Missing Data in Evaluation Research

Abstract

Although research conducted in applied settings is frequently hindered by missing data, there is surprisingly little practical advice concerning effective methods for dealing with the problem. The purpose of this article is to describe several alternative methodsfor dealing with incomplete multivariate data and to examine the effectiveness of these methods. It is concluded that pairwise deletion and listwise deletion are among the least effective methods in terms of approximating the results that would have been obtained had the data been complete, whereas replacing missing values with estimates based on correlationalprocedures generally produces the most accurate results. In addition, some descriptive statistical procedures are recommended that permit researchers to investigate the causes and consequences of incomplete data more fully.