Latent Variable Modeling and Adaptive Testing for Experimental Cognitive Psychopathology Research

Datos Bibliográficos

ID20282953
AutoresMichael L Thomas (0000-0002-6896-272X, Colorado State University, Fort Collins, CO, USA), Gregory G Brown (0000-0002-6430-9854, University of California San Diego, La Jolla, CA, USA), Virginie M Patt (VA Boston Healthcare System, MA, USA), Virginie Patt (0000-0002-5966-2397, VA Boston Healthcare System), John R Duffy (0000-0003-3548-2313, Colorado State University, Fort Collins, CO, USA)
Año2021
Volumen81
Número1
Páginas155-181
Fecha de publicación2021-02-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaEducational and Psychological Measurement (JOURNAL)
Identificadores de la revistaISSN: 0013-1644 • E-ISSN: 1552-3888
EditorialSAGE Publications (PUBLISHER • US)
DOI10.1177/0013164420919898
PMID33456066
OpenAlexW3034133837
IdiomaEN
Citas recibidas1
Referencias citadas50

The adaptation of experimental cognitive tasks into measures that can be used to quantify neurocognitive outcomes in translational studies and clinical trials has become a key component of the strategy to address psychiatric and neurological disorders. Unfortunately, while most experimental cognitive tests have strong theoretical bases, they can have poor psychometric properties, leaving them vulnerable to measurement challenges that undermine their use in applied settings. Item response theory–based computerized adaptive testing has been proposed as a solution but has been limited in experimental and translational research due to its large sample requirements. We present a generalized latent variable model that, when combined with strong parametric assumptions based on mathematical cognitive models, permits the use of adaptive testing without large samples or the need to precalibrate item parameters. The approach is demonstrated using data from a common measure of working memory—the N-back task—collected across a diverse sample of participants. After evaluating dimensionality and model fit, we conducted a simulation study to compare adaptive versus nonadaptive testing. Computerized adaptive testing either made the task 36% more efficient or score estimates 23% more precise, when compared to nonadaptive testing. This proof-of-concept study demonstrates that latent variable modeling and adaptive testing can be used in experimental cognitive testing even with relatively small samples. Adaptive testing has the potential to improve the impact and replicability of findings from translational studies and clinical trials that use experimental cognitive tasks as outcome measures

Cognition · Cognitive psychology · Cognitive test · Computerized adaptive testing · Item response theory · Latent variable · Latent variable model · Machine learning · Neurocognitive · Parametric statistics · Psychometrics · Sample size determination · Statistics · Structural equation modeling · Clinical Psychology · Cognitive Abilities and Testing · Computer Science · Mathematics · Mental Health Research Topics · Psychology · Psychometric Methodologies and Testing