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Autor/inn/en | Manapat, Patrick D.; Edwards, Michael C. |
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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 |
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 |
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 |