Comparing Methods for Handling Missing Data in Depression Scale
Author Information
Author(s): Fiona M Shrive, Heather Stuart, Hude Quan, William A Ghali
Primary Institution: University of Calgary
Hypothesis
This study aims to compare six different imputation techniques for dealing with missing data in the Zung Self-reported Depression scale (SDS).
Conclusion
Multiple imputation is the most accurate method for dealing with missing data in most scenarios assessed for the SDS.
Supporting Evidence
- Multiple imputation produced the highest Kappa statistic (0.89) indicating near perfect agreement.
- Individual mean imputation performed comparably well in many scenarios.
- Random selection method yielded poor correlations as missing data increased.
Takeaway
When researchers have missing answers in surveys, using a method called multiple imputation helps fill in those gaps more accurately.
Methodology
The study compared six imputation methods using simulated missing data scenarios on responses from 1580 participants who completed the SDS.
Limitations
The random simulations may not reflect real missing data patterns, and the study excluded participants with truly incomplete questionnaires.
Participant Demographics
Participants were surgical patients, with a total of 1931 surveyed, of which 1580 completed all items.
Digital Object Identifier (DOI)
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