We provide conceptual introductions to missingness mechanisms—missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR)—and state-of-the-art methods of handling missing data—full-information maximum likelihood (FIML) and multiple imputation (MI)—followed by a discussion of planned missing designs: multiform questionnaire protocols, two-method measurement models, and wave-missing longitudinal designs. We reviewed 80 articles of empirical studies published in the 2012 issues of the Journal of Pediatric Psychology to present a picture of how adequately missing data are currently handled in this field. To illustrate the benefits of utilizing MI or FIML and incorporating planned missingness into study designs, we provide example analyses of empirical data gathered using a three-form planned missing design.
Applied Statistics | Kinesiology | Statistical Models
Todd D. Little, PhD, Terrence D. Jorgensen, MS, Kyle M. Lang, MA, E. Whitney G. Moore, PhD; On the Joys of Missing Data, Journal of Pediatric Psychology, Volume 39, Issue 2, 1 March 2014, Pages 151–162, https://doi.org/10.1093/jpepsy/jst048