Abstract
Detecting influence is an important diagnostic stage in every statistical analysis. This paper examines the local influence of minor perturbations of the model and data for the two-parameter logistic item response model for binary data. By applying Cook's approach in the corresponding EM algorithm used to obtain the maximum likelihood estimate of the model parameters, the normal curvature and the conformal normal curvature - building blocks for obtaining the diagnostic measures – are computed. The methodology is illustrated with two real examples and an artificial one.
Disclosure statement
No potential conflict of interest was reported by the author(s).