Estimating psychometric properties of computerized multistage testing

The computerized multistage testing (MST) has been utilized as an alternative testing mode for large-scale testing programs. Although the usage of MST has been discussed extensively, there is little literature that addresses its psychometric properties. The motivation of this dissertation arises from the need to properly estimate psychometric properties under the MST framework. The primary purpose of this dissertation is to propose an integrated set of formulas for estimating psychometric properties for MST, including error variance, reliability, and classification indices.

Results showed that, for the ML estimator, the proposed analytic approach performed better than the conventional approach. For the EAP estimator, results from using the Bayesian approach led to smaller values of marginal error variances and higher values of reliability, compared with the quadrature approach. It was also found that longer test resulted in decreased error variances and improved reliability. Across the approaches, conditional classification consistency and accuracy gradually decreased when the ability parameter was moving further away from the cut scores. All of the estimation formulas provided in this dissertation can be applied directly to MST data in practice.