Recoverability and Testability of Missing Data: Introduction and Summary of Results
23 Pages Posted: 26 Oct 2013
Date Written: September 25, 2013
Managing missing data is a problem in every experimental science. Sensors do not always work reliably, respondents do not ll out every question in the questionnaire, and medical patients are often unable to recall episodes, treatments or outcomes. The literature on this problem is huge and has resulted in a powerful software industry that makes missing data packages available through computer programs such as LISREL, M-plus and EQS. The availability of such software has engendered a culture that shares vocabulary, beliefs and expectations and uses common theoretical framework and default assumptions. Most practices are based on the seminal theoretical work of Rubin (Rubin, 1976; Little & Rubin, 2002) who have formulated procedures and conditions under which the damage of missingness can be minimized. This theory has also resulted in a number of performance guarantees when data obey certain statistical conditions. However, the theoretical guarantees provided by this theory are rather coarse, as will be shown in the discussion that follows.
Suggested Citation: Suggested Citation