Model Choice and Sample Size in Item Response Theory Analysis of Aphasia Tests

Autor/inn/en	Hula, William D.; Fergadiotis, Gerasimos; Martin, Nadine
Titel	Model Choice and Sample Size in Item Response Theory Analysis of Aphasia Tests
Quelle	In: American Journal of Speech-Language Pathology, 21 (2012) 2, (13 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1058-0360
DOI	10.1044/1058-0360(2011/11-0090)
Schlagwörter	Sample Size; Guessing (Tests); Aphasia; Item Response Theory; Adults; Models; Measurement Techniques; Simulation; Scoring; Reliability; Psychometrics; Speech Language Pathology; Communication Disorders; Diagnostic Tests; Statistical Analysis; Federal Aid; Difficulty Level; Test Items + Suchen Sie Ihr Suchwort? Erraten; Expressive Aphasie; Aphasie; Item-Response-Theorie; Analogiemodell; Messtechnik; Simulation program; Simulationsprogramm; Bewertung; Reliabilität; Psychometry; Psychometrie; Kommunikationsstörung; Diagnostic test; Diagnostischer Test; Statistische Analyse; Schwierigkeitsgrad; Test content; Testaufgabe
Abstract	Purpose: The purpose of this study was to identify the most appropriate item response theory (IRT) measurement model for aphasia tests requiring 2-choice responses and to determine whether small samples are adequate for estimating such models. Method: Pyramids and Palm Trees (Howard & Patterson, 1992) test data that had been collected from individuals with aphasia were analyzed, and the resulting item and person estimates were used to develop simulated test data for 3 sample size conditions. The simulated data were analyzed using a standard 1-parameter logistic (1-PL) model and 3 models that accounted for the influence of guessing: augmented 1-PL and 2-PL models and a 3-PL model. The model estimates obtained from the simulated data were compared to their known true values. Results: With small and medium sample sizes, an augmented 1-PL model was the most accurate at recovering the known item and person parameters; however, no model performed well at any sample size. Follow-up simulations confirmed that the large influence of guessing and the extreme easiness of the items contributed substantially to the poor estimation of item difficulty and person ability. Conclusion: Incorporating the assumption of guessing into IRT models improves parameter estimation accuracy, even for small samples. However, caution should be exercised in interpreting scores obtained from easy 2-choice tests, regardless of whether IRT modeling or percentage correct scoring is used. (Contains 7 tables and 3 figures.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10