Partial and latent ignorability in missing-data problems

• 成果类型: Article
• 署名作者: Harel, Ofer; Schafer, Joseph L.
• 署名单位: University of Connecticut; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asn069
• 发表日期: 2009
• 页码: 3750
• 关键词: multiple imputation; nonrandom dropout; mixture-models; inference; nonresponse; attitudes

摘要

When an assumption of missing at random is untenable, it becomes necessary to model missing-data indicators, which carry information about the parameters of the complete-data population. Within a given application, however, researchers may believe that some aspects of missingness are ignorable but others are not. We argue that there are two different ways to formalize the notion that only part of the missingness is ignorable. These approaches correspond to assumptions that we call partially missing at random and latently missing at random. We explain these concepts and apply them in a latent-class analysis of survey questions with item nonresponse.