01544nas a2200145   4500008003900000245009800039210006900137300001200206490000700218520104000225100002901265700002501294700003401319856004501353       2019                          d00aImputation Methods to Deal With Missing Responses in Computerized Adaptive Multistage Testing0 aImputation Methods to Deal With Missing Responses in Computerize  a495-5110 v793 aRouting 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.1 aCetin-Berber, Dee, Duygu1 aSari, Halil, Ibrahim1 aHuggins-Manley, Anne, Corinne  uhttps://doi.org/10.1177/0013164418805532