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方法统计学1,836人工智能与机器学习1,661决策科学932研究方法1,354测量1,745因果与证据532研究实践118
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research design

Quantitative-dominant explanatory sequential mixed methods

The quantitative-dominant explanatory sequential mixed methods design is a two-phase mixed methods approach in which a larger, primary quantitative study is conducted first, followed by a smaller, secondary qualitative phase that explains, elaborates, or contextualises the quantitative results. Quantitative evidence ca

2 个来源2007
research design

Quantitative-dominant intervention mixed methods

Quantitative-dominant intervention mixed methods design embeds a qualitative component within a predominantly quantitative intervention study — typically a randomized controlled trial or quasi-experiment — where the quantitative strand carries the primary weight in determining efficacy, while the qualitative strand exp

2 个来源2007
research design

Quantitative-dominant mixed methods meta-inference

Quantitative-dominant mixed methods meta-inference is an integration procedure in which the researcher draws an overarching conclusion by combining inferences from both quantitative and qualitative strands, while explicitly assigning greater evidential weight to the quantitative results. The qualitative strand serves a

2 个来源2003
research design

Quantitative-dominant multiphase mixed methods

A quantitative-dominant multiphase mixed methods design conducts a series of distinct research phases — at least two, often three or more — in which quantitative data and analyses bear the primary weight of answering the research questions, while qualitative components serve a supporting or explanatory role. Phases are

2 个来源2000
research design

Quantitative-dominant pragmatic mixed methods

A mixed methods design in which quantitative data and analysis carry the primary explanatory weight while a smaller qualitative component provides contextual depth. Grounded in philosophical pragmatism, design decisions — including timing, sequencing, and the scope of each strand — are driven by what best answers the r

2 个来源1998
research design

Quantitative-dominant transformative mixed methods

Quantitative-dominant transformative mixed methods design embeds a transformative theoretical lens — such as feminist, critical race, or disability theory — as the overarching framework of a study while assigning greater weight and priority to quantitative data collection and analysis. Qualitative data play a secondary

2 个来源2003
research design

Quantitative-priority mixed methods design

Quantitative-priority mixed methods design is a research approach in which quantitative data and analysis carry the primary explanatory weight, while qualitative data play a supplementary or corroborating role. The researcher collects and analyzes quantitative data first (or concurrently with greater emphasis), then us

2 个来源2003
experimental design

Randomized Complete Block Design

The Randomized Complete Block Design (RCBD) is a parametric experimental design and hypothesis-testing framework that isolates and removes a known source of heterogeneity — called a block — before comparing treatment means. Introduced by Ronald A. Fisher in his 1935 monograph The Design of Experiments, it remains the f

2 个来源1935
experimental design

Randomized Controlled Trial

A randomized controlled trial (RCT) is the gold standard experimental design in clinical and health research, in which participants are randomly allocated to a treatment group or a control group so that the effect of an intervention can be measured with the highest possible degree of internal validity. The modern paral

2 个来源1948
media studies

Reception Analysis

Reception Analysis is a methodological approach to studying media that focuses on how audiences actively interpret, engage with, and create meanings from media content rather than passively consuming predetermined messages. Developed from literary reception aesthetics and adapted to media studies by scholars like Stuar

4 个来源1972
qualitative research

Reflexivity in Qualitative Research

Reflexivity is the practice of examining how the researcher's identity, assumptions, relationships, and values influence the research process and findings. Rather than treating objectivity as achievable detachment, reflexivity acknowledges that the researcher is embedded within the research and cannot be fully separate

4 个来源1990
research methodology

Research Design Types

Research design is the overall structure and strategy of a study, encompassing decisions about how to collect, organize, and analyze data to answer research questions. Major design types include experimental (randomized controlled trials), quasi-experimental (non-random assignment), observational (no manipulation), and

3 个来源1963
research methodology

Research Question Formulation

Research question formulation is the process of defining clear, focused, and answerable questions that guide a research study. A well-formulated research question specifies what a researcher seeks to investigate, distinguishing between independent and dependent variables (or phenomena), and sets the scope for literatur

3 个来源1950
experimental design

Response Surface Methodology

Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was intro

2 个来源1951
experimental design

Risk-based Box-Behnken Design

Risk-based Box-Behnken Design combines the classical three-level Box-Behnken response surface design with a formal risk assessment step — typically a risk ranking tool such as FMEA or Ishikawa analysis — to prioritize which process or formulation factors deserve experimental investigation. Widely adopted in pharmaceuti

2 个来源2005
experimental design

Risk-based central composite design

Risk-based Central Composite Design (Risk-based CCD) integrates formal risk identification and uncertainty quantification into the classical CCD framework. By coupling the rotatable second-order experimental structure of CCD with probabilistic risk metrics, engineers and scientists can simultaneously optimize process r

2 个来源1951
experimental design

Risk-based control chart

A risk-based control chart extends the classical Shewhart control chart by explicitly incorporating the costs and probabilities of two error types — false alarms (Type I) and missed shifts (Type II) — along with sampling costs, into the design of chart parameters. Rather than using arbitrary 3-sigma limits, the method

2 个来源1956
experimental design

Risk-based design of experiments

Risk-based design of experiments (RB-DoE) integrates formal risk assessment — typically using tools such as FMEA or fault tree analysis — with classical experimental design to prioritize which process or product factors are most critical to investigate. Rather than treating all candidate factors equally, this approach

2 个来源2000
experimental design

Risk-based event tree analysis

Risk-based event tree analysis is a forward-looking, inductive risk assessment technique that models the consequences of an initiating event by tracing binary success/failure branches through safety barriers, then weights each outcome path by its probability to produce quantified risk estimates. Widely applied in nucle

2 个来源1975
experimental design

Risk-based failure mode and effects analysis

Risk-based failure mode and effects analysis (RBFMEA) is a structured engineering technique that identifies every way a system or process can fail, assesses the risk of each failure mode using a numerical Risk Priority Number (RPN = Occurrence × Severity × Detectability), and prioritises corrective actions accordingly.

2 个来源1949
experimental design

Risk-based fault tree analysis

Risk-based fault tree analysis (RB-FTA) combines classical fault tree analysis with explicit quantitative risk assessment. Starting from an undesired top event, the analyst decomposes it into contributing causes using AND/OR logic gates, assigns failure probabilities to basic events from reliability databases or histor

2 个来源1961
experimental design

Risk-based full factorial design

Risk-based full factorial design integrates formal risk analysis — typically Failure Mode and Effects Analysis (FMEA) or a comparable risk-ranking tool — with a full factorial experiment to ensure that factors posing the greatest quality or safety risk receive exhaustive experimental coverage. All combinations of selec

2 个来源2000
experimental design

Risk-based Process Capability Analysis

Risk-based Process Capability Analysis (RBPCA) combines classical process capability indices (Cp, Cpk, Pp, Ppk) with structured risk assessment tools — such as FMEA risk priority numbers — to prioritise improvement actions not merely by how capable a process is, but by the potential harm its failures can cause. The app

2 个来源1990
experimental design

Risk-based quality function deployment

Risk-based quality function deployment (Risk-based QFD) integrates formal risk analysis — most commonly Failure Mode and Effects Analysis (FMEA) or risk matrices — into the classic QFD House of Quality framework. By weighting customer requirements and engineering characteristics against their associated failure risks,

2 个来源1990
experimental design

Risk-based reliability analysis

Risk-based reliability analysis (RBRA) is an engineering methodology that combines classical reliability analysis — quantifying failure rates, component lifetimes, and system dependability — with risk assessment frameworks that weigh the severity and consequences of each failure mode. By ranking failures according to b

2 个来源1960
experimental design

Risk-based Response Surface Methodology

Risk-based Response Surface Methodology (Risk-based RSM) extends classical RSM by embedding probabilistic risk or reliability constraints into the experimental optimization process. Rather than seeking a single optimal point under deterministic conditions, it identifies factor settings that achieve performance goals wh

2 个来源1990
experimental design

Risk-based Root Cause Analysis

Risk-based Root Cause Analysis (RBRCA) integrates classical root cause investigation with quantitative or semi-quantitative risk assessment to ensure that corrective actions are directed first at the causes that carry the highest probability and consequence of recurrence. Unlike standard RCA, which identifies root caus

2 个来源1990
experimental design

Risk-based statistical process control

Risk-based statistical process control (Risk-based SPC) is an engineering quality method that integrates formal risk analysis — typically FMEA or a risk matrix — with statistical process monitoring to focus control chart resources on the process parameters that pose the greatest risk to product quality or system safety

2 个来源1920
experimental design

Risk-based Taguchi method

The risk-based Taguchi method combines Genichi Taguchi's robust parameter design framework with explicit risk identification and quantification. By overlaying a risk assessment layer — typically drawing on failure mode analysis or probabilistic criteria — onto the standard signal-to-noise ratio optimization process, th

2 个来源1950
experimental design

Robust Box-Behnken Design

Robust Box-Behnken design combines the efficiency of the Box-Behnken design (BBD) — a three-level response surface design requiring no corner runs — with robust parameter design principles to identify factor settings that optimize the mean response while simultaneously minimizing sensitivity to uncontrollable noise fac

2 个来源1960
experimental design

Robust Central Composite Design

Robust Central Composite Design (Robust CCD) combines the efficient quadratic fitting capability of the central composite design with robust optimization principles to find factor settings that simultaneously achieve a target mean response and minimize the effect of uncontrollable noise factors on response variability.

2 个来源1951
experimental design

Robust Control Chart

A robust control chart replaces the classical mean and standard deviation estimators in a Shewhart-style chart with resistant alternatives — such as the median and median absolute deviation (MAD) — so that a small fraction of outliers or non-normal process data cannot distort the control limits. The approach preserves

2 个来源1989
experimental design

Robust event tree analysis

Robust Event Tree Analysis (Robust ETA) extends classical event tree analysis by explicitly accounting for uncertainty in the probability estimates assigned to each branch. Rather than treating branch probabilities as precise point values, the robust approach represents them as intervals, probability distributions, or

2 个来源1960
experimental design

Robust Failure Mode and Effects Analysis

Robust Failure Mode and Effects Analysis extends the classical FMEA framework by explicitly incorporating noise factors, parameter variability, and environmental variation into the risk assessment process. Rather than treating failure likelihood as a single deterministic estimate, it uses robust design principles — mos

2 个来源1980
experimental design

Robust Fault Tree Analysis

Robust Fault Tree Analysis (Robust FTA) extends classical fault tree analysis by explicitly representing and propagating uncertainty in component failure probabilities. Rather than assigning single point estimates to basic events, it uses probability distributions, interval bounds, or imprecise probabilities, then prop

2 个来源1980
experimental design

Robust Fractional Factorial Design

Robust fractional factorial design combines the run-count efficiency of fractional factorial arrays with Taguchi's robust parameter design philosophy. By simultaneously manipulating control factors (inner array) and noise factors (outer array) — each structured as a fractional factorial — the method identifies factor s

2 个来源1980
experimental design

Robust Full Factorial Design

Robust full factorial design extends the classical full factorial experiment by explicitly including noise factors — uncontrollable variables that cause performance variation in real-world conditions. By crossing all control factor levels with all noise factor levels in a single combined array, engineers identify contr

2 个来源1980
research design

Robust Model Testing Research

Robust model testing research applies structural or path models to data while explicitly accounting for violations of multivariate normality and other distributional assumptions. Rather than discarding non-normal data or forcing transformations, it uses corrected estimators — most notably the Satorra-Bentler scaled chi

2 个来源1988
experimental design

Robust Process Capability Analysis

Robust process capability analysis extends classical capability indices (Cp, Cpk, Ppk) by replacing the sample mean and standard deviation with robust location and scale estimators — such as the median, trimmed mean, MAD, or IQR-based spread — so that outliers and non-normal process distributions do not inflate or dist

2 个来源1990
experimental design

Robust Quality Function Deployment

Robust Quality Function Deployment (Robust QFD) extends the classical House of Quality framework by explicitly modeling uncertainty and variability in customer requirements, perception ratings, and engineering correlation judgments. Instead of treating inputs as crisp single-point values, it applies fuzzy sets, interva

2 个来源2000
research design

Robust Quantitative Content Analysis

Robust quantitative content analysis is a systematic method for coding and counting manifest or latent features of communication content — texts, images, or media — while applying statistical estimators that are resistant to outliers, skewed distributions, and coding inconsistencies. By combining the structured coding

2 个来源1980
experimental design

Robust Reliability Analysis

Robust reliability analysis is an engineering method that combines classical reliability estimation with robustness principles to quantify and improve system dependability in the presence of parameter uncertainty and variability. Rather than assuming fixed input values, it propagates distributions of noise factors thro

2 个来源1980
experimental design

Robust Response Surface Methodology

Robust Response Surface Methodology (Robust RSM) is an experimental optimization strategy that simultaneously fits two regression models — one for the mean response and one for its variance (or standard deviation) — across a designed experiment. By jointly optimizing these dual surfaces, engineers identify factor setti

2 个来源1990
experimental design

Robust Root Cause Analysis

Robust Root Cause Analysis (Robust RCA) integrates classical root cause investigation techniques — such as the 5-Whys, Ishikawa diagrams, and fault trees — with Taguchi's robustness thinking to identify not only the primary cause of a failure but also the noise factors and variability sources that allow the failure to

2 个来源1990
experimental design

Robust Statistical Process Control

Robust Statistical Process Control (Robust SPC) is an engineering quality-monitoring framework that replaces the classical mean and standard deviation estimators used in Shewhart-type control charts with outlier-resistant alternatives — such as the median, MAD, or trimmed statistics — so that isolated contaminating obs

2 个来源1989
research methodology

Sampling Methods in Research

Sampling is the process of selecting a subset of individuals, observations, or units (the sample) from a larger population to study. Sampling methods are broadly classified into probability (random) and non-probability (non-random) approaches. Probability methods—random sampling, stratified sampling, cluster sampling,

3 个来源1950
qualitative

Selective Coding

Selective coding is the third and final analytic phase of grounded theory, in which the researcher systematically identifies one central or core category that integrates all other major categories developed during open and axial coding. The outcome is a coherent, data-grounded substantive theory that explains the main

2 个来源1967
qualitative

Semiotic Analysis

Semiotic analysis is a qualitative method for interpreting how signs — words, images, sounds, gestures, and objects — produce and communicate meaning within a cultural context. Drawing on the structural linguistics of Ferdinand de Saussure and the triadic sign theory of Charles Sanders Peirce, and popularised as a rese

2 个来源1906
media studies

Semiotics in Film Studies

Semiotics in Film Studies is a systematic method for analyzing how film produces meaning through signs, codes, and symbolic systems. Developed from linguistic semiotics and adapted to cinema by scholars like Roland Barthes, Christian Metz, and Umberto Eco, it examines how visual, auditory, and narrative elements functi

4 个来源1968
experimental design

Sensitivity Analysis with Box-Behnken Design

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

2 个来源1960
experimental design

Sensitivity analysis with central composite design

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 mo

2 个来源1951
experimental design

Sensitivity Analysis with Control Chart

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 cha

2 个来源1990
experimental design

Sensitivity analysis with event tree analysis

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

2 个来源1990
experimental design

Sensitivity analysis with failure mode and effects analysis

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 i

2 个来源1990
experimental design

Sensitivity analysis with fault tree analysis

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 f

2 个来源1961
experimental design

Sensitivity Analysis with Fractional Factorial Design

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, i

2 个来源1935
experimental design

Sensitivity Analysis with Process Capability Analysis

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 specifica

2 个来源1986
experimental design

Sensitivity Analysis with Quality Function Deployment

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 param

2 个来源1990
experimental design

Sensitivity Analysis with Reliability Analysis

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 fo

2 个来源1969
experimental design

Sensitivity analysis with root cause analysis

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 f

2 个来源1990
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