Transformers deep learning models for missing data imputation: an application of the ReMasker model on a psychometric scale.

Journal: Frontiers in psychology
Published Date: Dec 17, 2024

Abstract

INTRODUCTION: Missing data in psychometric research presents a substantial challenge, impacting the reliability and validity of study outcomes. Various factors contribute to this issue, including participant non-response, dropout, or technical errors during data collection. Traditional methods like mean imputation or regression, commonly used to handle missing data, rely upon assumptions that may not hold on psychological data and can lead to distorted results.

Authors

  • Monica Casella
    Natural and Artificial Cognition Laboratory, Department of Humanistic Studies, University of Naples "Federico II", Naples, Italy.
  • Nicola Milano
    Natural and Artificial Cognition Laboratory, Department of Humanistic Studies, University of Naples "Federico II", Naples, Italy.
  • Pasquale Dolce
    Department of Translational Medical Science, University of Naples "Federico II", Naples, Italy.
  • Davide Marocco
    Department of Humanistic Studies, University of Naples Federico II, Naples, Italy.

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