Model-based treatment of rapid guessing.

Autor/inn/en	Deribo, Tobias; Kröhne, Ulf; Goldhammer, Frank
Titel	Model-based treatment of rapid guessing.
Quelle	In: Journal of educational measurement, 58 (2021) 2, S. 281-303Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Link als defekt meldenVerfügbarkeit
Beigaben	Illustrationen
Sprache	englisch
Dokumenttyp	online; Zeitschriftenaufsatz
ISSN	1745-3984
DOI	10.1111/jedm.12290
Schlagwörter	Längsschnittuntersuchung; Panel; Testkonstruktion; Validität; Problemlösen; Verhalten; Item-Response-Theory; Multiple-Choice-Verfahren; Medienkompetenz; Deutschland; Frage; Längsschnittuntersuchung; Modell; Student; Validität; Verhalten; Problemlösen; Testkonstruktion; Computerunterstütztes Verfahren; Leistungsbeurteilung; Panel; Multiple-Choice-Verfahren; Dauer; Antwort; Medienkompetenz; Leistungsbeurteilung; Frage; Computerunterstütztes Verfahren; Messverfahren; Dauer; Antwort; Modell; Student; Deutschland
Abstract	The increased availability of time-related information as a result of computer-based assessment has enabled new ways to measure test-taking engagement. One of these ways is to distinguish between solution and rapid guessing behavior. Prior research has recommended response-level filtering to deal with rapid guessing. Response-level filtering can lead to parameter bias if rapid guessing depends on the measured trait or (un-)observed covariates. Therefore, a model based on Mislevy and Wu (1996) was applied to investigate the assumption of ignorable missing data underlying response-level filtering. The model allowed us to investigate different approaches to treating response-level filtered responses in a single framework through model parameterization. The study found that lower-ability test-takers tend to rapidly guess more frequently and are more likely to be unable to solve an item they guessed on, indicating a violation of the assumption of ignorable missing data underlying response-level filtering. Further ability estimation seemed sensitive to different approaches to treating response-level filtered responses. Moreover, model-based approaches exhibited better model fit and higher convergent validity evidence compared to more naïve treatments of rapid guessing. The results illustrate the need to thoroughly investigate the assumptions underlying specific treatments of rapid guessing as well as the need for robust methods. (DIPF/Orig.).
Erfasst von	DIPF \| Leibniz-Institut für Bildungsforschung und Bildungsinformation, Frankfurt am Main
Update	2022/1