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One catalogue of research methods: learn how each one works, when to use it, and what it can’t do.

methods: 8,178
fields: 11
method families: 7
languages: 40

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1,836 methods

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ANCOVAhypothesis-test - ANCOVA is a parametric hypothesis test that compares the adjusted means of two or more independent groups while statistically controlling for one or more continuous covariates. By removing the portion of outcome variance explained by the covariate, ANCOVA increases statistical precision and produces fairer group compar1source
ARIMAregression-model - ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed1source
Cox proportional hazardsprocess-pipeline - The Cox proportional hazards model is a semi-parametric regression method that estimates the effect of one or more covariates on the hazard — the instantaneous rate of an event such as death, relapse, or failure — while making no assumption about the shape of the baseline hazard function. Introduced by David Cox in 1972sources
Bayesian Causal Impact Analysisregression-model - Bayesian Causal Impact Analysis uses a Bayesian structural time series (BSTS) model to estimate the causal effect of an intervention on a time series outcome. Developed by Brodersen and colleagues at Google in 2015, it builds a probabilistic counterfactual — what the series would have looked like without the interventi2sources
Causal Impact Analysisregression-model - Causal Impact Analysis, introduced by Brodersen et al. (2015) at Google, uses Bayesian structural time-series models to estimate what would have happened to an outcome had an intervention never occurred. By constructing a probabilistic counterfactual from pre-treatment data and control covariates, it quantifies point-i2sources
Difference-in-Differencesregression-model - Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the trea2sources
Foster-Greer-Thorbecke Indexprocess-pipeline - The Foster-Greer-Thorbecke (FGT) index is a parametric class of poverty measures introduced by James Foster, Joel Greer, and Erik Thorbecke in 1984 that became the workhorse of applied poverty analysis. A single parameter alpha tunes how much weight the measure places on the depth and distribution of poverty: alpha = 01source
A/B Testhypothesis-test - An A/B test is a randomized controlled experiment that simultaneously exposes two groups of users to a control variant (A) and a treatment variant (B) in order to determine whether a measured outcome differs significantly between them. The modern online controlled experiment framework was systematized by Ron Kohavi and2sources
Anderson-Darling Testregression-model - The Anderson-Darling test is an empirical distribution function (EDF) goodness-of-fit test, introduced by Anderson and Darling in 1952, that checks whether a continuous sample comes from a specified distribution such as the normal, exponential, or Weibull. By weighting deviations more heavily in the tails, it detects d2sources
ARCH-LM Testregression-model - The ARCH-LM test is Robert Engle's (1982) Lagrange multiplier diagnostic for autoregressive conditional heteroscedasticity in the residuals of a fitted time-series model. It checks whether the error variance changes over time and clusters into calm and turbulent periods, and it is the standard pre-test run before fitti2sources
ARCH modelregression-model - The ARCH model, introduced by Robert Engle in 1982, captures time-varying volatility in financial and macroeconomic time series. It models the conditional variance of today's error as a function of past squared errors, explaining why volatile periods cluster together — a phenomenon known as volatility clustering.2sources
ARMA modelregression-model - The ARMA(p,q) model describes a stationary time series as a combination of two components: an autoregressive part that regresses the current value on its own past p values, and a moving average part that accounts for past q error terms. It is the foundational framework of the Box-Jenkins methodology for univariate time2sources
Augmented Dickey-Fuller unit root testregression-model - The Augmented Dickey-Fuller test is the standard procedure for determining whether a univariate time series contains a unit root — that is, whether the series is non-stationary. It extends the original Dickey-Fuller test by including lagged difference terms that absorb serial correlation in the residuals, making the te2sources
Autoregressive modelregression-model - An autoregressive model of order p — AR(p) — expresses the current value of a time series as a linear function of its own p most recent past values plus a white-noise error. It is the building block of the Box-Jenkins family of time-series models and is widely used for forecasting stationary economic and financial seri2sources
Barnard's Exact Testhypothesis-test - Barnard's exact test is an unconditional exact hypothesis test for comparing two independent proportions in a 2×2 contingency table, proposed by George A. Barnard in 1945. Unlike Fisher's exact test, it does not condition on both margins being fixed, and is generally more powerful when column totals are not predetermin3sources
Bayesian Confirmatory Factor Analysislatent-structure - Bayesian confirmatory factor analysis tests a pre-specified factor structure using Bayesian inference. Instead of point estimates with p-values, it produces full posterior distributions for loadings, factor correlations, and residual variances, allowing the researcher to incorporate prior knowledge and propagate parame2sources
Bayesian Counterfactual Impact Evaluationregression-model - Bayesian Counterfactual Impact Evaluation estimates the causal effect of an intervention by constructing a Bayesian posterior distribution over the counterfactual outcome — what would have happened without treatment. The method, popularized by Brodersen et al. (2015) through the CausalImpact framework, uses Bayesian st2sources
Bayesian Difference-in-Differencesregression-model - Bayesian Difference-in-Differences applies Bayesian statistical inference to the classic DiD design, replacing frequentist point estimates with full posterior distributions over the treatment effect. This yields not only an estimate of the causal effect but also a coherent probability statement about its magnitude and 2sources
Bayesian Random Effects Modelregression-model - The Bayesian random effects model combines panel-data random effects with a Bayesian prior framework, allowing unit-specific effects to be treated as draws from a population distribution whose hyperparameters are estimated from the data. This produces regularised, uncertainty-quantified estimates that borrow strength a2sources
Bayesian SARIMA Modelregression-model - The Bayesian SARIMA model combines the classical Box-Jenkins Seasonal ARIMA framework with Bayesian inference to handle seasonal time-series data. Rather than producing a single point estimate, it yields a full posterior distribution over model parameters, propagating parameter uncertainty directly into forecasts and e2sources
Bayesian System GMMregression-model - Bayesian System GMM combines the Blundell-Bond System Generalized Method of Moments estimator for dynamic panel data with Bayesian prior distributions and posterior inference via MCMC. It handles endogeneity, individual fixed effects, and weak-instrument problems while incorporating prior knowledge and delivering full 2sources
Benjamini-Hochberg Procedurehypothesis-test - The Benjamini-Hochberg (BH) procedure, introduced by Yoav Benjamini and Yosef Hochberg in 1995, controls the false discovery rate (FDR) — the expected proportion of false positives among all rejected hypotheses — rather than the probability of any false positive. By tolerating a controlled fraction of false discoveries2sources
Bland-Altman Analysishypothesis-test - The Bland-Altman analysis is a graphical and statistical technique for assessing agreement between two measurement methods applied to the same subjects. Introduced by J. Martin Bland and Douglas G. Altman in their landmark 1986 Lancet paper, it plots the difference between the two methods against their mean for each su2sources
Factor Analysis for Scale Developmentprocess-pipeline - Exploratory factor analysis (EFA) is a statistical method for discovering the underlying dimensional structure of a set of items or variables. Pioneered by Louis Thurstone in the mid-20th century, EFA is widely used to develop and validate psychometric scales by identifying groups of items that correlate together, ther3sources
Fourier Random Effects Modelregression-model - The Fourier Random Effects Model extends the standard random effects panel estimator by incorporating trigonometric (Fourier) terms to approximate smooth, gradual structural change in time trends or intercepts. It retains the GLS efficiency advantages of the random effects estimator while allowing parameters to shift c2sources
Granger Causalityregression-model - The Granger causality test, introduced by Clive W. J. Granger in 1969, assesses whether the past values of one time series help predict another beyond what the latter's own past already explains. It defines causality in a strictly predictive sense rather than as a structural or physical cause.1source
i10-Indexprocess-pipeline - The i10-index is a deliberately simple author-level metric introduced by Google Scholar in 2011 for its Scholar Citations profiles. It counts the number of a researcher's publications that have each accumulated at least ten citations. Unlike the h-index, whose threshold depends on the rank of the paper, the i10-index a2sources
MANCOVAhypothesis-test - MANCOVA (Multivariate Analysis of Covariance) is a parametric hypothesis test that simultaneously compares two or more groups on multiple continuous dependent variables while statistically controlling for one or more covariates. It extends MANOVA by incorporating covariate adjustment, a tradition consolidated in multiv1source
Marginal Structural Modelregression-model - A marginal structural model is a causal modeling framework designed to estimate the effect of a time-varying treatment in the presence of time-varying confounders that are themselves affected by prior treatment. By reweighting observations with inverse probability of treatment weights, MSMs create a pseudo-population i2sources
Model Testing Researchprocess-pipeline - Model testing research is a confirmatory quantitative design in which the researcher specifies a theoretical model — depicting hypothesized relationships among constructs — and then tests how well that model fits empirical data. Drawing primarily on structural equation modeling (SEM) and confirmatory factor analysis (C2sources
Multivariate Regressionregression-model - Multivariate regression is a linear regression method that predicts several continuous dependent variables at the same time from a shared set of predictors. As developed in standard treatments such as Johnson and Wichern's Applied Multivariate Statistical Analysis (2007), each response equation can be fitted by ordinar1source
One-sample t-testhypothesis-test - The one-sample t-test is a parametric hypothesis test that determines whether the mean of a single sample differs significantly from a known or hypothesized population value. Derived from Student's (Gosset's) 1908 t-distribution, it assumes continuous, approximately normally distributed data and is one of the most fund2sources
Partisan Business Cycle Analysisregression-model - Partisan business cycle analysis tests whether left-wing and right-wing governments produce systematically different macroeconomic outcomes. Douglas Hibbs's 1977 partisan theory argued that because left and right parties represent constituencies with different exposures to unemployment and inflation, left governments d2sources
Threshold Regressionregression-model - Threshold regression is a nonlinear, regime-switching model in which the regression parameters take different values above and below an estimated threshold value of a threshold variable. The sample-splitting and threshold-estimation framework was developed by Bruce E. Hansen (2000) and is widely used for time-series an1source
Two-Step Floating Catchment Areaprocess-pipeline - The two-step floating catchment area (2SFCA) method measures spatial accessibility to constrained services — most famously physicians and hospitals — by accounting not only for how close supply is but for how many other people are competing for it. Introduced by Wei Luo and Fahui Wang in 2003, it works in two passes: f1source
Weber Industrial Location Modelregression-model - Weber's industrial location model is the classic least-cost theory of where a manufacturing plant should locate. Developed by Alfred Weber in 1909, it finds the site that minimizes total transport cost between the sources of raw materials and the market, then adjusts that site for savings in labour cost and for the ben1source
15-Minute City Analysisprocess-pipeline - 15-minute city analysis assesses how many of life's daily needs — shops, schools, healthcare, work, recreation — residents can reach within a short walk or bike ride, typically fifteen minutes, from their homes. Articulated by Carlos Moreno in 2021 under the banner of chrono-urbanism, the concept reorients planning aro1source
2SLS Regressionregression-model - Two-Stage Least Squares is a two-step instrumental-variables estimator that addresses endogeneity, the situation where a regressor is correlated with the error term. In the first stage the endogenous regressor is predicted from instrumental variables, and in the second stage the structural equation is estimated using t1source
Abridged Life Tableprocess-pipeline - The abridged life table is the workhorse of demography and population health for summarizing the mortality experience of a population in a single, age-grouped table. Instead of a single-year (complete) life table, it works on broad age intervals — typically <1, 1-4, then five-year groups up to an open-ended oldest inte2sources
Accelerated Failure Time Modelsurvival - The Accelerated Failure Time model is a parametric regression approach to survival analysis — formally reviewed and advocated by L. J. Wei in 1992 — in which covariates act as multiplicative factors that directly stretch or compress the time-to-event scale. Unlike the Cox proportional-hazards model, which models how co3sources
Accelerometer Cut-Point Calibrationregression-model - Accelerometer cut-point calibration solves the central translation problem of objective physical-activity measurement: a wearable accelerometer outputs dimensionless 'counts,' but researchers and health guidelines speak in intensities — sedentary, light, moderate, vigorous. Calibration establishes the count thresholds 2sources
Accessibility Analysisprocess-pipeline - Accessibility analysis measures how easily opportunities — jobs, shops, clinics, parks — can be reached from a given location, combining the attractiveness (size) of destinations with the cost of travelling to them. The gravity-based formulation introduced by Walter Hansen in 1959 sums the opportunities at all destinat2sources
Accessibility Equity Analysisprocess-pipeline - Accessibility equity analysis asks not just how much access to opportunities a place has, but how that access is distributed across people and social groups — who can reach jobs, healthcare, and education, and who is left behind. It pairs an accessibility measure, in the tradition formalized by Karst Geurs and Bert van1source
Active learning Gaussian processml-model - Active Learning Gaussian Process (GP-AL) combines a Gaussian process probabilistic model with an active learning query strategy, using the GP's posterior uncertainty to select the most informative unlabeled examples for labeling. This iterative approach minimizes labeling effort while maximizing predictive accuracy, ma2sources
Active Learning Logistic Regressionml-model - Active Learning with Logistic Regression is an iterative label-efficient framework in which a logistic regression model selects the unlabeled examples it is most uncertain about, an oracle (human annotator) labels them, and the model is retrained — repeating until a labeling budget or accuracy target is met. It dramati2sources
Active Life Expectancy Estimationsurvival - Active life expectancy (ALE) estimates how many of an older person's remaining years are expected to be lived in an active, independent state — free of disability in basic activities of daily living — as opposed to a dependent state requiring help. Introduced by Sidney Katz, Laurence Branch, and colleagues in 1983 in t1source
Actor-Partner Interdependence Modelregression-model - The Actor-Partner Interdependence Model (APIM), formalized by Kenny, Kashy, and Cook, is the standard framework for analyzing dyadic data in which two people's outcomes depend on both their own and their partner's characteristics. For each member of a dyad, the model estimates an actor effect -- the influence of a pers1source
Adaptive A/B testprocess-pipeline - An Adaptive A/B test is an experimental design that dynamically reallocates traffic or participants toward better-performing variants during the experiment itself, rather than holding allocations fixed until the end. Drawing on multi-armed bandit algorithms such as Thompson Sampling or Upper Confidence Bound (UCB), it 2sources
Adaptive Competing Risks Analysisprocess-pipeline - Adaptive competing risks analysis combines the Fine-Gray subdistribution hazard framework — which models the cumulative incidence of one cause of failure in the presence of other mutually exclusive causes — with adaptive or group-sequential interim monitoring rules. This allows a clinical trial or observational study t2sources
Adaptive Cox Proportional Hazardsprocess-pipeline - The Adaptive Cox Proportional Hazards model extends the classic Cox regression for time-to-event outcomes by adding adaptive LASSO (or related) penalization. It simultaneously estimates hazard ratios and performs variable selection, shrinking irrelevant covariate coefficients exactly to zero. This makes it especially v2sources
Adjusted Boxplotregression-model - The Adjusted Boxplot is a robust descriptive tool introduced by Hubert and Vandervieren (2008) that corrects the classical IQR-based boxplot for skewness using the medcouple statistic, reducing the false labelling of outliers in asymmetric data.1source
Age-Crime Curve Modelingregression-model - Age-crime curve modeling fits statistical functions to the well-known relationship between age and offending: crime rises sharply in adolescence, peaks in the late teens or early twenties, and declines through adulthood. Brought to prominence by Hirschi and Gottfredson's 1983 claim that this curve is invariant, and ela2sources
Age-Period-Cohort Analysisregression-model - Age-period-cohort (APC) analysis decomposes variation in disease or mortality rates into three temporal components: the effect of age (biological and accumulated risk), the effect of period (influences hitting everyone alive at a given calendar time, such as a new treatment or a recession), and the effect of cohort (la2sources
Age-Period-Cohort Modelregression-model - The age-period-cohort (APC) model decomposes variation in a vital rate — mortality, incidence, fertility — into three temporal dimensions: the age of individuals, the calendar period of observation, and the birth cohort to which they belong. It is the standard framework for asking whether a trend reflects how risk chan2sources
Aggregative Parish Register Analysisprocess-pipeline - Aggregative parish register analysis is the foundational technique of English historical demography, compiling the simple monthly and annual counts of baptisms, marriages and burials recorded in parish registers into continuous long-run series. Unlike family reconstitution, it does not identify individuals; it treats t2sources
Agricultural Household Modelregression-model - The agricultural household model treats the farm household as a single unit that is simultaneously a producer and a consumer, choosing how much to grow, how much labour to hire or supply, and how much to consume — decisions that ordinary firm theory and consumer theory treat as separate. Synthesised in Singh, Squire, a2sources
Agricultural Market Integration Analysisregression-model - Agricultural market integration analysis asks whether prices for the same commodity in geographically separated markets move together — and how quickly and symmetrically a price change in one market is transmitted to another. Martin Ravallion's 1986 article 'Testing Market Integration', using Bangladesh rice prices, se2sources
Aligned Rank Transform ANOVAhypothesis-test - The Aligned Rank Transform ANOVA (ART-ANOVA) is a nonparametric factorial hypothesis test that detects main effects and interactions in designs with two or more independent variables, without requiring normality. The procedure was formalized by Wobbrock, Findlater, Gergle, and Higgins in their 2011 CHI paper and operat1source
Almost Ideal Demand Systemregression-model - The Almost Ideal Demand System (AIDS), introduced by Angus Deaton and John Muellbauer in 1980, is the workhorse flexible demand system in applied microeconomics. It models each good's budget share as a linear function of the logarithms of all prices and of log real total expenditure, derived from a flexible (PIGLOG) co1source
Altman Z-Scoreregression-model - The Altman Z-Score is a linear discriminant model developed by Edward I. Altman in 1968 to predict corporate bankruptcy using five accounting-based financial ratios. Derived through multiple discriminant analysis on a matched sample of 66 US manufacturing firms, the model combines liquidity, profitability, leverage, so1source

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