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

TL;DR

This paper introduces a new method for imputing missing data and correcting statistics in large-scale behavioral databases, demonstrated on the DLP database, with practical Matlab tools provided.

Contribution

It proposes a novel imputation technique and corrected statistics for large-scale behavioral data, enabling more accurate analysis and model testing.

Findings

The methodology effectively imputes missing data in large databases.

Corrected statistics improve the accuracy of behavioral data analysis.

Tools are provided for practical implementation in Matlab.

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.