Research On IRT Equating For Tests Containing Testlets

The research on test equating is very important for fairness of examnation, item banking, teaching quality assessing and computerized adaptive test. Along with the development of research on examination, testlets appeared in different examnations increasingly, such as reading comprehension, mathematics, map etc. How to equate tests composed of testlets is a problem we are facing. When item response theory (IRT) models are applied in test equating, strong statistical assumptions—local independence (LI)—must be met. However, previous studies have shown that local independence is likely to be violated when testlets are contained in test. Hence, when equating tests composed of testlets, that local dependence is ignored can lead to distortion of equating coefficients using standard IRT model. In order to solve this problem, a testlets-based model—2 Parameters Testlet Model (2PTM) is used, which is derived from IRT 2 Parameters Logistic Model by adding random-effect parameters associated with each testlet. Local dependence is considered in 2PTM. IRT characteristic curve method and specific procedures for calculating equating coefficients are presented in this thesis. In terms of the recovery of estimating equating coefficients and based on Wilcoxon sign-rank test, a lot of experiments were done using Monte Carlo simulation method. The effectiveness of equating tests containing testlets was investigated in terms of the accuracy of the estimation of item parameters (AEIP), the number of examinees and the degree of local dependence. The findings of equating tests made up of testlets using 2PTM were compared with standard IRT model—2PLM, which does not account for local dependence among items from a common testlet. Results suggest that 2PTM is better than 2PLM in recovery and has significant differences mostly, so 2PTM is suitable for equating tests based testlets. In addition, the findings of using six different equating criterions for 2PTM were also compared with each other. The results showed that, generally speaking, when the value of coefficient A is between 0.5 and 0.9, the performance of SLcrit is the best, SQRcrit is proper for 0.9