55: Adapting Multiple Imputation for Compositional Survey Data

Thomas M. Menino Convention & Exhibition Center 

Compositional data, where each component is a proportion of a whole, presents unique statistical challenges, particularly when data are incomplete. An acceptable missing data method for compositional data must maintain its characteristics, such as the inverse relationships between components and the constraint on each observation's sum. Multiple Imputation (MI) has become a standard method for imputing incomplete quantitative, ordinal, or categorical data, but there are not any proposed imputation methods for incomplete compositional data that are able to preserve the characteristics of the compositions. We propose methods for imputing compositional data by adapting MI, and use the imputed datasets to conduct analysis on exercise motivation survey data. The novel method will be used to impute missingness in the original dataset. The analysis results will be used to evaluate the performance of our proposal against standard methods.

Applied Bayesian Statistics

Exercise Motivation

Missing Data

Multiple Imputation

Multivariate Statistics

Survey Methodology 

Survey Research Methods Section