Computation and Statistical Inference for Decision Consistency Indexes Based on the Rasch Model

Abstract

Procedures based on latent-trait models are described for computation and asymptotic statistical inference for two decision consistency indexes often used in mastery or competency testing. Details are given for the Rasch model. Simulations were conducted for some typical mastery testing situations with tests of 30 to 60 items and for raw score frequency distributions with small to marked levels of negative skewness. The results appear to indicate that, for these situations, the asymptotic normal theory may be safely used with samples of 100 subjects.