This paper presents a new methodology to solve problems resulting from
missing data in large-scale item performance behavioral databases. Useful
statistics corrected for missing data are described, and a new method of
imputation for missing data is proposed. This methodology is applied to the DLP
database recently published by Keuleers et al. (2010), which allows us to
conclude that this database fulfills the conditions of use of the method
recently proposed by Courrieu et al. (2011) to test item performance models.
Two application programs in Matlab code are provided for the imputation of
missing data in databases, and for the computation of corrected statistics to
test models.