In the modern world information has become the new power. An increasing amount of efforts are being made to gather data, resources being allocated, time being invested and tools being developed. Data collection is no longer a myth; however, it remains a great challenge to create value out of the enormous data that is being collected. Data modeling…

Walking readers step by step through complex concepts, this book translates missing data techniques into something that applied researchers and graduate students can understand and utilize in their own research. Enders explains the rationale and procedural details for maximum likelihood estimation, Bayesian estimation, multiple imputation, and…

One of six summaries of workshop sessions (See TM 000 130), designed to strengthen the evaluation of costly programs and their effects, this handbook presents an analysis of both random and nonrandom sampling errors by application of the Bayesian model. This model attempts to formalize the process and procedures of inference from data through…

To test the use of Bayes's theorem to adjust for nonresponse bias, 600 hospitals were used in a simulated sample survey. On the basis of known information on five variables, Bayes's formula correctly predicted the status of 92 of the 100 "nonrespondents" relative to a sixth variable. (Author/BW)

In this article, the author comments on an article by Dunn, Kadane, and Garrow, "Comparing Harm Done by Mobility and Class Absence: Missing Students and Missing Data." He believes the research reported in that article should serve as a model for future applications of Bayesian methods in important educational research problems. The author lauds…