Statistics — Page 23
Explore statistical methods, their assumptions, interpretation and limitations.
Research field
Method family
Frailty Model
The shared frailty model, introduced by Vaupel, Manton, and Stallard in 1979, extends standard survival regression by incorporating a random effect — the 'frailty' — that captures unobserved heterogeneity among subjects or clusters. When su...
Free Time Motivation Scale
The Free Time Motivation Scale for Adolescents (FTMS-A), developed by Cheryl Baldwin and Linda Caldwell in 2003, is a self-report instrument that measures why young people do what they do in their free time, grounded in self-determination t...
Frees Test
The Frees test, introduced by Edward Frees in 1995, is a non-parametric diagnostic procedure for detecting cross-sectional dependence in panel data. It is designed for settings where N (number of units) is large and T (time periods) is mode...
Frequency analysis
Frequency analysis is a fundamental descriptive technique that tallies how often each distinct value or category appears in a dataset. It produces absolute counts, relative percentages, and cumulative frequencies, giving an immediate pictur...
Friedman test
The Friedman test is a nonparametric hypothesis test that compares three or more related conditions measured on the same blocks or subjects, serving as the rank-based alternative to repeated-measures ANOVA. It was introduced by Milton Fried...
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...
Full Factorial Design
A full factorial design is a parametric experimental method in which every combination of factor levels is tested simultaneously, enabling the estimation of all main effects and all interaction effects in a single study. Rooted in R. A. Fis...
Fuzzy ANOVA
Fuzzy ANOVA extends classical analysis of variance to fuzzy data where observations and group memberships are imprecise or uncertain. Developed by Viertl and others, Fuzzy ANOVA tests whether fuzzy-valued groups differ significantly while a...
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 re...
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...
Fuzzy-Set Qualitative Comparative Analysis
Fuzzy-Set Qualitative Comparative Analysis (fsQCA) is a set-theoretic method developed by Charles Ragin in the early 2000s that combines the configurational logic of qualitative case studies with the mathematical rigor of fuzzy sets. It bri...
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...
G-Theory
Generalizability Theory, developed by Lee J. Cronbach and colleagues in the 1960s and formalised by Brennan (2001), is an ANOVA-based framework that extends Classical Test Theory by decomposing observed score variance into multiple, separat...
GAMLSS
GAMLSS is a broad class of semi-parametric regression models introduced by Robert Rigby and Mikis Stasinopoulos in 2005. Unlike classical regression, which models only the mean of a response, GAMLSS allows each parameter of a chosen paramet...
GARCH
GARCH is an econometric model for the time-varying volatility of financial time series, introduced by Tim Bollerslev in 1986 as a generalisation of Engle's ARCH model. It treats the conditional variance as a function of past squared shocks...
GARCH Model
The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effec...
GARCH-MIDAS
GARCH-MIDAS decomposes volatility into short-term (GARCH) and long-term (MIDAS) components, allowing low-frequency macroeconomic variables to drive medium-term volatility while high-frequency returns govern daily fluctuations. Introduced by...
GJR-GARCH
GJR-GARCH is a variant of the GARCH conditional-volatility model that captures the asymmetric effect of negative shocks on volatility using an indicator variable. It was introduced by Glosten, Jagannathan and Runkle (1993), with a closely r...
GM(1,1) Grey Forecasting
GM(1,1) is the core forecasting model of grey system theory, introduced by Julong Deng in 1982, designed to predict from very few observations and incomplete information — situations where classical time-series models like ARIMA need far mo...
GMM
The Growth Mixture Model, introduced by Muthén and Shedden in 1999, is a longitudinal latent variable method that identifies distinct subpopulations — latent trajectory classes — each following its own growth curve over time. It extends the...
GMM Estimation
The Generalized Method of Moments is a general-purpose econometric estimator that recovers parameters from population moment conditions, introduced by Lars Peter Hansen in 1982. It is widely used for instrumental-variable estimation, dynami...
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 paramete...
Games-Howell Test
The Games-Howell test is a parametric post-hoc multiple comparison procedure that identifies which pairs of group means differ significantly after an omnibus ANOVA reveals a significant overall effect. Proposed by Games and Howell in 1976,...
Gamma Regression
Gamma regression is a generalized linear model that uses the gamma distribution to model a positive, right-skewed continuous outcome. Developed within the GLM framework of McCullagh and Nelder (1989), it is an alternative to ordinary linear...