Revising and Evaluating the Expected Likelihood Ratio as a New Item Selection Strategy in Unidimensional Computerized Classification Tests

Bo-Hsien Hu ； Ching-Lin Shih

《International Journal of Intelligent Technologies and Applied Statistics》 14卷2期 (2021/06) Pp. 93-105

https://doi.org/10.6148/IJITAS.202106_14(2).0002

摘要

Purpose: The item selection strategy and termination rule are two crucial factors that determine the measurement efficiency of computerized classification tests (CCTs). The expected likelihood ratio (ELR) strategy combined with the sequential probability ratio test (SPRT) has been proposed to promote the efficiency of CCTs. However, the SPRT only compares two simple hypotheses, whereas the generalized likelihood ratio (GLR) can consider a range of trait levels along the latent continuum. It is a straightforward suggestion that the ELR should also be combined with the GLR in CCT contexts. Because the GLR technique divides the likelihood ratio into two conditions in which its calculation depends on the relative position of the latent trait estimates and boundaries of the indifference region, the ELR is revised accordingly in this study. Design: The performance of this newly proposed ELR strategy in terms of the probability of correct classification (PCC) and average test length (ATL) in CCT contexts was investigated through a series of simulation studies. Findings: The results showed that the revised ELR combined with GLR introduced better measurement efficiency than the two other combinations of methods. The conclusions and implications of this study are discussed.

關鍵字

Computerized classification test ； Sequential probability ratio test ； Generalized likelihood ratio ； Expected likelihood ratio