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Flexible Multiple Imputation of Missing Data in Time-Structured Longitudinal Designs
Mark Lustig1, Oliver Lüdtke2,3, Alexander Robitzsch2,3
1Department of Psychology, University of Hamburg, Germany.
Multivariate Behavioral Research
|August 7, 2026
Summary
Single-level multiple imputation (MI) offers a flexible approach to handling missing data in longitudinal studies. This method requires fewer assumptions and is more adaptable than multilevel MI for various analyses.
Area of Science:
- Statistics
- Longitudinal Data Analysis
- Psychometrics
Background:
- Missing data are prevalent in longitudinal research.
- Multiple imputation (MI) is a common technique, with single-level and multilevel approaches.
- Existing research often focuses on growth modeling, where multilevel MI assumptions are met.
Purpose of the Study:
- To advocate for single-level MI as a more flexible method for longitudinal data.
- To explore the application of single-level MI in longitudinal multiple-indicator designs.
- To compare the performance of single-level MI against multilevel MI.
Main Methods:
- Two simulation studies were conducted.
- Single-level MI was applied to time-structured and longitudinal multiple-indicator designs.
- Partial least squares (PLS) was used as a dimension reduction technique for single-level MI.
Main Results:
- Single-level MI demonstrated flexibility in handling missing data.
- Partial least squares (PLS) effectively facilitated single-level MI in designs with numerous variables.
- Single-level MI accommodates a wider range of analyses compared to multilevel MI.
Conclusions:
- Single-level MI is a versatile and assumption-light method for missing data in longitudinal studies.
- Integrating techniques like PLS enhances the utility of single-level MI for complex designs.
- The findings have significant implications for applied longitudinal research.
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