Missing data imputation and corrected statistics for large-scale behavioral databases
Copyright policy )Missing data imputation and corrected statistics for large-scale behavioral databases
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.
Behavior Research Methods (2011) in press
- Aix-Marseille University France
- Laboratoire de Psychologie Cognitive France
- University of Provence France
- Centre Hospitalier Saint-Charles France
- Institut des Sciences Biologiques France
FOS: Computer and information sciences, [STAT.ME] Statistics [stat]/Methodology [stat.ME], Models, Statistical, Databases, Factual, item performance behavioral databases, Statistics as Topic, model goodness of fit, Models, Psychological, Methodology (stat.ME), missing data imputation, Research Design, statistics corrected for missing data, model goodness of fit., Statistics - Methodology, Problem Solving
FOS: Computer and information sciences, [STAT.ME] Statistics [stat]/Methodology [stat.ME], Models, Statistical, Databases, Factual, item performance behavioral databases, Statistics as Topic, model goodness of fit, Models, Psychological, Methodology (stat.ME), missing data imputation, Research Design, statistics corrected for missing data, model goodness of fit., Statistics - Methodology, Problem Solving
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).16 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!