title: Multiple imputation as one tool to provide longitudinal databases for modelling human height and weight development.
abstract: Besides large efforts regarding field work, provision of valid databases requires statistical and informational infrastructure to enable long-term access to longitudinal data sets on height, weight and related issues.
  
  
  
  To foster use of longitudinal data sets within the scientific community, provision of valid databases has to address data-protection regulations. It is, therefore, of major importance to hinder identifiability of individuals from publicly available databases. To reach this goal, one possible strategy is to provide a synthetic database to the public allowing for pretesting strategies for data analysis. The synthetic databases can be established using multiple imputation tools. Given the approval of the strategy, verification is based on the original data.
  
  
  
  Multiple imputation by chained equations is illustrated to facilitate provision of synthetic databases as it allows for capturing a wide range of statistical interdependencies. Also missing values, typically occurring within longitudinal databases for reasons of item non-response, can be addressed via multiple imputation when providing databases.
  
  
  
  The provision of synthetic databases using multiple imputation techniques is one possible strategy to ensure data protection, increase visibility of longitudinal databases and enhance the analytical potential.
authors.affiliation: Chair of Statistics and Econometrics, Otto-Friedrich-Universität, Bamberg, Germany.|Leibniz Institute for Educational Trajectories, Bamberg, Germany.
authors.forename: C
authors.initials: C
authors.lastname: Aßmann
mesh_terms: D001699:Biometry; D001827:Body Height / Q000502:physiology*; D001835:Body Weight / Q000502:physiology*; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D006128:Growth / Q000502:physiology*; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D012016:Reference Values