This chapter provides an overview of missing data issues that can occur in a meta-analysis. Common approaches to missing data in meta-analysis are discussed. The chapter focuses on the problem of missing data in moderators of effect size. The examples demonstrate the use of maximum likelihood methods and multiple imputation, the only two methods that produce unbiased estimates under the assumption that data are missing at random. The methods discussed in this chapter are most useful in testing the sensitivity of results to missing data.
CITATION STYLE
Pigott, T. D. (2012). Missing Data in Meta-analysis: Strategies and Approaches. In Advances in Meta-Analysis (pp. 79–107). Springer US. https://doi.org/10.1007/978-1-4614-2278-5_7
Mendeley helps you to discover research relevant for your work.