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October 5, 2025Open Access

Adapting Multiple Imputation for Compositional Survey Data

Key Points

The proposed methods effectively preserve composition characteristics while imputing missing data.
Analysis using imputed datasets assessed the performance of the new approach against traditional methods.
These innovations could enhance the accuracy of studies involving compositional data, including survey analyses.
The evaluation focused on exercise motivation survey data, aiming to address gaps in existing methodologies.

Abstract

Compositional data, where each component is a proportion of a whole, presents unique statistical challenges, particularly when incomplete. 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, 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, which serves as the basis for the model development. The results of the analysis will be used to evaluate the performance of our proposal against standard methods. KEYWORDS: Applied Bayesian Statistics; Exercise Motivation; Missing Data; Multiple Imputation; Multivariate Statistics; Survey Methodology

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