Effect of Missing Data on Test Equating Methods Under NEAT Design

Abstract

In this study, it was aimed to examine the effect of missing data in different patterns and sizes on test equating methods under the NEAT design for different factors. For this purpose, as part of this study, factors such as sample size, average difficulty level difference between the test forms, difference between the ability distribution, missing data rate, and missing data mechanisms were manipulated. The effects of these factors on the equating error of test equating methods (chained-equipercentile equating, Tucker, frequency estimation equating, and Braun-Holland) were investigated. In the study, two separate sets of 10,000 dichotomous data were generated consistent with a 2-parameter logistic model. While generating data, the MCAR and MAR missing data mechanisms were used. All analyses were conducted by R 4.2.2. As a result of the study, it was seen that the RMSE of the equating methods increased significantly as the missing data rate increased. The results indicate that the RMSE of the equating methods with imputed missing data are reduced compared to equating without imputed missing data. Furthermore, the percentage of missing data, along with the difference between ability levels and the average difficulty difference between forms, was found to significantly affect equating errors in the presence of missing data. Although increasing sample size did not have a significant effect on equating error in the presence of missing data, it did lead to more accurate equating when there was no missing data present.