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Related Experiment Video

Updated: Mar 11, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Explicating the Conditions Under Which Multilevel Multiple Imputation Mitigates Bias Resulting from Random

Nisha C Gottfredson1, Sonya K Sterba2, Kristina M Jackson3

  • 1Department of Health Behavior, University of North Carolina at Chapel Hill, Campus Box 7440, 135 Dauer Drive, Chapel Hill, NC, 27599-7440, USA. gottfredson@unc.edu.

Prevention Science : the Official Journal of the Society for Prevention Research
|November 21, 2016
PubMed
Summary

Random coefficient-dependent (RCD) missingness in longitudinal data can cause bias. Multilevel multiple imputation (MMI) effectively reduces this bias, outperforming standard methods, especially with high intraclass correlation and more data waves.

Keywords:
DeterminacyLongitudinal missing dataMultilevel multiple imputationRandom coefficient dependent

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Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Missing data in longitudinal studies is common.
  • Random coefficient-dependent (RCD) missingness is a non-ignorable mechanism that can lead to biased results.
  • Standard imputation methods often fail to account for within-person dependency.

Purpose of the Study:

  • To evaluate the effectiveness of multilevel multiple imputation (MMI) in addressing bias caused by RCD missingness.
  • To compare MMI with single-level imputation methods for longitudinal data.
  • To identify factors influencing the performance of MMI under RCD missingness.

Main Methods:

  • A simulation study was conducted to evaluate different imputation methods.
  • Three factors were manipulated: intraclass correlation (ICC), number of data waves, and percentage of missing data.
  • The performance of MMI was compared to single-level wide-format imputation.

Main Results:

  • MMI significantly outperformed the single-level wide-format method in reducing bias under RCD missingness.
  • Bias reduction with MMI was greatest when ICC was high, the number of waves was larger, and the amount of missing data was smaller.
  • MMI demonstrated superior performance in handling non-ignorable missing data mechanisms.

Conclusions:

  • MMI is a recommended approach for handling RCD missingness in longitudinal data.
  • Researchers should consider MMI to ensure accurate parameter estimation and inference.
  • Practical guidelines for managing missing longitudinal data are provided based on simulation findings.