Gestion des données manquantes dans les bases de données : la méthode d’imputation multiple sous XLSTAT

The objective of this study is to evaluate the robustness of the missing data management method, called multiple imputation, in the series of secondary data in social sciences. We use a simulation using data observed to see the scope of the multiple imputation method. Results show a close similarity between the observed data and imputed data

Gestion des données manquantes dans les bases de données : la méthode d’imputation multiple sous XLSTAT

The objective of this study is to evaluate the robustness of the missing data management method, called multiple imputation, in the series of secondary data in social sciences. We use a simulation using data observed to see the scope of the multiple imputation method. Results show a close similarity between the observed data and imputed data

Gestion Des Donnees Manquantes Dans Les Bases De Donnees En Sciences Sociales : Algorithme Nipals Ou Imputation Multiple?

The main objective of this paper is to assess the robustness of imputation methods to fill up the series of secondary data in social sciences. The methodology used, especially that of mean imputation, multiple imputation and NIPALS algorithm, is based on a simulation using observed data. Results show a close similarity between the observed data and the data obtained by multiple imputation, mean imputation and NIPALS algorithm. The results also suggest that multiple imputation provides values substantially similar to observed data