Latent Variable Modeling and Adaptive Testing for Experimental Cognitive Psychopathology Research

Autor/inn/en	Thomas, Michael L.; Brown, Gregory G.; Patt, Virginie M.; Duffy, John R.
Titel	Latent Variable Modeling and Adaptive Testing for Experimental Cognitive Psychopathology Research
Quelle	In: Educational and Psychological Measurement, 81 (2021) 1, S.155-181 (27 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Thomas, Michael L.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164420919898
Schlagwörter	Adaptive Testing; Computer Assisted Testing; Cognitive Tests; Psychopathology; Psychometrics; Item Response Theory; Mathematical Models; Short Term Memory; Neurological Impairments + Suchen Sie Ihr Suchwort? Kognitiver Fähigkeitstest; Psychopathologie; Psychometry; Psychometrie; Item-Response-Theorie; Mathematical model; Mathematisches Modell; Kurzzeitgedächtnis; Neurodegenerative Erkrankung
Abstract	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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01