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Multiple imputation of multilevel data with single-level models: A fully conditional specification approach using

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A new adjusted group means (AGM) method for multilevel multiple imputation (MI) effectively handles missing data in complex designs. This approach offers reliable results, even outperforming traditional multilevel MI in challenging applications.

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Area of Science:

  • Statistics
  • Multilevel Modeling
  • Data Analysis

Background:

  • Missing data pose significant challenges in multilevel designs.
  • Multilevel multiple imputation (MI) is a common technique, but traditional methods can be unstable with less pronounced multilevel structures.
  • Existing methods struggle with reliability in practical applications where multilevel structures are not the primary focus.

Purpose of the Study:

  • To introduce a novel fully conditional specification (FCS) approach for multilevel MI using group means (GM) or adjusted group means (AGM).
  • To evaluate the performance of FCS-based multilevel MI methods, including GM and AGM, in various scenarios.
  • To compare the effectiveness of these new methods against conventional multilevel MI approaches.

Main Methods:

  • Developed a fully conditional specification (FCS) approach for multilevel MI.
  • Combined single-level imputation methods with group means (GM) or adjusted group means (AGM).
  • Conducted theoretical investigations and multiple simulation studies across balanced and unbalanced designs with varying numbers of variables.

Main Results:

  • The adjusted group means (AGM) approach demonstrated strong performance across most investigated scenarios.
  • The AGM approach outperformed conventional multilevel MI methods in challenging applications.
  • The group means (GM) approach did not consistently provide reliable results.

Conclusions:

  • The adjusted group means (AGM) method offers a robust and reliable solution for handling missing data in multilevel designs, particularly in complex or less pronounced multilevel structures.
  • This FCS-based approach provides a valuable alternative to conventional multilevel MI, enhancing data analysis practices.
  • The study provides practical implementation guidance and highlights the implications for statistical analysis in research settings.