@article {2741,
	title = {Imputation Methods to Deal With Missing Responses in Computerized Adaptive Multistage Testing},
	journal = {Educational and Psychological Measurement},
	volume = {79},
	number = {3},
	year = {2019},
	pages = {495-511},
	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.},
	doi = {10.1177/0013164418805532},
	url = {https://doi.org/10.1177/0013164418805532},
	author = {Dee Duygu Cetin-Berber and Halil Ibrahim Sari and Anne Corinne Huggins-Manley}
}
@article {2625,
	title = {What Information Works Best?: A Comparison of Routing Methods},
	journal = {Applied Psychological Measurement},
	volume = {42},
	number = {6},
	year = {2018},
	pages = {499-515},
	abstract = {There are many item selection methods proposed for computerized adaptive testing (CAT) applications. However, not all of them have been used in computerized multistage testing (ca-MST). This study uses some item selection methods as a routing method in ca-MST framework. These are maximum Fisher information (MFI), maximum likelihood weighted information (MLWI), maximum posterior weighted information (MPWI), Kullback{\textendash}Leibler (KL), and posterior Kullback{\textendash}Leibler (KLP). The main purpose of this study is to examine the performance of these methods when they are used as a routing method in ca-MST applications. These five information methods under four ca-MST panel designs and two test lengths (30 items and 60 items) were tested using the parameters of a real item bank. Results were evaluated with overall findings (mean bias, root mean square error, correlation between true and estimated thetas, and module exposure rates) and conditional findings (conditional absolute bias, standard error of measurement, and root mean square error). It was found that test length affected the outcomes much more than other study conditions. Under 30-item conditions, 1-3 designs outperformed other panel designs. Under 60-item conditions, 1-3-3 designs were better than other panel designs. Each routing method performed well under particular conditions; there was no clear best method in the studied conditions. The recommendations for routing methods in any particular condition were provided for researchers and practitioners as well as the limitations of these results.},
	doi = {10.1177/0146621617752990},
	url = {https://doi.org/10.1177/0146621617752990},
	author = {Halil Ibrahim Sari and Anthony Raborn}
}
