Missing data techniques for multilevel data: implications of model misspecification

When modeling multilevel data, it is important to accurately represent the interdependence of observations within clusters. Ignoring data clustering may result in parameter misestimation. However, it is not well established to what degree parameter estimates are affected by model misspecification wh...

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Bibliographic Details
Published inJournal of applied statistics Vol. 38; no. 9; pp. 1845 - 1865
Main Authors Black, Anne C., Harel, Ofer, Betsy McCoach, D.
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 01.09.2011
Taylor and Francis Journals
Taylor & Francis Ltd
SeriesJournal of Applied Statistics
Subjects
Cluster analysis
Mathematical models
maximum likelihood
Maximum likelihood method
Missing data
Monte Carlo
multilevel data
multiple imputation
Parameter estimation
Studies
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Summary:When modeling multilevel data, it is important to accurately represent the interdependence of observations within clusters. Ignoring data clustering may result in parameter misestimation. However, it is not well established to what degree parameter estimates are affected by model misspecification when applying missing data techniques (MDTs) to incomplete multilevel data. We compare the performance of three MDTs with incomplete hierarchical data. We consider the impact of imputation model misspecification on the quality of parameter estimates by employing multiple imputation under assumptions of a normal model (MI/NM) with two-level cross-sectional data when values are missing at random on the dependent variable at rates of 10%, 30%, and 50%. Five criteria are used to compare estimates from MI/NM to estimates from MI assuming a linear mixed model (MI/LMM) and maximum likelihood estimation to the same incomplete data sets. With 10% missing data (MD), techniques performed similarly for fixed-effects estimates, but variance components were biased with MI/NM. Effects of model misspecification worsened at higher rates of MD, with the hierarchical structure of the data markedly underrepresented by biased variance component estimates. MI/LMM and maximum likelihood provided generally accurate and unbiased parameter estimates but performance was negatively affected by increased rates of MD.
ISSN:0266-4763
1360-0532
DOI:10.1080/02664763.2010.529882