Survey Research

survey methodology
questionnaire research
survey design
survey study

Late 19th century; methodologically systematised 1940s–1960s

Francis Galton, Charles Booth, and early social statisticians; systematised by Paul Lazarsfeld and colleagues at Columbia in the 1940s

Survey research is a quantitative (and sometimes mixed-methods) design in which a researcher collects standardised self-report data from a sample drawn from a defined population, using a questionnaire or structured interview. It is the dominant non-experimental strategy for describing population characteristics, estimating prevalence, mapping attitude distributions, and testing bivariate or multivariate associations across social, behavioural, and health sciences.

Quantitative (and mixed) non-experimental design

Self-report responses to structured questionnaires (ordinal, interval, ratio, nominal)

Fowler, F. J. (2014). Survey Research Methods (5th ed.). Sage Publications.

Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, Phone, Mail, and Mixed-Mode Surveys: The Tailored Design Method (4th ed.). Wiley.

descriptive-research cross-sectional-research longitudinal-research correlational-research quantitative-content-analysis panel-research


Survey Weighting

Survey Calibration
Post-Stratification Weighting
Raking Adjustment
Ağırlıklandırma (Anket)

Survey weighting is a statistical procedure that assigns a numeric weight to each sampled unit so that the weighted sample reproduces known population totals. Rooted in classical sampling theory and systematically synthesized by Sharon Lohr (2010), the approach corrects for unequal selection probabilities, unit nonresponse, and coverage gaps, producing estimates that are more representative of the target population than raw sample means or totals would be.

Input: Probability sample with auxiliary population totals

Output: Calibrated weights for each sampled unit

Lohr, S. L. (2010). Sampling: Design and Analysis (2nd ed.). Brooks/Cole.


Survey

questionnaire survey
survey research
self-report survey
questionnaire study

Late 19th century; systematic social-science use from 1940s

Francis Galton, Charles Booth, and early social statisticians; formalised by Paul Lazarsfeld in the 1940s

A survey is a systematic data-collection method in which a standardised set of questions is posed to a sample of respondents to measure attitudes, behaviours, demographics, or other constructs. Surveys can be administered via paper, telephone, online platforms, or face-to-face. They are among the most widely used instruments in social, behavioural, health, and educational research because they can reach large, geographically dispersed samples at relatively low cost.

Quantitative (primarily) or mixed-methods data-collection instrument

Structured self-report responses (Likert scales, closed/open items)

Fowler, F. J. (2013). Survey Research Methods (5th ed.). Sage.

structured-interview online-survey delphi-technique face-to-face-survey longitudinal-survey mixed-methods


Survival Analysis

Kaplan-Meier analysis
Cox regression
TTE analysis

Survival analysis is a collection of statistical methods for modeling time from a defined starting point until an event of interest occurs (disease, recovery, death, equipment failure). Kaplan and Meier's nonparametric estimator (1958) and David Cox's proportional hazards model (1972) jointly enabled analysis of censored data—individuals whose event times are unknown because they left the study or were still event-free at follow-up. Indispensable in oncology, cardiology, infectious disease research, engineering reliability, and any field where time-to-event matters.

Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.

Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society, Series B, 34(2), 187–220.


Survival Regression

accelerated failure time model
AFT model
parametric survival model
time-to-event regression

Survival regression models the time until an event occurs — such as death, failure, or relapse — as a function of covariates. Unlike ordinary regression, it properly accounts for censored observations (cases where the event had not yet occurred at the end of follow-up) by specifying a parametric distribution for the survival time and estimating covariate effects via maximum likelihood.

Kalbfleisch, J. D., & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley.

Cox, D. R., & Oakes, D. (1984). Analysis of Survival Data. Chapman and Hall.

cox-regression kaplan-meier weibull-regression competing-risks-regression multilevel-survival-regression robust-survival-regression


SCS

sustainable lifestyle and consumption practices

Anna M. Sundström, Iris Vermeir, Wim Verbeke

The Sustainable Consumption Scale (SCS) measures the extent to which individuals adopt sustainable and ethical consumption practices across multiple life domains including food, clothing, household products, transportation, and waste. Developed within ecological economics and consumer behavior frameworks (Sundström, 2014; Vermeir & Verbeke, 2008), the SCS captures integrated sustainable lifestyle rather than isolated green behaviors. The scale is widely used in research on sustainable consumption patterns, consumer segmentation for green marketing, and evaluation of sustainability interventions targeting lifestyle transformation.

Self-report frequency and behavior scale

Sundström, A. M. (2014). An investigation of the relationship between sustainable values and consumption patterns. In Interdisciplinary book of sustainable development. InTech Press.

Vermeir, I., & Verbeke, W. (2008). Sustainable food consumption among young adults in Belgium: Theory of planned behaviour and the role of confidence and values. Ecological Economics, 64(3), 542–553.


SVAR

Structural VAR
Identified VAR
SVAR Model
Yapısal Vektör Otoregresyon

Structural Vector Autoregression (SVAR) is a multivariate time-series model, developed by Christopher Sims (1980), that extends the reduced-form VAR by imposing economically motivated identifying restrictions on contemporaneous relationships among variables. SVAR enables researchers to isolate orthogonal structural shocks and trace their causal dynamic effects through impulse response functions and forecast error variance decompositions, making it a cornerstone of modern empirical macroeconomics.

Structural multivariate time-series model

Sims awarded Nobel Memorial Prize in Economics, 2011

Short-run restrictions, long-run restrictions (Blanchard-Quah), sign restrictions

Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48(1), 1–48.


Support Vector Machine

Support Vector Machine (SVM — Classification)

Destek Vektör Makinesi (SVM — Sınıflandırma)

The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.

Maximum-margin classifier (kernel method)

Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.

svm-regression logistic-regression naive-bayes knn random-forest


Support Vector Regression

Destek Vektör Regresyonu (SVR)
SVR
epsilon-SVR
support vector machine for regression

Support Vector Regression (SVR), described in Smola and Schölkopf's 2004 tutorial, predicts a continuous outcome by fitting a function that stays within an epsilon-wide tube around the data while incurring as little error as possible. It extends the support vector machine idea from classification to regression, using a kernel to capture nonlinear relationships.

Kernel-based supervised model (epsilon-insensitive regression)

Smola, A.J. & Schölkopf, B. (2004). A Tutorial on Support Vector Regression. Statistics and Computing, 14, 199–222.

svm-classification ridge-regression linear-regression lasso-regression knn


Swallowing Quality of Life Questionnaire

The Swallowing Quality of Life (SWAL-QoL) Questionnaire is a comprehensive 44-item self-report measure of the psychosocial and functional impact of dysphagia across 11 quality-of-life domains, including eating burden, food selection, social participation, emotional impact, and fatigue. Developed by McHorney and colleagues (2002), SWAL-QoL captures the patient perspective on swallowing-related disability, complementing objective clinical measures (dysphagia severity, aspiration risk) with data on lived experience and psychological burden. A brief 15-item version, SWAL-CARE, enables efficient monitoring of treatment response.

McHorney, C. A., Bricker, D. E., Kramer, A. E., et al. (2000). The SWAL-QoL Outcomes Tool for Oropharyngeal Dysphagia in Adults: I. Conceptualization and Item Development. Dysphagia, 15(3), 115–121.

McHorney, C. A., Robbins, J., Lomax, K., et al. (2002). The SWAL-QoL and SWAL-CARE Outcomes Tool for Oropharyngeal Dysphagia in Adults: III. Documentation of Reliability and Validity. Dysphagia, 17(2), 97–114.

Guilcher, S. J., Mazzuca, N., Markham, J., & Craven, B. C. (2012). Hopelessness and Catastrophizing Mediate the Relationship Between Chronic Pain and Health Related Quality of Life in a Spinal Cord Injury Sample. Clin J Pain, 28(2), 163–167.


SWARA II

Step-wise Weight Assessment Ratio Analysis - Improved (SWARA II)

Keršuliene, Zavadskas, and Turskis; extended by Zolfani et al.

SWARA II (Step-wise Weight Assessment Ratio Analysis - Improved) is an enhanced variant of the SWARA method for deriving criterion weights from expert assessments. Instead of requiring pairwise comparisons or absolute weight assignments, SWARA II asks experts to rank criteria, then assess the relative importance of each criterion compared to the next-ranked one. Improved variants enhance robustness and interpretability of weight derivation.

Expert-based stepwise weight derivation with ratio refinement

Keršuliene, V., Zavadskas, E. K., & Turskis, Z. (2010). Selection of rational dispute resolution method by evaluating opposing parties' interest in civil litigation. Journal of Civil Engineering and Management, 16(3), 412-422.

Zolfani, S. H., Chatterjee, P., & Yazdani, M. (2016). The extended SWARA method for determining weights of criteria. International Journal of Computational Intelligence Systems, 9(5), 890-900.

swara merec-g critic-m bwm entropy-method


SWARA

SWARA (Step-Wise Weight Assessment Ratio Analysis) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Keršulienė, V., Zavadskas, E. K., Turskis, Z. in 2010. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Sequential step-ratio subjective weighting

Keršulienė, V., Zavadskas, E. K., Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis (SWARA). Journal of Business Economics and Management

ahpsort aploco aras aroman artasi cobra cocoso codas


SWAT Model

The Soil and Water Assessment Tool (SWAT) is a process-based watershed model that simulates the hydrological cycle, sediment transport, nutrient cycling, pesticide fate, and land management impacts across a watershed or large basin. Developed by Jeff Arnold and colleagues at USDA-ARS in 1998, SWAT has become a standard tool for evaluating non-point source pollution, assessing climate change impacts on water resources, and designing best management practices.

Process-based watershed and water quality simulation

Arnold, J. G., Srinivasan, R., Muttiah, R. S., & Williams, J. R. (1998). Large area hydrologic modeling and assessment part I: Model development. Journal of the American Water Resources Association, 34(1), 73-89.

Neitsch, S. L., Arnold, J. G., Kiniry, J. R., & Williams, J. R. (2011). Soil and Water Assessment Tool theoretical documentation. USDA Agricultural Research Service.


Swelling and Degradation

hydrogel swelling
polymer degradation
mass loss assay

The swelling and degradation assay measures how biomaterial scaffolds absorb water (swelling) and lose mass over time due to degradation. Developed by Wichterle and Lim in 1960 for hydrogels, the assay is fundamental for characterizing hydrogels, synthetic polymers, and composite scaffolds intended for tissue engineering. The assay provides quantitative data on swelling kinetics (equilibrium water content, swelling ratio), degradation kinetics (mass loss rate, half-life), and mechanisms of degradation (chain scission, enzymatic breakdown).

Wichterle, O., & Lim, D. (1960). Hydrophilic gels for biological use. Nature, 185(4706), 117-118.

Amsden, B. G., Sukarto, A., & Kilicalp, A. (2002). Assessment of an interpenetrating network of gelatin and poly (ethylene oxide) for cell encapsulation. Biomacromolecules, 3(3), 597-603.

Peppas, N. A., & Narasimhan, B. (1998). Mathematical models of protein release from degrading biopolymers. Journal of Controlled Release, 53(1-3), 233-243.

bmp-release dynamic-mechanical-analysis gpc-sec electrospinning


Swin Transformer

The Swin Transformer is a hierarchical vision transformer introduced by Liu et al. in 2021 that uses shifted window attention to achieve computational efficiency while maintaining strong performance on computer vision tasks. Unlike the original Vision Transformer which applies global self-attention, Swin uses local window-based attention with periodic shifting to balance expressiveness and efficiency.

Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021). Swin Transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 10012-10022).

vision-transformer vision-mamba detr masked-autoencoders


SWING

SWING (Swing Weighting — importance weights derived from worst-to-best swing utility gains) is a weight subjective multi-criteria decision-making (MCDM) method introduced by von Winterfeldt, D., Edwards, W. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Weight_Subjective (swing from worst to best, relative gain assessment)

von Winterfeldt, D., Edwards, W. (1986). Decision Analysis and Behavioral Research. Cambridge University Press


Satisfaction with Life Scale

Well-being and life satisfaction assessment

Ed Diener, Richard A. Emmons, Richard J. Larsen, and Sharon Griffin

The Satisfaction with Life Scale (SWLS) is a brief, five-item self-report measure of global life satisfaction developed by Diener, Emmons, Larsen, and Griffin in 1985. It assesses the degree to which individuals are satisfied with their lives as a whole, reflecting a cognitive-judgmental component of subjective well-being. The scale has become a cornerstone instrument in well-being research, psychology, gerontology, and quality-of-life assessment across diverse populations.

Diener, E., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985). The Satisfaction with Life Scale. Journal of Personality Assessment, 49(1), 71-75.

Pavot, W., & Diener, E. (1993). Review of the Satisfaction with Life Scale. Psychological Assessment, 5(2), 164-172.

panas ghq-12 gds-geriatric-depression ces-d hads


Symbolic Data Analysis

SDA
Interval Data Analysis
Distributional Data Analysis
Sembolik Veri Analizi

Symbolic Data Analysis (SDA) is a statistical framework designed to analyze complex, aggregate, or set-valued data — called symbolic data — in which each observation represents a group or concept rather than a single scalar. Introduced in its modern statistical form by Lynne Billard and Edwin Diday in 2003, SDA extends classical statistics to handle interval-valued, histogram-valued, and multi-valued variables, enabling rigorous inference at the level of knowledge rather than raw individual records.

Statistical framework for aggregate and set-valued data

Interval, histogram, multi-valued, modal

Billard, L., & Diday, E. (2003). From the statistics of data to the statistics of knowledge: symbolic data analysis. Journal of the American Statistical Association, 98(462), 470–487.


Symbolic Execution

symbolic execution
symbolic analysis
concolic execution

Symbolic execution is a program analysis technique that executes programs using symbolic (non-concrete) values instead of actual inputs, tracking how symbolic values flow through the program. Introduced by James C. King in 1976, symbolic execution builds mathematical constraints on program variables and can determine which inputs cause specific program behaviors, enabling automatic test generation and vulnerability detection. Modern symbolic execution tools like KLEE, S2E, and Z3 have become powerful instruments for finding subtle bugs and security vulnerabilities.

King, J. C. (1976). Symbolic execution and program testing. Communications of the ACM, 19(7), 385-394.

Cadar, C., & Sen, K. (2013). Symbolic execution for software testing: Three decades later. Communications of the ACM, 56(2), 82-90.


Symmetric Key Cryptanalysis

Cryptanalytic Analysis of Symmetric Encryption Algorithms

Symmetric Cryptanalysis
Block Cipher Analysis
Stream Cipher Cryptanalysis

Symmetric key cryptanalysis is the study of attacks against symmetric encryption algorithms (such as DES, AES, and stream ciphers) to evaluate their security and identify weaknesses. Classical techniques include differential cryptanalysis and linear cryptanalysis, which have shaped the design of modern ciphers and remain essential tools for cryptographers assessing algorithm robustness.

Biham, E., & Shamir, A. (1991). Differential cryptanalysis of DES. Journal of Cryptology, 4(1), 3–72.

Matsui, M. (1993). Linear cryptanalysis method for DES cipher. Advances in Cryptology – EUROCRYPT '93, 386–397.

Daemen, J., & Rijmen, V. (2002). The Design of Rijndael: AES—The Advanced Encryption Standard. Springer-Verlag.


Symmetric MAPE

Symmetric Mean Absolute Percentage Error is a refinement of MAPE that addresses its asymmetry by using the average of actual and predicted values as the denominator. Proposed by J. Scott Armstrong and refined by Makridakis (1993) and Hyndman & Koehler (2006), sMAPE treats over- and under-predictions symmetrically.

Symmetric percentage-based evaluation metric

Armstrong, J. S. (1985). Long-range forecasting: from crystal ball to computer (2nd ed.). New York: John Wiley & Sons.

Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679-688.

Makridakis, S. (1993). Accuracy measures for a robust comparison of forecasting methods. International Journal of Forecasting, 9(4), 679-688.

mean-absolute-percentage-error mean-absolute-scaled-error mean-absolute-error root-mean-squared-error


Symmetrical Components

Symmetrical Components Method for Unbalanced Circuit Analysis

Symmetrical component analysis
Phase component decomposition

Symmetrical Components is a mathematical technique for analyzing unbalanced three-phase electrical circuits by decomposing them into balanced component sets. Introduced by Charles Fortescue in 1918, the method transforms the complex analysis of unbalanced systems into simpler balanced equivalent circuits. Symmetrical components are fundamental to understanding faults, protection coordination, and stability in power systems, remaining essential in modern grid operations.

Decomposition method for analyzing unbalanced three-phase circuits

Fortescue, C. L. (1918). Method of symmetrical co-ordinates applied to the solution of polyphase networks. AIEE Transactions, 37(2), 1027-1044.

Blackburn, J. L. (1993). Symmetrical Components for Power Systems Engineering. Marcel Dekker.

Saadat, H. (2010). Power System Analysis (3rd ed.). PSA Publishing.


Synchrosqueezing Transform

Synchrosqueezing Transform for Time-Frequency Analysis

The synchrosqueezing transform is a time-frequency reassignment technique that sharpens the output of the continuous wavelet transform by concentrating energy along instantaneous frequency ridges. Introduced by Ingrid Daubechies and colleagues in 2011, it addresses the fundamental limitation of the standard wavelet transform: poor frequency localization. This method is particularly valuable for analyzing signals with time-varying frequency content.

Daubechies, I., Lu, J., & Wu, H. T. (2011). Synchrosqueezed wavelet transforms: An empirical tool for time-frequency analysis. Applied and Computational Harmonic Analysis, 30(2), 243–261.

Thakur, G., Brevdo, E., Fučkar, N. S., & Wu, H. T. (2015). The synchrosqueezing algorithm for time-varying spectral analysis and its application to signals with discontinuities. Applied and Computational Harmonic Analysis, 39(1), 1–31.

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


Synthesis Route Planning

Retrosynthetic Analysis and Synthesis Route Planning

retrosynthesis
retrosynthetic analysis
synthetic route design

Synthesis route planning, grounded in retrosynthetic analysis, is a strategic approach to designing efficient chemical syntheses. Formalized by Elias James Corey in the 1960s (earning him the Nobel Prize in 1990), this methodology systematically deconstructs target molecules into simpler precursors and starting materials, enabling chemists to discover logical, economical, and practical synthesis routes.

Corey, E. J., & Cheng, X. M. (1991). The Logic of Chemical Synthesis. John Wiley & Sons.

Warren, S., & Wyatt, P. (2008). Organic Synthesis: Strategy and Control. John Wiley & Sons.


Synthetic Control Method in Education Research

Synthetic Control Method Applied to Education Policy and Research

SCM in education
synthetic control
synthetic comparator
SCM

Alberto Abadie, Alexis Diamond, and Jens Hainmueller

The Synthetic Control Method (SCM) estimates the causal effect of an education policy or intervention by constructing a weighted combination of untreated comparison units — the synthetic control — that closely mimics the treated unit's pre-intervention trajectory. Developed by Abadie, Diamond, and Hainmueller, it is especially valuable when only one or a small number of schools, districts, or countries receive a policy change and no natural comparison exists.

Aggregate panel data (donor pool of control units over time)

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

Abadie, A., Diamond, A., & Hainmueller, J. (2015). Comparative Politics and the Synthetic Control Method. American Journal of Political Science, 59(2), 495-510.

difference-in-differences propensity-score-matching interrupted-time-series regression-discontinuity-design panel-fixed-effects causal-impact-analysis


Synthetic Control Method

Synthetic Control Method for Comparative Case Studies

SCM
synthetic control
synth estimator
Abadie-Diamond-Hainmueller method

Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)

The Synthetic Control Method estimates the causal effect of a treatment or policy on a single treated unit by constructing a weighted combination of untreated units — the synthetic control — that closely resembles the treated unit before the intervention. The gap between the treated unit and its synthetic counterpart after the intervention is the estimated treatment effect.

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

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

difference-in-differences regression-discontinuity-design instrumental-variables propensity-score-matching event-study-design causal-impact-analysis


Synthetic Control

synthetic control method
SCM
synthetic counterfactual
Sentetik Kontrol Yöntemi (SCM)

The Synthetic Control Method, introduced by Abadie, Diamond and Hainmueller in 2010, builds a weighted counterfactual for a single treated unit from a pool of untreated donor units. It is widely regarded as the gold standard for evaluating large policy interventions, natural experiments, and N=1 case studies where no obvious comparison unit exists.

Convex-weighted donor combination minimising pre-treatment fit

Abadie, A. (2021). Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects. Journal of Economic Literature, 59(2), 391-425.

interrupted-time-series regression-discontinuity iv-2sls matching-methods panel-fixed-effects


Synthetic Data Generation

Synthetic Data Generation for Disclosure Control

Fully Synthetic Data
Partial Synthetic Data
Statistical Data Synthesis
Sentetik Veri Üretimi

Synthetic data generation is a statistical disclosure limitation technique introduced by Donald Rubin in 1993, in which values in a confidential dataset are replaced by draws from a fitted posterior predictive distribution rather than released directly. The resulting artificial records preserve the joint statistical structure of the original data while preventing the identification of real individuals, enabling analysts to work with a publicly releasable dataset that behaves like the original for most inferential purposes.

Artificial dataset replacing or augmenting real records

Rubin, D. B. (1993). Statistical disclosure limitation. Journal of Official Statistics, 9(2), 461–468.


Synthetic Difference-in-Differences

Synthetic Control Difference-in-Differences Estimator

Arkhangelsky, Athey, Hirshberg, Imbens, and Wager

Synthetic Difference-in-Differences (SDID) combines synthetic control and difference-in-differences approaches to estimate treatment effects when a policy or intervention affects one unit (country, firm) at a point in time. Introduced by Arkhangelsky et al. (2021), it improves upon both methods alone by using weighted combinations of controls to match treated units' pre-treatment trends and levels. This yields more precise and robust estimates than classical DiD or synthetic control.

Arkhangelsky, D., Athey, S., Hirshberg, D. A., Imbens, G. W., & Wager, S. (2021). Synthetic difference-in-differences. American Economic Review, 111(12), 4088-4118.

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


System Dynamics

stock-flow modelling
Sistem Dinamiği (Stock-Flow Modelleme)
SD modelling
feedback simulation

System dynamics is a continuous simulation method, developed by Jay W. Forrester at MIT in 1961, that represents a complex system through stocks (accumulations), flows (rates of change), and feedback loops. By expressing these relationships as coupled ordinary differential equations, it reproduces how policies, delays, and nonlinear feedbacks drive system behaviour over time — making it a cornerstone tool in policy analysis, organisational modelling, and sustainability research.

Continuous simulation / feedback modelling

Stocks, flows, feedback loops (reinforcing R, balancing B)

Output: Time-series trajectories of system state variables

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

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

discrete-event-simulation monte-carlo-simulation latin-hypercube-sampling agent-based-modelling scenario-analysis


System GMM

System Generalized Method of Moments Estimator (Arellano-Bover / Blundell-Bond)

Sistem GMM (Arellano-Bover / Blundell-Bond)

Arellano & Bover (1995); Blundell & Bond (1998)

System GMM is a generalized method of moments estimator for dynamic panel models that contain a lagged dependent variable. Introduced by Blundell and Bond (1998), building on Arellano and Bover, it augments the differenced equation of the earlier difference GMM (Arellano-Bond) with the equation in levels to deliver consistent estimates when N is large and T is small.

System GMM (two-step, Windmeijer-corrected standard errors)

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.

Blundell, R. & Bond, S. (1998). Initial Conditions and Moment Restrictions in Dynamic Panel Data Models. Journal of Econometrics, 87(1), 115-143.

Roodman, D. (2009). How to Do xtabond2: An Introduction to Difference and System GMM in Stata. Stata Journal, 9(1), 86-136.

panel-fixed-effects panel-var ols-regression panel-random-effects


System Usability Scale

The System Usability Scale (SUS) is a rapid, standardized 10-item questionnaire for measuring perceived system usability in a single summary score. Developed by John Brooke in 1986, SUS has become one of the most widely used post-use usability instruments in industry and research. The scale is administered after a user has interacted with a system, capturing perceived ease of use, learnability, error recovery, and overall satisfaction with a quick, economical assessment that correlates well with comprehensive usability testing.

Rapid, post-use questionnaire scale for measuring perceived usability

Brooke, J. (1986). System Usability Scale (SUS): A quick and dirty usability scale. In B. Shackel & S. J. Richardson (Eds.), Usability Evaluation in Industry (pp. 189–194). Taylor & Francis.

Bangor, A., Kortum, P. T., & Miller, J. T. (2008). An empirical evaluation of the System Usability Scale. International Journal of Human-Computer Interaction, 24(6), 574–594.

nasa-tlx kano-model attrakdiff-ueq think-aloud-protocol


Systematic Literature Review

SLR
systematic review
evidence synthesis review
structured literature review

1993 (Cochrane Collaboration); 2004 (Kitchenham SLR guidelines)

Archie Cochrane (conceptual foundation); formalized by the Cochrane Collaboration (1993) and Barbara Kitchenham in software engineering (2004)

A systematic literature review (SLR) is a structured, reproducible method for identifying, appraising, and synthesizing all relevant studies on a research question. Unlike a narrative review, it follows an explicit, pre-specified protocol — from database search strings through inclusion criteria to data extraction — so that the process is transparent, auditable, and replicable by other researchers. It is widely used in medicine, education, software engineering, and the social sciences to produce the most comprehensive possible evidence base on a topic.

Published studies, journal articles, conference papers, grey literature

Kitchenham, B. (2004). Procedures for Performing Systematic Reviews. Keele University Technical Report TR/SE-0401.

Higgins, J. P. T., & Green, S. (Eds.). (2019). Cochrane Handbook for Systematic Reviews of Interventions (2nd ed.). Wiley.

meta-analysis scoping-review bibliometric-analysis narrative-review umbrella-review prisma-based-review


Systematic Mapping Review

Systematic Mapping Review (Scoping Review)

scoping review
systematic mapping
literature mapping
evidence mapping

Arksey & O'Malley (2005); Joanna Briggs Institute methodology

A systematic mapping review (also called a 'scoping review') is a literature review methodology that aims to comprehensively identify and categorize the published evidence on a topic without necessarily assessing the quality of individual studies or synthesizing findings quantitatively. Developed by Arksey and O'Malley (2005) and formalized by the Joanna Briggs Institute, systematic mapping reviews chart the landscape of evidence: what has been studied, what are the research gaps, and how is evidence distributed across study types, populations, and outcomes. Unlike systematic reviews that answer specific research questions with rigorous study selection and synthesis, mapping reviews provide a broad overview of the research terrain, making them ideal for defining scope, identifying knowledge gaps, and guiding future research priorities.

Arksey, H., & O'Malley, L. (2005). Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19–32.

Tricco, A. C., Lillie, E., Zarin, W., et al. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Annals of Internal Medicine, 169(7), 467–473.

Petticrew, M., & Roberts, H. (2009). Systematic reviews in the social sciences: A practical guide. Blackwell.

science-mapping research-front-identification co-citation-analysis bibliographic-coupling


Systematic Review

Systematic Review (Evidence Synthesis with Systematic Literature Search)

systematic literature review
evidence synthesis
scoping review
mapping review

A systematic review is a structured, transparent synthesis of all available evidence addressing a specific research question. Unlike narrative reviews, systematic reviews employ comprehensive database searches, predefined selection criteria, quality assessment, and rigorous reporting (PRISMA guideline). The Cochrane Collaboration (founded 1992) established this methodology as the gold standard for evidence synthesis in healthcare and social sciences.

Page, M. J., et al. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372, n71.

Higgins, J. P. T., & Thomas, J. (Eds.). (2023). Cochrane Handbook for Systematic Reviews of Interventions (Version 6.4). Cochrane.

Shamseer, L., et al. (2015). Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) 2015: elaboration and explanation. BMJ, 349, g7647.

meta-analysis-article literature-review-article original-research-article PICO-framework literature-search-strategy


Systematic Sampling

interval sampling
systematic random sampling
equal-interval sampling
fixed-interval sampling

Mid-20th century (Cochran 1953; Kish 1965)

William G. Cochran; formalized in survey sampling theory

Systematic sampling is a probability sampling technique in which every k-th element is selected from an ordered list of the population after a random starting point. With population size N and desired sample size n, the sampling interval k = N/n is computed and one unit is chosen at random from the first interval; all subsequent units are selected by adding k repeatedly. The method is operationally simple, yields a spread-out sample, and often achieves lower variance than simple random sampling when the list has no harmful periodicity.

Any data type collected from a list-based population (quantitative or qualitative)

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

Kish, L. (1965). Survey Sampling. John Wiley & Sons.


Systematic Search Strategy

Comprehensive and Reproducible Literature Search Protocol

search protocol
systematic search
comprehensive search strategy

1990s (formalized in Cochrane methodology)

Cochrane Collaboration and systematic review methodologists

A systematic search strategy is a comprehensive, transparent protocol for retrieving all relevant literature addressing a well-defined research question. Developed by the Cochrane Collaboration and formalized in guidelines like PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), systematic search strategies are essential for conducting unbiased literature reviews, systematic reviews, and meta-analyses. Unlike ad hoc searches (searching Google Scholar or PubMed without a protocol), systematic searches document every step—which databases were searched, what search terms were used, how many results were retrieved, and what inclusion/exclusion criteria were applied—enabling other researchers to reproduce the search and verify that no relevant studies were missed.

Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA statement. PLoS Medicine, 6(7), e1000097.

Higgins, J. P. T., & Thomas, J. (Eds.). (2019). Cochrane handbook for systematic reviews of interventions (Version 6.0). The Cochrane Collaboration.

Bramer, W. M., Rethlefsen, M. L., Murad, M. H., & Landhuis, E. (2016). When updating systematic reviews, how often should new searches be applied to increase the chance of finding relevant studies? Systematic Reviews, 5(1), 94.

pico-framework boolean-search-operators grey-literature-search citation-analysis


Systemic Functional Linguistics

Systemic Functional Linguistics (SFL) Framework

Systemic Functional Linguistics (SFL) is a framework for analyzing language developed by Michael Halliday, viewing language as a system of meaning-making choices where speakers select from available options to express meanings. The approach emphasizes the relationship between language form and social context, analyzing how register (field, tenor, mode) shapes linguistic choices and how language constructs meaning through metafunctional systems (ideational, interpersonal, textual). SFL is widely applied to discourse analysis, language education, and computational linguistics.

Halliday, M. A. K. (1994). An Introduction to Functional Grammar (2nd ed.). London: Edward Arnold.

Halliday, M. A. K., & Matthiessen, C. M. I. M. (2004). An Introduction to Functional Grammar (3rd ed.). London: Routledge.

Eggins, S. (2004). An Introduction to Systemic Functional Linguistics (2nd ed.). London: Continuum.


SBI

James M. Holland, Jill M. Currier, & Robert A. Neimeyer

The Systems of Belief Inventory (SBI), developed by Holland, Currier, and Neimeyer in 2011, is a 15-item self-report measure designed to assess the coherence, flexibility, and adaptive function of an individual's worldview and meaning-making system. Originally validated in bereavement research, the SBI captures dimensions of spiritual and existential belief that predict psychological adjustment following loss or trauma. It measures three key aspects: existential meaning-making, negative religious coping, and hope. The scale is useful in grief counseling, trauma recovery, and any clinical context where worldview disruption occurs.

Holland, J. M., Currier, J. M., & Neimeyer, R. A. (2011). The Systems of Belief Inventory: Factor structure and association with psychosocial outcome in bereavement. Psychological Assessment, 23(2), 311–321.


t-SNE

t-SNE (Boyut İndirgeme / Görselleştirme)

t-distributed stochastic neighbor embedding

t-SNE (t-Distributed Stochastic Neighbor Embedding) is a nonlinear dimensionality-reduction method introduced by Laurens van der Maaten and Geoffrey Hinton in 2008 that maps high-dimensional data into a 2D or 3D space for visualization. It preserves probabilistic local similarities, so points that are neighbours in the original space stay close together, revealing cluster structure and local neighbourhoods.

Nonlinear dimensionality reduction (manifold visualization)

Visualization & exploratory cluster inspection

van der Maaten, L. & Hinton, G. (2008). Visualizing Data using t-SNE. Journal of Machine Learning Research, 9(86), 2579–2605.

pca umap kmeans-clustering gaussian-mixture shap-analysis


T5 (Text-to-Text Transfer Transformer)

T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Raffel, C.; Shazeer, N.; Roberts, A.; et al. (Google Brain)

T5 is a unified sequence-to-sequence deep learning framework introduced by Raffel et al. at Google Brain in 2020, published in the Journal of Machine Learning Research (Vol. 21, No. 140). It reframes every NLP task — classification, translation, summarisation, question answering, and more — as a text-to-text problem: both input and output are always character strings, enabling a single encoder-decoder Transformer to be pre-trained once and fine-tuned across tasks with a consistent interface. T5 introduced span-corruption pre-training and the C4 corpus, and its largest variant (11B parameters) achieved state-of-the-art results across a wide range of NLP benchmarks at the time of publication.

Pre-trained encoder-decoder Transformer (sequence-to-sequence)

Any NLP task cast as text-to-text generation

Span corruption (masked-span prediction)

Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2020). Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. Journal of Machine Learning Research, 21(140), 1–67.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is All You Need. Advances in Neural Information Processing Systems, 30.

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, 4171–4186.

bert gpt bart flan-t5 transformer attention-mechanism transfer-learning


Tablet Questionnaire for Medication Adherence

The Tablet Questionnaire is a brief, simple self-report tool designed to assess medication non-adherence through direct questions about dose-skipping behavior and reasons for non-adherence. Developed by Adeniji and Brown in 2012, it prioritizes simplicity and cultural accessibility, making it particularly valuable in low-resource settings and populations with limited health literacy. Despite its brevity, the measure demonstrates good sensitivity for detecting non-adherence and has been validated across African and international populations.

Adeniji, B., & Brown, C. (2012). Tablet Questionnaire: A simple tool to assess medication non-adherence. Annals of African Medicine, 11(4), 202-205.


Tabu Search

Tabu Araması (Tabu Search)
TS
tabu metaheuristic

Tabu Search is a local-search metaheuristic introduced by Fred Glover in 1989 that uses a tabu list — a short-term memory of recently visited solutions — to prevent cycling and escape local optima. By explicitly forbidding moves that reverse recent decisions, the algorithm explores the search space more broadly and, through long-term memory structures such as aspiration criteria, aims to approach the global optimum even in large, complex combinatorial problems.

Short-term tabu list + optional long-term memory (aspiration criteria)

Glover, F. (1989). Tabu Search — Part I. ORSA Journal on Computing, 1(3), 190–206.

Glover, F. & Laguna, M. (1997). Tabu Search. Springer.

simulated-annealing genetic-algorithm ant-colony-optimization particle-swarm-optimization hill-climbing


Taguchi Method

Taguchi Method (Orthogonal Arrays, Signal-to-Noise Ratio)

Taguchi Yöntemi (Ortogonal Dizi, S/N Oranı)

The Taguchi Method is a robust design methodology developed by Genichi Taguchi, first systematized in his 1987 work, that uses orthogonal arrays to study many control factors in a minimum number of experimental runs while quantifying product or process quality through Signal-to-Noise (S/N) ratios. Its central goal is to design products and processes that are insensitive — or robust — to uncontrollable noise factors such as environmental variation, material inconsistency, or user behavior.

Taguchi, G. (1987). System of Experimental Design. UNIPUB/Kraus.

Phadke, M. S. (1989). Quality Engineering Using Robust Design. Prentice Hall.

full-factorial-design fractional-factorial-design response-surface-methodology one-way-anova latin-square-design


Tail Risk Measures

Tail Risk Measures (Expected Shortfall, Spectral and Expectile Risk)

Kuyruk Riski Ölçüleri (ES, Spectral, Expectile)

Artzner, Delbaen, Eber & Heath (coherent risk axioms); Acerbi & Tasche (Expected Shortfall)

Tail risk measures quantify the loss distribution beyond Value-at-Risk (VaR). Expected Shortfall — the expected loss given that VaR is exceeded — is the leading coherent risk measure, formalised by Artzner, Delbaen, Eber and Heath (1999) and shown to be coherent by Acerbi and Tasche (2002). Spectral and expectile-based measures generalise it.

Expected loss beyond a VaR threshold (historical or parametric)

Artzner, P., Delbaen, F., Eber, J.-M. & Heath, D. (1999). Coherent Measures of Risk. Mathematical Finance, 9(3), 203–228.

Acerbi, C. & Tasche, D. (2002). On the Coherence of Expected Shortfall. Journal of Banking & Finance, 26(7), 1487–1503.

quantile-regression garch-model extreme-value-theory regime-switching-finance ols-regression


Taint Analysis

taint analysis
information flow
data tainting

Taint analysis is a data flow analysis technique that tracks how untrusted (tainted) input flows through a program to identify vulnerabilities where tainted data reaches dangerous operations (sinks). Formalized by Newsome and Song in 2005, taint analysis marks input data as tainted and propagates taint labels through the program, alerting when tainted data reaches sensitive operations like SQL queries or system calls. Taint analysis is fundamental to detecting injection vulnerabilities and is widely used in dynamic analysis tools and security monitoring systems.

Newsome, J., & Song, D. X. (2005). Dynamic taint analysis for automatic detection, analysis, and signature generation of exploits on commodity software. In Network and Distributed System Security Symposium (NDSS 2005).

Schwartz, E. J., Avgerinos, T., & Brumley, D. (2010). All you ever wanted to know about dynamic taint analysis and forward symbolic execution (but might have been afraid to ask). In IEEE Symposium on Security and Privacy (SP), 2010, pp. 317-331.


Technology Acceptance Model Questionnaire

Technology Acceptance Model (TAM) Questionnaire

The Technology Acceptance Model (TAM) is a foundational framework introduced by Fred Davis in 1989 to explain user adoption of information technology. Published in MIS Quarterly, TAM posits that perceived usefulness and perceived ease of use are the primary determinants of technology acceptance, regardless of an individual's prior computer experience or technical background.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.

Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475-487.

utaut-questionnaire tam2-questionnaire is-success-model technology-readiness-index


TAM2 Questionnaire

Technology Acceptance Model 2 (TAM2) Questionnaire

The Technology Acceptance Model 2 (TAM2) was developed by Davis and Venkatesh in 2000 and published in Management Science. TAM2 extends the original TAM by incorporating social influence factors and job relevance moderators that explain how users form perceived usefulness beliefs. TAM2 is grounded in longitudinal field studies across multiple organizational contexts and systems.

Davis, F. D., & Venkatesh, V. (2000). A critical assessment of potential measurement biases in the Technology Acceptance Model: Two experiments. International Journal of Human-Computer Studies, 45(1), 23-45.

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186-204.

tam-questionnaire utaut-questionnaire is-success-model technology-readiness-index


TAR / SETAR

Threshold / Self-Exciting Threshold Autoregression (TAR/SETAR)

Threshold Autoregression
Self-Exciting Threshold Autoregression
SETAR Model
Eşik Otoregresyon

TAR and SETAR are nonlinear autoregressive models introduced by Howell Tong (1990) that allow a time series to follow different linear dynamics in distinct regimes, separated by one or more threshold values. SETAR is the self-exciting variant, in which the threshold variable is a lagged value of the series itself, making it particularly suited to cycles, asymmetric adjustment, and limit-cycle behavior observed in economic and financial data.

Nonlinear time-series model with regime switching

Lagged value of the series itself (SETAR) or an exogenous variable (TAR)

Tong, H. (1990). Non-linear Time Series: A Dynamical System Approach. Oxford University Press.


AIMS

Tardive dyskinesia and antipsychotic medication side effects

The Abnormal Involuntary Movement Scale (AIMS) is the standard clinical rating scale for assessing tardive dyskinesia, a iatrogenic movement disorder resulting from chronic antipsychotic medication exposure. Developed by the National Institute of Mental Health in 1976, the 12-item scale systematically measures involuntary movements across facial, oral, limb, and trunk regions. The AIMS is mandatory screening tool for patients on long-term antipsychotic therapy and essential for monitoring antipsychotic-associated movement complications.

National Institute of Mental Health (1976). Abnormal Involuntary Movement Scale (AIMS). In: Rockland, L. H., Schooler, N. R., & Levine, J. (Eds.), Drug Treatment of Mental Disorders. New York: Raven Press.


Target-Mediated Drug Disposition

Target-mediated drug disposition (TMDD) is a mechanistic framework describing nonlinear pharmacokinetics arising from drug binding to a target receptor or protein. Developed by Mager and Jusko in 2001, TMDD explains saturable clearance, dose-dependent half-lives, and time-dependent changes in plasma concentrations observed with protein therapeutics and some small-molecule drugs.

Mager, D. E., & Jusko, W. J. (2001). General pharmacokinetic model for drugs exhibiting target-mediated drug disposition. Journal of Pharmacokinetics and Pharmacodynamics, 28(6), 507-532.

Levy, G. (2004). Carrier-mediated active transport: pharmacokinetic consequences and examples in humans. Clinical Pharmacokinetics, 37(6), 429-445.


Targeted Maximum Likelihood Estimation

Hedeflenmiş Maksimum Olabilirlik Tahmini

Targeted Maximum Likelihood Estimation (TMLE) is a semiparametric, doubly robust causal inference method introduced by Mark van der Laan and Daniel Rubin in 2006. It combines flexible machine learning models for both the outcome and the treatment assignment mechanism, then applies a targeting step that re-fits the initial outcome model specifically to reduce bias for a pre-specified causal estimand such as the average treatment effect. TMLE is widely used in epidemiology, biostatistics, and health economics when estimating causal effects from observational data.

Consistent if either the outcome model or the propensity score model is correctly specified

Provides valid asymptotic confidence intervals via influence-function-based standard errors

van der Laan, M. J., & Rubin, D. (2006). Targeted maximum likelihood learning. The International Journal of Biostatistics, 2(1).


Task and Ego Orientation in Sport Questionnaire (TEOSQ)

The TEOSQ is a 13-item questionnaire measuring achievement goal orientation in sport: the underlying reasons athletes define success and pursue achievement. Developed by Duda and Nicholls in 1992, the TEOSQ has become a cornerstone instrument for understanding athlete motivation, resilience, and response to failure.

Self-report achievement goal orientation questionnaire

Duda, J. L., & Nicholls, J. G. (1992). Dimensions of achievement motivation in schoolwork and sport. Journal of Educational Psychology, 84(3), 290–299.

Duda, J. L. (2001). Achievement goal theory in sport: Recent extensions and future directions. In R. N. Singer, H. A. Hausenblas, & C. M. Janelle (Eds.), Handbook of Sport Psychology (2nd ed., pp. 417–443). New York: Wiley.

sport-motivation-scale athletic-identity-measurement-scale sport-confidence-inventory mental-toughness-questionnaire


Tau Estimator

tau regression estimator
robust tau regression
Tau-Tahmin Edici

The Tau estimator is a robust linear regression method introduced by Yohai and Zamar in 1988 that fits the model by minimising an efficient τ-scale of the residuals. It builds on the scale estimate of the S-estimator to combine a high breakdown point with high statistical efficiency, and is often used as an alternative to the MM-estimator in small samples.

Minimisation of an efficient τ-scale of the residuals

Yohai, V. J., & Zamar, R. H. (1988). High Breakdown-Point Estimates of Regression by Means of the Minimization of an Efficient Scale. Journal of the American Statistical Association, 83(402), 406-413.

Maronna, R. A., & Zamar, R. H. (2002). Robust Estimates of Location and Dispersion for High-Dimensional Datasets. Technometrics, 44(4), 307-317.

mm-estimator s-estimator theil-sen-estimator huber-m-estimator least-trimmed-squares


TAXONOMY

TAXONOMY (Taxonomy Method — Wrocław Taxonomic Development Measure) is a ranking multi-criteria decision-making (MCDM) method introduced by Florek, K., Łukaszewicz, J., Perkal, J., Steinhaus, H., Zubrzycki, S. in 1951. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Taxonomic distance composite index (z-score + Euclidean ideal)

Florek, K., Łukaszewicz, J., Perkal, J., Steinhaus, H., Zubrzycki, S. (1951). Taksonomia wrocławska. Przegląd Antropologiczny

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


Taylor Tool Life

Taylor's Tool Life Equation and Cutting Parameter Optimization

Taylor's equation
Tool life prediction
VT relationship

Taylor's tool life equation is an empirical relationship predicting how long a cutting tool remains usable before dulling or breaking, expressed as a function of cutting speed, feed rate, and depth of cut. Formulated by Frederick Winslow Taylor in 1907 from systematic experiments on metal cutting, this method provides a practical framework for optimizing machining operations by balancing productivity against tool wear and cost.

Taylor, F. W. (1907). On the art of cutting metals. Transactions of the American Society of Mechanical Engineers, 28, 31-350.

Elbestawi, M. A., Papazafiriou, T., & Du, R. (1994). In-process detection of tool wear in milling using cutting force signature. International Journal of Machine Tools and Manufacture, 34(4), 555-566.

Karpuschewski, B., Wehmeyer, K., & Schmidt, K. (2008). Advances in precision grinding and polishing processes. CIRP Annals, 57(2), 621-642.

cnc-tool-path-generation design-for-manufacturing-and-assembly modal-analysis additive-manufacturing-slicing


TBATS

Trigonometric, Box-Cox, ARMA, Trend and Seasonal Components Model

TBATS — Çoklu Mevsimsel Üstel Düzleştirme

TBATS is an innovations state space forecasting model, introduced by De Livera, Hyndman and Snyder (2011), that combines a Box-Cox transformation, ARMA errors and trigonometric (Fourier) seasonal terms. It is built to handle continuous time series with several nested seasonal cycles at once — for example hourly data that also repeats daily, weekly and yearly.

Maximum likelihood (innovations state space)

De Livera, A. M., Hyndman, R. J. & Snyder, R. D. (2011). Forecasting Time Series with Complex Seasonal Patterns Using Exponential Smoothing. Journal of the American Statistical Association, 106(496), 1513-1527.

Hyndman, R. J. & Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.). OTexts.

arima sarima ets-exponential-smoothing prophet stl-decomposition


TCAS

TCAS (Traffic Collision Avoidance System) is an airborne safety system that detects nearby aircraft using radar and mode C altitude reports, then provides traffic advisories (TAs) and recommended collision avoidance maneuvers (RAs) to flight crews. Mandated globally on commercial aircraft since 2000, TCAS is considered a last line of defense against mid-air collisions. TCAS II is the most common variant; TCAS I is a simplified advisory-only version for general aviation.

Federal Aviation Administration (2017). Traffic Collision Avoidance System (TCAS II). Technical Standard Order TSO-C119c.

International Civil Aviation Organization (2020). Annex 10 — Aeronautical Telecommunications, Volume IV. ICAO.

Billingsley, D., & Hoh, R. H. (2013). An overview of TCAS overview. In Proceedings of the Integrated Communications Navigation and Surveillance Conference (ICNS).


Teaching Effectiveness Scale

The Teaching Effectiveness Scale (TES) is a validated instrument designed to measure students' perceptions of instructor effectiveness across multiple dimensions. The most widely known version, the Student Evaluation of Educational Quality (SEEQ), developed by Marsh (1982), assesses nine dimensions of teaching including learning value, enthusiasm, organization, group interaction, and course difficulty, providing comprehensive feedback on instructor performance.

Marsh, H. W. (1982). SEEQ: a reliable, valid, and useful instrument for collecting students' evaluations of university teaching. British Journal of Educational Psychology, 52(1), 77-95.

Wachtel, H. K. (1998). Student evaluation of college teaching effectiveness: a brief review. Assessment and Evaluation in Higher Education, 23(2), 191-211.

student-satisfaction-survey course-experience-questionnaire school-climate-scale student-engagement-scale


Group Environment Questionnaire

Albert Carron, W. Neil Widmeyer, Lawrence Brawley

The GEQ is an 18-item instrument measuring team cohesion—the degree to which team members feel attracted to the group and perceive the group as unified around shared task and social goals. Developed by Carron, Widmeyer, and Brawley in 1985, the GEQ has become the gold standard for measuring cohesion in sport teams and is used to predict team performance, member satisfaction, and team retention.

Self-report team/group cohesion questionnaire

Carron, A. V., Widmeyer, W. N., & Brawley, L. R. (1985). The development of an instrument to assess cohesiveness in sport teams: The Group Environment Questionnaire. Journal of Sport Psychology, 7(3), 244–266.

Eys, M. A., Carron, A. V., Bray, S. R., & Beauchamp, M. R. (2003). The relationship between correlates of cohesion and attendance in interactive and coactive sport teams. Journal of Sport & Exercise Psychology, 25(1), 59–72.

sport-motivation-scale athletic-identity-measurement-scale mental-toughness-questionnaire task-ego-orientation-sport


Team Situation Awareness Scale

The Team Situation Awareness Scale (TSAS) extends individual situational awareness measurement to the team level, assessing how well team members collectively perceive the task environment, understand shared information, and coordinate their actions. Developed by Endsley, Salas, and collaborators in the 1990s–2000s, the TSAS measures team-level SA, recognizing that in complex operations (emergency response, military command, operating rooms), task success depends not just on individual operator awareness but on shared mental models, communication, and coordinated decision-making.

Salas, E., Prince, C., & Brannick, M. T. (1992). Team performance assessment in military tasks. In R. Guzzo & E. Salas (Eds.), Team Effectiveness and Decision Making in Organizations (pp. 90-120). Jossey-Bass.

Endsley, M. R. (2004). Team situation awareness. In S. Banbury & S. Tremblay (Eds.), A Cognitive Approach to Situation Awareness: Theory and Application (pp. 328-355). Ashgate.

situational-awareness-rating nasa-task-load-index workload-profile team-situation-awareness


Agency for Healthcare Research and Quality (AHRQ), Department of Defense, and team members from the TeamSTEPPS program

The TeamSTEPPS Teamwork Perceptions Questionnaire (T-TPQ) is a 35-item self-report instrument designed to measure team members' perceptions of teamwork and communication in clinical units. Developed by the Agency for Healthcare Research and Quality and the Department of Defense, the T-TPQ was created specifically to evaluate the impact of TeamSTEPPS (Team Strategies and Tools to Enhance Performance and Patient Safety) training on teamwork behaviors and safety outcomes. The questionnaire assesses five core domains of teamwork: team structure, leadership, situational monitoring, mutual support, and communication.

Sorra, J., Nieva, V. F., Famolaro, T., & Dyer, N. (2014). Detailed Evaluation of the Psychometric Properties of the Teamwork Perceptions Questionnaire. Agency for Healthcare Research and Quality. Technical Report.

Sexton, J. B., Helmreich, R. L., & Pronovost, P. J. (2011). The Safety Attitudes Questionnaire as a tool for evaluating the impact of patient safety initiatives on safety attitudes and outcomes. American Journal of Medical Quality, 25(3), 186–191.

Clements, P. T., Kitchens, E., Lebel, N., & Schrecengost, S. (2007). Critical incident stress debriefing: an evaluation of existing research. Prehospital and Disaster Medicine, 22(2), 87–95.

safety-attitudes-questionnaire hospital-survey-patient-safety healthcare-teamwork-scale clinical-handover-quality


Technical Debt Measurement

Technical Debt Quantification and Assessment

debt metrics
code health scoring
maintenance burden assessment

Technical debt represents accumulated shortcuts, deferred maintenance, and design compromises that incur future costs through slower development, higher defect rates, and deployment difficulty. Introduced by Ward Cunningham (1992), technical debt measurement quantifies these burdens using metrics like code complexity, duplication, test coverage gaps, and maintainability indices. Organizations use debt measurement to balance immediate delivery with long-term sustainability.

Cunningham, W. (1992). The WyCash Portfolio Management System. OOPSLA 92 Experience Report.

Seaman, C. B., & Guo, Y. (2011). Measuring and monitoring technical debt. Advances in Computers, 82, 25–46.

Tom, E., Aurum, A., & Vidgen, R. (2013). An exploration of technical debt. Journal of Systems and Software, 86(6), 1498–1516.

software-complexity-metrics code-coverage-analysis static-code-analysis defect-prediction-model


Technical Debt Quantification

debt measurement
refactoring cost estimation

Technical Debt Quantification is the measurement and monetization of technical shortcuts taken during development (incomplete refactoring, outdated dependencies, deferred testing). Coined by Cunningham in 1992, the metaphor frames accumulated shortcuts as financial debt: taking shortcuts saves immediate time but incurs interest (slower future development) and risk (outages).

Cunningham, W. (1992). The WyCash portfolio management system. OOPSLA '92 Experience Report.


Technoference Scale

Technoference Scale (or Smartphone Interference in Relationships Scale)

The Technoference Scale measures the degree to which smartphone and technology use interferes with interpersonal relationships, particularly in romantic partnerships, families, and close relationships. Developed by McDaniel and Coyne in the mid-2010s, this construct captures a modern phenomenon where digital devices create physical or psychological distance during face-to-face interaction, reducing relationship quality and satisfaction.

McDaniel, B. T., & Coyne, S. M. (2016). Technology interference in the context of romantic relationships. In R. E. Ackerman (Ed.), The psychology of social networking (Vol. 1, pp. 86–102). Nova Publishers.

smartphone-addiction-scale-short social-media-disorder-scale fear-of-missing-out-scale passive-social-media-use-scale


Technology Readiness Index

The Technology Readiness Index (TRI) was developed by Ajay Parasuraman in 2000 to measure individual propensity to adopt and use new technologies. The TRI assesses a person's personal attitudes toward technology across four dimensions: optimism, innovativeness, discomfort, and insecurity. Updated in 2015 with a streamlined 16-item version, the TRI helps identify technology adopter segments and predict behavior across diverse technology contexts.

Parasuraman, A., & Colby, C. L. (2015). An updated and streamlined Technology Readiness Index. Journal of Service Research, 18(1), 59-74.

Parasuraman, A. (2000). Technology Readiness Index (TRI): A multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307-320.

tam-questionnaire utaut-questionnaire computer-anxiety-scale online-trust-scale


Technostress Scale

The Technostress Scale, developed by Tarafdar, Tu, Ragu-Nathan, and colleagues (2007), measures the stress and negative emotions experienced by employees due to information technology use in the workplace. The scale captures five dimensions of technostress: techno-overload (excessive workload from technology demands), techno-invasion (inability to disconnect from work), techno-complexity (difficulty mastering new technology), techno-insecurity (fear of job loss due to automation), and techno-uncertainty (constant changes in technology). Technostress is linked to decreased productivity, increased burnout, and job dissatisfaction.

Tarafdar, M., Tu, Q., Ragu-Nathan, B. S., & Ragu-Nathan, T. S. (2007). The impact of technostress on role stress and productivity. Journal of Management Information Systems, 24(1), 301-328.

Ragu-Nathan, T. S., Tarafdar, M., Ragu-Nathan, B. S., & Tu, Q. (2008). The consequences of technostress for end users in organizations: Conceptual development and validation. Information Systems Research, 19(4), 417-433.

computer-anxiety-scale technology-readiness-index tam-questionnaire utaut-questionnaire


Telemedicine Satisfaction Scale

The Telemedicine Satisfaction Scale measures patient satisfaction with remote clinical encounters, assessing perceptions of communication quality, technical usability, provider competence, and perceived benefit. While no single universal scale dominates the literature, core satisfaction domains—connection quality, provider accessibility, clinical effectiveness, and likelihood to recommend—are consistently measured across telemedicine studies to evaluate user acceptance and identify barriers to adoption.

Or, Z., & Kartak, F. (2009). Review of the empirical literature on telemedicine in the OECD countries: Does telemedicine improve outcomes? In M. Rechel, B. Goddard (Eds.), Improving healthcare quality in Europe. WHO Regional Office for Europe.

Stacey, D., Samant, R., & Bennett, C. (2008). Decision-making in oncology: a review of patient decision aids for preference-sensitive treatments. Cancer, 113(8), 1721–1730.


Telephone-assisted Delphi Technique

telephone Delphi
phone-based Delphi
CATI Delphi
telephone consensus method

1963 (Delphi); telephone-assisted variant prominent 1970s–1990s

Norman Dalkey & Olaf Helmer (RAND Corporation); telephone adaptation used throughout 1970s–1990s applied research

The telephone-assisted Delphi Technique applies the classic iterative expert-consensus framework through structured telephone interviews rather than mailed or online questionnaires. Experts participate in sequential rounds of data collection by phone, enabling the researcher to clarify ambiguous responses in real time and reach consensus on complex, contested, or forward-looking questions without requiring participants to convene in person.

Iterative expert consensus technique delivered by telephone

Expert ratings, rankings, and open-ended verbal responses (converted to structured data across rounds)

Dalkey, N., & Helmer, O. (1963). An experimental application of the Delphi method to the use of experts. Management Science, 9(3), 458–467.

Hasson, F., Keeney, S., & McKenna, H. (2000). Research guidelines for the Delphi survey technique. Journal of Advanced Nursing, 32(4), 1008–1015.

delphi-technique online-delphi-technique structured-interview telephone-assisted-structured-interview nominal-group-technique expert-panel


Telephone-assisted Field Notes

Telephone-assisted Field Notes Collection

phone-dictated field notes
telephone field recording
remote field note dictation
phone-assisted observation notes

Adapted from traditional fieldwork practice; telephone dictation variant developed in qualitative health and social research circa 1980s–1990s

Telephone-assisted field notes is a data collection technique in which a field researcher verbally dictates observational notes via telephone — either to a live transcriptionist, an answering service, or a voicemail/recording system — immediately after or during a field encounter. It preserves the immediacy and richness of traditional field notes while enabling the researcher to record observations quickly and hands-free when written note-taking is impractical or disruptive.

Observational text data, verbally dictated descriptive notes

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

Bernard, H. R. (2011). Research Methods in Anthropology: Qualitative and Quantitative Approaches (5th ed.). AltaMira Press.

field-notes telephone-assisted-structured-interview remote-field-notes mobile-field-notes non-participant-observation voice-memo-data-collection


Telephone-assisted Focus Group

Telephone-assisted Focus Group Discussion

telephone focus group
phone focus group
TAFG
teleconference focus group

Adapted from in-person focus group methodology (Robert Merton et al., 1950s); telephone modality adopted in market and health research from the 1980s onward

A telephone-assisted focus group is a qualitative data collection technique in which a moderator facilitates a structured group discussion among multiple participants connected simultaneously via a telephone conference bridge or audio platform. It preserves the core interactive dynamics of traditional focus groups — group synergy, probing, and spontaneous reactions — while eliminating the need for geographic co-location, making it suitable for hard-to-reach, geographically dispersed, or mobility-constrained populations.

Qualitative group data collection technique

Audio-recorded verbal group discussion (text after transcription)

Greenbaum, T. L. (1998). The Handbook for Focus Group Research (2nd ed.). Sage. [Chapter on telephone and technology-mediated focus groups]

Krueger, R. A., & Casey, M. A. (2015). Focus Groups: A Practical Guide for Applied Research (5th ed.). Sage.


Telephone-assisted In-depth Interview

telephone in-depth interview
phone-based qualitative interview
TIDI
telephone qualitative interview

Developed from qualitative interview traditions; telephone variant documented from the 1980s onward

A telephone-assisted in-depth interview is a qualitative data collection method in which a researcher conducts a lengthy, open-ended, exploratory conversation with a participant via telephone. It preserves the depth and flexibility of face-to-face in-depth interviewing while overcoming geographic and mobility barriers, making it particularly useful when participants are dispersed, housebound, or when travel is impractical.

Sturges, J. E., & Hanrahan, K. J. (2004). Comparing telephone and face-to-face qualitative interviewing: A research note. Qualitative Research, 4(1), 107–118.

Carr, E. C. J., & Worth, A. (2001). The use of the telephone interview for research. NT Research, 6(1), 511–524.

structured-interview semi-structured-interview online-in-depth-interview face-to-face-in-depth-interview remote-in-depth-interview telephone-assisted-survey


Telephone-assisted Research Diary

Telephone-assisted Research Diary Method

phone-prompted diary
telephone diary method
telephone-based research diary
CATI diary

1980s–1990s (telephone-prompted diary variants)

Diary methods: Ronald Burgess and colleagues (field research tradition); telephone-prompted variants emerged from experience sampling and health research

The telephone-assisted research diary combines the longitudinal depth of diary methods with structured telephone prompting. Participants are contacted by researchers at scheduled intervals — daily, weekly, or event-contingent — and guided to reflect on and record recent experiences, behaviours, or feelings. The telephone call functions as both a prompt to ensure timely entries and as a brief interview that deepens the diary record beyond what participants might write unsupported.

Longitudinal qualitative/quantitative data collection

Text, structured ratings, or open-ended responses elicited via telephone call

Burgess, R. G. (1984). In the Field: An Introduction to Field Research. Allen & Unwin.

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

diary-method research-diary experience-sampling-method mobile-experience-sampling telephone-assisted-structured-interview longitudinal-diary-method


telephone semi-structured interview
phone-based semi-structured interview
TASI
telephone qualitative interview

1970s–1980s (widespread adoption in health and social research)

Adapted from face-to-face semi-structured interviewing; telephone use in social research documented from the 1970s onward

A telephone-assisted semi-structured interview is a qualitative data collection technique in which a researcher conducts a guided conversation with a participant over the telephone, using a pre-designed topic guide that balances predetermined questions with freedom to probe and explore. It combines the flexibility of semi-structured interviewing with the geographic reach and logistical convenience of telephone communication, making it widely used in health, social, and organizational research.

Audio-recorded or note-based verbal responses (qualitative text data)

Novick, G. (2008). Is there a bias against telephone interviews in qualitative research? Research in Nursing & Health, 31(4), 391–398.

Britten, N. (1995). Qualitative interviews in medical research. BMJ, 311(6999), 251–253.

semi-structured-interview structured-interview telephone-assisted-survey online-semi-structured-interview face-to-face-semi-structured-interview in-depth-interview


Telephone-assisted Sensor Data Collection

phone-based sensor data collection
telephone-mediated sensor monitoring
mobile phone sensor data collection
TASDC

2000s–2010s (aligned with smartphone proliferation)

Emerging from ubiquitous computing and digital health research communities; no single originator

Telephone-assisted sensor data collection uses participants' mobile phones as sensing platforms to gather continuous or triggered streams of physical and behavioral data — such as movement, location, and ambient sound — without requiring them to attend a lab. A research application installed on the phone captures sensor readings and transmits them to a central server, enabling large-scale, ecologically valid measurement of real-world behavior over days or weeks.

Passive and active data collection via telephone/smartphone sensors

Sensor streams (accelerometer, GPS, microphone, gyroscope, ambient light, proximity)

Lane, N. D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. T. (2010). A survey of mobile phone sensing. IEEE Communications Magazine, 48(9), 140–150.

Harari, G. M., Lane, N. D., Wang, R., Crosier, B. S., Campbell, A. T., & Gosling, S. D. (2016). Using smartphones to collect behavioral data in psychological science: Opportunities, practical considerations, and challenges. Perspectives on Psychological Science, 11(6), 838–854.

sensor-data-collection mobile-sensor-data-collection mobile-experience-sampling ecological-momentary-assessment remote-sensor-data-collection api-based-data-collection


Telephone-assisted Survey

CATI survey
computer-assisted telephone interview
telephone survey
phone survey

1970s (widespread from mid-1970s; Groves & Kahn 1979 seminal text)

Groves & Kahn (foundational comparative study); CATI systems developed by Charles Cannell and colleagues at University of Michigan

A telephone-assisted survey is a structured data-collection method in which a trained interviewer administers a standardised questionnaire to respondents over the telephone, often supported by Computer-Assisted Telephone Interviewing (CATI) software. It combines the efficiency of remote administration with the response-quality advantages of live interviewer guidance, making it widely used in social, public-health, market-research, and political polling contexts.

Quantitative / mixed-mode data collection

Structured or semi-structured responses collected via telephone

Groves, R. M., & Kahn, R. L. (1979). Surveys by telephone: A national comparison with personal interviews. Academic Press.

Lavrakas, P. J. (1993). Telephone survey methods: Sampling, selection, and supervision (2nd ed.). Sage.

survey structured-interview online-survey face-to-face-survey mobile-survey computer-assisted-personal-interview


TABC

Temperament Assessment Battery for Children

William Fullard, Sean McDevitt, William Carey

The TABC is a parent-completed questionnaire assessing infant and toddler temperament characteristics in children aged 3 months to 3 years. Developed by Fullard, McDevitt, and Carey (1984), it measures nine temperament dimensions derived from the New York Longitudinal Study of Thomas and Chess: activity level, rhythmicity, approach/withdrawal, adaptability, intensity of reaction, threshold of responsiveness, mood, distractibility, and persistence. The TABC is widely used in pediatric and developmental psychology research to characterize individual differences in behavioral style and predict developmental trajectories.

Fullard, W., McDevitt, S. C., & Carey, W. B. (1984). Assessing Temperament in One- to Three-Year-Old Children. Journal of Pediatric Psychology, 9(2), 205-217.

Carey, W. B., & McDevitt, S. C. (1978). Revision of the Infant Temperament Questionnaire. Pediatrics, 61(5), 735-739.


Template Matching

Correlation-based matching
Similarity matching

Template matching is a straightforward technique for locating a known pattern (template) within a larger image. By sliding a template image across the target image and computing a similarity measure at each position, template matching identifies locations where the template appears. It is effective for simple object detection when templates are well-defined and appearance variation is limited.

Lewis, J. P. (2004). Fast normalized cross-correlation. Vision Interface, 120–123.

Briechle, K., & Hanebeck, U. D. (2001). Template matching using fast normalized cross correlation. SPIE Proceedings, 4387, 95–102.

harris-corner-detection sift-feature-detection hough-transform contour-analysis blob-detection


Tempo Estimation

tempo detection
BPM estimation
pulse rate detection

Tempo estimation is the task of automatically determining the beats per minute (BPM) or tempo of a musical recording. Introduced by Scheirer (1998), it is fundamental to rhythm analysis, music classification, and synchronization applications. Tempo is one of the most perceptually salient features of music; accurate estimation enables music-aware systems and human-machine interaction. Unlike beat tracking, which produces discrete beat times, tempo estimation yields a single BPM value (or a distribution of likely tempi).

Scheirer, E. D. (1998). Tempo and beat analysis of acoustic musical signals. The Journal of the Acoustical Society of America, 103(1), 588-601.

Davies, M. E., Böck, S., & Flexer, A. (2013). Towards end-of-life music recommendations based on listening and moving. In Proceedings of the International Society for Music Information Retrieval Conference.

Gkiokas, A., Katsouros, V., Pikrakis, A., & Theodoridis, S. (2012). Music tempo estimation and beat tracking by applying source separation and metrical learning. In Proceedings of the International Society for Music Information Retrieval Conference.

beat-tracking music-segmentation pitch-detection-algorithm music-genre-classification music-similarity-measure


Temporal Betweenness Centrality

Temporal Betweenness Centrality (Time-Respecting Path Betweenness)

TBC
time-varying betweenness centrality
dynamic betweenness centrality
time-respecting betweenness

Kim, H. & Anderson, R.; Holme, P. & Saramäki, J.

Temporal Betweenness Centrality (TBC) extends classical betweenness centrality to time-stamped networks by counting how often a node lies on time-respecting shortest paths — paths that traverse edges in chronological order. It identifies nodes that act as temporal brokers, controlling information or resource flow as it evolves over time, rather than in a static snapshot.

Centrality measure for temporal networks

Timestamped edge lists, temporal contact sequences

Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.

Kim, H., & Anderson, R. (2012). Temporal node centrality in complex networks. Physical Review E, 85(2), 026107.

betweenness-centrality temporal-social-network-analysis temporal-closeness-centrality temporal-degree-centrality temporal-network-diffusion-analysis directed-betweenness-centrality


Temporal Closeness Centrality

Temporal Closeness Centrality in Time-Varying Networks

time-varying closeness centrality
dynamic closeness centrality
TCC
temporal reachability-based centrality

Temporal closeness centrality extends the classical closeness measure to time-varying networks by replacing static shortest paths with time-respecting (foremost) paths. It quantifies how quickly a node can reach all other nodes when interactions occur at specific moments in time, giving a more realistic picture of information flow, disease spread, and influence in dynamic systems.

Timestamped edge lists (temporal networks)

Pan, R. K., & Saramaki, J. (2011). Path lengths, correlations, and centrality in temporal networks. Physical Review E, 84(1), 016105.

Holme, P., & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.

closeness-centrality temporal-betweenness-centrality temporal-degree-centrality temporal-social-network-analysis betweenness-centrality temporal-pagerank


Temporal Community Detection

Temporal Community Detection in Dynamic Networks

dynamic community detection
time-varying community detection
evolutionary community detection
longitudinal community detection

Temporal community detection identifies cohesive groups (communities) in networks whose structure changes over time. By treating each time snapshot as a network layer and coupling consecutive layers, it reveals how communities form, merge, split, grow, or dissolve — turning a sequence of static snapshots into a continuous narrative of group evolution.

Time-stamped or snapshot-based network data

Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878.

Rossetti, G., & Cazabet, R. (2018). Community discovery in dynamic networks: A survey. ACM Computing Surveys, 51(2), 1–37.

modularity-analysis temporal-network-analysis multiplex-network-analysis social-network-analysis weighted-community-detection directed-community-detection


Temporal Degree Centrality

Temporal Degree Centrality in Time-Varying Networks

time-varying degree centrality
dynamic degree centrality
temporal node degree
TDC

Holme, P.; Saramaki, J.; Kim, H.; Anderson, R.

Temporal degree centrality extends the classic degree centrality to time-varying networks by counting how many distinct contacts a node accumulates over time. Rather than collapsing a dynamic network into a single static graph, it preserves the temporal order of edges, yielding a more faithful measure of a node's activity and reachability across the observation window.

Temporal edge lists or contact sequences with timestamps

Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.

Kim, H. & Anderson, R. (2012). Temporal node centrality in complex networks. Physical Review E, 85(2), 026107.

degree-centrality temporal-betweenness-centrality temporal-closeness-centrality temporal-social-network-analysis temporal-eigenvector-centrality temporal-pagerank


Temporal Dominance of Sensations

Temporal Dominance of Sensations (TDS) is a time-dynamic sensory evaluation method developed by Pineau and colleagues in 2009 that tracks which sensory attribute is perceived as dominant at each moment during the consumption of a food product. Unlike static descriptive methods, TDS captures the dynamic evolution of flavor, aroma, and texture sensations from the initial bite to swallowing, providing insight into the temporal structure of the eating experience.

Pineau, N., Schlich, P., Cordelle, S., Mathonniere, C., Issanchou, S., Imbert, A., ... & Köster, E. P. (2009). Temporal Dominance of Sensations: Construction of the TDS curves and comparison with time-intensity. Food Quality and Preference, 20(6), 450-455.

Lenfant, F., Loret, C., Pineau, N., Hartmann, C., & Martin, N. (2009). Perception of oral food breakdown and texture changes during eating. Physiology & Behavior, 98(5), 588-594.


Temporal Eigenvector Centrality

Temporal Eigenvector Centrality (Dynamic Eigenvector-Based Node Importance in Time-Varying Networks)

dynamic eigenvector centrality
time-varying eigenvector centrality
TEC
temporal communicability centrality

Grindrod, P.; Higham, D. J.; Taylor, D. et al.

Temporal eigenvector centrality extends the classical eigenvector centrality to networks that change over time. By accounting for the ordering and timing of connections, it identifies nodes that are influential not merely because of many simultaneous connections, but because they sit at the crossroads of sequentially important pathways across multiple time slices of the network.

Time-stamped edge lists or ordered adjacency matrices

Grindrod, P., Parsons, M. C., Higham, D. J., & Estrada, E. (2011). Communicability across evolving networks. Physical Review E, 83(4), 046120.

Taylor, D., Myers, S. A., Clauset, A., Porter, M. A., & Mucha, P. J. (2017). Eigenvector-based centrality measures for temporal networks. Multiscale Modeling and Simulation, 15(1), 537-574.

eigenvector-centrality temporal-social-network-analysis temporal-degree-centrality temporal-betweenness-centrality temporal-pagerank multilayer-eigenvector-centrality


Temporal Expression Extraction

Temporal Expression Extraction and Normalisation (TIMEX)

TIMEX
temporal tagging
TIMEX3 extraction
Zamansal İfade Çıkarma (TIMEX)

Temporal expression extraction is a natural-language-processing task that detects dates, times, durations, and frequencies in text and normalises them to the TimeML/TIMEX3 standard. Building on the TempEval shared task introduced by Verhagen et al. (2007), it turns time references scattered through free text into structured, machine-readable values that support event timelines and chronological analysis.

Dates, times, durations, and recurring frequencies

Output: Normalised temporal expressions for timelines and chronological analysis

Verhagen, M. et al. (2007). SemEval-2007 Task 15: TempEval Temporal Relation Identification.

Strötgen, J. & Gertz, M. (2013). Multilingual and Cross-Domain Temporal Tagging. Language Resources and Evaluation (LRE).


Temporal Fusion Transformer

Temporal Fusion Transformer for Interpretable Multi-Horizon Time Series Forecasting

interpretable multi-horizon forecasting transformer

The Temporal Fusion Transformer (TFT), introduced by Lim, Arık, Loeff and Pfister in 2021, is an interpretable deep learning architecture for multi-horizon time series forecasting. It combines variable selection, gating, multi-horizon attention and quantile outputs, processing static, past and known-future inputs together to produce multi-step forecasts.

Attention-based deep learning forecasting architecture

Lim, B., Arık, S. Ö., Loeff, N. & Pfister, T. (2021). Temporal Fusion Transformers for Interpretable Multi-Horizon Time Series Forecasting. International Journal of Forecasting, 37(4), 1748–1764.

Lim, B. & Zohren, S. (2021). Time-Series Forecasting with Deep Learning: A Survey. Philosophical Transactions of the Royal Society A, 379(2194), 20200209.

informer deepar nhits patchtst arima random-forest


Temporal Knowledge Graph Analysis

Temporal Knowledge Graph Analysis (TKG Analysis)

TKG analysis
temporal KG analysis
dynamic knowledge graph analysis
time-aware knowledge graph analysis

Trivedi, R. et al.; Dasgupta, S. S. et al.

Temporal Knowledge Graph Analysis extends standard knowledge graph methods to data where facts and relationships carry timestamps or validity intervals. It enables reasoning about how entities and relations evolve over time, supporting tasks such as link prediction for future facts, temporal relation classification, and event forecasting in dynamic relational data.

Timestamped relational triples (subject, predicate, object, time)

Trivedi, R., Dai, H., Wang, Y., & Song, L. (2017). Know-Evolve: Deep temporal reasoning for dynamic knowledge graphs. Proceedings of the 34th International Conference on Machine Learning (ICML), pp. 3462–3471.

Dasgupta, S. S., Ray, S. N., & Talukdar, P. (2018). HyTE: Hyperplane-based temporally aware knowledge graph embedding. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 2001–2011.

knowledge-graph-analysis temporal-network-diffusion-analysis temporal-social-network-analysis temporal-community-detection temporal-exponential-random-graph-model multilayer-knowledge-graph-analysis


Temporal Modularity Analysis

Temporal Modularity Analysis (Dynamic Community Detection via Modularity Optimization)

dynamic modularity
time-varying modularity
longitudinal community detection
temporal community structure analysis

Temporal modularity analysis extends standard modularity-based community detection to time-varying networks by treating each time slice as a network layer and coupling adjacent layers with inter-temporal links. This allows researchers to identify how communities form, persist, merge, split, and dissolve over time in dynamic relational data.

Community detection (temporal extension of modularity optimization)

Time-stamped or time-sliced network data (adjacency matrices at multiple time points)

Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876-878.

Holme, P., & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97-125.

modularity-analysis temporal-community-detection temporal-social-network-analysis weighted-modularity-analysis multilayer-modularity-analysis dynamic-community-detection


Temporal Multiplex Network Analysis

Temporal Multiplex Network Analysis (Time-varying Multi-layer Network Analysis)

TMNA
time-varying multiplex network analysis
dynamic multiplex network analysis
temporal multilayer network analysis

Kivela, M.; Holme, P.; Saramaki, J. (among foundational contributors)

Temporal multiplex network analysis studies relational systems in which actors are connected by multiple distinct types of relationships that all evolve over time. By simultaneously tracking layer heterogeneity and temporal dynamics, the method reveals how different interaction channels co-evolve, which actors hold persistent cross-layer influence, and how structural changes propagate across relationship types and time periods.

Time-stamped multiplex edge lists; longitudinal relational data across multiple relationship types

Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271.

Holme, P., & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.

multiplex-network-analysis temporal-network-analysis multilayer-network-analysis temporal-community-detection dynamic-community-detection social-network-analysis


Temporal Network Analysis

Temporal Network Analysis (Dynamic Networks)

Zamansal Ağ Analizi (Temporal / Dynamic Networks)

Holme & Saramäki (2012) — seminal framework

Temporal network analysis, formalised by Holme and Saramäki in their landmark 2012 Physics Reports survey, is the study of networks in which edges appear and disappear over time. Rather than collapsing all contacts into a single static graph, the approach preserves the precise timing of interactions — whether as contact sequences, time-stamped event lists, or windowed snapshots — and uses that timing to track how influence, disease, or information can actually propagate through the system.

Time-stamped edge lists or windowed snapshots

Output: Temporal centrality, reachability, event-driven diffusion metrics

Holme, P. & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97-125.

Masuda, N. & Lambiotte, R. (2016). A Guide to Temporal Networks. World Scientific.

multilayer-network-analysis centrality-analysis community-detection social-network-analysis time-series-analysis


Temporal Network Diffusion Analysis

Temporal Network Diffusion Analysis (Time-Varying Spreading Processes on Dynamic Networks)

TNDA
dynamic network diffusion
time-varying network spreading
diffusion on temporal networks

Temporal Network Diffusion Analysis studies how information, disease, influence, or other contagions spread through networks whose structure changes over time. By modeling edges as time-stamped contacts rather than static links, it captures the critical role of timing and ordering in determining which nodes get reached, how fast, and through which pathways — producing conclusions that static network models systematically miss.

Time-stamped edge lists, event sequences, contact networks

Holme, P. & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.

network-diffusion-analysis temporal-social-network-analysis temporal-community-detection temporal-betweenness-centrality multiplex-network-analysis exponential-random-graph-model


Temporal PageRank

Temporal PageRank (Time-Aware Node Importance Ranking in Temporal Networks)

TPR
time-aware PageRank
streaming PageRank
dynamic PageRank

Temporal PageRank extends the classic PageRank algorithm to time-evolving networks by incorporating the recency and ordering of interactions. Edges are weighted by a decay function so that recent contacts contribute more to a node's score than old ones. The result is a dynamic importance ranking that captures who is influential right now, rather than over the entire history of the network.

Centrality / ranking algorithm for temporal networks

Timestamped edge lists (temporal graphs)

Rozenshtein, P. & Gionis, A. (2016). Temporal PageRank. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Part II, LNCS 9852, pp. 674–689. Springer.

Lerman, K. & Ghosh, R. (2010). Information Contagion: An Empirical Study of the Spread of News on Digg and Twitter Social Networks. In Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media (ICWSM), pp. 90–97. AAAI Press.

temporal-social-network-analysis temporal-betweenness-centrality temporal-eigenvector-centrality directed-pagerank network-diffusion-analysis temporal-community-detection


Temporal Social Network Analysis

Temporal Social Network Analysis (Longitudinal and Time-Varying Network Analysis)

TSNA
longitudinal social network analysis
time-varying network analysis
dynamic SNA

Temporal Social Network Analysis (TSNA) extends classic social network analysis by treating networks as time-varying structures. Rather than aggregating all ties into a single static snapshot, TSNA tracks when ties form, persist, and dissolve, enabling researchers to study how social structures evolve and how dynamic connectivity shapes diffusion, influence, and inequality over time.

Time-stamped relational / panel network data

Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.

Moody, J., McFarland, D., & Bender-deMoll, S. (2005). Dynamic network visualization. American Journal of Sociology, 110(4), 1206–1241.

social-network-analysis dynamic-social-network-analysis multiplex-network-analysis exponential-random-graph-model temporal-community-detection network-diffusion-analysis


Temporal Stochastic Block Model

Temporal Stochastic Block Model (Dynamic Community Detection via SBM)

TSBM
dynamic stochastic block model
time-varying SBM
evolving block model

Xu, K. S. & Hero, A. O.; Matias, C. & Miele, V.

The Temporal Stochastic Block Model (TSBM) extends the classic Stochastic Block Model to sequences of network snapshots, jointly inferring latent community memberships and how those memberships evolve across time. It combines a generative edge-probability model with a Markov process over block assignments, enabling principled statistical detection of community structure that changes over time.

Time-stamped edge lists or adjacency matrices across discrete time steps

Matias, C. & Miele, V. (2017). Statistical clustering of temporal networks through a dynamic stochastic block model. Journal of the Royal Statistical Society: Series B, 79(4), 1119–1141.

Xu, K. S. & Hero, A. O. (2014). Dynamic stochastic blockmodels for time-evolving social networks. IEEE Journal of Selected Topics in Signal Processing, 8(4), 552–562.

stochastic-block-model temporal-community-detection temporal-modularity-analysis exponential-random-graph-model temporal-exponential-random-graph-model multilayer-stochastic-block-model


Temporal Two-Mode Network Analysis

Temporal Two-Mode (Bipartite) Network Analysis

longitudinal affiliation network analysis

Borgatti, S. P. & Everett, M. G. (two-mode foundations); extended to temporal setting by multiple authors

Temporal two-mode network analysis tracks relationships between two distinct classes of nodes — such as authors and publications, or actors and events — across multiple time points. By combining bipartite structure with longitudinal observation, it reveals how affiliation patterns, collaborations, and community memberships form, evolve, and dissolve over time.

Longitudinal bipartite (two-mode) relational data

Borgatti, S. P., & Everett, M. G. (1997). Network analysis of 2-mode data. Social Networks, 19(3), 243–269.

Latapy, M., Magnien, C., & Del Vecchio, N. (2008). Basic notions for the analysis of large two-mode networks. Social Networks, 30(1), 31–48.

two-mode-network-analysis temporal-network-analysis social-network-analysis modularity-analysis temporal-community-detection multilayer-network-analysis


RDC/TMD

Schiffman, Ohrbach, and International Consortium

The Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD) is a comprehensive, evidence-based diagnostic system for identifying and classifying temporomandibular disorders (TMD)—a group of painful and functional conditions affecting the jaw joint, muscles of mastication, and related structures. Originally developed in 1992 by Schiffman and colleagues and updated to the Diagnostic Criteria for TMD (DC/TMD) in 2014, the RDC/TMD is the international gold standard for TMD diagnosis in research and clinical practice. It combines structured patient history, pain questionnaires, and standardized clinical examination to reliably diagnose muscle disorders, intra-articular disorders, and headache associated with TMD.

Structured diagnostic interview and clinical examination

Schiffman, E., Ohrbach, R., Truelove, E., Look, J., Anderson, G., Goulet, J.-P., & Drangsholt, M. (2014). Diagnostic criteria for temporomandibular disorders (DC/TMD) for clinical and research applications: Recommendations of the International RDC/TMD Consortium Network and Orofacial Pain Special Interest Group. Journal of Oral & Facial Pain and Headache, 28(1), 6-27.