Enhancing generalizability theory with mixed-effects models for heteroscedasticity in psychological measurement: A theoretical introduction with an application from EEG data.

Abstract read

In one paragraph

Article in The British journal of mathematical and statistical psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Beyond classical metrics: Generalizability theory across psychophysiological modalities.International journal of psychophysiology : official journal of the International Organization of Psychophysiology · 2026
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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Philippe RastDepartment of Psychology, University of California Davis, Davis, California, USA.ORCID https://orcid.org/0000-0003-3630-6629

Peter E ClaysonDepartment of Psychology, University of South Florida, Tampa, Florida, USA.

Funding

PsyRAT: Extensible Open-Source Software for Applying Generalizability Theory to Assess Psychometric Reliability of Trial-Wise Scores and Optimize Tasks for RDoCR01MH128208 · NIMH · UNIVERSITY OF SOUTH FLORIDA · PI Peter Eugene Clayson · 2022 to 2026

$3.0M

NIMH NIH HHS R01 MH128208NIMH NIH HHS R01MH128208

6 · The paper itself

Abstract

Generalizability theory (G-theory) defines a statistical framework for assessing measurement reliability by decomposing observed variance into meaningful components attributable to persons, facets, and error. Classic G-theory assumes homoscedastic residual variances across measurement conditions, an assumption that is often violated in psychological and behavioural data. The main focus of this work is to extend G-theory using a mixed-effects location-scale model (MELSM) that allows residual error variance to vary systematically across conditions and persons. By modeling heteroscedasticity, we can extend the computation of condition-specific generalizability (

Indexed as

generalizability theoryheteroscedasticitymixed effects location scale model

Identifiers

PMID41549064

DOI10.1111/bmsp.70026

PMCPMC13410726

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.