TY - JOUR T1 - Imputation Methods to Deal With Missing Responses in Computerized Adaptive Multistage Testing JF - Educational and Psychological Measurement Y1 - 2019 A1 - Dee Duygu Cetin-Berber A1 - Halil Ibrahim Sari A1 - Anne Corinne Huggins-Manley AB - 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. VL - 79 UR - https://doi.org/10.1177/0013164418805532 ER -