title: The stochastically curtailed generalized likelihood ratio: A new termination criterion for variable-length computerized classification tests.
abstract: Computerized classification tests (CCTs) are used to classify examinees into categories in the context of professional certification testing. The term "variable-length" refers to CCTs that terminate (i.e., cease administering items to the examinee) when a classification can be made with a prespecified level of certainty. The sequential probability ratio test (SPRT) is a common criterion for terminating variable-length CCTs, but recent research has proposed more efficient methods. Specifically, the stochastically curtailed SPRT (SCSPRT) and the generalized likelihood ratio criterion (GLR) have been shown to classify examinees with accuracy similar to the SPRT while using fewer items. This article shows that the GLR criterion itself may be stochastically curtailed, resulting in a new termination criterion, the stochastically curtailed GLR (SCGLR). All four criteria-the SPRT, SCSPRT, GLR, and the new SCGLR-were compared using a simulation study. In this study, we examined the criteria in testing conditions that varied several CCT design features, including item bank characteristics, pass/fail threshold, and examinee ability distribution. In each condition, the termination criteria were evaluated according to their accuracy (proportion of examinees classified correctly), efficiency (test length), and loss (a single statistic combing both accuracy and efficiency). The simulation results showed that the SCGLR can yield increased efficiency without sacrificing accuracy, relative to the SPRT, SCSPRT, and GLR in a wide variety of CCT designs.
authors.affiliation: University of Notre Dame, 153 Hurley Hall, Notre Dame, IN, 46556, USA, Alan.Huebner.10@nd.edu.
authors.forename: Alan R
authors.initials: AR
authors.lastname: Huebner
authors.forename: Anthony D
authors.initials: AD
authors.lastname: Fina
mesh_terms: D001077:Aptitude Tests / Q000706:statistics & numerical data; D002965:Classification / Q000379:methods*; D003205:Computing Methodologies; D003627:Data Interpretation, Statistical; D004650:Employee Performance Appraisal / Q000379:methods / Q000706:statistics & numerical data; D006801:Humans; D016013:Likelihood Functions; D013269:Stochastic Processes