title: Handling Missing Data With Multilevel Structural Equation Modeling and Full Information Maximum Likelihood Techniques.
abstract: With increasing access to population-based data and electronic health records for secondary analysis, missing data are common. In the social and behavioral sciences, missing data frequently are handled with multiple imputation methods or full information maximum likelihood (FIML) techniques, but healthcare researchers have not embraced these methodologies to the same extent and more often use either traditional imputation techniques or complete case analysis, which can compromise power and introduce unintended bias. This article is a review of options for handling missing data, concluding with a case study demonstrating the utility of multilevel structural equation modeling using full information maximum likelihood (MSEM with FIML) to handle large amounts of missing data. MSEM with FIML is a parsimonious and hypothesis-driven strategy to cope with large amounts of missing data without compromising power or introducing bias. This technique is relevant for nurse researchers faced with ever-increasing amounts of electronic data and decreasing research budgets. © 2016 Wiley Periodicals, Inc.
authors.affiliation: Assistant Professor and Roberts Scholar, School of Nursing, University of Virginia, 202 Jeanette Lancaster Way PO Box 800782, Charlottesville, VA, 22903.
authors.forename: Donna L
authors.initials: DL
authors.lastname: Schminkey
authors.affiliation: Assistant Professor, College of Arts and Sciences, University of Virginia, Charlottesville, VA.
authors.forename: Timo
authors.initials: T
authors.lastname: von Oertzen
authors.affiliation: Jeanette Lancaster Alumni Professor of Nursing, Associate Dean for Research, School of Nursing, University of Virginia, Charlottesville, VA.
authors.forename: Linda
authors.initials: L
authors.lastname: Bullock
mesh_terms: D000328:Adult; D003625:Data Collection; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D016013:Likelihood Functions
keywords: full information maximum likelihood; missing data; multilevel structural equation modeling; secondary data analysis