New Method of Calibrating IRT Models.

This discussion of new methods for calibrating item response theory (IRT) models looks into new optimization procedures, such as the Genetic Algorithm (GA) to improve on the use of the Newton-Raphson procedure. The advantages of using a global optimization procedure like GA is that this kind of procedure is not easily affected by local optima and saddle points. Because these procedures do not use gradient information, they can be implemented easily to higher dimensional data, even though they converge more slowly than the Newton-Raphson approach. However, the two approaches can be combined to exploit the advantages of both. That is, GA can be used to find a suitable starting point close to the global optima, and then Newton-Raphson can be used to speed up the convergence. The focus in this paper is on calibrating the unidimensional three-parameter logistic model (3PL) because that is the model most widely used in large-scale standardized tests. Using recent 3PL model estimates from recent Test of English as a Foreign Language administrations to generate examinee responses, the effectiveness of the new method is demonstrated using simulated data. How to implement the new methods with multidimensional data is discussed. (Contains 3 tables, 2 figures, and 10 references.) (SLD)