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July 26, 2026Quality and Reliability Engineering InternationalOpen Access

Not All Missing Data are Equal: Choosing the Right Imputation Method for Binary Datasets

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Authors

MDManuel DelfinoFRFabio Rapallo

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Overview

Monte Carlo study comparing imputation methods for binary data, highlighting optimal choices for various analytical goals.

Key Points

  • The aim is to identify effective imputation methods for missing binary predictors in different scenarios of missingness.
  • Conducted a Monte Carlo study comparing five imputation methods across different missingness mechanisms.
  • Evaluated performance based on recovery of missing binary cells, logistic regression coefficients, and classification accuracy.
  • Tested missingness rates from 5% to 50% with two predictor-dependence structures.
  • KNN achieved the best recovery of exact missing binary cells under MCAR and MAR conditions.
  • MissForest outperformed others in scenarios with MNAR missingness.
  • MICE provided the most reliable predictive performance across various machine learning models.

Cite This Study

Delfino et al. (2026) studied this question.

synapsesocial.com/papers/6a65a422d3aea3239cd76f02https://doi.org/10.1002/qre.70342
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  5. 5Performance of Multiple Imputation Methods for Binary Logistic Regression with Missing Covariates: Simulation Study2026