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dc.creatorRioux, Charlie
dc.creatorLittle, Todd D.
dc.date.accessioned2021-03-24T19:30:16Z
dc.date.available2021-03-24T19:30:16Z
dc.date.issued2021
dc.identifier.citationRioux, C. & Little, T.D. (2021). Missing data treatments in intervention studies: What was, what is, and what should be. International Journal of Behavioral Development, 45(1), 51-58. https://doi.org/10.1177/0165025419880609en_US
dc.identifier.urihttps://doi.org/10.1177/0165025419880609
dc.identifier.urihttps://hdl.handle.net/2346/86879
dc.description.abstractMissing data are ubiquitous in studies examining preventive interventions. This missing data need to be handled appropriately for data analyses to yield unbiased results. After a brief discussion of missing data mechanisms, inappropriate missing data treatments and appropriate missing data treatments, we review the current state of missing data treatments in intervention studies as well as how they have evolved over the years. Although missing data treatments have improved over the years, antiquated missing data treatments associated with biased results are still prevalent. Furthermore, many studies do not appropriately report their rates of missing data and missing data treatments. Using appropriate missing data treatments is elemental to accurately identify effective preventive interventions and properly inform practice and policy.en_US
dc.language.isoengen_US
dc.subjectMissing Dataen_US
dc.subjectAttritionen_US
dc.subjectDropouten_US
dc.subjectMultiple Imputationen_US
dc.subjectFull Information Maximum Likelihooden_US
dc.subjectInterventionsen_US
dc.subjectClinical Trialsen_US
dc.titleMissing data treatments in intervention studies: What was, what is, and what should been_US
dc.typeArticleen_US


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