title: On the joys of missing data.
abstract: We provide conceptual introductions to missingness mechanisms--missing completely at random, missing at random, and missing not at random--and state-of-the-art methods of handling missing data--full-information maximum likelihood and multiple imputation--followed by a discussion of planned missing designs: Multiform questionnaire protocols, 2-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 using multiple imputation or full-information maximum likelihood and incorporating planned missingness into study designs, we provide example analyses of empirical data gathered using a 3-form planned missing design.
authors.affiliation: PhD, Center for Research Methods and Data Analysis, University of Kansas, Lawrence, KS 66045, USA. yhat@ku.edu.
authors.forename: Todd D
authors.initials: TD
authors.lastname: Little
authors.forename: Terrence D
authors.initials: TD
authors.lastname: Jorgensen
authors.forename: Kyle M
authors.initials: KM
authors.lastname: Lang
authors.forename: E Whitney G
authors.initials: EW
authors.lastname: Moore
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D011584:Psychology; D012107:Research Design
keywords: full-information maximum likelihood; missing data analysis; missingness mechanisms; multiple imputation; planned missing design