Sensitivity Analysis-Based Purposive Sampling

purposive sampling with sensitivity checks

Rooted in Patton's purposive sampling typology; sensitivity analysis practices formalized in research synthesis literature

Sensitivity analysis-based purposive sampling extends conventional purposive sampling by systematically testing whether key findings or case-selection decisions change when the inclusion criteria, selection logic, or boundary conditions are altered. It applies the logic of sensitivity analysis — standard in quantitative research and systematic reviews — to qualitative case selection, giving researchers explicit evidence of how robust their purposive choices are to plausible alternative selection rules.

Purposive qualitative sampling with robustness verification

Qualitative or mixed-methods data; case selection records

Patton, M. Q. (2015). Qualitative Research and Evaluation Methods (4th ed.). Sage Publications.

Teddlie, C., & Yu, F. (2007). Mixed methods sampling: A typology with examples. Journal of Mixed Methods Research, 1(1), 77–100.

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


Sensitivity Analysis for Causal Inference in Education Research

Rosenbaum sensitivity analysis
hidden-bias sensitivity analysis
causal sensitivity analysis
SA for causal education studies

Paul R. Rosenbaum (formal framework); applied in education research by Briggs and others

Sensitivity analysis for causality in education research tests how robust a quasi-experimental finding is to unmeasured confounding. Rather than assuming all bias has been removed, it quantifies how large a hidden bias would need to be to overturn a causal conclusion — a critical safeguard when randomisation is impossible, which is common in educational settings.

Observational panel, matched samples, quasi-experimental data

Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer.

Briggs, D. C. (2008). Using meta-analytic results to inform causal claims about the effects of educational interventions. Journal of Research on Educational Effectiveness, 1(3), 148-175.

propensity-score-matching difference-in-differences regression-discontinuity instrumental-variables interrupted-time-series matching-methods


Sensitivity Analysis for Causality

Sensitivity Analysis for Hidden Bias in Causal Inference

sensitivity analysis
hidden-bias sensitivity analysis
Rosenbaum sensitivity analysis
omitted-variable sensitivity

Paul R. Rosenbaum (hidden-bias framework); extended by Cinelli & Hazlett (omitted-variable approach)

Sensitivity analysis for causality assesses how robust a causal conclusion is to unobserved confounding. Rather than assuming all confounders are controlled, it asks: how strong would an unmeasured variable need to be to overturn the estimated effect? It is an indispensable robustness check after any quasi-experimental or observational causal analysis.

Observational or quasi-experimental panel / cross-sectional data

Cinelli, C., & Hazlett, C. (2020). Making sense of sensitivity: Extending omitted variable bias. Journal of the Royal Statistical Society: Series B, 82(1), 39-67.

propensity-score-matching difference-in-differences placebo-test instrumental-variables regression-discontinuity-design doubly-robust-estimation


Sensitivity analysis-integrated design of experiments

factor screening with sensitivity analysis

1990s–2000s (formal integration emerged in simulation and engineering optimization literature)

Integrated approach drawing on Saltelli et al. (sensitivity analysis) and Montgomery (DoE); no single originator

Sensitivity Analysis-Integrated Design of Experiments (SA-DoE) combines systematic experimental planning with formal sensitivity analysis to identify which input factors most strongly influence a response, then efficiently characterises those factors' effects. By embedding sensitivity screening into the DoE workflow, experimenters avoid wasting trials on inert variables and focus resources on the factors that truly drive system behaviour — making it especially valuable in simulation studies, product engineering, and complex process optimisation.

Quantitative simulation outputs or physical experimental measurements

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

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

full-factorial-design fractional-factorial-design response-surface-methodology variance-based-sensitivity-analysis latin-hypercube-sampling taguchi-methods


Sensitivity Analysis-Integrated Full Factorial Design

full factorial design with sensitivity analysis

Rooted in factorial experimentation (Fisher, 1935) combined with variance-based sensitivity analysis formalized by Saltelli and colleagues (1990s–2000s)

Sensitivity analysis-integrated full factorial design combines exhaustive factorial experimentation — where every combination of factor levels is tested — with systematic sensitivity analysis to quantify how much each input factor drives variation in the output response. This hybrid approach provides both reliable effect estimates and a ranked picture of factor importance, guiding engineers and scientists toward the levers that truly matter for system performance.

Experimental design with factor importance ranking

Continuous and categorical factor settings; measured response outputs

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

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


Sensitivity analysis-integrated response surface methodology

Sensitivity Analysis-Integrated Response Surface Methodology

response surface methodology with factor screening

Box & Wilson (RSM, 1951); Saltelli et al. (global SA framework, 1990s–2000s)

Sensitivity analysis-integrated RSM couples a structured experimental design with a formal sensitivity analysis of the fitted response surface model. After estimating a polynomial surrogate from designed experiments, global or local sensitivity indices are computed to quantify each input factor's relative contribution to output variability. This allows practitioners to identify which factors truly drive the response before committing to full optimization, reducing cost and improving the reliability of the final optimum.

Continuous experimental measurements; fitted response surface model

Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley.

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


Sensitivity Analysis-integrated Taguchi Method

Taguchi method with sensitivity analysis

1950s–1980s (Taguchi DOE); integrated workflow formalized from 1990s onward

Genichi Taguchi (Taguchi method); Andrea Saltelli et al. (global sensitivity analysis)

The sensitivity analysis-integrated Taguchi method augments the classical Taguchi robust design workflow with a systematic sensitivity analysis step that quantifies how much each control factor and noise factor contributes to response variability. By combining Taguchi orthogonal arrays with variance-based or ANOVA-based sensitivity indices, engineers can both optimize process settings and rank factors by their influence on output uncertainty, yielding more transparent and defensible engineering decisions.

Integrated experimental design and sensitivity analysis workflow

Continuous experimental response data from designed experiments (orthogonal arrays)

Taguchi, G. (1987). System of Experimental Design: Engineering Methods to Optimize Quality and Minimize Costs (Vols. 1–2). UNIPUB/Kraus International Publications.

taguchi-method design-of-experiments fractional-factorial-design robust-taguchi-method response-surface-methodology optimization-assisted-taguchi-method


Sensitivity Analysis for Unmeasured Confounding

Sensitivity Analysis for Hidden Bias in Observational Studies (Rosenbaum Bounds / E-value)

Duyarlılık Analizi — Gizli Yanlılık (Rosenbaum / E-value)

Paul R. Rosenbaum (bounds); Tyler J. VanderWeele & Peng Ding (E-value)

Sensitivity analysis for hidden bias is a family of methods that quantify how strongly an unmeasured confounder would have to operate before it could overturn a causal conclusion drawn from observational data. It was crystallised by Paul Rosenbaum's sensitivity bounds (2002) and extended by VanderWeele and Ding's E-value (2017).

Sensitivity analysis for causal inference

Rosenbaum sensitivity parameter Γ; E-value bound

Robustness of a causal estimate to hidden bias

VanderWeele, T. J. & Ding, P. (2017). Sensitivity Analysis in Observational Research: Introducing the E-Value. Annals of Internal Medicine, 167(4), 268-274.

propensity-score-matching iv-2sls frontdoor-adjustment placebo-tests-causal local-average-treatment-effect


Sensitivity Analysis with Box-Behnken Design

Sensitivity Analysis Integrated with Box-Behnken Design

1960 (BBD); sensitivity integration formalized 2000s–2010s

Box & Behnken (design, 1960); Saltelli et al. (sensitivity framework, 2000s)

Sensitivity analysis with Box-Behnken design combines a resource-efficient three-level response surface experiment with a systematic assessment of how much each input factor drives variation in the response. The Box-Behnken design (BBD) fits a second-order polynomial model using fewer runs than a full central composite design, while the overlaid sensitivity analysis quantifies each factor's relative influence — helping engineers and researchers distinguish the vital few drivers from the inconsequential many.

Integrated experimental-design and sensitivity-analysis technique

Continuous factor levels; quantitative response variables

Box, G. E. P., & Behnken, D. W. (1960). Some new three level designs for the study of quantitative variables. Technometrics, 2(4), 455–475.


Sensitivity analysis with central composite design

Sensitivity Analysis with Central Composite Design

SA-CCD
CCD sensitivity analysis
RSM sensitivity analysis
response surface sensitivity study

1951 (CCD); SA integration throughout 1970s–2000s

G. E. P. Box and K. B. Wilson (CCD); sensitivity analysis formalised within RSM by Montgomery and subsequent practitioners

Sensitivity analysis with Central Composite Design (CCD) combines a structured, space-filling experimental layout with a systematic examination of how much each input factor drives changes in the response. CCD supports estimation of a full quadratic response surface model; sensitivity analysis then interrogates that model to rank factors by influence, identify interactions, and map the performance landscape — guiding engineers and researchers toward robust operating conditions and efficient optimisation.

Quantitative experimental design with post-hoc sensitivity assessment

Continuous numeric responses measured at designed experimental runs

Box, G. E. P., & Wilson, K. B. (1951). On the Experimental Attainment of Optimum Conditions. Journal of the Royal Statistical Society: Series B, 13(1), 1–45.

central-composite-design response-surface-methodology box-behnken-design factorial-design one-factor-at-a-time variance-based-sensitivity-analysis


Sensitivity Analysis with Control Chart

Sensitivity Analysis Integrated with Statistical Process Control Charts

SA-SPC integration
control chart sensitivity analysis
SPC sensitivity assessment
sensitivity-enhanced control charting

Integration practice documented from the 1990s onward

Rooted in Shewhart (control charts, 1920s) and Saltelli et al. (global sensitivity analysis, 1990s–2000s); integration practice developed in quality engineering literature

Sensitivity analysis integrated with control charting evaluates how uncertain or varying inputs — such as sample size, subgroup frequency, distribution assumptions, or measurement error — affect the detection performance of a statistical process control chart. By quantifying which parameters most strongly influence chart metrics such as the average run length (ARL) or false alarm rate, engineers can design more robust monitoring schemes and understand where control chart conclusions are fragile.

Continuous or attribute process measurement data; model parameter distributions

Montgomery, D. C. (2020). Introduction to Statistical Quality Control (8th ed.). Wiley.


Sensitivity Analysis Integrated with Event Tree Analysis

probabilistic sensitivity analysis with ETA

Combination formalized in risk and reliability engineering from the 1990s onward

Sensitivity analysis: Saltelli et al. (1990s–2000s); Event tree analysis: Watson (1961, WASH-1400 formalization 1975)

Sensitivity analysis with event tree analysis (SA-ETA) is a quantitative risk assessment approach that systematically varies the input probabilities of an event tree model to determine which branch probabilities or initiating event frequencies most strongly influence the calculated probability of undesired outcomes. It extends classical event tree analysis by ranking the uncertainty contributions of individual inputs, thereby guiding risk-reduction efforts toward the parameters that matter most.

Hybrid quantitative risk analysis method

Probability estimates for initiating events and branch probabilities; numerical model outputs


Sensitivity Analysis-Integrated Failure Mode and Effects Analysis

SA-FMEA
FMEA with sensitivity analysis
sensitivity-enhanced FMEA
SA-integrated FMEA

Grumman Aircraft (FMEA origin, 1950s); sensitivity analysis integration developed by reliability engineering community

Sensitivity analysis with failure mode and effects analysis (SA-FMEA) combines classical FMEA risk scoring with systematic sensitivity analysis to determine which input parameters — severity, occurrence, and detectability ratings — drive the Risk Priority Number (RPN) most strongly. This integration helps teams focus improvement resources where they matter most, revealing how uncertain or variable scoring assumptions propagate into final risk rankings.

Hybrid risk analysis and sensitivity technique

Expert judgment scores, failure rate data, design parameters

Stamatis, D. H. (2003). Failure Mode and Effect Analysis: FMEA from Theory to Execution (2nd ed.). ASQ Quality Press.


Sensitivity analysis with fault tree analysis

Sensitivity Analysis Integrated with Fault Tree Analysis

probabilistic sensitivity analysis in fault trees

1961 (FTA); sensitivity integration formalised 1970s–1980s

H. A. Watson (Bell Labs, FTA, 1961); integrated sensitivity extensions developed through nuclear safety research (Vesely et al., 1981)

Sensitivity analysis integrated with fault tree analysis (FTA-SA) is a quantitative reliability engineering method that first models how system failure can occur through a hierarchical Boolean logic tree, then systematically varies the probability of each basic event to determine which components drive overall system failure risk most strongly. Widely used in nuclear, aerospace, chemical, and safety-critical system design, it prioritises mitigation effort and reveals which uncertainty in input data matters most.

Quantitative reliability and risk analysis technique

Component failure probability data, event probabilities, Boolean logic tree structures

Vesely, W. E., Goldberg, F. F., Roberts, N. H., & Haasl, D. F. (1981). Fault Tree Handbook. US Nuclear Regulatory Commission, NUREG-0492.

fault-tree-analysis event-tree-analysis failure-mode-effects-analysis monte-carlo-simulation probabilistic-risk-assessment importance-measures-analysis


Sensitivity Analysis with Fractional Factorial Design

Sensitivity Analysis Using Fractional Factorial Experimental Design

fractional factorial sensitivity screening

1935 (factorial design); 1990s–2000s (systematic SA integration)

R. A. Fisher (factorial design foundations); combined with sensitivity analysis frameworks developed by A. Saltelli and colleagues

Sensitivity analysis with fractional factorial design (SA-FFD) is an experimental screening method that uses a carefully chosen fraction of all possible factor combinations to identify which input variables most strongly influence a system's output. By running only 2^(k-p) experiments instead of a full 2^k factorial, it makes sensitivity ranking feasible when many factors are present. The approach is widely used in engineering, product development, simulation modeling, and process optimization.

Quantitative experimental screening method

Continuous or categorical input factors; continuous response outputs

Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley-Interscience.

full-factorial-design plackett-burman-design response-surface-methodology taguchi-method sobol-sensitivity-analysis two-level-factorial-design


Sensitivity Analysis with Process Capability Analysis

Sensitivity Analysis Combined with Process Capability Analysis

Sensitivity-Capability Analysis
PCA with Sensitivity Analysis
Process Capability Sensitivity Study
Cp/Cpk Sensitivity Analysis

1986–2000s (Cp/Cpk indices from Kane 1986; integration formalized in Six Sigma era)

Synthesized from work by V. E. Kane (process capability indices) and A. Saltelli (sensitivity analysis); integrated in Six Sigma and quality engineering practice

Sensitivity analysis with process capability analysis is a quantitative engineering method that combines the measurement of process performance — via capability indices such as Cp and Cpk — with systematic variation of input factors to identify which factors most strongly influence whether a process meets its specification limits. It is widely used in Six Sigma projects, manufacturing quality improvement, and Design of Experiments contexts to prioritize where corrective action will yield the greatest gain in process capability.

Continuous measurement data from process outputs and controllable input factors

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

design-of-experiments response-surface-methodology statistical-process-control taguchi-methods monte-carlo-simulation failure-mode-effects-analysis


Sensitivity Analysis with Quality Function Deployment

Sensitivity Analysis Integrated with Quality Function Deployment

SA-QFD
QFD sensitivity analysis
robust QFD
House of Quality sensitivity analysis

Yoji Akao (QFD foundation); sensitivity extension attributed to multiple QFD researchers (1990s–2000s)

Sensitivity analysis integrated with Quality Function Deployment (QFD) tests how stable the prioritization of engineering characteristics remains when customer requirement weights or relationship matrix scores are varied. By systematically perturbing the inputs of the House of Quality, teams identify which design parameters are truly critical and which rankings would flip under different assumptions — turning QFD from a one-shot prioritization tool into a robust decision framework.

Integrated engineering design and decision analysis technique

Customer requirement ratings, relationship matrix weights, engineering characteristic scores

Fung, R. Y. K., Tang, J., Tu, Y., & Wang, D. (2006). Product design resources optimization using a non-linear fuzzy quality function deployment model. International Journal of Production Research, 44(12), 2483–2504.

Akao, Y. (Ed.). (1990). Quality Function Deployment: Integrating Customer Requirements into Product Design. Productivity Press.

quality-function-deployment house-of-quality sensitivity-analysis analytic-hierarchy-process failure-mode-effects-analysis robust-design


Sensitivity Analysis with Reliability Analysis

Sensitivity Analysis Integrated with Reliability Analysis

SA-RA
reliability sensitivity analysis
importance measures in reliability
reliability-based sensitivity analysis

1969 (importance measures); 2000s (global SA integration)

Birnbaum (importance measures, 1969); Saltelli et al. (global SA formalization, 2000s)

Sensitivity analysis integrated with reliability analysis is a quantitative engineering method that determines how uncertainty or variation in each system input — such as component failure rates, material properties, or load distributions — propagates into overall system reliability. By computing importance measures for every uncertain parameter, analysts can rank components and assumptions by their influence on system dependability, focusing improvement efforts where they matter most.

Quantitative integrated engineering method

Numerical / probabilistic (failure rates, probability distributions, component data)

Borgonovo, E., & Apostolakis, G. E. (2001). A new importance measure for risk-informed decision making. Reliability Engineering and System Safety, 72(2), 193–212.


Sensitivity analysis with root cause analysis

Sensitivity Analysis Integrated with Root Cause Analysis

parameter sensitivity with failure analysis

1990s–2000s (formalized integration in reliability and quality engineering literature)

Integrated practice drawing on sensitivity analysis (Saltelli et al.) and root cause analysis (Ishikawa, Kepner-Tregoe)

Sensitivity Analysis with Root Cause Analysis (SA-RCA) is an integrated engineering method that first quantifies how much each input parameter or process variable drives variability in a system output, then applies structured root cause analysis to the most influential factors to identify and eliminate the underlying failure mechanisms. The combination transforms numerical rankings of influence into actionable diagnoses, making it particularly effective in quality engineering, reliability analysis, and process improvement contexts.

Integrated diagnostic and optimization method

Quantitative process/system data, model outputs, failure records

Andersen, B., & Fagerhaug, T. (2006). Root Cause Analysis: Simplified Tools and Techniques (2nd ed.). ASQ Quality Press.

failure-mode-and-effects-analysis fault-tree-analysis design-of-experiments monte-carlo-simulation pareto-analysis fishbone-diagram


Sensitivity Analysis with Six Sigma DMAIC

Sensitivity Analysis Integrated with Six Sigma DMAIC

SA-DMAIC
DMAIC sensitivity analysis
sensitivity-informed Six Sigma
Six Sigma sensitivity integration

Integration of Six Sigma DMAIC (Motorola / Mikel Harry, 1980s–2000) with sensitivity analysis techniques (Saltelli et al., 1990s–2000s)

Sensitivity analysis integrated with Six Sigma DMAIC augments the classic Define-Measure-Analyze-Improve-Control cycle with formal quantification of how much each input variable contributes to output variation. By embedding local or global sensitivity indices inside the Analyze phase, practitioners move beyond correlation screening to rigorously rank which process factors drive defect rates, guiding improvement resources to where they matter most.

Hybrid process-improvement and uncertainty-quantification pipeline

Continuous process measurements, simulation outputs, designed experiment data

Harry, M., & Schroeder, R. (2000). Six Sigma: The Breakthrough Management Strategy Revolutionizing the World's Top Corporations. Doubleday.


SENSITIVITY-ANALYSIS

SENSITIVITY-ANALYSIS (Sensitivity Analysis — Systematic assessment of output variation w.r.t. input perturbations) is a ranking multi-criteria decision-making (MCDM) method introduced by Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. in 2004. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Robustness wrapper — parameter / weight perturbation sensitivity indices

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


Sensitivity and Specificity

Sensitivity and Specificity in Diagnostic Testing and Binary Classification

diagnostic accuracy
true positive rate
true negative rate
receiver operating characteristic

Multiple sources in medical diagnosis and signal detection

Sensitivity and specificity are fundamental metrics of diagnostic test accuracy. Sensitivity is the probability that a test correctly identifies a person with the disease (true positive rate: TP / (TP + FN)). Specificity is the probability that a test correctly identifies a person without the disease (true negative rate: TN / (TN + FP)). Every test involves a trade-off: increasing sensitivity (catching all sick people) often reduces specificity (more false alarms). Choice of test threshold depends on the clinical context: screening for serious diseases favors sensitivity; confirming a diagnosis favors specificity.

Altman, D. G., & Bland, J. M. (1994). Diagnostic tests 1: Sensitivity and specificity. BMJ, 308(6943), 1552.

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

Metz, C. E. (1978). Basic principles of ROC analysis. Seminars in Nuclear Medicine, 8(4), 283–298.

type-i-type-ii-error p-value-significance effect-size null-hypothesis


Sensor Data Collection

sensor measurement
instrumented data collection
physical sensor logging
IoT data collection

1990s–2000s (widespread deployment with IoT ~2000s)

Multidisciplinary; sensor networks formalized in engineering and computer science from the 1990s onward

Sensor data collection uses physical or digital instruments to automatically capture quantitative measurements from the environment, human bodies, or machines over time. Common sensors measure temperature, motion, heart rate, location, light, sound, or chemical properties. Because the recording is automated and continuous, the method can produce high-frequency datasets with minimal researcher burden, making it central to IoT, environmental monitoring, wearable research, and behavioral studies.

Quantitative / mixed data collection technique

Continuous or event-triggered numeric readings (temperature, acceleration, biometric, location, etc.)

Chong, C.-Y., & Kumar, S. P. (2003). Sensor networks: Evolution, opportunities, and challenges. Proceedings of the IEEE, 91(8), 1247–1256.

mobile-experience-sampling ecological-momentary-assessment api-based-data-collection longitudinal-sensor-data-collection web-scraping field-notes


Sensor Fusion

Multisensor Data Fusion
Multi-Sensor Integration
Information Fusion
Sensör Füzyonu

Sensor fusion is a computational process that combines data from multiple heterogeneous sensors to produce an estimate of the environment that is more accurate, complete, and reliable than any single source alone. Systematized as a formal field by Khaleghi, Khamis, Karray, and Razavi in their 2013 state-of-the-art review in Information Fusion, the discipline addresses imperfections such as noise, incompleteness, temporal misalignment, and conflicting readings that arise whenever multiple sensing modalities operate in parallel.

Multi-source information integration pipeline

Imperfect, conflicting, and uncertain sensor data

Khaleghi, B., Khamis, A., Karray, F. O., & Razavi, S. N. (2013). Multisensor data fusion: A review of the state-of-the-art. Information Fusion, 14(1), 28–44.


Sentence Embeddings

Sentence Embeddings (Dense Vector Representations of Sentences)

sentence vectors
sentence representations
SBERT
semantic sentence encoding

Kiros et al. (Skip-Thought, 2015); Reimers & Gurevych (Sentence-BERT, 2019)

Sentence Embeddings convert a sentence or short text into a single fixed-length dense vector that captures its semantic meaning. These vectors allow downstream tasks — semantic similarity, clustering, retrieval, and classification — to operate on numerical representations instead of raw text, making them one of the most versatile building blocks in modern NLP pipelines.

Text (sentences, paragraphs, short documents)

Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 3980–3990.

Kiros, R., Zhu, Y., Salakhutdinov, R., Zemel, R. S., Torralba, A., Urtasun, R., & Fidler, S. (2015). Skip-Thought Vectors. Advances in Neural Information Processing Systems (NeurIPS), 28.

bert-based-classification roberta-based-classification transformer long-short-term-memory topic-modeling convolutional-neural-network


Sentiment Analysis

opinion mining
polarity detection
duygu analizi

Sentiment analysis, also called opinion mining, is a natural-language-processing task that detects the emotional tone of text — typically classifying it as positive, negative, or neutral. It turns unstructured opinion text into structured, quantifiable polarity signals using one of three families of approaches: sentiment lexicons, trained machine-learning classifiers, or pretrained transformer models.

Lexicon-based / machine-learning / transformer-based

Output: Polarity label (positive / negative / neutral)

Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.


Separation Anxiety Questionnaire

The Separation Anxiety Questionnaire (SAQ) is a self-report instrument measuring the intensity of separation anxiety experienced during or anticipated during separation from attachment figures. Developed by Sharpley and colleagues in 2000, the SAQ assesses worry, fear, and distress related to being apart from parents, partners, or other significant caregivers. It is used to evaluate separation anxiety in children, adolescents, and adults, and to monitor treatment response in separation anxiety disorder.

Sharpley, C. F., & Lavers, R. J. (2000). The Separation Anxiety to Mother Separation Scale: Development and psychometric properties. Journal of Clinical Psychology, 56(9), 1131–1146.


Sequence-to-Sequence Model

Sequence-to-Sequence (Seq2Seq) Encoder-Decoder Model

Dizi-Dizi Modeli (Seq2Seq — Encoder-Decoder)

The sequence-to-sequence (Seq2Seq) model, introduced by Sutskever, Vinyals and Le and by Cho and colleagues in 2014, is an encoder-decoder neural network that maps a variable-length input sequence to a variable-length output sequence. It is the foundation of machine translation, text summarization, dialogue systems and code generation.

Encoder-decoder neural network (deep learning)

Sequence transduction (variable-length input to variable-length output)

Sutskever, I., Vinyals, O. & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks. NeurIPS.

Cho, K., van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H. & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. EMNLP, 1724–1734.

attention-mechanism self-attention-transformer bert-finetuning random-forest xgboost


Sequence Alignment

pairwise alignment
multiple sequence alignment
MSA
sequence comparison

1970 (global alignment); 1981 (local alignment)

Saul B. Needleman & Christian D. Wunsch (global); Temple F. Smith & Michael S. Waterman (local)

Sequence alignment is a foundational bioinformatics technique that arranges two or more DNA, RNA, or protein sequences to reveal regions of similarity, infer evolutionary relationships, identify functional domains, and map sequencing reads to reference genomes. It underpins virtually every downstream genomic analysis, from variant calling and gene expression quantification to phylogenetics and structural annotation.

Computational sequence analysis technique

Nucleotide or amino acid sequences (FASTA/FASTQ)

Needleman, S. B., & Wunsch, C. D. (1970). A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology, 48(3), 443–453.

Smith, T. F., & Waterman, M. S. (1981). Identification of common molecular subsequences. Journal of Molecular Biology, 147(1), 195–197.

phylogenetic-analysis variant-calling rna-seq-differential-expression genome-wide-association-study chip-seq-peak-calling single-cell-rna-seq-analysis


Sequential Pattern Mining

Sequence Pattern Mining
Sequential Data Mining
Temporal Pattern Mining
Ardışık Örüntü Madenciliği

Sequential Pattern Mining discovers ordered patterns that recur across multiple event sequences in a database. Introduced by Agrawal and Srikant in 1995, it extends association-rule mining to time-ordered transactions. A pattern is frequent when it appears as an ordered subsequence in at least a user-specified fraction of all sequences. The method is widely applied wherever the order of events carries meaning, such as customer purchase histories, clickstream logs, electronic health records, and DNA sequence analysis.

Exponential in pattern length, polynomial in database size

Output: Ordered itemset sequences with minimum support

Agrawal, R., & Srikant, R. (1995). Mining sequential patterns. IEEE International Conference on Data Engineering (ICDE), 3–14.


Sequential Analysis

Sıralı Analiz (Sequential Testing / Group Sequential Design)

P. C. O'Brien & T. R. Fleming; P. C. Pocock

Sequential analysis is a framework for conducting hypothesis tests with pre-planned interim looks at accumulating data, allowing a study to stop early for efficacy or futility while controlling the overall Type I error rate. The group sequential approach was formalised by Pocock (1977) and O'Brien and Fleming (1979), and remains the standard for confirmatory clinical trials and rigorous A/B experiments.

O'Brien-Fleming, Pocock, alpha-spending function

O'Brien, P.C. & Fleming, T.R. (1979). A Multiple Testing Procedure for Clinical Trials. Biometrics, 35(3), 549–556.

Jennison, C. & Turnbull, B.W. (1999). Group Sequential Methods with Applications to Clinical Trials. CRC Press.

sample-size-planning adaptive-design bayesian-power-analysis one-sample-t-test one-way-anova simulation-based-power


Sequential Case-Focused Mixed Methods

Sequential Case-Focused Mixed Methods Design

sequential case study mixed methods
case-focused sequential design
sequential mixed case design
SCFMM

Robert K. Yin (case study integration); John W. Creswell & Vicki L. Plano Clark (sequential mixed methods typology)

Sequential case-focused mixed methods design combines the depth of case study methodology with the phased data-collection logic of sequential mixed methods. Quantitative and qualitative data are gathered in distinct, ordered phases — either QUAN then QUAL or QUAL then QUAN — and both strands are anchored within one or more bounded cases. The design is suited to research questions that require understanding how and why phenomena unfold within specific real-world contexts.

Quantitative and qualitative data collected in sequential phases from one or more bounded cases

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

Yin, R. K. (2006). Mixed methods research: Are the methods genuinely integrated or merely parallel? Research in the Schools, 13(1), 41–47.


Sequential Design

Sequential / Group Sequential Trial Design

Ardışık Deneme Tasarımı (Sequential / Group Sequential)

Sequential and group sequential trial designs allow a study to be stopped early — or continued — based on interim analyses conducted as data accumulate. The core framework was formalised by O'Brien and Fleming in 1979 and extended by Lan and DeMets's alpha-spending approach, and it controls the overall Type I error rate across all planned looks by pre-specifying both efficacy and futility boundaries before enrolment begins.

Jennison, C. & Turnbull, B.W. (2000). Group Sequential Methods with Applications to Clinical Trials. CRC Press.

randomized-controlled-trial adaptive-design sample-size-determination power-analysis lan-demets-spending


Sequential Exploratory Mixed Methods Design

exploratory sequential design
SEQEXP
qual → quan design
instrument-development mixed methods

The sequential exploratory mixed methods design begins with a qualitative phase to explore a poorly understood phenomenon, then builds on those findings in a second quantitative phase — most commonly to develop and test a measurement instrument, or to test whether themes identified qualitatively generalise across a broader population. The two phases are conducted in sequence, with qualitative results explicitly informing the design and content of the quantitative strand.

Qualitative data (Phase 1) followed by quantitative data (Phase 2)

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


Sequential Intervention Mixed Methods

Sequential Intervention Mixed Methods Design

sequential mixed methods intervention study

Creswell & Plano Clark (intervention design framework); extended by health and evaluation researchers

Sequential intervention mixed methods is a research design in which quantitative and qualitative data collection phases are arranged in sequence — one after the other — within the context of a planned intervention or experimental study. The sequencing allows each phase to build on the other: quantitative data may establish whether an intervention works, while qualitative data explain how and why it works (or does not) for specific participants or contexts.

Quantitative outcome data (trials, surveys) followed or preceded by qualitative data (interviews, focus groups)

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

Tariq, S., & Woodman, J. (2013). Using mixed methods in health research. JRSM Short Reports, 4(6), 1–8.


Sequential Mixed Methods Matrix

mixed methods joint display matrix
sequential explanatory matrix
sequential exploratory matrix
mixed methods integration matrix

John W. Creswell & Vicki L. Plano Clark (systematized); Timothy Guetterman et al. (joint display formalization)

The sequential mixed methods matrix is an integration tool used in sequential mixed methods designs — explanatory (QUAN → qual) or exploratory (qual → QUAN) — to display and synthesize quantitative results and qualitative findings side-by-side in a structured table. Also called a joint display matrix, it makes the process of connecting the two strands of evidence explicit, transparent, and auditable, helping researchers draw meta-inferences that neither strand alone could support.

Mixed methods integration and visualization tool

Quantitative results + qualitative findings (text and numbers combined)

Guetterman, T. C., Fetters, M. D., & Creswell, J. W. (2015). Integrating quantitative and qualitative results in health science mixed methods research through joint displays. Annals of Family Medicine, 13(6), 554–561.

mixed-methods-research explanatory-sequential-design exploratory-sequential-design convergent-mixed-methods thematic-analysis content-analysis


Sequential Monte Carlo with Measurement Error

sequential particle filtering with observation noise

Gordon, Salmond & Smith (1993); extended by Doucet, de Freitas & Gordon (2001)

Sequential Monte Carlo (SMC) with measurement error is a particle-based Bayesian filtering method for tracking hidden states in dynamical systems when observations are corrupted by noise. It propagates a weighted cloud of particles through time, updating weights at each step to reflect how well each particle explains the noisy measurement, and produces a full posterior distribution over the latent state at every time point.

Time series, state-space data with noisy observations

Doucet, A., de Freitas, N., & Gordon, N. (Eds.). (2001). Sequential Monte Carlo Methods in Practice. Springer New York.

Cappe, O., Godsill, S. J., & Moulines, E. (2007). An overview of existing methods and recent advances in sequential Monte Carlo. Proceedings of the IEEE, 95(5), 899-924.


Sequential Monte Carlo with Missing Data

particle filter with missing observations

Gordon, Salmond & Smith (particle filter, 1993); missing-data extensions formalised by Doucet et al. (2000s)

Sequential Monte Carlo (SMC) with missing data extends the standard particle filter to state-space models in which some observations are absent. When an observation is missing at a given time step the update step is simply skipped: particles are propagated forward through the transition model without reweighting, preserving exact Bayesian inference under any missing-data pattern as long as missingness is ignorable (missing at random or missing completely at random).

Sequential Bayesian filtering / smoothing

Time-series or sequential data with intermittent missing observations

Doucet, A., de Freitas, N., & Gordon, N. (Eds.) (2001). Sequential Monte Carlo Methods in Practice. Springer, New York.

Chopin, N., & Papaspiliopoulos, O. (2020). An Introduction to Sequential Monte Carlo. Springer, Cham.

sequential-monte-carlo particle-filter kalman-filter-with-missing-data bayesian-inference-with-missing-data gibbs-sampling-with-missing-data dynamic-sequential-monte-carlo


Sequential Monte Carlo

SMC
particle filter
sequential importance resampling
SMC sampler

1993 (particle filter); 2006 (SMC samplers)

Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)

Sequential Monte Carlo (SMC) is a family of simulation-based algorithms that approximate evolving probability distributions by propagating and reweighting a cloud of weighted random draws called particles. It handles nonlinear, non-Gaussian models and streams of data naturally, making it the method of choice for real-time state estimation and posterior approximation over complex distributions.

Sequential / time-series / multimodal continuous data

Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F - Radar and Signal Processing, 140(2), 107–113.

Del Moral, P., Doucet, A., & Jasra, A. (2006). Sequential Monte Carlo samplers. Journal of the Royal Statistical Society: Series B, 68(3), 411–436.

gibbs-sampling mcmc particle-filter kalman-filter hamiltonian-monte-carlo approximate-bayesian-computation


Sequential Pragmatic Mixed Methods

Sequential Pragmatic Mixed Methods Design

pragmatism-guided sequential mixed methods

Creswell & Plano Clark; Tashakkori & Teddlie (pragmatic worldview formalization)

Sequential pragmatic mixed methods is a mixed-methods research design in which quantitative and qualitative data strands are collected and analyzed in a defined sequence — one strand following and building on the other — with the entire design anchored in a pragmatic philosophical worldview. Pragmatism foregrounds research usefulness and problem-solving, treating method choice as a practical decision rather than an ideological commitment, and it provides the integrative logic for combining the two strands into actionable insights.

Quantitative and qualitative data collected in sequential phases

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


Sequential Qualitative-Priority Mixed Design

Sequential Qualitative-Priority Mixed Methods Design

sequential exploratory qualitative-priority design

Creswell & Plano Clark; Teddlie & Tashakkori

Sequential qualitative-priority mixed design is a two-phase mixed methods approach in which a qualitative strand is conducted first and carries greater weight in the overall study. The quantitative phase follows and serves to extend, test, or generalize the qualitative findings. The QUAL-first, QUAL-dominant logic makes this design well suited to exploratory research where theory or instruments are underdeveloped and must be grounded in participants' own words before being scaled up.

Qualitative data (phase 1) followed by quantitative data (phase 2)

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


Sequential Quantitative-Priority Mixed Design

Sequential Quantitative-Priority Mixed Methods Design

QUAN-dominant sequential design
quantitative-priority sequential MMR
quan-first sequential mixed methods
quantitative-led sequential design

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

The sequential quantitative-priority mixed design collects and analyzes quantitative data first, then follows with a qualitative strand to elaborate, explain, or contextualize the quantitative findings. The quantitative component is given greater weight in the overall study, meaning the primary research questions and conclusions are primarily grounded in the quantitative evidence, with the qualitative strand playing a supplementary, explanatory role.

Quantitative data (primary strand) followed by qualitative data (secondary strand)

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

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


Sequential Transformative Mixed Methods

Sequential Transformative Mixed Methods Design

sequential transformative design
transformative sequential MMR
Seq-TRAN mixed methods
sequential transformative research

2003–2007 (Mertens 2003; Creswell & Plano Clark 2007)

Donna M. Mertens (transformative framework); John W. Creswell & Vicki L. Plano Clark (mixed methods typology)

Sequential transformative mixed methods design combines the temporal structure of sequential mixed methods — collecting qualitative and quantitative data in two distinct, ordered phases — with a transformative theoretical framework that centres social justice, equity, and the perspectives of marginalized communities. Either the qualitative or the quantitative phase may come first; the sequence is determined by what the transformative research question demands. The design is guided by the work of Donna Mertens and is systematized in the Creswell and Plano Clark mixed methods typology.

Qualitative and quantitative data collected in sequential phases

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


SERS

Surface-enhanced Raman scattering
SERS spectroscopy

Surface-Enhanced Raman Spectroscopy (SERS) amplifies weak Raman signals by many orders of magnitude when analyte molecules are adsorbed on specially prepared metal (typically silver or gold) nanostructured surfaces. Discovered by Fleischmann, Hendra, and McQuillan in 1974, SERS enables detection of vibrational signatures of single molecules and ultra-trace contaminants, revolutionizing analytical chemistry and forensics.

Fleischmann, M., Hendra, P. J., & McQuillan, A. J. (1974). Raman spectra of pyridine adsorbed at a silver electrode. Chemical Physics Letters, 26(2), 163-166.

Jeanmaire, D. L., & Van Duyne, R. P. (1977). Surface raman spectroelectrochemistry: Part I. Heterocyclic, aromatic, and aliphatic amines adsorbed on the anodized silver electrode. Journal of Electroanalytical Chemistry, 84(1), 1-20.

Kneipp, K., Wang, Y., Kneipp, H., Perelman, L. T., Itzkan, I., Dasari, R. R., & Feld, M. S. (1997). Single molecule detection using surface-enhanced Raman scattering (SERS). Physical Review Letters, 78(9), 1667-1670.


Servant Leadership Scale

Servant Leadership Scale (SLS) - Multidimensional Assessment

Robert K. Greenleaf (concept); Robert C. Liden et al. (measurement scale)

The Servant Leadership Scale (SLS), developed by Liden and colleagues in 2008, measures the extent to which leaders prioritize others' well-being and development. Building on Robert Greenleaf's 1970 concept of servant leadership, the SLS operationalizes servant leadership across seven dimensions: emotional healing, creating value for community, conceptual skills, empowering others, helping followers grow and succeed, putting followers first, and behaving ethically. The scale enables assessment of leadership styles that foster trust, engagement, and organizational effectiveness.

Liden, R. C., Wayne, S. J., Zhao, H., & Henderson, D. (2008). Servant leadership: development of a multidimensional measure and multi-level assessment. The Leadership Quarterly, 19(2), 161-177.

Greenleaf, R. K. (1970). The servant as leader. Indianapolis: Robert K. Greenleaf Center for Servant Leadership.

transformational-leadership-scale organizational-justice-scale psychological-safety-scale organizational-commitment-scale job-satisfaction-survey


Service Area Analysis

Network Service Area (Isochrone) Analysis

Isochrone Analysis
Network Catchment Area Analysis
Travel-Time Polygon Analysis
Hizmet Alanı Analizi

Service Area Analysis delineates the geographic region reachable from one or more origin facilities within a specified travel cost — typically time, distance, or generalized impedance — by traversing a real road or transit network. It is widely used by urban planners, public health officials, logistics managers, and emergency response coordinators who need to understand actual accessibility rather than simple straight-line buffers.

Input: Road network + origin point(s) + impedance threshold

Output: Polygon(s) delineating reachable area within given cost

Miller, H. J., & Shaw, S.-L. (2001). Geographic Information Systems for Transportation: Principles and Applications. Oxford University Press.


SERVPERF Scale

Perception-Only Service Quality Scale
SERVPERF-Performance Model

SERVPERF, developed by Cronin and Taylor in 1992, is a streamlined service quality measurement instrument that evaluates perceived service performance only, without the expectation component. Using 22 items identical in content to SERVQUAL but applied to perception alone, SERVPERF reduces survey burden while maintaining dimensional coverage of Tangibles, Reliability, Responsiveness, Assurance, and Empathy. Empirical evidence suggests SERVPERF performs equally well or better than SERVQUAL in explaining overall satisfaction.

Cronin, J. J., & Taylor, S. A. (1992). Measuring Service Quality: A Reexamination and Extension. Journal of Marketing, 56(3), 55-68.

Taylor, S. A., & Cronin, J. J. (1994). Modeling Patient Satisfaction and Service Quality in the Healthcare Industry. Journal of Health Care Marketing, 14(1), 34-44.

servqual e-servqual customer-satisfaction-index hedqual


SERVQUAL Service Quality Scale

A. Parasuraman, Valerie A. Zeithaml, Leonard L. Berry

SERVQUAL is a 22-item, multi-dimensional scale developed by Parasuraman, Zeithaml, and Berry in 1988 to measure consumer perceptions of service quality. It captures the gap between customer expectations and actual service performance across five core dimensions: Tangibles, Reliability, Responsiveness, Assurance, and Empathy. The instrument has become the most widely used tool for service quality assessment in marketing research and practice.

Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A Multiple-Item Scale for Measuring Consumer Perceptions of Service Quality. Journal of Retailing, 64(1), 12-40.

Zeithaml, V. A., Parasuraman, A., & Berry, L. L. (1990). Delivering Quality Service: Balancing Customer Perceptions and Expectations. Free Press.

servperf e-servqual hedqual customer-satisfaction-index


Session Rating Scale

The Session Rating Scale (SRS) is a 4-item ultra-brief measure of client perceptions of session quality and therapeutic alliance, developed by Miller and Duncan to support real-time feedback in psychotherapy. Administered after each session, the SRS captures client satisfaction with the relationship, alignment on goals and topics, and the therapist's approach, offering immediate insight for therapeutic adjustment. The measure is designed to operationalize common factors of psychotherapy outcome and enable therapists to respond to client feedback in vivo.

Miller, S. D., Duncan, B. L., Brown, J., Sparks, J. A., & Claud, D. A. (2003). The Outcome Rating Scale: Preliminary validity studies of a brief, visual, general measure of session effectiveness. Journal of Brief Therapy, 5(2), 23–33.

Campbell, A., & Hemsley, S. (2012). Outcome Rating Scale and Session Rating Scale in psychological practice: Clinical utility of ultra-brief measures. Clinical Psychology, 16(1), 37–46.

working-alliance-inventory outcome-rating-scale therapeutic-alliance-scale common-factors-questionnaire


Session RPE

Session Rate of Perceived Exertion and Training Load Quantification

Session rate of perceived exertion (sRPE) is a simple, athlete-centered method to quantify training load by combining perceived exertion intensity (RPE, 0-10 scale) with session duration. Introduced by Carl Foster (2001), sRPE avoids the need for external equipment (heart rate monitors, GPS, force plates) and captures the integrated physiological and psychological demands of any training modality. Despite its simplicity, sRPE correlates well with objective physiological markers (heart rate, lactate, VO2) and is widely adopted in elite and recreational sports for load management and recovery planning.

Foster, C., Florhaug, J. A., Franklin, J., Gottschall, L., Hrovatin, L. A., Parker, S., & Dodge, C. (2001). A new approach to monitoring exercise training. Journal of Strength and Conditioning Research, 15(1), 109-115.

Sweet, T. W., Foster, C., McGuigan, M. R., & Brice, G. (2014). Quantitation of resistance training using the session rating of perceived exertion scale. Journal of Strength and Conditioning Research, 28(3), 619-622.

Impellizzeri, F. M., Rampinini, E., Coutts, A. J., Sassi, A., & Marcora, S. M. (2004). Use of RPE-based training load in soccer. Medicine & Science in Sports & Exercise, 36(6), 1042-1047.


Sexual Harassment Experiences Questionnaire (SHEQ)

The Sexual Harassment Experiences Questionnaire measures employee exposure to unwanted sexual behavior, comments, and coercion in the workplace. Developed by Fitzgerald, Gelfand, and Drasgow, the SHEQ distinguishes between gender harassment, unwanted sexual attention, and sexual coercion—recognizing that sexual harassment spans a continuum from crude jokes to assault. The scale is foundational for occupational health surveillance, legal compliance documentation, and evaluation of organizational prevention efforts.

Fitzgerald, L. F., Gelfand, M. J., & Drasgow, F. (1995). Measuring sexual harassment: Theoretical and psychometric advances. Basic Appl Soc Psychol, 17(4), 425–445.

Gruber, J. E. (1998). The impact of male work environments and organizational policies on women's experiences of sexual harassment. Gender Work Organ, 5(2), 86–104.


Sexual Satisfaction Scale

SSS
Kansas City Sexual Satisfaction Scale

The Sexual Satisfaction Scale (also known as the Kansas City Sexual Satisfaction Scale) is a brief, unidimensional self-report measure designed to assess subjective satisfaction with sexual life and sexual relationships. First published by Ponticas in 2003, it comprises typically 5–7 items measuring global sexual satisfaction on simple Likert or semantic differential scales. Its brevity and conceptual clarity make it useful for rapid assessment in clinical and research settings.

Ponticas, Y. (2003). Validity and reliability of a brief sexual satisfaction scale. Dissertation Abstracts International, 64(3-A), 835.

Zou, H., Ong, K. L., Johnson, M. P., & Sanders, K. A. (2009). Epidemiology of erectile dysfunction: a systematic review of prevalence and incidence studies. Journal of Sexual Medicine, 5(3), 650–660.


SF-12 Health Survey

John E. Ware Jr., Mark Kosinski, and Susan Keller

The SF-12 is a brief, 12-item version of the SF-36 health survey developed by Ware, Kosinski, and Keller in 1996. Designed to reduce respondent burden while maintaining psychometric validity, it has become the standard instrument for large-scale surveys, epidemiological studies, and health outcomes research where administration time is critical.

Brief self-report health status instrument

Ware, J. E., Kosinski, M., & Keller, S. D. (1996). A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity. Medical Care, 34(3), 220–233.

Ware, J. E., Kosinski, M., & Keller, S. D. (2002). SF-12: How to score the SF-12 Physical and Mental Health Summary Scales (3rd ed.). QualityMetric.

Gandek, B., Ware, J. E., Aaronson, N. K., et al. (2004). Cross-validation of item selection and scoring for the SF-12 Health Survey in nine countries. Journal of Clinical Epidemiology, 51(11), 1171–1178.


SF-36 Health Survey

SF-36 Questionnaire
Medical Outcomes Study SF-36

John E. Ware Jr. and Cathy D. Sherbourne

The SF-36 is a generic, self-administered 36-item questionnaire measuring eight dimensions of health status. Developed by Ware and Sherbourne in 1992, it has become the most widely used health survey in clinical trials, outcomes research, and population health monitoring. It assesses perceived health across physical and mental domains relevant to the general adult population.

Ware, J. E., & Sherbourne, C. D. (1992). The MOS 36-item Short-Form Health Survey (SF-36): I. Conceptual framework and item selection. Medical Care, 30(6), 473–483.

Ware, J. E., Snow, K. K., Kosinski, M., & Gandek, B. (1993). SF-36 Health Survey: Manual and interpretation guide. New England Medical Center.

McHorney, C. A., Ware, J. E., & Raczek, A. E. (1994). The MOS 36-Item Short-Form Health Survey (SF-36): II. Psychometric and clinical tests of validity. Medical Care, 32(3), 217–226.


SF-8 Health Survey

SF-8 Questionnaire
Medical Outcomes Study SF-8

John E. Ware Jr., Mark Kosinski, and colleagues

The SF-8 is an ultra-brief, 8-item version of the SF-36 health survey developed by Ware and colleagues in 2005. Designed for extreme time-constraint settings and large-scale epidemiological surveys, the SF-8 maintains strong correlation with SF-36 and SF-12 domains while requiring only 1–2 minutes to complete.

Ultra-brief self-report health status instrument

Ware, J. E., Kosinski, M., Dewey, J. E., & Gandek, B. (2005). How to score and interpret single-item health status measures: a manual for users of the SF-8 Health Survey. QualityMetric Inc.

Ware, J. E., & Kosinski, M. (2001). The SF-8 Health Survey vs. the SF-12 Health Survey for the assessment of health-related quality of life. Quality of Life Newsletter, 27, 3–4.

Jenkinson, C., Chandola, T., Stafford, M., & Marmot, M. (2005). The Whitehall II Study: the short Form 8 health survey questionnaire (SF-8): psychometric properties and normative data for the general population. Journal of Public Health Medicine, 27(1), 65–71.


SF-ARAS

SF-ARAS (Spherical extension of ARAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Kutlu Gündoğdu, F. Kahraman, C. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Spherical outranking/ranking — Spherical Fuzzy Set (SFS: μ, ν, π; μ²+ν²+π² ≤ 1)

Kutlu Gündoğdu, F., Kahraman, C. (2019). Spherical fuzzy sets and spherical fuzzy TOPSIS method. Journal of Intelligent & Fuzzy Systems

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


SF-COCOSO

SF-COCOSO (SF-CoCoSo — Spherical extension of COCOSO) is a ranking multi-criteria decision-making (MCDM) method introduced by Kutlu Gündoğdu, F. Kahraman, C. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


SF-CODAS

SF-CODAS (Spherical extension of CODAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Karaşan, Boltürk & Kutlu Gündoğdu (book chapter); earlier Kutlu Gündoğdu-Kahraman 2019c JMVLSC in 2019 / 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Karaşan, A., Boltürk, E., Kutlu Gündoğdu, F. (2021). Assessment of Livability Indices of Suburban Places of Istanbul by Using Spherical Fuzzy CODAS Method. In: Kahraman C., Kutlu Gündoğdu F. (eds.) Decision Making with Spherical Fuzzy Sets — Theory and Applications. Studies in Fuzziness and Soft Computing vol. 392. Springer


SF-COPRAS

SF-COPRAS (Spherical extension of COPRAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Kutlu Gündoğdu, F. Kahraman, C. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


SF-EDAS

SF-EDAS (Spherical extension of EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Garg, H., Sharaf, I.M. in 2022. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Garg, H., Sharaf, I.M. (2022). A new spherical aggregation function with the concept of spherical fuzzy difference for spherical fuzzy EDAS and its application to industrial robot selection. Computational and Applied Mathematics


SF-GRA

SF-GRA (Spherical extension of GRA) is a ranking multi-criteria decision-making (MCDM) method introduced by Kutlu Gündoğdu, F. Kahraman, C. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


SF-MABAC

SF-MABAC (Spherical extension of MABAC) is a ranking multi-criteria decision-making (MCDM) method introduced by Kutlu Gündoğdu, F. Kahraman, C. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


SF-MARCOS

SF-MARCOS (Spherical extension of MARCOS) is a ranking multi-criteria decision-making (MCDM) method introduced by Kutlu Gündoğdu, F. Kahraman, C. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


SF-MOORA

SF-MOORA (Spherical extension of MOORA) is a ranking multi-criteria decision-making (MCDM) method introduced by Kutlu Gündoğdu, F. Kahraman, C. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


SF-PROMETHEE

SF-PROMETHEE (Spherical extension of PROMETHEE) is a outranking multi-criteria decision-making (MCDM) method introduced by Sharaf (book chapter); SF foundation Kutlu Gündoğdu-Kahraman 2019 in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Sharaf, I. M. (2021). Evaluating Geothermal Energy Systems Using Spherical Fuzzy PROMETHEE. In: Kahraman C., Kutlu Gündoğdu F. (eds.) Decision Making with Spherical Fuzzy Sets — Theory and Applications. Studies in Fuzziness and Soft Computing vol. 392. Springer


SF-SAW

SF-SAW (Spherical extension of SAW) is a ranking multi-criteria decision-making (MCDM) method introduced by Kutlu Gündoğdu & Yörükoğlu in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Kutlu Gündoğdu, F., Yörükoğlu, M. (2021). Simple Additive Weighting and Weighted Product Methods Using Spherical Fuzzy Sets. In: Kahraman C., Kutlu Gündoğdu F. (eds.) Decision Making with Spherical Fuzzy Sets — Theory and Applications. Studies in Fuzziness and Soft Computing vol. 392. Springer


SF-TODIM

SF-TODIM (Spherical extension of TODIM) is a ranking multi-criteria decision-making (MCDM) method introduced by Kutlu Gündoğdu, F. Kahraman, C. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


SF-TOPSIS

SF-TOPSIS (Spherical extension of TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Kutlu Gündoğdu & Kahraman in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


SF-VIKOR

SF-VIKOR (Spherical extension of VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Sharaf (book chapter); SF foundation Kutlu Gündoğdu-Kahraman 2019 in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Sharaf, I. M. (2021). Spherical Fuzzy VIKOR with SWAM and SWGM Operators for MCDM. In: Kahraman C., Kutlu Gündoğdu F. (eds.) Decision Making with Spherical Fuzzy Sets — Theory and Applications. Studies in Fuzziness and Soft Computing vol. 392. Springer


SF-WASPAS

SF-WASPAS (Spherical extension of WASPAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Boltürk & Kutlu Gündoğdu in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Boltürk, E., Kutlu Gündoğdu, F. (2021). Prioritizing Manufacturing Challenges of a Contract Manufacturing Company for Personal Auto by Using Spherical WASPAS Method. In: Kahraman C., Kutlu Gündoğdu F. (eds.) Decision Making with Spherical Fuzzy Sets — Theory and Applications. Studies in Fuzziness and Soft Computing vol. 392. Springer


SF-WPM

SF-WPM (Spherical extension of WPM) is a ranking multi-criteria decision-making (MCDM) method introduced by Kutlu Gündoğdu & Yörükoğlu in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


SFZN-CRADIS

SFZN-CRADIS (Spherical Fuzzy Z-Number CRADIS Ranking) is a distance multi-criteria decision-making (MCDM) method introduced by Niu, J. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Compromise ranking via dual distance to global ideal and anti-ideal under spherical Z-number uncertainty

Niu, J. (2024). Spherical Fuzzy Z-Numbers-based CRITIC CRADIAS and MARCOS Approaches for Evaluating English Teacher Performance. International Journal of Advanced Computer Science and Applications (IJACSA)

sfzn-critic sfzn-ahp sf-ahp any-sfzn-compatible-weighting


SFZN-CRITIC

SFZN-CRITIC (Spherical Fuzzy Z-Number CRITIC Weighting) is a weighting multi-criteria decision-making (MCDM) method introduced by Niu, J. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Objective weighting from criterion variability + cross-criterion correlation under spherical Z-number uncertainty

sfzn-cradis sfzn-marcos sfzn-topsis sfzn-waspas


SFZN-MARCOS

SFZN-MARCOS (Spherical Fuzzy Z-Number MARCOS Ranking) is a distance multi-criteria decision-making (MCDM) method introduced by Niu, J. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Compromise ranking via extended-matrix utility degrees relative to anti-ideal and ideal anchors under spherical Z-number uncertainty


SGP4 TLE Propagation

Simplified General Perturbations 4 with Two-Line Element Set

SGP4
TLE propagation
simplified perturbations

SGP4 (Simplified General Perturbations 4) is a rapid orbital propagation method that predicts satellite position and velocity from Two-Line Element (TLE) sets published by NORAD. Developed in the 1970s, SGP4 accounts for atmospheric drag, gravitational perturbations, and solar radiation pressure using simplified analytical models. SGP4 is the de facto standard for space surveillance, conjunction assessment, and satellite tracking.

Vallado, D. A., Crawford, P., Hujsa, R., & Kelso, T. S. (2006). Revisiting Spacetrack Report Number 3. In AIAA/AAS Astrodynamics Specialist Conference.

Kelso, T. S. (1995). Analysis of the Iridium 33/Cosmos 2251 Collision. CelesTrak.

Hoots, F. R., & Roehrich, R. L. (1980). Models for Propagation of NORAD Element Sets. Spacetrack Report No. 3.


SHA Hash Function

National Institute of Standards and Technology (NIST)

The Secure Hash Algorithm (SHA) is a family of cryptographic hash functions standardized by NIST starting in 1993. SHA functions produce fixed-length digests from arbitrary-length input data, serving as a fundamental building block for digital signatures, message authentication, and data integrity verification across security-critical applications.

National Institute of Standards and Technology (1993). Secure Hash Standard (SHS). Federal Information Processing Standards (FIPS) Publication 180.

Wang, X., Yin, Y. L., & Yu, H. (2005). Finding collisions in the full SHA-1. Proceedings of CRYPTO 2005, Lecture Notes in Computer Science, 3621, 17–36.

Stevens, M., Bursztein, E., Karpman, P., Albertini, A., & Markov, Y. (2013). The first collision for full SHA-1. Advances in Cryptology – CRYPTO 2017, 570–596.

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


Shade Selection in Dentistry

Tooth Shade Selection for Esthetic Restorations

shade matching
color selection
tooth shade guide
spectrophotometry

Multiple innovators in restorative dentistry

Shade selection in dentistry is the process of determining the appropriate color and translucency of tooth-colored restorative materials to match the patient's natural dentition. Systematic shade selection involves visual assessment using calibrated shade guides, spectrophotometric measurement, and sometimes digital imaging to ensure esthetic harmony. Accurate shade selection is critical for patient satisfaction with restorations and requires understanding of tooth color dimensions (hue, value, chroma) and the optical properties of restorative materials.

Guan, X. Y., Hao, Z. X., Zhou, G. Z., & Fang, C. X. (2005). Clinical evaluation of shade matching in tooth-colored restoratives. Chinese Journal of Dental Research, 8(1), 35-38.

O'Neill, O. N., Eustaquio, A. R., & Nishii, Y. (2018). Tooth shade matching: The role of spectrophotometry and colorimetry in restorative dentistry. Compendium of Continuing Education in Dentistry, 39(4), 234-239.

Paul, S., Peter, A., Pietrobon, N., & Hämmerle, C. H. (2007). Visual and spectrophotometric shade matching of tooth-colored restorations: A clinical comparison. Quintessence International, 38(1), 15-23.


Shannon Capacity

channel capacity
information theory bound

Shannon's channel capacity theorem, published in 1948, establishes the maximum rate at which information can be reliably transmitted over a noisy channel. Expressed as C = B log2(1 + S/N) for additive white Gaussian noise (AWGN), it is a fundamental bound in information theory and communications engineering. Shannon proved that reliable communication is possible at any rate below capacity, and impossible above it. This theorem underpins the design of all modern communication systems and motivates coding theory, modulation, and signal processing techniques.

Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379-423.

Cover, T. M., & Thomas, J. A. (1991). Elements of Information Theory. John Wiley & Sons.

ofdm mimo turbo-code ldpc-codes polar-codes


SHAP

SHAP Değerleri (Model Açıklanabilirlik)
Shapley additive explanations
SHAP values
model explainability

SHAP is a model-explanation method, introduced by Scott Lundberg and Su-In Lee in 2017, that uses Shapley values from cooperative game theory to measure how much each feature contributes to an individual prediction, making the output of black-box machine-learning models interpretable. It supports both global explanations (overall feature importance) and local explanations (why one specific prediction came out the way it did).

Model-explanation method (Shapley-value attribution)

Interpreting predictions of trained ML models

Global (feature importance) and local (single-prediction) explanations

Lundberg, S.M. & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems, 30, 4766–4777.

random-forest xgboost decision-tree gaussian-mixture logistic-regression


Shapiro-Wilk test

Shapiro-Wilk W test
W test for normality
Shapiro-Wilk normallik testi

The Shapiro-Wilk test is a hypothesis test that checks whether a continuous variable was drawn from a normal distribution. It was introduced by Samuel Shapiro and Martin Wilk in 1965 and is regarded as one of the most powerful normality tests, recommended for sample sizes below 5000.

not applicable (statistic bounded in (0, 1])

Shapiro, S. S. & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3-4), 591–611.

kolmogorov-smirnov-test independent-t-test one-way-anova levene-test


Shapley Value

Fair Division
Cooperative Game Solution
Dividend Vector

The Shapley Value is a solution concept for coalition games that distributes total payoff fairly among players based on their marginal contributions to coalitions. Introduced by Lloyd Shapley in 1953, the Shapley Value is the unique payoff distribution that satisfies four intuitive axioms: efficiency (total payoff is distributed), symmetry (identical players receive equal payoff), null player (players contributing nothing receive nothing), and additivity across games.

Shapley, L. S. (1953). A value for n-person games. In H. W. Kuhn & A. W. Tucker (Eds.), Contributions to the Theory of Games II (pp. 307-317). Princeton University Press.

Roth, A. E. (1988). The Shapley value as a von Neumann-Morgenstern utility. Econometrica, 56(4), 745-794.

nash-equilibrium vcg-mechanism top-trading-cycles principal-agent-model


Sheehan Disability Scale

The Sheehan Disability Scale is a brief three-item self-report measure designed by David V. Sheehan to assess functional impairment across work/school, social life, and family life domains in psychiatric and medical disorders. First described in Sheehan's 1983 book 'The Anxiety Disease' and validated in multiple studies since, the SDS quantifies the degree to which a patient's illness interferes with major life domains. It is widely used in psychiatric research and clinical practice to assess the functional impact of depression, anxiety, ADHD, and other conditions, complementing symptom severity measures by capturing real-world impairment.

Sheehan, D. V. (1983). The Anxiety Disease. New York: Scribner.

Leon, A. C., Olfson, M., & Portera, L. (1997). Assessing psychiatric impairment in primary care with the Sheehan Disability Scale. International Journal of Psychiatry in Medicine, 27(2), 93–105.

Arbuckle, R., Freston, M., & Witt, R. (2009). A systematic literature review of the psychometric properties and interpretation of the Sheehan Disability Scale. Current Medical Research and Opinion, 25(10), 2517–2526.

phq-9 quick-inventory-depressive patient-global-impression-change clinical-global-impressions-scale


Shewhart Control Chart

Shewhart Variables Control Chart (X-bar and R)

X-bar and R chart
Shewhart chart
variables control chart
process control chart
Shewhart kontrol kartı

The Shewhart control chart, invented by Walter Shewhart at Bell Labs in the 1920s and set out in his 1931 book, is the foundational tool of statistical process control. It plots a process statistic — typically the subgroup mean (X-bar) and range (R) — over time against a center line and three-sigma control limits, distinguishing the natural common-cause variation inherent in a stable process from special-cause variation that signals something has changed and warrants investigation.

Statistical process control chart for variables

Process mean and within-subgroup variation

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

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

cusum-chart ewma-chart attributes-control-chart descriptive-statistics


Shift-Share IV

Shift-Share Instrumental Variable (Bartik Instrument)

Shift-Share Araç Değişkeni (Bartik Instrument)

Bartik (1991); identification framework by Goldsmith-Pinkham, Sorkin & Swift (2020) and Borusyak, Hull & Jaravel (2022)

The shift-share instrumental variable, widely known as the Bartik instrument, is a causal-inference strategy that builds an instrument by interacting national or sector-level shocks (the shifts) with local composition weights (the shares). Its modern identification framework was set out by Goldsmith-Pinkham, Sorkin and Swift (2020) and Borusyak, Hull and Jaravel (2022).

Two-stage least squares with a constructed shift-share instrument

Goldsmith-Pinkham, P., Sorkin, I. & Swift, H. (2020). Bartik Instruments: What, When, Why, and How. American Economic Review, 110(8), 2586–2624.

Borusyak, K., Hull, P. & Jaravel, X. (2022). Quasi-Experimental Shift-Share Research Designs. Review of Economic Studies, 89(1), 181–213.

iv-2sls regression-kink-design regression-discontinuity panel-fixed-effects difference-in-differences


Shor's Algorithm

Shor's Algorithm for Integer Factorization and Discrete Logarithm

Shor factorization
quantum factorization

Shor's Algorithm is a polynomial-time quantum algorithm for factoring large integers and computing discrete logarithms, problems believed to be intractable on classical computers. Discovered by Peter Shor in 1994, it demonstrated the potential of quantum computers to break widely used cryptographic systems like RSA, marking a landmark in quantum computing theory.

Shor, P. W. (1994). Algorithms for quantum computation: discrete logarithms and factoring. Proceedings of the 35th Annual Symposium on Foundations of Computer Science, 124–134.

Shor, P. W. (1997). Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM Review, 41, 303–332.

Ekert, A. K., Raussendorf, R. (2014). A short introduction to quantum computing. Reviews of Modern Physics, 74, 339–373.


Short-Form CFA

SF-CFA
abbreviated scale CFA
short-form validation
brief scale factor analysis

Building on CFA methodology (Jöreskog, 1969) applied to abbreviated scale contexts

Short-form confirmatory factor analysis applies CFA to a reduced subset of items drawn from a longer validated scale, testing whether the abbreviated version preserves the original factor structure with acceptable model fit and reliability. It is a standard step in short-form scale development and validation.

Ordinal or continuous item responses from an abbreviated scale

Byrne, B. M. (2008). Structural Equation Modeling with EQS: Basic Concepts, Applications, and Programming (2nd ed.). Lawrence Erlbaum Associates.

Smith, G. T., McCarthy, D. M., & Anderson, K. G. (2000). On the sins of short-form development. Psychological Assessment, 12(1), 102–111.

confirmatory-factor-analysis short-form-exploratory-factor-analysis measurement-invariance scale-development item-response-theory cronbachs-alpha


Short form construct validity

abbreviated scale construct validity
short-form scale validation
brief scale validity
short measure construct validation

Multiple contributors; Smith, McCarthy, & Anderson (2000) formalized short-form validation criteria

Short form construct validity is the systematic evaluation of whether an abbreviated version of a psychological scale still measures the same underlying construct as the original full-length instrument. It combines item selection procedures with confirmatory factor analysis, convergent and discriminant validity tests, and criterion-related evidence to demonstrate that scale shortening has not compromised the meaning of measurement.

Ordinal or interval survey items (short-form scores vs. full-form scores)

Stanton, J. M., Sinar, E. F., Balzer, W. K., & Smith, P. C. (2002). Issues and strategies for reducing the length of self-report scales. Personnel Psychology, 55(1), 167–194.

construct-validity confirmatory-factor-analysis convergent-validity discriminant-validity short-form-reliability-analysis measurement-invariance


Short form content validity

abbreviated scale content validity
short-scale content coverage
brief form content validity
content validity for short forms

Messick (validity framework); Smith et al. (short-form standards)

Short-form content validity evaluates whether items retained in an abbreviated scale still adequately represent every substantive facet of the construct measured by the original full-length instrument. It ensures that shortening a scale does not hollow out the conceptual domain it was designed to cover.

Messick, S. (1995). Validity of psychological assessment: Validation of inferences from persons' responses and performances as scientific inquiry into score meaning. American Psychologist, 50(9), 741–749.

content-validity construct-validity short-form-scale-development short-form-item-analysis convergent-validity discriminant-validity


Short-form Cronbach's alpha

Short-Form Cronbach's Alpha Reliability Estimation

abbreviated scale alpha
brief scale internal consistency
short-scale Cronbach's alpha
reduced-item alpha

1951 (alpha); short-form practice codified 1980s–2000s

L. J. Cronbach (alpha); short-form application formalized across scale-abbreviation literature

Short-form Cronbach's alpha quantifies the internal consistency reliability of an abbreviated version of a psychological scale. It applies Cronbach's alpha formula to a reduced item set, verifying that the shortened instrument retains sufficient reliability to support valid score interpretation in research and applied contexts.

Internal consistency reliability coefficient

Ordinal or continuous items from an abbreviated scale

Smith, G. T., McCarthy, D. M. & Anderson, K. G. (2000). On the sins of short-form development. Psychological Assessment, 12(1), 102–111.

Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.

cronbachs-alpha mcdonalds-omega short-form-reliability-analysis short-form-scale-development item-response-theory confirmatory-factor-analysis


Short form differential item functioning

Short-Form Differential Item Functioning Analysis

Short-form DIF
abbreviated scale DIF
DIF in short forms
short-scale DIF detection

1970s–1990s (DIF); short-form context developed in parallel with scale abbreviation literature

Angoff, W. H. and subsequent DIF methodologists

Short-form differential item functioning (DIF) analysis examines whether individual items in an abbreviated scale function equivalently across demographic or subgroup comparisons. When a scale is shortened, retained items must still behave fairly for all relevant groups — DIF analysis verifies this, ensuring that score differences reflect true ability or trait differences rather than item bias.

Item bias / measurement fairness analysis

Ordinal or binary item responses from abbreviated scales

Millsap, R. E. (2012). Statistical Approaches to Measurement Invariance. Routledge.

Smith, R. M. (2000). Fit analysis in latent trait measurement models. Journal of Applied Measurement, 1(2), 199–218.

differential-item-functioning short-form-item-response-theory short-form-measurement-invariance item-response-theory short-form-confirmatory-factor-analysis measurement-invariance


Short form generalizability theory

G-theory for abbreviated scales
short-form G-study
abbreviated test generalizability
short-form D-study

1963–1972 (G-theory); short-form extension ongoing from 1980s

Lee J. Cronbach, Goldine Gleser, Harinder Nanda, Nageswari Rajaratnam

Short form generalizability theory applies the G-theory variance-component framework to abbreviated measurement instruments, using G-studies and D-studies to estimate how many items a short scale must retain to achieve a desired reliability and to evaluate the accuracy of decisions made with a condensed instrument.

Ordinal or continuous item scores from abbreviated instruments

Brennan, R. L. (2001). Generalizability Theory. Springer.

Shavelson, R. J., & Webb, N. M. (1991). Generalizability Theory: A Primer. Sage Publications.

generalizability-theory short-form-reliability-analysis short-form-scale-development short-form-item-response-theory cronbachs-alpha multilevel-reliability-analysis


Short-form item analysis

abbreviated scale item analysis
short-scale item evaluation
item screening for short forms
SFIA

Psychometric tradition; methodological articulation by Smith, McCarthy & Anderson (2000)

Short-form item analysis is the systematic psychometric evaluation and selection of items when constructing an abbreviated version of a longer measurement instrument. It applies classical and modern item-analysis criteria — item-total correlations, reliability estimates, and factor structure — to identify the smallest item subset that preserves the original scale's psychometric integrity.

Ordinal or interval item-level responses

scale-development item-response-theory cronbachs-alpha mcdonalds-omega exploratory-factor-analysis confirmatory-factor-analysis


Short-Form IRT

SF-IRT
abbreviated scale IRT
short-form calibration
shortened instrument IRT

Multiple contributors; IRT adapted to short-form contexts from Lord & Novick (1968) and subsequent applied psychometricians

Short-form item response theory applies IRT calibration and scoring to abbreviated or shortened psychological scales. It uses item information functions to guide which items to retain from a full-length instrument, then estimates latent trait scores from the reduced item set while preserving psychometric rigor and linkage to the full-scale metric.

Binary or polytomous item responses from abbreviated psychological scales

Embretson, S. E. & Reise, S. P. (2000). Item Response Theory for Psychologists. Lawrence Erlbaum Associates.


Short-form McDonald's omega

Short-Form McDonald's Omega Reliability Estimation

omega for abbreviated scales
short-scale omega
omega-total short form
abbreviated scale reliability

1999 (omega); short-form application 1990s–2000s

Roderick P. McDonald (omega); short-form application systematised across psychometric literature

Short-form McDonald's omega applies the omega reliability coefficient to abbreviated or shortened versions of psychological scales. It provides a theoretically sound reliability estimate that accounts for the multidimensional structure of the short instrument, enabling researchers to evaluate whether abbreviation has preserved the reliability of the original full-length scale.

Reliability coefficient for abbreviated scales

Ordinal or continuous item scores from a shortened instrument

McDonald, R. P. (1999). Test theory: A unified treatment. Lawrence Erlbaum Associates.

mcdonalds-omega cronbachs-alpha short-form-scale-development short-form-reliability-analysis confirmatory-factor-analysis item-response-theory


Short Form Measurement Invariance

Short Form Measurement Invariance Testing

SF-MI
abbreviated scale invariance
short-form factorial invariance
brief measure invariance

Adapted from Vandenberg & Lance (2000) and Millsap & Kwok (2004) invariance framework applied to short-form scales

Short form measurement invariance testing evaluates whether an abbreviated version of a psychological scale measures the same latent construct equivalently across groups or conditions. It applies the hierarchical multigroup confirmatory factor analysis invariance sequence — configural, metric, scalar, and strict — specifically to short-form instruments, ensuring that brevity does not introduce measurement bias when comparing subgroups.

Ordinal or continuous item scores from abbreviated scales

Millsap, R. E., & Kwok, O. M. (2004). Evaluating the impact of partial factor loading and intercept invariance on selection in two populations. Psychological Methods, 9(1), 93–115.

Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70.

measurement-invariance confirmatory-factor-analysis short-form-confirmatory-factor-analysis multi-group-measurement-invariance differential-item-functioning item-response-theory


Short form nomological validity

nomological validity of abbreviated scales

1955 (concept); 2000 (short-form context)

Cronbach & Meehl (nomological network concept); Smith et al. for short-form application

Short form nomological validity examines whether an abbreviated version of a psychological scale preserves the pattern of theoretically expected correlations with conceptually related and unrelated constructs. It is a cornerstone step in justifying the use of a shortened instrument in research and applied settings.

Correlations and regression coefficients between short-form scores and external criterion variables

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

confirmatory-factor-analysis convergent-validity discriminant-validity criterion-validity scale-abbreviation construct-validity


Short form Rasch model

Rasch analysis for abbreviated scales
short scale Rasch calibration
brief instrument Rasch model

1960 (Rasch model); short-form application from 1980s onward

The short form Rasch model applies Rasch measurement theory to abbreviated instrument versions. Rather than using all items from a full scale, researchers select a reduced item set and calibrate it under the Rasch model to verify that the shortened instrument preserves interval-level measurement, adequate person separation, and item fit, enabling efficient yet rigorous measurement with fewer items.

Dichotomous or polytomous ordinal item responses

Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. Danmarks Paedagogiske Institut.

Smith, E. V. Jr. (2000). Metric development and score reporting in Rasch measurement. Journal of Applied Measurement, 1(3), 303-326.

item-response-theory rasch-model short-form-item-response-theory short-form-confirmatory-factor-analysis differential-item-functioning measurement-invariance


Short-form reliability analysis

abbreviated scale reliability
short-form validation
scale shortening
item reduction reliability

Conventional practice; codified notably by Smith, McCarthy & Anderson (2000) and Stanton et al. (2002)

Short-form reliability analysis evaluates whether an abbreviated version of a psychological scale maintains acceptable internal consistency, validity, and structural integrity after items are removed. It is used in survey and assessment research to create briefer instruments that reduce respondent burden without sacrificing measurement quality.

Scale development / psychometric evaluation

Stanton, J. M., Sinar, E. F., Balzer, W. K. & Smith, P. C. (2002). Issues and strategies for reducing the length of self-report scales. Personnel Psychology, 55(1), 167–194.

cronbach-alpha exploratory-factor-analysis confirmatory-factor-analysis item-response-theory mcdonald-omega test-retest-reliability


Short-Form Scale Development

scale abbreviation
abbreviated scale development
short-scale construction
item reduction methodology

Multiple contributors; foundational critique by Smith, McCarthy & Anderson (2000); practical guidance by Stanton et al. (2002)

Short-form scale development is the systematic process of reducing a full-length psychological scale to a smaller subset of items while preserving the construct validity, reliability, and measurement properties of the original instrument. It is widely used when administration burden must be minimised without sacrificing psychometric quality.

Smith, G. T., McCarthy, D. M., & Anderson, K. G. (2000). On the sins of short-form development. Psychological Assessment, 12(1), 102–111.

scale-development exploratory-factor-analysis confirmatory-factor-analysis item-response-theory cronbachs-alpha measurement-invariance


Short-form test-retest reliability

abbreviated scale temporal stability
short-form temporal consistency
retest reliability of short forms
SF test-retest

Derived from classical test-retest reliability; short-form methodology formalised by Smith, McCarthy & Anderson (2000) among others

Short-form test-retest reliability quantifies how consistently an abbreviated version of a measurement instrument produces the same scores across two administrations separated by a defined time interval. It is a critical validation step whenever a full-length scale is shortened for practical use, confirming that item reduction has not degraded temporal stability.

Repeated ordinal or interval scale scores from abbreviated instruments

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

test-retest-reliability cronbach-alpha internal-consistency-reliability parallel-forms-reliability item-response-theory confirmatory-factor-analysis


SPPB

The Short Physical Performance Battery (SPPB) is a performance-based assessment developed by Guralnik and colleagues in 1994 at the National Institute on Aging to measure lower extremity physical function and functional mobility in older adults. It is widely used in clinical practice and epidemiological research to predict disability, institutionalization, and mortality in community-dwelling seniors.

Guralnik, J. M., Simonsick, E. M., Ferrucci, L., et al. (1994). A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol, 49(2), M85-M94.

Guralnik, J. M., Ferrucci, L., Pieper, C. F., et al. (2000). Lower extremity function and subsequent disability: consistency across studies, predictive models, and value of gait speed alone compared with the short physical performance battery. J Gerontol A Biol Sci Med Sci, 55(4), M221-M231.

Pahor, M., Guralnik, J. M., Ambrosius, W. T., et al. (2006). Effect of structured physical activity on prevention of major mobility disability in older adults: the LIFE study randomized clinical trial. JAMA, 311(23), 2387-2396.

tinetti-balance-assessment activities-balance-confidence frail-scale edmonton-frail-scale life-space-assessment


Short-Time Fourier Transform

Short-Time Fourier Transform (STFT) Analysis

STFT
Windowed Fourier Transform
Time-Frequency Analysis

The Short-Time Fourier Transform (STFT) is a fundamental signal analysis technique that computes the frequency content of a signal as it evolves over time by applying the Fourier transform to short, overlapping windows of the signal. Introduced conceptually by Dennis Gabor in 1946, the STFT provides a time-frequency representation essential for analyzing non-stationary signals where frequency content changes over time.

Gabor, D. (1946). Theory of Communication. Journal of the Institution of Electrical Engineers, 93(3), 429–457.

Oppenheim, A. V., Schafer, R. W., & Buck, J. R. (1999). Discrete-Time Signal Processing (2nd ed.). Prentice Hall.

power-spectral-density matched-filter fir-filter-design blind-source-separation


Shrinking Core Model

Shrinking Core Model for Leaching and Roasting

Shrinking Unreacted Core Model
SCM
Leaching Kinetics Model

The Shrinking Core Model, formalized by Szekely, Evans, and Sohn in 1976, describes the kinetics of chemical reactions between solid ore particles and surrounding fluids (leaching solutions, roasting gases). As the reaction proceeds from the particle surface inward, an unreacted core shrinks while products accumulate in a product layer. The model enables prediction of leaching times and optimization of hydrometallurgical processes.

Reaction kinetics model for solid-fluid reactions

Szekely, J., Evans, J. W., & Sohn, H. Y. (1976). Gas-solid reactions. Academic Press, New York.

Levenspiel, O. (1999). Chemical reaction engineering (3rd ed.). John Wiley & Sons.


Siamese Network

Siamese Neural Network (Deep Metric Learning)

twin network
Siamese neural network
contrastive metric network
Siyam ağı

Jane Bromley & Yann LeCun et al.; popularized by Koch et al.

A Siamese network is a deep architecture with two (or more) identical, weight-sharing branches that map inputs into an embedding space where similar inputs land close together and dissimilar ones far apart. Introduced by Bromley, LeCun, and colleagues in 1993 for signature verification and revived by Koch et al. (2015) for one-shot image recognition, it learns a similarity metric rather than fixed class labels, making it ideal for verification, matching, and few-shot tasks.

An embedding where similar inputs are close

Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., & Shah, R. (1993). Signature verification using a 'Siamese' time delay neural network. Advances in Neural Information Processing Systems, 6.

Koch, G., Zemel, R., & Salakhutdinov, R. (2015). Siamese neural networks for one-shot image recognition. ICML Deep Learning Workshop.

convolutional-neural-network visual-contrastive-learning k-nearest-neighbors autoencoder


SIAR Mixing Model

Stable Isotope Analysis in R (SIAR) Mixing Model

isotope mixing model
Bayesian mixing model
source apportionment
diet analysis

The Stable Isotope Analysis in R (SIAR) mixing model is a Bayesian framework for estimating the proportional contributions of dietary sources to a consumer, using stable isotope ratios. Developed by Parnell and colleagues (2010) and implemented in the R package siar (and its successor MixSIAR), this method integrates isotopic data from potential food sources and consumers to infer diets. It accounts for uncertainty in isotope fractionation (the shift in isotope ratios between diet and tissue) and natural variation among source populations, producing probability distributions rather than point estimates of diet composition.

Parnell, A. C., Inger, R., Bearhop, S., & Jackson, A. L. (2010). Source partitioning using stable isotopes: coping with too much variation. PLoS ONE, 5(3), e9672.

Jackson, A. L., Inger, R., Parnell, A. C., & Bearhop, S. (2011). Comparing isotopic niche widths among sympatric species: the role of phylogenetic relatedness. Ecology Letters, 14(8), 841-851.

Phillips, D. L., Inger, R., Bearhop, S., Jackson, A. L., Moore, J. W., Parnell, A. C., Semmens, B. X., & Ward, E. J. (2014). Best practices for use of stable isotope mixing models in food-web studies. Canadian Journal of Zoology, 92(10), 823-835.

food-web-topology leslie-matrix population-viability-analysis bioaccumulation-model


SIBTEST

SIBTEST (Simultaneous Item Bias Test) is a non-parametric method for detecting differential item functioning (DIF) and differential test functioning (DTF) developed by Shealy and Stout (1993). Unlike parametric approaches, SIBTEST does not assume a particular item response model and directly tests whether groups differ in their probability of correct responses at equal levels of overall ability.

Differential item functioning (DIF) assessment

Shealy, R., & Stout, W. F. (1993). A model-based standardization approach that separates true bias/DIF from group differences and detects test bias/DTF. Psychometrika, 58(2), 159-194.

Chang, H. H., Mazzeo, J., & Roussos, L. (1996). Detecting DIF for polytomously scored items: An adaptation of the SIBTEST procedure. Journal of Educational Measurement, 33(3), 333-353.

Stout, W. F. (1987). A nonparametric approach for assessing latent trait unidimensionality. Psychometrika, 52(4), 589-617.

rule-space-methodology dina-model dino-model cognitive-diagnostic-cat necessary-condition-analysis