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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, e
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 effe
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
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-base
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
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 rat
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 bro
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 estab
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 proc
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 rese
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 make
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 c
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. E
Simulation-assisted Box-Behnken design couples the three-level, near-spherical Box-Behnken experimental matrix with computer simulation models — such as finite-element analysis, computational fluid dynamics, or discrete-event simulation — to map how multiple controllable factors jointly affect one or more output respon
Simulation-assisted causal-comparative research is a hybrid observational design that combines the ex post facto logic of causal-comparative studies — comparing groups that differ on a naturally occurring variable — with computational simulation to strengthen causal inference, test counterfactuals, and assess the robus
Simulation-assisted cross-sectional research combines the one-time, population-wide snapshot of a classic cross-sectional survey with computational simulation — such as agent-based modelling or Monte Carlo methods — to extend what can be inferred from data collected at a single point in time. Empirical cross-sectional
Simulation-assisted design of experiments (SA-DoE) integrates computational simulation tools — such as finite element analysis (FEA), computational fluid dynamics (CFD), or discrete-event simulation — with classical DoE principles to systematically explore the factor space of a system. Rather than running costly or haz
Simulation-assisted ex post facto design is a non-experimental observational approach in which the researcher examines already-occurred events or conditions using existing records and then supplements the empirical analysis with computational simulation to approximate counterfactual scenarios that cannot be observed in
Simulation-assisted fractional factorial design (SA-FFD) combines the statistical efficiency of fractional factorial experimentation with computerized simulation models to screen and estimate factor effects when physical experiments are too costly, hazardous, or time-consuming. A carefully chosen subset of factor-level
Simulation-assisted full factorial design integrates full factorial design of experiments (DOE) with computer simulation models — such as discrete-event simulation, finite element analysis, or Monte Carlo methods — to systematically explore every combination of factor levels and quantify their effects on system respons
Simulation-assisted quality function deployment (SA-QFD) integrates computational simulation into the classic QFD framework to replace or supplement costly physical prototypes when evaluating how engineering design decisions satisfy customer requirements. By embedding simulation models — such as finite element analysis
Simulation-assisted response surface methodology (SA-RSM) combines computer simulation models — such as finite element analysis, computational fluid dynamics, or discrete-event simulation — with the statistical framework of response surface methodology to efficiently map, model, and optimize system responses. Instead o
Simulation-assisted root cause analysis (Sim-RCA) integrates computational simulation — such as discrete-event simulation, Monte Carlo methods, or finite-element analysis — into the structured root cause analysis process to diagnose the underlying causes of complex failures or defects. By running virtual experiments on
The simulation-assisted Taguchi method replaces or supplements physical prototypes with computer simulation models (finite element analysis, computational fluid dynamics, discrete-event simulation, etc.) to execute Taguchi orthogonal-array experiments. Signal-to-noise ratios and effects are computed from virtual runs,
The single-blind AB design is a single-subject experimental design that combines the two-phase AB structure — a baseline phase (A) followed by an intervention phase (B) — with assessor or observer masking. The individual collecting or rating outcome data is kept unaware of which phase is being measured, preventing know
The single-blind ABA design combines the three-phase reversal logic of the ABA single-subject design — baseline (A1), intervention (B), and withdrawal (A2) — with single-blind masking, in which outcome assessors are kept unaware of the current phase or treatment condition while the participant and intervention team rem
The single-blind ABAB design is a single-case experimental approach that sequences two baseline phases (A) and two intervention phases (B) to demonstrate experimental control over a target behavior, while keeping one party — typically the outcome assessor or the participant — unaware of current phase assignment. This b
A single-blind control group experimental design is a controlled experiment in which participants are kept unaware of whether they are receiving the active treatment or a control condition, while researchers and outcome assessors remain unmasked. The design uses a designated control group as the baseline for comparison
A single-blind factorial experiment combines factorial design — simultaneously varying two or more independent factors across all their level combinations — with single-blinding, in which participants are unaware of which treatment condition they have been assigned to while researchers and administrators remain unmaske
A single-blind field experiment combines real-world experimental conditions with partial blinding: either participants or outcome assessors — but not both — are kept unaware of treatment assignment. This design reduces demand characteristics or observer bias while preserving ecological validity, making it a practical m
A single-blind fractional factorial experiment studies multiple factors simultaneously by testing only a strategically chosen subset — a fraction — of all possible factor-level combinations, while keeping participants unaware of which treatment condition they receive. This design yields substantial information about ma
A single-blind full factorial experiment systematically tests every combination of all factor levels while keeping participants unaware of their treatment assignment. This design allows simultaneous estimation of all main effects and all interaction effects between factors, with single-blind masking reducing participan
A single-blind laboratory experiment is a controlled study conducted in a laboratory setting in which participants do not know which condition (e.g., treatment or control) they have been assigned to, while the researchers administering the conditions are aware. This masking of participants reduces demand characteristic
A single-blind multi-arm experiment is a controlled experimental design that simultaneously compares three or more treatment conditions while blinding participants — but not investigators — to their group assignment. This configuration reduces response bias driven by participants' expectations, preserves operational fe
A single-blind natural experiment leverages an exogenous, researcher-uncontrolled event — such as a policy change, lottery, or natural disaster — to create treatment and comparison groups, while applying single-blind procedures so that either the participants or the outcome assessors (but not both) are unaware of group
The single-blind pretest-posttest experimental design combines two protective strategies: measuring outcomes both before and after treatment to quantify change, and keeping participants unaware of which condition they are in. This pairing controls for preexisting group differences and expectancy-driven response bias, m
A single-blind randomized controlled trial (SB-RCT) is a rigorous experimental design in which participants are randomly assigned to treatment or control conditions while remaining unaware of which condition they have received. Investigators, outcome assessors, and data analysts are not blinded. By masking participants
A single-blind single-subject experimental design (SB-SSED) applies a single-blind protocol to an N-of-1 experiment: one individual participant is studied intensively across alternating or sequential phases, and either the participant or the assessor — but not both — is kept unaware of the current treatment condition.
A single-case study is a qualitative research design that investigates one bounded instance — an organization, program, event, individual, or community — in its real-world context through multiple converging sources of evidence. Developed into a rigorous social-science method chiefly by Robert Yin and Robert Stake, it
Single-subject experimental design (SSED) establishes experimental control by repeatedly measuring one individual (or a small number of individuals) across baseline and intervention phases, using the participant as their own control. Instead of comparing groups, it compares the participant's own behavior across conditi
The Social Capital Index measures the stock of social connections, networks, and civic participation within an individual's or community's social ecosystem. Rooted in the theoretical work of Pierre Bourdieu and popularized by Robert Putnam, social capital encompasses bonding capital (ties within homogeneous groups), br
The Social Cohesion Scale measures the degree to which members of a community feel integrated, connected, and unified by shared values and mutual support. Developed across multiple traditions—notably by Robert Sampson and colleagues in criminology and urban sociology, and by Forrest & Kearns in housing research—it asse
The Solomon Four-Group Design extends the classic pretest-posttest control-group design by adding two groups that receive no pretest, enabling researchers to detect whether the pretest itself alters participants' responses to the treatment. Introduced by Richard L. Solomon in 1949, it remains the gold standard for isol
Space syntax is a quantitative method that analyzes the spatial configuration of buildings and settlements to understand social organization and movement patterns. Developed by Bill Hillier and Julienne Hanson in the 1980s, space syntax measures how open or segregated spaces are, and how these properties relate to soci
Statistical Process Control (SPC) is a data-driven quality method that uses statistical techniques — primarily control charts — to monitor a manufacturing or service process over time. By distinguishing natural process variation (common cause) from unusual, actionable variation (special cause), SPC enables practitioner
Straussian Grounded Theory is a systematic qualitative methodology developed by Anselm Strauss and Juliet Corbin that generates theory inductively from data through structured coding procedures. Unlike exploratory description, it aims to produce a substantive mid-range theory that explains how a social process unfolds,
STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) is a 22-item evidence-based checklist published in 2007 by Von Elm et al. to improve the quality of reporting of cohort, case-control, and cross-sectional observational studies. Like CONSORT for RCTs, STROBE is endorsed by over 300 journals a
Strontium isotope provenance analysis uses the ratios of strontium-87 to strontium-86 in human skeletal remains to determine geographic origin and track human mobility and migration. Developed by Jonathan Ericson in the 1980s, this method exploits the fact that strontium isotope ratios in the environment vary geographi
Survey research is a quantitative (and sometimes mixed-methods) design in which a researcher collects standardised self-report data from a sample drawn from a defined population, using a questionnaire or structured interview. It is the dominant non-experimental strategy for describing population characteristics, estima
The Taguchi Method is a robust design methodology developed by Genichi Taguchi, first systematized in his 1987 work, that uses orthogonal arrays to study many control factors in a minimum number of experimental runs while quantifying product or process quality through Signal-to-Noise (S/N) ratios. Its central goal is t
Tephrochronology is a chronometric and stratigraphic technique that uses volcanic ash layers (tephra) as time markers to date and correlate archaeological and geological deposits. Pioneered by Icelandic geologist Sigurdur Thorarinsson in 1944, it exploits the fact that large explosive volcanic eruptions deposit distinc
Textual criticism is a systematic philological method for identifying, comparing, and evaluating variant readings across multiple manuscript or print witnesses of a text in order to reconstruct the most accurate version of the original — or the author's intended — text. Applied since antiquity to classical, biblical, a
Transcendental phenomenology, founded by Edmund Husserl, is a qualitative method that seeks the universal essential structures — the invariant essences — of a consciously lived experience. By bracketing all assumptions and prior theories (epoché) and applying eidetic reduction, the researcher uncovers what an experienc
Transformative mixed methods design embeds a social-justice or advocacy theoretical framework — such as feminism, critical race theory, disability studies, or indigenous worldviews — as the overarching lens that guides every decision about data collection, integration, and use. Both quantitative and qualitative strands
Trend research is a longitudinal quantitative design that tracks changes in a characteristic of a general population over time by surveying different, independently drawn samples at two or more time points. Unlike panel studies, the same individuals are not followed; rather, each wave draws a fresh sample from the same
Trustworthiness is a framework for evaluating the quality and rigor of qualitative research, developed by Lincoln and Guba (1985) as an alternative to quantitative criteria (internal validity, external validity, reliability, objectivity). The framework comprises five criteria: credibility (findings are accurate and gro
Typography Legibility Testing is a systematic method for evaluating how easily and accurately audiences can read typefaces in specific contexts. Pioneered by Miles A. Tinker in the mid-twentieth century, this pipeline combines perceptual metrics, user testing, and psychophysical measurement to ensure text achieves opti
Typological analysis is a systematic method for grouping objects, texts, legal categories, or social phenomena into defined types based on shared attributes. Originating in archaeology and linguistics, it is now widely applied across the humanities and social sciences to impose analytical order on diverse corpora, trac
An unstructured interview is a qualitative data-collection method in which the researcher enters the conversation with a broad topic or grand-tour question rather than a fixed questionnaire, allowing the participant to direct the flow and depth of the discussion. The approach prioritises the participant's own conceptua
Use-wear analysis (also called microwear or tool-use analysis) is a method that infers the function of stone tools from microscopic wear patterns on their cutting edges and surfaces. Pioneered by Lawrence Keeley in the 1970s-1980s, this technique examines damage patterns, polishes, and edge rounding produced as tools c