Abstract
Introduction One research challenge faced when conducting a longitudinal study is selecting a method for handling missing data. Incomplete assessment histories for longitudinal study participants are ubiquitous (Allison, 2002; Jelicic, Phelps & Lerner, 2009), and are due to multiple factors, such as participants’ attrition, illness, unwillingness or inability to answer certain questions, and problems related to the methods of data collection. When considering how longitudinal data are inherently structured – with repeated measurements (at level-one) clustered or nested within individual participants (at level-two) – such data are in effect multilevel or hierarchical
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