Jelajahi sains berdasarkan metode, bidang, dan bukti.

Cyberbullying Victimization Scale

The Cyberbullying Victimization Scale measures the frequency and nature of bullying experienced through digital channels—social media, text messages, gaming platforms, email, and online forums. Developed by Smith and colleagues (2008) and refined through meta-analytic synthesis by Kowalski and colleagues (2014), the sc

2 sumber2008

DAG Causal Identification

DAG causal identification is a framework, developed by Judea Pearl (2009), that encodes causal assumptions as a directed acyclic graph and uses the do-calculus rules to determine whether and how a causal effect can be identified from observational data. It systematically handles confounders, instrumental variables, and

2 sumber2009

health education

DASH

The DASH is a 20-item observer-rated instrument measuring the quality of debriefing—the structured, facilitated reflection following a healthcare simulation activity. Developed by Rudolph, Simon, and Raemer in 2006 at Massachusetts General Hospital, the DASH evaluates the debriefing facilitator's ability to create a ps

2 sumber2006

health economics

Decision Analytic Modeling

Decision analytic modeling is a systematic framework for comparing health interventions by integrating evidence on probabilities, outcomes, costs, and patient preferences into a quantitative model. Developed by Pauker and Kassirer in 1975, decision analysis structures clinical uncertainty and economic trade-offs, enabl

3 sumber1975

Difference-in-Differences in Education Research

Difference-in-Differences (DiD) in education research applies the classic quasi-experimental DiD estimator to evaluate education policies, programs, and reforms. Researchers compare changes in student, school, or district outcomes between a group exposed to an intervention and a comparable unexposed group across pre- a

2 sumber1990

Difference-in-Discontinuities

Difference-in-Discontinuities is a hybrid quasi-experimental design that fuses regression discontinuity (RDD) with difference-in-differences (DID), introduced by Grembi, Nannicini and Troiano (2016). It compares the discontinuity at the same cutoff value across two periods to isolate a causal effect.

2 sumber2016

Digital Health Acceptance Scale

The Digital Health Acceptance Scale measures the extent to which patients and providers perceive digital health technologies as useful, easy to use, and worth adopting. Grounded in Davis's Technology Acceptance Model (TAM) and extended by Venkatesh and colleagues through the Unified Theory of Acceptance and Use of Tech

2 sumber1989

health economics

Disability-Adjusted Life Year

A DALY quantifies disease burden as the sum of years of life lost to premature death and years lived with disability. Developed by the World Health Organization and World Bank in 1990 as part of the Global Burden of Disease (GBD) study, DALYs enable epidemiologists and public health planners to compare disease burden a

3 sumber1990

DLQI

The DLQI is the primary patient-centered outcome measure in dermatology research and clinical practice. Developed by Andrew Finlay and Gul Khan in 1994, this 10-item self-report questionnaire quantifies the impact of skin disease on patients' daily functioning, emotional well-being, social relationships, and work capac

3 sumber1994

epidemiology

Dose-Response Analysis

Dose-response analysis quantifies the relationship between the magnitude of an exposure (the dose) and the probability or rate of an outcome (the response). It is a core analytical strategy in epidemiology and toxicology, providing evidence that increasing exposure systematically increases — or decreases — the risk of

2 sumber

evidence synthesis

Dose-Response Meta-Analysis

Dose-response meta-analysis is a specialized evidence synthesis method that models the relationship between exposure dose (or intensity, duration, quantity) and health outcome across multiple studies, assessing whether effects follow a linear trend, nonlinear curve, or threshold pattern. Pioneered by Greenland and Long

3 sumber1992

Doubly Robust Estimation

Doubly Robust Estimation, also called Augmented Inverse Probability Weighting (AIPW), is a semiparametric method for estimating causal treatment effects that combines an outcome regression model with a propensity (treatment) model. Developed in the work of Robins & Rotnitzky (1995) and Bang & Robins (2005), it stays co

2 sumber2005

Doubly Robust Estimation in Education Research

Doubly robust estimation (DR) is a semiparametric causal inference approach that combines an outcome regression model with a propensity score model. In education research, it is used to estimate the causal effect of educational programs, interventions, or policies on student outcomes when treatment assignment is non-ra

2 sumber1994

DQOL

The DQOL is a patient-reported measure of quality of life impact in people with diabetes. Developed by the Diabetes Control and Complications Trial (DCCT) Research Group in 1988, this 46-item questionnaire assesses how diabetes affects daily functioning, emotional well-being, worry about complications, and satisfaction

3 sumber1988

Drug Abuse Screening Test

The Drug Abuse Screening Test (DAST) is a brief, validated self-report instrument developed by Skinner in 1982 to screen for drug abuse and dependence in medical and psychiatric populations. The 10-item DAST-10 comprises yes/no questions assessing drug use patterns, consequences, and interference with life functioning.

3 sumber1982

Dynamic Counterfactual Impact Evaluation

Dynamic Counterfactual Impact Evaluation (dynamic CIE) extends standard counterfactual program evaluation to settings where treatment is assigned sequentially across multiple periods. Rather than comparing a single treated versus untreated state, it estimates the causal effect of entire treatment trajectories or regime

2 sumber1986

Dynamic Difference-in-Differences

Dynamic Difference-in-Differences extends the classic DiD framework to settings where units adopt treatment at different times. Rather than collapsing all variation into a single 2x2 comparison, it estimates group-time average treatment effects for each adoption cohort at each calendar period, then aggregates them into

Dynamic Entropy Balancing

Dynamic Entropy Balancing extends the entropy balancing reweighting approach to settings with time-varying treatments in panel or longitudinal data. It constructs unit weights at each time period such that the covariate distributions of treated and comparison units are balanced on specified moments, adjusting sequentia

2 sumber2012

Dynamic Event Study Design

The dynamic event study design extends the standard difference-in-differences framework by estimating treatment effects at each period before and after the event, rather than collapsing everything into a single post-treatment coefficient. By plotting lead and lag coefficients against relative event time, researchers ca

Dynamic Fuzzy Regression Discontinuity

Dynamic Fuzzy Regression Discontinuity Design extends the standard fuzzy RDD to a panel or multi-period setting, allowing researchers to estimate how the causal effect of a probabilistic threshold-based treatment evolves over time. By combining an IV-based fuzzy first stage with time-indexed outcomes, it traces treatme

2 sumber2001

Dynamic Instrumental Variables

Dynamic Instrumental Variables estimation addresses endogeneity in panel models where the outcome depends on its own past values. By first-differencing to remove unit fixed effects and then using lagged levels as instruments for the differenced lagged outcome, it produces consistent causal estimates even when standard

2 sumber1991

Dynamic Interrupted Time Series

Dynamic Interrupted Time Series (Dynamic ITS) extends the standard ITS design by allowing intervention effects to build up, decay, or shift over multiple time lags rather than assuming a single instantaneous level change. It estimates how an intervention's impact evolves across time periods, making it especially suited

2 sumber2002

Dynamic Inverse Probability Weighting

Dynamic Inverse Probability Weighting (Dynamic IPW) estimates the causal effect of a time-varying treatment sequence by reweighting observed data to mimic a hypothetical randomised trial. Developed by Robins and colleagues in the context of marginal structural models, it handles the challenge that in longitudinal setti

2 sumber1986

Dynamic Matching Estimator

The Dynamic Matching Estimator extends standard matching methods to settings where treatment is assigned sequentially over multiple periods. Instead of a single treatment decision, units receive or forgo treatment at each time point, and the estimator identifies causal effects of entire treatment histories by matching

2 sumber2010

Dynamic Panel Event Study

The dynamic panel event study is a quasi-experimental method that uses panel data to trace out how a treatment effect evolves over time — before and after a defining event — by estimating a flexible regression of leads and lags around the treatment date. It simultaneously tests for pre-existing parallel trends and maps

Dynamic Propensity Score Matching

Dynamic Propensity Score Matching (DPSM) extends classic propensity score matching to settings where treatment is assigned repeatedly over time and earlier treatment choices influence later ones. It estimates the causal effect of entire treatment sequences or regime changes by constructing matched comparisons at each d

2 sumber1986

Dynamic Synthetic Control Method

The Dynamic Synthetic Control Method extends the classic synthetic control framework to evaluate treatments that unfold over multiple periods or change in intensity over time. It constructs a weighted combination of untreated units that matches the treated unit in pre-treatment outcomes, then traces the full time path

2 sumber2010

implementation science

EBPAS-36

The EBPAS-36 is a 36-item self-report questionnaire that assesses clinicians' and organizational leaders' attitudes toward adopting and implementing evidence-based practices (EBP). Developed by Aarons in 2005 and refined through multiple validation studies, it measures four core dimensions: perceived requirements to ad

1 sumber2005

eHealth Literacy Scale

The eHealth Literacy Scale measures individuals' ability to seek, find, understand, and appraise health information from electronic sources and apply that knowledge to health decision-making. Developed by Norman and Skinner in 2006, it assesses functional, communicative, and critical digital health literacy competencie

1 sumber2006

epidemiology

Endemic Compartmental Models

Endemic compartmental models extend the classical SIR framework to capture diseases that persist indefinitely in a population rather than burning out after a single epidemic wave. The SIS model allows recovered individuals to return to susceptibility immediately; SIRS introduces temporary immunity before loss; SIRV add

1 sumber2000

Entropy Balancing

Entropy balancing is a preprocessing method for causal inference that assigns weights to control-group units so that the reweighted control sample matches the treatment group exactly on a chosen set of covariate moments (means, variances, skewness). Introduced by Hainmueller (2012), it replaces trial-and-error propensi

2 sumber2012

EORTC QLQ-C30

The EORTC QLQ-C30 is the most widely used international instrument for assessing quality of life in cancer patients. Developed by the European Organisation for Research and Treatment of Cancer in 1993, it measures physical, emotional, cognitive, and social functioning alongside cancer-specific symptoms and financial im

3 sumber1993

Epworth Sleepiness Scale

The Epworth Sleepiness Scale (ESS) is a brief, validated self-report instrument developed by Johns in 1991 to quantify the level of daytime somnolence or excessive daytime sleepiness. The ESS comprises eight items asking patients to rate the likelihood of dozing off in various everyday situations. It is the most common

3 sumber1991

academic writing

EQUATOR Network Reporting Guidelines

EQUATOR (Enhancing QUAlity and Transparency Of health Research) is a global network that develops, endorses, and promotes reporting guidelines for health and life sciences research. Founded in 2006 and hosted by the University of Oxford, EQUATOR maintains a library of 500+ guidelines covering study designs (randomized

3 sumber2006

Event Study Design

The event study design is a generalised difference-in-differences model that estimates a separate treatment-effect coefficient for each period before and after an intervention, tracing the dynamics of the effect over event time. Its modern, heterogeneity-robust form was developed by Sun & Abraham (2021) and Callaway &

Event Study Design in Education Research

An event study design tracks how educational outcomes evolve before and after a clearly defined event — such as a school finance reform, accountability policy, or curriculum change — for affected and unaffected units. By estimating period-by-period treatment effects relative to a baseline period, it delivers both a cau

2 sumber1993

health behavior

Exercise Self-Efficacy Scale

The Exercise Self-Efficacy Scale measures an individual's confidence in their ability to exercise regularly and maintain physical activity despite challenges. Grounded in Albert Bandura's Social Cognitive Theory, self-efficacy is the belief that one has the capability to execute a specific behavior and achieve desired

2 sumber1997

Fagerström Test for Nicotine Dependence

The Fagerström Test for Nicotine Dependence (FTND) is a brief, validated self-report instrument originally developed by Fagerstrom in 1978 and revised by Heatherton and colleagues in 1991 to quantify the severity of nicotine dependence in cigarette smokers. The FTND comprises six items assessing morning cigarette use,

3 sumber1991

FCI Algorithm

The Fast Causal Inference (FCI) algorithm is a constraint-based causal discovery method introduced by Spirtes, Glymour, and Scheines in their landmark 2000 book Causation, Prediction, and Search. Unlike its predecessor the PC algorithm, FCI is specifically designed to handle the presence of latent (unmeasured) common c

1 sumber2000

public health

Fear of COVID-19 Scale

The Fear of COVID-19 Scale (FCV-19S) is a 7-item, self-report instrument assessing fear of COVID-19 infection across cognitive, emotional, and physiological domains. Developed by Ahorsu and colleagues in 2020, it measures threat perception, anxiety symptoms triggered by disease-related triggers, and avoidance behaviors

1 sumber2020

implementation science

Fidelity Assessment in Implementation

Fidelity Assessment is the systematic measurement of the degree to which an intervention is delivered as designed in real-world practice. Formalized by the National Institutes of Health Behavior Change Consortium (Bellg et al. 2004) and expanded in MRC guidance (Moore et al. 2015), fidelity assessment is critical to im

3 sumber2004

implementation science

Fidelity Scale

Fidelity of Implementation refers to the degree to which an evidence-based practice or intervention is delivered as originally designed and intended. The Fidelity of Implementation Scale (or fidelity assessment framework) operationalizes this concept by specifying the core components of an intervention, defining each c

2 sumber2007

scientometrics

Field-mapping Scoping review

A field-mapping scoping review is a purposive variant of the scoping review in which the overarching goal is to chart the conceptual and empirical landscape of a research field — identifying what has been studied, by whom, using which methods, and where knowledge gaps remain. It follows the Arksey and O'Malley scoping

2 sumber2005

FIQ

The FIQ is the most widely used patient-reported outcome measure for fibromyalgia disease burden. Developed by Cynthia Burckhardt and colleagues in 1991, this 10-item questionnaire quantifies how fibromyalgia affects physical function, work capacity, depression, anxiety, sleep, pain, and fatigue. The revised version (F

3 sumber1991

Frontdoor Adjustment

Frontdoor adjustment is Judea Pearl's graphical identification strategy, introduced in 1995, that recovers the causal effect of a treatment on an outcome through a fully mediating variable even when an unobserved confounder sits between the treatment and the outcome. It is the go-to tool when the backdoor criterion can

2 sumber1995

Fuzzy Regression Discontinuity

Fuzzy Regression Discontinuity Design (Fuzzy RDD) estimates causal effects when eligibility for a treatment is determined by a threshold on a running variable but actual take-up of that treatment is imperfect — some eligible units do not receive treatment and some ineligible units do. The cutoff acts as an instrument,

2 sumber2001

Fuzzy Regression Discontinuity in Education Research

Fuzzy Regression Discontinuity Design (Fuzzy RDD) is a quasi-experimental causal method that exploits a known score threshold — such as a test cutoff — to estimate the effect of a program or intervention when assignment is imperfect. Widely used in education research to evaluate summer school, remedial programs, schola

2 sumber1990

public health nutrition

FWQ

The FWQ is a self-report questionnaire assessing pregnant women's subjective perception of fetal wellbeing, maternal physical and emotional health, and prenatal bonding. Developed by DiPietro and colleagues studying fetal development and maternal-fetal attachment, the FWQ captures non-clinical dimensions of pregnancy e

2 sumber2008

G-Computation

G-computation is a causal inference method for estimating the effect of an intervention or treatment on an outcome from observational data. Developed by James M. Robins in 1986, it provides a parametric approach to standardization that can handle time-varying exposures and confounders. The method estimates what the pop

3 sumber1986

speech language pathology

GES Algorithm

Greedy Equivalence Search (GES) is a score-based algorithm for learning the causal structure of a set of variables from observational data. Introduced by David Maxwell Chickering in 2002, GES operates directly on Markov equivalence classes of directed acyclic graphs (DAGs), represented as completed partially directed a

1 sumber2002

research methodology

GRADE Evidence Profiling

GRADE (Grading of Recommendations Assessment, Development and Evaluation) is a systematic, transparent framework for assessing the certainty of evidence and determining the strength of clinical recommendations in healthcare. Published in 2008 by Guyatt et al., GRADE has become the international standard for guideline d

1 sumber2008

GRBAS Voice Perceptual Scale

The GRBAS Scale (Grade, Roughness, Breathiness, Asthenia, Strain) is a clinician-rated perceptual assessment tool for classifying voice quality across five distinct vocal dimensions. Developed by Hirano in 1981, GRBAS provides a standardized language for voice clinicians and physicians to describe dysphonia characteris

3 sumber1981

psychometrics

GRM

The Graded Response Model is an item response theory model developed by Fumiko Samejima in 1969 for ordered polytomous items such as Likert-type scales. It estimates both the discriminating power of each item and a set of threshold parameters marking the boundaries between adjacent response categories, while simultaneo

2 sumber1969

healthcare management

HCAHPS Hospital Consumer Assessment Survey

The Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) is a 27-item, CMS-mandated patient experience survey administered to a random sample of hospital inpatients after discharge. Launched in 2006 by the Centers for Medicare & Medicaid Services and the Agency for Healthcare Research and Quality,

3 sumber2006

public health nutrition

HDDS

The HDDS is a simple, 12-item food group checklist that captures the diversity of the household diet in the preceding 24 hours. Developed by the FAO in 2011 as a proxy indicator of dietary quality and nutrient adequacy, the HDDS enables rapid assessment of the nutritional vulnerability of households in resource-limited

1 sumber2011

Health App Usability Scale

The System Usability Scale (SUS) is a rapid, validated tool for measuring perceived usability of digital products, widely adapted for health applications. Developed by John Brooke in 1996 and extensively validated by Bangor and colleagues, the 10-item SUS generates a single composite score reflecting users' subjective

2 sumber1996

health behavior

Health Belief Model Scale

The Health Belief Model (HBM) is a foundational psychological framework developed by Marshall Rosenstock in 1966 to predict and explain preventive health behavior. Based on the central premise that people take health action to avoid illness when they perceive susceptibility to a health threat and believe that taking ac

2 sumber1966