This chapter describes a selected set of techniques that are available to educational researchers to deal with the problem of missing data in group randomized trials.25 As discussed in Chapter 1, the methods were selected based on a review of several recent articles by experts in the field (Graham, 2009 Schafer & Graham, 2002; Allison, 2002; and Peugh & Enders, 2004)26 as well as a review of the techniques that have been used in RCTs recently sponsored by the U.S. Department of Education.27
As shown in the chart below, some of the methods discussed in this chapter can only be used to address missing data for the dependent or outcome "Y" variable (e.g., student post-test scores), others are only applicable for missing data on the independent "X" variables (e.g., student demographics and pretest score), while some can be used to address missing data problems for both types of variables.
|Methods Discussed||Can be used for missing data inů|
|X Variables||Y Variables|
|Maximum Likelihood Estimation||√||√|
|Dummy Variable Adjustment||√|
|"Fully-Specified" Regression Models||√|
|Pattern Mixture Modeling||√|
The discussion of these different methods is organized into two parts. The first deals with what we refer to as "standard" missing data methods that are in common use, particularly when one can assume that missing data are MAR:29 imputation methods, maximum likelihood estimation, dummy variable adjustment, weighting methods, and fully-specified regression models. The second section focuses on two methods that have been developed to address situations where the missing data can be considered to be NMAR:30 selection modeling and pattern-mixture modeling. In this second section, we also discuss the use of sensitivity testing that can be used to enhance the reporting of RCT findings under either missing data circumstance.