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Khám phá khoa học theo phương pháp, lĩnh vực và bằng chứng.

Một danh mục duy nhất về các phương pháp nghiên cứu — tìm hiểu cách mỗi phương pháp hoạt động, khi nào nên dùng và điều nó không làm được.

6,435 phương pháp11 lĩnh vực7 họ phương pháp40 ngôn ngữ
Atlas khoa họcLập bản đồ cấu trúc của khoa học trước khi sử dụng.Lĩnh vực · phương pháp · lộ trình bằng chứngKhám phá bản đồ
Lĩnh vựcHealth & Medicine716Psychology570Business & Finance410Engineering330Life Sciences263Education261Research Practice
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Thư viện tham khảo về phương pháp nghiên cứu, đặt nội dung lên hàng đầu — mỗi phương pháp là gì, hoạt động ra sao và bắt nguồn từ đâu.

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Các mục từ được biên soạn từ những nguồn đã công bố nhằm mục đích tham khảo. Việc kiểm chứng tính chính xác và mức độ phù hợp của bất kỳ thông tin nào cho mục đích sử dụng của bạn vẫn thuộc trách nhiệm của bạn.

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Natural Sciences236
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Phương phápThống kê1,836Trí tuệ nhân tạo & học máy1,661Khoa học quyết định932Phương pháp nghiên cứu1,354Đo lường1,745Nhân quả & bằng chứng532Thực hành nghiên cứu118
493 phương pháp · Nhân quả & bằng chứngXóa
Các phương pháp thực khớp với bộ lọc của bạn.
Sắp xếpĐộ phổ biếnA–ZZ–AMới nhất
causal inference

Robust Counterfactual Impact Evaluation

Robust Counterfactual Impact Evaluation (Robust CIE) strengthens causal impact estimates by combining multiple quasi-experimental estimators, placebo tests, and formal sensitivity analyses. Rather than relying on a single method, it cross-validates findings across approaches — such as matching, difference-in-difference

2 nguồn2010
causal inference

Robust Difference-in-Differences

Robust Difference-in-Differences is a family of modern DiD estimators designed to remain valid when treatment timing is staggered across units and treatment effects are heterogeneous over time or across groups. Classical two-way fixed-effects (TWFE) DiD can be severely biased in such settings; robust variants estimate

2 nguồn2021
causal inference

Robust Fuzzy Regression Discontinuity

Robust Fuzzy Regression Discontinuity Design estimates a local average treatment effect (LATE) at a threshold where crossing the cutoff raises — but does not guarantee — treatment receipt. Introduced by Calonico, Cattaneo, and Titiunik (2014), the robust framework applies bias-corrected local polynomial estimation with

2 nguồn2014
causal inference

Robust Instrumental Variables

Robust Instrumental Variables estimation extends standard IV and two-stage least squares (2SLS) by guarding against weak-instrument bias and non-standard inference. Methods such as the Anderson-Rubin test, Limited Information Maximum Likelihood (LIML), and the Conditional Likelihood Ratio test provide valid confidence

2 nguồn1949
causal inference

Robust Interrupted Time Series

Robust Interrupted Time Series Analysis is a quasi-experimental method that estimates the causal effect of a policy or intervention on an aggregate outcome over time, using segmented regression fitted with outlier-resistant or heteroskedasticity-consistent standard errors. It is widely used in health services research

2 nguồn2010
causal inference

Robust Inverse Probability Weighting

Robust Inverse Probability Weighting is a causal inference estimator that reweights observed units by stabilized or trimmed propensity score weights, then applies sandwich or bootstrap variance estimation to guard against model misspecification, extreme weights, and inflated standard errors. It extends standard IPW to

2 nguồn2000
causal inference

Robust Marginal Structural Model

Robust Marginal Structural Models (robust MSMs) extend the standard MSM framework — which uses inverse probability of treatment weighting to handle time-varying confounding — by pairing IPTW estimation with sandwich (robust) standard errors or doubly-robust estimators. This combination yields valid causal estimates and

2 nguồn2000
causal inference

Robust Matching Estimator

The robust matching estimator, developed by Abadie and Imbens (2006, 2011), extends nearest-neighbor matching by adding a regression-based bias correction that removes the finite-sample bias arising when matched units are not perfectly alike. It yields consistent, asymptotically normal estimates of average treatment ef

2 nguồn2006
causal inference

Robust Panel Event Study

A robust panel event study extends the standard panel event study design by applying heteroskedasticity- and autocorrelation-robust (HAC) standard errors and, where staggered treatment adoption exists, interaction-weighted estimators that remain valid even when treatment effects are heterogeneous across cohorts and tim

2 nguồn2021
causal inference

Robust Propensity Score Matching

Robust Propensity Score Matching (robust PSM) is a quasi-experimental causal inference method that pairs treated and control units on their estimated probability of receiving treatment (the propensity score), then estimates the average treatment effect using variance estimators that account for the uncertainty introduc

2 nguồn2016
causal inference

Robust Propensity Score Weighting

Robust Propensity Score Weighting extends standard inverse probability weighting by incorporating safeguards against misspecification of the propensity score model and extreme weights. It combines techniques such as weight trimming, overlap weighting, or augmented outcome models to ensure that causal effect estimates r

2 nguồn1994
causal inference

Robust Synthetic Control Method

The robust synthetic control method extends the classic synthetic control estimator by providing statistically valid uncertainty quantification and inference. Developed by Cattaneo, Feng and Titiunik (2021), it addresses a core limitation of the original approach — the lack of formal prediction intervals — making causa

2 nguồn2021
health education

RPQ

The RPQ is a self-report instrument measuring the degree to which healthcare students and professionals engage in reflective practice—the deliberate examination of their clinical experiences, decisions, and actions to extract learning and improve future practice. Developed by Sobral and refined by Saarikoski and collea

2 nguồn2000
healthcare management

Safety Attitudes Questionnaire

The Safety Attitudes Questionnaire (SAQ) is a 60-item self-report instrument developed by Sexton and colleagues in the early 2000s to measure organizational safety culture in healthcare settings. Adapted from crew resource management research in aviation, the SAQ assesses clinician and non-clinician perceptions of safe

2 nguồn2000
implementation science

Scaling Up Health Interventions

Scaling Up is the deliberate expansion of successful health interventions from pilot sites to entire health systems, regions, or countries. Formalized by the World Health Organization (WHO) and Simmons et al. (2007), scaling up is distinct from simple dissemination; it requires systematic planning, financial modeling,

3 nguồn2007
scientometrics

Scoping Review

A scoping review is a systematic evidence-synthesis method that maps the breadth and nature of research on a topic — identifying key concepts, evidence types, and gaps — without necessarily appraising study quality or pooling effect sizes. Developed by Arksey and O'Malley (2005) and refined by Levac and colleagues (201

2 nguồn2005
evidence synthesis

Scoping Review Methodology

A scoping review is a structured, transparent literature mapping method that identifies and synthesizes evidence across a defined topic without formally assessing study quality or generating pooled effect estimates. Developed by Arksey and O'Malley (2005) and refined by the Joanna Briggs Institute (JBI) and PRISMA-ScR

3 nguồn2005
health education

SCPS

The SCPS is a self-report questionnaire measuring students' overall satisfaction with their clinical placement experience, including satisfaction with the learning environment, educator support, clinical opportunities, and facility resources. Originally developed by Papastavrou and colleagues in Cyprus (2007–2010), the

2 nguồn2007
epidemiology

Screening Test Evaluation

Screening test evaluation is a systematic epidemiological approach for assessing whether a test or program can accurately and cost-effectively identify individuals with a condition before symptoms appear. It quantifies diagnostic performance metrics — sensitivity, specificity, predictive values, and the ROC curve — and

2 nguồn1968
epidemiology

SEIR Model

The SEIR model is a deterministic compartmental model that partitions a closed population into four epidemiological states: Susceptible (S), Exposed (E), Infectious (I), and Recovered (R). It extends the classic SIR framework by explicitly incorporating a latent period during which individuals have been infected but ar

1 nguồn1991
causal inference

Sensitivity Analysis for Causality

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

2 nguồn1983
causal inference

Sensitivity Analysis for Unmeasured Confounding

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-val

2 nguồn2002
causal inference

Shift-Share IV

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

2 nguồn2020
epidemiology

SIR Model

The SIR model is a foundational mathematical framework for describing the spread of infectious diseases through a population. Introduced by William Ogilvy Kermack and Anderson Gray McKendrick in 1927, it partitions a closed population of size N into three mutually exclusive compartments: Susceptible (S), Infectious (I)

1 nguồn1927
implementation science

SoC

The Stages of Concern Questionnaire (SoC) is a 35-item self-report instrument that measures the types and intensity of concerns individuals experience when adopting new practices, technologies, or innovations. Developed by Hall and colleagues in the 1970s as part of the Concerns-Based Adoption Model (CBAM), the SoC mea

2 nguồn1977
health informatics

Social Media Anxiety Scale

The Social Media Anxiety Scale measures the extent to which individuals experience anxiety, apprehension, and psychological distress related to social media use. Developed by Przybylski and colleagues (2013) and expanded by Elhai and colleagues, the scale captures the 'Fear of Missing Out' (FOMO) construct—anxiety abou

2 nguồn2013
causal inference

Spatial Causal Impact Analysis

Spatial causal impact analysis estimates the causal effect of a spatially-targeted intervention — a policy, shock, or treatment applied to particular locations — while explicitly accounting for geographic spillovers between treated and untreated units. By combining quasi-experimental designs such as difference-in-diffe

2 nguồn2010
causal inference

Spatial Coarsened Exact Matching

Spatial Coarsened Exact Matching applies the Coarsened Exact Matching framework to study designs involving geographic units — neighbourhoods, census tracts, municipalities, or grid cells. Covariates are coarsened into discrete bins and units are matched exactly on those bins, with spatial attributes (location, adjacenc

2 nguồn2012
causal inference

Spatial Counterfactual Impact Evaluation

Spatial Counterfactual Impact Evaluation (SCIE) is a family of quasi-experimental methods that estimate the causal effect of geographically targeted policies — such as EU Cohesion Funds, enterprise zones, or place-based subsidies — by constructing a spatial counterfactual: what outcomes the treated region would have ex

2 nguồn2010
causal inference

Spatial Doubly Robust Estimation

Spatial doubly robust estimation is a semiparametric causal inference method that combines propensity score weighting with outcome regression modeling — providing protection against misspecification of either component — while explicitly accounting for spatial autocorrelation among units. It extends the classical augme

2 nguồn2010
causal inference

Spatial Entropy Balancing

Spatial entropy balancing extends standard entropy balancing to observational settings where units are embedded in geographic space, incorporating spatial structure into the reweighting process so that balance is achieved while respecting spatial proximity, clustering, or spillover dependencies between units.

2 nguồn2010
causal inference

Spatial Event Study Design

Spatial event study design estimates the dynamic causal effects of a geographically concentrated shock or policy by plotting how outcomes in affected locations evolve relative to unaffected locations across time periods, while explicitly accounting for spatial spillovers and autocorrelation across geographic units. It

2 nguồn2000
causal inference

Spatial Fuzzy Regression Discontinuity

Spatial Fuzzy Regression Discontinuity Design (Spatial Fuzzy RDD) estimates a local average treatment effect when a geographic boundary determines treatment eligibility but some units on either side of the boundary fail to comply with their assigned status. It combines the spatial running-variable logic of geographic R

2 nguồn2015
causal inference

Spatial Instrumental Variables

Spatial Instrumental Variables (Spatial IV) is a causal inference method for settings where units — regions, firms, neighborhoods — are spatially interdependent, creating endogeneity that standard IV approaches ignore. It constructs instruments from the spatially lagged values of exogenous characteristics of neighborin

2 nguồn1988
causal inference

Spatial Interrupted Time Series

Spatial Interrupted Time Series (Spatial ITS) extends the classic ITS design to settings where units are geo-referenced and outcomes in one location may spill over into or correlate with outcomes in neighbouring locations. It estimates the causal effect of a discrete intervention on an outcome time series while explici

2 nguồn1990
causal inference

Spatial Inverse Probability Weighting

Spatial Inverse Probability Weighting extends the classical IPW estimator to settings where units are geo-referenced and spatial location is a confounding dimension. By incorporating geographic coordinates or spatial proximity into the propensity score model, it reweights the observed sample so that treatment and contr

2 nguồn2010
causal inference

Spatial Marginal Structural Model

The Spatial Marginal Structural Model (Spatial MSM) extends the classical marginal structural model to settings where units are geographically distributed and spatial dependencies — such as neighborhood spillovers, clustering, and spatial confounding — may bias causal estimates. It estimates causal effects of spatially

2 nguồn2000
causal inference

Spatial Matching Estimator

The Spatial Matching Estimator estimates causal treatment effects by pairing each treated geographic unit with one or more similar untreated units nearby, exploiting the assumption that units close in space share similar unobserved characteristics. By restricting matches to a geographic neighbourhood or weighting by sp

2 nguồn2000
causal inference

Spatial Panel Event Study

Spatial panel event study extends the classical panel event-study design to settings where units are geographically located and outcomes may spill over across space. By combining event-time indicators with spatial weights matrices, it estimates dynamic treatment effects while explicitly accounting for spatial autocorre

2 nguồn2010
causal inference

Spatial Placebo Test

A spatial placebo test is a falsification check used in geographic or spatial causal-inference studies. The analyst applies the same estimation procedure to spatial units, boundaries, or zones where no treatment effect should exist — fake borders, shifted cutoffs, or buffer areas beyond spillover range — and checks whe

2 nguồn2000
causal inference

Spatial Propensity Score Matching

Spatial Propensity Score Matching (Spatial PSM) extends the classic propensity score matching framework to settings where units are embedded in geographic space and treatment assignment or outcomes may be spatially correlated. By incorporating spatial covariates and adjacency structure into the propensity model and mat

2 nguồn2000
causal inference

Spatial Regression Discontinuity Design

Spatial Regression Discontinuity Design uses a geographic or administrative boundary as the threshold that assigns units to treatment. Observations just inside one side of the boundary are compared with those just outside it, exploiting the near-random variation in treatment status near the cutoff to recover a local ca

2 nguồn2010
causal inference

Spatial Sensitivity Analysis for Causality

Spatial sensitivity analysis for causality systematically tests whether a causal estimate derived from georeferenced data holds up as spatial structure, spillovers, and the choice of spatial weights matrix are varied. Because nearby units often share unmeasured confounders — soil quality, local infrastructure, neighbou

2 nguồn1988
causal inference

Spatial Synthetic Control Method

The Spatial Synthetic Control Method adapts the classic synthetic control framework to settings where treated and donor units are defined by geographic location. By constructing a weighted combination of spatially proximate or comparable control regions, the method estimates what would have happened to a treated area a

2 nguồn2003
healthcare management

Staffing Ratio Analysis

Staffing Ratio Analysis is a systematic method for determining appropriate healthcare worker levels (nurses, physicians, technicians) based on patient volume, acuity, and task requirements. Research shows that staffing levels directly impact patient safety, quality, and staff burnout; systematic analysis supports evide

3 nguồn1990
health behavior

Stages of Change Questionnaire

The Transtheoretical Model (TTM), also called the Stages of Change model, is a framework developed by James Prochaska and Carlo DiClemente in 1983 to understand how people modify problematic behaviors and adopt healthier ones. The central premise is that behavior change is not an all-or-nothing event but a process that

2 nguồn1983
causal inference

Staggered Difference-in-Differences

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

2 nguồn2021
causal inference

Stepped Wedge Cluster Randomized Trial

A stepped wedge cluster randomized trial is an experimental design where clusters (e.g., schools, hospitals, communities) are randomized to receive an intervention in a phased, staggered manner over time. First formally described by Hussey and Hughes in 2007, this design combines the benefits of cluster randomization w

3 nguồn2007
patient centered care

SURE Test

The SURE Test is a four-item screening questionnaire designed to rapidly identify patients experiencing decisional conflict—uncertainty or difficulty in making healthcare decisions. Developed by Annette O'Connor and colleagues, the SURE Test is an abbreviated, practical version of the longer Decisional Conflict Scale (

2 nguồn2010
causal inference

Synthetic Control

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

2 nguồn2010
causal inference

Synthetic Control Method

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

2 nguồn2003
causal inference

Synthetic Control Method in Education Research

The Synthetic Control Method (SCM) estimates the causal effect of an education policy or intervention by constructing a weighted combination of untreated comparison units — the synthetic control — that closely mimics the treated unit's pre-intervention trajectory. Developed by Abadie, Diamond, and Hainmueller, it is es

2 nguồn2003
scientometrics

Systematic Literature Review

A systematic literature review (SLR) is a structured, reproducible method for identifying, appraising, and synthesizing all relevant studies on a research question. Unlike a narrative review, it follows an explicit, pre-specified protocol — from database search strings through inclusion criteria to data extraction — so

2 nguồn1993
bibliometrics

Systematic Mapping Review

A systematic mapping review (also called a 'scoping review') is a literature review methodology that aims to comprehensively identify and categorize the published evidence on a topic without necessarily assessing the quality of individual studies or synthesizing findings quantitatively. Developed by Arksey and O'Malley

3 nguồn2005
academic writing

Systematic Review

A systematic review is a structured, transparent synthesis of all available evidence addressing a specific research question. Unlike narrative reviews, systematic reviews employ comprehensive database searches, predefined selection criteria, quality assessment, and rigorous reporting (PRISMA guideline). The Cochrane Co

3 nguồn1992
causal inference

Targeted Maximum Likelihood Estimation

Targeted Maximum Likelihood Estimation (TMLE) is a semiparametric, doubly robust causal inference method introduced by Mark van der Laan and Daniel Rubin in 2006. It combines flexible machine learning models for both the outcome and the treatment assignment mechanism, then applies a targeting step that re-fits the init

1 nguồn2006
healthcare management

TeamSTEPPS Teamwork Perceptions Questionnaire

The TeamSTEPPS Teamwork Perceptions Questionnaire (T-TPQ) is a 35-item self-report instrument designed to measure team members' perceptions of teamwork and communication in clinical units. Developed by the Agency for Healthcare Research and Quality and the Department of Defense, the T-TPQ was created specifically to ev

3 nguồn2008
health informatics

Telemedicine Satisfaction Scale

The Telemedicine Satisfaction Scale measures patient satisfaction with remote clinical encounters, assessing perceptions of communication quality, technical usability, provider competence, and perceived benefit. While no single universal scale dominates the literature, core satisfaction domains—connection quality, prov

2 nguồn2009
implementation science

Theoretical Domains Framework

The Theoretical Domains Framework (TDF) is a 14-domain model that integrates constructs from 33 behavior change and implementation theories to identify barriers and facilitators to professional and public behavior change. Developed by Michie et al. (2005) to provide a practical tool for implementation scientists and be

3 nguồn2005
health behavior

Theory of Planned Behavior Questionnaire

The Theory of Planned Behavior (TPB) is a psychological framework developed by Icek Ajzen in 1991 to predict and understand deliberate human behavior. The TPB questionnaire measures four core constructs that explain why people intend to perform (or not perform) a specific behavior: attitudes toward the behavior, subjec

1 nguồn1991
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