Skip to Main content Skip to Navigation
Journal articles

Missing Data Imputation and Corrected Statistics for Large-Scale Behavioral Databases

Abstract : Abstract. 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.
Document type :
Journal articles
Complete list of metadata

Cited literature [13 references]  Display  Hide  Download
Contributor : Pierre Courrieu Connect in order to contact the contributor
Submitted on : Friday, February 18, 2011 - 3:54:26 PM
Last modification on : Tuesday, October 19, 2021 - 10:58:50 PM
Long-term archiving on: : Thursday, May 19, 2011 - 2:35:45 AM


Files produced by the author(s)




Pierre Courrieu, Arnaud Rey. Missing Data Imputation and Corrected Statistics for Large-Scale Behavioral Databases. Behavior Research Methods, Psychonomic Society, Inc, 2011, in press, doi: 10.3758/s13428-011-0071-2. ⟨10.3758/s13428-011-0071-2⟩. ⟨hal-00567176⟩



Record views


Files downloads