Examining the Robustness of the Graded Response and 2-Parameter Logistic Models to Violations of Construct Normality

Autor/inn/en	Manapat, Patrick D.; Edwards, Michael C.
Titel	Examining the Robustness of the Graded Response and 2-Parameter Logistic Models to Violations of Construct Normality
Quelle	In: Educational and Psychological Measurement, 82 (2022) 5, S.967-988 (22 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Manapat, Patrick D.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/00131644211063453
Schlagwörter	Robustness (Statistics); Computational Linguistics; Item Response Theory; Psychological Patterns; Correlation; Validity; Inferences; Models; Evaluation Methods; Simulation + Suchen Sie Ihr Suchwort? Widerstandsfähigkeit; Linguistics; Computerlinguistik; Item-Response-Theorie; Korrelation; Gültigkeit; Inference; Inferenz; Analogiemodell; Simulation program; Simulationsprogramm
Abstract	When fitting unidimensional item response theory (IRT) models, the population distribution of the latent trait ([theta]) is often assumed to be normally distributed. However, some psychological theories would suggest a nonnormal [theta]. For example, some clinical traits (e.g., alcoholism, depression) are believed to follow a positively skewed distribution where the construct is low for most people, medium for some, and high for few. Failure to account for nonnormality may compromise the validity of inferences and conclusions. Although corrections have been developed to account for nonnormality, these methods can be computationally intensive and have not yet been widely adopted. Previous research has recommended implementing nonnormality corrections when [theta] is not "approximately normal." This research focused on examining how far [theta] can deviate from normal before the normality assumption becomes untenable. Specifically, our goal was to identify the type(s) and degree(s) of nonnormality that result in unacceptable parameter recovery for the graded response model (GRM) and 2-parameter logistic model (2PLM). (As Provided).
Anmerkungen	SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01