CHAPTER 6 - Parametric Estimation of Effect Size from a Series of Experiments

Abstract

This chapter discusses the parametric estimation of effect size from a series of experiments. It presents two methods for obtaining optimal combinations of estimates of effect size from a series of studies: (1) a direct weighted linear combination of estimators from different studies and (2) a maximum likelihood estimator. Both the estimators have the same asymptotic distribution and, hence, are asymptotically equivalent. The chapter highlights two other methods that involve a transformation of the effect size estimates. Statistical properties of procedures for combining results from a series of experiments depend on the structural model for the results of the experiments. There are several alternative methods for estimating the effect size from a large series of studies, each of which has a small sample size. One method is based on weighted linear combinations of estimators. The second method is analogous to maximum likelihood and is based on a suggestion of Neyman and Scott.