A Comparison of Three IRT Approaches to Examinee Ability Change Modeling in a Single-Group Anchor Test Design

Autor/inn/en	Paek, Insu; Park, Hyun-Jeong; Cai, Li; Chi, Eunlim
Titel	A Comparison of Three IRT Approaches to Examinee Ability Change Modeling in a Single-Group Anchor Test Design
Quelle	In: Educational and Psychological Measurement, 74 (2014) 4, S.659-676 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164413507062
Schlagwörter	Item Response Theory; Comparative Analysis; Test Items; Equated Scores; Models; Ability; Change; Longitudinal Studies; Grade 7; Mathematics Tests; Achievement Tests; Computation; Statistical Distributions; Error of Measurement; Goodness of Fit; Foreign Countries; South Korea + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Test content; Testaufgabe; Analogiemodell; Fähigkeit; Fertigkeit; Wandel; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; School year 07; 7. Schuljahr; Schuljahr 07; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Wahrscheinlichkeitsverteilung; Messfehler; Ausland; Korea; Republik
Abstract	Typically a longitudinal growth modeling based on item response theory (IRT) requires repeated measures data from a single group with the same test design. If operational or item exposure problems are present, the same test may not be employed to collect data for longitudinal analyses and tests at multiple time points are constructed with unique item sets, as well as a set of common items (i.e., anchor test) for a study of examinee growth. In this study, three IRT approaches to examinee growth modeling were applied to a single-group anchor test design and their examinee growth estimates were compared. In terms of tracking individual growth, growth patterns in the examinee population distribution, and the overall model-data fit, results show the importance of modeling the serial correlation over multiple time points and other additional dependence coming from the use of the unique item sets, as well as the anchor test. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10