연구 방법을 한데 모은 단 하나의 카탈로그 — 각 방법이 어떻게 작동하는지, 언제 쓰는지, 무엇을 할 수 없는지 알아보세요.
The machine learning-augmented panel event study extends the classical panel event study by replacing or augmenting parametric counterfactual models with machine learning estimators — such as LASSO, random forests, or matrix completion — to construct more accurate pre-event baselines, detect violations of parallel tren
Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matche
Publication bias analysis examines whether the set of studies included in a meta-analysis is a representative sample of all conducted research, or whether studies with non-significant or unfavorable results have been systematically suppressed. Matthias Egger and colleagues introduced the regression-based funnel plot as
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 impossi
The ACT is a simple, rapid, patient-centered measure of asthma control. Developed by Robert Nathan and colleagues in 2004, this 5-item questionnaire quantifies how asthma symptoms, activity limitation, and nighttime awakening affect daily life. It is the most widely used asthma control measure in clinical practice and
An adaptive case series is an observational study design that documents a consecutive group of patients with a shared condition or exposure while incorporating pre-specified rules for modifying data collection, monitoring, or stopping criteria as accumulating evidence warrants. It combines the descriptive richness of t
An adaptive case-control study is a case-control design that incorporates pre-specified rules allowing modification of study parameters — such as sample size, case-to-control ratio, or matching criteria — based on interim data, without compromising validity. It combines the efficiency of adaptive methodology with the r
An adaptive cohort study is a longitudinal observational design that follows a defined group of individuals over time to assess exposure-outcome relationships, while incorporating pre-specified adaptation rules that allow protocol modifications — such as sample-size re-estimation, subgroup enrichment, or measurement sc
An adaptive cross-sectional epidemiological study combines the core logic of a cross-sectional survey — measuring exposures and outcomes simultaneously in a defined population at one point in time — with pre-specified adaptive rules that allow modifications to sampling strategy, sample size, or subgroup allocation base
An adaptive diagnostic accuracy study evaluates how well an index test distinguishes between patients with and without a target condition, while incorporating pre-specified interim analyses that allow modifications — such as sample size re-estimation, threshold adjustment, or subgroup enrichment — based on accumulating
Adaptive dose-response analysis combines pre-specified dose-response modeling with planned interim looks that allow modifications — such as dropping ineffective doses or reallocating sample size — while maintaining statistical integrity. The most widely cited framework is MCP-Mod (Multiple Comparisons and Modeling), en
An adaptive ecological study is an observational epidemiological design in which the unit of analysis is a group or population (e.g., a region, country, or community) rather than an individual. It extends the classical ecological study by incorporating pre-specified interim decision rules that allow modifications — suc
An adaptive nested case-control study embeds a case-control comparison within a defined cohort and incorporates pre-specified interim decision rules that allow modifications — such as control-to-case ratio adjustment or biomarker sub-sampling revision — based on accumulating data, without compromising the study's valid
An adaptive Phase I clinical trial is a first-in-human or early-phase dose-finding study that continuously updates the recommended dose after each patient cohort using a prespecified statistical model, rather than following a fixed rule. The goal is to identify the maximum tolerated dose (MTD) or the recommended Phase
An adaptive Phase II clinical trial is a prospective experimental design in which pre-specified rules allow the study protocol to be modified — such as dropping arms, adjusting sample size, or narrowing the patient population — based on accumulating interim data, without inflating the Type I error rate. The design is w
An adaptive Phase III clinical trial is a confirmatory randomized controlled trial that incorporates pre-specified rules allowing modifications to the trial design — such as sample size re-estimation, dose selection, or population enrichment — based on accumulating interim data, while preserving the Type I error rate.
An Adaptive Phase IV study is a post-marketing surveillance study conducted after a drug or intervention has received regulatory approval, augmented with pre-specified adaptive design elements that allow pre-planned modifications to the study protocol in response to accumulating data. These modifications may include sa
An adaptive randomized clinical trial (adaptive RCT) is a prospective experimental study that uses pre-specified rules to modify one or more trial aspects — such as sample size, allocation ratios, or treatment arms — based on accumulating data collected during the trial itself, while maintaining statistical validity an
Adaptive screening test evaluation is a psychometric and epidemiological framework for designing and assessing screening instruments whose item selection or stopping rules adjust dynamically to each respondent's response pattern. Rooted in item response theory (IRT) and computerized adaptive testing (CAT), the method u
Adaptive survival analysis integrates adaptive clinical trial design with time-to-event statistical methods, allowing pre-specified modifications to sample size, event targets, or allocation ratios at interim stages based on accumulating survival data. It is widely used in oncology, cardiovascular, and infectious disea
Innovation Adoption refers to the extent to which an innovation, evidence-based practice, or new technology is actually used by the target population or in the target setting. Adoption is typically measured as the percentage of eligible users/staff who have adopted the innovation by a specific time point, or the trajec
The Balanced Scorecard is a strategic performance management framework that translates an organization's mission and strategy into a comprehensive set of performance measures across four perspectives: financial, customer, internal processes, and learning and growth. Developed by Kaplan and Norton in 1992 for general bu
The Barriers to Physical Activity Questionnaire (BPA) is a scale designed to identify and measure perceived obstacles to exercise engagement. Rooted in the Health Belief Model and Health Promotion Model, the BPA assesses multiple categories of barriers—time constraints, lack of motivation, physical discomfort, cost, la
The Basic Psychological Needs Questionnaire (BPNQ), developed by Gagné (2003) and grounded in Self-Determination Theory by Deci and Ryan, measures satisfaction of three fundamental human psychological needs: Autonomy, Competence, and Relatedness. According to Self-Determination Theory, these three needs are universally
The Behavioral Regulation in Exercise Questionnaire—3 (BREQ-3) is a 24-item measure developed by Wilson and colleagues (2012) to assess the type and quality of motivation underlying exercise behavior. Grounded in Self-Determination Theory, the BREQ-3 measures six regulation types positioned on a continuum from amotivat
The Behaviour Change Wheel (BCW) is a systematic, evidence-based framework for designing behavior change interventions. Developed by Michie et al. (2011) and built on the COM-B model (Capability, Opportunity, Motivation→Behavior), the BCW guides practitioners through a structured process: diagnose behavior change barri
A bibliometrix-assisted PRISMA-based review combines the structured, transparent reporting framework of PRISMA with the quantitative science-mapping capabilities of the bibliometrix R package. The approach embeds bibliometric analyses — such as citation analysis, co-authorship mapping, and keyword co-occurrence — into
A bibliometrix-assisted systematic literature review integrates the R package bibliometrix — developed by Aria and Cuccurullo (2017) — into the standard systematic review pipeline to automate and visualize bibliometric performance and science-mapping analyses. It combines the transparency and reproducibility of a proto
Budget impact analysis estimates the financial consequences (net costs or savings) of implementing a new health technology in a specific healthcare system or population over a short time horizon (typically 1–5 years). Distinct from cost-effectiveness analysis (which compares health outcomes per dollar), BIA answers a b
The Consumer Assessment of Healthcare Providers and Systems (CAHPS) is a family of evidence-based surveys developed by the Agency for Healthcare Research and Quality (AHRQ) beginning in 1995. It systematically measures patient experiences across diverse healthcare settings including hospitals, ambulatory clinics, and h
The Care Transitions Measure (CTM-3) is a three-item patient-reported outcome instrument that assesses how well patients feel prepared for the transition from one care setting to another—for example, from hospital to home, from acute care to rehabilitation, or from hospital to primary care. Developed by Carla Parry and
A case series is a descriptive observational study that documents the characteristics, clinical course, and outcomes of a group of patients who share a common condition, exposure, or intervention. Unlike case reports, which focus on a single patient, a case series aggregates data across multiple patients (typically thr
A case-control study is a retrospective observational design in which individuals who have developed a disease or outcome of interest (cases) are compared with individuals who have not (controls) to determine whether prior exposure to a putative risk factor differs between the two groups. The primary measure of associa
The case-crossover design is an observational epidemiological method that estimates whether a transient exposure triggers an acute event by comparing each case's exposure during a brief hazard window immediately before the event to their own exposure during earlier control periods. Because each person serves as their o
Causal discovery is a family of algorithms that automatically learn a directed acyclic graph (DAG) describing causal structure directly from observational data. The constraint-based PC and FCI algorithms were developed by Spirtes, Glymour and Scheines (2000), while the LiNGAM model of Shimizu et al. (2006) exploits lin
Causal mediation analysis is a counterfactual framework that splits a treatment's total effect into a Natural Direct Effect (NDE) and a Natural Indirect Effect (NIE) that runs through a mediator. The modern general approach was formalised by Pearl (2001) and Imai, Keele and Tingley (2010), giving the decomposition a pr
The Children's Dietary Questionnaire (CDQ) is a parent-proxy or child self-report food frequency questionnaire designed to assess usual dietary intake in children and adolescents aged 6–18 years. Developed by Rockett and colleagues at Harvard School of Public Health in the 1990s, it captures consumption of 60–120 commo
The CHQ is a disease-specific quality of life measure for chronic heart failure (CHF). Developed by Luc Guyonnet and colleagues in 2000, this 20-item questionnaire assesses how heart failure affects dyspnea, fatigue, emotional function, and activity limitation. It is used in heart failure clinical trials and research t
The CLES+T is a 34-item self-report questionnaire measuring nursing students' perceptions of their clinical learning environment and the quality of supervision received from their clinical preceptor or teacher. Originally developed by Saarikoski and colleagues in 2007 and expanded in 2008 to include a specific teacher
Clinical audit is a systematic, cyclical process that measures the quality of clinical care against evidence-based standards and benchmarks, identifies gaps, and implements improvements to bring practice into alignment with current best evidence. Originating in the UK NHS, clinical audit is now a fundamental quality as
The Clinical Handover Quality Scale (CHQS) is a comprehensive framework and measurement tool for assessing the quality of clinical handovers—the critical communication process by which responsibility for a patient's care is transferred from one provider or team to another. Handovers occur multiple times daily in health
Coarsened Exact Matching is a preprocessing method that achieves covariate balance by temporarily coarsening continuous variables into bins, exactly matching treated and control units within those bins, and then discarding all unmatched units. Introduced by Iacus, King, and Porro (2011, 2012), it bounds imbalance on ea
Coarsened Exact Matching (CEM) is a pre-processing matching strategy that reduces imbalance between treated and comparison groups before outcome analysis. In education research it is used to create balanced comparison groups from administrative records, survey data, or quasi-experimental study designs — for example com
A cohort study assembles a group of individuals who share a common starting point — typically freedom from the outcome of interest — and follows them over time to observe who develops the outcome. By comparing incidence rates between exposed and unexposed subgroups, researchers can estimate relative risk and absolute r
CollaboRATE is a three-item patient-reported outcome measure designed to assess shared decision making (SDM) quality in clinical consultations. Developed by Glyn Elwyn and colleagues in 2013, it measures the degree to which clinicians involve patients in decisions about their care through simple, actionable items that
Conditional process analysis is Andrew F. Hayes's regression-based PROCESS framework (2018) that combines mediation and moderation in a single model, testing how an indirect effect changes across levels of a moderator. It quantifies conditional indirect and conditional direct effects and tests them with bootstrap confi
The Consolidated Framework for Implementation Research (CFIR) is a five-domain model designed to systematically evaluate the factors influencing implementation success of evidence-based interventions in health systems. Developed by Damschroder et al. (2009) and refined through extensive use across health domains, CFIR
The Control Preferences Scale (CPS) is a five-item measure that assesses a patient's preferred role in healthcare decision making, ranging from a passive (physician-directed) to active (patient-directed) or shared approach. Developed by Lois Degner and colleagues in 1997, the CPS measures the degree of control patients
Convergent Cross Mapping (CCM) is a nonlinear, state-space method for detecting causality between time-series variables embedded in a shared dynamical system. Introduced by George Sugihara and colleagues in their landmark 2012 Science paper, CCM exploits Takens' embedding theorem: if variable X causally influences Y, t
Cost-benefit analysis compares the total monetary value of benefits produced by a program against its total monetary costs, reporting net present value (NPV) or benefit-cost ratio (BCR). Rooted in welfare economics and used extensively in public policy (transportation, environmental, education, health), CBA answers the
Cost-effectiveness analysis compares the incremental cost per unit of health benefit gained by one intervention relative to a comparator (standard care or best alternative). Developed rigorously in the 1980s by Drummond, Stoddart, and colleagues, CEA is now the standard framework for technology appraisal globally. NICE
Cost-Effectiveness Analysis (CEA) is an economic evaluation method that compares the cost and health benefits of alternative treatments to determine whether an intervention provides good value for money. Within Health Technology Assessment, CEA is the primary tool for recommending reimbursement and coverage decisions.
Counterfactual Impact Evaluation is a family of causal methods that estimates the effect of an intervention by comparing what actually happened to participants with what would have happened had the intervention not taken place. Formalised in the Rubin Causal Model and extended by Heckman, Imbens and others, CIE underli
Counterfactual impact evaluation (CIE) is the systematic application of causal inference designs — such as difference-in-differences, regression discontinuity, matching, and instrumental variables — to measure the genuine effect of education programs, policies, or interventions by constructing a credible counterfactual
The COVID-19 Anxiety Scale (CAS) is a brief, self-administered instrument designed to assess anxiety symptoms specifically related to the COVID-19 pandemic. Developed by Lipp and colleagues in 2020, it captures worry about infection, social isolation, and pandemic-related uncertainties. The scale is widely used in epid
The COVID-19 Mental Health Impact Scale (CMHIS) is a brief, multidimensional instrument assessing anxiety, depression, and stress symptoms triggered by the COVID-19 pandemic. Developed by Wang and colleagues in 2020 during the initial pandemic wave in China, it captures the spectrum of psychological distress across mul
Cross-sectional descriptive research collects data from a population or sample at a single point in time to portray the current distribution of characteristics, attitudes, behaviors, or conditions. It answers 'what is happening now?' questions without manipulating variables or following participants over time. Widely u
A cross-sectional epidemiological study measures the exposure(s) and outcome(s) of interest simultaneously in a defined population at a single point in time (or over a short period). Because there is no follow-up, it is the most efficient observational design for estimating disease prevalence and for generating hypothe
A cross-sectional study (or prevalence study) measures exposure and outcome simultaneously at a single point in time, producing a 'snapshot' of a population. Respondents are recruited and surveyed (or examined) on the same occasion, capturing current prevalence of both exposure and disease. Cross-sectional studies are
Cross-sectional survey research administers a structured questionnaire or interview to a representative sample of a population at one point in time. It is the workhorse design for estimating prevalence, describing group characteristics, and mapping associations among variables across a wide range of disciplines — from