cacIRT-package: Classification accuracy and consistency under Item Response Theory

Description

Computes classification accuracy and consistency under Item Response Theory by the approach proposed by Lee, Hanson & Brennen (2002) and Lee (2010) or the approach proposed by Rudner (2001, 2005).

Arguments

Details

ll{ Package: cacIRT Type: Package Version: 1.2 Date: 2011-01-26 License: GPL (>= 2) LazyLoad: yes } This packages computes classification accuracy and consistency with two recent approaches proposed by Lee, Hanson & Brennan (2002) and Lee (2010) or by Rudner (2001, 2005), for dichotomous IRT models. The two functions class.Lee and class.Rud are the wrapper functions for the respective approaches. They accept a range of inputs: ability estimates, quadrature points, or response data matrix and item parameters. Marginal indices are computed with either the D (given distribution) or P (sample mean) method (see Lee (2010)). The function recursive.raw computes the probabilities of total scores given ability and item parameters and may be of interest outside of classification.

References

Lathrop, Q. N. & Cheng, Y. (2013) Two Approaches to Estimation of Classification Accuracy Rate Under Item Response Theory. Applied Psychological Measurement, 37, 226--241. Lee, W. (2010) Classification consistency and accuracy for complex assessments using item response theory. Journal of Educational Measurement, 47, 1--17. Lee, W., Hanson, B. A., & Brennan, R. L. (2002) Estimating consistency and accuracy indices for multiple classifications. Applied Psychological Measurement, 26, 412--432. Lee, W., & Kolen, M. J. (2008) Irt-class: Irt classification consistency and accuracy (version 2.0).

Rudner, L. M. (2001) Computing the expected proportions of misclassified examinees. PracticalAssessment, Research & Evaluation, 7(14), 1--5.

Rudner, L. M. (2005) Expected classification accuracy. Practical Assessment Research & Evaluation, 10(13), 1--4.