Using Transformers for Missing Data Imputation in Psychology
Author Information
Author(s): Casella Monica, Milano Nicola, Dolce Pasquale, Marocco Davide
Primary Institution: University of Naples “Federico II”
Hypothesis
Can transformer-based deep learning models effectively impute missing data in psychometric research?
Conclusion
The ReMasker model outperforms traditional imputation techniques and shows comparable performance to other machine learning methods.
Supporting Evidence
- The ReMasker model consistently provided the most accurate imputations across all tested scenarios.
- Machine learning techniques, particularly ReMasker, achieve superior performance in terms of reconstruction error.
- Conventional methods showed the least favorable performance compared to machine learning techniques.
Takeaway
This study shows that a new computer model can fill in missing information in psychological surveys better than older methods.
Methodology
The study compared the ReMasker model with traditional and machine learning imputation methods using a psychometric dataset.
Potential Biases
The reliance on specific assumptions about missing data mechanisms may introduce bias.
Limitations
The study focused only on continuous data imputation and did not explore all possible imputation techniques.
Participant Demographics
Responses from 15,740 individuals aged 18 and older from 137 countries.
Digital Object Identifier (DOI)
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