Computerized classification testing with the Rasch model
Theo J. H. M. Eggen · Educational Research and Evaluation · 2011
If classification in a limited number of categories is the purpose of testing, computerized adaptive tests (CATs) with algorithms based on sequential statistical testing perform better than estimation-based CATs (e.g., Eggen & Straetmans, 2000 Eggen, T.J.H.M and Straetmans, G.J.J.M. 2000. Computerized adaptive testing for classifying examinees into three categories. Educational and Psychological Measurement, 66: 713–734. [Crossref] , [Google Scholar]). In these computerized classification tests (CCTs), the Sequential Probability Ratio Test (SPRT) (Wald, 1947 Wald, A. 1947. Sequential analysis, New York, NY: Wiley. [Google Scholar]) is applied to determine when and which classification decision is to be taken. In practice, the procedure is always truncated at a maximum test length (TSPRT). Stochastically Curtailed SPRT (SCSPRT) (Finkelman, 2008 Finkelman, M. 2008. On using stochastic curtailment to shorten the SPRT in sequential mastery testing. Journal of Educational and Behavioral Statistics, 33: 442–463. [Crossref], [Web of Science ®] , [Google Scholar]) uses additional stopping rules. If the Rasch model (1960) is used as the item response theory (IRT) model, applying the TSPRT and SCSPRT is elegant and simple. The performance of the TSPRT- and SCSPRT-based CATs are compared using different item selection methods. It is shown that the TSPRT and SCSPRT procedures are much better than optimal traditional linear tests. Results with the Rasch model are compared to results with other IRT models.