facet: framework for Generalizability Theory

Michaelides, George; Jackson, Duncan J. R.

facet: framework for Generalizability Theory

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Michaelides, George and Jackson, Duncan J. R. (2026) facet: framework for Generalizability Theory. UNSPECIFIED.

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Abstract

facet provides a comprehensive framework for conducting Generalizability Theory (G-theory) analyses using variance component models. It provides a bridge between classical test theory and standard linear mixed-effects models, offering: Multiple backends: Frequentist approaches using mom (Method of Moments/ANOVA) and lme4 (Restricted Maximum Likelihood); Bayesian approachs using brms (NUTS / Hamiltonian Monte Carlo) Univariate and multivariate analyses Built-in datasets: Includes classic G-theory datasets like brennan and rajaratnam Rich Visualization: Built-in support for plotting results

Item Type:	Other
Faculty \ School:	Faculty of Social Sciences > Norwich Business School
UEA Research Groups:	Faculty of Social Sciences > Research Groups > Employment Systems and Institutions
Related URLs:	https://github.com/michaelides/facet
Depositing User:	LivePure Connector
Date Deposited:	09 Jun 2026 08:04
Last Modified:	09 Jun 2026 08:04
URI:	https://ueaeprints.uea.ac.uk/id/eprint/103327
DOI:

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