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Handling Missing Data With Multilevel Structural Equation Modeling and Full Information Maximum Likelihood

Donna L Schminkey1, Timo von Oertzen2, Linda Bullock3

  • 1Assistant Professor and Roberts Scholar, School of Nursing, University of Virginia, 202 Jeanette Lancaster Way PO Box 800782, Charlottesville, VA, 22903.

Research in Nursing & Health
|May 14, 2016
PubMed
Summary
This summary is machine-generated.

Healthcare researchers can effectively manage missing data using multilevel structural equation modeling with full information maximum likelihood (MSEM with FIML). This method prevents power loss and bias in analyses of large datasets.

Keywords:
full information maximum likelihoodmissing datamultilevel structural equation modelingsecondary data analysis

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Area of Science:

  • Health Services Research
  • Biostatistics
  • Nursing Research

Background:

  • Missing data are prevalent in population-based and electronic health record analyses.
  • Traditional methods like complete case analysis or simpler imputation can introduce bias and reduce statistical power.
  • Healthcare researchers lag in adopting advanced missing data techniques compared to social sciences.

Observation:

  • This review examines strategies for handling missing data in health research.
  • A case study illustrates the application of multilevel structural equation modeling with full information maximum likelihood (MSEM with FIML).

Findings:

  • MSEM with FIML offers a parsimonious and hypothesis-driven approach to address substantial missing data.
  • This technique effectively handles missing data without compromising statistical power or introducing bias.

Implications:

  • MSEM with FIML is a valuable tool for nurse researchers managing large electronic datasets.
  • This methodology supports robust data analysis amidst shrinking research budgets and increasing data complexity.