The use of statistical methods to combine the results of independent empirical research studies (meta-analysis) has a long history. Meta-analytic work can be divided into two traditions: tests of the statistical significance of combined results and methods for combining estimates across studies. The principal classes of combined significance tests are reviewed, and the limitations of these tests are discussed. Fixed effects approaches treat the effect magnitude parameters to be estimated as a consequence of a model involving fixed but unknown constants. Random effects approaches treat effect magnitude parameters as if they were sampled from a universe of effects and attempt to estimate the mean and variance of the hyperpopulation of effects. Mixed models incorporate both fixed and random effects. Finally, areas of current research are summarized, including methods for handling missing data, models for publication selection, models to handle studies that are not independent, and distribution-free models for random effects.