDAS

DHF-EDAS (Dual Hesitant Fuzzy extension of EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Ning, B., Lin, R., Wei, G., Chen, X. in 2023. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Ning, B., Lin, R., Wei, G., Chen, X. (2023). EDAS method for multiple attribute group decision making with probabilistic dual hesitant fuzzy information and its application to suppliers selection. Technological and Economic Development of Economy


DHF-TODIM

DHF-TODIM (Dual Hesitant Fuzzy extension of TODIM) is a ranking multi-criteria decision-making (MCDM) method introduced by Liu, Y., Tariq, M., Khan, S., Abdullah, S. in 2023. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Liu, Y., Tariq, M., Khan, S., Abdullah, S. (2023). Complex dual hesitant fuzzy TODIM method and their application in Russia–Ukraine war's impact on global economy. Complex & Intelligent Systems


DHF-TOPSIS

DHF-TOPSIS (Dual Hesitant Fuzzy extension of TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Wang, R., Li, W., Zhang, T., Han, Q. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Wang, R., Li, W., Zhang, T., Han, Q. (2020). New Distance Measures for Dual Hesitant Fuzzy Sets and Their Application to Multiple Attribute Decision Making. Symmetry


DHF-VIKOR

DHF-VIKOR (Dual Hesitant Fuzzy extension of VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by An, J., Zhang, X., Liu, L., Zuo, W. in 2025. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

An, J., Zhang, X., Liu, L., Zuo, W. (2025). A dual hesitation fuzzy VIKOR method with incomplete attribute weights for property service quality evaluation. International Journal of Strategic Property Management


Diabetes Distress Scale

diabetes-specific emotional and psychosocial distress

The Diabetes Distress Scale (DDS) is a 17-item self-report measure that quantifies emotional and psychosocial distress specifically related to living with and managing diabetes. Developed by Polonsky and colleagues in 2005, the DDS captures diabetes-specific worries (e.g., regimen burden, fear of complications, social stigma, lack of support) that are distinct from generalized depression or anxiety, making it essential for identifying and addressing the emotional obstacles to optimal diabetes self-management.

Polonsky, W. H., Fisher, L., Earles, J., Dudl, R. J., Lees, J., Mulcahy, K., & Jackson, R. A. (2005). Assessing psychosocial distress in diabetes: Development of the Diabetes Distress Scale. Diabetes Care, 28(3), 626–631.


DQOL

DQOL
Diabetes Quality of Life
Diabetes QOL

The DQOL is a patient-reported measure of quality of life impact in people with diabetes. Developed by the Diabetes Control and Complications Trial (DCCT) Research Group in 1988, this 46-item questionnaire assesses how diabetes affects daily functioning, emotional well-being, worry about complications, and satisfaction with life and health. It is widely used in diabetes clinical trials and research to capture the burden of diabetes management and impact on psychosocial well-being.

Self-report quality of life questionnaire

The Diabetes Control and Complications Trial Research Group. (1988). The Diabetes Quality of Life (DQOL) measure. Diabetes Care, 11(10), 725-732.

Jacobson, A. M., de Groot, M., & Samson, J. A. (1994). The evaluation of two measures of quality of life in patients with type I and type II diabetes. Diabetes Care, 17(4), 267-274.

Bott, U., Hendrieckx, C., Mohr-Riehle, B., & Nagaraja, H. N. (2016). Psychosocial outcomes of therapy with real-time continuous glucose monitoring systems. Diabetes Technology & Therapeutics, 18(5), 314-322.

eortc-qlq-c30 copd-assessment-test chronic-heart-failure-questionnaire asthma-control-test


Diabetes Self-Efficacy Scale

diabetes self-management confidence and efficacy

The Diabetes Self-Efficacy Scale (DASES) is an 8-item self-report measure that assesses a patient's confidence in their ability to manage key diabetes self-care tasks: medication adherence, glucose monitoring, diet management, exercise, and coping with symptoms or complications. Developed by Lorig and colleagues based on social-cognitive theory, the DASES is grounded in Bandura's self-efficacy framework and demonstrates strong predictive validity for glycemic control, treatment adherence, and quality of life outcomes.

Lorig, K. R., Ritter, P. L., Villa, F. J., & Armas, J. (2009). Community-based peer-led diabetes self-management: A randomized trial. Diabetes Educator, 35(4), 641–651.

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


Diabetes Symptom Checklist

Diabetes Symptom Checklist: Patient-Reported Symptom Dimensions

Diabetes-specific symptom and impact assessment

Peta Grootenhuis, Frans Snoek, Rolf Heine, Lex Bouter

The Diabetes Symptom Checklist is a 34-item patient-reported outcome measure assessing the frequency and burden of symptoms directly related to diabetes and its complications. Developed by Grootenhuis and colleagues in 1994, it captures eight symptom dimensions including hyperglycemic symptoms, hypoglycemic symptoms, fatigue, polyuria, neuropathic pain, and psychological distress. The instrument is used to quantify symptom experience and treatment response across diverse diabetes populations.

Grootenhuis, P. A., Snoek, F. J., Heine, R. J., & Bouter, L. M. (1994). Development of a type 2 diabetes symptom checklist: A measure of symptom severity. Diabet Med, 11(3), 253-261.

Snoek, F. J., Pouwer, F., Welch, G. W., & Polonsky, W. H. (1997). Diabetes-related distress in Dutch and U.S. diabetic patients: Relationship to perceived health status and depression. Diabetes Care, 20(10), 1554-1558.


Diagnostic Accuracy Study Design

Diagnostic Test Accuracy Study (Sensitivity, Specificity, Likelihood Ratios)

diagnostic accuracy study
test accuracy
STARD
diagnostic evaluation

Bossuyt, Reitsma, and STARD group (2003); clinical epidemiology pioneers

A diagnostic accuracy study evaluates how well a new diagnostic test (or biomarker, imaging modality, clinical assessment) detects the presence or absence of disease compared to a reference standard (gold standard). Standardized since 2003 by the STARD (Standards for Reporting of Diagnostic Accuracy Studies) initiative, diagnostic accuracy studies are fundamental to clinical medicine, determining whether and how new tests can improve patient diagnosis and treatment.

Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L. M., ... & de Vet, H. C. (2003). Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. Annals of Internal Medicine, 138(1), 40–44.

Cohen, J. F., Korevaar, D. A., Altman, D. G., Bruns, D. E., Gatsonis, C. A., Hooft, L., ... & Bossuyt, P. M. (2016). STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration. BMJ Open, 6(11), e012799.

Jaeschke, R., Guyatt, G. H., & Sackett, D. L. (1994). Users' guides to the medical literature. III. How to use an article about a diagnostic test. A. Are the results of the study valid? JAMA, 271(5), 389–391.

randomized-controlled-trial cohort-study-design predictive-value roc-curve reference-standard


Dialectical Behavior Therapy

Dialectical Behavior Therapy (DBT) is a comprehensive, multimodal psychosocial intervention developed by Marsha M. Linehan to treat individuals with Borderline Personality Disorder (BPD) and chronic suicidality. Combining cognitive-behavioral principles with dialectical philosophy and Zen principles, DBT is delivered through individual therapy, skills training, phone coaching, and therapist consultation and has become the gold-standard treatment for BPD.

Linehan, M. M. (1993). Cognitive-behavioral treatment of borderline personality disorder. Guilford Press.

Linehan, M. M., Korslund, K. E., Harned, M. S., et al. (2015). Dialectical behavior therapy for high suicide risk in individuals with borderline personality disorder: A randomized clinical trial and component analysis. JAMA Psychiatry, 72(5), 475–482.


Dialectometry

Linguistic Distance Measurement
Quantitative Dialect Analysis

Dialectometry is a quantitative method for measuring linguistic distances between dialects or languages using objective metrics applied to phonological, lexical, or phonetic data. Pioneered by Jean Seguy in 1973, dialectometry compares word lists, pronunciations, or phonetic transcriptions across speech varieties to calculate similarity scores. The resulting distance matrices and dendrograms reveal patterns of dialect relatedness and geographic or social clustering. This method complements traditional dialectology and contributes to historical linguistics and sociolinguistics.

Seguy, J. (1973). La dialectométrie dans l'étude de l'espace linguistique. Revue de Linguistique Romane, 37, 1-24.

Nerbonne, J., & Heeringa, W. (2009). Measuring dialect differences. In P. Auer & J. E. Schmidt (Eds.), Language and Space: An International Handbook of Linguistic Variation. Berlin: De Gruyter.

Heeringa, W. (2004). Measuring Dialect Pronunciation Differences Using Levenshtein Distance. Groningen: University of Groningen.


Dialogue Act Classification

Diyalog Eylem Sınıflandırma (Dialogue Act Classification)

Dialogue act classification is a natural-language-processing task that automatically labels the communicative function of each utterance in a conversation — such as question, answer, greeting, or rejection. Consolidated by Jurafsky et al. (1997) and Stolcke et al. (2000), it is a foundational component for chatbots and discourse analysis.

Output: Dialogue act label per utterance (question, answer, greeting, rejection, etc.)

Stolcke, A. et al. (2000). Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech. Computational Linguistics, 26(3), 339-373.

Jurafsky, D. et al. (1997). Automatic Detection of Discourse Structure for Speech Recognition and Understanding. ICASSP.

intent-detection slot-filling sentiment-analysis text-classification


DMP Model
Search and Matching Model
Mortensen-Pissarides Model

Peter Diamond, Dale Mortensen, Christopher Pissarides

The Diamond-Mortensen-Pissarides (DMP) model, developed by Peter Diamond, Dale Mortensen, and Christopher Pissarides in the early 1980s, is a fundamental framework for understanding labor market dynamics through the lens of search and matching frictions. It explains how workers and firms meet, form employment relationships, and separate, endogenously determining unemployment, vacancies, and wages.

Mortensen, D. T., & Pissarides, C. A. (1994). Job Reallocation, Employment Fluctuations and Unemployment. In J. B. Taylor & M. Woodford (Eds.), Handbook of Macroeconomics, 1A, 1171–1227.

Diamond, P. A. (1982). Wage Determination and Efficiency in Search Equilibrium. Review of Economic Studies, 49(2), 217–227.

Pissarides, C. A. (1985). Short-Run Equilibrium Dynamics of Unemployment, Vacancies, and Real Wages. American Economic Review, 75(4), 676–690.


Diary Method

diary study
diary technique
self-report diary
daily diary method

1920s–1940s (systematised by Allport, 1942)

Gordon Allport (systematic social-science use); Nels Anderson (early fieldwork diaries)

The diary method is a data-collection technique in which participants record their thoughts, behaviours, events, or experiences in their own words at regular or event-contingent intervals over a defined study period. By capturing data close in time to the event, diaries reduce retrospective recall bias and give researchers access to the texture of everyday life as it unfolds — something one-off surveys and retrospective interviews cannot provide.

Qualitative / mixed-methods data-collection technique

Participant-generated text entries (qualitative), structured ratings or counts (quantitative), or both

Alaszewski, A. (2006). Using Diaries for Social Research. Sage.

Bolger, N., Davis, A., & Rafaeli, E. (2003). Diary methods: Capturing life as it is lived. Annual Review of Psychology, 54(1), 579–616.

experience-sampling-method field-notes participant-observation longitudinal-survey semi-structured-interview non-participant-observation


DIBR

DIBR (Defining Interrelationships Between Ranked criteria) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Pamučar, D., Žižović, M., Marinković, D., Doljanica, D. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Ranked criteria interrelationship weighting (non-pairwise sequential)

Pamučar, D., Žižović, M., Marinković, D., Doljanica, D. (2021). Development of an alternative approach for the selection of suppliers under the conditions of fuzzy sets. Symmetry

ahpsort aploco aras aroman artasi cobra cocoso codas


Dichotic Listening

Shadowing Task
Divided Attention Listening

The Dichotic Listening Task is an auditory measure of selective attention and hemispheric lateralization. Different speech stimuli (words, digits, or syllables) are presented simultaneously to each ear via headphones. Participants attend to one ear (shadowing or repeating that information) while ignoring the other. Accuracy and reaction times reveal the capacity for selective attention, and asymmetries in correct identification between ears reveal hemispheric dominance for speech processing (typically stronger in the right ear, reflecting left-hemisphere language dominance).

Broadbent, D. E. (1958). Perception and communication. Pergamon Press.

Cherry, E. C. (1953). Some experiments on the recognition of speech, with one and with two ears. Journal of the Acoustical Society of America, 25(5), 975-979.

Kimura, D. (1961). Cerebral dominance and the perception of verbal stimuli. Canadian Journal of Psychology, 15(3), 166-171.

attention selective-attention auditory-processing hemispheric-lateralization


Dictator Game

The Dictator Game is a simple economic decision task measuring generosity and prosocial behavior. One player (dictator) receives money and unilaterally decides how to allocate it between themselves and an anonymous second player (recipient). The recipient cannot reject the offer; they simply receive what the dictator gives. Unlike the Ultimatum Game, the dictator faces no punishment for selfishness. Thus, the Dictator Game reveals baseline generosity without strategic calculation, revealing intrinsic prosocial preferences.

Forsythe, R., Horowitz, J. L., Savin, N. E., & Sefton, M. (1994). Fairness in simple bargaining experiments. Games and Economic Behavior, 6(3), 347-369.

Camerer, C. F. (2003). Behavioral game theory: Experiments in strategic interaction. Princeton University Press.

Henrich, J., Boyd, R., Bowles, S., et al. (2005). 'Economic man' in cross-cultural perspective: Behavioral experiments in 15 small-scale societies. Behavioral and Brain Sciences, 28(6), 795-855.

ultimatum-game generosity social-preference altruism


Staggered Difference-in-Differences

Staggered Difference-in-Differences (Callaway-Sant'Anna / Sun-Abraham Estimators)

Kademeli DID (Staggered Difference-in-Differences)

Staggered Difference-in-Differences is a generalisation of DID for panel designs in which treatment is rolled out to different groups at different times. Introduced in the modern form by Callaway and Sant'Anna (2021) and Sun and Abraham (2021), it corrects the bias that classical two-way fixed-effects (TWFE) estimators suffer when treatment effects are heterogeneous across cohorts and over time.

Quasi-experimental panel causal estimator

Group-time average treatment effects ATT(g,t), aggregated

panel or time series with staggered treatment timing

Callaway, B. & Sant'Anna, P. H. C. (2021). Difference-in-Differences with Multiple Time Periods. Journal of Econometrics, 225(2), 200-230.

Sun, L. & Abraham, S. (2021). Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects. Journal of Econometrics, 225(2), 175-199.

did-classic event-study-causal synthetic-control regression-discontinuity panel-fixed-effects


Diebold-Mariano Test

Diebold-Mariano Test of Equal Predictive Accuracy

Diebold-Mariano Forecast Comparison Test

The Diebold-Mariano (DM) test, introduced by Diebold and Mariano in 1995, is a widely used non-parametric procedure for formally comparing the predictive accuracy of two competing forecasting models. It evaluates whether the difference in forecast errors between two models is statistically significant, without requiring nested models or specific distributional assumptions about the forecasts, making it broadly applicable across economics, finance, and time-series analysis.

Equal predictive accuracy between two competing forecasts

Flexible; supports squared error, absolute error, and user-defined loss

Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics, 13(3), 253–263.


DQI-I

Sungwon Kim, Pamela S. Haines, Aileen M. Siega-Riz, Barry M. Popkin

The Dietary Quality Index-International is a comprehensive dietary quality assessment tool developed to evaluate overall diet quality based on food and nutrient intake data. Introduced by Kim and colleagues in 2003, the DQI-I incorporates four key dimensions of diet quality: adequacy (adequate intake of essential nutrients and food groups), moderation (limiting excess intake of less healthful components), variety (diversity of food groups), and appropriate macronutrient distribution. It is widely used in epidemiological research to assess population dietary patterns and to examine relationships between diet quality and chronic disease outcomes.

Derived from dietary assessment data (food frequency questionnaire, 24-hour recall)

Kim, S., Haines, P. S., Siega-Riz, A. M., & Popkin, B. M. (2003). The Diet Quality Index-International (DQI-I) provides an effective tool for assessing the quality of various diet profiles. The Journal of Nutrition, 133(12), 3911-3919.

Newby, P. K., & Tucker, K. L. (2007). Empirically derived eating patterns using factor or cluster analysis: A review. Nutrition Reviews, 65(6), 280-293.

mediterranean-diet-adherence food-frequency-questionnaire mini-nutritional-assessment nutrition-self-efficacy-scale intuitive-eating-scale


DIF Analysis

Madde Yanlılık Analizi (DIF — Differential Item Functioning)

Paul W. Holland & Dorothy T. Thayer (Mantel-Haenszel approach, 1988)

Differential Item Functioning analysis examines whether examinees from different groups — such as gender, ethnicity, or language background — who have the same underlying ability respond differently to a test item. First formalised by Holland and Thayer in 1988 via the Mantel-Haenszel procedure, it is the principal tool in modern test development for detecting and removing item bias.

Item-level fairness / measurement equivalence analysis

DIF flags per item with effect-size classification (ETS A/B/C)

Binary or ordinal item responses with a known group variable

Mantel-Haenszel, logistic regression, Lord chi-square

Bonferroni correction recommended across items

Holland, P. W. & Thayer, D. T. (1988). Differential Item Performance and the Mantel-Haenszel Procedure. ETS Research Report Series.

Magis, D., Beland, S., Tuerlinckx, F. & De Boeck, P. (2010). A General Framework and an R Package for the Detection of Dichotomous Differential Item Functioning. Behavior Research Methods, 42(3), 847–862.

irt-rasch exploratory-factor-analysis confirmatory-factor-analysis item-analysis measurement-invariance


Difference GMM

First-Differenced Generalized Method of Moments Estimator

Arellano-Bond estimator
AB-GMM
first-difference GMM
difference GMM estimator

Difference GMM, introduced by Arellano and Bond (1991), estimates dynamic panel data models by first-differencing the equation to remove fixed effects, then using lagged levels of the endogenous variables as GMM instruments. It is the standard approach when a lagged dependent variable or other endogenous regressors are present in a panel with many units and few time periods.

Balanced or unbalanced short panel data (large N, small T)

Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58(2), 277–297.

Roodman, D. (2009). How to do xtabond2: An introduction to difference and system GMM in Stata. Stata Journal, 9(1), 86–136.

arellano-bond-gmm-estimator dynamic-panel-data-model panel-system-gmm fixed-effects-model random-effects-model panel-data-analysis


Difference-in-Differences in Education Research

Difference-in-Differences Estimator Applied to Education Research

DiD in education
education DiD
quasi-experimental education design
education policy DiD

Dynarski, Card, Angrist, and colleagues — applied in education economics from the 1990s onward

Difference-in-Differences (DiD) in education research applies the classic quasi-experimental DiD estimator to evaluate education policies, programs, and reforms. Researchers compare changes in student, school, or district outcomes between a group exposed to an intervention and a comparable unexposed group across pre- and post-intervention periods, isolating policy effects from background trends.

Panel data or repeated cross-sections with student, school, or district outcomes

Dynarski, S. M. (2003). Does Aid Matter? Measuring the Effect of Student Aid on College Attendance and Completion. American Economic Review, 93(1), 279-288.

Angrist, J. D., & Lavy, V. (1999). Using Maimonides' Rule to Estimate the Effect of Class Size on Scholastic Achievement. Quarterly Journal of Economics, 114(2), 533-575.

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


Difference-in-Differences

diff-in-diff
DiD
Farkların Farkı (Diff-in-Diff)

Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)

Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes.

Interaction coefficient of treatment × time (ATT)

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

Card, D., & Krueger, A. B. (1994). Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania. American Economic Review, 84(4), 772-793.

ols-regression panel-fixed-effects propensity-score-matching instrumental-variables granger-causality


Difference-in-Discontinuities

Difference-in-Discontinuities is a hybrid quasi-experimental design that fuses regression discontinuity (RDD) with difference-in-differences (DID), introduced by Grembi, Nannicini and Troiano (2016). It compares the discontinuity at the same cutoff value across two periods to isolate a causal effect.

Hybrid quasi-experimental causal design (RDD + DID)

Difference of two local-polynomial RDD discontinuity estimates

Grembi, V., Nannicini, T. & Troiano, U. (2016). Do Fiscal Rules Matter? A Difference-in-Discontinuities Design. American Economic Journal: Applied Economics, 8(3), 1-30.

Cattaneo, M. D., Idrobo, N. & Titiunik, R. (2020). A Practical Introduction to Regression Discontinuity Designs: Foundations. Cambridge University Press.

regression-discontinuity difference-in-differences instrumental-variables panel-fixed-effects ols-regression


Differential ChIP-seq peak calling

Differential Chromatin Immunoprecipitation Sequencing Peak Calling

differential chromatin occupancy analysis

Rory Stark and Gordon Brown (DiffBind framework); broader ENCODE community

Differential ChIP-seq peak calling identifies genomic loci where a protein of interest — typically a transcription factor or histone mark — shows significantly altered binding or occupancy between two or more biological conditions. By combining standard ChIP-seq peak detection with count-based statistical testing, the method reveals condition-specific regulatory elements, providing a genome-wide map of dynamic chromatin interactions underlying cellular state changes.

Comparative genomic signal analysis pipeline

Replicated ChIP-seq BAM/peak files across two or more biological conditions

Ross-Innes, C. S., Stark, R., Teschendorff, A. E., Holmes, K. A., Ali, H. R., Dunning, M. J., Brown, G. D., Gojis, O., Ellis, I. O., Green, A. R., Ali, S., Chin, S. F., Palmieri, C., Caldas, C., & Carroll, J. S. (2012). Differential oestrogen receptor binding is associated with clinical outcome in breast cancer. Nature, 481(7381), 389-393.

Stark, R., & Brown, G. (2011). DiffBind: differential binding analysis of ChIP-Seq peak data. Bioconductor Package, Cancer Research UK Cambridge Research Institute.

chip-seq-peak-calling rna-seq-differential-expression atac-seq-analysis epigenome-wide-association-study gene-set-enrichment-analysis variant-calling


Differential Copy Number Variation Analysis

dCNV analysis
comparative CNV analysis
somatic copy number alteration analysis
SCNA analysis

Adam Olshen, E. S. Venkatraman and colleagues (CBS); Rameen Beroukhim, Gad Getz and colleagues (GISTIC)

Differential copy number variation (dCNV) analysis identifies genomic regions where DNA copy numbers differ systematically between two conditions — such as tumor versus normal tissue, case versus control cohorts, or treated versus untreated cells. By combining probe-level read-depth or array-intensity data with statistical segmentation and group-level testing, it pinpoints somatic amplifications and deletions that may drive disease, and distinguishes recurrent driver events from passenger noise across a cohort.

Array CGH, SNP array, or WGS/WES read-depth data from two conditions or cohorts

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.

Mermel, C. H., Schumacher, S. E., Hill, B., Meyerson, M. L., Beroukhim, R., & Getz, G. (2011). GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biology, 12(4), R41.

copy-number-variation-detection genome-wide-association-study somatic-mutation-calling structural-variant-detection comparative-genomic-hybridization segmentation-analysis


Differential Cryptanalysis

differential attack
differential path
differential probability

Differential cryptanalysis is a statistical attack technique on symmetric block ciphers that analyzes differences in inputs and outputs to recover secret keys. Introduced by Eli Biham and Adi Shamir in 1990, differential cryptanalysis was the first practical attack on DES that outperformed brute force search. The technique exploits non-random properties of cipher transformations by studying how small changes in plaintext propagate through the cipher rounds. Differential cryptanalysis has shaped cipher design for three decades.

Biham, E., & Shamir, A. (1990). Differential cryptanalysis of DES-like cryptosystems. In Advances in Cryptology - CRYPTO 1990, LNCS 537, pp. 2-21.

Knudsen, L. R. (2005). Block ciphers and public key cryptosystems. In Information Security and Cryptography, pp. 1-25.


epigenome-wide differential methylation analysis

Rakyan, Down, Balding & Beck (2011); Irizarry group for differential methylation methods (~2009–2014)

A Differential Epigenome-Wide Association Study (Differential EWAS) scans hundreds of thousands of CpG methylation sites across the genome to identify those whose methylation levels differ significantly between two or more comparison groups — such as cases vs. controls, exposed vs. unexposed, or distinct developmental stages. It is the standard epigenomic analogue of a differential expression analysis but operates at the level of DNA methylation marks rather than RNA counts.

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

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

Jaffe, A. E., & Irizarry, R. A. (2014). Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biology, 15(2), R31.

epigenome-wide-association-study genome-wide-association-study chip-seq-peak-calling copy-number-variation-analysis rna-seq-differential-expression pathway-enrichment-analysis


Differential eQTL Analysis

Differential Expression Quantitative Trait Loci Analysis

deQTL analysis
context-specific eQTL
interaction eQTL
conditional eQTL

Pioneered by GTEx Consortium and Stranger et al.; formal differential testing approaches developed ~2007–2012

Differential eQTL analysis identifies genetic variants — expression quantitative trait loci — whose regulatory effect on gene expression varies systematically across biological conditions such as tissue types, disease states, developmental stages, or treatment groups. By testing for statistical interactions between genotype and condition, the method pinpoints loci where the same allele has different transcriptional consequences depending on context, revealing the molecular basis of condition-specific gene regulation.

Genotype array or WGS data + RNA-seq expression data from two or more conditions/tissues

Stranger, B. E., et al. (2007). Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science, 315(5813), 848–853.

Huang, Q. Q., et al. (2018). Dissecting super-enhancer hierarchy based on chromatin interactions. Nature Communications, 9(1), 943.

eqtl-analysis genome-wide-association-study rna-seq-differential-expression bayesian-eqtl-analysis single-cell-eqtl-analysis pathway-enrichment-analysis


Differential Evolution

DE algorithm
Diferansiyel Evrim (DE)
DE optimization

Differential Evolution (DE), introduced by Rainer Storn and Kenneth Price in 1997, is a population-based stochastic optimisation algorithm designed for continuous parameter spaces. It generates candidate solutions by combining vector differences between existing population members, making it a powerful and parameter-lean alternative to Genetic Algorithms and Particle Swarm Optimisation when the search landscape is non-convex, multimodal, or poorly suited to gradient-based methods.

Scale factor F, crossover rate CR, population size NP

Storn, R. & Price, K. (1997). Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization, 11(4), 341–359.

Das, S., Mullick, S. S., & Suganthan, P. N. (2016). Recent advances in differential evolution – An updated survey. Swarm and Evolutionary Computation, 27, 1–30.

genetic-algorithm bayesian-regression pca neural-architecture-search deep-reinforcement-learning


Differential Item Functioning

DIF
item bias analysis
measurement non-equivalence
item-level measurement bias

William H. Angoff and colleagues (ETS); systematized by Holland & Wainer

Differential item functioning identifies test or survey items that behave differently for examinees from different groups — such as gender, ethnicity, or language background — after controlling for the underlying ability or trait being measured. DIF analysis is essential for fairness evaluation in educational testing and psychological scale development.

Holland, P. W. & Wainer, H. (Eds.) (1993). Differential Item Functioning. Lawrence Erlbaum Associates.

Dorans, N. J. & Kulick, E. (1986). Demonstrating the utility of the standardization approach to assessing unexpected differential item performance on the Scholastic Aptitude Test. Journal of Educational Measurement, 23(4), 355–368.


Differential Metabolomics Analysis

comparative metabolomics
differential metabolite profiling
metabolomic differential analysis
DMA

2000s–2010s (field formalised alongside mass spectrometry advances)

Developed through convergent contributions by multiple groups; XCMS (Siuzdak lab, 2006) and MetaboAnalyst (Wishart lab, 2009–2015) are foundational computational implementations

Differential metabolomics analysis is a computational pipeline that identifies metabolites whose abundance levels differ significantly between two or more biological conditions — such as disease versus control, treated versus untreated, or different developmental stages. By integrating mass spectrometry or NMR data with statistical modelling and pathway databases, it translates raw spectral measurements into biologically interpretable lists of perturbed metabolic features and the pathways they implicate.

LC-MS, GC-MS, or NMR metabolomics data matrices (samples × metabolite features)

Xia, J., Sinelnikov, I. V., Han, B., & Wishart, D. S. (2015). MetaboAnalyst 3.0 — making metabolomics more meaningful. Nucleic Acids Research, 43(W1), W251–W257.

Smith, C. A., Want, E. J., O'Maille, G., Abagyan, R., & Siuzdak, G. (2006). XCMS: Processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Analytical Chemistry, 78(3), 779–787.


Differential Pathway Enrichment Analysis

differential enrichment analysis
comparative pathway enrichment
DPEA
cross-condition pathway analysis

Extended from Over-Representation Analysis (Draghici et al. 2003) and competitive gene-set testing (Smyth lab, ~2004–2012)

Differential pathway enrichment analysis identifies biological pathways whose enrichment signals differ significantly between two or more experimental conditions — for example, between two diseases, two treatments, or two cell types. Rather than asking which pathways are enriched in one condition, it asks which pathways show a statistically meaningful change in enrichment level across conditions, revealing condition-specific or context-dependent biology.

Gene expression matrices (RNA-seq counts or microarray intensities), with biological condition labels

Wu, D., & Smyth, G. K. (2012). Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Research, 40(17), e133.

Väremo, L., Nielsen, J., & Nookaew, I. (2013). Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical inferences. Nucleic Acids Research, 41(8), 4378–4391.


Differential Privacy

DP
epsilon-differential privacy
randomized privacy
Diferansiyel Gizlilik

Differential privacy is a mathematical framework for releasing statistical information about a dataset while providing rigorous guarantees that individual records cannot be identified or inferred. Introduced by Cynthia Dwork in 2006, it formalizes privacy as a probabilistic bound: any single individual's presence or absence in the dataset changes the output distribution by at most a multiplicative factor of e^ε, where ε is the privacy budget controlling the privacy–utility tradeoff.

Dwork, C. (2006). Differential privacy. International Colloquium on Automata, Languages and Programming (ICALP), 1–12.


Differential proteomics analysis

differential protein expression analysis

Late 1990s–2000s (mass spectrometry-based approaches matured ~1999–2004)

Pioneered broadly by Matthias Mann and colleagues; SILAC introduced by Ong et al. (2002)

Differential proteomics analysis is a quantitative pipeline that identifies proteins whose abundance levels change significantly between two or more biological conditions — such as healthy versus diseased tissue, treated versus untreated cells, or different developmental stages. By combining mass spectrometry-based detection with statistical testing, the method generates ranked lists of differentially expressed proteins that can be linked to biological pathways, disease mechanisms, or drug targets.

Mass spectrometry intensity data (label-based or label-free); protein abundance tables

Ong, S.-E., Blagoev, B., Kratchmarova, I., Kristensen, D. B., Steen, H., Pandey, A., & Mann, M. (2002). Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Molecular & Cellular Proteomics, 1(5), 376–386.

Bantscheff, M., Lemeer, S., Savitski, M. M., & Kuster, B. (2012). Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present. Analytical and Bioanalytical Chemistry, 404(4), 939–965.

mass-spectrometry-proteomics label-free-quantification silac two-dimensional-gel-electrophoresis gene-ontology-enrichment-analysis pathway-enrichment-analysis


Differential Scanning Calorimetry

DSC
differential thermal analysis
thermal analysis

Differential Scanning Calorimetry (DSC) is a thermal characterization technique that measures the heat flow required to maintain a sample and an inert reference at the same temperature while both are heated or cooled. Invented by Watson, O'Neill, and colleagues in 1964, DSC directly quantifies enthalpy changes during phase transitions, crystallization, melting, and chemical reactions. It is the standard tool in materials science, chemistry, and pharmaceutical research for determining thermodynamic properties, thermal stability, and kinetics of thermal transitions.

Watson, E. S., O'Neill, M. J., Justin, J., & Brenner, N. (1964). A differential scanning calorimeter for quantitative differential thermal analysis. Analytical Chemistry, 36(7), 1233-1238.

Haines, P. J. (Ed.). (2012). Principles of Thermal Analysis and Calorimetry (2nd ed.). Royal Society of Chemistry.

Schick, C., & Mathot, V. (2019). Fast Scanning Calorimetry. Springer.


Differential single-cell RNA-seq analysis

Differential Single-Cell RNA Sequencing Analysis

single-cell differential expression analysis

Multiple contributors; pseudobulk framework formalized by Squair et al. (2021); Seurat/FindMarkers by Satija lab (~2015)

Differential single-cell RNA-seq (scRNA-seq) analysis is a computational pipeline that compares transcriptomic profiles across biological conditions — such as treated versus untreated, disease versus healthy, or time points — at single-cell resolution. It identifies which genes, cell types, and cell states change between conditions, providing mechanistic insight that bulk RNA-seq comparisons cannot offer. The approach combines clustering, cell-type annotation, and statistical testing, typically using pseudobulk aggregation to account for within-sample correlation.

Single-cell RNA sequencing count matrices; cell-type annotation metadata; sample condition labels

Hafemeister, C., & Satija, R. (2019). Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biology, 20, 296.

Squair, J. W., Gautier, M., Kathe, C., Anderson, M. A., James, N. D., Hutson, T. H., Lefoulon, E., Tani, N., Bhatt, D. L., Rossetti, A., & Courtine, G. (2021). Confronting false discoveries in single-cell differential expression. Nature Communications, 12, 5692.


Differential Variant Calling

Differential Variant Calling in Genomics

somatic variant calling
comparative variant analysis
tumor-normal variant calling
differential SNV/indel calling

2009–2013 (field matured with NGS; seminal tools 2009–2013)

Multiple groups; key tools: VarScan (Koboldt et al.), MuTect (Cibulskis et al.), GATK Haplotype Caller (DePristo et al.)

Differential variant calling is a bioinformatics pipeline that identifies genetic variants — single nucleotide variants (SNVs), small insertions/deletions (indels), and structural variants — that are present in one biological sample or condition but absent (or significantly enriched) in a paired reference sample. The canonical application is tumor-versus-normal cancer genomics, where somatic mutations unique to the tumor are distinguished from germline variants shared with normal tissue. The same logic applies to comparing treated vs. untreated cell lines, evolved vs. ancestral strains, or case vs. control cohorts in population genomics.

Aligned short-read sequencing data (BAM/CRAM files) from paired or multi-sample experiments

Koboldt, D.C., Zhang, Q., Larson, D.E., Shen, D., McLellan, M.D., Lin, L., Miller, C.A., Mardis, E.R., Ding, L., & Wilson, R.K. (2012). VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Research, 22(3), 568–576.

Cibulskis, K., Lawrence, M.S., Carter, S.L., Sivachenko, A., Jaffe, D., Sougnez, C., Gabriel, S., Meyerson, M., Lander, E.S., & Getz, G. (2013). Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nature Biotechnology, 31(3), 213–219.


Difficulties in Emotion Regulation Scale (DERS)

The DERS is a 36-item self-report measure assessing multidimensional emotion dysregulation across six related but distinct facets. Developed by Gratz and Roemer in 2004, it has become a cornerstone transdiagnostic measure in emotion regulation research, capturing emotional avoidance, behavioral dyscontrol, and limited coping awareness that cut across psychiatric conditions.

Gratz, K. L., & Roemer, L. (2004). Multidimensional assessment of emotion regulation and dysregulation: Development, factor structure, and initial validation of the Difficulties in Emotion Regulation Scale. Journal of Psychopathology and Behavioral Assessment, 26(1), 41–54.

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


Diffie-Hellman Key Exchange

DH Key Exchange
Diffie-Hellman Key Agreement

The Diffie-Hellman key exchange, invented by Whitfield Diffie and Martin Hellman in 1976, is a foundational protocol for establishing a shared secret over an insecure communication channel. Two parties who have never previously communicated can use Diffie-Hellman to agree on a symmetric encryption key that an eavesdropper cannot easily derive, even after observing all public exchanges.

Diffie, W., & Hellman, M. E. (1976). New directions in cryptography. IEEE Transactions on Information Theory, 22(6), 644–654.

Menezes, A. J., van Oorschot, P. C., & Vanstone, S. A. (1997). Handbook of Applied Cryptography. CRC Press.

Boyd, C., & Mathuria, A. (2003). Protocols for Authentication and Key Establishment. Springer-Verlag.

rsa-cryptosystem-analysis tls-protocol-analysis symmetric-key-analysis digital-signature-scheme


DiffServ

DiffServ is a QoS architecture providing scalable, class-based service differentiation in networks. Introduced by IETF (1998), DiffServ marks packets with a Differentiated Services Code Point (DSCP) in the IP header, enabling routers to apply per-hop-behaviors (PHBs) based on markings. Unlike IntServ (which reserves resources per-flow), DiffServ is stateless and scalable to Internet scale. DiffServ remains the primary QoS mechanism in ISP and enterprise networks.

Blake, S., Black, D., Carlson, M., et al. (1998). An Architecture for Differentiated Services. RFC 2475.

Nichols, K., Jacobson, V., & Zhang, L. (1999). A Two-bit Differentiated Services Architecture for the Internet. RFC 2638.


Diffusion Kurtosis Imaging

DKI
non-Gaussian diffusion
diffusion kurtosis

Diffusion Kurtosis Imaging (DKI) is an advanced diffusion MRI technique that quantifies non-Gaussian diffusion of water molecules, providing detailed information about tissue microstructure beyond conventional diffusion tensor imaging. Introduced by Jensen and colleagues in 2005, DKI detects deviations from normal Gaussian diffusion, revealing information about cellular compartmentalization and fiber heterogeneity.

Jensen, J. H., Helpern, J. A., Ramani, A., et al. (2005). Diffusional kurtosis imaging: the quantification of non-Gaussian water diffusion by magnetic resonance imaging. Magnetic Resonance in Medicine, 53(6), 1432–1440.

Lu, H., Jensen, J. H., Topgaard, D., & Helpern, J. A. (2018). Effective medium theory of apparent diffusion coefficient in fibrous media. Magnetic Resonance in Medicine, 81(5), 3245–3260.


Diffusion Model

Denoising Diffusion Probabilistic Model (DDPM / Latent Diffusion)

Difüzyon Modeli (DDPM / Stable Diffusion)

A diffusion model is a generative deep-learning method, introduced by Ho, Jain and Abbeel in 2020 (DDPM), that learns to produce high-quality images, audio and molecular structures by reversing a step-by-step noising process. It has largely displaced GANs as the current state of the art in generative modelling.

Generative deep learning (denoising diffusion)

Generation of images, audio and molecular structures

Ho, J., Jain, A. & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. NeurIPS.

Rombach, R., Blattmann, A., Lorenz, D., Esser, P. & Ommer, B. (2022). High-Resolution Image Synthesis with Latent Diffusion Models. CVPR.

variational-autoencoder score-based-diffusion neural-ode pca


Diffusion of Innovation Model

Rogers Diffusion of Innovation Framework

DOI Model
Innovation Adoption Curve
S-Curve Adoption

The Diffusion of Innovation (DOI) model is a theoretical framework developed by Everett Rogers in 1962 to explain how innovations spread through populations over time. The framework categorizes adopters into five groups based on when they adopt an innovation and describes the characteristic S-shaped curve that typically describes market adoption of new products, services, and technologies.

Rogers, E. M. (1962). Diffusion of Innovations. Free Press.

Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). Free Press.

Bass, F. M. (1969). A New Product Growth for Model Consumer Durables. Management Science, 15(5), 215-227.

marketing-mix-modeling advertising-effectiveness-study customer-journey-mapping brand-equity-measurement market-segmentation-analysis


Digital Autoethnography

online autoethnography
virtual autoethnography
digital self-ethnography
networked autoethnography

Annette Markham; expanded through netnography work by Robert Kozinets

Digital autoethnography is a qualitative research design in which the researcher systematically examines their own lived experience within digital environments — social media platforms, online communities, gaming worlds, digital workplaces, or other networked spaces — to illuminate broader cultural and social phenomena. Combining autoethnography's first-person reflexivity with the study of digital life, it treats personal digital traces, interactions, and self-representations as primary data.

Personal digital artifacts, online communication logs, social media posts, digital field notes

Markham, A. N. (2013). Undermining 'data': A critical examination of a core term in scientific inquiry. First Monday, 18(10).

Kozinets, R. V. (2010). Netnography: Doing Ethnographic Research Online. Sage.

autoethnography netnography digital-ethnography narrative-inquiry reflexive-thematic-analysis virtual-ethnography


Digital Case Study

online case study
virtual case study
internet-based case study
digital ethnographic case study

2000s–2010s (building on Yin's 1984 foundational case study framework)

Robert K. Yin (case study foundations); extended to digital contexts by multiple scholars in the 2000s–2010s

Digital case study research applies the classic bounded case study framework to phenomena that are situated in, or mediated by, digital environments. Drawing on Robert Yin's foundational case study methodology, it investigates a contemporary phenomenon in depth within its real-world digital context — using online documents, social media archives, virtual interviews, website content, and other digital artifacts as primary evidence. The approach is particularly suited to studying how individuals, groups, or organisations behave in online spaces.

Digital documents, online interactions, social media data, digital artifacts, virtual interviews

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

Merriam, S. B. (2009). Qualitative Research: A Guide to Design and Implementation. Jossey-Bass.

case-study ethnography netnography content-analysis narrative-analysis document-analysis


Digital Classic Grounded Theory

Digital CGT
online classic grounded theory
Glaserian digital grounded theory
classic GT in digital contexts

1967 (classic GT); digital adaptation from early 2000s onward

Barney G. Glaser and Anselm L. Strauss (classic GT); digital application developed by subsequent methodologists

Digital Classic Grounded Theory applies Glaser and Strauss's original (Glaserian) grounded theory methodology to data collected from online and digital environments — including social media, online forums, email threads, and chat logs. It preserves the inductive, emergence-focused logic of classic GT while adapting sampling, data collection, and ethical practices to the digital context, aiming to generate a grounded substantive theory that explains a social or psychological process as it unfolds online.

Digital text data: online forum posts, social media content, email exchanges, chat logs, web documents

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

Glaser, B. G. (1978). Theoretical Sensitivity: Advances in the Methodology of Grounded Theory. Sociology Press.

classic-grounded-theory grounded-theory digital-grounded-theory digital-constructivist-grounded-theory digital-straussian-grounded-theory netnography


Digital Constructivist grounded theory

Digital CGT
online constructivist grounded theory
digital-context CGT
constructivist GT with digital data

Kathy Charmaz (CGT); applied to digital contexts by qualitative internet researchers

Digital Constructivist Grounded Theory (Digital CGT) applies Kathy Charmaz's constructivist variant of grounded theory to data generated in digital environments — social media platforms, online communities, forums, digital interviews, and other internet-mediated spaces. It treats meaning as co-constructed between researcher and participant in digitally-mediated contexts, and generates theory grounded in how people make sense of experience through and within digital life.

Digital text data — online interviews, social media posts, forum discussions, emails, digital documents

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

Salmons, J. (2014). Qualitative Online Interviews: Strategies, Design, and Skills (2nd ed.). Sage.

constructivist-grounded-theory digital-grounded-theory digital-ethnography netnography digital-thematic-analysis classic-grounded-theory


Digital Content analysis

DCA
online content analysis
web content analysis
digital media content analysis

1950s (classical); digital adaptation 2000s–2010s

Building on Berelson (1952) and Krippendorff (1980); adapted for digital contexts by Herring (2010) and Neuendorf (2002+)

Digital Content Analysis is a systematic research method for describing, categorising, and interpreting the content of digital materials — social media posts, websites, online forums, blogs, emails, and video transcripts. It applies the rigorous coding logic of classical content analysis to digitally native or digitally collected text, enabling researchers to move from raw online data to structured, interpretable findings about communication, meaning, and social phenomena.

Qualitative/quantitative hybrid research approach

Digital texts: social media posts, websites, online forums, emails, blogs, video transcripts

Neuendorf, K. A. (2017). The Content Analysis Guidebook (2nd ed.). Sage.

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

content-analysis qualitative-content-analysis discourse-analysis thematic-analysis digital-ethnography social-media-analysis


Digital Conversation Analysis

DCA
online conversation analysis
digital CA
computer-mediated conversation analysis

1974 (CA foundations); 1990s–2000s (digital adaptation)

Harvey Sacks, Emanuel Schegloff, Gail Jefferson (CA foundations); Susan Herring (computer-mediated discourse adaptation)

Digital Conversation Analysis (DCA) applies the systematic, turn-by-turn analytical procedures of Conversation Analysis (CA) to digital and computer-mediated interactions — including chat logs, social media threads, instant messages, and online forums. Rooted in the foundational CA framework of Sacks, Schegloff, and Jefferson, DCA adapts classical concepts such as turn-taking, adjacency pairs, and sequential organisation to account for the asynchronous, multimodal, and textual character of online communication.

Digital text data: chat logs, forum threads, social media exchanges, instant messages, emails

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

Herring, S. C. (2007). A faceted classification scheme for computer-mediated discourse. Language@Internet, 4(1).

conversation-analysis discourse-analysis digital-discourse-analysis critical-discourse-analysis digital-ethnography thematic-analysis


Digital Critical Discourse Analysis

Digital CDA
Online Critical Discourse Analysis
Multimodal Digital CDA
DCDA

Scholars extending Ruth Wodak and Norman Fairclough's CDA tradition into digital contexts; notably Crispin Thurlow, Michele Zappavigna, and Jannis Androutsopoulos

Digital Critical Discourse Analysis (Digital CDA) is a qualitative research approach that applies the theoretical and methodological tools of Critical Discourse Analysis to digital and online communicative contexts. It examines how language, multimodal elements, and digital affordances are mobilized in online spaces to produce, reproduce, or contest power relations, ideologies, and social inequalities. Drawing on traditions established by Fairclough, Wodak, and van Dijk, Digital CDA treats digital texts — from social media posts to comment threads and websites — as sites of ideological struggle shaped by the platforms that host them.

Digital text, multimodal online content (social media posts, websites, comment threads, digital images, videos)

Unger, J. W., Krzyżanowski, M., & Wodak, R. (Eds.). (2016). Multilingual Encounters in Europe's Institutional Spaces. Bloomsbury Academic.

Thurlow, C., & Mroczek, K. (Eds.). (2011). Digital Discourse: Language in the New Media. Oxford University Press.

critical-discourse-analysis multimodal-discourse-analysis social-media-analysis content-analysis thematic-analysis narrative-analysis