An Information-Correction Method for Testlet-Based Test Analysis: From the Perspectives of Item Response Theory and Generalizability Theory. Research Report. ETS RR-17-27

Autor/in	Li, Feifei
Titel	An Information-Correction Method for Testlet-Based Test Analysis: From the Perspectives of Item Response Theory and Generalizability Theory. Research Report. ETS RR-17-27
Quelle	In: ETS Research Report Series, (2017), (27 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2330-8516
Schlagwörter	Item Response Theory; Generalizability Theory; Tests; Error of Measurement; Test Items; Methods; Test Theory; Test Construction; Test Length; Reading Tests; Grade 3; Statistical Analysis; Factor Analysis + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Examination; Prüfung; Examen; Messfehler; Test content; Testaufgabe; Method; Methode; Testtheorie; Testaufbau; Lesetest; School year 03; 3. Schuljahr; Schuljahr 03; Statistische Analyse; Faktorenanalyse
Abstract	An information-correction method for testlet-based tests is introduced. This method takes advantage of both generalizability theory (GT) and item response theory (IRT). The measurement error for the examinee proficiency parameter is often underestimated when a unidimensional conditional-independence IRT model is specified for a testlet dataset. By using a design effect ratio composed of random variances that can be easily derived from GT analysis, it becomes possible to adjust the underestimated measurement error from the unidimensional IRT models to a more appropriate level. In this paper, it is demonstrated how the information-correction method can be implemented in the context of a testlet design. Also, through the simulation study, it is shown that the underestimated measurement errors from IRT estimates can be adjusted to the appropriate level despite the varying magnitudes of local item dependence (LID), testlet length, balance of testlet length, and number of item parameters in the model. The real data example provides more details about when and how the information-correction method should be used in a test analysis. Estimation by the information-correction method should be adequate for practical work, given the robustness of the variance ratio. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01