Imputation Methods to Deal with Missing Responses in Computerized Adaptive Multistage Testing

Autor/inn/en	Cetin-Berber, Dee Duygu; Sari, Halil Ibrahim; Huggins-Manley, Anne Corinne
Titel	Imputation Methods to Deal with Missing Responses in Computerized Adaptive Multistage Testing
Quelle	In: Educational and Psychological Measurement, 79 (2019) 3, S.495-511 (17 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164418805532
Schlagwörter	Computer Assisted Testing; Adaptive Testing; Error of Measurement; Research Problems; Test Items; Response Style (Tests); Maximum Likelihood Statistics; Scoring; Statistical Bias + Suchen Sie Ihr Suchwort? Messfehler; Forschungskritik; Test content; Testaufgabe; Antwortverhalten; Bewertung
Abstract	Routing examinees to modules based on their ability level is a very important aspect in computerized adaptive multistage testing. However, the presence of missing responses may complicate estimation of examinee ability, which may result in misrouting of individuals. Therefore, missing responses should be handled carefully. This study investigated multiple missing data methods in computerized adaptive multistage testing, including two imputation techniques, the use of full information maximum likelihood and the use of scoring missing data as incorrect. These methods were examined under the missing completely at random, missing at random, and missing not at random frameworks, as well as other testing conditions. Comparisons were made to baseline conditions where no missing data were present. The results showed that imputation and the full information maximum likelihood methods outperformed incorrect scoring methods in terms of average bias, average root mean square error, and correlation between estimated and true thetas. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01