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

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 shou...

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Bibliographic Details
Published in:Educational and psychological measurement 2019-06, Vol.79 (3), p.495-511
Main Authors: Cetin-Berber, Dee Duygu, Sari, Halil Ibrahim, Huggins-Manley, Anne Corinne
Format: Article
Language:English
Subjects:
Adaptive Testing
Computer Assisted Testing
Educational tests & measurements
Error of Measurement
Maximum Likelihood Statistics
Missing data
Psychological tests
Research Problems
Response Style (Tests)
Scoring
Statistical Bias
Test Items
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Summary: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.
ISSN:0013-1644
1552-3888
DOI:10.1177/0013164418805532