Multiple imputation (MI) is a popular method for handling missing data. In education research, it can be challenging to use MI because the data often have a clustered structure that need to be accommodated during MI. Although much research has considered applications of MI in hierarchical data, little is known about its use in cross-classified…

Interest in variation in program impacts--How big is it? What might explain it?--has inspired recent work on the analysis of data from multi-site experiments. One critical aspect of this problem involves the use of random or fixed effect estimates to visualize the distribution of impact estimates across a sample of sites. Unfortunately, unless the…

This "What Works Clearinghouse Procedures and Standards Handbook (Version 3.0)" provides a detailed description of the standards and procedures of the What Works Clearinghouse (WWC). The remaining chapters of this Handbook are organized to take the reader through the basic steps that the WWC uses to develop a review protocol, identify…

This article discusses the use of propensity scoring in experimental program evaluations to estimate impacts for subgroups defined by program features and participants' program experiences. The authors discuss estimation issues and provide specification tests. They also discuss the use of an overlooked data collection design--obtaining predictions…

This paper details the challenges encountered by authors summarizing evidence from a primary study to describe a treatment's effectiveness using an effect size (ES) estimate. Dilemmas that are encountered, including how to calculate and interpret the pertinent standardized mean difference ES for results from studies of various research designs,…